Table

of Contents


APPLIED SOCIAL RESEARCH METHODS SERIES
Title Page
Copyright Page
Foreword
Preface
Dedication
ABSTRACT
Chapter 1 - Introduction

THE CASE STUDY AS A RESEARCH METHOD
COMPARING CASE STUDIES WITH OTHER RESEARCH
METHODS IN THE SOCIAL SCIENCES
DIFFERENT KINDS OF CASE STUDIES, BUT A COMMON
DEFINITION
SUMMARY
NOTES
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 1
ABSTRACT

Chapter 2 - Designing Case Studies Identifying Your Case(s) and Establishing ...

GENERAL APPROACH TO DESIGNING CASE STUDIES
CRITERIA FOR JUDGING THE QUALITY OF RESEARCH
DESIGNS
CASE STUDY DESIGNS
MODEST ADVICE IN SELECTING CASE STUDY DESIGNS
NOTES
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 2
ABSTRACT

Chapter 3 - Preparing to Collect Case Study Evidence What You Need to Do ...

 THE CASE STUDY INVESTIGATOR: DESIRED SKILLS
PREPARATION AND TRAINING FOR A SPECIFIC CASE STUDY
THE CASE STUDY PROTOCOL
SCREENING THE CANDIDATE “CASES” FOR YOUR CASE
STUDY
THE PILOT CASE STUDY
SUMMARY
NOTES
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 3
ABSTRACT

Chapter 4 - Collecting Case Study Evidence The Principles You Should Follow
in ...

SIX SOURCES OF EVIDENCE
THREE PRINCIPLES OF DATA COLLECTION
SUMMARY
NOTES
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 4
ABSTRACT

Chapter 5 - Analyzing Case Study Evidence How to Start Your Analysis, Your ...

AN ANALYTIC STRATEGY: MORE THAN FAMILIARITY WITH
ANALYTIC TOOLS
FIVE ANALYTIC TECHNIQUES
PRESSING FOR A HIGH-QUALITY ANALYSIS
SUMMARY
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 5
ABSTRACT

Chapter 6 - Reporting Case Studies How and What to Compose

TARGETING CASE STUDY REPORTS
CASE STUDY REPORTS AS PART OF LARGER, MIXED METHODS
STUDIES
 ILLUSTRATIVE STRUCTURES FOR CASE STUDY
COMPOSITIONS
PROCEDURES IN DOING A CASE STUDY REPORT
WHAT MAKES AN EXEMPLARY CASE STUDY?
NOTES
REFERENCE TO EXPANDED CASE STUDY MATERIALS FOR
CHAPTER 6

References
Author Index
Subject Index
About the Author
 APPLIED SOCIAL RESEARCH METHODS
SERIES

Series Editors
LEONARD BICKMAN, Peabody College, Vanderbilt University, Nashville
DEBRA J. ROG, Westat

1. SURVEY RESEARCH METHODS (Fourth Edition) by FLOYD J.

FOWLER, Jr.
2. SYNTHESIZING RESEARCH (Third Edition) by HARRIS
COOPER
3. METHODS FOR POLICY RESEARCH by ANN MAJCHRZAK
4. SECONDARY RESEARCH (Second Edition) by DAVID W.
STEWART and MICHAEL A. KAMINS
5. CASE STUDY RESEARCH (Fourth Edition) by ROBERT K. YIN
6. META-ANALYTIC PROCEDURES FOR SOCIAL RESEARCH
(Revised Edition) by ROBERT ROSENTHAL
7. TELEPHONE SURVEY METHODS (Second Edition) by PAUL J.
LAVRAKAS
8. DIAGNOSING ORGANIZATIONS (Second Edition) by MICHAEL
I. HARRISON
9. GROUP TECHNIQUES FOR IDEA BUILDING (Second Edition) by
CARL M. MOORE
10. NEED ANALYSIS by JACK McKILLIP
11. LINKING AUDITING AND META EVALUATION by THOMAS
A. SCHWANDT and EDWARD S. HALPERN
12. ETHICS AND VALUES IN APPLIED SOCIAL RESEARCH by
ALLAN J. KIMMEL
13. ON TIME AND METHOD by JANICE R. KELLY and JOSEPH E.
McGRATH
14. RESEARCH IN HEALTH CARE SETTINGS by KATHLEEN E.
GRADY and BARBARA STRUDLER WALLSTON
15. PARTICIPANT OBSERVATION by DANNY L. JORGENSEN
16. INTERPRETIVE INTERACTIONISM (Second Edition) by
NORMAN K. DENZIN
17. ETHNOGRAPHY (Second Edition) by DAVID M. FETTERMAN
18. STANDARDIZED SURVEY INTERVIEWING by FLOYD J.
FOWLER, Jr. and THOMAS W. MANGIONE
19. PRODUCTIVITY MEASUREMENT by ROBERT O.
BRINKERHOFF and DENNIS E. DRESSLER
20. FOCUS GROUPS (Second Edition) by DAVID W. STEWART,
PREM N. SHAMDASANI, and DENNIS W. ROOK
21. PRACTICAL SAMPLING by GART T. HENRY
22. DECISION RESEARCH by JOHN S. CARROLL and ERIC J.
JOHNSON
23. RESEARCH WITH HISPANIC POPULATIONS by GERARDO
MARIN and BARBARA VANOSS MARIN
24. INTERNAL EVALUATION by ARNOLD J. LOVE
25. COMPUTER SIMULATION APPLICATIONS by MARCIA LYNN
WHICKER and LEE SIGELMAN
26. SCALE DEVELOPMENT by ROBERT F. DeVELLIS
27. STUDYING FAMILIES by ANNE P. COPELAND and KATHLEEN
M. WHITE
28. EVENT HISTORY ANALYSIS by KAZUO YAMAGUCHI
29. RESEARCH IN EDUCATIONAL SETTINGS by GEOFFREY
MARUYAMA and STANLEY DENO
30. RESEARCHING PERSONS WITH MENTAL ILLNESS by
ROSALIND J. DWORKIN
31. PLANNING ETHICALLY RESPONSIBLE RESEARCH by JOAN
E. SIEBER
32. APPLIED RESEARCH DESIGN by TERRY E. HEDRICK,
LEONARD BICKMAN, and DEBRA J. ROG
33. DOING URBAN RESEARCH by GREGORY D. ANDRANOVICH
and GERRY RIPOSA
34. APPLICATIONS OF CASE STUDY RESEARCH by ROBERT K.
YIN
35. INTRODUCTION TO FACET THEORY by SAMUEL SHYE and
DOV ELIZUR with MICHAEL HOFFMAN
36. GRAPHING DATA by GARY T. HENRY
37. RESEARCH METHODS IN SPECIAL EDUCATION by DONNA
M. MERTENS and JOHN A. McLAUGHLIN
38. IMPROVING SURVEY QUESTIONS by FLOYD J. FOWLER, Jr.
39. DATA COLLECTION AND MANAGEMENT by MAGDA
STOUTHAMER-LOEBER and WELMOET BOK VAN KAMMEN
40. MAIL SURVEYS by THOMAS W. MANGIONE
 41. QUALITATIVE RESEARCH DESIGN by JOSEPH A. MAXWELL
42. ANALYZING COSTS, PROCEDURES, PROCESSES, AND
OUTCOMES IN HUMAN SERVICES by BRIAN T. YATES
43. DOING LEGAL RESEARCH by ROBERT A. MORRIS, BRUCE D.
SALES, and DANIEL W. SHUMAN
44. RANDOMIZED EXPERIMENTS FOR PLANNING AND
EVALUATION by ROBERT F. BORUCH
45. MEASURING COMMUNITY INDICATORS by PAUL J.
GRUENEWALD, ANDREW J. TRENO, GAIL TAFF, and MICHAEL
KLITZNER
46. MIXED METHODOLOGY by ABBAS TASHAKKORI and
CHARLES TEDDLIE
47. NARRATIVE RESEARCH by AMIA LIEBLICH, RIVKA TUVAL-
MASHIACH, and TAMAR ZILBER
48. COMMUNICATING SOCIAL SCIENCE RESEARCH TO
POLICYMAKERS by ROGER VAUGHAN and TERRY F. BUSS
49. PRACTICAL META-ANALYSIS by MARK W. LIPSEY and DAVID
B. WILSON
50. CONCEPT MAPPING FOR PLANNING AND EVALUATION by
MARY KANE and WILLIAM M. K. TROCHIM
51. COMPARATIVE METHODS by BENOÎT RIHOUX and CHARLES
C. RAGIN


 Copyright © 2009 by SAGE Publications, Inc.

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Library of Congress Cataloging-in-Publication Data

Yin, Robert K.
Case study research : design and methods/Robert K. Yin.—4th ed.
p. cm.—(Applied social research methods v. 5)
Includes bibliographical references and index. ISBN 978-1-4129-6099-1 (pbk.)
1. Case method. 2. Social sciences—Research—Methodology. I. Title.

H62.Y56 2009
300.72′2—dc22
2008019313

This book is printed on acid-free paper.

08 09 10 11 12 10 9 8 7 6 5 4 3 2 1

Acquisitions Editor: Vicki Knight
Associate Editor: Sean Connelly
Editorial Assistant: Lauren Habib
Production Editor: Catherine M. Chilton
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Cover Designer: Candice Harman
Marketing Manager: Stephanie Adams
 Foreword

It is a privilege to provide the foreword for this fine book. It epitomizes a
research method for attempting valid inferences from events outside the
laboratory while at the same time retaining the goals of knowledge shared with
laboratory science.
More and more I have come to the conclusion that the core of the scientific
method is not experimentation per se but rather the strategy connoted by the
phrase “plausible rival hypotheses.” This strategy may start its puzzle solving
with evidence, or it may start with hypothesis. Rather than presenting this
hypothesis or evidence in the context-independent manner of positivistic
confirmation (or even of postpositivistic corroboration), it is presented instead in
extended networks of implications that (although never complete) are
nonetheless crucial to its scientific evaluation.
This strategy includes making explicit other implications of the hypotheses for
other available data and reporting how these fit. It also includes seeking out rival
explanations of the focal evidence and examining their plausibility. The
plausibility of these rivals is usually reduced by ramification extinction, that is,
by looking at their other implications on other data sets and seeing how well
these fit. How far these two potentially endless tasks are carried depends on the
scientific community of the time and what implications and plausible rival
hypotheses have been made explicit. It is on such bases that successful scientific
communities achieve effective consensus and cumulative achievements, without
ever reaching foundational proof. Yet, these characteristics of the successful
sciences were grossly neglected by the logical positivists and are underpracticed
by the social sciences, quantitative or qualitative.
Such checking by other implications and the ramification-extinction of rival
hypotheses also characterizes validity-seeking research in the humanities,
including the hermeneutics of Schleiermacher, Dilthey, Hirst, Habermas, and
current scholarship on the interpretation of ancient texts. Similarly, the strategy
is as available for a historian’s conjectures about a specific event as for a
scientist’s assertion of a causal law. It is tragic that major movements in the
social sciences are using the term hermeneutics to connote giving up on the goal
of validity and abandoning disputation as to who has got it right. Thus, in
addition to the quantitative and quasi-experimental case study approach that Yin
teaches, our social science methodological armamentarium also needs a
humanistic validity-seeking case study methodology that, although making no
use of quantification or tests of significance, would still work on the same
questions and share the same goals of knowledge.
As versions of this plausible rival hypotheses strategy, there are two
paradigms of the experimental method that social scientists may emulate. By
training, we are apt to think first of the randomized-assignment-to-treatments
model coming to us from agricultural experimentation stations, psychological
laboratories, randomized trials of medical and pharmaceutical research, and the
statistician’s mathematical models. Randomization purports to control an infinite
number of rival hypotheses without specifying what any of them are.
Randomized assignment never completely controls these rivals but renders them
implausible to a degree estimated by the statistical model.
The other and older paradigm comes from physical science laboratories and is
epitomized by experimental isolation and laboratory control. Here are the
insulated and lead-shielded walls; the controls for pressure, temperature, and
moisture; the achievement of vacuums; and so on. This older tradition controls
for a relatively few but explicitly specified rival hypotheses. These are never
controlled perfectly, but well enough to render them implausible. Which rival
hypotheses are controlled for is a function of the disputations current in the
scientific community at the time. Later, in retrospect, it may be seen that other
controls were needed.
The case study approach as presented here, and quasi-experimentation more
generally, is more similar to the experimental isolation paradigm than to the
randomized-assignment-to-treatments model in that each rival hypothesis must
be specified and specifically controlled for. The degree of certainty or consensus
that the scientific community is able to achieve will usually be less in out-of-
doors social science, due to the lesser degree of plausibility-reduction of rival
hypotheses that is likely to be achieved. The inability to replicate at will (and
with variations designed to rule out specific rivals) is part of the problem. We
should use those singular-event case studies (which can never be replicated) to
their fullest, but we should also be alert for opportunities to do intentionally
replicated case studies.
Given Robert Yin’s background (Ph.D. in experimental psychology, with a
dozen publications in that field), his insistence that the case study method be
done in conformity with science’s goals and methods is perhaps not surprising.
But such training and career choice are usually accompanied by an intolerance of
the ambiguities of nonlaboratory settings. I like to believe that this shift was
facilitated by his laboratory research on that most hard-to-specify stimulus, the
human face, and that this experience provided awareness of the crucial role of
pattern and context in achieving knowledge.
 This valuable background has not kept him from thoroughly immersing
himself in the classic social science case studies and becoming in the process a
leader of nonlaboratory social science methodology. I know of no comparable
text. It meets a longstanding need. I am confident that it will become a standard
text in social science research methods courses.

—Donald T. Campbell
Bethlehem, Pennsylvania
 Preface

Congratulations! You are reading the best edition of Case Study Research to
date. This fourth edition contains more material, is more readable, and has more
practical value than previous editions. The book was first published 25 years
ago, and this fourth edition is actually the book’s fifth published version, because
there was a revised edition (1989) in addition to the three earlier editions (1984,
1994, and 2003).
The book’s enduring objective is to guide you and other investigators and
students to do case study research rigorously. The book claims to be distinctive
in several ways. First, it presents the breadth of the case study method, but also
at a detailed level. Other texts do not offer this same combination. Thus, the
earlier versions of this book have been used as a complete portal to the world of
case study research. Among its most distinctive features, the book provides
• a workable technical definition of the case study method and its
differentiation from other social science research methods (Chapter 1),
• an extensive discussion of case study design (Chapter 2), and
• a continually expanding presentation of case study analytic techniques
(Chapter 5).

These features are important because case study design and analysis tend to
create the greatest challenges for people doing case studies.1 Sandwiched
between Chapters 2 and 5, the book also has two extensive and important
chapters pertaining to preparing for and then collecting case study evidence.
Second, the book refers to numerous case studies, in different academic and
applied fields. These references will increase your access to existing and (often)
exemplary case studies. Most of the citations are contemporary, making the
works easy to retrieve. However, to avoid losing connectivity with “roots,” the
citations also include older works that might be out of print but still deserving of
being recognized. The specific references are found in BOXES sprinkled
throughout the chapters. Each BOX contains one or more concrete examples of
published case studies, to illustrate points made in the text. In this fourth edition,
the BOXES now cover more than 50 different case studies, about a quarter of
them newly cited in comparison to the earlier editions of this book.
Third, the new material in the BOXES complements other new technical
material located throughout the book. The new information demonstrates how
the case study as a research method appears to be advancing, despite vigorous
attention to (and disproportionate funding support for) other methods, such as
experimental designs.
In fact, Chapter 1 discusses the complementarity between case studies and
experiments, including an important new reference to the centrality of case
studies in clinical psychology (Veerman & van Yperen, 2007). Chapter 1 also
contains a more elaborate discussion of the limitations of randomized field trials
when the unit of analysis is a collective rather than an individual. Similarly, this
new edition points out several features that parallel Paul Rosenbaum’s (2002)
important work in nonexperimental research designs. The parallel features
include the desirability of having elaborate theories as starting points; the use of
“case control” or “retrospective” designs; the importance of collecting and
presenting data to support or reject rival explanations, as if to represent theories
of their own; the value of the nonequivalent, dependent variables design as a
form of pattern matching; and replication strategies as an essential approach to
multiple-case analysis.
This edition also gives greater attention to two critical topics now addressed
more fully in Chapter 2. The first is the definition of the “case” being studied (a
concrete entity, event, occurrence, action, but not an abstract topic such as a
concept, argument, hypothesis, or theory). The second is more guidance on the
substance (not just the form) of a case study’s initial questions and a suggested
three-stage approach that may help readers to define their initial questions.
Similarly, the new edition devotes more attention to the mixing of quantitative
and qualitative data as part of the same case study. The possibilities and
variations in mixed methods designs gain explicit attention at the end of Chapter
2, and Chapter 6 has modest guidance on composing case studies in relation to
mixed methods research. New examples of quantitative analyses, including the
use of hierarchical linear models and structural equation models as applied to
certain facets of a case study, appear in Chapter 5. These examples reinforce this
book’s original and continuing position regarding the case study method as one
that can embrace both quantitative and qualitative data.2
Finally, new material in Chapter 3 discusses human subjects protection, the
role of institutional review boards (IRBs), and the interplay between obtaining
IRB approval and the final development of the case study protocol and conduct
of a pilot case.
Aside from these technical enhancements, this fourth edition contains several
features aimed at making the book more useful and practical. First, each chapter
starts with a “tip.” The tip poses key questions and answers for the core material
in the entire chapter. The tips therefore enable readers to know quickly how hard
they will want to focus on any given chapter. An easily understood tip might
suggest that the chapter only needs brief perusal. Conversely, a tip that appears
confusing or obscure might suggest the need for a close reading.
Second, the practical exercises for each chapter have been upgraded. Previous
editions also had five such exercises for each chapter, but the fourth edition
revises some of them and then locates them throughout each chapter, rather than
at the end of the chapter as in the past. Each exercise therefore appears next to
the chapter section that is most pertinent to the exercise. The upgrading and
relocation of the exercises should increase their practical value.
Third, the end of each chapter, besides having one or more endnotes, now has
a new cross-referencing table. The table indicates where readers may seek more
extensive excerpts or fuller renditions of the case studies referenced in the
chapter’s BOXES and text. Although readers always can refer to the original
case study publication, the table indicates whether excerpts also appear in either
of two anthologies that deliberately collected these materials (Yin, 2003, 2004).
The anthologies only contain excerpts, but they nevertheless serve to broaden the
exposure to the case studies for readers who may not be ready (or willing) to
work with the original literature.
Finally, the chapter titles and subtitles have been revised to be more friendly.
They should still communicate the basic coverage of each chapter but also
suggest what readers will gain by studying the chapter. Likewise, this preface is
entirely new and attempts to point out the new edition’s important features. As
with previous editions, the chapter titles are followed with a brief abstract that
summarizes the chapter’s contents.
One possible motivation for all these changes, expanding technical topics and
making the book more practical, may derive from an observation that I (and
many others) have long had (but cannot explain): the remarkable ability of
young people to conduct computer and video game operations easily and with
little apparent instructional guidance. The young learn fast. However, they also
may come equipped with more skills and intuitions than previous generations.
This observation has, curiously, influenced the revisions in the fourth edition.
As being suggested by this preface, I have not hesitated to add some more
difficult concepts in doing case study research. As a result of these changes,
readers should be forewarned that I think this edition is “harder” (hopefully not
more arcane) than earlier editions. However, successful adoption of this edition’s
techniques and guidance also means that case study research will be better than
in the past. The ultimate goal, as always, is to improve our social science
methods and practices over those of previous generations. Only in this manner
can every generation make its own mark, much less establish its own
competitive niche.
 Given this context, two places where the book has not changed very much
deserve attention. Reviewers of the third edition suggested reducing the material
in Chapter 6, because many of the compositional issues seem to be related to the
writing of research more generally, not limited to the writing of case studies.
However, my experience has been that the writing of case studies is more critical
to their communication than the writing of other types of research. Furthermore,
those who have done exemplary case studies appear also to have a flair for
writing (and may have been attracted to the case study method in the first place
because they wanted to have the opportunity to do some good writing). Thus,
Chapter 6 serves as a reminder about the importance of writing and the
investigator’s skills, when doing case study research.
Second, Donald Campbell’s insightful foreword remains unchanged. His
succinct text, written nearly 30 years ago, still stands as a masterpiece about
social science methods. Within the context of today’s research debates,
Campbell’s work continues, remarkably, to speak with freshness and direct
relevance. His foreword also positions well the role of case study research as
portrayed in this book. I am deeply honored by the inclusion of this foreword
and have attempted to provide but a modest repayment in a subsequent
publication (Yin, 2000).

Over the years, the initiation and continued evolution of this book have
benefited from the advice and support of many people. I will resist creating a
cumulative list acknowledging all of these people from, in some cases, many
years ago. However, Prof. Leonard Bickman and Dr. Debra Rog invited me to
submit the first manuscript of this book as part of their (then) new series on
Applied Social Research Methods. Under their editorship, the series has become
a bellwether among all of Sage’s publications. I will be forever grateful to them
for providing the opportunity as well as the initial feedback and encouragement
in completing the manuscript. Similarly, in relation to the book’s still-early
editions, colleagues such as Larry Susskind at the Department of Urban Studies
and Planning (Massachusetts Institute of Technology), Nanette Levinson at the
Department of Computer Sciences (The American University), and Eric Maaloe
(the Aarhus School of Business in Denmark) all provided opportunities to teach
and learn about the case study method in different settings.
Flashing forward to this fourth edition, and as part of its preparation, Sage
Publications invited seven persons to share in writing their experience in using
the third edition. I did not expect Sage to divulge their identities, and they
remained anonymous until well after I had integrated the comments, reworked
the manuscript, and started the production process with Sage’s editors. At that
point, Sage chose to make the identities known. Though surprised, I nevertheless
can now thank these reviewers by name. I hope they will see that their comments
have influenced the edition’s enhancements and updating, although I could not
respond to all of the suggestions. The reviewers’ diverse array of teaching
experiences also appears to reflect the breadth of courses and disciplines that
have found the book to be relevant:
• qualitative research methods to Ph.D. nursing students (Martha Ann
Carey, Azusa Pacific University);
• doctoral course in IT research methodologies, for degree in management
(Alan McCord, Lawrence Technological University);
• foundation and capstone seminars for master’s in public administration
(Nolan J. Argyle, Valdosta State University);
• political science (Jeffrey L. Bernstein, Eastern Michigan University);
• case study research for doctoral students in educational administration
(Vincent A. Anfara Jr., University of Tennessee);
• first-year doctoral seminar in education (Pam Bishop, University of
Calgary); and
• qualitative research for graduate-level course in public policy (William S.
Lynn, Tufts University).

Research methods editors at Sage Publications also have, over the years, been
extremely helpful in identifying ways of making the book more useful and
usable for readers. For this most recent edition, I have had the pleasure of
working first with Lisa Cuevas Shaw and then with Vicki Knight and Catherine
Chilton. Lisa set us on a straight and productive course, and Vicki and Catherine
then made sure that the final manuscript would be converted into a distinctive
book, even as a fourth edition. As you can guess, we all have worked hard to
make the book have its own identity, beyond being a mere retread of earlier
work. Nonetheless, as with the earlier versions, I alone bear the responsibility for
this fourth edition.
At the same time, I conclude this preface by repeating a portion from the
preface to the third edition. In it, I suggested that anyone’s ideas about case
studies—and about social science methods more generally—must have deeper
roots. Mine go back to the two disciplines in which I was trained: history as an
undergraduate and brain and cognitive sciences as a graduate. History and
historiography first raised my consciousness regarding the importance (and
challenge) of methodology in the social sciences. The unique brand of basic
research in brain and cognitive science that I learned at MIT then taught me that
empirical research advances only when it is accompanied by theory and logical
inquiry, and not when treated as a mechanistic data collection endeavor. This
lesson turns out to be a basic theme of the case study method. I have therefore
dedicated this book to the person at MIT who taught me this best and under
whom I completed a dissertation on face recognition, though he might only
barely recognize the resemblances between past and present, were he alive today.
NOTES

1 Readers familiar with earlier versions of this book will find that a discussion of
pattern matching that formerly appeared as part of a design discussion in Chapter
2 is now found in its more appropriate place under pattern matching in Chapter
5.

2 Esteemed quantitative researchers may even agree with this. One of them has
been the lead author of an article using “case study” in its title (Cook & Foray,
2007). Readers should not take this as an example of how to do case study
research, however. The article mainly contains the authors’ rendition of a set of
events (which apparently could not be told with quantitative methods) but does
not present much evidence to support that rendition. (The rendition may be
important, but whether it should be accepted as an example of case study
research remains an open question.)

REFERENCES

Cook, T. D., & Foray, D. (2007). Building the capacity to experiment in schools:
A case study of the Institute of Educational Sciences in the US Department of
Education. Economics of Innovation and New Technology, 16(5), 385-402.
Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York: Springer.
Veerman, J. W., & van Yperen, T. A. (2007). Degrees of freedom and degrees of
certainty: A developmental model for the establishment of evidence-based youth
care. Evaluation and Program Planning, 30, 212-221.
Yin, R. K. (2000). Rival explanations as an alternative to “reforms as
experiments.” In L. Bickman (Ed.), Validity & social experimentation: Donald
Campbell’s legacy (pp. 239-266). Thousand Oaks, CA: Sage.
Yin, R. K. (2003). Applications of case study research (2nd ed.). Thousand Oaks,
CA: Sage.
Yin, R. K. (Ed.). (2004). The case study anthology. Thousand Oaks, CA: Sage.
This book is dedicated to Hans-Lukas Teuber,
who made research a lifelong goal for
all who studied with him.

Doing Case Study Research: A linear but iterative process

ABSTRACT

The case study is but one of several ways of doing social science research. Other
ways include but are not limited to experiments, surveys, histories, and
economic and epidemiologic research.
Each method has peculiar advantages and disadvantages, depending upon
three conditions: the type of research question, the control an investigator has
over actual behavioral events, and the focus on contemporary as opposed to
historical phenomena. In general, case studies are the preferred method when (a)
“how” or “why” questions are being posed, (b) the investigator has little control
over events, and (c) the focus is on a contemporary phenomenon within a real-
life context. This situation distinguishes case study research from other types of
social science research. Nevertheless, the methods all overlap in many ways, not
marked by sharp boundaries.
In case studies, the richness of the phenomenon and the extensiveness of the
real-life context require case study investigators to cope with a technically
distinctive situation: There will be many more variables of interest than data
points. In response, an essential tactic is to use multiple sources of evidence,
with data needing to converge in a triangulating fashion. This challenge is but
one of the ways that makes case study research “hard,” although it has
classically been considered a “soft” form of research.
 1

Introduction

How to Know Whether and When to Use Case Studies as a Research

Method
THE CASE STUDY AS A RESEARCH METHOD

Using case studies for research purposes remains one of the most challenging of
all social science endeavors. The purpose of this book is to help you—an
experienced or budding social scientist—to deal with the challenge. Your goal is
to design good case studies and to collect, present, and analyze data fairly. A
further goal is to bring the case study to closure by writing a compelling report
or book.
Do not underestimate the depth of your challenge. Although you may be ready
to focus on designing and doing case study research, others may espouse and
advocate other research methods. Similarly, prevailing federal or other research
funds may favor other methods, but not the case study. As a result, you may need
to have ready responses to some inevitable questions.
First and foremost, you should explain and show how you are devoting
yourself to following a rigorous methodological path. The path begins with a
thorough literature review and the careful and thoughtful posing of research
questions or objectives. Equally important will be a dedication to formal and
explicit procedures when doing your research. Along these lines, this book offers
much guidance. It shows how case study research includes procedures central to
all types of research methods, such as protecting against threats to validity,
maintaining a “chain of evidence,” and investigating and testing “rival
explanations.” The successful experiences of scholars and students, for over 25
years, may attest to the potential payoffs from using this book.
Second, you should understand and openly acknowledge the strengths and
limitations of case study research. Such research, like any other, complements
the strengths and limitations of other types of research. In the face of those who
might only see the need for a single research method, this book believes that, just
as different scientific methods prevail in the natural sciences, different social
science research methods fill different needs and situations for investigating
social science topics. For instance, in the natural sciences, astronomy is a science
but does not rely on the experimental method. Similarly, much
neurophysiological and neuroanatomical research does not rely on statistical
methods. For social science, later portions of this chapter present more about the
potential “niches” of different research methods.


Tip: How do I know if I should use the case study method?

There’s no formula, but your choice depends in large part on your research
question(s). The more that your questions seek to explain some present
circumstance (e.g., “how” or “why” some social phenomenon works), the
more that the case study method will be relevant. The method also is
relevant the more that your questions require an extensive and “in-depth”
description of some social phenomenon.


What are some other reasons you might cite for using or not using the
case study method?
As a research method, the case study is used in many situations, to contribute
to our knowledge of individual, group, organizational, social, political, and
related phenomena. Not surprisingly, the case study has been a common research
method in psychology, sociology, political science, anthropology, social work,
business, education, nursing, and community planning. Case studies are even
found in economics, in which the structure of a given industry or the economy of
a city or a region may be investigated. In all of these situations, the distinctive
need for case studies arises out of the desire to understand complex social
phenomena. In brief, the case study method allows investigators to retain the
holistic and meaningful characteristics of real-life events—such as individual
life cycles, small group behavior, organizational and managerial processes,
neighborhood change, school performance, international relations, and the
maturation of industries.
This book covers the distinctive characteristics of the case study as a research
method. The book will help you to deal with some of the more difficult questions
still frequently neglected by available research texts. So often, for instance, the
author has been confronted by a student or colleague who has asked (a) how to
define the “case” being studied, (b) how to determine the relevant data to be
collected, or (c) what to do with the data, once collected. This book answers
these questions and more, by covering all of the phases of design, data
collection, analysis, and reporting.
 At the same time, the book does not cover all uses of case studies. For
example, it is not intended to help those who might use case studies as a teaching
tool, popularized in the fields of law, business, medicine, or public policy (see
Garvin, 2003; Llewellyn, 1948; Stein, 1952; Towl, 1969; Windsor & Greanias,
1983) but now prevalent in virtually every academic field, including the natural
sciences. For teaching purposes, a case study need not contain a complete or
accurate rendition of actual events. Rather, the purpose of the “teaching case” is
to establish a framework for discussion and debate among students. The criteria
for developing good cases for teaching—usually of the single-and not multiple-
case variety—are different from those for doing research (e.g., Caulley &
Dowdy, 1987). Teaching case studies need not be concerned with the rigorous
and fair presentation of empirical data; research case studies need to do exactly
that.
Similarly, this book is not intended to cover those situations in which cases are
used as a form of record keeping. Medical records, social work files, and other
case records are used to facilitate some practice, such as medicine, law, or social
work. Again, the criteria for developing good cases for practice differ from those
for doing case study research.
In contrast, the rationale for this book is that case studies are commonly used
as a research method in the social science disciplines—psychology (e.g., D. T.
Campbell, 1975; Hersen & Barlow, 1976), sociology (e.g., Hamel, 1992; Platt,
1992; Ragin & Becker, 1992), political science (e.g., George & Bennett, 2004;
Gerring, 2004), and anthropology—and for doing research in different
professional fields, such as social work (e.g., Gilgun, 1994), business and
marketing (e.g., Benbasat, Goldstein, & Mead, 1987; Bonoma, 1985; Ghauri &
Grønhaug, 2002; Gibbert & Ruigrok, 2007; Graebner & Eisenhardt, 2004;
Voelpel, Leibold, Tekie, & von Krogh, 2005), public administration (e.g.,
Agranoff & Radin, 1991; Perry & Kraemer, 1986), public health (e.g., Pluye,
Potvin, Denis, Pelletier, & Mannoni, 2005; Richard et al., 2004), education (e.g.,
Yin, 2006a; Yin & Davis, 2006), accounting (e.g., Bruns, 1989), and evaluation
(e.g., U.S. Government Accountability Office, 1990).
You as a social scientist would like to know how to design and conduct single-
or multiple-case studies to investigate a research issue. You may only be doing a
case study or may be using it as part of a larger mixed methods study (see
Chapter 2). Whichever, this book covers the entire range of issues in designing
and doing case studies, including how to start a case study, collect case study
evidence, analyze case study data, and compose a case study report.
COMPARING CASE STUDIES WITH OTHER RESEARCH
METHODS IN THE SOCIAL SCIENCES

When and why would you want to do case studies on some topic? Should you
consider doing an experiment instead? A survey? A history? An analysis of
archival records, such as modeling economic trends or student performance in
schools?1
These and other choices represent different research methods. Each is a
different way of collecting and analyzing empirical evidence, following its own
logic. And each method has its own advantages and disadvantages. To get the
most out of using the case study method, you need to appreciate these
differences.
A common misconception is that the various research methods should be
arrayed hierarchically. Many social scientists still deeply believe that case
studies are only appropriate for the exploratory phase of an investigation, that
surveys and histories are appropriate for the descriptive phase, and that
experiments are the only way of doing explanatory or causal inquiries. This
hierarchical view reinforces the idea that case studies are only a preliminary
research method and cannot be used to describe or test propositions.
This hierarchical view, however, may be questioned. Experiments with an
exploratory motive have certainly always existed. In addition, the development
of causal explanations has long been a serious concern of historians, reflected by
the subfield known as historiography. Likewise, case studies are far from being
only an exploratory strategy. Some of the best and most famous case studies
have been explanatory case studies (e.g., see BOX 1 for a vignette on Allison
and Zelikow’s Essence of Decision: Explaining the Cuban Missile Crisis, 1999).
Similarly, famous descriptive case studies are found in major disciplines such as
sociology and political science (e.g., see BOX 2 for two vignettes). Additional
examples of explanatory case studies are presented in their entirety in a
companion book cited throughout this text (Yin, 2003, chaps. 4-7). Examples of
descriptive case studies are similarly found there (Yin, 2003, chaps. 2 and 3).

BOX 1

A Best-Selling, Explanatory, Single-Case Study
 For over 30 years, Graham Allison’s (1971) original study of a single case,
the 1962 Cuban missile crisis, has been a political science best seller. In this
crisis, a U.S.-Soviet Union confrontation could have produced nuclear
holocaust and doomed the entire world. The book posits three competing
but also complementary theories to explain the crisit—that the U.S. and
Soviets performed as (a) rationale actors, (b) complex bureaucracies, or (c)
politically motivated groups of persons. Allison compares the ability of
each theory to explain the actual cource of events in the crisis: why the
Soviet Union placed offensive (and not merely defensive) missiles in Cuba
in the first place, why the United States responded to the deployement with
a blockade (and not an air strike or invasion—the missiles already were in
Cuba!), and why the Soviet Union eventually withdrew the missiles.
The case study shows the explanatory and not just descriptive or exploratory
functions of single-case studies. Furthermore, the lessons from the case study are
intended to be generalizable to foreign affairs more broadly and also to a whole
variety of complex governemental actions. In this way, the book, even more
thoghtfully presented in its second edition (Allison & Zelikow, 1999), forcelly
demonstrates how a single case study can be the basis for significant
explanations and generalizations.

Distinguishing among the various research methods and their advantages and
disadvantages may require going beyond the hierarchical stereotype. The more
appropriate view may be an inclusive and pluralistic one: Every research method
can be used for all three purposes—exploratory, descriptive, and explanatory.
There may be exploratory case studies, descriptive case studies, or explanatory
case studies. Similarly, there may be exploratory experiments, descriptive
experiments, and explanatory experiments. What distinguishes the different
methods is not a hierarchy but three important conditions discussed below. As an
important caution, however, the clarification does not imply that the boundaries
between the methods—or the occasions when each is to be used—are always
sharp. Even though each method has its distinctive characteristics, there are large
overlaps among them. The goal is to avoid gross misfits—that is, when you are
planning to use one type of method but another is really more advantageous.

BOX 2

Two Famous Descriptive Case Studies

2A. A Neighborhood Scene


Street Corner Society (1943/1955), by William F.Whyte, has for decades
been recommended reading in community sociology. The book is a classic
example of a descriptive case study. It traces the sequence of interpersonal
events over time, describes a subculture that had rarely been the topic of
previous study, and discovers key phenomena-such as the career
advancement of lower income youths and their ability (or inability) to break
neighborhood ties.
The study has been highly regarded despite its being a single-case study,
covering one neighborhood (under the pseudonym of “Cornerville”) and a
time period now nearly 100 years old. The value of the book is,
paradoxically, its generalizability even to contemporary issues of individual
performance, group structure, and the social structure of neighborhoods.
Later investigators have repeatedly found remnants of Cornerville in their
work, even though they have studied different neighborhoods and different
time periods (also see BOX 20, Chapter 4, p. 111).


2B. A National Crisis

Neustadt and Fineberg’s excellent analysis of a mass immunization
campaign was issued originally as a government report in 1978, The Swine
Flu Affair: Decision-Making on a Slippery Disease. The case study
describes the immunization of 40 million Americans when the United
States was faced with a threat of epidemic proportions from a new and
potentially lethal influenza strain.
Although the case study became known as an exemplary example of a
thorough having been published by the U.S. Government Printing Office,
which, according to the authors, “has many virtues, ... but ... filling orders
which do not have exact change and precise stock numbers is not one of
them ” (Neustadt & Fineberg, 1983, p.xxiv). As a result, a revised version
of the original case study—adding new material to the original case—was
later published as The Epidemic That Never Was (1983). p. xxiv). As a
result, a revised version of the original case study-adding new material to
the original case-was later published as The Epidemic That Never Was
(1983).
 When to Use Each Method

The three conditions consist of (a) the type of research question posed, (b) the
extent of control an investigator has over actual behavioral events, and (c) the
degree of focus on contemporary as opposed to historical events. Figure 1.1
displays these three conditions and shows how each is related to the five major
research methods being discussed: experiments, surveys, archival analyses,
histories, and case studies. The importance of each condition, in distinguishing
among the five methods, is as follows.

Figure 1.1 Relevant Situations for Different Research Methods
SOURCE: COSMOS Corporation.

Types of research questions (Figure 1.1, column 1). The first condition covers
your research question(s) (Hedrick, Bickman, & Rog, 1993). A basic
categorization scheme for the types of questions is the familiar series: “who,”
“what,” “where,” “how,” and “why” questions.
If research questions focus mainly on “what” questions, either of two
possibilities arises. First, some types of “what” questions are exploratory, such as
“What can be learned from a study of a startup business?” This type of question
is a justifiable rationale for conducting an exploratory study, the goal being to
develop pertinent hypotheses and propositions for further inquiry. However, as
an exploratory study, any of the five research methods can be used—for
example, an exploratory survey (testing, for instance, the ability to survey
startups in the first place), an exploratory experiment (testing, for instance, the
potential benefits of different kinds of incentives), or an exploratory case study
(testing, for instance, the importance of differentiating “first-time” startups from
startups by entrepreneurs who had previously started other firms).
The second type of “what” question is actually a form of a “how many” or
“how much” line of inquiry—for example, “What have been the ways that
communities have assimilated new immigrants?” Identifying such ways is more
likely to favor survey or archival methods than others. For example, a survey can
be readily designed to enumerate the “what,” whereas a case study would not be
an advantageous method in this situation.
Similarly, like this second type of “what” question, “who” and “where”
questions (or their derivatives—“how many” and “how much”) are likely to
favor survey methods or the analysis of archival data, as in economic studies.
These methods are advantageous when the research goal is to describe the
incidence or prevalence of a phenomenon or when it is to be predictive about
certain outcomes. The investigation of prevalent political attitudes (in which a
survey or a poll might be the favored method) or of the spread of a disease like
AIDS (in which an epidemiologic analysis of health statistics might be the
favored method) would be typical examples.
In contrast, “how” and “why” questions are more explanatory and likely to
lead to the use of case studies, histories, and experiments as the preferred
research methods. This is because such questions deal with operational links
needing to be traced over time, rather than mere frequencies or incidence. Thus,
if you wanted to know how a community successfully overcame the negative
impact of the closing of its largest employer—a military base (see Bradshaw,
1999, also presented in BOX 26, Chapter 5, p. 138)—you would be less likely to
rely on a survey or an examination of archival records and might be better off
doing a history or a case study. Similarly, if you wanted to know how research
investigators may possibly (but unknowingly) bias their research, you could
design and conduct a series of experiments (see Rosenthal, 1966).
Let us take two more examples. If you were studying “who” had suffered as a
result of terrorist acts and “how much” damage had been done, you might survey
residents, examine government records (an archival analysis), or conduct a
“windshield survey” of the affected area. In contrast, if you wanted to know
“why” the act had occurred, you would have to draw upon a wider array of
documentary information, in addition to conducting interviews; if you focused
on the “why” question in more than one terrorist act, you would probably be
doing a multiple-case study.
Similarly, if you wanted to know “what” the outcomes of a new governmental
program had been, you could answer this question by doing a survey or by
examining economic data, depending upon the type of program involved.
Questions—such as “How many clients did the program serve?” “What kinds of
benefits were received?” “How often were different benefits produced?”—all
could be answered without doing a case study. But if you needed to know “how”
or “why” the program had worked (or not), you would lean toward either a case
study or a field experiment.
To summarize, the first and most important condition for differentiating
among the various research methods is to classify the type of research question
being asked. In general, “what” questions may either be exploratory (in which
case, any of the methods could be used) or about prevalence (in which surveys
or the analysis of archival records would be favored). “How” and “why”
questions are likely to favor the use of case studies, experiments, or histories.

EXERCISE 1.1 Defining a Case Study Question

Develop a “how” or “why” question that would be the rationale for a case
study that you might conduct. Instead of doing a case study, now imagine
that you only could do a history, a survey, or an experiment (but not a case
study) in order to answer this question. What would be the distinctive
advantage of doing a case study, compared to these other methods, in order
to answer this question?

Defining the research questions is probably the most important step to be
taken in a research study, so you should be patient and allow sufficient time for
this task. The key is to understand that your research questions have both
substance—for example, What is my study about?—and form—for example, am
I asking a “who,” “what,” “where,” “why,” or “how” question? Others have
focused on some of the substantively important issues (see J. P. Campbell, Daft,
& Hulin, 1982); the point of the preceding discussion is that the form of the
question can provide an important clue regarding the appropriate research
method to be used. Remember, too, the large areas of overlap among the
methods, so that, for some questions, a choice among methods might actually
exist. Be aware, finally, that you (or your academic department) may be
predisposed to favor a particular method regardless of the study question. If so,
be sure to create the form of the study question best matching the method you
were predisposed to favor in the first place.

EXERCISE 1.2 Identifying the Research Questions Covered

When Other Research Methods Are Used

Locate a research study based solely on the use of survey, historical, or
experimental (but not case study) methods. Identify the research question(s)
addressed by the study. Does the type of question differ from those that
might have appeared as part of a case study on the same topic, and if so,
how?

Extent of control over behavioral events (Figure 1.1, column 2) and degree of
focus on contemporary as opposed to historical events (Figure 1.1, column 3).
Assuming that “how” and “why” questions are to be the focus of study, a further
distinction among history, case study, and experiment is the extent of the
investigator’s control over and access to actual behavioral events. Histories are
the preferred method when there is virtually no access or control. The distinctive
contribution of the historical method is in dealing with the “dead” past—that is,
when no relevant persons are alive to report, even retrospectively, what occurred
and when an investigator must rely on primary documents, secondary
documents, and cultural and physical artifacts as the main sources of evidence.
Histories can, of course, be done about contemporary events; in this situation,
the method begins to overlap with that of the case study.
The case study is preferred in examining contemporary events, but when the
relevant behaviors cannot be manipulated. The case study relies on many of the
same techniques as a history, but it adds two sources of evidence not usually
included in the historian’s repertoire: direct observation of the events being
studied and interviews of the persons involved in the events. Again, although
case studies and histories can overlap, the case study’s unique strength is its
ability to deal with a full variety of evidence—documents, artifacts, interviews,
and observations—beyond what might be available in a conventional historical
study. Moreover, in some situations, such as participant-observation (see Chapter
4), informal manipulation can occur.
Finally, experiments are done when an investigator can manipulate behavior
directly, precisely, and systematically. This can occur in a laboratory setting, in
which an experiment may focus on one or two isolated variables (and presumes
that the laboratory environment can “control” for all the remaining variables
beyond the scope of interest), or it can be done in a field setting, where the term
field or social experiment has emerged to cover research where investigators
“treat” whole groups of people in different ways, such as providing them with
different kinds of vouchers to purchase services (Boruch & Foley, 2000). Again,
the methods overlap. The full range of experimental science also includes those
situations in which the experimenter cannot manipulate behavior but in which
the logic of experimental design still may be applied. These situations have been
commonly regarded as “quasi-experimental” situations (e.g., D. T. Campbell &
Stanley, 1966; Cook & Campbell, 1979) or “observational” studies (e.g., P. R.
Rosenbaum, 2002). The quasi-experimental approach even can be used in a
historical setting, where, for instance, an investigator may be interested in
studying race riots or lynchings (see Spilerman, 1971) and use a quasi-
experimental design because no control over the behavioral event was possible.
In this case, the experimental method begins to overlap with histories.
In the field of evaluation research, Boruch and Foley (2000) have made a
compelling argument for the practicality of one type of field experiment—
randomized field trials. The authors maintain that the field trials design,
emulating the design of laboratory experiments, can be and has been used even
when evaluating complex community initiatives. However, you should be
cautioned about the possible limitations of this design.
In particular, the design may work well when, within a community, individual
consumers or users of services are the unit of analysis. Such a situation would
exist if a community intervention consisted, say, of a health promotion campaign
and the outcome of interest was the incidence of certain illnesses among the
community’s residents. The random assignment might designate a few
communities to have the campaign, compared to a few that did not, and the
outcomes would compare the condition of the residents in both sets of
communities.
In many community studies, however, the outcomes of interest and therefore
the appropriate unit of analysis are at the community or collective level and not
at the individual level. For instance, efforts to upgrade neighborhoods may be
concerned with improving a neighborhood’s economic base (e.g., the number of
jobs per residential population). Now, although the candidate communities still
can be randomly assigned, the degrees of freedom in any later statistical analysis
are limited by the number of communities rather than the number of residents.
Most field experiments will not be able to support the participation of a
sufficiently large number of communities to overcome the severity of the
subsequent statistical constraints.
The limitations when communities or collective entities are the unit of
analysis are extremely important because many public policy objectives focus on
the collective rather than individual level. For instance, the thrust of federal
education policy in the early 2000s focused on school performance. Schools
were held accountable for year-to-year performance even though the
composition of the students enrolled at the schools changed each year. Creating
and implementing a field trial based on a large number of schools, as opposed to
a large number of students, would present an imposing challenge and the need
for extensive research resources. In fact, Boruch (2007) found that a good
number of the randomized field trials inadvertently used the incorrect unit of
analysis (individuals rather than collectives), thereby making the findings from
the trials less usable.
Field experiments with a large number of collective entities (e.g.,
neighborhoods, schools, or organizations) also raise a number of practical
challenges:
• any randomly selected control sites may adopt important components of
the intervention of interest before the end of the field experiment and no
longer qualify as “no-treatment” sites;
• the funded intervention may call for the experimental communities to
reorganize their entire manner of providing certain services—that is, a
“systems” change—thereby creating site-to-site variability in the unit of
assignment (the experimental design assumes that the unit of assignment
is the same at every site, both intervention and control);
• the same systems change aspect of the intervention also may mean that the
organizations or entities administering the intervention may not
necessarily remain stable over the course of time (the design requires
such stability until the random field trials have been completed); and
• the experimental or control sites may be unable to continue using the same
instruments and measures (the design, which will ultimately “group” the
data to compare intervention sites as a group with comparison sites as a
second group, requires common instruments and measures across sites).

The existence of any of these conditions will likely lead to the need to find
alternatives to randomized field trials.

Summary. You should be able to identify some situations in which all research
methods might be relevant (such as exploratory research) and other situations in
which two methods might be considered equally attractive. You also can use
multiple methods in any given study (for example, a survey within a case study
or a case study within a survey). To this extent, the various methods are not
mutually exclusive. But you should also be able to identify some situations in
which a specific method has a distinct advantage. For the case study, this is
when
• A “how” or “why” question is being asked about
• a contemporary set of events,
• over which the investigator has little or no control.


To determine the questions that are most significant for a topic, as well as to
gain some precision in formulating these questions requires much preparation.
One way is to review the literature on the topic (Cooper, 1984). Note that such a
literature review is therefore a means to an end, and not—as many people have
been taught to think—an end in itself. Novices may think that the purpose of a
literature review is to determine the answers about what is known on a topic; in
contrast, experienced investigators review previous research to develop sharper
and more insightful questions about the topic.
 Traditional Prejudices against the Case Study Method

Although the case study is a distinctive form of empirical inquiry, many
research investigators nevertheless disdain the strategy. In other words, as a
research endeavor, case studies have been viewed as a less desirable form of
inquiry than either experiments or surveys. Why is this?
Perhaps the greatest concern has been over the lack of rigor of case study
research. Too many times, the case study investigator has been sloppy, has not
followed systematic procedures, or has allowed equivocal evidence or biased
views to influence the direction of the findings and conclusions. Such lack of
rigor is less likely to be present when using the other methods—possibly because
of the existence of numerous methodological texts providing investigators with
specific procedures to be followed. In contrast, only a small (though increasing)
number of texts besides the present one cover the case study method in similar
fashion.
The possibility also exists that people have confused case study teaching with
case study research. In teaching, case study materials may be deliberately altered
to demonstrate a particular point more effectively (e.g., Garvin, 2003). In
research, any such step would be strictly forbidden. Every case study
investigator must work hard to report all evidence fairly, and this book will help
her or him to do so. What is often forgotten is that bias also can enter into the
conduct of experiments (see Rosenthal, 1966) and the use of other research
methods, such as designing questionnaires for surveys (Sudman & Bradburn,
1982) or conducting historical research (Gottschalk, 1968). The problems are not
different, but in case study research, they may have been more frequently
encountered and less frequently overcome.

EXERCISE 1.3 Examining Case Studies Used for Teaching

Purposes

Obtain a copy of a case study designed for teaching purposes (e.g., a case in
a textbook used in a business school course). Identify the specific ways in
which this type of “teaching” case is different from research case studies.
Does the teaching case cite primary documents, contain evidence, or
 display data? Does the teaching case have a conclusion? What appears to be
the main objective of the teaching case?

A second common concern about case studies is that they provide little basis
for scientific generalization. “How can you generalize from a single case?” is a
frequently heard question. The answer is not simple (Kennedy, 1976). However,
consider for the moment that the same question had been asked about an
experiment: “How can you generalize from a single experiment?” In fact,
scientific facts are rarely based on single experiments; they are usually based on
a multiple set of experiments that have replicated the same phenomenon under
different conditions. The same approach can be used with multiple-case studies
but requires a different concept of the appropriate research designs, discussed in
detail in Chapter 2. The short answer is that case studies, like experiments, are
generalizable to theoretical propositions and not to populations or universes. In
this sense, the case study, like the experiment, does not represent a “sample,” and
in doing a case study, your goal will be to expand and generalize theories
(analytic generalization) and not to enumerate frequencies (statistical
generalization). Or, as three notable social scientists describe in their single case
study done years ago, the goal is to do a “generalizing” and not a
“particularizing” analysis (Lipset, Trow, & Coleman, 1956, pp. 419-420).2
A third frequent complaint about case studies is that they take too long, and
they result in massive, unreadable documents. This complaint may be
appropriate, given the way case studies have been done in the past (e.g., Feagin,
Orum, & Sjoberg, 1991), but this is not necessarily the way case studies—yours
included—must be done in the future. Chapter 6 discusses alternative ways of
writing the case study—including ones in which the traditional, lengthy narrative
can be avoided altogether. Nor need case studies take a long time. This
incorrectly confuses the case study method with a specific method of data
collection, such as ethnography (e.g., Fetterman, 1989) or participant-
observation (e.g., Jorgensen, 1989). Ethnographies usually require long periods
of time in the “field” and emphasize detailed, observational evidence.
Participant-observation may not require the same length of time but still assumes
a hefty investment of field efforts. In contrast, case studies are a form of inquiry
that does not depend solely on ethnographic or participant-observer data. You
could even do a valid and high-quality case study without leaving the telephone
or Internet, depending upon the topic being studied.
A fourth possible objection to case studies has seemingly emerged with the
renewed emphasis, especially in education and related research, on randomized
field trials or “true experiments.” Such studies aim to establish causal
relationships—that is, whether a particular “treatment” has been efficacious in
producing a particular “effect” (e.g., Jadad, 1998). In the eyes of many, the
emphasis has led to a downgrading of case study research because case studies
(and other types of nonexperimental methods) cannot directly address this issue.
Overlooked has been the possibility that case studies can offer important
evidence to complement experiments. Some noted methodologists suggest, for
instance, that experiments, though establishing the efficacy of a treatment (or
intervention), are limited in their ability to explain “how” or “why” the treatment
necessarily worked, whereas case studies could investigate such issues (e.g.,
Shavelson & Townes, 2002, pp. 99-106).3 Case studies may therefore be valued
“as adjuncts to experiments rather than as alternatives to them” (Cook & Payne,
2002). In clinical psychology, a “large series of single case studies,” confirming
predicted behavioral changes after the initiation of treatment, even may provide
additional evidence of efficaciousness (e.g., Veerman & van Yperen, 2007).
Despite the fact that these four common concerns can be allayed, as above,
one major lesson is that good case studies are still difficult to do. The problem is
that we have little way of screening for an investigator’s ability to do good case
studies. People know when they cannot play music; they also know when they
cannot do mathematics beyond a certain level, and they can be tested for other
skills, such as the bar examination in law. Somehow, the skills for doing good
case studies have not yet been formally defined. As a result, “most people feel
that they can prepare a case study, and nearly all of us believe we can understand
one. Since neither view is well founded, the case study receives a good deal of
approbation it does not deserve” (Hoaglin, Light, McPeek, Mosteller, & Stoto,
1982, p. 134). This quotation is from a book by five prominent statisticians.
Surprisingly, from another field, even they recognize the challenge of doing
good case studies.
DIFFERENT KINDS OF CASE STUDIES, BUT A COMMON
DEFINITION

Our discussion has progressed without a formal definition of case studies.

Moreover, commonly asked questions about case studies still have been
unanswered. For example, is it still a case study when more than one case is
included in the same study? Do case studies preclude the use of quantitative
evidence? Can case studies be used to do evaluations? Let us now attempt to
define the case study strategy and answer these questions.
 Definition of the Case Study as a Research Method

The most frequently encountered definitions of case studies have merely
repeated the types of topics to which case studies have been applied. For
example, in the words of one observer,
The essence of a case study, the central tendency among all types of case
study, is that it tries to illuminate a decision or set of decisions: why they
were taken, how they were implemented, and with what result. (Schramm,
1971, emphasis added)

This definition thus cites cases of “decisions” as the major focus of case
studies. Other common cases include “individuals,” “organizations,”
“processes,” “programs,” “neighborhoods,” “institutions,” and even “events.”
However, citing a case topic4 is surely insufficient to establish the needed
definition of case studies as a research method.
Alternatively, many of the earlier social science textbooks failed to consider
the case study a formal research method at all (the major exception is the book
by five statisticians from Harvard University—Hoaglin et al., 1982). As
discussed previously, one common flaw was to consider the case study as the
exploratory stage of some other type of research method, and the case study
itself was only mentioned in a line or two of text.
Another definitional flaw has been to confuse case studies with ethnographies
or with participant-observation, so that a textbook’s presumed discussion of case
studies was in reality a description either of the ethnographic method or of
participant-observation as a data collection technique. Many earlier
methodological texts (e.g., see L. Kidder & Judd, 1986; Nachmias & Nachmias,
1992), in fact, only covered “fieldwork” as a data collection technique and
omitted any further discussion of case studies.
In a historical overview of the case study in American methodological
thought, Jennifer Platt (1992) explains the reasons for these treatments. She
traces the practice of doing case studies back to the conduct of life histories, the
work of the Chicago school of sociology, and casework in social work. She then
shows how “participant-observation” emerged as a data collection technique,
leaving the further definition of any distinctive case study method in suspension.
Finally, she explains how the first edition of this book (1984) definitively
dissociated the case study strategy from the limited perspective of only doing
participant-observation (or any type of fieldwork). The case study strategy, in her
words, begins with “a logic of design . . . a strategy to be preferred when
circumstances and research problems are appropriate rather than an ideological
commitment to be followed whatever the circumstances” (Platt, 1992, p. 46).
And just what is this logic of design? The critical features had been worked
out prior to the first edition of this book (Yin, 1981a, 1981b) but now may be
restated as part of a twofold, technical definition of case studies. The first part
begins with the scope of a case study:
1. A case study is an empirical inquiry that
• investigates a contemporary phenomenon in depth and within its
real-life context, especially when
• the boundaries between phenomenon and context are not clearly
evident.


In other words, you would use the case study method because you wanted to
understand a real-life phenomenon in depth, but such understanding
encompassed important contextual conditions—because they were highly
pertinent to your phenomenon of study (e.g., Yin & Davis, 2007). This first part
of the logic of design therefore helps to continue to distinguish case studies from
the other research methods that have been discussed.
An experiment, for instance, deliberately divorces a phenomenon from its
context, attending to only a few variables (typically, the context is “controlled”
by the laboratory environment). A history, by comparison, does deal with the
entangled situation between phenomenon and context but usually with non-
contemporary events. Finally, surveys can try to deal with phenomenon and
context, but their ability to investigate the context is extremely limited. The
survey designer, for instance, constantly struggles to limit the number of
variables to be analyzed (and hence the number of questions that can be asked)
to fall safely within the number of respondents who can be surveyed.
Second, because phenomenon and context are not always distinguishable in
real-life situations, other technical characteristics, including data collection and
data analysis strategies, now become the second part of our technical definition
of case studies:
2. The case study inquiry
• copes with the technically distinctive situation in which there will
be many more variables of interest than data points, and as one
 result
• relies on multiple sources of evidence, with data needing to
converge in a triangulating fashion, and as another result
• benefits from the prior development of theoretical propositions to
guide data collection and analysis.


In essence, the twofold definition shows how case study research comprises
an all-encompassing method—covering the logic of design, data collection
techniques, and specific approaches to data analysis. In this sense, the case study
is not limited to being a data collection tactic alone or even a design feature
alone (Stoecker, 1991). How the method is practiced is the topic of this entire
book.

EXERCISE 1.4 Finding and Analyzing an Existing Case

Study from the Literature

Retrieve an example of case study research from the literature. The case
study can be on any topic, but it must have used some empirical method
and presented some empirical (qualitative or quantitative) data. Why is this
a case study? What, if anything, is distinctive about the findings that could
not be learned by using some other social science method focusing on the
same topic?

Certain other features of the case study method are not critical for defining the
method, but they may be considered variations within case study research and
also provide answers to common questions.
 Variations within Case Studies as a Research Method

Yes, case study research includes both single-and multiple-case studies.
Though some fields, such as political science and public administration, have
tried to distinguish between these two approaches (and have used such terms as
the comparative case method as a distinctive form of multiple-case studies; see
Agranoff & Radin, 1991; Dion, 1998; Lijphart, 1975), single-and multiple-case
studies are in reality but two variants of case study designs (see Chapter 2 for
more).
And yes, case studies can include, and even be limited to, quantitative
evidence. In fact, any contrast between quantitative and qualitative evidence
does not distinguish the various research methods. Note that, as analogous
examples, some experiments (such as studies of perceptions) and some survey
questions (such as those seeking categorical rather than numerical responses)
rely on qualitative and not quantitative evidence. Likewise, historical research
can include enormous amounts of quantitative evidence.
As a related but important note, the case study method is not just a form of
“qualitative research,” even though it may be recognized among the array of
qualitative research choices (e.g., Creswell, 2007). Some case study research
goes beyond being a type of qualitative research, by using a mix of quantitative
and qualitative evidence. In addition, case studies need not always include the
direct and detailed observational evidence marked by other forms of “qualitative
research.”
And yes, case studies have a distinctive place in evaluation research (see
Cronbach & Associates, 1980; Patton, 2002; U.S. Government Accountability
Office, 1990). There are at least four different applications. The most important
is to explain the presumed causal links in real-life interventions that are too
complex for the survey or experimental strategies. A second application is to
describe an intervention and the real-life context in which it occurred. Third,
case studies can illustrate certain topics within an evaluation, again in a
descriptive mode. Fourth, the case study strategy may be used to enlighten those
situations in which the intervention being evaluated has no clear, single set of
outcomes. Whatever the application, one constant theme is that program
sponsors—rather than research investigators alone—may have the prominent
role in defining the evaluation questions and desired data categories (U.S.
Government Accountability Office, 1990).
And finally, yes, case studies can be conducted and written with many
different motives. These motives vary from the simple presentation of individual
cases to the desire to arrive at broad generalizations based on case study
evidence but without presenting any of the individual case studies separately
(see BOX 3).

BOX 3

Multiple-Case Studies: Case Studies Containing Multiple “Cases”

Case studies can cover multiple cases and then draw a single set of “cross-
case” conclusions. The two examples below both focused on a topic of
continuing public interest: identifying successful programs to improve U.S.
social conditions.



3A. A Cross-Case Analysis following the Presentation of Separate,
Single Cases

Jonathan Crane (1998) edited a book that had nine social programs as
separate cases. Each case had a different author and was presented in its
own chapter. The programs had in common strong evidence of their
effectiveness, but they varied widely in their focus—from education to
nutrition to drug prevention to preschool programs to drug treatment for
delinquent youths. The editor then presents a cross-program analysis in a
final chapter, attempting to draw generalizable conclusions that could apply
to many other programs.


3B. A Book Whose Entire Text Is Devoted to the Multiple-Case
(“Cross-Case”) Analysis

Lisbeth Schorr’s (1997) book is about major strategies for improving social
conditions, illustrated by four policy topics: welfare reform, strengthening
the child protection system, education reform, and transforming
neighborhoods. The book continually refers to specific case of successful
 programs,but thes programs do not appear as separate, individual chapters.
Also citing data from the literature, the author develops numerous
generalizations based on the case studies, including the need for successful
programs to be “results oriented:‘ Similarly, she identifies six other
attributes of highly effective programs (also see BOX 41 A and 41 B,
Chapter 6, p. 173).

EXERCISE 1.5 Defining Different Types of Case Studies Used

for Research Purposes

Define the three types of case studies used for research (but not teaching)
purposes: (a) explanatory or causal case studies, (b) descriptive case
studies, and (c) exploratory case studies. Compare the situations in which
these different types of case studies would be most applicable. Now name a
case study that you would like to conduct. Would it be explanatory,
descriptive, or exploratory? Why?

SUMMARY

This chapter has introduced the importance of the case study as a research
method. Like other research methods, it is a way of investigating an empirical
topic by following a set of prespecified procedures. Articulating these
procedures will dominate the remainder of this book.
The chapter has provided an operational definition of the case study and has
identified some of the variations in case studies. The chapter also has attempted
to distinguish the case study from alternative research methods in social science,
indicating the situations in which doing a case study may be preferred, for
instance, to doing a survey. Some situations may have no clearly preferred
method, as the strengths and weaknesses of the various methods may overlap.
The basic goal, however, is to consider all the methods in an inclusive and
pluralistic fashion—as part of your repertoire from which you may draw
according to a given situation to do social science research.
Finally, the chapter has discussed some of the major criticisms of case study
research, also suggesting possible responses to these criticisms. However, we
must all work hard to overcome the problems of doing case study research,
including the recognition that some of us were not meant, by skill or disposition,
to do such research in the first place. Case study research is remarkably hard,
even though case studies have traditionally been considered to be “soft”
research, possibly because investigators have not followed systematic
procedures. This book tries to make your research study easier by offering an
array of such procedures.
NOTES

1 The discussion only pertains to the use of these methods in the social sciences,
making no claims for commenting on the use of experiments, for instance, in
physics, biology, or other fields.

2 There nevertheless may be exceptional circumstances when a single case is so

unique or important that a case study investigator has no desire to generalize to
any other cases. See Stake’s (2005) “intrinsic” case studies and Lawrence-
Lightfoot and Davis’s (1997) “portraits.”

3 Scholars also point to the possibility that the classic experiments tend to test
simple causal relationships—that is, when a single treatment such as a new drug
is hypothesized to produce an effect. However, for many social and behavioral
topics, the relevant causes may be complex and involve multiple interactions,
and investigating these may well be beyond the capability of a single experiment
(George & Bennett, 2004, p. 12).

4 Robert Stake (2005, p. 443) similarly considers the “case,” and not any method
of inquiry, to be the defining criterion for case study. Furthermore, Stake (1995,
pp. 1-2) says that the preferred case must be a well-bounded, specific, complex,
and functioning “thing” (e.g., a person or a program) and not a generality (such
as the relationship among schools or an education policy).

REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 1

For selected case studies cited in the text of this chapter, two anthologies contain
either a more extensive excerpt or the full case study. The table below
crosswalks the reference in this book to the location of the excerpt or full
rendition.


ABSTRACT

A research design is the logic that links the data to be collected (and the
conclusions to be drawn) to the initial questions of study. Every empirical study
has an implicit, if not explicit, research design. Articulating “theory” about what
is being studied and what is to be learned helps to operationalize case study
designs and make them more explicit.
Case study designs need to maximize their quality through four critical
conditions related to design quality: (a) construct validity, (b) internal validity,
(c) external validity, and (d) reliability. How investigators can deal with these
aspects of quality control in doing case studies is discussed in Chapter 2 but also
is a major theme throughout the remainder of the book.
Among the actual case study designs, four major types are relevant, following
a 2 × 2 matrix. The first pair consists of single-case and multiple-case designs.
The second pair, which can occur in combination with either of the first pair, is
based on the unit or units of analysis to be covered--and distinguishes between
holistic and embedded designs. Among these designs, most multiple-case
designs are likely to be stronger than single-case designs. Trying to use even a
“two-case” design is therefore a worthy objective, compared to doing a single-
case study. Case studies also can be part of a larger mixed methods study.
 2

Designing Case Studies Identifying Your Case(s) and Establishing

the Logic of Your Case Study

GENERAL APPROACH TO DESIGNING CASE STUDIES

In identifying the method for your research project, Chapter 1 has shown when
you might choose to use the case study method, as opposed to other methods.
The next task is to design your case study. For this purpose, as in designing any
other type of research investigation, you need a plan or research design.
The development of this research design is a difficult part of doing case
studies. Unlike other research methods, a comprehensive “catalog” of research
designs for case studies has yet to be developed. There are no textbooks, like
those in the biological and psychological sciences, covering such design
considerations as the assignment of subjects to different “groups,” the selection
of different stimuli or experimental conditions, or the identification of various
response measures (see Cochran & Cox, 1957; Fisher, 1935, cited in Cochran &
Cox, 1957; Sidowski, 1966). In a laboratory experiment, each of these choices
reflects an important logical connection to the issues being studied. Similarly,
there are not even textbooks like the well-known volumes by Campbell and
Stanley (1966) or by Cook and Campbell (1979) that summarize the various
research designs for quasi-experimental situations. Nor have there emerged any
common designs—for example, “panel” studies—such as those recognized in
doing survey research (see L. Kidder & Judd, 1986, chap. 6).
One pitfall to be avoided, however, is to consider case study designs to be a
subset or variant of the research designs used for other methods, such as
experiments. For the longest time, scholars incorrectly thought that the case
study was but one type of quasi-experimental design (the “one-shot post-test-
only” design). This misperception has finally been corrected, with the following
statement appearing in a revision on quasi-experimental designs (Cook &
Campbell, 1979): “Certainly the case study as normally practiced should not be
demeaned by identification with the one-group post-test-only design” (p. 96). In
other words, the one-shot, post-test-only design as a quasi-experimental design
still may be considered flawed, but the case study has now been recognized as
something different. In fact, the case study is a separate research method that has
its own research designs.


Tip: How should I select the case(s) for my case study?

You need sufficient access to the potential data, whether to interview
people, review documents or records, or make observations in the “field.”
Given such access to more than a single candidate case, you should choose
the case(s) that will most likely illuminate your research questions. Absent
such access, you should consider changing your research questions,
hopefully leading to new candidates to which you do have access.


Do you think access should be so important?
Unfortunately, case study research designs have not been codified. The
following chapter therefore expands on the new methodological ground broken
by earlier editions of this book and describes a basic set of research designs for
doing single-and multiple-case studies. Although these designs will need to be
continually modified and improved in the future, in their present form they will
nevertheless help you to design more rigorous and methodologically sound case
studies.
 Definition of Research Designs

Every type of empirical research has an implicit, if not explicit, research
design. In the most elementary sense, the design is the logical sequence that
connects the empirical data to a study’s initial research questions and, ultimately,
to its conclusions. Colloquially, a research design is a logical plan for getting
from here to there, where here may be defined as the initial set of questions to be
answered, and there is some set of conclusions (answers) about these questions.
Between “here” and “there” may be found a number of major steps, including
the collection and analysis of relevant data. As a summary definition, another
textbook has described a research design as a plan that
guides the investigator in the process of collecting, analyzing, and
interpreting observations. It is a logical model of proof that allows the
researcher to draw inferences concerning causal relations among the
variables under investigation. (Nachmias & Nachmias, 1992, pp. 77-78,
emphasis added)

Another way of thinking about a research design is as a “blueprint” for your
research, dealing with at least four problems: what questions to study, what data
are relevant, what data to collect, and how to analyze the results (Philliber,
Schwab, & Samsloss, 1980).
Note that a research design is much more than a work plan. The main purpose
of the design is to help to avoid the situation in which the evidence does not
address the initial research questions. In this sense, a research design deals with
a logical problem and not a logistical problem. As a simple example, suppose
you want to study a single organization. Your research questions, however, have
to do with the organization’s relationships with other organizations—their
competitive or collaborative nature, for example. Such questions can be
answered only if you collect information directly from the other organizations
and not merely from the one you started with. If you complete your study by
examining only one organization, you cannot draw unbiased conclusions about
interorganizational partnerships. This is a flaw in your research design, not in
your work plan. The outcome could have been avoided if you had developed an
appropriate research design in the first place.
 Components of Research Designs

For case studies, five components of a research design are especially
important:
1. a study’s questions;
2. its propositions, if any;
3. its unit(s) of analysis;
4. the logic linking the data to the propositions; and
5. the criteria for interpreting the findings.

Study questions. This first component has already been described in Chapter 1,
which suggested that the form of the question—in terms of “who,” “what,”
“where,” “how,” and “why”—provides an important clue regarding the most
relevant research method to be used. The case study method is most likely to be
appropriate for “how” and “why” questions, so your initial task is to clarify
precisely the nature of your study questions in this regard.
More troublesome may be coming up with the substance of the questions.
Many students take an initial stab, only to be discouraged when they find the
same question(s) already well covered by previous research. Other less desirable
questions focus on too trivial or minor parts of an issue. A helpful hint is to
move in three stages. In the first, try to use the literature to narrow your interest
to a key topic or two, not worrying about any specific research questions. In the
second, examine closely—even dissect—a few key studies on your topic of
interest. Identify the questions in those few studies and whether they conclude
with new questions or loose ends for future research. These may then stimulate
your own thinking and imagination, and you may find yourself articulating some
potential questions of your own. In the third stage, examine another set of
studies on the same topic. They may provide support for your potential questions
or even suggest ways of sharpening them.

EXERCISE 2.1 Defining the Boundaries of a Case Study

Select a topic for a case study you would like to do. Identify some research
questions to be answered or propositions to be examined by your case
study. How does the naming of these questions or propositions clarify the
 boundaries of your case study with regard to the time period covered by the
case study; the relevant social group, organization, or geographic area; the
type of evidence to be collected; and the priorities for data collection and
analysis?

Study propositions. As for the second component, each proposition directs
attention to something that should be examined within the scope of study. For
instance, assume that your research, on the topic of interorganizational
partnerships, began with the following question: How and why do organizations
collaborate with one another to provide joint services (for example, a
manufacturer and a retail outlet collaborating to sell certain computer products)?
These “how” and “why” questions, capturing what you are really interested in
answering, led you to the case study as the appropriate method in the first place.
Nevertheless, these “how” and “why” questions do not point to what you should
study.
Only if you are forced to state some propositions will you move in the right
direction. For instance, you might think that organizations collaborate because
they derive mutual benefits. This proposition, besides reflecting an important
theoretical issue (that other incentives for collaboration do not exist or are
unimportant), also begins to tell you where to look for relevant evidence (to
define and ascertain the extent of specific benefits to each organization).
At the same time, some studies may have a legitimate reason for not having
any propositions. This is the condition—which exists in experiments, surveys,
and the other research methods alike—in which a topic is the subject of
“exploration.” Every exploration, however, should still have some purpose.
Instead of propositions, the design for an exploratory study should state this
purpose, as well as the criteria by which an exploration will be judged
successful. Consider the analogy in BOX 4 for exploratory case studies. Can you
imagine how you would ask for support from Queen Isabella to do your
exploratory study?

BOX 4

“Exploration” as an Analogy Exploratory Case Study

When Christopher Columbūs went to Queen Isabella to ask for support for
his “exploration” of the New World, he had to have some reasons for asking
for three ships (Why not one? Why not five?), and he hād some rationale
for going westward (Why not south? Why not south and then east?). He
 also had some (mistaken) criteria for recognizing the Indies when he
actually encoūntered it. In short, his exploration began with some rationale
and direction, even if his initial assumptions might later have been proved
wrong (Wilford, 1992). This same degree of rātionale and direction should
underlie even an exploratory case study.

Unit of analysis. This third component is related to the fundamental problem of
defining what the “case” is—a problem that has plagued many investigators at
the outset of case studies (e.g., Ragin & Becker, 1992). For instance, in the
classic case study, a “case” may be an individual. Jennifer Platt (1992) has noted
how the early case studies in the Chicago school of sociology were life histories
of such persons as juvenile delinquents or derelict men. You also can imagine
case studies of clinical patients, of exemplary students, or of certain types of
leaders. In each situation, an individual person is the case being studied, and the
individual is the primary unit of analysis. Information about the relevant
individual would be collected, and several such individuals or “cases” might be
included in a multiple-case study.
You would still need study questions and study propositions to help identify
the relevant information to be collected about this individual or individuals.
Without such questions and propositions, you might be tempted to cover
“everything” about the individual(s), which is impossible to do. For example, the
propositions in studying these individuals might involve the influence of early
childhood or the role of peer relationships. Such seemingly general topics
nevertheless represent a vast narrowing of the relevant data. The more a case
study contains specific questions and propositions, the more it will stay within
feasible limits.
Of course, the “case” also can be some event or entity other than a single
individual. Case studies have been done about decisions, programs, the
implementation process, and organizational change. Feagin et al. (1991) contains
some classic examples of these single cases in sociology and political science.
Beware of these types of cases—none is easily defined in terms of the beginning
or end points of the “case.” For example, a case study of a specific program may
reveal (a) variations in program definition, depending upon the perspective of
different actors, and (b) program components that preexisted the formal
designation of the program. Any case study of such a program would therefore
have to confront these conditions in delineating the unit of analysis.
As a general guide, your tentative definition of the unit of analysis (which is
the same as the definition of the “case”) is related to the way you have defined
your initial research questions. Suppose, for example, you want to study the role
of the United States in the global economy. Years ago, Peter Drucker (1986)
wrote a provocative essay (not a case study) about fundamental changes in the
world economy, including the importance of “capital movements” independent
of the flow of goods and services. Using Drucker’s work or some similar
theoretical framework, the unit of analysis (or “case”) for your case study might
be a country’s economy, an industry in the world marketplace, an economic
policy, or the trade or capital flow between countries. Each unit of analysis and
its related questions and propositions would call for a slightly different research
design and data collection strategy.
Selection of the appropriate unit of analysis will start to occur when you
accurately specify your primary research questions. If your questions do not lead
to the favoring of one unit of analysis over another, your questions are probably
either too vague or too numerous—and you may have trouble doing a case study.
However, when you do eventually arrive at a definition of the unit of analysis, do
not consider closure permanent. Your choice of the unit of analysis, as with other
facets of your research design, can be revisited as a result of discoveries during
your data collection (see discussion and cautions about flexibility throughout this
book and at the end of this chapter).
Sometimes, the unit of analysis may have been defined one way, even though
the phenomenon being studied actually follows a different definition. Most
frequently, investigators have confused case studies of neighborhoods with case
studies of small groups (as another example, confusing a new technology with
the workings of an engineering team in an organization; see BOX 5A). How a
geographic area such as a neighborhood copes with racial transition, upgrading,
and other phenomena can be quite different from how a small group copes with
these same phenomena. For instance, Street Corner Society (Whyte, 1943/1955;
see BOX 2A in Chapter 1 of this book) and Tally’s Corner (Liebow, 1967; see
BOX 9, this chapter) often have been mistaken for being case studies of
neighborhoods when in fact they are case studies of small groups (note that in
neither book is the neighborhood geography described, even though the small
groups lived in a small area with clear neighborhood implications). BOX 5B,
however, presents a good example of how units of analyses can be defined in a
more discriminating manner—in the field of world trade.

BOX 5

Defining the Unit of Analysis

5A. What Is the Unit of Analysis?


The Soul of a New Machine (1981) was a Pulitzer Prize-winning book by
Tracy Kidder. The book, also a best seller, is about the development of a
new minicomputer, produced by Data General Corporation, intended to
compete with one produced by a direct competitor, Digital Equipment
Corporation (also see BOX 28,
Chapter 5, p. 142). This easy-to-read book describes how Data General’s
engineering team invented and developed the new computer. The book
begins with the initial conceptualization of the computer and ends when the
engineering team relinquishes control of the machine to Data General’s
marketing staff.
The book is an excellent example of a case study. However, the book also
illustrates a fundamental problem in doing case studies-that of defining the
unit of analysis. Is the “case” being studied the minicomputer, or is it about
the dynamics of a small group-the engineering team? the answer is critical
for understanding how the case study mightrelate to any broader body of
knowledge-that is, whether to generalize to a technology topic or to a group
dynamics topic. Because the book is not an academic study, it does not need
to, nor does it, provide an answer.


5B. A Clearer Choice among Units of Analysis

Ira Magaziner and Mark Patinkin’s(1989) book, The Silent War: Inside the
Global Business Battles Shaping America’s Future, presents nine individual
case studies (also see BOX 35, Chapter 5, p. 161). Each case helps the
reader to understand a real-life situation of international economic
competition.
Two of the cases appear similar but in fact have different main units of
analysis. One case, about the Korean firm Samsung, is a case study of the
critical policies that make the firm competitive. Understanding Korean
economic development is part of the context, and the case study also
contains an embedded unit-Samsung’s development of the microwave oven
as an illustrative product. The other case, about the development of an
Apple computer factory in Singapore, is in fact a case study of Singapore’s
critical policies that make the country competitive. The Apple computer
factory experience-an embedded unit of analysis-is actually an illustrative

 example of how the national policies affected foreign investments. These
two case show how the definition of the main and embedded units of
analyses, as well as the definition of the cintextual events surrouding these
units depends on the level of inquiry. The main unit of analysis is likely to
be at the level being addressed by the main study questions.
Most investigators will encounter this type of confusion in defining the unit of
analysis or “case.” To reduce the confusion, one recommended practice is to
discuss the potential case with a colleague. Try to explain to that person what
questions you are trying to answer and why you have chosen a specific case or
group of cases as a way of answering those questions. This may help you to
avoid incorrectly identifying the unit of analysis.
Once the general definition of the case has been established, other
clarifications in the unit of analysis become important. If the unit of analysis is a
small group, for instance, the persons to be included within the group (the
immediate topic of the case study) must be distinguished from those who are
outside it (the context for the case study). Similarly, if the case is about local
services in a specific geographic area, you need to decide which services to
cover. Also desirable, for almost any topic that might be chosen, are specific
time boundaries to define the beginning and end of the case (e.g., whether to
include the entire or only some part of the life cycle of the entity that is to be the
case). Answering all of these types of questions will help to determine the scope
of your data collection and, in particular, how you will distinguish data about the
subject of your case study (the “phenomenon”) from data external to the case
(the “context”).
These latter cautions regarding the need for spatial, temporal, and other
concrete boundaries underlie a key but subtle aspect in defining your case. The
desired case should be some real-life phenomenon, not an abstraction such as a
topic, an argument, or even a hypothesis. These abstractions, absent the
identification of specific examples or cases, would rightfully serve as the
subjects of research studies using other kinds of methods but not case studies. To
justify using the case study method, you need to go one step further: You need to
define a specific, real-life “case” to represent the abstraction. (For examples of
more concrete and less concrete case study topics, see Figure 2.1.)
Take the concept of “neighboring.” Alone, it could be the subject of research
studies using methods other than the case study method. The other methods
might include a survey of the relationships among neighbors, a history of the
evolution of the sense of neighboring and the setting of boundaries, or an
experiment in which young children do tasks next to each other to determine the
distracting effects, if any, of their neighbors. These examples show how the
abstract concept of “neighboring” does not alone produce the grounds for a case
study. However, the concept could readily become a case study topic if it were
accompanied by your selecting a specific neighborhood (“case”) to be studied
and posing study questions and propositions about the neighborhood in relation
to the concept of “neighboring.”
One final point pertains to the role of the available research literature and
needs to be made about defining the case and the unit of analysis. Most
researchers will want to compare their findings with previous research. For this
reason, the key definitions used in your study should not be idiosyncratic.
Rather, each case study and unit of analysis either should be similar to those
previously studied by others or should innovate in clear, operationally defined
ways. In this manner, the previous literature also can become a guide for
defining the case and unit of analysis.

Figure 2.1 Illustrative Case Study Topics

EXERCISE 2.2 Defining the Unit of Analysis (and the

“Case”) for a Case Study

Examine Figure 2.1. Discuss each subject, which illustrates a different unit
of analysis. Find a published case study on at least one of these subjects,
 indicating the actual “case” that was being studied. Understanding that each
subject illustrates a different unit of analysis and involves the selection of
different cases to be studied, do you think that the more concrete units
might be easier to define than the less concrete ones? Why?

Linking data to propositions and criteria for interpreting the findings. The fourth
and fifth components have been increasingly better developed in doing case
studies. These components foreshadow the data analysis steps in case study
research. Because the analytic techniques and choices are covered in detail in
Chapter 5, your main concern during the design phase is to be aware of the main
choices and how they might suit your case study. In this way, your research
design can create a more solid foundation for the later analysis.
All of the analytic techniques in Chapter 5 represent ways of linking data to
propositions: pattern matching, explanation building, time-series analysis, logic
models, and cross-case synthesis. The actual analyses will require that you
combine or calculate your case study data as a direct reflection of your initial
study propositions. For instance, knowing that some or all of your propositions
cover a temporal sequence would mean that you might eventually use some type
of time-series analysis. Noting this strong likelihood during the design phase
would call your attention to the need to be sure you had sufficient procedures to
collect time markers as part of your data collection plans.
If you have had limited experience in conducting empirical studies, you will
not easily identify the likely analytic technique(s) or anticipate the needed data
to use the techniques to their full advantage. More experienced researchers will
note how often they have either (a) collected too much data that were not later
used in any analysis or (b) collected too little data that prevented the proper use
of a desired analytic technique. Sometimes, the latter situation even may force
researchers to return to their data collection phase (if they can), to supplement
the original data. The more you can avoid any of these situations, the better off
you will be.

Criteria for interpreting a study’s findings. Statistical analyses offer some
explicit criteria for such interpretations. For instance, by convention, social
science considers a p level of less than .05 to demonstrate that observed
differences were “statistically significant.” However, much case study analysis
will not rely on the use of statistics and therefore calls attention to other ways of
thinking about such criteria.
A major and important alternative strategy is to identify and address rival
explanations for your findings. Again, Chapter 5 discusses this strategy and how
it works more fully. At the design stage of your work, the challenge is to
anticipate and enumerate the important rivals, so you will include information
about them as part of your data collection. If you only think of rival explanations
after data collection has been completed, you will be starting to justify and
design a future study, but you will not be helping to complete your current case
study. For this reason, specifying important rival explanations is a part of a case
study’s research design work.

Summary. A research design should include five components. Although the
current state of the art does not provide detailed guidance on the last two, the
complete research design should indicate what data are to be collected—as
indicated by a study’s questions, its propositions, and its units of analysis. The
design also should tell you what is to be done after the data have been collected
—as indicated by the logic linking the data to the propositions and the criteria
for interpreting the findings.
 The Role of Theory in Design Work

Covering these preceding five components of research designs will effectively
force you to begin constructing a preliminary theory related to your topic of
study. This role of theory development, prior to the conduct of any data
collection, is one point of difference between case studies and related methods
such as ethnography (Lincoln & Guba, 1985; Van Maanen, 1988) and “grounded
theory” (Corbin & Strauss, 2007). Typically, these related methods deliberately
avoid specifying any theoretical propositions at the outset of an inquiry. As a
result, students confusing these methods with case studies wrongly think that, by
having selected the case study method, they can proceed quickly into the data
collection phase of their work, and they may have been encouraged to make their
“field contacts” as quickly as possible. No guidance could be more misleading.
Among other considerations, the relevant field contacts depend upon an
understanding—or theory—of what is being studied.

Theory development. For case studies, theory development as part of the design
phase is essential, whether the ensuing case study’s purpose is to develop or to
test theory. Using a case study on the implementation of a new management
information system (MIS) as an example (Markus, 1983), the simplest ingredient
of a theory is a statement such as the following:
The case study will show why implementation only succeeded when the
organization was able to re-structure itself, and not just overlay the new
MIS on the old organizational structure. (Markus, 1983)

The statement presents the nutshell of a theory of MIS implementation—that
is, that organizational restructuring is needed to make MIS implementation
work.
Using the same case, an additional ingredient might be the following
statement:
The case study will also show why the simple replacement of key persons
was not sufficient for successful implementation. (Markus, 1983)

This second statement presents the nutshell of a rival theory—that is, that MIS
implementation fails because of the resistance to change on the part of individual
people and that the replacement of such people is the main requirement for
implementation to succeed.
You can see that as these two initial ingredients are elaborated, the stated ideas
will increasingly cover the questions, propositions, units of analysis, logic
connecting data to propositions, and criteria for interpreting the findings—that
is, the five components of the needed research design. In this sense, the complete
research design embodies a “theory” of what is being studied.
This theory should by no means be considered with the formality of grand
theory in social science, nor are you being asked to be a masterful theoretician.
Rather, the simple goal is to have a sufficient blueprint for your study, and this
requires theoretical propositions, usefully noted by Sutton and Staw (1995) as “a
[hypothetical] story about why acts, events, structure, and thoughts occur” (p.
378). Then, the complete research design will provide surprisingly strong
guidance in determining what data to collect and the strategies for analyzing the
data. For this reason, theory development prior to the collection of any case
study data is an essential step in doing case studies. As noted for
nonexperimental studies more generally, a more elaborate theory desirably
points to a more complex pattern of expected results (P. R. Rosenbaum, 2002,
pp. 5-6 and 277-279). The benefit is a stronger design and a heightened ability to
interpret your eventual data.
However, theory development takes time and can be difficult (Eisenhardt,
1989). For some topics, existing works may provide a rich theoretical framework
for designing a specific case study. If you are interested in international
economic development, for instance, Peter Drucker’s (1986) “The Changed
World Economy” is an exceptional source of theories and hypotheses. Drucker
claims that the world economy has changed significantly from the past. He
points to the “uncoupling” between the primary products (raw materials)
economy and the industrial economy, a similar uncoupling between low labor
costs and manufacturing production, and the uncoupling between financial
markets and the real economy of goods and services. To test these propositions
might require different studies, some focusing on the different uncouplings,
others focusing on specific industries, and yet others explaining the plight of
specific countries. Each different study would likely call for a different unit of
analysis. Drucker’s theoretical framework would provide guidance for designing
these studies and even for collecting relevant data.
In other situations, the appropriate theory may be a descriptive theory (see
BOX 2A in Chapter 1 for another example), and your concern should focus on
such issues as (a) the purpose of the descriptive effort, (b) the full but realistic
range of topics that might be considered a “complete” description of what is to
be studied, and (c) the likely topic(s) that will be the essence of the description.
Good answers to these questions, including the rationales underlying the
answers, will help you go a long way toward developing the needed theoretical
base—and research design—for your study.
For yet other topics, the existing knowledge base may be poor, and the
available literature will provide no conceptual framework or hypotheses of note.
Such a knowledge base does not lend itself to the development of good
theoretical statements, and any new empirical study is likely to assume the
characteristic of an “exploratory” study. Nevertheless, as noted earlier with the
illustrative case in BOX 4, even an exploratory case study should be preceded by
statements about what is to be explored, the purpose of the exploration, and the
criteria by which the exploration will be judged successful.
Overall, you may want to gain a richer understanding of how theory is used in
case studies by reviewing specific case studies that have been successfully
completed. For instance, Yin (2003, chap. 1) shows how theory was used in
exploratory, descriptive, and explanatory situations by discussing five actual case
studies.

Illustrative types of theories. In general, to overcome the barriers to theory
development, you should try to prepare for your case study by doing such things
as reviewing the literature related to what you would like to study (also see
Cooper, 1984), discussing your topic and ideas with colleagues or teachers, and
asking yourself challenging questions about what you are studying, why you are
proposing to do the study, and what you hope to learn as a result of the study.
As a further reminder, you should be aware of the full range of theories that
might be relevant to your study. For instance, note that the MIS example
illustrates MIS “implementation” theory and that this is but one type of theory
that can be the subject of study. Other types of theories for you to consider
include
• individual theories—for example, theories of individual development,
cognitive behavior, personality, learning and disability, individual
perception, and interpersonal interactions;
• group theories—for example, theories of family functioning, informal
groups, work teams, supervisory-employee relations, and interpersonal
networks;
• organizational theories—for example, theories of bureaucracies,
organizational structure and functions, excellence in organizational
performance, and interorganizational partnerships; and
• societal theories—for example, theories of urban development,
 international behavior, cultural institutions, technological development,
and marketplace functions.

Other examples cut across these illustrative types. Decision-making theory
(Carroll & Johnson, 1992), for instance, can involve individuals, organizations,
or social groups. As another example, a common topic of case studies is the
evaluation of publicly supported programs, such as federal, state, or local
programs. In this situation, the development of a theory of how a program is
supposed to work is essential to the design of the evaluation. In this situation,
Bickman (1987) reminds us that the theory needs to distinguish between the
substance of the program (e.g., how to make education more effective) and the
process of program implementation (e.g., how to install an effective program).
The distinction would avoid situations where policy makers might want to know
the desired substantive remedies (e.g., findings about a newly effective
curriculum) but where an evaluation unfortunately focused on managerial issues
(e.g., the need to hire a good project director). Such a mismatch can be avoided
by giving closer attention to the substantive theory.

Generalizing from case study to theory. Theory development does not only
facilitate the data collection phase of the ensuing case study. The appropriately
developed theory also is the level at which the generalization of the case study
results will occur. This role of theory has been characterized throughout this
book as “analytic generalization” and has been contrasted with another way of
generalizing results, known as “statistical generalization.” Understanding the
distinction between these two types of generalization may be your most
important challenge in doing case studies.
Let us first take the more commonly recognized way of generalizing
—statistical generalization—although it is the less relevant one for doing case
studies. In statistical generalization, an inference is made about a population (or
universe) on the basis of empirical data collected about a sample from that
universe. This is shown as a Level One inference in Figure 2.2.1 This method of
generalizing is commonly recognized because research investigators have ready
access to quantitative formulas for determining the confidence with which
generalizations can be made, depending mostly upon the size and internal
variation within the universe and sample. Moreover, this is the most common
way of generalizing when doing surveys (e.g., Fowler, 1988; Lavrakas, 1987) or
analyzing archival data.
A fatal flaw in doing case studies is to conceive of statistical generalization as
the method of generalizing the results of your case study. This is because your
cases are not “sampling units” and should not be chosen for this reason. Rather,
individual case studies are to be selected as a laboratory investigator selects the
topic of a new experiment. Multiple cases, in this sense, resemble multiple
experiments. Under these circumstances, the mode of generalization is analytic
generalization, in which a previously developed theory is used as a template with
which to compare the empirical results of the case study.2 If two or more cases
are shown to support the same theory, replication may be claimed. The empirical
results may be considered yet more potent if two or more cases support the same
theory but do not support an equally plausible, rival theory. Graphically, this
type of generalization is shown as a Level Two inference in Figure 2.2.

Figure 2.2 Making Inferences: Two Levels

Analytic generalization can be used whether your case study involves one or
several cases, which shall be later referenced as single-case or multiple-case
studies. Furthermore, the logic of replication and the distinction between
statistical and analytic generalization will be covered in greater detail in the
discussion of multiple-case study designs. The main point at this juncture is that
you should try to aim toward analytic generalization in doing case studies, and
you should avoid thinking in such confusing terms as “the sample of cases” or
the “small sample size of cases,” as if a single-case study were like a single
respondent in a survey or a single subject in an experiment. In other words, in
terms of Figure 2.2, you should aim for Level Two inferences when doing case
studies.
Because of the importance of this distinction between the two ways of
generalizing, you will find repeated examples and discussion throughout the
remainder of this chapter as well as in Chapter 5.

Summary. This subsection has suggested that a complete research design,
covering the four components described earlier, in fact requires the development
of a theoretical framework for the case study that is to be conducted. Rather than
resisting such a requirement, a good case study investigator should make the
effort to develop this theoretical framework, no matter whether the study is to be
explanatory, descriptive, or exploratory. The use of theory, in doing case studies,
is an immense aid in defining the appropriate research design and data
collection. The same theoretical orientation also becomes the main vehicle for
generalizing the results of the case study.
CRITERIA FOR JUDGING THE QUALITY OF RESEARCH
DESIGNS

Because a research design is supposed to represent a logical set of statements,

you also can judge the quality of any given design according to certain logical
tests. Concepts that have been offered for these tests include trustworthiness,
credibility, confirmability, and data dependability (U.S. Government
Accountability Office, 1990).
Four tests, however, have been commonly used to establish the quality of any
empirical social research. Because case studies are one form of such research,
the four tests also are relevant to case studies. An important innovation of this
book is the identification of several tactics for dealing with these four tests when
doing case studies. Figure 2.3 lists the four widely used tests and the
recommended case study tactics, as well as a cross-reference to the phase of
research when the tactic is to be used. (Each tactic is described in detail in the
referenced chapter of this book.)
Because the four tests are common to all social science methods, the tests
have been summarized in numerous textbooks (see L. Kidder & Judd, 1986, pp.
26-29):
• Construct validity: identifying correct operational measures for the
concepts being studied
• Internal validity (for explanatory or causal studies only and not for
descriptive or exploratory studies): seeking to establish a causal
relationship, whereby certain conditions are believed to lead to other
conditions, as distinguished from spurious relationships
• External validity: defining the domain to which a study’s findings can be
generalized
• Reliability: demonstrating that the operations of a study—such as the data
collection procedures—can be repeated, with the same results

Each item on this list deserves explicit attention. For case studies, an
important revelation is that the several tactics to be used in dealing with these
tests should be applied throughout the subsequent conduct of the case study, not
just at its beginning. Thus, the “design work” for case studies may actually
continue beyond the initial design plans.

Figure 2.3 Case Study Tactics for Four Design Tests

 Construct Validity

This first test is especially challenging in case study research. People who
have been critical of case studies often point to the fact that a case study
investigator fails to develop a sufficiently operational set of measures and that
“subjective” judgments are used to collect the data.3 Take an example such as
studying “neighborhood change”—a common case study topic (e.g., Bradshaw,
1999; Keating & Krumholz, 1999).
Over the years, concerns have arisen over how certain urban neighborhoods
have changed their character. Any number of case studies has examined the
types of changes and their consequences. However, without any prior
specification of the significant, operational events that constitute “change,” a
reader cannot tell whether the claimed changes in a case study genuinely reflect
the events in a neighborhood or whether they happen to be based on an
investigator’s impressions only.
Neighborhood change can cover a wide variety of phenomena: racial turnover,
housing deterioration and abandonment, changes in the pattern of urban services,
shifts in a neighborhood’s economic institutions, or the turnover from low-to
middle-income residents in revitalizing neighborhoods. The choice of whether to
aggregate blocks, census tracts, or larger areas also can produce different results
(Hipp, 2007).
To meet the test of construct validity, an investigator must be sure to cover
two steps:
1. define neighborhood change in terms of specific concepts (and relate
them to the original objectives of the study) and
2. identify operational measures that match the concepts (preferably citing
published studies that make the same matches).

For example, suppose you satisfy the first step by stating that you plan to
study neighborhood change by focusing on trends in neighborhood crime. The
second step now demands that you select a specific measure, such as police-
reported crime (which happens to be the standard measure used in the FBI
Uniform Crime Reports) as your measure of crime. The literature will indicate
certain known shortcomings in this measure, mainly that unknown proportions
of crimes are not reported to the police. You will then need to discuss how the
shortcomings nevertheless will not bias your study of neighborhood crime and
hence neighborhood change.
As Figure 2.3 shows, three tactics are available to increase construct validity
when doing case studies. The first is the use of multiple sources of evidence, in a
manner encouraging convergent lines of inquiry, and this tactic is relevant during
data collection (see Chapter 4). A second tactic is to establish a chain of
evidence, also relevant during data collection (also Chapter 4). The third tactic is
to have the draft case study report reviewed by key informants (a procedure
described further in Chapter 6).
 Internal Validity

This second test has been given the greatest attention in experimental and
quasi-experimental research (see Campbell & Stanley, 1966; Cook & Campbell,
1979). Numerous “threats” to validity have been identified, mainly dealing with
spurious effects. However, because so many textbooks already cover this topic,
only two points need to be made here.
First, internal validity is mainly a concern for explanatory case studies, when
an investigator is trying to explain how and why event x led to event y. If the
investigator incorrectly concludes that there is a causal relationship between x
and y without knowing that some third factor—z—may actually have caused y,
the research design has failed to deal with some threat to internal validity. Note
that this logic is inapplicable to descriptive or exploratory studies (whether the
studies are case studies, surveys, or experiments), which are not concerned with
this kind of causal situation.
Second, the concern over internal validity, for case study research, extends to
the broader problem of making inferences. Basically, a case study involves an
inference every time an event cannot be directly observed. An investigator will
“infer” that a particular event resulted from some earlier occurrence, based on
interview and documentary evidence collected as part of the case study. Is the
inference correct? Have all the rival explanations and possibilities been
considered? Is the evidence convergent? Does it appear to be airtight? A research
design that has anticipated these questions has begun to deal with the overall
problem of making inferences and therefore the specific problem of internal
validity.
However, the specific tactics for achieving this result are difficult to identify.
This is especially true in doing case studies. As one set of suggestions, Figure
2.3 shows that the analytic tactic of pattern matching, described further in
Chapter 5, is one way of addressing internal validity. Three other analytic tactics,
explanation building, addressing rival explanations, and using logic models, also
are described in Chapter 5.
 External Validity

The third test deals with the problem of knowing whether a study’s findings
are generalizable beyond the immediate case study. In the simplest example, if a
study of neighborhood change focused on one neighborhood, are the results
applicable to another neighborhood? The external validity problem has been a
major barrier in doing case studies. Critics typically state that single cases offer a
poor basis for generalizing. However, such critics are implicitly contrasting the
situation to survey research, in which a sample is intended to generalize to a
larger universe. This analogy to samples and universes is incorrect when dealing
with case studies. Survey research relies on statistical generalization, whereas
case studies (as with experiments) rely on analytic generalization. In analytical
generalization, the investigator is striving to generalize a particular set of results
to some broader theory (see three examples in BOX 6).
For example, the theory of neighborhood change that led to a case study in the
first place is the same theory that will help to identify the other cases to which
the results are generalizable. If a study had focused on population transition in an
urban neighborhood (e.g., Flippen, 2001), the procedure for selecting a
neighborhood for study would have begun with identifying a neighborhood
within which the hypothesized transitions were occurring. Theories about
transition would then be the domain to which the results could later be
generalized.

BOX 6

How Case Studies Can Be Generalized to Theory: Three Examples

6A. The Origins of Social Class Theory


The first example is about the uncovering and labeling of a social class
structure based on a case study of a typical American city, Yankee City
(garner & Lunt, 1941). This classic case study in sociology made a critical
contribution to social stratifica middle,” “middle-middle,” “upper-lower,”
and “lower” classes.


6B. Contributions to Urban Planning Theory

The second example is Jane Jacobs and her famous book, The Death and
Life of Great American Cities (1961). The book is based mostly on
experiences from a single case, New York City. However, the chapter
topics, rather than reflecting the single experi ences of New York, cover
broader theoretical issues in urban planning, such as the role of sidewalks,
the role of neighborhood parks, the need for primary mixed uses, the need
for small blocks, and the processes of slumming and unslumming. In the
aggregate, these issues in fact represent Jacobs’s building of a theory or
urban planning.
Jacobs’s book created heated controversy in the planning profession. As
a partial result, new empirical inquiries were made in other locales, to
examine one or another facet of her rich and provocative ideas. Her theory,
in essence, became the vehicle for examining other cases, and the theory
still stands as a significant contribution to the field of urban planning.


6C. A More Contemporary Example

A third example covers a 5-year ethnographic study of a single
neighborhood at the edge of Chicago (Carr, 2003). The study shows how
the neighborhood successfully thwarted undesirable youth-related crime.
The experience, in the author’s view, challenged existing theories claiming
that strong social ties are crucial to effective neighborhood control. Instead,
the author offers nether theories of informal social control that he believes
may be especially pertinent to youth crime prevention in contemporary
suburban neighborhoods.
The generalization is not automatic, however. A theory must be tested by
replicating the findings in a second or even a third neighborhood, where the
theory has specified that the same results should occur. Once such direct
replications have been made, the results might be accepted as providing strong
support for the theory, even though further replications had not been performed.
This replication logic is the same that underlies the use of experiments (and
allows scientists to cumulate knowledge across experiments). The logic will be
discussed further in this chapter in the section on multiple-case designs.
 Reliability

Most people are probably already familiar with this final test. The objective is
to be sure that, if a later investigator followed the same procedures as described
by an earlier investigator and conducted the same case study all over again, the
later investigator should arrive at the same findings and conclusions. (Note that
the emphasis is on doing the same case over again, not on “replicating” the
results of one case by doing another case study.) The goal of reliability is to
minimize the errors and biases in a study.
One prerequisite for allowing this other investigator to repeat an earlier case
study is the need to document the procedures followed in the earlier case.
Without such documentation, you could not even repeat your own work (which
is another way of dealing with reliability). In the past, case study research
procedures have been poorly documented, making external reviewers suspicious
of the reliability of the case study method.4 Figure 2.3 indicates two specific
tactics to overcome these shortcomings—the use of a case study protocol to deal
with the documentation problem in detail (discussed in Chapter 3) and the
development of a case study database (discussed in Chapter 4).
The general way of approaching the reliability problem is to make as many
steps as operational as possible and to conduct research as if someone were
always looking over your shoulder. Accountants and bookkeepers always are
aware that any calculations must be capable of being audited. In this sense, an
auditor also is performing a reliability check and must be able to produce the
same results if the same procedures are followed. A good guideline for doing
case studies is therefore to conduct the research so that an auditor could in
principle repeat the procedures and arrive at the same results.
 Summary

Four tests may be considered relevant in judging the quality of a research
design. In designing and doing case studies, various tactics are available to deal
with these tests, though not all of the tactics occur at the formal stage of
designing a case study. Some of the tactics occur during the data collection, data
analysis, or compositional phases of the research and are therefore described in
greater detail in subsequent chapters of this book.

EXERCISE 2.3 Defining the Criteria for Judging the Quality

of Research Designs

Define the four criteria for judging the quality of research designs: (a)
construct validity, (b) internal validity, (c) external validity, and (d)
reliability. Give an example of each type of criterion in a case study you
might want to do.

CASE STUDY DESIGNS

These general characteristics of research designs serve as a background for

considering the specific designs for case studies. Four types of designs will be
discussed, based on a 2 × 2 matrix (see Figure 2.4). The matrix first shows that
every type of design will include the desire to analyze contextual conditions in
relation to the “case,” with the dotted lines between the two signaling that the
boundaries between the case and the context are not likely to be sharp. The
matrix then shows that single-and multiple-case studies reflect different design
situations and that, within these two variants, there also can be unitary or
multiple units of analysis. The resulting four types of designs for case studies are
(Type 1) single-case (holistic) designs, (Type 2) single-case (embedded) designs,
(Type 3) multiple-case (holistic) designs, and (Type 4) multiple-case (embedded)
designs. The rationale for these four types of designs is as follows.

Figure 2.4 Basic Types of Designs for Case Studies
SOURCE: COSMOS Corporation.

 What Are the Potential Single-Case Designs (Types 1 and 2)?

Rationale for single-case designs. A primary distinction in designing case
studies is between single- and multiple-case designs. This means the need for a
decision, prior to any data collection, on whether a single case or multiple cases
are going to be used to address the research questions. The single-case study is
an appropriate design under several circumstances, and five rationales are given
below. Recall that a single-case study is analogous to a single experiment, and
many of the same conditions that justify a single experiment also justify a single-
case study.
One rationale for a single case is when it represents the critical case in testing
a well-formulated theory (again, note the analogy to the critical experiment ).
The theory has specified a clear set of propositions as well as the circumstances
within which the propositions are believed to be true. A single case, meeting all
of the conditions for testing the theory, can confirm, challenge, or extend the
theory. The single case can then be used to determine whether a theory’s
propositions are correct or whether some alternative set of explanations might be
more relevant. In this manner, like Graham Allison’s comparison of three
theories and the Cuban missile crisis (described in Chapter 1, BOX 2), the single
case can represent a significant contribution to knowledge and theory building.
Such a study can even help to refocus future investigations in an entire field.
(See BOX 7 for another example, in the field of organizational innovation.)
A second rationale for a single case is where the case represents an extreme
case or a unique case. Either of these situations commonly occurs in clinical
psychology, where a specific injury or disorder may be so rare that any single
case is worth documenting and analyzing. For instance, one rare clinical
syndrome is the inability of certain clinical patients to recognize familiar faces.
Given visual cues alone, such patients are unable to recognize loved ones,
friends, pictures of famous people, or (in some cases) their own image in a
mirror. This syndrome appears to be due to some physical injury to the brain. Yet
the syndrome occurs so rarely that scientists have been unable to establish any
common patterns (Yin, 1970, 1978). In such circumstances, the single-case study
is an appropriate research design whenever a new person with this syndrome—
known as prosopagnosia—is encountered. The case study would document the
person’s abilities and disabilities, determine the precise nature of the face
recognition deficit, but also ascertain whether related disorders exist.

BOX 7

The Critical Case as a Single-Case Study

One rationale for selecting a single-case rather than a multiple-case design
is that the single case can represent the critical test of a significant theory.
Gross, Bernstein, and Giacquinta (1971) used such a design by focusing on
a single school in their book, Implementing Organizational Innovations
(also see BOX 19B, Chapter 3, p. 110).
The school was selected because it had a prior history of innovation and
could not be claimed to suffer from “barriers to innovation.” In the
prevailing theories, such barriers had been prominently cited as the major
reason that innovations failed. Gross et al. (1971) showed that, in this
school, an innovation also failed but that the failure could not be attributed
to any barries. Implementation processes, rather than barriers, appeared to
account for the failure.
In this manner, the book, though limited to a single case, represented a
water-shed in organizational innovation theory. Prior to the study, analysts
had focused on the identification of barriers to innovation; since the study,
the literature has been much more dominated by studies of the
implementation process.

Conversely, a third rationale for a single case is the representative or typical
case. Here, the objective is to capture the circumstances and conditions of an
everyday or commonplace situation (see BOX 8; also see BOX 14, p. 75). The
case study may represent a typical “project” among many different projects, a
manufacturing firm believed to be typical of many other manufacturing firms in
the same industry, a typical urban neighborhood, or a representative school, as
examples. The lessons learned from these cases are assumed to be informative
about the experiences of the average person or institution.
A fourth rationale for a single-case study is the revelatory case. This situation
exists when an investigator has an opportunity to observe and analyze a
phenomenon previously inaccessible to social science inquiry, such as Whyte’s
(1943/1955) Street Corner Society, previously described in Chapter 1, BOX 2A.
Another example is Elliot Liebow’s (1967) famous case study of unemployed
men, Tally’s Corner (see BOX 9). Liebow had the opportunity to meet the men
in an African American neighborhood in Washington, D.C. and to learn about
their everyday lives. His observations of and insights into the problems of
unemployment formed a significant case study, because few social scientists had
previously had the opportunity to investigate these problems, even though the
problems were common across the country. When other investigators have
similar types of opportunities and can uncover some prevalent phenomenon
previously inaccessible to social scientists, such conditions justify the use of a
single-case study on the grounds of its revelatory nature.

BOX 8

The Average Case as a Single-Case Study

A famous community case study in sociology, Middletown, is about an
average American city. The investigators, Robert and Helen Lynd (1929),
deliberately chose to study a small town in middle Amrica during the early
20th century (also see BOX 14, p. 75). Their purpose was to show how the
transition from an agricultural to an industrial economy occured in the
average town—and thereby to provide a case study about a significant
development in all of American history.

BOX 9

The Revelatory Case as a Single-Case Study

Another rationale for selecting a single-case rather than a multiple-case
design is that the investigator has access to a situation previusly
inaccessible to scientific observation. The case study is therefore worth
conducting because the descriptive information alone will be revelatory.
Such was the situation in Elliot Liebow’s (1967) sociological classic,
Tally’s Corner. The book is about a single group of African American men
living in a poor, inner-city neighborhhood. By befriending these men, the
author was able to learn about their lifestyles, their coping behavior, and in
particular their sensitivity to unemployment and failure. The book provided
insights into a subculture that has prevailed in many U.S. cities foor a long
 period of time, but one that had been only obscurely understood. lating
much further research and eventually the develompment of policy actions.
The single case showed how investigations of such topics could be done,
thus stimulating much further research and eventually the development of
policy actions.

A fifth rationale for a single-case study is the longitudinal case: studying the
same single case at two or more different points in time. The theory of interest
would likely specify how certain conditions change over time, and the desired
time intervals would presumably reflect the anticipated stages at which the
changes should reveal themselves.
These five serve as major reasons for conducting a single-case study. There
are other situations in which the single-case study may be used as a pilot case
that is the first of a multiple-case study. However, in these latter instances, the
single-case study cannot be regarded as a complete study on its own.
Whatever the rationale for doing single-case studies (and there may be more
than the five mentioned here), a potential vulnerability of the single-case design
is that a case may later turn out not to be the case it was thought to be at the
outset. Single-case designs therefore require careful investigation of the potential
case to minimize the chances of misrepresentation and to maximize the access
needed to collect the case study evidence. A fair warning is not to commit
yourself to any single-case study until all of these major concerns have been
covered.

Holistic versus embedded case studies. The same single-case study may involve
more than one unit of analysis. This occurs when, within a single case, attention
is also given to a subunit or subunits (see BOX 10). For instance, even though a
case study might be about a single organization, such as a hospital, the analysis
might include outcomes about the clinical services and staff employed by the
hospital (and possibly even some quantitative analyses based on the employee
records of the staff). In an evaluation study, the single case might be a public
program that involves large numbers of funded projects—which would then be
the embedded units. In either situation, these embedded units can be selected
through sampling or cluster techniques (McClintock, 1985). No matter how the
units are selected, the resulting design would be called an embedded case study
design (see Figure 2.4, Type 2). In contrast, if the case study examined only the
global nature of an organization or of a program, a holistic design would have
been used (see Figure 2.4, Type 1).
These two variants of single-case studies both have their strengths and
weaknesses. The holistic design is advantageous when no logical subunits can be
identified or when the relevant theory underlying the case study is itself of a
holistic nature. Potential problems arise, however, when a global approach
allows an investigator to avoid examining any specific phenomenon in
operational detail. Thus, a typical problem with the holistic design is that the
entire case study may be conducted at an unduly abstract level, lacking
sufficiently clear measures or data.

BOX 10

An Embedded, Single-Case Design

Union Democracy (1956) is a highly regarded case study by three eminent
academicians-Union Democracy (1956) is a highly regarded case study by
three eminent academicians—Seymour Martin Lipset, Martin Trow, ānd
James Coleman. The case study is about the inside politics of the
International Typographical Union and involves several units of analysis
(see “Kinds of Data” table). The māīn ūnit was the organization as a whole,
the smallest unit was the individual member, and several intermediary units
also were important. At eāch level of analysis, different datā collection
techniques were used, ranging from historical to survey analysis.


SOURCE: Lipset, Trew, & Coleman (1956, p. 622). Reprinced by
permission.

A further problem with the holistic design is that the entire nature of the case
study may shift, unbeknownst to the researcher, during the course of study. The
initial study questions may have reflected one orientation, but as the case study
proceeds, a different orientation may emerge, and the evidence begins to address
different research questions. Although some people have claimed such flexibility
to be a strength of the case study approach, in fact the largest criticism of case
studies is based on this type of shift—in which the implemented research design
is no longer appropriate for the research questions being asked (see COSMOS
Corporation, 1983). Because of this problem, you need to avoid such
unsuspected slippage; if the relevant research questions really do change, you
should simply start over again, with a new research design. One way to increase
the sensitivity to such slippage is to have a set of subunits. Thus, an embedded
design can serve as an important device for focusing a case study inquiry.
An embedded design, however, also has its pitfalls. A major one occurs when
the case study focuses only on the subunit level and fails to return to the larger
unit of analysis. For instance, an evaluation of a program consisting of multiple
projects may include project characteristics as a subunit of analysis. The project-
level data may even be highly quantitative if there are many projects. However,
the original evaluation becomes a project study (i.e., a multiple-case study of
different projects) if no investigating is done at the level of the original case—
that is, the program. Similarly, a study of organizational climate may involve
individual employees as a subunit of study. However, if the data focus only on
individual employees, the study will in fact become an employee and not an
organizational study. In both examples, what has happened is that the original
phenomenon of interest (a program or organizational climate) has become the
context and not the target of study.

Summary. Single cases are a common design for doing case studies, and two
variants have been described: those using holistic designs and those using
embedded units of analysis. Overall, the single-case design is eminently
justifiable under certain conditions—where the case represents (a) a critical test
of existing theory, (b) a rare or unique circumstance, or (c) a representative or
typical case, or where the case serves a (d) revelatory or (e) longitudinal
purpose.
A major step in designing and conducting a single case is defining the unit of
analysis (or the case itself). An operational definition is needed, and some
caution must be exercised—before a total commitment to the whole case study is
made—to ensure that the case in fact is relevant to the issues and questions of
interest.
Within the single case may still be incorporated subunits of analyses, so that a
more complex—or embedded—design is developed. The subunits can often add
significant opportunities for extensive analysis, enhancing the insights into the
single case. However, if too much attention is given to these subunits, and if the
larger, holistic aspects of the case begin to be ignored, the case study itself will
have shifted its orientation and changed its nature. If the shift is justifiable, you
need to address it explicitly and indicate its relationship to the original inquiry.
 What Are the Potential Multiple-Case Designs (Types 3 and 4)?

The same study may contain more than a single case. When this occurs, the
study has used a multiple-case design, and such designs have increased in
frequency in recent years. A common example is a study of school innovations
(such as the use of new curricula, rearranged school schedules, or a new
educational technology), in which individual schools adopt some innovation.
Each school might be the subject of an individual case study, but the study as a
whole covers several schools and in this way uses a multiple-case design.

Multiple-versus single-case designs. In some fields, multiple-case studies have
been considered a different “methodology” from single-case studies. For
example, both anthropology and political science have developed one set of
rationales for doing single-case studies and a second set for doing what have
been considered “comparative” (or multiple-case) studies (see Eckstein, 1975;
Lijphart, 1975). This book, however, considers single-and multiple-case designs
to be variants within the same methodological framework—and no broad
distinction is made between the so-called classic (that is, single) case study and
multiple-case studies. The choice is considered one of research design, with both
being included under the case study method.
Multiple-case designs have distinct advantages and disadvantages in
comparison to single-case designs. The evidence from multiple cases is often
considered more compelling, and the overall study is therefore regarded as being
more robust (Herriott & Firestone, 1983). At the same time, the rationale for
single-case designs cannot usually be satisfied by multiple cases. By definition,
the unusual or rare case, the critical case, and the revelatory case all are likely to
involve only single cases. Moreover, the conduct of a multiple-case study can
require extensive resources and time beyond the means of a single student or
independent research investigator. Therefore, the decision to undertake multiple-
case studies cannot be taken lightly.
Selecting the multiple cases also raises a new set of questions. Here, a major
insight is to consider multiple cases as one would consider multiple experiments
—that is, to follow a “replication” design. This is far different from a mistaken
analogy in the past, which incorrectly considered multiple cases to be similar to
the multiple respondents in a survey (or to the multiple subjects within an
experiment)—that is, to follow a “sampling” design. The methodological
differences between these two views are revealed by the different rationales
underlying the replication as opposed to sampling designs.

Replication, not sampling logic, for multiple-case studies. The replication logic
is analogous to that used in multiple experiments (see Hersen & Barlow, 1976).
For example, upon uncovering a significant finding from a single experiment, an
ensuing and pressing priority would be to replicate this finding by conducting a
second, third, and even more experiments. Some of the replications might
attempt to duplicate the exact conditions of the original experiment. Other
replications might alter one or two experimental conditions considered
unimportant to the original finding, to see whether the finding could still be
duplicated. Only with such replications would the original finding be considered
robust.
The logic underlying the use of multiple-case studies is the same. Each case
must be carefully selected so that it either (a) predicts similar results (a literal
replication) or (b) predicts contrasting results but for anticipatable reasons (a
theoretical replication). The ability to conduct 6 or 10 case studies, arranged
effectively within a multiple-case design, is analogous to the ability to conduct 6
to 10 experiments on related topics; a few cases (2 or 3) would be literal
replications, whereas a few other cases (4 to 6) might be designed to pursue two
different patterns of theoretical replications. If all the cases turn out as predicted,
these 6 to 10 cases, in the aggregate, would have provided compelling support
for the initial set of propositions. If the cases are in some way contradictory, the
initial propositions must be revised and retested with another set of cases. Again,
this logic is similar to the way scientists deal with conflicting experimental
findings.
An important step in all of these replication procedures is the development of
a rich, theoretical framework. The framework needs to state the conditions under
which a particular phenomenon is likely to be found (a literal replication) as well
as the conditions when it is not likely to be found (a theoretical replication). The
theoretical framework later becomes the vehicle for generalizing to new cases,
again similar to the role played in cross-experiment designs. Furthermore, just as
with experimental science, if some of the empirical cases do not work as
predicted, modification must be made to the theory. Remember, too, that theories
can be practical and not just academic.
For example, one might consider the initial proposition that an increase in
using a new technology in school districts will occur when the technology is
used for both administrative and instructional applications, but not either alone.
To pursue this proposition in a multiple-case study design, 3 or 4 cases might be
selected in which both types of applications are present, to determine whether, in
fact, technology use did increase over a period of time (the investigation would
be predicting a literal replication in these 3 or 4 cases). Three or 4 additional
cases might be selected in which only administrative applications are present,
with the prediction being little increase in use (predicting a theoretical
replication). Finally, 3 or 4 other cases would be selected in which only
instructional applications are present, with the same prediction of little increase
in use, but for different reasons than the administrative-only cases (another
theoretical replication). If this entire pattern of results across these multiple cases
is indeed found, the 9 to 12 cases, in the aggregate, would provide substantial
support for the initial proposition.
Another example of a multiple-case replication design comes from the field of
urban studies (see BOX 11). You also can find examples of three entire case
studies, all following a replication design but covering HIV/AIDS prevention,
university administration, and the transformation of business firms, in the
companion text (Yin, 2003, chaps. 8-10).
This replication logic, whether applied to experiments or to case studies, must
be distinguished from the sampling logic commonly used in surveys. The
sampling logic requires an operational enumeration of the entire universe or pool
of potential respondents and then a statistical procedure for selecting a specific
subset of respondents to be surveyed. The resulting data from the sample that is
actually surveyed are assumed to reflect the entire universe or pool, with
inferential statistics used to establish the confidence intervals for which this
representation is presumed accurate. The entire procedure is commonly used
when an investigator wishes to determine the prevalence or frequency of a
particular phenomenon.

BOX 11

A Multiple-Case, Replication Design

A common problem in the 1960s and 1970s was how to get good advice to
city governments. Peter Szanton’s (1981) book, Not Well Advised, reviewed
the experiences of numerous attempts by university and research groups to
collaborate with city officials.
The book is an excellent example of a multiple-case, replication desing.
Szanton starts with eight case studies, showing how different university
 groups all failed to help cities. The eight cases are sufficient “replications”
to convince the reader of a general phenomenon. Szanton then provides five
more case studies, in which nonuniversity groups also failed, concluding
that failure was therefore not necessarily inherent in the academic
enterprise. Yet a third group of cases shows how university groups have
successfully helped business, engineering firms, and sectors other than city
government. A final set of three cases shows that those few groups able to
help city government were concerden with implementation and not just
with the production of newi ideas, leading th the major conclusion that city
governments may have peculiar needs in receiving but also then putting
advice into
practice. Within each of have peculiar of case studies, Szanton has
illustrated the principle of literal replication. Across the four groups, he has
illustrated theoretical replication. This potent case study design can and
should be applied to many other topics.

Any application of this sampling logic to case studies would be misplaced.
First, case studies are not the best method for assessing the prevalence of
phenomena. Second, a case study would have to cover both the phenomenon of
interest and its context, yielding a large number of potentially relevant variables.
In turn, this would require an impossibly large number of cases—too large to
allow any statistical consideration of the relevant variables.
Third, if a sampling logic had to be applied to all types of research, many
important topics could not be empirically investigated, such as the following
problem: Your investigation deals with the role of the presidency of the United
States, and you are interested in doing a multiple-case study of a (few) presidents
to test your theory about presidential leadership. However, the complexity of
your topic means that your choice of a small number of cases could not
adequately represent all the 44 presidents since the beginning of the Republic.
Critics using a sampling logic might therefore deny the acceptability of your
study. In contrast, if you use a replication logic, the study is eminently feasible.
The replication approach to multiple-case studies is illustrated in Figure 2.5.
The figure indicates that the initial step in designing the study must consist of
theory development, and then shows that case selection and the definition of
specific measures are important steps in the design and data collection process.
Each individual case study consists of a “whole” study, in which convergent
evidence is sought regarding the facts and conclusions for the case; each case’s
conclusions are then considered to be the information needing replication by
other individual cases. Both the individual cases and the multiple-case results
can and should be the focus of a summary report. For each individual case, the
report should indicate how and why a particular proposition was demonstrated
(or not demonstrated). Across cases, the report should indicate the extent of the
replication logic and why certain cases were predicted to have certain results,
whereas other cases, if any, were predicted to have contrasting results.
An important part of Figure 2.5 is the dashed-line feedback loop. The loop
represents the situation where important discovery occurs during the conduct of
one of the individual case studies (e.g., one of the cases did not in fact suit the
original design). Such a discovery even may require you to reconsider one or
more of the study’s original theoretical propositions. At this point, “redesign”
should take place before proceeding further. Such redesign might involve the
selection of alternative cases or changes in the case study (i.e., data collection)
protocol (see Chapter 3). Without such redesign, you risk being accused of
distorting or ignoring the discovery, just to accommodate the original design.
This condition leads quickly to a further accusation—that you have been
selective in reporting your data, to suit your preconceived ideas (i.e., the original
theoretical propositions).

Figure 2.5 Case Study Methed
SOURCE: COSMOS Corporation.

Overall, Figure 2.5 depicts a very different logic from that of a sampling
design. The logic as well as its contrast with a sampling design may be difficult
to follow and is worth extensive discussion with colleagues before proceeding
with any multiple case study.
When using a multiple-case design, a further question you will encounter has
to do with the number of cases deemed necessary or sufficient for your study.
However, because a sampling logic should not be used, the typical criteria
regarding sample size also are irrelevant. Instead, you should think of this
decision as a reflection of the number of case replications—both literal and
theoretical—that you need or would like to have in your study.
For the number of literal replications, an appropriate analogy from statistics is
the selection of the criterion for establishing the sample size desired to detect an
“effect.” Designating a “p < .05” or “p < .01” likelihood of detection as part of a
power analysis is not based on any formula but is a matter of discretionary,
judgmental choice. Analogously, designating the number of replications depends
upon the certainty you want to have about your multiple-case results (as with the
higher criterion for establishing the likelihood of detection, the greater certainty
lies with the larger number of cases). For example, you may want to settle for
two or three literal replications when your theory is straightforward and the issue
at hand does not demand an excessive degree of certainty. However, if your
theory is subtle or if you want a high degree of certainty, you may press for five,
six, or more replications.
For the number of theoretical replications, the important consideration is
related to your sense of the importance of rival explanations. The stronger the
rivals, the more additional cases you might want, each case showing a different
result when some rival explanation had been taken into account. For example,
your original hypothesis might be that summer reading programs improve
students’ reading scores, and you already might have shown this result through
several cases that served as literal replications. A rival explanation might be that
parents also work more closely with their children during the summer and that
this circumstance can account for improved reading scores. You would then find
another case, with parent participation but no summer reading program, and in
this theoretical replication you would predict that the scores would not improve.
Rationale for multiple-case designs. In short, the rationale for multiple-case
designs derives directly from your understanding of literal and theoretical
replications. The simplest multiple-case design would be the selection of two or
more cases that are believed to be literal replications, such as a set of cases with
exemplary outcomes in relation to some evaluation questions, such as “how and
why a particular intervention has been implemented smoothly.” Selecting such
cases requires prior knowledge of the outcomes, with the multiple-case inquiry
focusing on how and why the exemplary outcomes might have occurred and
hoping for literal (or direct) replications of these conditions from case to case.5
More complicated multiple-case designs would likely result from the number
and types of theoretical replications you might want to cover. For example,
investigators have used a “two-tail” design in which cases from both extremes
(of some important theoretical condition, such as good and bad outcomes) have
been deliberately chosen. Multiple-case rationales also can derive from the prior
hypothesizing of different types of conditions and the desire to have subgroups
of cases covering each type. These and other similar designs are more
complicated because the study should still have at least two individual cases
within each of the subgroups, so that the theoretical replications across
subgroups are complemented by literal replications within each subgroup.

Multiple-case studies: Holistic or embedded. The fact that a design calls for
multiple-case studies does not eliminate the variation identified earlier with
single cases: Each individual case may still be holistic or embedded. In other
words, a multiple-case study may consist of multiple holistic cases (see Figure
2.4, Type 3) or of multiple embedded cases (see Figure 2.4, Type 4).
The difference between these two variants depends upon the type of
phenomenon being studied and your research questions. In an embedded design,
a study even may call for the conduct of a survey at each case study site. For
instance, suppose a study is concerned with the impact of the same type of
curriculum adopted by different schools. Each school may be the topic of a case
study, with the theoretical framework dictating that nine such schools be
included as case studies, three to replicate a direct result (literal replication) and
six others to deal with contrasting conditions (theoretical replications).
For all nine schools, an embedded design is used because surveys of the
students (or, alternatively, examination of students’ archival records) are needed
to address research questions about the performance of the schools. However,
the results of each survey will not be pooled across schools. Rather, the survey
data will be part of the findings for each individual school, or case. These data
may be highly quantitative, focusing on the attitudes and behavior of individual
students, and the data will be used along with archival information to interpret
the success and operations at the given school. If, in contrast, the survey data are
pooled across schools, a replication design is no longer being used. In fact, the
study has now become a single-case study, in which all nine schools and their
students have now become part of some larger, main unit of analysis. Such a new
case study would then require a complete redefinition of the main unit of
analysis, with extensive revisions to the original theories and propositions of
interest also a likely need.

Summary. This section has dealt with situations in which the same investigation
may call for multiple-case studies. These types of designs are becoming more
prevalent, but they are more expensive and time-consuming to conduct.
Any use of multiple-case designs should follow a replication, not a sampling
logic, and an investigator must choose each case carefully. The cases should
serve in a manner similar to multiple experiments, with similar results (a literal
replication) or contrasting results (a theoretical replication) predicted explicitly
at the outset of the investigation.
The individual cases within a multiple-case study design may be either
holistic or embedded. When an embedded design is used, each individual case
study may in fact include the collection and analysis of quantitative data,
including the use of surveys within each case.

EXERCISE 2.4 Defining a Case Study Research Design

Select one of the case studies described in the BOXES of this book,
reviewing the entire case study (not just the material in the BOX). Describe
the research design of this case study. How did it justify the relevant
evidence to be sought, given the basic research questions to be answered?
What methods were used to draw conclusions, based on the evidence? Is
the design a single-or multiple-case design? Is it holistic or does it have
embedded units of analysis?

MODEST ADVICE IN SELECTING CASE STUDY DESIGNS

Now that you know how to define case study designs and are prepared to carry
out design work, three pieces of advice may be offered.
 Single-or Multiple-Case Designs?

The first word of advice is that, although all designs can lead to successful
case studies, when you have the choice (and resources), multiple-case designs
may be preferred over single-case designs. Even if you can do a “two-case” case
study, your chances of doing a good case study will be better than using a single-
case design. Single-case designs are vulnerable if only because you will have put
“all your eggs in one basket.” More important, the analytic benefits from having
two (or more) cases may be substantial.
To begin with, even with two cases, you have the possibility of direct
replication. Analytic conclusions independently arising from two cases, as with
two experiments, will be more powerful than those coming from a single case
(or single experiment) alone. Alternatively you may have deliberately selected
your two cases because they offered contrasting situations, and you were not
seeking a direct replication. In this design, if the subsequent findings support the
hypothesized contrast, the results represent a strong start toward theoretical
replication—again vastly strengthening your findings compared to those from a
single case alone (e.g., Eilbert & Lafronza, 2005; Hanna, 2005; also see BOX
12).

BOX 12

Two, “Two-Case” Case Studies 12A. Contrasting Cases for Community
Building

Chaskin (2001) used two case studies to illustrate contrasting strategies for
capacity building at the neighborhood level. The author’s overall
conceptual framework, which was the main topic of inquiry, claimed that
there could be two approaches to building community capacity—using a
collaborative organization to (a) reinforce existing networks of community
organizations or (b) initiate a new organization in the neighborhood. After
thoroughly airing the framework on theoretical grounds, the author presents
the two case studies, showing the viability of each approach.
12B. Contrasting Strategies for Educational Accountability
In a directly complementary manner, Elmore, Abelmann, and Fuhrman
 (1997) chose two case studies to illustrate contrasting strategies for
designing and implementing educational accountability (i.e., holding
schools accountable for the academic performance of their students). One
case represented a higher cost, basic version of an accountability system.
The other represented a higher cost, more complex version.

In general, criticisms about single-case studies usually reflect fears about the
uniqueness or artifactual conditions surrounding the case (e.g., special access to
a key informant). As a result, the criticisms may turn into skepticism about your
ability to do empirical work beyond having done a single-case study. Having two
cases can begin to blunt such criticism and skepticism. Having more than two
cases will produce an even stronger effect. In the face of these benefits, having at
least two cases should be your goal. If you do use a single-case design, you
should be prepared to make an extremely strong argument in justifying your
choice for the case.

EXERCISE 2.5 Establishing the Rationale for a Multiple-

Case Study

Develop some preliminary ideas about a “case” for your case study.
Alternatively, focus on one of the single-case studies presented in the
BOXES in this book. In either situation, now think of a companion “case”
that might augment the single case. In what ways might the companion
case’s findings supplement those of the first case? Could the data from the
second case fill a gap left by the first case or respond better to some
obvious shortcoming or criticism of the first case? Would the two cases
together comprise a stronger case study? Could yet a third case make the
findings even more compelling?

 Closed Designs or Flexible Designs?

Another word of advice is that, despite this chapter’s details about design
choices, you should not think that a case study’s design cannot be modified by
new information or discovery during data collection. Such revelations can be
enormously important, leading to your altering or modifying your original
design.
As examples, in a single-case study, what was thought to be a critical or
unique case might have turned out not to be so, after initial data collection had
started; ditto a multiple-case study, where what was thought to be parallel cases
for literal replication turn out not to be so. With these revelations, you have
every right to conclude that your initial design needs to be modified. However,
you should undertake any alterations only given a serious caution. The caution is
to understand precisely the nature of the alteration: Are you merely selecting
different cases, or are you also changing your original theoretical concerns and
objectives? The point is that the needed flexibility should not lessen the rigor
with which case study procedures are followed.
 Mixed Methods Designs: Mixing Case Studies with Other Methods?

Researchers have given increasing attention to “mixed methods research”—a
“class of research where the researcher mixes or combines quantitative and
qualitative research techniques, methods, approaches, concepts or language into
a single study” (Johnson & Onwuegbuzie, 2004, p. 17, emphasis added).
Confinement to a single study forces the methods being mixed into an integrated
mode. The mode differs from the conventional situation whereby different
methods are used in separate studies that may later be synthesized.
Mixed methods research forces the methods to share the same research
questions, to collect complementary data, and to conduct counterpart analyses
(e.g., Yin, 2006b)—in short, to follow a mixed methods design. As such, mixed
methods research can permit investigators to address more complicated research
questions and collect a richer and stronger array of evidence than can be
accomplished by any single method alone. Depending upon the nature of your
research questions and your ability to use different methods, mixed methods
research opens a class of research designs that deserve your consideration.
The earlier discussion of embedded case study designs in fact points to the
fact that certain kinds of case studies already represent a form of mixed methods
research. The embedded case studies rely on more holistic data collection
strategies for studying the main case but then call upon surveys or other more
quantitative techniques to collect data about the embedded unit(s) of analysis. In
this situation, other research methods are embedded within your case study.
The opposite relationship also can occur. Your case study may be part of a
larger, mixed methods study. The main investigation may rely on a survey or
other quantitative techniques, and your case study may help to investigate the
conditions within one of the entities being surveyed. The contrasting
relationships (survey within case or case within survey) are illustrated in Figure
2.6.
At the same time, mixed methods research need not include the use of the case
study strategy at all. For instance, much historical work embraces the
quantitative analysis of archival records, such as newspapers and other file
material. And, in an even broader sense, mixed methods research need not be
limited to combinations of quantitative and qualitative methods. For instance, a
study could employ a survey to describe certain conditions, complemented by an
experiment that tried to manipulate some of those conditions (e.g., Berends &
Garet, 2002).

Figure 2.6 Mixed Methods: Two Nested Arrangements

By definition, studies using mixed methods research are more difficult to

execute than studies limited to single methods. However, mixed methods
research can enable you to address broader or more complicated research
questions than case studies alone. As a result, mixing case studies with other
methods should be among the possibilities meriting your consideration.
NOTES

1 Figure 2.2 focuses only on the formal research design process, not on data
collection activities. For all three types of research (survey, case study, and
experiment), data collection techniques might be depicted as the level below
Level One in the figure. For example, for case studies, this might include using
multiple sources of evidence, as described further in Chapter 4. Similar data
collection techniques can be described for surveys or experiments—for example,
questionnaire design for surveys or stimulus presentation strategies for
experiments.

2 See Gomm, Hammersley, and Foster (2000) for more explanation of analytic
generalization, though their work uses different labels for the same concept.

3 One of the anonymous reviewers of the third edition of this book pointed out
that construct validity also has to do with whether interviewees understand what
is being asked of them.

4 For other suggested guidelines for reviewers of case study proposals or

manuscripts, see Yin (1999).

5 Strictly quantitative studies that select cases with known outcomes follow the
same design and have alternatively been called “case-control,” “retrospective,”
or “case referent” studies (see P. R. Rosenbaum, 2002, p. 7).

REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 2

For selected case studies cited in the text of this chapter, two anthologies contain
either a more extensive excerpt or the full case study. The table on the next page
crosswalks the reference in this book to the location of the excerpt or full
rendition.


ABSTRACT

Preparing to do a case study starts with the prior skills of the investigator and
covers the preparation and training for the specific case study (including
procedures for protecting human subjects), the development of a case study
protocol, the screening of candidate cases to be part of the case study, and the
conduct of a pilot case study.
With regard to prior skills, many people incorrectly believe they are
sufficiently skilled to do case studies because they think the method is easy to
use. In fact, case study research is among the hardest types of research to do
because of the absence of routine procedures. Case study investigators therefore
need to feel comfortable in addressing procedural uncertainties during the course
of a study. Other desirable traits include the ability to ask good questions,
“listen,” be adaptive and flexible, have a firm grasp of the issues being studied,
and know how to avoid bias.
An investigator can prepare to do a high-quality case study through intensive
training. A case study protocol should be developed and refined. These
procedures are especially desirable if the research is based on a multiple-case
design or involves multiple investigators, or both.
 3

Preparing to Collect Case Study Evidence What You Need to Do

Before Starting to Collect Case Study Data

Chapters 1 and 2 have shown that doing a case study begins with the research
questions to be addressed and the development of a case study design. However,
most people associate the “doing” of a case study with the collection of the case
study data, and this and the following chapter focus on the data collection
activity. This chapter deals with the needed preparation. The next covers the
actual data collection techniques.
Preparing for data collection can be complex and difficult. If not done well,
the entire case study investigation can be jeopardized, and all of the earlier work
—in defining the research questions and designing the case study—will have
been for naught.
Good preparation begins with the desired skills on the part of the case study
investigator. These skills have seldom been the subject of separate attention in
the past. Yet, some are critical and can be learned or practiced. Four additional
topics also should be a formal part of any case study preparation: training for a
specific case study, developing a protocol for the investigation, screening
candidate cases, and conducting a pilot case study. The protocol is an especially
effective way of dealing with the overall problem of increasing the reliability of
case studies. However, success with all five topics is needed to ensure that case
study data collection will proceed smoothly. All demand a certain amount of
patience, which has too frequently been overlooked in the past.
THE CASE STUDY INVESTIGATOR: DESIRED SKILLS

Too many people are drawn to the case study strategy because they believe it is
“easy.” Many social scientists—especially budding ones—think the case study
strategy can be mastered without much difficulty. Their belief is that they will
have to learn only a minimal set of technical procedures; that any of their own
shortcomings in formal, analytic skills will be unimportant; and that a case study
will allow them simply to “tell it like it is.” No belief could be farther from the
truth.

Tip: When am I ready to start collecting the case study data?

You have just completed designing your case study, following the
suggestions in Chapter 2, and you are anxious to start collecting the case
study data because time is short, and available data collection opportunities
are present. Your readiness, however, should not be defined by external
time constraints or conditions. Instead, your “readiness” depends upon your
own skill levels for doing case studies, as well as your having completed
formal and preparatory procedures prior to collecting actual data.


Have you practiced these skills, and do you think case study research
needs to follow formal procedures to prepare for data collection?
In actuality, the demands of a case study on your intellect, ego, and emotions
are far greater than those of any other research method. This is because the data
collection procedures are not routinized. In laboratory experiments or in surveys,
for instance, the data collection phase of a research project can be largely, if not
wholly, conducted by one (or more) research assistant(s). The assistant is to
carry out the data collection activities with a minimum of discretionary behavior,
and in this sense, the activity is routinized—and analytically boring.
Conducting case studies offers no such parallel. Rather, a well-trained and
experienced investigator is needed to conduct a high-quality case study because
of the continuous interaction between the theoretical issues being studied and the
data being collected. During data collection, only a more experienced
investigator will be able to take advantage of unexpected opportunities rather
than being trapped by them—and also will exercise sufficient care against
potentially biased procedures.
Unfortunately, there are no tests for distinguishing those persons likely to
become good case study investigators from those who are not. Compare this
situation to that in mathematics or even a profession such as law. In math, people
are able to score themselves for their abilities and to screen themselves from
further advancement because they simply cannot carry out higher levels of math
problems. To practice law, a person must pass the bar examination in a particular
state. Again, many people screen themselves out of the field by failing to pass
this test.
No such gatekeepers exist for assessing case study skills. However, a basic list
of commonly required skills is as follows:
• A good case study investigator should be able to ask good questions—and
interpret the answers.
• An investigator should be a good “listener” and not be trapped by her or
his own ideologies or preconceptions.
• An investigator should be adaptive and flexible, so that newly encountered
situations can be seen as opportunities, not threats.
• An investigator must have a firm grasp of the issues being studied, even if
in an exploratory mode. Such a grasp reduces the relevant events and
information to be sought to manageable proportions.
• A person should be unbiased by preconceived notions, including those
derived from theory. Thus, a person should be sensitive and responsive to
contradictory evidence.

Each of these attributes is described below. Any absence of these attributes is
remediable, as anyone missing one or more of the skills can work on developing
them. But everyone must be honest in assessing her or his capabilities in the first
place.
 Asking Good Questions

More than with the other research methods discussed in Chapter 1, case
studies require an inquiring mind during data collection, not just before or after
the activity. The ability to pose and ask good questions is therefore a prerequisite
for case study investigators. The desired result is for the investigator to create a
rich dialogue with the evidence, an activity that encompasses
pondering the possibilities gained from deep familiarity with some aspect of
the world, systematizing those ideas in relation to kinds of information one
might gather, checking the ideas in the light of that information, dealing
with the inevitable discrepancies between what was expected and what was
found by rethinking the possibilities of getting more data, and so on.
(Becker, 1998, p. 66)

Case study data collection does follow a formal protocol, but the specific
information that may become relevant to a case study is not readily predictable.
As you collect case study evidence, you must quickly review the evidence and
continually ask yourself why events or facts appear as they do. Your judgments
may lead to the immediate need to search for additional evidence. If you are able
to ask good questions throughout the data collection process, a good prediction
is that you also will be mentally and emotionally exhausted at the end of each
day. This depletion of analytic energy is far different from the experience in
collecting experimental or survey data—that is, testing “subjects” or
administering questionnaires. In these situations, data collection is highly
routinized, and the data collector must complete a certain volume of work but
exercise minimal discretionary behavior. Furthermore, any substantive review of
the evidence does not come until some later time. The result is that such a data
collector may become physically exhausted but will have been mentally untested
after a day of data collection.
One insight into asking good questions is to understand that research is about
questions and not necessarily about answers. If you are the type of person for
whom one tentative answer immediately leads to a whole host of new questions,
and if these questions eventually aggregate to some significant inquiry about
how or why the world works as it does, you are likely to be a good asker of
questions.
 Being a Good “Listener”

For case studies, “listening” means receiving information through multiple
modalities—for example, making keen observations or sensing what might be
going on—not just using the aural modality. Being a good listener means being
able to assimilate large amounts of new information without bias. As an
interviewee recounts an incident, a good listener hears the exact words used by
the interviewee (sometimes, the terminology reflects an important orientation),
captures the mood and affective components, and understands the context from
which the interviewee is perceiving the world.
The listening skill also needs to be applied to the inspection of documentary
evidence, as well as to observations of real-life situations. In reviewing
documents, listening takes the form of worrying whether there is any important
message between the lines; any inferences, of course, would need to be
corroborated with other sources of information, but important insights may be
gained in this way. Poor “listeners” may not even realize that there can be
information between the lines. Other listening deficiencies include having a
closed mind or simply having a poor memory.
 Exercising Adaptiveness and Flexibility

Few case studies will end up exactly as planned. Inevitably, you will have to
make minor if not major changes, ranging from the need to pursue an
unexpected lead (potentially minor) to the need to identify a new “case” for
study (potentially major). The skilled investigator must remember the original
purpose of the investigation but then must be willing to adapt procedures or
plans if unanticipated events occur (see BOX 13).

BOX 13

Maintaining Flexibility in Designing a Case Study

Peter Blau’s study of behavior in large government agencies (The
Dynamics of Bureaucracy, 1955) is still valued for its insights into the
relationship between the formal and informal organization of work groups,
even over 50 years later. Although his studyfocused on two government
agencies, that was not Blau’s initial design. As the author notes, he first
intended to study a single orga-
nization and later switched to a plan to compare two organizations—a
public one and a private one (Blau, 1955, pp. 272-273). However, his initial
attempts to gain access to a private firm were unsuccesful, and in the
meanwhile, he had developed a stronger rationalefor comparing two
government agencies but of different kinds. These shifts in the initial plans
are examples of the kinds of changes that can occur in the design of a case
study. Blau’s experience show show a skilled investigator can take
advantage of changing opportunities, as well as shifts in theoretical
concerns, to produce a classic case study. take advantage of changing
opportunities, as well as shifts in theoretical concerns, to produce a classic
case study.

When a shift is made, you must maintain an unbiased perspective and
acknowledge those situations in which, in fact, you may have inadvertently
begun to pursue a totally new investigation. When this occurs, many completed
steps—including the initial design of the case study—must be repeated and
redocumented. One of the worst complaints about the conduct of case study
research is that investigators change directions without knowing that their
original research design was inadequate for the revised investigation, thereby
leaving unknown gaps and biases. Thus, the need to balance adaptiveness with
rigor—but not rigidity—cannot be overemphasized.
 Having a Firm Grasp of the Issues Being Studied

The main way of staying on target, of course, is to understand the purpose of
the case study investigation in the first place. Each case study investigator must
understand the theoretical or policy issues because analytic judgments have to be
made throughout the data collection phase. Without a firm grasp of the issues,
you could miss important clues and would not know when a deviation was
acceptable or even desirable. The point is that case study data collection is not
merely a matter of recording data in a mechanical fashion, as it is in some other
types of research. You must be able to interpret the information as it is being
collected and to know immediately, for instance, if several sources of
information contradict one another and lead to the need for additional evidence
—much like a good detective.
In fact, the detective role offers some keen insights into case study fieldwork.
Note that the detective arrives on a scene after a crime has occurred and is
basically being called upon to make inferences about what actually transpired.
The inferences, in turn, must be based on convergent evidence from witnesses
and physical evidence, as well as some unspecifiable element of common sense.
Finally, the detective may have to make inferences about multiple crimes, to
determine whether the same perpetrator committed them. This last step is similar
to the replication logic underlying multiple-case studies.
 Avoiding Bias

All of the preceding conditions will be negated if an investigator seeks only to
use a case study to substantiate a preconceived position. Case study investigators
are especially prone to this problem because they must understand the issues
beforehand (see Becker, 1958, 1967). You also may have selected the case study
method to enable you (wrongly) to pursue or (worse yet) advocate particular
issues.1 In contrast, the traditional research assistant, though mechanistic and
possibly even sloppy, is not likely to introduce a substantive bias into the
research.
One test of this possible bias is the degree to which you are open to contrary
findings. For example, researchers studying “nonprofit” organizations may be
surprised to find that many of these organizations have entrepreneurial and
capitalistic motives (even though the organizations don’t formally make profits).
If such findings are based on compelling evidence, the conclusions of the case
study would have to reflect these contrary findings. To test your own tolerance
for contrary findings, report your preliminary findings—possibly while still in
the data collection phase—to two or three critical colleagues. The colleagues
should offer alternative explanations and suggestions for data collection. If the
quest for contrary findings can produce documentable rebuttals, the likelihood of
bias will have been reduced.

EXERCISE 3.1 Identifying the Skills for Doing Case Studies

Name the various skills that are important for a case study investigator to
have. Do you know any people that have been successful in doing case
study research? What strengths and weaknesses do they have as research
investigators? Are these similar to the ones you have just named?

EXERCISE 3.2 Analyzing Your Own Skills for Doing Case

Studies

What distinctive skills do you believe equip you to do a case study? Have
 you done previous studies requiring the collection and analysis of original
data? Have you done any fieldwork, and if so, in what ways are you a good
“listener” or an observant person? If you identify some case study skills that
you still might need to strengthen, how would you go about the task?

PREPARATION AND TRAINING FOR A SPECIFIC CASE
STUDY

 Human Subjects Protection

Some time between the completion of your design and the start of your data
collection, you will need to show how you plan to protect the human subjects in
your case study. You will need to obtain formal approval for your plan. Such
approval should not merely be viewed as an oversight process, because you
should always conduct all of your research with the highest ethical standard.
The specific need for protecting human subjects comes from the fact that
nearly all case studies, like those covered by this book, are about contemporary
human affairs. In this single manner, you and other social scientists differ from
scientists who study physical, chemical, or other nonhuman systems or from
historians who may be studying the “dead past.” The study of “a contemporary
phenomenon in its real-life context” obligates you to important ethical practices
akin to those followed in medical research.
As part of the protection, you are responsible for conducting your case study
with special care and sensitivity—going beyond the research design and other
technical considerations covered throughout this book. The care usually involves
• gaining informed consent from all persons who may be part of your case
study, by alerting them to the nature of your case study and formally
soliciting their volunteerism in participating in the study;
• protecting those who participate in your study from any harm, including
avoiding the use of any deception in your study;
• protecting the privacy and confidentiality of those who participate so that,
as a result of their participation, they will not be unwittingly put in any
undesirable position, even such as being on a roster to receive requests to
participate in some future study, whether conducted by you or anyone
else; and
• taking special precautions that might be needed to protect especially
vulnerable groups (for instance, research involving children).

Exactly how you exercise the needed care and sensitivity will vary, depending
on your case study. General guidance comes from your own professional ethics.
Professional research associations also promulgate their own standards for doing
human subjects research, not just case studies (e.g., Joint Committee on
Standards for Educational Evaluation, 1981). Most important, however, your
institutional setting will have its own expectations, whether you are part of a
university or of an independent research organization, and you need to follow its
specific guidance.
In particular, every institution now has an Institutional Review Board (IRB).
The board is charged with reviewing and approving all human subjects research
before such research can proceed. The board’s review will cover the objectives
of your study and how you plan to protect the human subjects that may be part of
the study. Note that your interactions with the specific human subjects in your
study take place through both direct contact (as in interviews) and the potential
use of personal records (as in client records). Case studies present a more
challenging situation than when using other research methods because these
interactions are not necessarily as structured as with other methods (such as in
administering a closed-ended questionnaire). The board will want to know such
information as how you plan to interact with those being studied, the protocols
or data collection instruments you are planning to use, and how you will ensure
such protections as informed consent and confidentiality.
As a result, the most imperative step before proceeding with your case study is
to seek out the IRB at your institution, follow its guidance, and obtain its
approval. The IRB’s concerns will vary from institution to institution and IRB to
IRB. Do not hesitate to speak with a member or two of the IRB informally and
ahead of time, to gain insight into the review process and its expectations.
 Case Study Training as a Seminar Experience

Training also is a necessary step in doing case study research. The timing of
the training, relative to the timing for seeking human subjects approval, will not
always be linear. You need to have some data collection plans before seeking
approval, but, as pointed out below, the finalization of the plans cannot occur
until after the approval has been granted. The training activities described below
may therefore take place over an extended period of time, as in a regular
seminar.
For case study research, the key to understanding the needed training is to
understand that every case study investigator must be able to operate as a
“senior” investigator. Once you have started collecting data, you should think of
yourself as an independent investigator who cannot rely on a rigid formula to
guide your inquiry. You must be able to make intelligent decisions throughout
the data collection process.
In this sense, training for a case study investigation actually begins with the
definition of the questions being addressed and the development of the case
study design. If these steps have been satisfactorily conducted, as described in
Chapters 1 and 2, only minimal further effort may be needed, especially if there
is only a single case study investigator.
However, it often happens that a case study investigation must rely on a case
study team,2 for any of three reasons:
1. a single case calls for intensive data collection at the same site, requiring
a “team” of investigators (see BOX 14);
2. a case study involves multiple cases, with different persons being needed
to cover each site or to rotate among the sites (Stake, 2006, p. 21); or
3. a combination of the first two conditions.

Under these circumstances, all team members should have contributed to the
development of a draft case study protocol. This draft would then have been the
version submitted for IRB approval, with the IRB-approved version
subsequently being considered the final version of the protocol.

BOX 14

The Logistique of Fiels Research, Circa 1924-1925

Research, Circa 1924-1925 Arranging schedules and gaining access to
relevant sources of evidence are important to the management of a case
study. The modern researcher may feel that these activities have only
emerged with the growth of “big” social science during the 1960s and
1970s.
In a famous field study done decades ago, however, many of the same
management techniques already had been practiced. The two principal
investigators and their staff secretary opened a local office in the city they
were studying. This office was used by other project staff for extebded
periods of time . From this vantage point, the research team participated in
local life, examined documentary materials, compiled local statistics,
conducted interviews, and distributed and collected questionnaires. This
extensive fieldwork resulted 5 years later in the publication of the now-
classic study of small-town America, Middletown (1929), by Robert and
Helen Lynd (also see BOX 8, Chapter 2, p. 48). now-classic study of small-
town America, Middletown (1929), by Robert and Helen Lynd (also see
BOX 8, Chapter 2, p. 48).

When multiple investigators or team members participate in the same case
study, all need to learn to be “senior” investigators. Training takes the form of a
seminar rather than didactic instruction. As in a seminar, much time has to be
allowed for reading, preparing for the training, and holding the training. (See
Figure 3.1 for an agenda of an illustrative training session.)

Figure 3.1 Multisession Agenda for Case Study Training


Typically, the seminar will cover all phases of the planned case study
investigation, including readings on the subject matter, the theoretical issues that
led to the case study design, and case study methods and tactics. You might
review examples of tools used in other case studies (see BOX 15), to add to the
methodological portion of the training.
The goal of the training is to have all participants understand the basic
concepts, terminology, and methodological issues relevant to the study. Each
team member needs to know
• why the study is being done,
• what evidence is being sought,

BOX 15

Reviewing the Tools and Methods Used in Other Case Studies,
Circa the 21st Century

Web sites have provided new opportunities to access the tools and
methods used in other case studies. For example, in online versions
of articles, academic journals may reproduce supplementary
materials that might not have appeared in the printed version of the
 article. For one case study, the supplementary materials included the
formal case study protocol, case study coding book, evidentiary
tables linking claims to sections of the case study database, and a
list of documents in the case study database (Randolph & Eronen,
2007). database (Randolph & Eronen, 2007).

• what variations can be anticipated (and what should be done if such
variations occur), and
• what would constitute supportive or contrary evidence for any given
proposition.

Discussions, rather than lectures, are the key part of the training effort, to
ensure that the desired level of understanding has been achieved.
This seminar approach to case study training can be contrasted to the training
for other types of data collection—for example, group training for survey
interviewers. The survey training does involve discussions, but it mainly
emphasizes the questionnaire items or terminology to be used and takes place
over an intensive but short period of time. Moreover, the survey training may not
cover the global or conceptual concerns of the study, as interviewers may not
need to have any broader understanding beyond the mechanics of the survey
instrument. Survey training rarely involves any outside reading about the
substantive issues, and the survey interviewer generally does not know how the
survey data are to be analyzed or what issues are to be investigated. Such an
approach may feed the strengths of doing surveys but would be insufficient for
case study training.
 Protocol Development and Review

The next subsection will say more about the content of the case study
protocol. However, a legitimate and desirable training task is the understanding
of the protocol by all of the case study investigators.
To reinforce such an understanding, each investigator or team member may be
assigned one portion of the substantive topics covered by the protocol. Each
investigator is then responsible for reviewing the appropriate reading materials
related to the assigned portion, adding any other information that may be
relevant, and leading a discussion that clarifies that portion of the protocol’s
questions. In this manner, such an arrangement should ensure that each team
member has mastered the content of the protocol.
 Problems to Be Addressed

The training also has the purpose of uncovering problems within the case
study plan or with the research team’s capabilities. If such problems do emerge,
one consolation is that they will be more troublesome if they are not recognized
until later, after the data collection begins. Good case study investigators should
therefore press to be certain, during the training period, that potential problems
are brought into the open.
The most obvious problem is that the training may reveal flaws in the case
study design or even the initial definition of the study questions. If this occurs,
you must be willing to make the necessary revisions, even if more time and
effort are necessary. Sometimes, the revisions will challenge the basic purpose of
the investigation, as in a situation in which the original objective may have been
to investigate a technological phenomenon, such as the use of personal
computers, but in which the case study really turns out to be about an
organizational phenomenon, such as poor supervision. Any revisions, of course,
also may lead to the need to review a slightly different literature and to recast the
entire study and its audience. You also should check your IRB’s procedures to
see whether it will need to conduct a new human subjects review. Despite these
unexpected developments, changing the basic premise of your case study is fully
warranted if the training has demonstrated the unrealistic (or uninteresting)
nature of the original plan.
A second problem is that the training may reveal incompatibilities among the
investigating team—and in particular, the fact that some of the team members
may not share the orientation of the project or its sponsors. In one multiple-case
study of community organizations, for instance, team members varied in their
beliefs regarding the efficacy of such organizations (U.S. National Commission
on Neighborhoods, 1979). When such biases are discovered, one way of dealing
with the contrary orientations is to suggest to the team that contrary evidence
will be respected if it is collected and verifiable. A team member still has the
choice, of course, of continuing to participate in the study or deciding to drop
out.
A third problem is that the training may reveal some impractical time
deadlines or expectations regarding available resources. For instance, a case
study may have assumed that 20 persons were to be contacted for open-ended
interviews during a site visit, as part of the data collection. The training may
have revealed, however, that the time needed for meeting with these persons is
likely to be much longer than anticipated. Under such circumstances, any
expectation for interviewing 20 persons would have to depend on revising the
original data collection schedule.
Finally, the training may uncover some positive features, such as the fact that
two or more team members have complementary skills and are able to work
productively together. Such rapport and productivity during the training session
may readily extend to the actual data collection period and may therefore suggest
certain pairings for the fieldwork teams. In general, the training should have the
effect of creating group norms for the ensuing data collection activity. This
norm-building process is more than an amenity; it will help ensure supportive
reactions, should unexpected problems arise during the data collection.

EXERCISE 3.3 Conducting Training for Doing a Case Study

Describe the major ways in which the preparation and training to do a case
study project are different from those for doing projects using other types of
research strategies (e.g., surveys, experiments, histories, and archival
analysis). Develop a training agenda to prepare for a case study you might
be considering, in which two or three persons are to collaborate.

THE CASE STUDY PROTOCOL

A case study protocol has only one thing in common with a survey
questionnaire: Both are directed at a single data point—either a single case (even
if the case is part of a larger, multiple-case study) or a single respondent.
Beyond this similarity are major differences. The protocol is more than a
questionnaire or instrument. First, the protocol contains the instrument but also
contains the procedures and general rules to be followed in using the protocol.
Second, the protocol is directed at an entirely different party than that of a survey
questionnaire, explained below. Third, having a case study protocol is desirable
under all circumstances, but it is essential if you are doing a multiple-case study.
The protocol is a major way of increasing the reliability of case study research
and is intended to guide the investigator in carrying out the data collection from
a single case (again, even if the single case is one of several in a multiple-case
study). Figure 3.2 gives a table of contents from an illustrative protocol, which
was used in a study of innovative law enforcement practices supported by
federal funds. The practices had been defined earlier through a careful screening
process (see later discussion in this chapter for more detail on “screening case
study nominations”). Furthermore, because data were to be collected from 18
such cases as part of a multiple-case study, the information about any given case
could not be collected in great depth, and thus the number of the case study
questions was minimal.


Figure 3.2 Table of Contents of Protocol for Conducting Case Studies of
Innovative Law Enforcement Practices


As a general matter, a case study protocol should have the following sections:
• an overview of the case study project (project objectives and auspices,
case study issues, and relevant readings about the topic being
investigated),
• field procedures (presentation of credentials, access to the case study
“sites,” language pertaining to the protection of human subjects, sources
of data, and procedural reminders),
• case study questions (the specific questions that the case study
investigator must keep in mind in collecting data, “table shells” for
specific arrays of data, and the potential sources of information for
answering each question—see Figure 3.3 for an example), and
• a guide for the case study report (outline, format for the data, use and
presentation of other documentation, and bibliographical information).

A quick glance at these topics will indicate why the protocol is so important.
First, it keeps you targeted on the topic of the case study. Second, preparing the
protocol forces you to anticipate several problems, including the way that the
case study reports are to be completed. This means, for instance, that you will
have to identify the audience for your case study report even before you have
conducted your case study. Such forethought will help to avoid mismatches in
the long run.

Figure 3.3 Illustrative Protocol Question (from a Study of School Practices)

The table of contents of the illustrative protocol in Figure 3.2 reveals another
important feature of the case study report: In this instance, the desired report
starts by calling for a description of the innovative practice being studied (see
item C1 in Figure 3.2)—and only later covers the agency context and history
pertaining to the practice (see item C4). This choice reflects the fact that most
investigators write too extensively on history and background conditions. While
these are important, the description of the subject of the study—the innovative
practice—needs more attention.
Each section of the protocol is discussed next.
 Overview of the Case Study Project

The overview should cover the background information about the project, the
substantive issues being investigated, and the relevant readings about the issues.
As for background information, every project has its own context and
perspective. Some projects, for instance, are funded by government agencies
having a general mission and clientele that need to be remembered in conducting
the research. Other projects have broader theoretical concerns or are offshoots of
earlier research studies. Whatever the situation, this type of background
information, in summary form, belongs in the overview section.
A procedural element of this background section is a statement about the
project which you can present to anyone who may want to know about the
project, its purpose, and the people involved in conducting and sponsoring the
project. This statement can even be accompanied by a letter of introduction, to
be sent to all major interviewees and organizations that may be the subject of
study. (See Figure 3.4 for an illustrative letter.) The bulk of the overview,
however, should be devoted to the substantive issues being investigated. This
may include the rationale for selecting the case(s), the propositions or
hypotheses being examined, and the broader theoretical or policy relevance of
the inquiry. For all of these topics, relevant readings should be cited, and the
essential reading materials should be made available to everyone on the case
study team.
A good overview will communicate to the informed reader (that is, someone
familiar with the general topic of inquiry) the case study’s purpose and setting.
Some of the materials (such as a summary describing the project) may be needed
for other purposes anyway, so that writing the overview should be seen as a
doubly worthwhile activity. In the same vein, a well-conceived overview even
may later form the basis for the background and introduction to the final case
study report.
 Field Procedures

Chapter 1 has previously defined case studies as studies of events within their
real-life context. This has important implications for defining and designing the
case study, which have been discussed in Chapters 1 and 2.
For data collection, however, this characteristic of case studies also raises an
important issue, for which properly designed field procedures are essential. You
will be collecting data from people and institutions in their everyday situations,
not within the controlled confines of a laboratory, the sanctity of a library, or the
structured limitations of a survey questionnaire. In a case study, you must
therefore learn to integrate real-world events with the needs of the data
collection plan. In this sense, you do not have the control over the data collection
environment as others might have in using the other research methods discussed
in Chapter 1.
Note that in a laboratory experiment, human “subjects” are solicited to enter
into the laboratory—an environment controlled nearly entirely by the research
investigator. The subject, within ethical and physical constraints, must follow the
investigator’s instructions, which carefully prescribe the desired behavior.
Similarly, the human “respondent” to a survey questionnaire cannot deviate from
the agenda set by the questions. Therefore, the respondent’s behavior also is
constrained by the ground rules of the investigator. Naturally, the subject or
respondent who does not wish to follow the prescribed behaviors may freely
drop out of the experiment or survey. Finally, in the historical archive, pertinent
documents may not always be available, but the investigator can inspect what
exists at his or her own pace and at a time convenient to her or his schedule. In
all three situations, the research investigator closely controls the formal data
collection activity.

Figure 3.4 Illustrative Letter of Introduction


Doing case studies involves an entirely different situation. For interviewing

key persons, you must cater to the interviewee’s schedule and availability, not
your own. The nature of the interview is much more open-ended, and an
interviewee may not necessarily cooperate fully in sticking to your line of
questions. Similarly, in making observations of real-life activities, you are
intruding into the world of the subject being studied rather than the reverse;
under these conditions, you are the one who may have to make special
arrangements, to be able to act as an observer (or even as a participant-observer).
As a result, your behavior—and not that of the subject or respondent—is the one
likely to be constrained.
This contrasting process of doing data collection leads to the need to have
explicit and well-planned field procedures encompassing guidelines for “coping”
behaviors. Imagine, for instance, sending a youngster to camp; because you do
not know what to expect, the best preparation is to have the resources to be
prepared. Case study field procedures should be the same way.
With the preceding orientation in mind, the field procedures of the protocol
need to emphasize the major tasks in collecting data, including
• gaining access to key organizations or interviewees;
• having sufficient resources while in the field—including a personal
computer, writing instruments, paper, paper clips, and a preestablished,
quiet place to write notes privately;
• developing a procedure for calling for assistance and guidance, if needed,
from other case study investigators or colleagues;
• making a clear schedule of the data collection activities that are expected
to be completed within specified periods of time; and
• providing for unanticipated events, including changes in the availability of
interviewees as well as changes in the mood and motivation of the case
study investigator.

These are the types of topics that can be included in the field procedures section
of the protocol. Depending upon the type of study being done, the specific
procedures will vary.
The more operational these procedures are, the better. To take but one minor
issue as an example, case study data collection frequently results in the
accumulation of numerous documents at the field site. The burden of carrying
such bulky documents can be reduced by two procedures. First, the case study
team may have had the foresight to bring large, prelabeled envelopes, to mail the
documents back to the office rather than carry them. Second, field time may
have been set aside for perusing the documents and then going to a local copier
facility and copying only the few relevant pages of each document—and then
returning the original documents to the informants at the field site. These and
other operational details can enhance the overall quality and efficiency of case
study data collection.
A final part of this portion of the protocol should carefully describe the
procedures for protecting human subjects. First, the protocol should repeat the
rationale for the IRB-approved field procedures. Then, the protocol should
include the “scripted” words or instructions for the team to use in obtaining
informed consent or otherwise informing case study interviewees and other
participants of the risks and conditions associated with the research.
 Case Study Questions

The heart of the protocol is a set of substantive questions reflecting your
actual line of inquiry. Some people may consider this part of the protocol to be
the case study “instrument.” However, two characteristics distinguish case study
questions from those in a survey instrument. (Refer back to Figure 3.3 for an
illustrative question from a study of a school program; the complete protocol
included dozens of such questions.)

General orientation of questions. First, the questions are posed to you, the
investigator, not to an interviewee. In this sense, the protocol is directed at an
entirely different party than a survey instrument. The protocol’s questions, in
essence, are your reminders regarding the information that needs to be collected,
and why. In some instances, the specific questions also may serve as prompts in
asking questions during a case study interview. However, the main purpose of
the protocol’s questions is to keep the investigator on track as data collection
proceeds.
Each question should be accompanied by a list of likely sources of evidence.
Such sources may include the names of individual interviewees, documents, or
observations. This crosswalk between the questions of interest and the likely
sources of evidence is extremely helpful in collecting case study data. Before
arriving on the case study scene, for instance, a case study investigator can
quickly review the major questions that the data collection should cover. (Again,
these questions form the structure of the inquiry and are not intended as the
literal questions to be asked of any given interviewee.)

Levels of questions. Second, the questions in the case study protocol should
distinguish clearly among different types or levels of questions. The potentially
relevant questions can, remarkably, occur at any of five levels:
Level 1: questions asked of specific interviewees;
Level 2: questions asked of the individual case (these are the questions in
the case study protocol to be answered by the investigator during a single
case, even when the single case is part of a larger, multiple-case study);
Level 3: questions asked of the pattern of findings across multiple cases;
 Level 4: questions asked of an entire study—for example, calling on
information beyond the case study evidence and including other literature
or published data that may have been reviewed; and
Level 5: normative questions about policy recommendations and
conclusions, going beyond the narrow scope of the study.

Of these five levels, you should concentrate heavily on Level 2 for the case
study protocol.
The difference between Level 1 and Level 2 questions is highly significant.
The two types of questions are most commonly confused because investigators
think that their questions of inquiry (Level 2) are synonymous with the specific
questions they will ask in the field (Level 1). To disentangle these two levels in
your own mind, think again about a detective, especially a wily one. The
detective has in mind what the course of events in a crime might have been
(Level 2), but the actual questions posed to any witness or suspect (Level 1) do
not necessarily betray the detective’s thinking. The verbal line of inquiry is
different from the mental line of inquiry, and this is the difference between Level
1 and Level 2 questions. For the case study protocol, explicitly articulating the
Level 2 questions is therefore of much greater importance than any attempt to
identify the Level 1 questions.
In the field, keeping in mind the Level 2 questions while simultaneously
articulating Level 1 questions in conversing with an interviewee is not easy. In a
like manner, you can lose sight of your Level 2 questions when examining a
detailed document that will become part of the case study evidence (the common
revelation occurs when you ask yourself, “Why am I reading this document?”).
To overcome these problems, successful participation in the earlier seminar
training helps. Remember that being a “senior” investigator means maintaining a
working knowledge of the entire case study inquiry. The (Level 2) questions in
the case study protocol embody this inquiry.
The other levels also should be understood clearly. A cross-case question, for
instance (Level 3), may be whether the larger school districts among your cases
are more responsive than smaller school districts or whether complex
bureaucratic structures make the larger districts more cumbersome and less
responsive. However, this Level 3 question should not be part of the protocol for
collecting data from the single case, because the single case only can address the
responsiveness of a single school district. The Level 3 question cannot be
addressed until the data from all the single cases (in a multiple-case study) are
examined. Thus, only the multiple-case analysis can cover Level 3 questions.
Similarly, the questions at Levels 4 and 5 also go well beyond any individual
case study, and you should note this limitation if you include such questions in
the case study protocol. Remember: The protocol is for the data collection from
a single case (even when part of a multiple-case study) and is not intended to
serve the entire project.

Undesired confusion between unit of data collection and unit of analysis.
Related to the distinction between Level 1 and Level 2 questions, a more subtle
and serious problem can arise in articulating the questions in the case study
protocol. The questions should cater to the unit of analysis of the case study,
which may be at a different level from the unit of data collection of the case
study. Confusion will occur if, under these circumstances, the data collection
process leads to an (undesirable) distortion of the unit of analysis.
The common confusion begins because the data collection sources may be
individual people (e.g., interviews with individuals), whereas the unit of analysis
of your case study may be a collective (e.g., the organization to which the
individual belongs)—a frequent design when the case study is about an
organization, community, or social group. Even though your data collection may
have to rely heavily on information from individual interviewees, your
conclusions cannot be based entirely on interviews as a source of information
(you would then have collected information about individuals’ reports about the
organization, not necessarily about organizational events as they actually had
occurred). In this example, the protocol questions therefore need to be about the
organization, not the individual.
However, the reverse situation also can be true. Your case study may be about
an individual, but the sources of information can include archival records (e.g.,
personnel files or student records) from an organization. In this situation, you
also would want to avoid basing your conclusions about the individual from the
organizational sources of information only. In this example, the protocol
questions therefore need to be about the individual, not the organization.

Figure 3.5 Design versus Data Collection: Different Units of Analysis


Figure 3.5 illustrates these two situations, where the unit of analysis for the
case study is different from the unit of data collection.

Other data collection devices. The protocol questions also can include empty
“table shells” (for more detail, see Miles & Huberman, 1994). These are the
outlines of a table, defining precisely the “rows” and “columns” of a data array
—but in the absence of having the actual data. In this sense, the table shell
indicates the data to be collected, and your job is to collect the data called forth
by the table. Such table shells help in several ways. First, the table shells force
you to identify exactly what data are being sought. Second, they ensure that
parallel information will be collected at different sites, where a multiple-case
design is being used. Finally, the table shells aid in understanding what will be
done with the data once they have been collected.
 Guide for the Case Study Report

This element is generally missing in most case study plans. Investigators
neglect to think about the outline, format, or audience for the case study report
until after the data have been collected. Yet, some planning at this preparatory
stage—admittedly out of sequence in the typical conduct of most research—
means that a tentative outline can (and should) appear in the case study protocol.
(Such planning accounts for the arrow between “prepare” and “share” in the
figure at the outset of this chapter.)
Again, one reason for the traditional, linear sequence is related to practices
with other research methods. One does not worry about the report from an
experiment until after the experiment has been completed, because the format of
the report and its likely audience already have been dictated by the conventional
formats of academic journals. Most reports of experiments follow a similar
outline: the posing of the research questions and hypotheses; a description of the
research design, apparatus, and data collection procedures; the presentation of
the data collected; the analysis of the data; and a discussion of findings and
conclusions.
Unfortunately, case study reports do not have such a uniformly acceptable
outline. Nor, in many instances, do case study reports end up in journals (Feagin
et al., 1991, pp. 269-273). For this reason, each investigator must be concerned,
throughout the conduct of a case study, with the design of the final case study
report. The problem is not easy to deal with.
In addition, the protocol also can indicate the extent of documentation for the
case study report. Properly done, the data collection is likely to lead to large
amounts of documentary evidence, in the form of published reports,
publications, memoranda, and other documents collected about the case. What is
to be done with this documentation, for later presentation? In most studies, the
documents are filed away and seldom retrieved. Yet, this documentation is an
important part of the “database” for a case study (see Chapter 4) and should not
be ignored until after the case study has been completed. One possibility is to
have the case study report include an annotated bibliography in which each of
the available documents is itemized. The annotations would help a reader (or the
investigator, at some later date) to know which documents might be relevant for
further inquiry.
In summary, to the extent possible, the basic outline of the case study report
should be part of the protocol. This will facilitate the collection of relevant data,
in the appropriate format, and will reduce the possibility that a return visit to the
case study site will be necessary. At the same time, the existence of such an
outline should not imply rigid adherence to a predesigned protocol. In fact, case
study plans can change as a result of the initial data collection, and you are
encouraged to consider these flexibilities—if used properly and without bias—to
be an advantage of the case study method.

EXERCISE 3.4 Developing a Case Study Protocol

Select some phenomenon in need of explanation from the everyday life of
your university or school (past or present). Illustrative topics might be, for
example, why the university or school changed some policy or how it
makes decisions about its curriculum requirements. For these illustrative
topics (or some topics of your own choosing), design a case study protocol
to collect the information needed to make an adequate explanation. What
would be your main research questions or propositions? What specific
sources of data would you seek (e.g., persons to be interviewed, documents
to be sought, and field observations to be made)? Would your protocol be
sufficient in guiding you through the entire process of doing your case
study?

SCREENING THE CANDIDATE “CASES” FOR YOUR CASE
STUDY

Another preparatory step is the final selection of the case(s) to be part of your
case study. Sometimes, the selection is straightforward because you have chosen
to study a unique case whose identity has been known from the outset of your
inquiry. Or, you already may know the case you will study because of some
special arrangement or access that you have. However, at other times, there may
be many qualified case study candidates, and you must choose your final single
case or array of multiple cases from among them. The goal of the screening
procedure is to be sure that you identify the final cases properly prior to formal
data collection. The worst scenario would occur when, after having started
formal data collection, the case turns out not to be viable or to represent an
instance of something other than what you had intended to study.
When you have only a score or so (20 to 30) of possible candidates that can
serve as your cases (whether these candidates are “sites” or individuals or some
other entity depends on your unit of analysis), the screening may consist of
querying people knowledgeable about each candidate. You even may collect
limited documentation about each candidate. To be avoided, at all costs, is an
extensive screening procedure that effectively becomes a “mini” case study of
every candidate case. Prior to collecting the screening data, you should have
defined a set of operational criteria whereby candidates will be deemed qualified
to serve as cases. If doing a single-case study, choose the case that is likely, all
other things being equal, to yield the best data. If doing a multiple-case study,
select cases that best fit your (literal or theoretical) replication design.
When the eligible number of candidates is larger, a two-stage screening
procedure is warranted. The first stage should consist of collecting relevant
quantitative data about the entire pool, from some archival source (e.g.,
statistical databases about individual schools or firms). You may have to obtain
the archival data from some central source (e.g., a federal, state, or local agency
or a national association). Once obtained, you should define some relevant
criteria for either stratifying or reducing the number of candidates. The goal is to
reduce the number of candidates to 20 to 30 and then to conduct the second
screening stage, which consists of carrying out the procedure in the previous
paragraph. Such a two-stage procedure was followed in a case study of local
economic development, and the experience is fully reported in the companion
text (Yin, 2003, chap. 6, pp. 9-14).
In completing the screening process, you may want to revisit your earlier
decision about the total number of cases to be studied. Regardless of any
resource constraints, if multiple candidates are qualified to serve as cases, the
larger the number you can study, the better.
THE PILOT CASE STUDY

Pilot cases may be conducted for several reasons unrelated to the criteria for
selecting the final cases in the case study design. For example, the informants at
a pilot site may be unusually congenial and accessible, or the site may be
geographically convenient or may have an unusual amount of documentation
and data. One other possibility is that a pilot case represents a most complicated
case, compared to the likely real cases, so that nearly all relevant data collection
issues will be encountered in the pilot case.
A pilot case study will help you to refine your data collection plans with
respect to both the content of the data and the procedures to be followed. In this
regard, it is important to note that a pilot test is not a pretest. The pilot case is
more formative, assisting you to develop relevant lines of questions—possibly
even providing some conceptual clarification for the research design as well. In
contrast, the pretest is the occasion for a formal “dress rehearsal,” in which the
data collection plan is used as the final plan as faithfully as possible. As a result,
the pilot test might preferably occur before seeking final approval from an IRB,
as discussed earlier in this chapter.
The pilot case study can be so important that more resources may be devoted
to this phase of the research than to the collection of data from any of the actual
cases. For this reason, several subtopics are worth further discussion: the
selection of pilot cases, the nature of the inquiry for the pilot cases, and the
nature of the reports from the pilot cases.
 Selection of Pilot Cases

In general, convenience, access, and geographic proximity can be the main
criteria for selecting a pilot case or cases. This will allow for a less structured
and more prolonged relationship between yourself and the case than might occur
in the “real” cases. The pilot case can then assume the role of a “laboratory” in
detailing your protocol, allowing you to observe different phenomena from many
different angles or to try different approaches on a trial basis.
One study of technological innovations in local services (Yin, 2003, pp. 6-9)
actually had seven pilot cases, each focusing on a different type of technology.
Four of the cases were located in the same metropolitan area as the research
team’s and were visited first. Three of the cases, however, were located in
different cities and were the basis for a second set of visits. The cases were not
chosen because of their distinctive technologies or for any other substantive
reason. The main criterion, besides proximity, was the fact that access to the
cases was made easy by some prior personal contact on the part of the research
team. Finally, the interviewees in the cases also were congenial to the notion that
the investigators were at an early stage of their research and would not have a
fixed agenda.
In return for serving as a pilot case, the main informants usually expect to
receive some feedback from you about their case. Your value to them is as an
external observer, and you should be prepared to provide such feedback. To do
so, even though you should already have developed a draft protocol representing
the topics of interest to your case study, you should adapt parts of the protocol to
suit the informants’ needs. You should then conduct the pilot case by following
(and pilot-testing) your formal field procedures. Under no circumstance should
the pilot case be the occasion for an overly informal or highly personalized
inquiry.
 Scope of the Pilot Inquiry

Nevertheless, the scope of the inquiry for the pilot case can be much broader
and less focused than the ultimate data collection plan. Moreover, the inquiry can
cover both substantive and methodological issues.
In the above-mentioned example, the research team used the seven pilot cases
to improve its conceptualization of different types of technologies and their
related organizational effects. The pilot studies were done prior to the selection
of specific technologies for the final data collection—and prior to the final
articulation of the study’s theoretical propositions. Thus, the pilot data provided
considerable insight into the basic issues being studied. This information was
used in parallel with an ongoing review of relevant literature, so that the final
research design was informed both by prevailing theories and by a fresh set of
empirical observations. The dual sources of information help to ensure that the
actual study reflected significant theoretical or policy issues as well as questions
relevant to contemporary cases.
Methodologically, the work on the pilot cases can provide information about
relevant field questions and about the logistics of the field inquiry. In the
technology pilot cases, one important logistical question was whether to observe
the technology in action first or to collect information about the prevalent
organizational issues first. This choice interacted with a further question about
the deployment of the field team: If the team consisted of two or more persons,
what assignments required the team to work together and what assignments
could be completed separately? Variations in these procedures were tried during
the pilot case studies, the trade-offs were acknowledged, and eventually a
satisfactory procedure was developed for the formal data collection plan.
 Reports from the Pilot Cases

The pilot case reports are mainly of value to the investigators and need to be
written clearly, even if in the form of memoranda. One difference between the
pilot reports and the actual case study reports is that the pilot reports should be
explicit about the lessons learned for both research design and field procedures.
The pilot reports might even contain subsections on these topics.
If more than a single pilot case is planned, the report from one pilot case also
can indicate the modifications to be attempted in the next pilot case. In other
words, the report can contain the agenda for the ensuing pilot case. If enough
pilot cases are done in this manner, the final agenda may actually become a good
prototype for the final case study protocol.

EXERCISE 3.5 Selecting a Case for Doing a Pilot Study

Define the desired features for a pilot case, as a prelude to a new case study
research project. How would you go about contacting and using such a
case? Describe why you might want only one pilot case, as opposed to two
or more pilot cases.

SUMMARY

This chapter has reviewed the preparations for data collection. Depending upon
the scope of a case study—whether single or multiple cases will be involved or
whether single or multiple investigators will be involved—the preparatory tasks
will be correspondingly straightforward or complex.
The major topics have been the desired skills of the case study investigator,
the preparation and training of the case study investigators for a specific case
study, the nature of the case study protocol, the screening of candidate cases, and
the role and purpose of a pilot case study. Every case study should follow these
different steps to varying degrees, depending upon the specific inquiry.
As with the management of other affairs, the expertise with which these
activities are conducted will improve with practice. Thus, one desirable sequence
is for you to complete a relatively straightforward case study before attempting
to do a more complex one, from a managerial standpoint. With the successful
completion of each case study, these preparatory tasks may even become second
nature. Furthermore, if the same case study team has conducted several different
studies together, the team will work with increasing efficiency and professional
satisfaction with each ensuing case study.
NOTES

1 Thacher (2006) argues forcefully in support of what he calls “normative” case

studies. In such studies, the investigators do use case studies to advocate specific
issues, at the risk of being challenged about the fairness of their data. Such risks
may be best left to very senior investigators but are not recommended for those
with less experience in doing case studies, much less novices.

2 The difference between having a single case study investigator and needing
multiple investigators can create a significantly different orientation to the entire
case study method. The classic single investigators have frequently been brilliant
and creative—quickly and intuitively adapting to new conditions during data
collection or finding newly appealing patterns during data analysis. With
multiple investigators, such talents may have to be curbed because of the need
for consistency across investigators, but the good discipline is rewarded by
minimizing the likelihood of introducing bias into the case study.

REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 3

For selected case studies cited in the text of this chapter, two anthologies contain
either a more extensive excerpt or the full case study. The table below
crosswalks the reference in this book to the location of the excerpt or full
rendition.


ABSTRACT

Case study evidence may come from six sources: documents, archival records,
interviews, direct observation, participant-observation, and physical artifacts.
Using these six sources calls for mastering different data collection procedures.
Throughout, a major objective is to collect data about actual human events and
behavior. This objective differs from (but complements) the typical survey
objective of capturing perceptions, attitudes, and verbal reports about events and
behavior (rather than direct evidence about the events and behavior).
In addition to the attention given to the six sources, some overriding principles
are important to any data collection effort in doing case studies. These include
the use of (a) multiple sources of evidence (evidence from two or more sources,
converging on the same facts or findings), (b) a case study database (a formal
assembly of evidence distinct from the final case study report), and (c) a chain of
evidence (explicit links among the questions asked, the data collected, and the
conclusions drawn). The incorporation of these principles into a case study will
increase its quality substantially.
 4

Collecting Case Study Evidence The Principles You Should Follow

in Working with Six Sources of Evidence

Case study evidence can come from many sources. This chapter discusses six of
them: documentation, archival records, interviews, direct observation,
participant-observation, and physical artifacts. Each source is associated with an
array of data or evidence. One purpose of this chapter is to review the six
sources briefly. A second purpose is to convey three essential data collection
principles, regardless of the sources used.

Supporting textbooks. You may find the six sources of evidence all potentially
relevant, even in doing the same case study. For this reason, having them briefly
reviewed, all in one place, may be helpful. For any given source of evidence,
extensive further detail is available in numerous methodological textbooks and
articles. Therefore, you also may want to check out some of these texts,
especially if any single source of evidence is especially important to your case
study. However, choosing among the texts and other works will require some
searching and careful selection.
First, at an earlier time, guidance on collecting data relevant for case studies
was available under three rubrics. One was “fieldwork” (e.g., Murphy, 1980;
Wax, 1971) and a second was “field research” (e.g., Bouchard, 1976; Schatzman
& Strauss, 1973). The third was “social science methods” more broadly (e.g., L.
Kidder & Judd, 1986; Webb, Campbell, Schwartz, Sechrest, & Grove, 1981).
Under these rubrics, the books also could cover the logistics of planning and
conducting the fieldwork (e.g., Fiedler, 1978). The array of data collection
techniques included under these rubrics was relevant to doing case studies,
although none focused on case studies. The texts are still valuable because they
are easy to use and discuss the basic data collection procedures to be followed.
Unfortunately, the texts are probably increasingly hard to locate.
Second, recent texts are more readily available, but your choices are more
complicated. Individual texts usually only cover some of the sources of evidence
(e.g., single interviews, focus group interviews, and field observations) but not
the others (e.g., archival and documentary sources), thereby losing the flavor of
the entire blend of multiple sources. Furthermore, the texts also may not suit
your needs because they may have a dominant substantive or disciplinary
orientation, such as (a) clinical research or research on primary care settings
(e.g., Crabtree & Miller, 1999), (b) program evaluations (e.g., Patton, 2002), or
(c) social work research (e.g., A. Rubin & Babbie, 1993). Yet other texts may not
have such an orientation, but they may focus on only a single source of evidence,
such as field interviewing (e.g., H. J. Rubin & Rubin, 1995), doing participant-
observation (e.g., Jorgensen, 1989), or using documentary evidence (e.g., Barzun
& Graff, 1985). In general, contemporary texts appear to have become more
specialized, and few span the needed breadth of data collection methods. In
particular, few texts combine data collection through communicative and
observational means (i.e., interviews and direct observations, including the use
of videotapes) with data collection through documentary and archival sources.

Tip: How much time and effort should I devote to collecting the
case study data? How do I know whether I’m finished collecting
the data?

Unlike other methods, there is no clear cut-off point. You should try to
collect enough data so that (a) you have confirmatory evidence (evidence
from two or more different sources) for most of your main topics, and (b)
your evidence includes attempts to investigate major rival hypotheses or
explanations.


What do you think are some of the cut-off points for other methods,
and why wouldn’t they work in doing case study research?
Third, books that might at first appear to be comprehensive methodological
texts also cover many topics in addition to data collection and, as a result, only
devote a fraction of their entire text to data collection procedures (e.g., 1 of 11
chapters in Creswell, 2007, and 1 of 26 chapters in Silverman, 2000). Other
books that do have a truly comprehensive range and that do discuss data
collection techniques in greater detail are nevertheless designed to serve more as
reference works than as textbooks to be used by individual investigators (e.g.,
Bickman & Rog, 2000).
Given these variations, you must overcome the complex if not fragmented
nature of the methodological marketplace represented by these various texts. To
do so will make your own data collection procedures even better.

Supporting principles. In addition to your need to be familiar with the data
collection procedures using the six different sources of evidence, you also need
to continue addressing the design challenges enumerated in Chapter 2: construct
validity, internal validity, external validity, and reliability. For this reason, this
chapter gives much emphasis to its second purpose, the discussion of three
principles of data collection.
These principles have been neglected in the past and are discussed at length:
(a) using multiple, not just single, sources of evidence; (b) creating a case study
database; and (c) maintaining a chain of evidence. The principles are extremely
important for doing high-quality case studies, are relevant to all six types of
sources of evidence, and should be followed whenever possible. In particular, the
principles, as noted in Chapter 2 (see Figure 2.5), will help to deal with the
problems of construct validity and reliability.

EXERCISE 4.1 Using Evidence

Select and obtain one of the case studies cited in the BOXES of this book.
Go through the case study and identify five “facts” important to the case
study. For each fact, indicate the source or sources of evidence, if any, used
to define the fact. In how many instances was there more than a single
source of evidence?

SIX SOURCES OF EVIDENCE

The sources of evidence discussed here are the ones most commonly used in
doing case studies: documentation, archival records, interviews, direct
observations, participant-observation, and physical artifacts. However, you
should be aware that a complete list of sources can be quite extensive—
including films, photographs, and videotapes; projective techniques and
psychological testing; proxemics; kinesics; “street” ethnography; and life
histories (Marshall & Rossman, 1989).
A useful overview of the six major sources considers their comparative
strengths and weaknesses (see Figure 4.1). You should immediately note that no
single source has a complete advantage over all the others. In fact, the various
sources are highly complementary, and a good case study will therefore want to
use as many sources as possible (see the later discussion in this chapter on
“multiple sources of evidence”).
 Documentation

Except for studies of preliterate societies, documentary information is likely to
be relevant to every case study topic.1 This type of information can take many
forms and should be the object of explicit data collection plans. For instance,
consider the following variety of documents:
Figure 4.1 Six Sources of Evidence: Strengths and Weaknesses


• letters, memoranda, e-mail correspondence, and other personal

documents, such as diaries, calendars, and notes;
• agendas, announcements and minutes of meetings, and other written
reports of events;
• administrative documents—proposals, progress reports, and other internal
records;
• formal studies or evaluations of the same “case” that you are studying;
 and
• news clippings and other articles appearing in the mass media or in
community newspapers.

These and other types of documents all are increasingly available through
Internet searches. The documents are useful even though they are not always
accurate and may not be lacking in bias. In fact, documents must be carefully
used and should not be accepted as literal recordings of events that have taken
place. Few people realize, for instance, that even the “verbatim” transcripts of
official U.S. Congress hearings have been deliberately edited—by the
congressional staff and others who may have testified—before being printed in
final form. In another field, historians working with primary documents also
must be concerned with the validity of a document.
For case studies, the most important use of documents is to corroborate and
augment evidence from other sources. First, documents are helpful in verifying
the correct spellings and titles or names of organizations that might have been
mentioned in an interview. Second, documents can provide other specific details
to corroborate information from other sources. If the documentary evidence is
contradictory rather than corroboratory, you need to pursue the problem by
inquiring further into the topic. Third, you can make inferences from documents
—for example, by observing the distribution list for a specific document, you
may find new questions about communications and networking within an
organization. However, you should treat inferences only as clues worthy of
further investigation rather than as definitive findings because the inferences
could later turn out to be false leads.
Because of their overall value, documents play an explicit role in any data
collection in doing case studies. Systematic searches for relevant documents are
important in any data collection plan. For example, prior to field visits, an
Internet search can produce invaluable information. During field visits, you
should allot time for using local libraries and other reference centers whose
documents, such as back issues of periodicals, may not be available
electronically. You should also arrange access to examine the files of any
organizations being studied, including a review of documents that may have
been put into cold storage. The scheduling of such retrieval activities is usually a
flexible matter, independent of other data collection activities, and the search can
usually be conducted at your convenience. For this reason, there is little excuse
for omitting a thorough review of documentary evidence. Among such evidence,
news accounts are excellent sources for covering certain topics, such as the two
in BOXES 16 and 17.
 BOX 16

Combining Personal Participation with Extensive Newspaper
Documentation

Improving educational conditions—especially for urban schools in the
United States—has become one of the biggest challenges for the 21 st
century. How the Houston, Texas, system dealt with constrained fiscal
resources, diverse student populations, and local political constituences is
the topic of an exciting and riveting case study by Donald McAdams
(2000). McAdams benefits from having been a member of the system’s
school board for three elected, 4-year terms. He writes as a storyteller, not a
social science analyst. At the same time, the book contains numerous
references to local news articles to corroborate events. The result is one of
the most readable but also well-documented case studies that readers will
encounter. most readable but also well-documented case studies that readers
will encounter.

BOX 17

Comparing Evidence from Two Archival Sources to Cover the Same
Community Events

One of the most inflammatory community events in the 1990s came to be
known as the “Rodney King crisis.” White police officers were
serendipitously videotaped in as the “Rodney King crisis.” White police
officers were serendipitously videotaped in the act of beating an African
American man, but a year later, they all were acquitted of any wrongdoing.
The acquittal sparked a major civil disturbance, in which 58 of any
wrongdoing. The acquittal sparked a major civil disturbance, in which 58
people were killed, 2,000 injured, and 11,000 arrested.
A case study of this crisis deliberately drew from two different
newspapers—the major daily for the metropolitan area and the most
significant newspaper for the area’s African American community (R. N.
Jacobs, 1996). For the pertinent period surrounding the crisis, the first
newspaper produced 357 articles and the second (a weekly, not daily,
publication) 137 articles. The case study traces the course of second (a
weekly, not daily, publication) 137 articles. The case study traces the course
 of events and shows how the two papers constructed different
understandings of the crisis, illustrating the potential biases of documentary
evidence and the need to address such biases. address such biases.

At the same time, many people have been critical of the potential overreliance
on documents in case study research. This is probably because the casual
investigator may mistakenly assume that all kinds of documents—including
proposals for projects or programs—contain the unmitigated truth. In fact,
important in reviewing any document is to understand that it was written for
some specific purpose and some specific audience other than those of the case
study being done. In this sense, the case study investigator is a vicarious
observer, and the documentary evidence reflects a communication among other
parties attempting to achieve some other objectives. By constantly trying to
identify these objectives, you are less likely to be misled by documentary
evidence and more likely to be correctly critical in interpreting the contents of
such evidence.2
A newer problem has arisen because of the abundance of materials available
through Internet searches. You may get lost in reviewing such materials and
actually waste a lot of time on them. Note, however, that the problem is not that
different from having an overabundance of numeric data about your case, as
might be available from sources such as the U.S. census (also see discussion of
archival records, next) if you were doing a neighborhood study. In both
situations, you need to have a strong sense of your case study inquiry and focus
on the most pertinent information. One suggestion is to sort or triage the
materials (documents or numeric data) by their apparent centrality to your
inquiry. Then, spend more time reading or reviewing what appears central, and
leave aside other, less important materials for later reading or review. The
procedure will not be perfect, but it will permit you to keep moving to other case
study tasks.
 Archival Records

For many case studies, archival records—often taking the form of computer
files and records as in the U.S. census data just mentioned—also may be
relevant. Examples of archival records include
• “public use files” such as the U.S. census and other statistical data made
available by federal, state, and local governments;
• service records, such as those showing the number of clients served over a
given period of time;
• organizational records, such as budget or personnel records;
• maps and charts of the geographical characteristics of a place; and
• survey data, such as data previously collected about a site’s employees,
residents, or participants.

These and other archival records can be used in conjunction with other
sources of information in producing a case study. However, unlike documentary
evidence, the usefulness of these archival records will vary from case study to
case study. For some studies, the records can be so important that they can
become the object of extensive retrieval and quantitative analysis (for example,
see a multiple-case study of 20 universities, in Yin, 2003, chap. 9). In other
studies, they may be of only passing relevance.
When archival evidence has been deemed relevant, an investigator must be
careful to ascertain the conditions under which it was produced as well as its
accuracy. Sometimes, the archival records can be highly quantitative, but
numbers alone should not automatically be considered a sign of accuracy. Nearly
every social scientist, for instance, is aware of the pitfalls of using the FBI’s
Uniform Crime Reports—or any other archival records based on crimes reported
by law enforcement agencies. The same general word of caution made earlier
with documentary evidence therefore also applies to archival evidence: Most
archival records were produced for a specific purpose and a specific audience
other than the case study investigation, and these conditions must be fully
appreciated in interpreting the usefulness and accuracy of the records.
 Interviews

One of the most important sources of case study information is the interview.
Such an observation may be surprising because of the usual association between
interviews and the survey method. However, interviews also are essential
sources of case study information. The interviews will be guided conversations
rather than structured queries. In other words, although you will be pursuing a
consistent line of inquiry, your actual stream of questions in a case study
interview is likely to be fluid rather than rigid (H. J. Rubin & Rubin, 1995).
Note that this means that, throughout the interview process, you have two
jobs: (a) to follow your own line of inquiry, as reflected by your case study
protocol, and (b) to ask your actual (conversational) questions in an unbiased
manner that also serves the needs of your line of inquiry (see distinction between
“Level 1” and “Level 2” questions in Chapter 3). For instance, you may want (in
your line of inquiry) to know “why” a particular process occurred as it did.
Becker (1998, pp. 58-60), however, has pointed to the important difference in
actually posing a “why” question to an informant (which, in his view, creates
defensiveness on the informant’s part) in contrast to posing a “how” question—
the latter in fact being his preferred way of addressing any “why” question in an
actual conversation. Thus, case study interviews require you to operate on two
levels at the same time: satisfying the needs of your line of inquiry (Level 2
questions) while simultaneously putting forth “friendly” and “nonthreatening”
questions in your open-ended interviews (Level 1 questions).
One type of case study interview is an in-depth interview. You can ask key
respondents about the facts of a matter as well as their opinions about events. In
some situations, you may even ask the interviewee to propose her or his own
insights into certain occurrences and may use such propositions as the basis for
further inquiry. The “interview” may therefore take place over an extended
period of time, not just a single sitting. The interviewee also can suggest other
persons for you to interview, as well as other sources of evidence.
The more that an interviewee assists in this manner, the more that the role may
be considered one of an “informant” rather than a respondent. Key informants
are often critical to the success of a case study. Such persons provide the case
study investigator with insights into a matter and also can initiate access to
corroboratory or contrary sources of evidence. Such a person, named “Doc,”
played an essential role in the conduct of the famous case study presented in
Street Corner Society (Whyte, 1943/1955; also see BOX 2A, Chapter 1, p. 7).
Similar key informants have been noted in other case studies. Of course, you
need to be cautious about becoming overly dependent on a key informant,
especially because of the interpersonal influence—frequently subtle—that the
informant may have over you. A reasonable way of dealing with this pitfall again
is to rely on other sources of evidence to corroborate any insight by such
informants and to search for contrary evidence as carefully as possible.
A second type of case study interview is a focused interview (Merton, Fiske,
& Kendall, 1990), in which a person is interviewed for a short period of time—
an hour, for example. In such cases, the interviews may still remain open-ended
and assume a conversational manner, but you are more likely to be following a
certain set of questions derived from the case study protocol.
For example, a major purpose of such an interview might simply be to
corroborate certain facts that you already think have been established (but not to
ask about other topics of a broader, open-ended nature). In this situation, the
specific questions must be carefully worded, so that you appear genuinely naive
about the topic and allow the interviewee to provide a fresh commentary about
it; in contrast, if you ask leading questions, the corroboratory purpose of the
interview will not have been served. Even so, you need to exercise caution when
different interviewees appear to be echoing the same thoughts—corroborating
each other but in a conspiratorial way.3 Further probing is needed. One way is to
test the sequence of events by deliberately checking with persons known to hold
different perspectives. If one of the interviewees fails to comment, even though
the others tend to corroborate one another’s versions of what took place, the
good case study investigator will even jot this down in the case study notes,
citing the fact that a person was asked but declined to comment, as done in good
journalistic accounts.
Yet a third type of interview entails more structured questions, along the lines
of a formal survey. Such a survey could be designed as part of an embedded case
study (see Chapter 2) and produce quantitative data as part of the case study
evidence (see BOX 18). This situation would be relevant, for instance, if you
were doing a case study of an urban design project and surveyed a group of
designers about the project (e.g., Crewe, 2001) or if you did a case study of an
organization that included a survey of workers and managers. This type of
survey would follow both the sampling procedures and the instruments used in
regular surveys, and it would subsequently be analyzed in a similar manner. The
difference would be the survey’s role in relation to other sources of evidence.
For example, residents’ perceptions of neighborhood decline or improvement
would not necessarily be taken as a measure of actual decline or improvement
but would be considered only one component of the overall assessment of the
neighborhood.

BOX 18

A Case Study Encompassing a Survey

Hanna (2000) used a variety of sources of data, including a survey, to
conduct a case study of an urban-rural estuarine setting. In this setting, an
integrated resource management program was established to help manage
environmental and eco nomic planning issues. The case study focused on
the estuarine setting, including its description and the policies and public
participation that appeared to affect it. Within the case study, participants in
the policy process served as an embedded unit of analysis. Hanna surveyed
these individuals, and the survey data were pre sented with statistical tests
as part of the single-case study.

Overall, interviews are an essential source of case study evidence because
most case studies are about human affairs or behavioral events. Well-informed
interviewees can provide important insights into such affairs or events. The
interviewees also can provide shortcuts to the prior history of such situations,
helping you to identify other relevant sources of evidence.
At the same time, even though your interviews may focus on behavioral
events because they are the key ingredients of your case study, the interviews
should always be considered verbal reports only. As such, even in reporting
about such events or explaining how they occurred, the interviewees’ responses
are subject to the common problems of bias, poor recall, and poor or inaccurate
articulation. Again, a reasonable approach is to corroborate interview data with
information from other sources.
Sometimes, you will be interested in an interviewee’s opinions or attitudes,
apart from explaining behavioral events. Corroborating these opinions or
attitudes against other sources would not be relevant, as in dealing with
behavioral events. You still may want to get a feeling for the prevalence of the
opinions or attitudes by comparing them with those of others, but the more you
do this, the more you are moving toward a conventional survey and should
follow survey procedures and precautions.
A common question about doing interviews is whether to record them. Using
recording devices is a matter of personal preference. Audiotapes certainly
provide a more accurate rendition of any interview than any other method.
However, a recording device should not be used when (a) an interviewee refuses
permission or appears uncomfortable in its presence, (b) there is no specific plan
for transcribing or systematically listening to the contents of the electronic
record—a process that takes enormous time and energy, (c) the investigator is
clumsy enough with mechanical devices that the recording creates distractions
during the interview itself, or (d) the investigator thinks that the recording device
is a substitute for “listening” closely throughout the course of an interview.
 Direct Observation

Because a case study should take place in the natural setting of the “case,” you
are creating the opportunity for direct observations. Assuming that the
phenomena of interest have not been purely historical, some relevant behaviors
or environmental conditions will be available for observation. Such observations
serve as yet another source of evidence in a case study.
The observations can range from formal to casual data collection activities.
Most formally, observational instruments can be developed as part of the case
study protocol, and the fieldworker may be asked to assess the occurrence of
certain types of behaviors during certain periods of time in the field (see the two
examples in BOX 19). This can involve observations of meetings, sidewalk
activities, factory work, classrooms, and the like. Less formally, direct
observations might be made throughout a field visit, including those occasions
during which other evidence, such as that from interviews, is being collected.
For instance, the condition of buildings or work spaces will indicate something
about the climate or impoverishment of an organization; similarly, the location
or the furnishings of an interviewee’s office may be one indicator of the status of
the interviewee within an organization.

BOX 19

Using Observational Evidence

19A. Reporting Field Observations


“Clean rooms” are a key part of the manufacturing process for producing
semiconductor chips. Among other features, employees wear “bunny suits”
of lint-free cloth and handle extremely small components in these rooms. In
their case study of high tech working life, Silicon Valley Fever, Rogers and
Larsen (1984) used observational evidence to show how employees adapted
to the working conditions in these clean rooms, adding that, at the time,
most of the employees were women while most of the supervisors were
men.


19B. Combining Field Observations with Other Types of Case Study
Evidence

Case studies need not be limited to a single source of evidence. In fact,
most of the better case studies rely on a variety of sources.
One example of a case study that used such a variety is a book by Gross
et al. (1971) covering events in a single school (also see BOX 7, Chapter 2,
p. 48). The case al. study included an observationel protocol for measuring
the time that students spent on various tasks but also relied on a structured
survey of a larger number of study included an observational protocol for
measuring the time that students teachers, open-ended interviews with a
smaller number of key persons, of organizational documents; Both the
observational and survey data led to quantitative information about attitudes
and behavior in the open-ended interviews and documentary evidence led to
qualitative information.
All sources of evidence were reviewed and analyzed together, so that the
case study’s finding were based on the convergence of information from
different sources, not quantitative or qualitative data alone. study’s findings
were based on the convergence of information from different sources, not
quantitative or qualitative data alone.
Observational evidence is often useful in providing additional information
about the topic being studied. If a case study is about a new technology or a
school curriculum, for instance, observations of the technology or curriculum at
work are invaluable aids for understanding the actual uses of the technology or
curriculum or any potential problems being encountered. Similarly, observations
of a neighborhood or of an organizational unit add new dimensions for
understanding either the context or the phenomenon being studied. The
observations can be so valuable that you may even consider taking photographs
at the case study site. At a minimum, these photographs will help to convey
important case characteristics to outside observers (see Dabbs, 1982). Note,
however, that in some situations—such as photographing students in public
schools—you will need written permission before proceeding.
A common procedure to increase the reliability of observational evidence is to
have more than a single observer making an observation—whether of the formal
or the casual variety. Thus, when resources permit, a case study investigation
should allow for the use of multiple observers.
 Participant-Observation

Participant-observation is a special mode of observation in which you are not
merely a passive observer. Instead, you may assume a variety of roles within a
case study situation and may actually participate in the events being studied. In
urban neighborhoods, for instance, these roles may range from having casual
social interactions with various residents to undertaking specific functional
activities within the neighborhood (see Yin, 1982a). The roles for different
illustrative studies in neighborhoods and organizations have included
• being a resident in a neighborhood that is the subject of a case study (see
BOX 20);
• taking some other functional role in a neighborhood, such as serving as a
store-keeper’s assistant;
• serving as a staff member in an organizational setting; and
• being a key decision maker in an organizational setting.

BOX 20

Paticipant-Observation in a Neighborhood Near “Street Corner
Society”

Participant-observation has been a method used frequently to study urban
neighborhoods. One such study of subsequent fame was conducted by
Herbert Gans, whowrote The Urban Villagers (1962), a study about “group
and class in the life of Italian-Americans.”
Gans’s methodology is documented in a separate chapter of his book,
titled “On Gans’s methodology is documented in a separate chapter of his
book, titled ”On the Methods Used in This Study.” He notes that his
evidence was based on six approaches: the use of the neighborhood’s
facilities, attendance at meetings, informal visiting with neigh bors and
friends, formal and informal interviewing, the use of informants, and direct
observation. Of all theses ources, the “participation role turned out to be
most productive” (pp.339-340). This role was based on Gans’s being an
actual resident, along with his wife, of the neighborhood he was studying.
 The result is a classic statement of neighborhood life undergoing urban
renewal and change, and a stark contrast to the stability found in a nearby
neighborhood, as covered in Whyte’s (1943/1955) Street Corner Society
some 20 years earlier (also see BOX 2A, Chapter 1, p. 7).

The participant-observation technique has been most frequently used in
anthropological studies of different cultural or social groups. The technique also
can be used in more everyday settings, such as a large organization (see BOX
21; also see BOX 16, earlier) or informal small groups.

BOX 21

A Participant-Observer Study in an “Everyday” Setting

Eric Redman provides an insider’s account of how Congress works in his
well-regarded case study, The Dance of Legislation (1973). The case study
traces the introduction and passage of the legislation that created the
National Health Service Corps during the 91st Congress in 1970.
Redman’s account, from the vantage point of an author who was on the
staff of one of the bill’s main supporters, Senator Warren G. Magnuson, is
well written and easy to read. The account also provides the reader with
great insight into the daily operations of Congress—from the introduction
of a bill to its eventual passage, including the Congress-from the
introduction of a bill to its eventual passage,
The account is an excellent example of participant-observation in a
contemporary setting. It contains information about insiders’ roles that few
persons have been rary setting. contains information about insiders’ roles
that few persons have been privileged to share. The subtle legislative
strategies, the overlooked role of committee clerks and lobbyists, and the
interaction between the legislative and executive branches of government
are all re-created by the case study, and all add to the reader’s general
understanding of the legislative process.

Participant-observation provides certain unusual opportunities for collecting
case study data, but it also involves major problems. The most distinctive
opportunity is related to your ability to gain access to events or groups that are
otherwise inaccessible to a study. In other words, for some topics, there may be
no way of collecting evidence other than through participant-observation.
Another distinctive opportunity is the ability to perceive reality from the
viewpoint of someone “inside” the case study rather than external to it. Many
have argued that such a perspective is invaluable in producing an “accurate”
portrayal of a case study phenomenon. Finally, other opportunities arise because
you may have the ability to manipulate minor events—such as convening a
meeting of a group of persons in the case. Only through participant-observation
can such manipulation occur, as the use of documents, archival records, and
interviews, for instance, assumes a passive investigator. The manipulations will
not be as precise as those in experiments, but they can produce a greater variety
of situations for the purposes of collecting data.
The major problems related to participant-observation have to do with the
potential biases produced (see Becker, 1958). First, the investigator has less
ability to work as an external observer and may, at times, have to assume
positions or advocacy roles contrary to the interests of good social science
practice. Second, the participant-observer is likely to follow a commonly known
phenomenon and become a supporter of the group or organization being studied,
if such support did not already exist. Third, the participant role may simply
require too much attention relative to the observer role. Thus, the participant-
observer may not have sufficient time to take notes or to raise questions about
events from different perspectives, as a good observer might. Fourth, if the
organization or social group being studied is physically dispersed, the
participant-observer may find it difficult to be at the right place at the right time,
either to participate in or to observe important events.
These trade-offs between the opportunities and the problems have to be
considered seriously in undertaking any participant-observation study. Under
some circumstances, this approach to case study evidence may be just the right
approach; under other circumstances, the credibility of a whole case study
project can be threatened.
 Physical Artifacts

A final source of evidence is a physical or cultural artifact—a technological
device, a tool or instrument, a work of art, or some other physical evidence. Such
artifacts may be collected or observed as part of a case study and have been used
extensively in anthropological research.
Physical artifacts have less potential relevance in the most typical kind of case
study. However, when relevant, the artifacts can be an important component in
the overall case. For example, one case study of the use of personal computers in
the classroom needed to ascertain the nature of the actual use of the machines.
Although use could be directly observed, an artifact—the computer printout—
also was available. Students displayed these printouts as the finished product of
their work and maintained notebooks of their printouts. Each printout showed
the type of schoolwork that had been done as well as the date and amount of
computer time used to do the work. By examining the printouts, the case study
investigators were able to develop a broader perspective concerning all of the
classroom applications over the length of a semester, far beyond that which
could be directly observed in the limited time of a field visit.
 Summary

This section has reviewed six commonly used sources of case study evidence.
The procedures for collecting each type of evidence must be developed and
mastered independently to ensure that each source is properly used. Not all
sources will be relevant for all case studies. However, the trained case study
investigator should be acquainted with the procedures associated with using each
source of evidence—or have colleagues who have the needed expertise and who
can work as members of the case study team.

EXERCISE 4.2 Identifying Specific Types of Evidence

Name a case study topic you would like to study. For some aspect of this
topic, identify the specific type of evidence that would be relevant—for
example, if a document, what kind of document? If an interview, what
respondent and what questions? If an archival record, what records and
what variables?

THREE PRINCIPLES OF DATA COLLECTION

The benefits from these six sources of evidence can be maximized if you follow
three principles. These principles are relevant to all six sources and, when used
properly, can help to deal with the problems of establishing the construct validity
and reliability of the case study evidence. The three are as follows.
 Principle 1: Use Multiple Sources of Evidence

Any of the preceding sources of evidence can and have been the sole basis for
entire studies. For example, some studies have relied only on participant-
observation but have not examined a single document; similarly, numerous
studies have relied on archival records but have not involved a single interview.
This isolated use of sources may be a function of the independent way that
sources have typically been conceived—as if an investigator should choose the
single most appropriate source or the one with which she or he is most familiar.
Thus, on many an occasion, investigators have announced the design of a new
study by identifying both the problem to be studied and the prior selection of a
single source of evidence—such as “interviews”—as the focus of the data
collection effort.

Triangulation: Rationale for using multiple sources of evidence. The approach to
individual sources of evidence as just described, however, is not recommended
for conducting case studies. On the contrary, a major strength of case study data
collection is the opportunity to use many different sources of evidence (see BOX
22 and BOX 19B, earlier, for examples of such studies). Furthermore, the need
to use multiple sources of evidence far exceeds that in other research methods,
such as experiments, surveys, or histories. Experiments, for instance, are largely
limited to the measurement and recording of actual behavior in a laboratory and
generally do not include the systematic use of survey or verbal information.
Surveys tend to be the opposite, emphasizing verbal information but not the
measurement or recording of individual behavior. Finally, histories are limited to
events in the “dead” past and therefore seldom have any contemporary sources
of evidence, such as direct observations of a phenomenon or interviews with key
actors.

BOX 22

A Case Study Combining Personal Experience with Extensive Field
Research

Most people across the country by now have heard of Head Start. Its
 development and growth into one of the most successful federal programs
is traced by Zigler and Muenchow (1992). Their book is exceptionālly
insightful, possibly because it is based on Zigler’s personal experiences
with the program, beginning with his role as its first director. However, the
book also calls on other independent sources of evidence, with the coauthor
contributing historical and field research, including interviews of more than
200 persons associated with Head Start. All of these multiple sources of
evidence are integrated into a coherent if not compelling case study of Head
Start. The result is a winning combination: a most readable but also well-
documented book.

Of course, each of these strategies can be modified, creating hybrid strategies
in which multiple sources of evidence are more likely to be relevant. An
example of this is the evolution of “oral history” studies in the past several
decades. Such studies involve extensive interviews with key leaders who have
retired, on the stipulation that the interview information will not be reported until
after the leader’s death. Later, the historian will join the interview data with the
more conventional array of historical evidence. Nevertheless, such a
modification of the traditional methods does not alter the fact that the case study
inherently deals with a wide variety of evidence, whereas the other methods do
not.
The use of multiple sources of evidence in case studies allows an investigator
to address a broader range of historical and behavioral issues. However, the most
important advantage presented by using multiple sources of evidence is the
development of converging lines of inquiry, a process of triangulation and
corroboration emphasized repeatedly in the previous section of this chapter.
Thus, any case study finding or conclusion is likely to be more convincing and
accurate if it is based on several different sources of information, following a
corroboratory mode (see BOX 23).

BOX 23

Triangulating from Multiple Sources of Evidence

Basu, Dirsmith, and Gupta (1999) conducted a case study of the federal
government’s audit agency, the U.S. Government Accountability Office.
Their case was theory oriented and examined the relations hip between an
organization’s actual work and the image it presents to external parties (the
finding was that they are loosely coupled). The case study used an
 impressive array of sources of evidence—an extended period of field
observations, with diaries; interviews of 55 persons; and reviews of
historical accounts, public records, administrators’ personal files, and news
artides-all triangulating on the same set of research questions.

Patton (2002) discusses four types of triangulation in doing evaluations—the
triangulation
1. of data sources (data triangulation),
2. among different evaluators (investigator triangulation),
3. of perspectives to the same data set (theory triangulation), and
4. of methods (methodological triangulation).

The present discussion pertains only to the first of these four types (data
triangulation), encouraging you to collect information from multiple sources but
aimed at corroborating the same fact or phenomenon. In pursuing such
corroboratory strategies, Figure 4.2 distinguishes between two conditions—
when you have really triangulated the data (upper portion) and when you have
multiple sources as part of the same study but that nevertheless address different
facts (lower portion). When you have really triangulated the data, the events or
facts of the case study have been supported by more than a single source of
evidence; when you have used multiple sources but not actually triangulated the
data, you typically have analyzed each source of evidence separately and have
compared the conclusions from the different analyses—but not triangulated the
data.
With data triangulation, the potential problems of construct validity also can
be addressed because the multiple sources of evidence essentially provide
multiple measures of the same phenomenon. Not surprisingly, one analysis of
case study methods found that those case studies using multiple sources of
evidence were rated more highly, in terms of their overall quality, than those that
relied on only single sources of information (see COSMOS Corporation, 1983).

Figure 4.2 Convergence and Nonconvergence of Multiple Sources of Evidence


Prerequisites for using multiple sources of evidence. At the same time, the use of
multiple sources of evidence imposes a greater burden, hinted at earlier, on
yourself or any other case study investigator. First is that the collection of data
from multiple sources is more expensive than if data were only collected from a
single source (Denzin, 1978, p. 61). Second and more important, each
investigator needs to know how to carry out the full variety of data collection
techniques. For example, a case study investigator may have to collect and
analyze documentary evidence as in history, to retrieve and analyze archival
records as in economics or operations research, and to design and conduct
surveys as in survey research. If any of these techniques is used improperly, the
opportunity to address a broader array of issues, or to establish converging lines
of inquiry, may be lost. This requirement for mastering multiple data collection
techniques therefore raises important questions regarding the training and
expertise of the case study investigator.
Unfortunately, many graduate training programs emphasize one type of data
collection activity over all others, and the successful student is not likely to have
a chance to master the others. To overcome such conditions, you should seek
other ways of obtaining the needed training and practice. One such way is to
work in a multidisciplinary research organization rather than being limited to a
single academic department. Another way is to analyze the methodological
writings of a variety of social scientists (see Hammond, 1968) and to learn of the
strengths and weaknesses of different data collection techniques as they have
been practiced by experienced scholars. Yet a third way is to design different
pilot studies that will provide an opportunity for practicing different techniques.
No matter how the experience is gained, every case study investigator should
be well versed in a variety of data collection techniques so that a case study can
use multiple sources of evidence. Without such multiple sources, an invaluable
advantage of the case study strategy will have been lost. Worse, what started out
as a case study may turn into something else. For example, you might overly rely
on open-ended interviews as your data, giving insufficient attention to
documentary or other evidence to corroborate the interviews. If you then
complete your analysis and study, you probably will have done an “interview”
study, similar to surveys that are entirely based on verbal reports that come from
open-ended interviews—but you would not have done a case study. In this
interview study, your text would constantly have to point out the self-reported
nature of your data, using such phrases as “as reported by the interviewees,” “as
stated in the interviews,” or “she/he reported that. . . .” and the like.

EXERCISE 4.3 Seeking Converging Evidence

Name a particular incident that occurred recently in your everyday life.
How would you go about establishing the “facts” of this incident, if you
wanted now (in retrospect) to demonstrate what had happened? Would you
interview any important persons (including yourself)? Would there have
been any artifacts or documentation to rely on?

 Principle 2: Create a Case Study Database

A second principle has to do with the way of organizing and documenting the
data collected for case studies. Here, case studies have much to borrow from the
practices followed by the other research methods defined in Chapter 1. Their
documentation commonly consists of two separate collections:
1. the data or evidentiary base and
2. the report of the investigator, whether in article, report, or book form.

With the advent of computer files, the distinction between these two
collections has been made even clearer. For example, investigators doing
psychological, survey, or economic research may exchange data files and other
electronic documentation that contain only the actual database—for example,
behavioral responses or test scores in psychology, itemized responses to various
survey questions, or economic indicators. The database then can be the subject
of separate, secondary analysis, independent of any reports by the original
investigator.
However, with case studies, the distinction between a separate database and
the case study report has not yet become an institutionalized practice. Too often,
the case study data are synonymous with the narrative presented in the case
study report, and a critical reader has no recourse if he or she wants to inspect
the raw data that led to the case study’s conclusions. The case study report may
not have presented adequate data, and without a case study database, the raw
data may not be available for independent inspection. A major exception to this
is where ethnographic studies have separated and stored data on their fieldwork,
to make these data available to new research investigators. The practice is
sufficiently important, however, that every case study project should strive to
develop a formal, presentable database, so that in principle, other investigators
can review the evidence directly and not be limited to the written case study
reports. In this manner, a case study database markedly increases the reliability
of the entire case study.
The lack of a formal database for most case studies is a major shortcoming of
case study research and needs to be corrected. There are numerous ways of
accomplishing the task, as long as you and other investigators are aware of the
need and are willing to commit the additional effort required to build the
database. At the same time, the existence of an adequate database does not
preclude the need to present sufficient evidence within the case study report
itself (to be discussed further in Chapter 6). Every report should still contain
enough data so that the reader of the report can draw independent conclusions
about the case study.
Nevertheless, the problem of initially establishing a case study database has
not been recognized by most of the books on field methods. Thus, the
subsections below represent an extension of the current state of the art. The
problem of developing the database is described in terms of four components:
notes, documents, tabular materials, and narratives.
Case study notes. For case studies, your own notes are likely to be the most
common component of a database. These notes take a variety of forms. The
notes may be a result of your interviews, observations, or document analysis.
The notes may be handwritten, typed, on audiotapes, or in word-processing or
other electronic files, and they may be assembled in the form of a diary, on index
cards, or in some less organized fashion.
Regardless of their form or content, these case study notes must be stored in
such a manner that other persons, yourself included, can retrieve them efficiently
at some later date. Most commonly, the notes can be organized according to the
major subjects—as outlined in the case study protocol—covered by a case study;
however, any classificatory system will do, as long as the system is usable by an
outside party. Only in this manner will the notes be available as part of the case
study database.
This identification of the notes as part of the case study database does not
mean, however, that you need to spend excessive amounts of time in rewriting
interviews or making extensive editorial changes to make the notes presentable.
Building such a formal case record, by editing and rewriting the notes, may be a
misplaced priority. Any such editing should be directed at the case study report
itself, not at the notes. The only essential characteristics of the notes are that they
be organized, categorized, complete, and available for later access.

Case study documents. Many documents relevant to a case study will be
collected during the course of a study. Chapter 3 indicated that the disposition of
these documents should be covered in the case study protocol and suggested that
one helpful way is to have an annotated bibliography of these documents. Such
annotations would again facilitate storage and retrieval, so that later investigators
can inspect or share the database.
The single, unique characteristic of these documents is that they are likely to
require a large amount of physical storage space, unless you trouble to make
portable document format (PDF) copies and store them electronically. In
addition, the documents may be of varying importance to the database, and you
may want to establish a primary file and a secondary file for such documents.
The main objective, again, is to make the documents readily retrievable for later
inspection or perusal. In those instances in which the documents have been
relevant to specific interviews, one additional cross-reference is to have the
interview notes cite the documents.

Tabular materials. The database may consist of tabular materials, either
collected from the site being studied or created by the research team. Such
materials also need to be organized and stored to allow for later retrieval.
The materials may include survey and other quantitative data. For example, a
survey may have been conducted at one or more of the case study sites as part of
an embedded case study. In such situations, the tabular materials may be stored
in computer files. As another example, in dealing with archival or observational
evidence, a case study may have called for “counts” of various phenomena (see
Miles & Huberman, 1994). The documentation of these counts, done by the case
study team, also should be organized and stored as part of the database. In brief,
any tabular materials, whether based on surveys, observational counts, or
archival data, can be treated in a manner similar to the way they are handled
when using other research methods.

Narratives. Certain types of narrative, produced by a case study investigator
upon completion of all data collection, also may be considered a formal part of
the database and not part of the final case study report. The narrative reflects a
special practice that should be used more frequently: to have case study
investigators compose open-ended answers to the questions in the case study
protocol. This practice has been used on several occasions in multiple-case
studies designed by the author (see BOX 24).

BOX 24

Narratives in the Case Study Database

A series of 12 case studieswas done on personal computer use in schools
(Yin, 2003, chap. 3). Each case study was based on open-ended answers to
about 50 protocol questions concerning matters such as the number and
location of the personal computers (an inventory question requiring tabular
 and narrative responses), the relationship between the computer units and
other computational systems within a school district, and the training and
coordination provided by the district.
After data collection has finished, the case study investigator’s first
responsibility was to answer these 50 questions as completely as possible,
citing specific sources of evidence in footnotes. These answers were
unedited but served as the basis for both the individual case reports and the
cross-case analysis. The availability of the database meant that other
members of the case study team could determine the events at each site,
even before the case study reports were completed.

In such a situation, each answer represents your attempt to integrate the
available evidence and to converge upon the facts of the matter or their tentative
interpretation. The process is actually an analytic one and is the start of the case
study analysis. The format for the answers may be considered analogous to that
of a comprehensive “take-home” exam, used in academic courses. You the
investigator are the respondent, and your goal is to cite the relevant evidence—
whether from interviews, documents, observations, or archival evidence—in
composing an adequate answer. The main purpose of the open-ended answer is
to document the connection between specific pieces of evidence and various
issues in the case study, generously using footnotes and citations.
The entire set of answers can be considered part of the case study database.
You, along with any other interested party, can then use this database to compose
the actual case study report. Or, if no reports are composed concerning the
individual cases (see Chapter 6 for such situations), the answers can serve as the
database for the subsequent cross-case analysis. Again, because the answers are
part of the database and not of the final report, you should not spend much time
trying to make the answers presentable. In other words, you need not perform
the standard editing and copyediting chores. (However, for an example of a case
study that was written entirely in the form of narrative answers to the protocol
questions and in which such editing was done, see Yin 2003, chap. 2.) The most
important attribute of good answers is that they indeed connect the pertinent
issues—through adequate citations—to specific evidence.

EXERCISE 4.4 Practicing the Development of a Database

For the topic you covered in Exercise 4.3, write a short report (no more than
two double-spaced pages) that adheres to the following outline: Start the
report by stating a major question you were attempting to answer (about the
 facts of the incident recalled from your everyday life). Now provide the
answer, citing the evidence you had used (your format should include
formal citations and footnotes). Repeat the procedure for another research
question (or the questions from your hypothetical case study protocol).
Envisage how this question-and-answer sequence might be one of many in
your total case study “database.”

 Principle 3: Maintain a Chain of Evidence

Another principle to be followed, to increase the reliability of the information
in a case study, is to maintain a chain of evidence. Such a principle is based on a
notion similar to that used in forensic investigations.
The principle is to allow an external observer—in this situation, the reader of
the case study—to follow the derivation of any evidence from initial research
questions to ultimate case study conclusions (see Figure 4.3). Moreover, this
external observer should be able to trace the steps in either direction (from
conclusions back to initial research questions or from questions to conclusions).
As with criminological evidence, the process should be tight enough that
evidence presented in “court”—the case study report—is assuredly the same
evidence that was collected at the scene of the “crime” during the data collection
process. Conversely, no original evidence should have been lost, through
carelessness or bias, and therefore fail to receive appropriate attention in
considering the “facts” of a case. If these objectives are achieved, a case study
also will have addressed the methodological problem of determining construct
validity, thereby increasing the overall quality of the case study.

Figure 4.3 Maintaining a Chain of Evidence

 Imagine the following scenario. You have read the conclusions in a case study
report and want to know more about the basis for the conclusions. You therefore
want to trace the evidentiary process backward.
First, the report itself should have made sufficient citation to the relevant
portions of the case study database—for example, by citing specific documents,
interviews, or observations. Second, the database, upon inspection, should reveal
the actual evidence and also indicate the circumstances under which the
evidence was collected—for example, the time and place of an interview. Third,
these circumstances should be consistent with the specific procedures and
questions contained in the case study protocol, to show that the data collection
had followed the procedures stipulated by the protocol. Finally, a reading of the
protocol should indicate the link between the content of the protocol and the
initial study questions.
In the aggregate, you have therefore been able to move from one part of the
case study process to another, with clear cross-referencing to methodological
procedures and to the resulting evidence. This is the ultimate “chain of
evidence” that is desired.

EXERCISE 4.5 Establishing a Chain of Evidence

State a hypothetical conclusion that might emerge from a case study you are
going to do. Now work backward and identify the specific data or evidence
that would have supported such a conclusion. Similarly, work backward and
define the protocol question that would have led to the collection of this
evidence, and then the study question that in turn would have led to the
design of the protocol question. Do you understand how this chain of
evidence has been formed and how one can move forward or backward in
tracing the chain?

SUMMARY

This chapter has reviewed six sources of case study evidence, how evidence can
be collected from these sources, and three important principles regarding the
data collection process.
The data collection process for case studies is more complex than those used
in other research methods. A case study investigator must have a methodological
versatility not necessarily required for using other methods and must follow
certain formal procedures to ensure quality control during the data collection
process. The three principles described above are steps in this direction. They are
not intended to straitjacket the inventive and insightful investigator. They are
intended to make the process as explicit as possible, so that the final results—the
data that have been collected—reflect a concern for construct validity and for
reliability, thereby becoming worthy of further analysis. How such analysis can
be carried out is the subject of the next chapter.
NOTES

1. Limited availability of print materials in low-income communities in the

United States—even including signage and materials in schools and
public libraries—has been the subject of study (Neuman & Celano,
2001). To the extent of such impoverishment, researchers studying such
neighborhoods and their community organizations (or schools) may find
the use of documentary sources of evidence also limited.
2. Excellent suggestions regarding the ways of verifying documentary
evidence, including the nontrivial problem of determining the actual
author of a document, are offered by Barzun and Graff (1985, pp. 109-
133). An exemplary quantitative study of the authorship problem is
found in Mosteller and Wallace (1984).
3. Such consistent responses are likely to occur when interviewing
members of a “closed” institution, such as the residents of a drug
treatment program or the teachers in a closely knit school. The apparent
conspiracy arises because those being interviewed all are aware of the
“socially desirable” responses and appear to be providing corroboratory
evidence when in fact they are merely repeating their institution’s
mantra.

REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 4

For selected case studies cited in the text of this chapter, two anthologies contain
either a more extensive excerpt or the full case study. The table below
crosswalks the reference in this book to the location of the excerpt or full
rendition.


ABSTRACT

Data analysis consists of examining, categorizing, tabulating, testing, or

otherwise recombining evidence, to draw empirically based conclusions.
Analyzing case study evidence is especially difficult because the techniques still
have not been well defined. To overcome this circumstance, every case study
analysis should follow a general analytic strategy, defining priorities for what to
analyze and why. Four strategies are relying on theoretical propositions,
developing case descriptions, using both quantitative and qualitative data, and
examining rival explanations. Using various computer aids to manipulate your
data will not substitute for the absence of a general analytic strategy.
Any of these strategies can be used in practicing five specific techniques for
analyzing case studies: pattern matching, explanation building, time-series
analysis, logic models, and cross-case synthesis. With appropriately fine-grained
data, the analyses can incorporate statistical models, such as regression or
structural equation models. Throughout, a persistent challenge is to produce
high-quality analyses, which require attending to all the evidence collected,
displaying and presenting the evidence separate from any interpretation, and
considering alternative interpretations.
 5

Analyzing Case Study Evidence How to Start Your Analysis, Your

Analytic Choices, and How They Work

AN ANALYTIC STRATEGY: MORE THAN FAMILIARITY
WITH ANALYTIC TOOLS

 Need for an Analytic Strategy

Introduction. The analysis of case study evidence is one of the least developed
and most difficult aspects of doing case studies. Too many times, investigators
start case studies without having the foggiest notion about how the evidence is to
be analyzed (despite Chapter 3’s recommendation that the analytic approaches
be considered when developing the case study protocol). Such investigations
easily become stalled at the analytic stage; this author has known colleagues who
have simply ignored their case study data for month after month, not knowing
what to do with the evidence.
Because of the problem, the experienced case study investigator is likely to
have great advantages over the novice at the analytic stage. Unlike statistical
analysis, there are few fixed formulas or cookbook recipes to guide the novice.
Instead, much depends on an investigator’s own style of rigorous empirical
thinking, along with the sufficient presentation of evidence and careful
consideration of alternative interpretations.
Investigators and especially novices do continue to search for formulas,
recipes, or tools, hoping that familiarity with these devices will produce the
needed analytic result. The tools are important and can be useful, but they are
usually most helpful if you know what to look for (i.e., have an overall analytic
strategy), which unfortunately returns you back to your original problem, if you
hadn’t noticed.

Computer-assisted tools. For instance, computer-assisted routines with
prepackaged software such as Atlas.ti, HyperRESEARCH, NVivo, or The
Ethnograph all are examples of computer-assisted qualitative data analysis
software (CAQDAS—e.g., Fielding & Lee, 1998). The software has become
more diverse and functional over the past decade. Essentially, the tools can help
you code and categorize large amounts of narrative text, as might have been
collected from open-ended interviews or from large volumes of written
materials, such as newspaper articles. Guidance on coding skills and techniques
also has improved (e.g., Boyatzis, 1998).


Tip: How do I start analyzing my case study data?

You might start with questions (e.g., the questions in your case study
protocol) rather than with the data. Start with a small question first, then
identify your evidence that addresses the question. Draw a tentative
conclusion based on the weight of the evidence, also asking how you
should display the evidence so that readers can check your assessment.
Continue to a larger question and repeat the procedure. Keep going until
you think you have addressed your main research question(s).


Could you have started with the data instead of the questions?
Key to your understanding of the value of these packages are two words:
assisted and tools. The software will not do any analysis for you, but it may
serve as an able assistant and reliable tool. For instance, if you enter your textual
data and then define an initial set of codes, one or another of the various
software packages will readily locate in the textual data all words and phrases
matching these codes, count the incidence or occurrence of the words or codes,
and even conduct Boolean searches to show when and where multiple
combinations are found together. You can do this process iteratively, gradually
building more complex categories or groups of codes. However, unlike statistical
analyses, you cannot use the software’s outputs themselves as if they were the
end of your analysis.
Instead, you will need to study the outputs to determine whether any
meaningful patterns are emerging. Quite likely, any patterns—such as the
frequency of codes or code combinations—will still be conceptually more
primitive (lower) than the initial “how” and “why” research questions that might
have led to your case study in the first place. In other words, developing a rich
and full explanation or even a good description of your case, in response to your
initial “how” or “why” questions, will require much post-computer thinking and
analysis on your part.
Backtracking, you also will need to have clarified the reasons for defining the
initial codes or subsequent codes, as well as connecting them to your original
research design (you, not the software, created them). In what ways do the codes
or concepts accurately reflect the meaning of the retrieved words and phrases,
and why? Answering these questions requires your own analytic rationale.
Under some circumstances, the computerized functions can nevertheless be
extremely helpful. The minimal conditions include when (a) the words or verbal
reports represent verbatim records and are the central part of your case study
evidence and (b) you have a large collection of such data. Such conditions
commonly occur in research using grounded theory strategies (e.g., Corbin &
Strauss, 2007), where the surfacing of a new concept or theme can be highly
valuable. However, even under the best of circumstances, nearly all scholars
express strong caveats about any use of computer-assisted tools: You must still
be prepared to be the main analyst and to direct the tools; they are the assistant,
not you.
Most case studies pose a more serious challenge in efforts to use computer-
assisted tools: Verbatim records such as interviewees’ responses are likely to be
only part of the total array of case study evidence. The case study will typically
be about complex events and behavior, occurring within a possibly more
complex, real-life context. Unless you convert all of your evidence—including
your field notes and the archival documents you might have collected—into the
needed textual form, computerized tools cannot readily handle this more diverse
array of evidence. Yet, as emphasized in Chapter 4, such an array should
represent an important strength of your case study. For a diverse set of evidence,
you therefore need to develop your own analytic strategies.
A helpful starting point is to “play” with your data. One set of analytic
manipulations has been comprehensively described and summarized by Miles
and Huberman (1994) and includes
• Putting information into different arrays
• Making a matrix of categories and placing the evidence within such
categories
• Creating data displays—flowcharts and other graphics—for examining the
data
• Tabulating the frequency of different events
• Examining the complexity of such tabulations and their relationships by
calculating second-order numbers such as means and variances
• Putting information in chronological order or using some other temporal
scheme

These are indeed useful and important manipulations and can put the evidence
in some preliminary order. Moreover, conducting such manipulations is one way
of overcoming the stalling problem mentioned earlier. Without a broader
strategy, however, you are still likely to encounter many false starts and
potentially waste large chunks of your time. Furthermore, if after playing with
the data, a general strategy does not emerge (or if you are not facile in playing
with the data to begin with), the entire case study analysis is likely to be in
jeopardy.
Any preliminary manipulations, such as the preceding, or any use of
computer-assisted tools therefore cannot substitute for having a general analytic
strategy in the first place. Put another way, all empirical research studies,
including case studies, have a “story” to tell. The story differs from a fictional
account because it embraces your data, but it remains a story because it must
have a beginning, end, and middle. The needed analytic strategy is your guide to
crafting this story, and only rarely will your data do the crafting for you.
Once you have a strategy, the tools may turn out to be extremely useful (or
irrelevant). The strategy will help you to treat the evidence fairly, produce
compelling analytic conclusions, and rule out alternative interpretations. The
strategy also will help you to use tools and make manipulations more effectively
and efficiently. Four such strategies are described below, after which five
specific techniques for analyzing case study data are reviewed. These strategies
or techniques are not mutually exclusive. You can use any number of them in
any combination. A continued alert is to be aware of these choices before
collecting your data, so that you can be sure your data will be analyzable.
 Four General Strategies

Relying on theoretical propositions. The first and most preferred strategy is to
follow the theoretical propositions that led to your case study. The original
objectives and design of the case study presumably were based on such
propositions, which in turn reflected a set of research questions, reviews of the
literature, and new hypotheses or propositions.
The propositions would have shaped your data collection plan and therefore
would have given priorities to the relevant analytic strategies. One example,
from a study of intergovernmental relationships, followed the proposition that
federal funds have redistributive dollar effects but also create new organizational
changes at the local level (Yin, 1980). The basic proposition—the creation of a
“counterpart bureaucracy” in the form of local planning organizations, citizen
action groups, and other new offices within a local government itself, but all
attuned to specific federal programs—was traced in case studies of several cities.
For each city, the purpose of the case study was to show how the formation and
modification in local organizations occurred after changes in related federal
programs and how these local organizations acted on behalf of the federal
programs even though they might have been components of local government.
This proposition is an example of a theoretical orientation guiding the case
study analysis. Clearly, the proposition helps to focus attention on certain data
and to ignore other data. (A good test is to decide what data you might cite if you
had only 5 minutes to defend a proposition in your case study.) The proposition
also helps to organize the entire case study and to define alternative explanations
to be examined. Theoretical propositions stemming from “how” and “why”
questions can be extremely useful in guiding case study analysis in this manner.

Developing a case description. A second general analytic strategy is to develop a
descriptive framework for organizing the case study. This strategy is less
preferable than relying on theoretical propositions but serves as an alternative
when you are having difficulty making the first strategy work. For instance, you
actually (but undesirably) may have collected a lot of data without having settled
on an initial set of research questions or propositions. Studies started this way
inevitably encounter challenges at their analytic phase.
 Sometimes, the original and explicit purpose of the case study may have been
a descriptive one. This was the objective of the famous sociological study
Middletown (Lynd & Lynd, 1929), which was a case study of a small
midwestern city. What is interesting about Middletown, aside from its classic
value as a rich and historic case, is its compositional structure, reflected by its
chapters:
• Chapter I: Getting a Living
• Chapter II: Making a Home
• Chapter III: Training the Young
• Chapter IV: Using Leisure
• Chapter V: Engaging in Religious Practices
• Chapter VI: Engaging in Community Activities

These chapters cover a range of topics relevant to community life in the early
20th century, when Middletown was studied. Note how the descriptive
framework organizes the case study analysis but also assumes that data were
collected about each topic in the first place. In this sense, you should have
thought (at least a little) about your descriptive framework before designing your
data collection instruments. As usual, the ideas for your framework should have
come from your initial review of literature, which may have revealed gaps or
topics of interest to you, spurring your interest in doing a case study. Another
suggestion is to note the structure of existing case studies (e.g., by examining the
original versions of those cited in the BOXES throughout this book) and at least
to observe their tables of contents as an implicit clue to different descriptive
approaches.
In other situations, the original objective of the case study may not have been
a descriptive one, but a descriptive approach may help to identify the appropriate
causal links to be analyzed—even quantitatively. BOX 25 gives an example of a
case study that was concerned with the complexity of implementing a local
public works program in Oakland, California. Such complexity, the investigators
realized, could be described in terms of the multiplicity of decisions, by public
officials, that had to occur in order for implementation to succeed. This
descriptive insight later led to the enumeration, tabulation, and hence
quantification of the various decisions. In this sense, the descriptive approach
was used to identify (a) an embedded unit of analysis (see Chapter 2) and (b) an
overall pattern of complexity that ultimately was used in a causal sense to
“explain” why implementation had failed.

BOX 25

Quatifying the Descriptive Elements of a Case Study

Pressman and Wildavsky’s (1973) book, Implementation: How Great
Expectations in Washington Are Dashed in Oakland, is regarded as one of
the breakthrough contributions to the study of implementation (Yin, 1982b).
This is the process whereby some programmatic activity-an economic
development project, a new curriculum in a school, or a crime prevention
program, for example—is installed in a specific setting (eg, organization or
community) The process is complex and involves numerous individuals,
organizational rules, social norms, and mixtures of good and bad intentions.


Can such a complex process also be the subject of quantitative inquiry
and analysis? Pressman and Wildavsky (1973) offer one innovative
solution. To the extent that successful implementation can be described as a
sequence of decisions, an analyst can focus part of the case study on the
number and types of such decisions or elements.

Thus,in their chapter titled “The Complexity of Joint Action,”the authors
analyze the difficulties in Oakland: To implement one public works
program required a total of 70 sequential decisions-project approvals,
negotiation of leases, letting of contracts, and soon. The analysis examined
the level of agreement and the time needed to reach agreement at each of
the 70 decision points.Given the normal diversity of opinion and slippage
intime,the an alysis illustrates—inaquantitative manner—the low
probability of implementation success.
Using both qualitative and quantitative data. This third strategy may be more
attractive to advanced students and scholars and can yield appreciable benefits.
Certain case studies can include substantial amounts of quantitative data. If these
data are subjected to statistical analyses at the same time that qualitative data
nevertheless remain central to the entire case study, you will have successfully
followed a strong analytic strategy.
The quantitative data may have been relevant to your case study for at least
two reasons. First, the data may cover the behavior or events that your case study
is trying to explain—typically, the “outcomes” in an evaluative case study.
Second, the data may be related to an embedded unit of analysis within your
broader case study. In either situation, the qualitative data may be critical in
explaining or otherwise testing your case study’s key propositions. So, imagine a
case study about a school, a neighborhood, an organization, a community, a
medical practice, or some other common case study topic. For these topics, the
outcomes of an evaluative case study might be, respectively, student
achievement (for the case study about the school), housing prices (for the
neighborhood), employees’ salaries (for the organization), various crime rates
(for the community), or the course of an illness (for the medical practice).
Alternatively, the embedded units might be students (or teachers), census blocks
(or single-family housing), employees (for the organization), persons arrested
(for the community), or patients (for the medical practice).
All of the illustrative outcomes or embedded units can be the occasion for
having collected fine-grained quantitative data. Yet, the main case study
questions might have been at a higher level: a single school (not its students), the
neighborhood (not its housing units), a business firm (not its employees), a
community (not its residents), or a new medical practice (not the patients). To
explore, describe, or explain events at this higher level, you would have
collected and used qualitative data. Thus, your case study would have
deliberately used both qualitative and quantitative data.
If you attempt this third strategy, be prepared for the skills you will need.
Beyond knowing how to do the case study well, you may have to master certain
statistical techniques. Mentioned later in this chapter (but only in passing) are
regression discontinuity analyses, hierarchical linear models, and structural
equation models. Do you believe that any of them can be part of a case study
analysis?

EXERCISE 5.1 Using Quantitative Data in a Case Study

Select one of your own empirical studies—but not a case study—in which
you analyzed some quantitative data (or choose such a study from the
literature). Describe how the data were analyzed in this study. Argue
whether this same analysis, virtually in its same form, could be found as
one part of a fuller case study analysis. Do you think that quantitative data
are less relevant to case studies than qualitative data?

Examining rival explanations. A fourth general analytic strategy, trying to define
and test rival explanations, generally works with all of the previous three: Initial
theoretical propositions (the first strategy above) might have included rival
hypotheses; the contrasting perspectives of participants and stakeholders may
produce rival descriptive frameworks (the second strategy); and data from
comparison groups may cover rival conditions to be examined as part of using
both quantitative and qualitative data (the third strategy).
For instance, the typical hypothesis in an evaluation is that the observed
outcomes were the result of an intervention supported by public or foundation
funds. The simple or direct rival explanation would be that the observed
outcomes were in fact the result of some other influence besides the intervention
and that the investment of funds may not actually have been needed. Being
aware (ahead of time) of this direct rival, your case study data collection should
then have included attempts to collect evidence about the possible “other
influences.” Furthermore, you should have pursued your data collection about
them vigorously—as if you were in fact trying to prove the potency of the other
influences rather than rejecting them (Patton, 2002, p. 553; P. R. Rosenbaum,
2002, pp. 8-10). Then, if you had found insufficient evidence, you would less
likely be accused of stacking the deck in favor of the original hypothesis.
The direct rival—that the original investment was not the reason for the
observed outcomes—is but one of several types of rival explanations. Figure 5.1
classifies and lists many types of rivals (Yin, 2000). For each type, an informal
and more understandable descriptor (in the parentheses and quotation marks in
Figure 5.1) accompanies the formal social science categorization, making the
gist of the rival thinking clearer.
The list reminds us of three “craft” rivals that underlie all of our social science
research, and textbooks have given much attention to these craft rivals. However,
the list also defines six “real-life” rivals, which have received virtually no
attention by other textbooks (nor, unfortunately, do most texts discuss the
challenges and benefits of rival thinking or the use of rival explanations). These
real-life rivals are the ones that you should carefully identify prior to your data
collection (while not ignoring the craft rivals). Some real-life rivals also may not
become apparent until you are in the midst of your data collection, and attending
to them at that point is acceptable and desirable. Overall, the more rivals that
your analysis addresses and rejects, the more confidence you can place in your
findings.
Rival explanations were a critical part of several of the case studies already
contained in the BOXES cited earlier (e.g., refer to BOXES 1 and 11 in Chapters
1 and 2, respectively). The authors of these case studies used the rivals to drive
their entire case study analysis. Additional examples—covering cases of
university innovation and of drug abuse prevention but deliberately focusing on
the essence of the evidence about rival explanations—are found in Yin (2003,
chaps. 4 and 5).

Figure 5.1 Brief Descriptions of Different Kinds of Rival Explanations
SOURCE: Yin (2000).

Summary. The best preparation for conducting case study analysis is to have a
general analytic strategy. Four have been described, relying on theoretical
propositions, case descriptions, a dual use of both quantitative and qualitative
data, and rival explanations. All four strategies underlie the analytic techniques
to be described below. Without such strategies (or alternatives to them), case
study analysis will proceed with difficulty.
The remainder of this chapter covers the specific analytic techniques, to be
used as part of and along with any of the general strategies. The techniques are
especially intended to deal with the previously noted problems of developing
internal validity and external validity in doing case studies (see Chapter 2).

EXERCISE 5.2 Creating a General Analytic Strategy

Assume that you have begun analyzing your case study data but still do not
 have an overall analytic strategy. Instead of staying stalled at this analytic
step, move to the next step and speculate how you might organize your
(later) case study report into separate chapters or sections. Within each
chapter or section, create substantive titles and headings (e.g., instead of
“introduction,” make the title say what the introduction is about, even if
more than a few words are needed). Try different sequences of titles and
headings, noting how such differences might dictate the creation of
different analytic strategies. Now choose one sequence and start sorting
your data into the designated chapters or sections. You should be on your
way to analyzing your case study data.

FIVE ANALYTIC TECHNIQUES

None of the analytic techniques should be considered easy to use, and all will
need much practice to be used powerfully. Your objective should be to start
modestly, work thoroughly and introspectively, and build your own analytic
repertoire over time. The reward will eventually emerge in the form of
compelling case study analyses and, ultimately, compelling case studies.
 Pattern Matching

For case study analysis, one of the most desirable techniques is to use a
pattern-matching logic. Such a logic (Trochim, 1989) compares an empirically
based pattern with a predicted one (or with several alternative predictions). If the
patterns coincide, the results can help a case study to strengthen its internal
validity.
If the case study is an explanatory one, the patterns may be related to the
dependent or the independent variables of the study (or both). If the case study is
a descriptive one, pattern matching is still relevant, as long as the predicted
pattern of specific variables is defined prior to data collection.

Nonequivalent dependent variables as a pattern. The dependent-variables
pattern may be derived from one of the more potent quasi-experimental research
designs, labeled a “nonequivalent, dependent variables design” (Cook &
Campbell, 1979, p. 118). According to this design, an experiment or quasi-
experiment may have multiple dependent variables—that is, a variety of relevant
outcomes. For instance, in quantitative health studies, some outcomes may have
been predicted to be affected by a treatment, whereas other outcomes may have
been predicted not to be affected (Rosenbaum, 2002, pp. 210-211). For these
studies as well as a case study, the pattern matching occurs in the following
manner: If, for each outcome, the initially predicted values have been found, and
at the same time alternative “patterns” of predicted values (including those
deriving from methodological artifacts, or “threats” to validity) have not been
found, strong causal inferences can be made.
For example, consider a single case in which you are studying the effects of a
newly decentralized office computer system. Your major proposition is that—
because each peripheral piece of equipment can work independently of any
server—a certain pattern of organizational changes and stresses will be
produced. Among these changes and stresses, you specify the following, based
on propositions derived from previous decentralization theory:
• employees will create new applications for the office system, and these
applications will be idiosyncratic to each employee;
• traditional supervisory links will be threatened, as management control
 over work tasks and the use of central sources of information will be
diminished;
• organizational conflicts will increase, due to the need to coordinate
resources and services across the decentralized units; but nevertheless,
• productivity will increase over the levels prior to the installation of the
new system.

In this example, these four outcomes each represent different dependent
variables, and you would assess each with different measures. To this extent, you
have a study that has specified nonequivalent dependent variables. You also have
predicted an overall pattern of outcomes covering each of these variables. If the
results are as predicted, you can draw a solid conclusion about the effects of
decentralization. However, if the results fail to show the entire pattern as
predicted—that is, even if one variable does not behave as predicted—your
initial proposition would have to be questioned (see BOX 26 for another
example).

BOX 26

Pattern Matching on Each of Multiple Outcomes

Researchers and politicians alike recognize that U.S. military bases, located
across the country, contribute significantly to a local economy’s housing,
employment, and other markets. When such bases close, a corresponding
belief is that the community will suffer in some catastrophic (both
economic and social) manner.
To test the latter proposition, Bradshaw (1999) conducted a case studyof
a closure that had occurred in a modestly sized California community. He
first identified a series of sectors (e.g., housing sales, civilian employment,
unemployment, population turnover and stability, and retail markets) where
catastrophic outcomes might have been feared, and he then collected data
about each sector before and after the base closure. A pattern-matching
procedure, examining the pre-post patterns of outcomes in every sector and
also in comparison to other communities and statewide trends, showed that
the outcomes were much less severe than antici pated. Some sectors did not
even show any decline. Bradshaw also presented evidence to explain the
pattern of outcomes, there by producing a compelling argument for his
conclusions.

 This first case could then be augmented by a second one, in which another
new office system had been installed, but of a centralized nature—that is, the
equipment at all of the individual workstations had been networked. Now you
would predict a different pattern of outcomes, using the same four dependent
variables enumerated above. And now, if the results show that the decentralized
system (Case A) had actually produced the predicted pattern and that this first
pattern was different from that predicted and produced by the centralized system
(Case B), you would be able to draw an even stronger conclusion about the
effects of decentralization. In this situation, you have made a theoretical
replication across cases. (In other situations, you might have sought a literal
replication by identifying and studying two or more cases of decentralized
systems.)
Finally, you might be aware of the existence of certain threats to the validity
of this logic (see Cook & Campbell, 1979, for a full list of these threats). For
example, a new corporate executive might have assumed office in Case A,
leaving room for a counterargument: that the apparent effects of decentralization
were actually attributable to this executive’s appointment and not to the newly
installed office system. To deal with this threat, you would have to identify some
subset of the initial dependent variables and show that the pattern would have
been different (in Case A) if the corporate executive had been the actual reason
for the effects. If you only had a single-case study, this type of procedure would
be essential; you would be using the same data to rule out arguments based on a
potential threat to validity. Given the existence of a second case, as in our
hypothetical example, you also could show that the argument about the corporate
executive would not explain certain parts of the pattern found in Case B (in
which the absence of the corporate executive should have been associated with
certain opposing outcomes). In essence, your goal is to identify all reasonable
threats to validity and to conduct repeated comparisons, showing how such
threats cannot account for the dual patterns in both of the hypothetical cases.

Rival explanations as patterns. The use of rival explanations, besides being a
good general analytic strategy, also provides a good example of pattern matching
for independent variables. In such a situation (for an example, see BOX 27),
several cases may be known to have had a certain type of outcome, and your
investigation has focused on how and why this outcome occurred in each case.

BOX 27

Pattern Matching for Rival Explanations and Replicating across
 Multiple Cases

A common policy problem is to understand the conditions under which new
research findings can be made useful to society. This topic was the subject
of a multiple-case study (Yin, 2003, chap. 1, pp. 20-22). For nine different
cases, the investigators first provided definitive evidence that important
research findings had indeed been put into practical use in every case.
The main research inquiry then dealt with “how” and “why” such
outcomes had occurred. The investigators compared three theories
(“rivals”) from the prevailing literature, that (a) researchers select their own
topics to study and then successfully disseminate their findings to the
practical world (technology “push”), (b) the practical world identifies
problems that attract researchers’ attention and that then leads to successful
problem solving (demand “pull”), and (c) researchers and practitioners
work together, customizing an elongated process of problem identification
and solution testing (“social interaction”).Each theory predicts a different
pattern of rival events that should precede the preestablished outcome. For
instance, the demand “pull” theory requires the prior existence of a problem
as a prelude to the initiation of a research project, but the same condition is
not present in the other
two theories. For the nine cases, the events turned out to match best a
combination of the second and third theories.The multiple-case study had
therefore pattern-matched the events in each case with different theoretical
predictions and also used a replication logic across the cases.

This analysis requires the development of rival theoretical propositions,
articulated in operational terms. The desired characteristic of these rival
explanations is that each involves a pattern of independent variables that is
mutually exclusive: If one explanation is to be valid, the others cannot be. This
means that the presence of certain independent variables (predicted by one
explanation) precludes the presence of other independent variables (predicted by
a rival explanation). The independent variables may involve several or many
different types of characteristics or events, each assessed with different measures
and instruments. The concern of the case study analysis, however, is with the
overall pattern of results and the degree to which the observed pattern matches
the predicted one.
This type of pattern matching of independent variables also can be done either
with a single case or with multiple cases. With a single case, the successful
matching of the pattern to one of the rival explanations would be evidence for
concluding that this explanation was the correct one (and that the other
explanations were incorrect). Again, even with a single case, threats to validity
—basically constituting another group of rival explanations—should be
identified and ruled out. Moreover, if this identical result were additionally
obtained over multiple cases, literal replication of the single cases would have
been accomplished, and the cross-case results might be stated even more
assertively. Then, if this same result also failed to occur in a second group of
cases, due to predictably different circumstances, theoretical replication would
have been accomplished, and the initial result would stand yet more robustly.

Simpler patterns. This same logic can be applied to simpler patterns, having a
minimal variety of either dependent or independent variables. In the simplest
case, where there may be only two different dependent (or independent)
variables, pattern matching is possible as long as a different pattern has been
stipulated for these two variables.
The fewer the variables, of course, the more dramatic the different patterns
will have to be to allow any comparisons of their differences. Nevertheless, there
are some situations in which the simpler patterns are both relevant and
compelling. The role of the general analytic strategy would be to determine the
best ways of contrasting any differences as sharply as possible and to develop
theoretically significant explanations for the different outcomes.

Precision of pattern matching. At this point in the state of the art, the actual
pattern-matching procedure involves no precise comparisons. Whether one is
predicting a pattern of nonequivalent dependent variables, a pattern based on
rival explanations, or a simple pattern, the fundamental comparison between the
predicted and the actual pattern may involve no quantitative or statistical criteria.
(Available statistical techniques are likely to be irrelevant because each of the
variables in the pattern will probably represent a single data point, and none will
therefore have a “variance.”) The most quantitative result will likely occur if the
study had set preestablished benchmarks (e.g., productivity will increase by
10%) and the value of the actual outcome was then compared to this benchmark.
Low levels of precision can allow for some interpretive discretion on the part
of the investigator, who may be overly restrictive in claiming a pattern to have
been violated or overly lenient in deciding that a pattern has been matched. You
can make your case study stronger by developing more precise measures. In the
absence of such precision, an important suggestion is to avoid postulating very
subtle patterns, so that your pattern matching deals with gross matches or
mismatches whose interpretation is less likely to be challenged.
 Explanation Building

A second analytic technique is in fact a special type of pattern matching, but
the procedure is more difficult and therefore deserves separate attention. Here,
the goal is to analyze the case study data by building an explanation about the
case.
As used in this chapter, the procedure is mainly relevant to explanatory case
studies. A parallel procedure, for exploratory case studies, has been commonly
cited as part of a hypothesis-generating process (see Glaser & Strauss, 1967), but
its goal is not to conclude a study but to develop ideas for further study.

Elements of explanations. To “explain” a phenomenon is to stipulate a presumed
set of causal links about it, or “how” or “why” something happened. The causal
links may be complex and difficult to measure in any precise manner (see BOX
28).
In most existing case studies, explanation building has occurred in narrative
form. Because such narratives cannot be precise, the better case studies are the
ones in which the explanations have reflected some theoretically significant
propositions. For example, the causal links may reflect critical insights into
public policy process or into social science theory. The public policy
propositions, if correct, can lead to recommendations for future policy actions
(see BOX 29A for an example); the social science propositions, if correct, can
lead to major contributions to theory building, such as the transition of countries
from agrarian to industrial societies (see BOX 29B for an example).

BOX 28 Explanation Building in a Single-Case Study

Why businesses succeed or fail continues to be a topic of popular as well as
research interest. Explanations are definitely needed when failure occurs
with a firm that, having successfully grown for 30 years, had risen to
become the number two computer maker in the entire country and, across
all industries, among the top 50 carporations in size. Edgar Schein’s (2003)
single-case study assumed exactly that challenge and contains much
documentation and interview data (also see BOX 46, Chapter 6, p. 188).
 Schein, a professor at MIT, had served as a consultant to the firm’s senior
management during nearly all of its history. His case study tries to explain
how and why the company had a “missing gene”—one that appeared
critical to the business’s survival. The author argues that the gene was
needed to overcome the firm’s other tendencies, Instead, the firm schould
have given more attention to its business and marketing operations. The
firm might then have overcome its inability to address layoffs that might
have prened deadwood in a more timely manner and set priorities among
competing development projects (the firm developed three different PCs,
not just one).

BOX 29

Explanation Building in Multiple-Case Stidies

29A. A Study of Multiple Communities


In a multiple-case study, one goal is to build a general explanation that fits
each individual case, even though the cases will vary in their details. The
objective is analogous to creating an overall explanation, in science, for the
findings from multiple experiments.
Martha Derthick’s (1972) New Towns In-Town: Why a Federal Program
Failed is a book about a housing program under President Lyndon
Johnson’s administration. The federal government was to give its surplus
land—located in choice inner-city areas—to local governments for housing
developments. But after 4 years, little progress had been made at the seven
sites—San Antonio, Texas; New Bedford, Massachusetts; San Francisco,
California; Washington, D.C.; Atlanta, Georgia; Louisville, Kentucky; and
Clinton Township, Michigan—and the program was considered a failure.
Derthick’s (1972) account first analyzes the events at each of the seven
sites. Then, a general explanation—that the projects failed to generate
sufficient local support—is found unsatisfactory because the condition was
not dominant at all of the sites. According to Derthick, local support did
exist, but “federal officials had nevertheless stated such ambitious
objectives that some degree of failure was certain” (p. 91). As a result,
Derthick builds a modified explanation and concludes that “the surplus
lands program failed both because the federal government had limited
 influence at the local level and because it set impossibly high objectives”
(p. 93).


29B. A Study of Multiple Societies

An analytic approach similar to Derthick’s is used by Barrington Moore
(1966) in his history on the Social Origins of Dictatorship and Democracy.
The book serves as another illustration of explanation building in multiple-
case studies, even though the cases are actually historical examples.
Moore’s (1966) book covers the transformation from agrarian to
industrial societies in six different countries—England, France, the United
States, China, Japan, and India—and the general explanation of the role of
the upper classes and the peasantry is a basic theme that emerges and that
became a significant contribution to the field of history.
Iterative nature of explanation building. The explanation-building process, for
explanatory case studies, has not been well documented in operational terms.
However, the eventual explanation is likely to be a result of a series of iterations:
• Making an initial theoretical statement or an initial proposition about
policy or social behavior
• Comparing the findings of an initial case against such a statement or
proposition
• Revising the statement or proposition
• Comparing other details of the case against the revision
• Comparing the revision to the facts of a second, third, or more cases
• Repeating this process as many times as is needed

In this sense, the final explanation may not have been fully stipulated at the
beginning of a study and therefore differs from the pattern-matching approaches
previously described. Rather, the case study evidence is examined, theoretical
positions are revised, and the evidence is examined once again from a new
perspective in this iterative mode.
The gradual building of an explanation is similar to the process of refining a
set of ideas, in which an important aspect is again to entertain other plausible or
rival explanations. As before, the objective is to show how these rival
explanations cannot be supported, given the actual set of case study events.

Potential problems in explanation building. You should be forewarned that this
approach to case study analysis is fraught with dangers. Much analytic insight is
demanded of the explanation builder. As the iterative process progresses, for
instance, an investigator may slowly begin to drift away from the original topic
of interest. Constant reference to the original purpose of the inquiry and the
possible alternative explanations may help to reduce this potential problem.
Other safeguards already have been covered by Chapters 3 and 4—that is, the
use of a case study protocol (indicating what data were to be collected), the
establishment of a case study database for each case (formally storing the entire
array of data that were collected, available for inspection by a third party), and
the following of a chain of evidence.

EXERCISE 5.3 Constructing an Explanation

Identify some observable changes that have been occurring in your
neighborhood (or the neighborhood around your campus). Develop an
explanation for these changes and indicate the critical set of evidence you
would collect to support or challenge this explanation. If such evidence
were available, would your explanation be complete? Compelling? Useful
for investigating similar changes in another neighborhood?

 Time-Series Analysis

A third analytic technique is to conduct a time-series analysis, directly
analogous to the time-series analysis conducted in experiments and quasi-
experiments. Such analysis can follow many intricate patterns, which have been
the subject of several major textbooks in experimental and clinical psychology
with single subjects (e.g., see Kratochwill, 1978); the interested reader is
referred to such works for further detailed guidance. The more intricate and
precise the pattern, the more that the time-series analysis also will lay a firm
foundation for the conclusions of the case study.

Simple time series. Compared to the more general pattern-matching analysis, a
time-series design can be much simpler in one sense: In time series, there may
only be a single dependent or independent variable. In these circumstances,
when a large number of data points are relevant and available, statistical tests
can even be used to analyze the data (see Kratochwill, 1978).
However, the pattern can be more complicated in another sense because the
appropriate starting or ending points for this single variable may not be clear.
Despite this problem, the ability to trace changes over time is a major strength of
case studies—which are not limited to cross-sectional or static assessments of a
particular situation. If the events over time have been traced in detail and with
precision, some type of time-series analysis always may be possible, even if the
case study analysis involves some other techniques as well (see BOX 30).

BOX 30

Using Time-Series Analysis in a Single-Case Study

In New York City, and following a parallel campaign to make the city’s
subways safer, the city’s police department took many actions to reduce
crime in the city more broadly. The actions included enforcing minor
violations (“order restoration and maintenance”), installing computer-based
crime-control techniques, and reorganizing the department to hold police
officers accountable for controlling crime.
Kelling and Coles (1997) first describe all of these actions in sufficient
 detail to make their potential effect on crime reduction understandable and
plausible. The case study then presents time series of the annual rates of
specific types of crime over a 7-year period. During this period, crime
initially rose for a couple of years and then declined for the remainder of
the period. The case study explains how the timthen declined for the
remainder of the period. The case study explains how the timing of the
relevant actions by the police department matched the changes in the crime
trends. The authors cite the plausibility of the actions effects, combined
with the timing of the actions in relation to the changes in crime trends, to
support their explanation for the reduction in crime rates in the New York
City of that era.

The essential logic underlying a time-series design is the match between the
observed (empirical) trend and either of the following: (a) a theoretically
significant trend specified before the onset of the investigation or (b) some rival
trend, also specified earlier. Within the same single-case study, for instance, two
different patterns of events may have been hypothesized over time. This is what
D. T. Campbell (1969) did in his now-famous study of the change in
Connecticut’s speed limit law, reducing the limit to 55 miles per hour in 1955.
The predicted time-series pattern was based on the proposition that the new law
(an “interruption” in the time series) had substantially reduced the number of
fatalities, whereas the other time-series pattern was based on the proposition that
no such effect had occurred. Examination of the actual data points—that is, the
annual number of fatalities over a period of years before and after the law was
passed—then determined which of the alternative time series best matched the
empirical evidence. Such comparison of “interrupted time series” within the
same case can be used in many different situations.
The same logic also can be used in doing a multiple-case study, with
contrasting time-series patterns postulated for different cases. For instance, a
case study about economic development in cities may have examined the
reasons that a manufacturing-based city had more negative employment trends
than those of a service-based city. The pertinent outcome data might have
consisted of annual employment data over a prespecified period of time, such as
10 years. In the manufacturing-based city, the predicted employment trend might
have been a declining one, whereas in the service-based city, the predicted trend
might have been a rising one. Similar analyses can be imagined with regard to
the examination of youth gangs over time within individual cities, changes in
health status (e.g., infant mortality), trends in college rankings, and many other
indicators. Again, with appropriate data, the analysis of the trends can be
subjected to statistical analysis. For instance, you can compute “slopes” to cover
time trends under different conditions (e.g., comparing student achievement
trends in schools with different kinds of curricula) and then compare the slopes
to determine whether their differences are statistically significant (see Yin,
Schmidt, & Besag, 2006). As another approach, you can use regression
discontinuity analysis to test the difference in trends before and after a critical
event, such as the passing of a new speed limit law (see D. T. Campbell, 1969).

Complex time series. The time-series designs can be more complex when the
trends within a given case are postulated to be more complex. One can postulate,
for instance, not merely rising or declining (or flat) trends but some rise followed
by some decline within the same case. This type of mixed pattern, across time,
would be the beginning of a more complex time series. The relevant statistical
techniques would then call for stipulating nonlinear models. As always, the
strength of the case study strategy would not merely be in assessing this type of
time series but also in having developed a rich explanation for the complex
pattern of outcomes and in comparing the explanation with the outcomes.
Greater complexities also arise when a multiple set of variables—not just a
single one—are relevant to a case study and when each variable may be
predicted to have a different pattern over time. Such conditions can especially be
present in embedded case studies: The case study may be about a single case, but
extensive data also cover an embedded unit of analysis (see Chapter 2, Figure
2.3). BOX 31 contains two examples. The first (see BOX 31A) was a single-case
study about one school system, but hierarchical linear models were used to
analyze a detailed set of student achievement data. The second (see BOX 31B)
was about a single neighborhood revitalization strategy taking place in several
neighborhoods; the authors used statistical regression models to analyze time
trends for the sales prices of single-family houses in the targeted and comparison
neighborhoods and thereby to assess the outcomes of the single strategy.

BOX 31

More Complex Time-Series Analyses: Using Quantitative Methods
When Single-Case Studies Have an Embedded Unit of Analysis

31A. Evaluating the Impact of Systemwide Reform in Education Supovitz
and Taylor (2005) conducted a case study of Duval County School District
in Florida, with the district’s students serving as an embedded unit of
analysis. A quantitative analysis of the students’ achievement scores over a
 4-year period, using hierarchical linear models adjusted for confounding
factors, showed “little evidence of sustained systemwide impacts on student
learning, in comparison to other districts.”
The case study includes a rich array of field observations and surveys of
principals, tracing the difficulties in implementing new systemwide changes
prior to and during tracing the difficulties in implementing new systemwide
changes prior to and during the 4-year period. The authors also discuss in
great detail their own insights about systemwide reform and the
implications for evaluators—that such an “intervention” is hardly self-
contained and that its evaluation may need to embrace more broadly the
institutional environment beyond the workings of the school system itself.



31B. Evaluating a Neighborhood Revitalization Strategy

Galster, Tatian, and Accordino (2006) do not present their work as a case
study. The aim of their study was nevertheless to evaluate a single
neighborhood revitalization strategy (as in a single-case study) begun in
1998 in Richmond, Virginia. The article presents the strategy’s nationale
and some of its implementation history, and the main conclusions are about
the revitalization strategy. However, the distinctive anamain conclusions are
about the revitalization strategy. However, the distinctive prices of single-
family homes. applicable to a prices of single-family homes. The overall
evaluation design is highly applicable to a
To test the effectiveness of the revitalization strategy, the authors used
regression models to compare pre-and postintervention (time series) trends
between housing prices in targered and comparison neighborhoods. The
findings showed that the revitalization strategy had “produced substantially
greater appreciation in the market values of single-family homes in the
targered areas than in comparable homes in similarly distressed
neighborhoods.”
In general, although a more complex time series creates greater problems for
data collection, it also leads to a more elaborate trend (or set of trends) that can
strengthen an analysis. Any match of a predicted with an actual time series,
when both are complex, will produce strong evidence for an initial theoretical
proposition.

Chronologies. The compiling of chronological events is a frequent technique in
case studies and may be considered a special form of time-series analysis. The
chronological sequence again focuses directly on the major strength of case
studies cited earlier—that case studies allow you to trace events over time.
You should not think of the arraying of events into a chronology as a
descriptive device only. The procedure can have an important analytic purpose—
to investigate presumed causal events—because the basic sequence of a cause
and its effect cannot be temporally inverted. Moreover, the chronology is likely
to cover many different types of variables and not be limited to a single
independent or dependent variable. In this sense, the chronology can be richer
and more insightful than general time-series approaches. The analytic goal is to
compare the chronology with that predicted by some explanatory theory—in
which the theory has specified one or more of the following kinds of conditions:
• Some events must always occur before other events, with the reverse
sequence being impossible.
• Some events must always be followed by other events, on a contingency
basis.
• Some events can only follow other events after a prespecified interval of
time.
• Certain time periods in a case study may be marked by classes of events
that differ substantially from those of other time periods.

If the actual events of a case study, as carefully documented and determined
by an investigator, have followed one predicted sequence of events and not those
of a compelling, rival sequence, the single-case study can again become the
initial basis for causal inferences. Comparison to other cases, as well as the
explicit consideration of threats to internal validity, will further strengthen this
inference.

Summary conditions for time-series analysis. Whatever the stipulated nature of
the time series, the important case study objective is to examine some relevant
“how” and “why” questions about the relationship of events over time, not
merely to observe the time trends alone. An interruption in a time series will be
the occasion for postulating potential causal relationships; similarly, a
chronological sequence should contain causal postulates.
On those occasions when the use of time-series analysis is relevant to a case
study, an essential feature is to identify the specific indicator(s) to be traced over
time, as well as the specific time intervals to be covered and the presumed
temporal relationships among events, prior to collecting the actual data. Only as
a result of such prior specification are the relevant data likely to be collected in
the first place, much less analyzed properly and with minimal bias.
In contrast, if a study is limited to the analysis of time trends alone, as in a
descriptive mode in which causal inferences are unimportant, a non-case study
strategy is probably more relevant—for example, the economic analysis of
consumer price trends over time.
Note, too, that without any hypotheses or causal propositions, chronologies
become chronicles—valuable descriptive renditions of events but having no
focus on causal inferences.

EXERCISE 5.4 Analyzing Time-Series Trends

Identify a simple time series—for example, the number of students enrolled
at your university for each of the past 20 years. How would you compare
one period of time with another within the 20-year period? If the university
admissions policies had changed during this time, how would you compare
the effects of such policies? How might this analysis be considered part of a
broader case study of your university?

 Logic Models

This fourth technique has become increasingly useful in recent years,
especially in doing case study evaluations (e.g., Mulroy & Lauber, 2004). The
logic model deliberately stipulates a complex chain of events over an extended
period of time. The events are staged in repeated cause-effect-cause-effect
patterns, whereby a dependent variable (event) at an earlier stage becomes the
independent variable (causal event) for the next stage (Peterson & Bickman,
1992; Rog & Huebner, 1992). Evaluators also have demonstrated the benefits
when logic models are developed collaboratively—that is, when evaluators and
the officials implementing a program being evaluated work together to define a
program’s logic model (see Nesman, Batsche, & Hernandez, 2007). The process
can help a group define more clearly its vision and goals, as well as how the
sequence of programmatic actions will (in theory) accomplish the goals.
As an analytic technique, the use of logic models consists of matching
empirically observed events to theoretically predicted events. Conceptually, you
therefore may consider the logic model technique to be another form of pattern
matching. However, because of their sequential stages, logic models deserve to
be distinguished as a separate analytic technique from pattern matching.
Joseph Wholey (1979) was at the forefront in developing logic models as an
analytic technique. He first promoted the idea of a “program” logic model,
tracing events when a public program intervention was intended to produce a
certain outcome or sequence of outcomes. The intervention could initially
produce activities with their own immediate outcomes; these immediate
outcomes could in turn produce some intermediate outcomes; and in turn, the
intermediate outcomes were supposed to produce final or ultimate outcomes.
To illustrate Wholey’s (1979) framework with a hypothetical example,
consider a school intervention aimed at improving students’ academic
performance. The hypothetical intervention involves a new set of classroom
activities during an extra hour in the school day (intervention). These activities
provide time for students to work with their peers on joint exercises (immediate
outcome). The result of this immediate outcome is evidence of increased
understanding and satisfaction with the educational process, on the part of the
participating students, peers, and teachers (intermediate outcome). Eventually,
the exercises and the satisfaction lead to the increased learning of certain key
concepts by the students, and they demonstrate their knowledge with higher test
scores (ultimate outcome).
Going beyond Wholey’s (1979) approach and using the strategy of rival
explanations espoused throughout this book, an analysis also could entertain
rival chains of events, as well as the potential importance of spurious external
events. If the data were supportive of the preceding sequence involving the extra
hour of schooling, and no rivals could be substantiated, the analysis could claim
a causal effect between the initial school intervention and the later increased
learning. Alternatively, the conclusion might be reached that the specified series
of events was illogical—for instance, that the school intervention had involved
students at a different grade level than whose learning had been assessed. In this
situation, the logic model would have helped to explain a spurious finding.
The program logic model strategy can be used in a variety of circumstances,
not just those where a public policy intervention has occurred. A key ingredient
is the claimed existence of repeated cause-and-effect sequences of events, all
linked together. The links may be qualitative or, with appropriate data involving
an embedded unit of analysis, even can be tested with structural equation models
(see BOX 32). The more complex the link, the more definitively the case study
data can be analyzed to determine whether a pattern match has been made with
these events over time. Four types of logic models are discussed next. They
mainly vary according to the unit of analysis that might be relevant to your case
study.

BOX 32

Testing a Logic Model of refomr in a Single School Sysgtem

An attempted transformation of a major urban school system took place in
the 1980s, based on the passage of a new law that decentralized the system
by installing powerful local school councils for each of the system’s
schools.
Bryk, Bebring, Kerbow, Rollow,and Rollow, and Easton (1998) evaluated
the transformation, including qualitative data about the system as a whole
and about individual schools (embedded units of analysis) in the system. At
the same time, the study also includes a major quantitative analysis, taking
the form of structural equation modeling with data from 269 of the
elementary schools in the system. The path analysis is made possible
because the single case (the school system) contains an embedded unit of
 analysis (individual schools).
The analysis tests a complex logic model whereby the investigators claim
that pre-reform restructuring will produce strong democracy for a school, in
turn producing the systemic restructuring of the school, and finally
producing innovative instruction. The results, being aggregated across
schools, pertain to the collective experience across all of the schools and
not to any single school—in the other words, the overall transformation of
the system (single case) as a whole.

Individual-level logic model. The first type assumes that your case study is about
an individual person, with Figure 5.2 depicting the behavioral course of events
for a hypothetical youth. The events flow across a series of boxes and arrows
reading from left to right in the figure. It suggests that the youth may be at risk
for becoming a member of a gang, may eventually join a gang and become
involved in gang violence and drugs, and even later may participate in a gang-
related criminal offense. Distinctive about this logic model is the series of 11
numbers associated with the various arrows in the figure. Each of the 11
represents an opportunity, through some type of planned intervention (e.g.,
community or public program), to prevent an individual youth from continuing
on the course of events. For instance, community development programs
(number 1) might bring jobs and better housing to a neighborhood and reduce
the youth’s chances of becoming at risk in the first place. How a particular youth
might have encountered and dealt with any or all of the 11 possible interventions
might be the subject of a case study, with Figure 5.2 helping you to define the
relevant data and their analysis.

Firm or organizational-level logic model. A second type of logic model traces
events taking place in an individual organization, such as a manufacturing firm.
Figure 5.3 shows how changes in a firm (Boxes 5 and 6 in Figure 5.3) are
claimed to lead to improved manufacturing (Box 8) and eventually to improved
business performance (Boxes 10 and 11). The flow of boxes also reflects a
hypothesis—that the initial changes were the result of external brokerage and
technical assistance services. Given this hypothesis, the logic model therefore
also contains rival or competing explanations (Boxes 12 and 13). The data
analysis for this case study would then consist of tracing the actual events over
time, at a minimum giving close attention to their chronological sequence. The
data collection also should have tried to identify ways in which the boxes were
actually linked in real life, thereby corroborating the layout of the arrows
connecting the boxes.

Figure 5.2Yoth Behavior and 11 Possible Interventions

Figure 5.3Changes in Performance in a Manufacturing Firm
SOURCE: Yin and Oldsman (1995).

An alternative configuration for an organizational-level logic model.
Graphically, nearly all logic models follow a linear sequence (e.g., reading from
left to right or from top to bottom). In real life, however, events can be more
dynamic, not necessarily progressing linearly. One such set of events might
occur in relation to the “reforming” or “transformation” of an organization. For
instance, business firms may undergo many significant operational changes, and
the business’s mission and culture (and even name) also may change. The
significance of these changes warrants the notion that the entire business has
been transformed (see Yin, 2003, chaps. 6 and 10, for a case study of a single
firm and then the cross-case analysis of a group of transformed firms). Similarly,
schools or school systems can sufficiently alter their way of doing business that
“systemic reform” is said to be occurring. In fact, major public initiatives
deliberately aim at improving schools by encouraging the reform of entire school
systems (i.e., school districts). However, neither the business transformation nor
school reform processes are linear, in at least two ways. First, changes may
reverse course and not just progress in one direction. Second, the completed
transformation or systemic reform is not necessarily an end point implied by the
linear logic model (i.e., the final box in the model); continued transforming and
reforming may be ongoing processes even over the long haul.
Figure 5.4 presents an alternatively configured, third type of logic model,
reflecting these conditions. This logic model tracks all of the main activities in a
school system (the initials are decoded in the key to the figure)—over four
periods of time (each time interval might represent a 2-or 3-year period of time).
Systemic reform occurs when all of the activities are aligned and work together,
and this occurs at t3 in Figure 5.4. At later stages, however, the reform may
regress, represented by t4, and the logic model does not assume that the
vacillations will even end at t4. As a further feature of the logic model, the entire
circle at each stage can be positioned higher or lower, representing the level of
student performance—the hypothesis being that systemic reform will be
associated with the highest performance. The pennants in the middle of the circle
indicate the number of schools or classrooms implementing the desired reform
practices, and this number also can vacillate. Finally, the logic model contains a
“metric,” whereby the positioning of the activities or the height of the circle can
be defined as a result of analyzing actual data.

Figure 5.4 Hypothetical States of an Education (K-12) Reforming System
SOURCE: Yin and Davis (2007).

Program-level logic model. Returning to the more conventional linear model,
Figure 5.5 contains a fourth and final type of logic model. Here, the model
depicts the rationale underlying a major federal program, aimed at reducing the
incidence of HIV/AIDS by supporting community planning and prevention
initiatives. The program provides funds as well as technical assistance to 65 state
and local health departments across the country. The model was used to organize
and analyze data from eight case studies, including the collection of data on rival
explanations, whose potential role also is shown in the model (see Yin, 2003
chap. 8, for the entire multiple-case study).

Summary. Using logic models represents a fourth technique for analyzing case
study data. Four types of logic models, applicable to different units of analysis
and situations, have been presented. You should define your logic model prior to
collecting data and then “test” the model by seeing how well the data support it
(see Yin, 2003, for several examples of case studies using logic models).
 Cross-Case Synthesis

A fifth technique applies specifically to the analysis of multiple cases (the
previous four techniques can be used with either single-or multiple-case studies).
The technique is especially relevant if, as advised in Chapter 2, a case study
consists of at least two cases (for a synthesis of six cases, see Ericksen & Dyer,
2004). The analysis is likely to be easier and the findings likely to be more
robust than having only a single case. BOX 33 presents an excellent example of
the important research and research topics that can be addressed by having a
“two-case” case study. Again, having more than two cases could strengthen the
findings even further.
Cross-case syntheses can be performed whether the individual case studies
have previously been conducted as independent research studies (authored by
different persons) or as a predesigned part of the same study. In either situation,
the technique treats each individual case study as a separate study. In this way,
the technique does not differ from other research syntheses—aggregating
findings across a series of individual studies (see BOX 34). If there are large
numbers of individual case studies available, the synthesis can incorporate
quantitative techniques common to other research syntheses (e.g., Cooper &
Hedges, 1994) or meta-analyses (e.g., Lipsey, 1992). However, if only a modest
number of case studies are available, alternative tactics are needed.
One possibility starts with the creation of word tables that display the data
from the individual cases according to some uniform framework. Figure 5.6 has
an f example of such a word table, capturing the findings from 14 case studies of
organizational centers, with each center having an organizational partner
(COSMOS Corporation, 1998). Of the 14 centers, 7 had received programmatic
support and were considered intervention centers; the other 7 were selected as
comparison centers. For both types of centers, data were collected about the
center’s ability to co-locate (e.g., share facilities) with its partnering organization
—this being only one of several outcomes of interest in the original study.

Figure 5.5Improving Community Planning for HIV/AIDS Prevention
SOURCE: Yin (2003, chap. 8).


BOX 33

Using a “Two-Case” Case Study to Test a Policy-Oriented Theory

The international marketplace of the 1970s and 1980s was marked by
Japan’s prominence. Much of its strength was attributable to the role of
centralized planning and support by a special governmental ministry—
considered by many to be an unfair competitive edge, compared to the
 policies in other countries. For instance, the United States was considered
to have no counterpart support structures. Gregory Hooks’s (1990) excellent
case study points to a counterexample frequently ignored by advocates: the
role of the U.S. defense department in implementing an industrial planning
policy within defense-related industries.
Hooks (1990) provides quantitative data on two cases—the aeronautics
industry and the microelectronics industry (the forerunner to the entire
computer chip market and its technologies, such as the personal computer).
One industry (aeronautics) has traditionally been known to be dependent
upon support support from the federal government, but the other has not. In
both cases, Hooks’s evidence showx how the defense departement
supported the critical early development of these industries through
financial support, the support of R&D, and the creationio of an initial
customer base for the industry’s products. The existence of both cases, and
not the aeronautics industry alone, makes the author’s entire argument
powerful and persuasive.

BOX 34

Eleven Program Evaluations and a Cross-“Case” Analysis

Dennis Rosenbaum (1986) collected 11 program evaluations as separate
chapters in an edited book. The 11 evaluations had been conducted by
different investigators, had used a variety of methods, and were not case
studies. Each evaluation was about a different community crime prevention
intervention, and some presented ample quantitative evidence and
employed statistical analyses. The evaluations were deliberatelyselected
because nearly all had shown positive results. A cross-case analysis was
conducted by the present author (Yin, 1986), treating each evaluation as if it
were a separate “case.” The analysis dissected and arrayed the evidence
from the 11 evaluations in the form of word tables. Generalizations about
successful community crime prevention, independent of any specific
intervention, were then derived by using a replication logic, given that all of
the evaluations had shown positive results.


Figure 5.6 Co-location of Interorganizational Partners (14 Centers and Their
Counterpart Organizations)
SOURCE: COSMOS Corporation (1998).

The overall pattern in the word table led to the conclusion that the intervention
and comparison centers did not differ with regard to this particular outcome.
Additional word tables, reflecting other processes and outcomes of interest, were
examined in the same way. The analysis of the entire collection of word tables
enabled the study to draw cross-case conclusions about the intervention centers
and their outcomes.
Complementary word tables can go beyond the single features of a case and
array a whole set of features on a case-by-case basis. Now, the analysis can start
to probe whether different groups of cases appear to share some similarity and
deserve to be considered instances of the same “type” of general case. Such an
observation can further lead to analyzing whether the arrayed case studies reflect
subgroups or categories of general cases—raising the possibility of a typology of
individual cases that can be highly insightful.
An important caveat in conducting this kind of cross-case synthesis is that the
examination of word tables for cross-case patterns will rely strongly on
argumentative interpretation, not numeric tallies. Chapter 2 has previously
pointed out, however, that this method is directly analogous to cross-experiment
interpretations, which also have no numeric properties when only a small
number of experiments are available for synthesis. A challenge you must be
prepared to meet as a case study investigator is therefore to know how to
develop strong, plausible, and fair arguments that are supported by the data.
PRESSING FOR A HIGH-QUALITY ANALYSIS

No matter what specific analytic strategy or techniques have been chosen, you
must do everything to make sure that your analysis is of the highest quality. At
least four principles underlie all good social science research (Yin, 1994a,
1994b, 1997, 1999) and require your attention.
First, your analysis should show that you attended to all the evidence. Your
analytic strategies, including the development of rival hypotheses, must
exhaustively cover your key research questions (you can now appreciate better
the importance of defining sharp as opposed to vague questions). Your analysis
should show how it sought to use as much evidence as was available, and your
interpretations should account for all of this evidence and leave no loose ends.
Without achieving this standard, your analysis may be vulnerable to alternative
interpretations based on the evidence that you had (inadvertently) ignored.
Second, your analysis should address, if possible, all major rival
interpretations. If someone else has an alternative explanation for one or more of
your findings, make this alternative into a rival. Is there evidence to address this
rival? If so, what are the results? If not, should the rival be restated as a loose
end to be investigated in future studies?
Third, your analysis should address the most significant aspect of your case
study. Whether it is a single-or multiple-case study, you will have demonstrated
your best analytic skills if the analysis focuses on the most important issue
(preferably defined at the outset of the case study). By avoiding a detour to a
lesser issue, your analysis will be less vulnerable to the possibility that the main
issue was being avoided because of possibly negative findings.
Fourth, you should use your own prior, expert knowledge in your case study.
The strong preference here is for you to demonstrate awareness of current
thinking and discourse about the case study topic. If you know your subject
matter as a result of your own previous investigations and publications, so much
the better.
The case study in BOX 35 was done by a research team with academic
credentials as well as strong and relevant practical experience. In their work, the
authors demonstrate a care of empirical investigation whose spirit is worth
considering in all case studies. The care is reflected in the presentation of the
cases themselves, not by the existence of a stringent methodology section whose
tenets might not have been fully followed in the actual case study. If you can
emulate the spirit of these authors, your case study analysis also will be given
appropriate respect and recognition.

BOX 35

Analytic Quality in a Multiple-Case Study of International Trade
Competition

The quality of a case study analysis is not dependent solely on the
techniques used, although they are important. Equally important is that the
investigator demonstrate expertise in carrying out the analysis. This
expertise was reflected in Magaziner and Patinkin’s (1989) book, The Silent
War. Inside the Global Business Battles Shaping America’s Future.
The authors organized their nine cases in excellent fashion. Across cases,
major themes regarding America’s competitive advantages (and
disadvantages) were covered in a replication design. Within each case, the
authors provided extensive inter view and other documentation, showing
the sources of their findings. (To keep the narrative reading smoothly, much
of the data—in word tables, footnotes, and quantitative tabulations—were
relegated to footnotes and appendices.) In addition, the authors showed that
they had extensive personal exposure to the issues being studied, as a result
of numerous domestic and overseas visits.
Technically, a more explicit methodological section might have been
helpful. However, the careful and detailed work, even in the absence of
such a section, helps to illustrate what all investigators should strive to
achieve (also see BOX 5B, Chapter 2, p. 31).

EXERCISE 5.5 Analyzing the Analytic Process

Select and obtain one of the case studies described in the BOXES in this
book. Find one of the case study’s chapters (usually in the middle of the
study) in which evidence is presented, but conclusions also are being made.
Describe how this linkage—from cited evidence to conclusions—occurs.
Are data displayed in tables or other formats? Are comparisons being
made?

SUMMARY

This chapter has presented several ways of analyzing case studies. First, the
potential analytic difficulties can be reduced if you have a general strategy for
analyzing the data—whether such a strategy is based on theoretical propositions,
rival explanations, or descriptive frameworks. In the absence of such strategies,
you may have to “play with the data” in a preliminary sense, as a prelude to
developing a systematic sense of what is worth analyzing and how it should be
analyzed.
Second, given a general strategy, several specific analytic techniques are
relevant. Of these, five (pattern matching, explanation building, time-series
analysis, logic models, and cross-case syntheses) can be effective in laying the
groundwork for high-quality case studies. For all five, a similar replication logic
should be applied if a study involves multiple cases. Comparisons to rival
propositions and threats to internal validity also should be made within each
individual case.
None of these techniques is easy to use. None can be applied mechanically,
following any simple cookbook procedure. Not surprisingly, case study analysis
is the most difficult stage of doing case studies, and novice investigators are
especially likely to have a troublesome experience. Again, one recommendation
is to begin with a simple and straightforward case study (or, more preferably, a
“two-case” design), even if the research questions are not as sophisticated or
innovative as might be desired. Experience gained in completing such
straightforward case studies will lead to the ability to tackle more difficult topics
in subsequent case studies.
REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 5

For selected case studies cited in the text of this chapter, two anthologies contain
either a more extensive excerpt or the full case study. The table below
crosswalks the reference in this book to the location of the excerpt or full
rendition.


ABSTRACT

Reporting a case study means bringing its results and findings to closure.
Regardless of the form of the report, similar steps underlie the case study
composition: identifying the audience for the report, developing its
compositional structure, and having drafts reviewed by others.
Once composed, the case study may be finished—or it may be joined with
data collected through other methods, as part of a broader, mixed methods study.
Such studies can be advantageous and represent a further challenge in doing case
study research.
Whether serving as a finished case study or as part of a mixed methods study,
creating a case study report is one of the most challenging aspects of doing case
studies. The best general advice is to compose portions of the case study early
(e.g., the bibliography and the methodology section), rather than waiting until
the end of the data analysis process. As for compositional structures, six
alternatives are suggested: linear-analytic, comparative, chronological, theory-
building, “suspense,” and unsequenced structures. The case study report also
presents a choice regarding the disclosure or anonymity of case identities. A
final plea is to worry about producing high-quality and not just run-of-the-mill
case studies.
 6

Reporting Case Studies How and What to Compose

As a general rule, the compositional phase puts the greatest demands on a case
study investigator. The case study report does not follow any stereotypic form,
such as a journal article in psychology. Because of this uncertain nature,
researchers who do not like to compose may want to question their interest in
doing case studies in the first place. Most of the notable case study scholars have
been ones who liked to compose and also actually had a flair for writing. Do
you?
Of course, most investigators can eventually learn to compose easily and well,
and inexperience in composing should not be a deterrent to doing case studies.
However, much practice will be needed. Furthermore, to do good case studies,
you should want to become good at composing—and not merely put up with it.
One indicator of success at this phase of the craft is whether you found term
papers easy or difficult to do in high school or college. The more difficult they
were, the more difficult it will be to compose a case study report. Another
indicator is whether composing is viewed as an opportunity or as a burden. The
successful investigator usually perceives the compositional phase as an
opportunity—to make a significant contribution to knowledge or practice.
Unfortunately, few people are forewarned about this problem that lies at the
end of designing and doing a case study. The smart investigator will begin to
compose the case study report even before data collection and analysis have
been completed. In general, the compositional phase is so important that you
should give it explicit attention throughout the earlier phases of your case study.
Despite this advice, most investigators typically ignore the compositional
phase until the very end of their case studies. Under these circumstances, all
sorts of “writer’s cramps” may appear, and the case study report may become
impossible to compose. Thus, a prelude to any case study research may be to
consult a textbook covering the writing of research reports more generally (e.g.,
Barzun & Graff, 1985; Becker, 1986). Such texts offer invaluable reminders for
taking notes, making outlines, using plain words, writing clear sentences,
establishing a schedule for composing, and combating the common urge not to
compose.


Tip: What’s the best way of getting my case study report

finished, with the least trouble and time?

Every investigator differs, so you have to develop your own style and
preferences. Improvement occurs with each case study you write. Thus,
don’t be surprised if your first one is more difficult. One possible strategy is
to think about writing “inside-out” and “backwards.” Inside-out: Start your
report with a table, exhibit, vignette, or quotation to be cited by the
narrative of your case study (but don’t try to write the narrative yet). In the
same manner, now amass all of the tables, exhibits, vignettes, or quotations
for your entire report, arraying them in the sequence they are to appear in
your report. Backwards: Now start by writing the narrative for the final
portion of the case study before the rest, then write the analytic narrative
that led to the final portion, and so on.


If you successfully follow the preceding suggestions, would you be
finished, or do you have but a first draft that now needs to be
recomposed so that it blends better?

EXERCISE 6.1 Reducing the Barriers to Composition

Everyone has difficulties in composing reports, whether case studies or not.
To succeed at composing, investigators must take specific steps during the
conduct of a study to reduce barriers to composition. Name five such steps
that you would take—such as starting on a portion of the composition at an
early stage. Have you used these five steps in the past?

The purpose of this chapter is not to repeat these general lessons, although
they are applicable to case studies. Most of the lessons are important to all forms
of research composition, and to describe them here would defeat the purpose of
providing information specific to case studies. Instead, the main purpose of this
chapter is to highlight those aspects of composition and reporting that are
directly related to case studies. These include the following topics, each covered
in a separate section:
• targeting case study reports;
• case study reports as part of larger, mixed methods studies;
• illustrative structures for case study compositions;
• procedures to be followed in doing a case study report; and
• in conclusion, speculations on the characteristics of an exemplary case
study (extending beyond the report itself and covering the design and
content of the case).

One reminder from Chapter 4 is that the case study report should not be the
main way of recording or storing the evidentiary base of the case study. Rather,
Chapter 4 advocated the use of a case study database for this purpose (see
Chapter 4, Principle 2), and the compositional efforts described in this chapter
are primarily intended to serve reporting, and not documentation, objectives.
TARGETING CASE STUDY REPORTS

Giving some initial thought to your likely or preferred audience and reporting
formats serves as a good starting point for composing your case study. It can
have a more diverse set of potential audiences than most other types of research,
including (a) academic colleagues; (b) policy makers, practitioners, community
leaders, and other professionals who do not specialize in case study or other
social science research; (c) special groups such as a dissertation or thesis
committee; and (d) funders of research.1
With most research reports, such as reports of experiments, the second
audience is not typically relevant, as few would expect the result of a laboratory
experiment to be directed to nonspecialists. However, for case studies, this
second audience may be a frequent target of the case study report. As another
contrast, the third audience would rarely be relevant for some types of research
—such as evaluations—because evaluations are not usually suitable as theses or
dissertations. However, for case studies, this third audience also is a frequent
consumer of the case study report, due to the large number of theses and
dissertations in the social sciences that rely on case studies.
Because case studies have more potential audiences than other types of
research, one of your essential tasks in designing the overall case study report is
to identify the specific audiences for the report. Each audience has different
needs, and no single report will serve all audiences simultaneously.
As examples, for academic colleagues, the relationships among the case study,
its findings, and previous theory or research are likely to be most important (see
BOX 36). For nonspecialists, the descriptive elements in portraying some real-
life situation, as well as the implications for action, are likely to be more
important. For a thesis committee, mastery of the methodology and theoretical
issues, along with an indication of the care with which the research was
conducted, is important. Finally, for research funders, the significance of the
case study findings, whether cast in academic or practical terms, is probably as
important as the rigor with which the research was conducted. Successful
communication with more than one audience may mean the need for more than
one version of a case study report. Investigators should seriously consider
catering to such a need (see BOX 37).
 BOX 36

Famous Case Study Reprinted

For many years, Philip Selznick’s TVA and the Grass Roots (1949) has
stood as a classic about public organizations. The case has been cited in
many subsequent studies of federal agencies, political behavior, and
organizational decentralization.
Fully 30 years after its original publication, this case was reprinted in
1980 as part lisher. This type of reissuance allows numerous other
researchers to have access to this famous case study and reflects its
substantial contribution to the field.

BOX 37

Two Versions of the Same Case Study

The city planning office of Broward County, Florida, implemented an office
automation system beginning in 1982 (“The Politics of Automating a
Plannig Office,”
Standerfer & Rider, 1983). The implementation strategies were
innovative and significant—especially in relation to tensions with the
county government’s computer department. As a result, the case study is
interesting and informative, and a popularized version—appearing in a
practioner journal—is fun and easy to read. Because this type of
implementation also covers complex technical issues, the authors made
supplementary information avalaible to the interested reader. The
popularized version provided a name, adress, and telephone number, so that
such a reader could obtain the additional information. This type of dual
availability of case study reports is but one example of how different
reports of the same case study may be useful for communicating with
different audiences.

EXERCISE 6.2 Defining the Audience

Name the alternative audiences for a case study you might compose. For
each audience, indicate the features of the case study report that you should
 highlight or de-emphasize. Would the same report serve all the audiences,
and why?

 Communicating with Case Studies

One additional difference between the case study and other types of research
is that your case study report can itself be a significant communication device.
For many nonspecialists, the description and analysis of a single case often
suggests implications about a more general phenomenon.
A related situation, often overlooked, occurs with testimony before a
legislative committee. If an elderly person, for instance, testifies about her or his
health services before such a committee, its members may assume that they have
acquired an understanding of health care for the elderly more generally—based
on this “case.” Only then might the members be willing to review broader
statistics about the prevalence of similar cases. Later, the committee may inquire
about the representative nature of the initial case, before proposing new
legislation. However, throughout this entire process, the initial “case”—
represented by a witness—may have been the essential ingredient in gaining
insight into the health care issue in the first place.
In these and other ways, your case study can communicate research-based
information about a phenomenon to a variety of nonspecialists. Your case study
may even assume the form of a videotape or other multimedia device and not a
narrative report (e.g., see Naumes & Naumes, 1999, chap. 10). The usefulness of
case studies therefore goes far beyond the role of the typical research report,
which is generally addressed to research colleagues rather than nonspecialists.
Obviously, descriptive as well as explanatory case studies can be important in
this role, and you should not overlook the potential descriptive impact of a well-
presented case study (see BOX 38).

BOX 38

Using a Metaphor too Organize Both Theory and Presentation in
Another Field

whether four “countries”—the American colonies, Russia, England, and
France—all underwent similar courses of events during their major political
revolutions is the topic of Crane Brinton’s (1938) famous historical study,
the Anatomy of a Revolution. Tracing and analyzing these events is done in
 a descriptive manner, as the author’s purpose is not so much to explain the
revolutions as to determine whether they followed similar courses (also see
BOX 41B, p. 173).
The “cross-case” analysis reveals majors similarities: All societies were
on the upgrade, economically; there were bitter class antagonisms; the
intellectuals deserted their governments; government machinery was
inefficient; and the ruling class exhibited immoral, dissolute, or inept
behavior (or all three). However, rather than relying solely on this “factors”
approach to description, the author also develops the metaphor of a human
body suffering from a fever as a way of describing the pattern of events
over time. The author adeptly uses the cyclic pattern of fever and chills,
rising to a critical point and followed by a false tranquility, to describe the
ebb and flow of events in the four revolutions.

 Orienting the Case Study Report to an Audience’s Needs

Overall, the preferences of the potential audience should dictate the form of
your case study report. Although the research procedures and methodology
should have followed other guidelines, suggested in Chapters 1 through 5, your
report should reflect emphases, detail, compositional forms, and even a length
suitable to the needs of the potential audience. The importance of the audience
suggests that you might want to collect formal information about what the
audiences need and their preferred types of communication (Morris, Fitz-
Gibbon, & Freeman, 1987, p. 13). Along these lines, this author has frequently
called the attention of thesis or dissertation students to the fact that the thesis or
dissertation committee may be their only audience. The ultimate report, under
these conditions, should attempt to communicate directly with this committee. A
recommended tactic is to integrate the committee members’ previous research
into the thesis or dissertation, creating greater conceptual (and methodological)
overlap and thereby increasing the thesis or dissertation’s potential
communicability to that particular audience.
Whatever the audience, the greatest error you can make is to compose a report
from an egocentric perspective. This error will occur if you complete your report
without identifying a specific audience or without understanding the specific
needs of such an audience. To avoid this error, you should identify the audience,
as previously noted. A second and equally important suggestion is to examine
prior case study reports that have successfully communicated with this audience.
Such earlier reports may offer helpful clues for composing a new report. For
instance, consider again the thesis or dissertation student. The student should
consult previous dissertations and theses that have passed the academic regimen
successfully—or are known to have been exemplary works. The inspection of
such works may yield sound information regarding the departmental norms (and
reviewers’ likely preferences) for designing a new thesis or dissertation.
 Formats for Written Case Study Reports

Among written forms of case studies, there are at least four important
varieties. The first is the classic single-case study. A single narrative is used to
describe and analyze the case. You may augment the narrative with tabular as
well as graphic and pictorial displays. Depending upon the depth of the case
study, these classic single cases are likely to appear as books, although some of
the best discipline-based journals also run rather long articles.
A second type of written product is the multiple-case version of the classic
single case. This type of multiple-case report will contain multiple narratives,
covering each of the cases singly, usually presented as separate chapters or
sections. In addition to these individual case narratives, your report also will
contain a chapter or section covering the cross-case analysis and results. Some
situations even may call for several cross-case chapters or sections, and the
cross-case portion of the final text may justify a volume separate from the
individual case narratives (see BOX 39). In these situations, a frequent form of
presentation is to have the bulk of the main report contain the cross-case
analysis, with the individual cases presented as part of a long appendix to that
basic volume.

BOX 39

A Multiple-Case Report

Multiple-case studies often contain both the individual case studies and
some cross-case chapters. The composition of such a mūltiple-case study
also may be shared among several authors.
This type of arrangement was used in a study of eight innovations in
mathematics and science education, edited by Raizen and Britton (1997).
The study, titled Bold Ventures, appears in three separate and lengthy
volumes (about 250, 350, and 650 pages, respectively). The indivudial case
studies appear in the last two volumes, while the seven chapters in Volume
1 cover cross-case issues. Many different and multiple authors conducted
both the indīvidual case studīes and the cross-case chapters, although the
entire study was orchestrated and coordinated as a single undertaking.

A third type of written product covers either a multiple-or a single-case study
but does not contain the traditional narrative. Instead, the composition for each
case follows a series of questions and answers, based on the questions and
answers in the case study database (see Chapter 4). For reporting purposes, the
content of the database is shortened and edited for readability, with the final
product still assuming the format, analogously, of a comprehensive examination.
(In contrast, the traditional case study narrative may be considered similar to the
format of a term paper.) This question-and-answer format may not reflect your
full creative talent, but the format helps to avoid the problems of writer’s
cramps. This is because you can proceed immediately to answer the required set
of questions. (Again, the comprehensive exam has a similar advantage over a
term paper.)
If you use this question-and-answer format to report a multiple-case study,
repeating the same set of questions in covering each individual case study, the
advantages are potentially enormous: Your reader(s) need only examine the
answers to the same question or questions within each case study to begin
making her or his own cross-case comparisons. Because each reader may be
interested in different questions, the entire format facilitates the development of
a cross-case analysis tailored to the specific interests of its readers (see BOX 40).
Yin (2003, chap. 2) contains a complete case study demonstrating this format.

BOX 40

A Question-and-Answer Format: Case Studies without the Traditional
Narrative

Case study evidence does not need to be presented in the traditional
narrative form. An alternative for mat for presenting the same evidence is to
write the narrative in question-and-answer form. A series of questions can
be posed, with the answer taking some reasonable length—for example,
three or four paragraphs each. Each answer can contain all the relevant
evidence and can even be augmented with tabular presentations and
citations.
This alternative was followed in 40 case studies of community
organizations produced by the U.S. National Commission on Neigh
borhoods (1979), People, Building Neighborhoods. The same question-and-
answer format was used in each case, so that the interested reader could do
her or his own cross-case analysis by following so the same question across
 all of the cases. The format allowed hurried readers to find exactly the
relevant portions of each case. For people offended by the absence of the
traditional narrative, each case also called for a summary, unconstrained in
its form (but no longer than three pages), allowing the author to exercise her
or his more literary talents.

The fourth and last type of written product applies to multiple-case studies
only. In this situation, there may be no separate chapters or sections devoted to
the individual cases. Rather, your entire report may consist of the cross-case
analysis, whether purely descriptive or also covering explanatory topics. In such
a report, each chapter or section would be devoted to a separate cross-case issue,
and the information from the individual cases would be dispersed throughout
each chapter or section. With this format, summary information about the
individual cases, if not ignored altogether (see BOX 41, as well as Chapter 1, p.
20, BOX 3B), might be presented in abbreviated vignettes.
As a final note, the specific type of case study composition, involving a choice
among at least these four alternatives, should be identified during the design of
the case study. Your initial choice always can be altered, as unexpected
conditions may arise, and a different type of composition may become more
relevant than the one originally selected. However, early selection will facilitate
both the design and the conduct of the case study. Such an initial selection
should be part of the case study protocol, alerting you to the likely nature of the
final composition and its requirements.

BOX 41

Writing a Multiple-Case Report

In a multiple-case study, the indivudial case studies need not always be
presented in the final manuscript. The individual cases, in a sense, serve
only as the evidentiary base for the study and may be cited sporadically in
the cross-case analysis (also see BOX 3B, Chapter 1, p. 20).



41 A. An Example in Which No Single Cases Are Presented

This approach was used in a book about six federal bureau chiefs, by
Herbert Kaufman (1981), The Administrative Behavior of Federal Bureau
Chiefs. Kaufman spent intensive periods of time with each chief to
understand his day-to-day rou tine. He interviewed the chiefs, listened in on
their phone calls, attended meetings, and was present during staff
discussions in the chiefs’ offices.
The book’s purpose, however, was not to portray any single one of these
chiefs. Rather, the book synthesizes the lessons from all of them and is
organized around such topics as how chiefs decide things, how they receive
and review information, and how they motivate their staffs. Under each
topic, Kaufman draws appropriate examples from the six cases, but none of
the six is presented as a single-case study.


41B. Another Example (from Another Field) in Which No Single Cases
Are Presented

A design similar to Kaufman’s is used in another field—history—in a
famous book by Crane Brinton (1938), The Anatomy of a Revolution.
Brinto’s book is based on four revolutions: the English, American, French,
and Russian revolutions (also see Box 38, p.169). The book is an analysis
and theory of revolutionary periods, with pertinent examples drawn from
each of the four “cases”; however, as in kaufman’s book, there is no attempt
to present the single revolutions as individual case studies.
CASE STUDY REPORTS AS PART OF LARGER, MIXED
METHODS STUDIES

Your completed case study may include data from other methods (e.g., surveys
or quantitative analysis of archival data such as health status indicators). In
particular, Chapter 2 pointed to the possibility that within a single case might
exist embedded units of analysis, which might have been the subject of data
collection through these other methods (see Chapter 2, Figure 2.3). In this
situation, the case study encompasses the other methods, and your completed
case study report would incorporate the reporting of the data from these other
methods (e.g., see Chapter 4, BOX 18).
A totally different situation occurs when your case study has been deliberately
designed to be part of a larger, mixed methods study (Yin, 2006b). In this
situation, the larger study encompasses the case study. The larger study will
contain your completed case study but also should report separately the findings
about the data from the other methods. The larger study’s overall report would
then be based on the pattern of evidence from both the case study and the other
methods.
This mixed methods situation deserves a bit more attention so that you will
understand its implications for your case study, even though you might not
compose your case study report any differently than if it had been a “stand-
alone” report. At least three different rationales might have motivated the larger
study to use mixed methods.
First, the larger study may have called for mixed methods simply to determine
whether converging evidence (triangulation) might be obtained even though
different methods had been used (Datta, 1997). In this scenario, your case study
would have shared the same initial research questions as those driving the other
methods, but you would likely have conducted, analyzed, and reported your case
study independently. Part of the larger study’s assessment would then be to
compare the case study results with those based on the other methods.
Second, the larger study may have been based on a survey or quantitative
analysis of archival data—for example, a study of households’ financial
situations under different income tax conditions. The larger study might then
have wanted case studies to illustrate, in greater depth, the experiences of
individual families. In this scenario, the questions for your case study might only
be surfaced after the survey or archival data had been analyzed, and the selection
of cases might come from the pool of those surveyed or contained within the
archival records. The main implications for your case study effort are that both
its timing and direction may depend on the progress and findings of the other
inquiries.
Third, the larger study might knowingly have called for case studies to
elucidate some underlying process and used another method (such as a survey)
to define the prevalence or frequency of such processes. In this scenario of
complementarity as opposed to convergence, the case study questions are likely
to be closely coordinated with those of the other methods, and the
complementary inquiries can occur simultaneously or sequentially. However, the
initial analysis and reports from each inquiry should be conducted independently
(even though the final analysis may merge findings from all the different
methods). BOX 42 contains two examples of larger studies done under this third
scenario.
These three different situations show how your case study and its reporting
may have to be coordinated within some broader context. Beware that when
your case study is not independent, you may have to coordinate deadlines and
technical directions, and your case study report may not proceed as you might
have expected initially. Also assess carefully your willingness and ability to be
part of a larger team before making any commitments.

Box 42

Integrating Case Study and Survey Evidence: Complementarity of
Findings

Multimethod studies can pose complementary qūestions that are to be
addressed by different methods. Most commonly, case studies are used to
gain insight into causal processes, whereas surveys provide an indication of
the prevalence of a phenomenon. Two studies illustrate this combination.
The first was a study of educationnal projects funded by the U.S.
Department of Education (Berman & McLaughlin, 1974-1978). The study
combined case studies of Education (Berman & McLaughlin, 1974-1978).
The study combined case studies of 29 projects with a survey of 293
projects, revealing invaluable information on the implementation process
and its outcomes. The second study (Yin, 1981c) combined case studies of
19 sites with a survey of 90 other sites. The findings contributed to
understanding the life cycle of technological innovations in local public
 services.

ILLUSTRATIVE STRUCTURES FOR CASE STUDY
COMPOSITIONS

The chapters, sections, subtopics, and other components of a report must be

organized in some way, and this constitutes your case study report’s
compositional structure. Attending to such structure has been a topic of attention
with other methodologies. For instance, L. Kidder and Judd (1986, pp. 430-431)
write of the “hourglass” shape of a report for quantitative studies. Similarly, in
ethnography, John Van Maanen (1988) has developed the concept of “tales” for
reporting fieldwork results. He identifies several different types of tales: realist
tales, confessional tales, impressionist tales, critical tales, formal tales, literary
tales, and jointly told tales. These different types may be used in different
combinations in the same report.
Alternatives also exist for structuring case study reports. This section suggests
six illustrative structures (see Figure 6.1) that may be used with any type of case
study formats just described. The illustrations are described mainly in relation to
the composition of a single-case study, although the principles are readily
translatable into multiple-case reports. As a further note and as indicated in
Figure 6.1, the first three are all applicable to descriptive, exploratory, and
explanatory case studies. The fourth is applicable mainly to exploratory and
explanatory case studies, the fifth to explanatory cases, and the sixth to
descriptive cases.

Figure 6.1 Six Structures and Their Application to Different Purposes of Case
Studies

 Linear-Analytic Structures

This is a standard approach for composing research reports. The sequence of
subtopics starts with the issue or problem being studied and a review of the
relevant prior literature. The subtopics then proceed to cover the methods used,
the findings from the data collected and analyzed, and the conclusions and
implications from the findings.
Most journal articles in experimental science reflect this type of structure, as
do many case studies. The structure is comfortable to most investigators and
probably is the most advantageous when research colleagues or a thesis or
dissertation committee comprise the main audience for a case study. Note that
the structure is applicable to explanatory, descriptive, or exploratory case studies.
For example, an exploratory case may cover the issue or problem being
explored, the methods of exploration, the findings from the exploration, and the
conclusions (for further research).
 Comparative Structures

A comparative structure repeats the same case study two or more times,
comparing alternative descriptions or explanations of the same case. This is best
exemplified in Graham Allison’s (1971) noted case study on the Cuban missile
crisis (see Chapter 1, BOX 1). In this book, the author repeats the “facts” of the
case study three times, each time in relation to a different conceptual model. The
purpose of the repetition is to show the degree to which the facts fit each model,
and the repetitions actually illustrate a pattern-matching technique at work.
A similar approach can be used even if a case study is serving descriptive, and
not explanatory, purposes. The same case can be described repeatedly, from
different points of view or with different descriptive models, to understand how
the case might best be categorized for descriptive purposes—similar to arriving
at the correct diagnosis for a clinical patient in psychology. Of course, other
variants of the comparative approach are possible, but the main feature is that the
entire case study (or the results of a cross-case analysis when doing a multiple-
case study) is repeated two or more times, in an overtly comparative mode.
 Chronological Structures

Because case studies generally cover events over time, a third type of
approach is to present the case study evidence in chronological order. Here, the
sequence of chapters or sections might follow the early, middle, and late phases
of a case history. This approach can serve an important purpose in doing
explanatory case studies because presumed causal sequences must occur linearly
over time. If a presumed cause of an event occurs after the event has occurred,
one would have reason to question the initial causal proposition.
Whether for explanatory or descriptive purposes, a chronological approach
has one pitfall to be avoided: giving disproportionate attention to the early events
and insufficient attention to the later ones. Most commonly, an investigator will
expend too much effort in composing the introduction to a case, including its
early history and background, and leave insufficient time to write about the
current status of the case. Yet, much of the interest in the case may be related to
the more recent events. Thus, one recommendation when using a chronological
structure is to draft the case study backward. Those chapters or sections that are
about the current status of the case should be drafted first, and only after these
drafts have been completed should the background to the case be drafted. Once
all drafts have been completed, you can then return to the normal chronological
sequence in then refining the final version of the case study.
 Theory-Building Structures

In this approach, the sequence of chapters or sections will follow some theory-
building logic. The logic will depend on the specific topic and theory, but each
chapter or section should reveal a new part of the theoretical argument being
made. If structured well, the entire sequence and its unfolding of key ideas can
produce a compelling and impressive case study.
The approach is relevant to both explanatory and exploratory case studies,
both of which can be concerned with theory building. Explanatory cases will be
examining the various facets of a causal argument; exploratory cases will be
debating the value of further investigating various hypotheses or propositions.
 Suspense Structures

This structure inverts the linear-analytic structure described previously. The
direct “answer” or outcome of a case study and its substantive significance is,
paradoxically, presented in the initial chapter or section. The remainder of the
case study—and its most suspenseful parts—are then devoted to the
development of an explanation of this outcome, with alternative explanations
considered in the ensuing chapters or sections.
This type of approach is relevant mainly to explanatory case studies, as a
descriptive case study has no especially important outcome. When used well, the
suspense approach is often an engaging compositional structure.
 Unsequenced Structures

An unsequenced structure is one in which the sequence of sections or chapters
assumes no particular importance. This structure is often sufficient for
descriptive case studies, as in the example of Middletown (Lynd & Lynd, 1929),
cited in Chapters 2 and 3 (BOXES 8 and 14). Basically, one could change the
order of the chapters in that book and not alter its descriptive value.
Descriptive case studies of organizations often exhibit the same characteristic.
Such case studies use separate chapters or sections to cover an organization’s
genesis and history, its ownership and employees, its product lines, its formal
lines of organization, and its financial status. The particular order in which these
chapters or sections is presented is not critical and may therefore be regarded as
an unsequenced approach (see BOX 43 for another example).

BOX 43

Unsequenced Chapters, but in a Best-Selling Book

A best-selling book, appealing to both popular and academic audiences, was
Peters and Waterman’s (1982) In Search of Excellence. Although the book
is based on more than 60 case studies of America’s most successful large
businesses, the text contains only the cross-case analysis, each chapter
covering an insightful set of general characteristics associated with
organizational excellence. However, the particular sequence of these
chapters is alterable. The book would have made a significant contribution
even if the chapters were in some other order.

If an unsequenced structure is used, the investigator does need to attend to one
other problem: a test of completeness. Thus, even though the order of the
chapters or sections may not matter, the overall collection does. If certain key
topics are left uncovered, the description may be regarded as incomplete. An
investigator must know a topic well enough—or have related models of case
studies to reference—to avoid such a shortcoming. If a case study fails to present
a complete description, the investigator can be accused of having assembled a
skewed version of the case—even though the case study was only descriptive.
PROCEDURES IN DOING A CASE STUDY REPORT

Every investigator should have a well-developed set of procedures for analyzing

social science data and for composing an empirical report. Numerous texts offer
good advice on how you can develop your own customized procedures,
including the benefits and pitfalls of using word-processing software (Becker,
1986, p. 160). One common warning is that writing means rewriting—a function
not commonly practiced by students and therefore underestimated during the
early years of research careers (Becker, 1986, pp. 43-47). The more rewriting,
especially in response to others’ comments, the better a report is likely to be. In
this respect, the case study report is not much different from other research
reports.
However, three important procedures pertain specifically to case studies and
deserve further mention. The first deals with a general tactic for starting a
composition, the second covers the problem of whether to leave the case
identities anonymous, and the third describes a review procedure for increasing
the construct validity of a case study.
 When and How to Start Composing

The first procedure is to start composing early in the analytic process. One
guide in fact admonishes that “you cannot begin writing early enough” (Wolcott,
1990, p. 20). From nearly the beginning of an investigation, certain sections of
your report will always be draftable, and this drafting should proceed even
before data collection and analysis have been completed.
For instance, after the literature has been reviewed and the case study has been
designed, two sections of a case study report can be drafted: the bibliography
and the methodological sections. The bibliography always can be augmented
later with new citations if necessary, but by and large, the major citations will
have been covered in relation to the literature review. This is therefore the time
to formalize the references, to be sure that they are complete, and to construct a
draft bibliography. If some references are incomplete, the remaining details can
be tracked down while the rest of the case study proceeds. This will avoid the
usual practice among researchers who do the bibliography last and who therefore
spend much clerical time at the very end of their research, rather than attending
to the more important (and pleasurable!) tasks of writing, rewriting, and editing.
The methodological section also can be drafted at this stage because the major
procedures for data collection and analysis should have become part of the case
study design. This section may not become a formal part of the final narrative
but may be designated as an appendix. Whether part of the text or an appendix,
however, the methodological section can and should be drafted at this early
stage. You will remember your methodological procedures with greater precision
at this juncture.
A third section is the preliminary literature review and how it led to or
complemented your research questions and the propositions being studied.
Because your case study will already have settled on these questions and
propositions in order to proceed with protocol development and data collection,
much of the connectivity to the literature will be known. Although you may need
to revisit this early version after completing your data collection and analysis,
having a preliminary draft never hurts.
After data collection but before analysis begins, a fourth section that can be
composed covers the descriptive data about the cases being studied. Whereas
the methodological section should have included the issues regarding the
selection of the case(s), the descriptive data should cover qualitative and
quantitative information about the case(s). At this stage in the research process,
you still may not have finalized your ideas about the type of case study format to
be used and the type of structure to be followed. However, the descriptive data
are likely to be useful regardless of the format or structure. Furthermore, drafting
the descriptive sections, even in abbreviated form, may stimulate your thinking
about the overall format and structure.
If you can draft these four sections before analysis has been completed, you
will have made a major advance. These sections also may call for substantial
documentation (e.g., copies of your final case study protocol), and an opportune
time to put such documentation into presentable form (possibly even “camera
ready”) occurs at this stage of the research. You also will be at an advantage if
all details—citations, references, organizational titles, and spellings of people’s
names—have been accurately recorded during data collection and are integrated
into the text at this time (Wolcott, 1990, p. 41).
If these sections are drafted properly, more attention can then be devoted to
the analysis itself, as well as to the findings and conclusions. To begin
composing early also serves another important psychological function: You may
get accustomed to the compositional process as an ongoing (possibly even daily)
practice and have a chance to routinize it before the task becomes truly
awesome. Thus, if you can identify other sections that can be drafted at these
early stages, you should draft them as well.
 Case Identities: Real or Anonymous?

Nearly every case study presents an investigator with a choice regarding the
anonymity of the case. Should the case study and its informants be accurately
identified, or should the names of the entire case and its participants be
disguised? Note that the anonymity issue can be raised at two levels: that of the
entire case (or cases) and that of an individual person within a case (or cases).
The most desirable option is to disclose the identities of both the case and the
individuals, within the constraints for protecting human subjects, discussed in
Chapter 3. Disclosure produces two helpful outcomes. First, the reader has the
opportunity to recollect any other previous information he or she may have
learned about the same case—from previous research or other sources—in
reading and interpreting your case study. This ability to become familiar with a
new case study in light of prior knowledge is invaluable, similar to the ability to
recall previous experimental results when reading about a new set of
experiments. Second, the absence of disguised names will make the entire case
easier to review, so that footnotes and citations can be checked, if necessary, and
appropriate external comments can be solicited about the published case.
Nevertheless, anonymity is necessary on some occasions. The most common
rationale occurs when a case study has been on a controversial topic. Anonymity
then serves to protect the real case and its real participants. A second occasion
occurs when the issuance of the final case report may affect the subsequent
actions of those that were studied. This rationale was used in Whyte’s
(1943/1955) famous case study, Street Corner Society (which was about an
anonymous neighborhood, “Cornerville”).2 As a third illustrative situation, the
purpose of the case study may be to portray an “ideal type,” and there may be no
reason for disclosing the true identities. This rationale was used by the Lynds in
their study Middletown (Lynd & Lynd, 1929), in which the names of the small
town, its residents, and its industries all were disguised.
On such occasions when anonymity may appear justifiable, however, other
compromises should still be sought. First, you should determine whether the
anonymity of the individuals alone might be sufficient, thereby leaving the case
itself to be identified accurately.
A second compromise would be to name the individuals but to avoid
attributing any particular point of view or comment to a single individual, again
allowing the case itself to be identified accurately. This second alternative is
most relevant when you want to protect the confidentiality of specific
individuals. However, the lack of attribution may not always be completely
protective—you also may have to disguise the comments so that no one involved
in the case can infer the likely source.
For multiple-case studies, a third compromise would be to avoid composing
any single-case reports and to report only a cross-case analysis. This last
situation would be roughly parallel to the procedure used in surveys, in which
the individual responses are not disclosed and in which the published report is
limited to the aggregate evidence.
Only if these compromises are impossible should you consider making the
entire case study and its informants anonymous. However, anonymity is not to
be considered a desirable choice. Not only does it eliminate some important
background information about the case, but it also makes the mechanics of
composing the case difficult. The case and its components must be
systematically converted from their real identities to fictitious ones, and you
must make a considerable effort to keep track of the conversions. The cost of
undertaking such a procedure should not be underestimated.

EXERCISE 6.3 Maintaining Anonymity in Case Studies

Identify a case study whose “case” has been given a fictitious name (or
check some of the boxes in this book for an example). What are the
advantages and disadvantages of using such a technique? What approach
would you use in reporting your own case study, and why?

 Reviewing the Draft Case Study: A Validating Procedure

A third procedure to be followed in doing the case study report is related to
the overall quality of the study. The procedure is to have the draft report
reviewed, not just by peers (as would be done for any research manuscript) but
also by the participants and informants in the case. If the comments are
exceptionally helpful, the investigator may even want to publish them as part of
the entire case study (see BOX 44).
Such review is more than a matter of professional courtesy. The procedure has
been correctly identified as a way of corroborating the essential facts and
evidence presented in a case report (Schatzman & Strauss, 1973, p. 134). The
informants and participants may still disagree with an investigator’s conclusions
and interpretations, but these reviewers should not disagree over the actual facts
of the case. If such disagreement emerges during the review process, an
investigator knows that the case study report is not finished and that such
disagreements must be settled through a search for further evidence. Often, the
opportunity to review the draft also produces further evidence, as the informants
and participants may remember new materials that they had forgotten during the
initial data collection period.

BOX 44

Reviewing Case Studies—and Printing the Comments

A major way of improving the quality of case studies and ensuring their
construct validity is to have the draft cases reviewed by those who have
been the subjects of study. This procedure was followed to an exemplary
degree in a set of five case studies by Alkin, Daillak, and White (1979).
Each case study was about a school district and the way that the district
used evaluative information about its students’ performance. As part of the
analytic and reporting procedure, the draft for each case was reviewed by
the informants from the relevant district. The comments were obtained in
part as a result of an open-ended questionnaire devised by the investigators
for just this purpose. In an some instances, the responses were so insightful
and helpful that the investigators modified their original material and also
 printed the responses as part of their book.
With such presentation of supplementary evidence and comments, any
reader can reach her or his own conclusions about the adequacy of the cases
—an opportunity that has occurred, unfortunately, all too seldom in
traditional case study reseach.

This type of review should be followed even if the case study or some of its
components are to remain anonymous. Under this condition, some recognizable
version of the draft must be shared with the case study informants or
participants. After they have reviewed this draft, and after any differences in
facts have been settled, the investigator can disguise the identities so that only
the informants or participants will know the true identities. When Whyte
(1943/1955) first completed Street Corner Society, he followed this procedure by
sharing drafts of his book with “Doc,” his major informant. He notes,
As I wrote, I showed the various parts to Doc and went over them with him
in detail. His criticisms were invaluable in my revision. (p. 341)

From a methodological standpoint, the corrections made through this process
will enhance the accuracy of the case study, hence increasing the construct
validity of the study. The likelihood of falsely reporting an event should be
reduced. In addition, where no objective truth may exist—as when different
participants indeed have different renditions of the same event—the procedure
should help to identify the various perspectives, which can then be represented
in the case study report. At the same time, you need not respond to all the
comments made about the draft. For example, you are entitled to your own
interpretation of the evidence and should not automatically incorporate your
informants’ reinterpretations. In this respect, your discretionary options are no
different from how you might respond to comments made in the conventional
peer review process.
The review of the draft case study by its informants will clearly extend the
period of time needed to complete the case study report. Informants, unlike
academic reviewers, may use the review cycle as an opportunity to begin a fresh
dialogue about various facets of the case, thereby extending the review period
even further. You must anticipate these extensions and not use them as an excuse
to avoid the review process altogether. When the process has been given careful
attention, the potential result is the production of a high-quality case study (see
BOX 45).

BOX 45

Formal Reviews of Case Studies

As with any other research product the review process plays an important
role in enhancing and ensuring the quality of the final results.For case
studies, such a review process should involve, at a minimum, a review of
the draft case study.
One set of case studies that followed this procedure, to an exemplary
degree, was case studies, which were about medical technologies, was
“seen by at least 20, and some by 40 or more, outside reviewers.”
Furthermore, the reviewers reflected different perspectives, including those
of government agencies, professional societies, consumer and public
interest groups, medical practice, academic medicine, and economics and
decision sciences.
In one of the studies, a contrary view of the case—put forth by one of the
reviewers—was included as part of the final published version of the case,
as well as a response by the case study authors. This type of open printed
interchange adds to the reader’s ability to interpret the case study’s
conclusions and therefore to the overall quality of the case study evidence.

EXERCISE 6.4 Anticipating the Difficulties of the Review

Process

Case study reports are likely to be improved by having some review by
informants—that is, those persons who were the subjects of the study.
Discuss the pros and cons of having such reviews. What specific advantage,
for quality control purposes, is served? What disadvantages are there? On
balance, are such reviews worthwhile?

WHAT MAKES AN EXEMPLARY CASE STUDY?

In all of case study research, one of the most challenging tasks is to define an
exemplary case study. Although no direct evidence is available, some
speculations seem an appropriate way of concluding this book.3
The exemplary case study goes beyond the methodological procedures already
highlighted throughout this book. Even if you, as a case study investigator, have
followed most of the basic techniques—using a case study protocol, maintaining
a chain of evidence, establishing a case study database, and so on—you still may
not have produced an exemplary case study. The mastering of these techniques
makes you a good technician but not necessarily an esteemed social scientist. To
take but one analogy, consider the difference between a chronicler and a
historian: The former is technically correct but does not produce the insights into
human or social processes provided by the latter.
Five general characteristics of an exemplary case study are described below.
They are intended to help your case study to be a lasting contribution to
research.

EXERCISE 6.5 Defining a Good Case Study

Select a case study that you believe is one of the best you know (again, the
selection can be from the BOXES in this book). What makes it a good case
study? Why are such characteristics so infrequently found in other case
studies? What specific efforts might you make to emulate such a good case
study?

 The Case Study Must Be Significant

The first general characteristic may be beyond the control of many
investigators. If an investigator has access to only a few “cases,” or if resources
are extremely limited, the ensuing case study may have to be on a topic of only
marginal significance. This situation is not likely to produce an exemplary case
study. However, where choice exists, the exemplary case study is likely to be one
in which
• the individual case or cases are unusual and of general public interest,
• the underlying issues are nationally important—either in theoretical terms
or in policy or practical terms, or
• your case meets both of the preceding conditions.

For instance, a single-case study may have been chosen because it was a
revelatory case—that is, one reflecting some real-life situation that social
scientists had not been able to study in the past. This revelatory case is in itself
likely to be regarded as a discovery and to provide an opportunity for doing an
exemplary case study. Alternatively, a critical case may have been chosen
because of the desire to compare two rival propositions; if the propositions are at
the core of a well-known debate in the literature—or reflect major differences in
public beliefs—the case study is likely to be significant. Finally, imagine the
situation in which both discovery and theory development are found within the
same case study, as in a multiple-case study in which each individual case
reveals a discovery but in which the replication across cases also adds up to a
significant theoretical breakthrough. This situation truly lends itself to the
production of an exemplary case study.
In contrast to these promising situations, many students select nondistinctive
cases or outmoded theoretical issues as the topics for their case studies. This
situation can be avoided, in part, by doing better homework with regard to the
existing body of research. Prior to selecting a case study, you should describe, in
detail, the contribution to be made, assuming that the intended case study were
to be completed successfully. If no satisfactory answer is forthcoming, you might
want to plan another case study.
 The Case Study Must Be “Complete”

This characteristic is extremely difficult to describe operationally. However, a
sense of completeness is as important in doing a case study as it is in defining a
complete series of laboratory experiments (or in completing a symphony or
finishing a painting). All have the problem of defining the boundaries of the
effort, but few guidelines are available.
For case studies, completeness can be characterized in at least three ways.
First, the complete case is one in which the boundaries of the case—that is, the
distinction between the phenomenon being studied and its context—are given
explicit attention. If this is done only mechanically—for example, by declaring
at the outset that only arbitrary time intervals or spatial boundaries will be
considered—a nonexemplary case study is likely to result. The best way is to
show, either through logical argument or the presentation of evidence, that as the
analytic periphery is reached, the information is of decreasing relevance to the
case study. Such testing of the boundaries can occur throughout the analytic and
reporting steps in doing case studies.
A second way involves the collection of evidence. The complete case study
should demonstrate convincingly that the investigator expended exhaustive
effort in collecting the relevant evidence. The documentation of such evidence
need not be placed in the text of the case study, thereby dulling its content.
Footnotes, appendices, and the like will do. The overall goal, nevertheless, is to
convince the reader that little relevant evidence remained untouched by the
investigator, given the boundaries of the case study. This does not mean that the
investigator should literally collect all available evidence—an impossible task—
but that the critical pieces have been given “complete” attention. Such critical
pieces, for instance, would be those representing rival propositions.
A third way concerns the absence of certain artifactual conditions. A case
study is not likely to be complete if the study ended only because resources were
exhausted, because the investigator ran out of time (when the semester ended),
or because she or he faced other, nonresearch constraints. When a time or
resource constraint is known at the outset of a study, the responsible investigator
should design a case study that can be completed within such constraints, rather
than reaching and possibly exceeding his or her limits. This type of design
requires much experience and some good fortune. Nevertheless, these are the
conditions under which an exemplary case study is likely to be produced.
Unfortunately, if in contrast a severe time or resource constraint suddenly
emerges in the middle of a case study, it is unlikely that the case study will
become exemplary.
 The Case Study Must Consider Alternative Perspectives

For explanatory case studies, one valuable approach is the consideration of
rival propositions and the analysis of the evidence in terms of such rivals (see
Chapter 5). The citing of rival claims or alternative perspectives also should be
part of a good abstract for your case study (Kelly & Yin, 2007). Even in doing an
exploratory or a descriptive case study, the examination of the evidence from
different perspectives will increase the chances that a case study will be
exemplary.
For instance, a descriptive case study that fails to account for different
perspectives may raise a critical reader’s suspicions. The investigator may not
have collected all the relevant evidence and only may have attended to the
evidence supporting a single point of view. Even if the investigator was not
purposefully biased, different descriptive interpretations might not have been
entertained, thereby presenting a one-sided case. To this day, this type of
problem persists whenever studies of organizations appear to represent the
perspectives of management and not workers, or when studies of social groups
appear to be insensitive to issues of gender or multiculturalism, or when studies
of youth programs appear to represent adult perspectives and ignore those of
youths.
To represent different perspectives adequately, an investigator must seek those
alternatives that most seriously challenge the assumptions of the case study.
These perspectives may be found in alternative cultural views, different theories,
variations among the stakeholders or decision makers who are part of the case
study, or some similar contrasts. If sufficiently important, the alternative
perspectives can appear as alternative renditions covering the same case, using
the comparative structure of composition described earlier in this chapter as one
of seven possible structures. Less prominently but still invaluable would be the
presentation of alternative views as separate chapters or sections of the main
case study (see BOX 46).

BOX 46

Adding Alternative Perspectives, Written by a Case Study’s
 Participants, as Supplements to a Case Study

Edgar Schein’s (2003) single-case study tried to explain the demise of a
computer firm that had been among the country’s top 50 corporations in
size (see BOX 28, Chapter 5, p. 142). The contemporary nature of the case
study meant that the firm’s former executives were still available to offer
their own rendition of the firm’s fate.
Schein supported his own explanation with much documentation and
interview data, but he made his case study distinctive īn another way: He
also included supplementary chapters, each giving a key executive the
opportunity to present his own rival explanation.

Many times, if an investigator describes a case study to a critical listener, the
listener will immediately offer an alternative interpretation of the facts of the
case. Under such circumstances, the investigator is likely to become defensive
and to argue that the original interpretation was the only relevant or correct one.
In fact, the exemplary case study anticipates these “obvious” alternatives, even
advocates their positions as forcefully as possible, and shows—empirically—the
basis upon which such alternatives might be rejected.
 The Case Study Must Display Sufficient Evidence

Although Chapter 4 encouraged investigators to create a case study database,
the critical pieces of evidence for a case study must still be contained within the
case study report. The exemplary case study is one that judiciously and
effectively presents the most relevant evidence, so that a reader can reach an
independent judgment regarding the merits of the analysis.
This selectiveness does not mean that the evidence should be cited in a biased
manner—for example, by including only the evidence that supports an
investigator’s conclusions. On the contrary, the evidence should be presented
neutrally, with both supporting and challenging data. The reader should then be
able to draw an independent conclusion about the validity of a particular
interpretation. The selectiveness is relevant in limiting the report to the most
critical evidence and not cluttering the presentation with supportive but
secondary information. Such selectiveness takes a lot of discipline among
investigators, who usually want to display their entire evidentiary base, in the
(false) hope that sheer volume or weight will sway the reader. (In fact, sheer
volume or weight will bore the reader.)
Another goal is to present enough evidence to gain the reader’s confidence
that the investigator “knows” his or her subject. In doing a field study, for
instance, the evidence presented should convince the reader that the investigator
has indeed been in the field, made penetrating inquiries while there, and has
become steeped in the issues about the case. A parallel goal exists in multiple-
case studies: The investigator should show the reader that all of the single cases
have been treated fairly and that the cross-case conclusions have not been biased
by undue attention to one or a few of the entire array of cases.
Finally, the display of adequate evidence should be accompanied by some
indication that the investigator attended to the validity of the evidence—in
maintaining a chain of evidence, for example. This does not mean that all case
studies need to be burdened with methodological treatises. A few judicious
footnotes will serve the purpose. Alternatively, some words in the preface of the
case study can cover the critical validating steps. Notes to a table or figure also
will help. As a negative example, a figure or table that presents evidence without
citing its source is an indication of sloppy research and cautions the reader to be
more critical of other aspects of the case study. This is not a situation that
produces exemplary case studies.
 The Case Study Must Be Composed in an Engaging Manner

One last global characteristic has to do with the composition of the case study
report. Regardless of the medium used (a written report, an oral presentation, or
some other form), the report should be engaging.
For written reports, this means a clear writing style, but one that constantly
entices the reader to continue reading. A good manuscript is one that “seduces”
the eye. If you read such a manuscript, your eye will not want to leave the page,
and you will continue to read paragraph after paragraph, page after page, until
exhaustion sets in. Anyone reading good fiction has had this experience. This
type of seduction should be the goal in composing any case study report.
The production of such seductive writing calls for talent and experience. The
more often that someone has written for the same audience, the more likely that
the communication will be effective. However, the clarity of writing also
increases with rewriting, which is highly recommended. With the use of
electronic writing tools, an investigator has no excuse for shortcutting the
rewriting process.
Engagement, enticement, and seduction—these are unusual characteristics of
case studies. To produce such a case study requires an investigator to be
enthusiastic about the investigation and to want to communicate the results
widely. In fact, the good investigator might even think that the case study
contains earth-shattering conclusions. This sort of inspiration should pervade the
entire investigation and will indeed lead to an exemplary case study.
NOTES

1 Ignored here is a frequent audience for case studies: students taking a course
using case studies as a curriculum material. Such use of case studies, as
indicated in Chapter 1, is for teaching and not research purposes, and the entire
case study strategy might be defined and pursued differently under these
conditions.

2 Of course, even when an investigator makes the identity of a case or its

participants anonymous, a few other colleagues—sharing the confidence of the
investigator—will usually know the real identities. In the case of both Street
Corner Society and Middletown, other sociologists, especially those working in
the same academic departments as Whyte and the Lynds, were quite aware of the
real identities.

3 The speculations also are based on some empirical findings. As part of an

earlier investigation, 21 prominent social scientists were asked to name the best
qualities of case studies (see COSMOS Corporation, 1983). Some of these
qualities are reflected in this discussion of exemplary case studies.

REFERENCE TO EXPANDED CASE STUDY MATERIALS
FOR CHAPTER 6

For selected case studies cited in the text of this chapter, one anthology contains
either a more extensive excerpt or the full case study. The table below
crosswalks the reference in this book to the location of the excerpt or full
rendition.

 References

Agranoff, R., & Radin, B. A. (1991). The comparative case study approach in
public administration. Research in Public Administration, 1, 203-231.
Alkin, M., Daillak, R., & White, P. (1979). Using evaluations: Does evaluation
make a difference? Beverly Hills, CA: Sage.
Allison, G. T. (1971). Essence of decision: Explaining the Cuban missile crisis.
Boston: Little, Brown.
Allison, G. T., & Zelikow, P. (1999). Essence of decision: Explaining the Cuban
missile crisis (2nd ed.). New York: Addison Wesley Longman.
Barzun, J., & Graff, H. (1985). The modern researcher (4th ed.). New York:
Harcourt Brace Jovanovich.
Basu, O. N., Dirsmith, M. W., & Gupta, P. P. (1999). The coupling of the
symbolic and the technical in an institutionalized context. American Sociological
Review, 64, 506-526.
Becker, H. S. (1958). Problems of inference and proof in participant observation.
American Sociological Review, 23, 652-660.
Becker, H. S. (1967). Whose side are we on? Social Problems, 14, 239-247.
Becker, H. S. (1986). Writing for social scientists: How to start and finish your
thesis, book, or article. Chicago: University of Chicago Press.
Becker, H. S. (1998). Tricks of the trade: How to think about your research while
you’re doing it. Chicago: University of Chicago Press.
Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in
studies of information systems. MIS Quarterly, 11, 369-386.
Berends, M., & Garet, M. S. (2002). In (re)search of evidence-based school
practices: Possibilities for integrating nationally representative surveys and
randomized field trials to inform educational policy. Peabody Journal of
Education, 77(4), 28-58.
Berman, P., & McLaughlin, M. (1974-1978). Federal programs supporting
educational change (8 vols.). Santa Monica, CA: RAND.
Bickman, L. (1987). The functions of program theory. In L. Bickman (Ed.),
Using program theory in evaluation (pp. 5-18). San Francisco: Jossey-Bass.
Bickman, L., & Rog, D. J. (Eds.). (2000). Handbook of applied research
methods. Thousand Oaks, CA: Sage.
Blau, P. M. (1955). The dynamics of bureaucracy. Chicago: University of
Chicago Press.
Bonoma, T. V. (1985). Case research in marketing: Opportunities, problems, and
a process. Journal of Marketing Research, 12, 199-208.
Boruch, R. (2007, October 12). The flight of error: Scientific questions,
evidential answers, and STEM education research. Presentation at workshop on
STEM Education Research Designs: Conceptual and Practical Considerations
for Planning Experimental Studies, Arlington, VA, sponsored by the University
of California, Irvine.
Boruch, R., & Foley, E. (2000). The honestly experimental society. In L.
Bickman (Ed.), Validity & social experimentation: Donald Campbell’s legacy
(pp. 193-238). Thousand Oaks, CA: Sage.
Bouchard, T. J., Jr. (1976). Field research methods. In M. D. Dunnette (Ed.),
Industrial and organizational psychology (pp. 363-413). Chicago: Rand
McNally.
Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis
and code development. Thousand Oaks, CA: Sage.
Bradshaw, T. K. (1999). Communities not fazed: Why military base closures
may not be catastrophic. Journal of the American Planning Association, 65, 193-
206.
Brinton, C. (1938). The anatomy of a revolution. Englewood Cliffs, NJ: Prentice
Hall.
Bruns, W. J., Jr. (1989). A review of Robert K. Yin’s ‘Case Study Research:
Design and Methods.’ Journal of Management Accounting Research, 1, 157-163.
Bryk, A. S., Bebring, P. B., Kerbow, D., Rollow, S., & Easton, J. Q. (1998).
Charting Chicago school reform: Democratic localism as a lever for change.
Boulder, CO: Westview.
Campbell, D. T. (1969). Reforms as experiments. American Psychologist, 24,
409-429.
Campbell, D. T. (1975). Degrees of freedom and the case study. Comparative
Political Studies, 8, 178-193.
Campbell, D. T., & Stanley, J. (1966). Experimental and quasi-experimental
designs for research. Chicago: Rand McNally.
Campbell, J. P., Daft, R. L., & Hulin, C. L. (1982). What to study: Generating
and developing research questions. Beverly Hills, CA: Sage.
Carr, P. J. (2003). The new parochialism: The implications of the Beltway case
for arguments concerning informal social control. American Journal of
Sociology, 108, 1249-1291.
Carroll, J., & Johnson, E. (1992). Decision research: A field guide. Journal of
the Operational Research Society, 43, 71-72.
Caulley, D. N., & Dowdy, I. (1987). Evaluation case histories as a parallel to
legal case histories. Evaluation and Program Planning, 10, 359-372.
Chaskin, R. J. (2001). Building community capacity: A definitional framework
and case studies from a comprehensive community initiative. Urban Affairs
Review, 36, 291-323.
Cochran, W. G., & Cox, G. M. (1957). Experimental designs (2nd ed.). New
York: John Wiley.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and
analysis issues for field settings. Chicago: Rand McNally.
Cook, T. D., & Payne, M. R. (2002). Objecting to the objections to using random
assignment in educational research. In F. Mosteller & R. Boruch (Eds.),
Evidence matters: Randomized trials in education research (pp. 150-178).
Washington, DC: Brookings Institution Press.
Cooper, H. M. (1984). The integrative research review. Beverly Hills, CA: Sage.
Cooper, H. M., & Hedges, L. V. (Eds.). (1994). The handbook of research
synthesis. New York: Russell Sage Foundation.
Corbin, J., & Strauss, A. (2007). Basics of qualitative research: Techniques and
procedures for developing grounded theory (3rd ed.). Thousand Oaks, CA: Sage.
COSMOS Corporation. (1983). Case studies and organizational innovation:
Strengthening the connection. Bethesda, MD: Author.
COSMOS Corporation. (1998). Evaluation of MEP-SBDC partnerships: Final
report. Report prepared for the National Institute of Standards and Technology,
U.S. Department of Commerce, Gaithersburg, MD.
Crabtree, B. F., & Miller, W. L. (Eds.). (1999). Doing qualitative research (2nd
ed.). Thousand Oaks, CA: Sage.
Crane, J. (Ed.). (1998). Social programs that work. New York: Russell Sage
Foundation.
Creswell, J. W. (2007). Qualitative inquiry & research design: Choosing among
five approaches (2nd ed.). Thousand Oaks, CA: Sage.
Crewe, K. (2001). The quality of participatory design: The effects of citizen
input on the design of the Boston Southwest Corridor. APA Journal, 67, 437-455.
Cronbach, L. J., & Associates. (1980). Toward reform of program evaluation:
Aims, methods, and institutional arrangements. San Francisco: Jossey-Bass.
Dabbs, J. M., Jr. (1982). Making things visible. In J. Van Maanen, J. M. Dabbs
Jr., & R. R. Faulkner (Eds.), Varieties of qualitative research (pp. 31-63).
Beverly Hills, CA: Sage.
Datta, L. (1997). Multimethod evaluations. In E. Chelimsky & W. R. Shadish
(Eds.), Evaluation for the 21st century (pp. 344-359). Thousand Oaks, CA: Sage.
Denzin, N. K. (1978). The logic of naturalistic inquiry. In N. K. Denzin (Ed.),
Sociological methods: A sourcebook. New York: McGraw-Hill.
Derthick, M. (1972). New towns in-town: Why a federal program failed.
Washington, DC: The Urban Institute.
Dion, D. (1998). Evidence and inference in the comparative case study.
Comparative Politics, 30, 127-145.
Drucker, P. F. (1986). The changed world economy. In P. F. Drucker (Ed.), The
frontiers of management (pp. 21-49). New York: Dutton.
Eckstein, H. (1975). Case study and theory in political science. In F. I.
Greenstein & N. W. Polsby (Eds.), Strategies of inquiry (pp. 79-137). Reading,
MA: Addison-Wesley.
Eilbert, K. W., & Lafronza, V. (2005). Working together for community health—
a model and case studies. Evaluation and Program Planning, 28, 185-199.
Eisenhardt, K. M. (1989). Building theories from case study research. Academy
of Management Review, 14, 532-550.
Elmore, R. F., Abelmann, C. H., & Fuhrman, S. H. (1997). The new
accountability in state education reform: From process to performance. In H. F.
Ladd (Ed.), Holding schools accountable (pp. 65-98). Washington, DC:
Brookings Institution.
Ericksen, J., & Dyer, L. (2004). Right from the start: Exploring the effects of
early team events on subsequent project team development and performance.
Administrative Science Quarterly, 49, 438-471.
Feagin, J. R., Orum, A. M., & Sjoberg, G. (Eds.). (1991). A case for the case
study. Chapel Hill: University of North Carolina Press.
Fetterman, D. (1989). Ethnography: Step by step. Newbury Park, CA: Sage.
Fiedler, J. (1978). Field research: A manual for logistics and management of
scientific studies in natural settings. San Francisco: Jossey-Bass.
Fielding, N. G., & Lee, R. M. (1998). Computer analysis and qualitative
research. Thousand Oaks, CA: Sage.
Flippen, C. (2001). Neighborhood transition and social organization: The White
to Hispanic case. Social Problems, 48, 299-321.
Fowler, F. J., Jr. (1988). Survey research methods (Rev. ed.). Newbury Park, CA:
Sage.
Galster, G., Tatian, P., & Accordino, J. (2006). Targeting investments for
neighborhood revitalization. Journal of the American Planning Association, 72,
457-474.
Gans, H. J. (1962). The urban villagers: Group and class in the life of Italian-
Americans. New York: Free Press.
Garvin, D.A. (2003, September-October). Making the case: Professional
education for the world of practice. Harvard Magazine, 106(1), 56-107.
George, A. L., & Bennett, A. (2004). Case studies and theory development in the
social sciences. Cambridge: MIT Press.
Gerring, J. (2004). What is a case study and what is it good for? American
Political Science Review, 98, 341-354.
Ghauri, P., & Grønhaug, K. (2002). Research methods in business studies: A
practical guide. Harlow, England: Pearson Education.
Gibbert, M., & Ruigrok, W. (2007). What passes as a rigorous case study?
Strategic Management Journal.
Gilgun, J. F. (1994). A case for case studies in social work research. Social Work,
39, 371-380.
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies
for qualitative research. Chicago: Aldine.
Gomm, R., Hammersley, M., & Foster, P. (Eds.). (2000). Case study method:
Key issues, key texts. Thousand Oaks, CA: Sage.
Gottschalk, L. (1968). Understanding history: A primer of historical method.
New York: Knopf.
Graebner, M. E., & Eisenhardt, K. M. (2004). The seller’s side of the story:
Acquisition as courtship and governance as syndicated in entrepreneurial firms.
Administrative Science Quarterly, 49, 366-403.
Gross, N., Bernstein, M., & Giacquinta, J. B. (1971). Implementing
organizational innovations: A sociological analysis of planned educational
change. New York: Basic Books.
Hamel, J. (Ed.). (1992). The case study method in sociology [Whole issue].
Current Sociology, 40.
Hammond, P. E. (1968). Sociologists at work: Essays on the craft of social
research. Garden City, NY: Doubleday.
Hanna, K. S. (2000). The paradox of participation and the hidden role of
information. Journal of the American Planning Association, 66, 398-410.
Hanna, K. S. (2005). Planning for sustainability. Journal of the American
Planning Association, 71, 27-40.
Hedrick, T., Bickman, L., & Rog, D. J. (1993). Applied research design.
Newbury Park, CA: Sage.
Herriott, R. E., & Firestone, W. A. (1983). Multisite qualitative policy research:
Optimizing description and generalizability. Educational Researcher, 12, 14-19.
Hersen, M., & Barlow, D. H. (1976). Single-case experimental designs:
Strategies for studying behavior. New York: Pergamon.
Hipp, J. R. (2007). Block, tract, and levels of aggregation: Neighborhood
structure and crime and disorder as a case in point. American Sociological
Review, 72, 659-680.
Hoaglin, D. C., Light, R. J., McPeek, B., Mosteller, F., & Stoto, M. A. (1982).
Data for decisions: Information strategies for policymakers. Cambridge, MA:
Abt Books.
Hooks, G. (1990). The rise of the Pentagon and U.S. state building: The defense
program as industrial policy. American Journal of Sociology, 96, 358-404.
Jacobs, J. (1961). The death and life of great American cities. New York:
Random House.
Jacobs, R. N. (1996). Civil society and crisis: Culture, discourse, and the Rodney
King beating. American Journal of Sociology, 101, 1238-1272.
Jadad, A. (1998). Randomised controlled trials. London: BMJ Books.
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A
research paradigm whose time has come. Educational Researcher, 33, 14-26.
Joint Committee on Standards for Educational Evaluation. (1981). Standards for
evaluations of educational programs, projects, and materials. New York:
McGraw-Hill.
Jorgensen, D. (1989). Participant observation: A methodology for human
studies. Newbury Park, CA: Sage.
Kaufman, H. (1981). The administrative behavior of federal bureau chiefs.
Washington, DC: Brookings Institution.
Keating, W. D., & Krumholz, N. (Eds.). (1999). Rebuilding urban
neighborhoods: Achievements, opportunities, and limits. Thousand Oaks, CA:
Sage.
Kelling, G. L., & Coles, C. M. (1997). Fixing broken windows: Restoring order
and reducing crime in our communities. New York: Simon & Schuster.
Kelly, A. E., & Yin, R. K. (2007). Strengthening structured abstracts for
education research: The need for claim-based structured abstracts. Educational
Researcher, 36, 133-138.
Kennedy, M. M. (1976). Generalizing from single case studies. Evaluation
Quarterly, 3, 661-678.
Kidder, L., & Judd, C. M. (1986). Research methods in social relations (5th ed.).
New York: Holt, Rinehart & Winston.
Kidder, T. (1981). The soul of a new machine. Boston: Little, Brown.
Kratochwill, T. R. (1978). Single subject research. New York: Academic Press.
Lavrakas, P. J. (1987). Telephone survey methods. Newbury Park, CA: Sage.
Lawrence-Lightfoot, S., & Davis, J. H. (1997). The art and science of
portraiture. San Francisco: Jossey-Bass.
Liebow, E. (1967). Tally’s corner. Boston: Little, Brown.
Lijphart, A. (1975). The comparable-cases strategy in comparative research.
Comparative Political Studies, 8, 158-177.
Lincoln, Y. S., & Guba, E. G. (1985). But is it rigorous? Trustworthiness and
authenticity in naturalistic evaluation. In D. D. Williams (Ed.), Naturalistic
evaluation (pp. 73-84). San Francisco: Jossey-Bass.
Lipset, S. M., Trow, M., & Coleman, J. (1956). Union democracy: The inside
politics of the International Typographical Union. New York: Free Press.
Lipsey, M. W. (1992). Meta-analysis in evaluation research: Moving from
description to explanation. In H. T. Chen & P. Rossi (Eds.), Using theory to
improve program and policy evaluations (pp. 229-241). New York: Greenwood.
Llewellyn, K. N. (1948). Case method. In E. Seligman & A. Johnson (Eds.),
Encyclopedia of the social sciences. New York: Macmillan.
Lynd, R. S., & Lynd, H. M. (1929). Middletown: A study in modern American
culture. New York: Harcourt Brace Jovanovich.
Magaziner, I. C., & Patinkin, M. (1989). The silent war: Inside the global
business battles shaping America’s future. New York: Random House.
Markus, M. L. (1983). Power, politics, and MIS implementation.
Communications of the ACM, 26, 430-444.
Marshall, C., & Rossman, G. B. (1989). Designing qualitative research.
Newbury Park, CA: Sage.
McAdams, D. R. (2000). Fighting to save our urban schools . . . and winning!
Lessons from Houston. New York: Teachers College Press.
McClintock, C. (1985). Process sampling: A method for case study research on
administrative behavior. Educational Administration Quarterly, 21, 205-222.
Merton, R. K., Fiske, M., & Kendall, P. L. (1990). The focused interview: A
manual of problems and procedures (2nd ed.). New York: Free Press.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An
expanded sourcebook. Thousand Oaks, CA: Sage.
Moore, B., Jr. (1966). Social origins of dictatorship and democracy: Lord and
peasant in the making of the modern world. Boston: Beacon.
Morris, L. L., Fitz-Gibbon, C. T., & Freeman, M. E. (1987). How to
communicate evaluation findings. Beverly Hills, CA: Sage.
Mosteller, F., & Wallace, D. L. (1984). Applied Bayesian and classical
inference: The case of “The Federalist” papers (2nd ed.). New York: Springer
Verlag.
Mulroy, E. A., & Lauber, H. (2004). A user-friendly approach to program
evaluation and effective community interventions for families at risk of
homelessness. Social Work, 49, 573-586.
Murphy, J. T. (1980). Getting the facts: A fieldwork guide for evaluators and
policy analysts. Santa Monica, CA: Goodyear.
Nachmias, D., & Nachmias, C. (1992). Research methods in the social sciences.
New York: St. Martin’s.
Naumes, W., & Naumes, M. J. (1999). The art & craft of case writing. Thousand
Oaks, CA: Sage.
Nesman, T. M., Batsche, C., & Hernandez, M. (2007). Theory-based evaluation
of a comprehensive Latino education initiative: An interactive evaluation
approach. Evaluation and Program Planning, 30, 267-281.
Neuman, S. B., & Celano, D. (2001). Access to print in low-income and middle-
income communities: An ecological study of four neighborhoods. Reading
Research Quarterly, 36, 8-26.
Neustadt, R. E., & Fineberg, H. (1983). The epidemic that never was:
Policymaking and the swine flu affair. New York: Vintage.
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.).
Thousand Oaks, CA: Sage.
Perry, J. M., & Kraemer, K. L. (1986). Research methodology in the public
administration review. Public Administration Review, 46, 215-226.
Peters, T. J., & Waterman, R. H., Jr. (1982). In search of excellence. New York:
Harper & Row.
Peterson, K. A., & Bickman, L. (1992). Using program theory in quality
assessments of children’s mental health services. In H. T. Chen & P. Rossi
(Eds.), Using theory to improve program and policy evaluations (pp. 165-176).
New York: Greenwood.
Philliber, S. G., Schwab, M. R., & Samsloss, G. (1980). Social research: Guides
to a decision-making process. Itasca, IL: Peacock.
Platt, J. (1992). “Case study” in American methodological thought. Current
Sociology, 40, 17-48.
Pluye, P., Potvin, L., Denis, J.-L., Pelletier, J., & Mannoni, C. (2005). Program
sustainability begins with the first events. Evaluation and Program Planning, 28,
123-137.
Pressman, J. L., & Wildavsky, A. (1973). Implementation: How great
expectations in Washington are dashed in Oakland. Berkeley: University of
California Press.
Ragin, C. C., & Becker, H. S. (Eds.). (1992). What is a case? Exploring the
foundations of social inquiry. New York: Cambridge University Press.
Raizen, S. A., & Britton, E. D. (Eds.). (1997). Bold ventures (3 vols.). Dordrecht,
The Netherlands: Kluwer Academic.
Randolph, J. J., & Eronen, P. J. (2007). Developing the Learner Door: A case
study in youth participatory program planning. Evaluation and Program
Planning, 30, 55-65.
Redman, E. (1973). The dance of legislation. New York: Simon & Schuster.
Richard, L., Lehoux, P., Breton, É., Denis, J.-L., Labrie, L., & Léonard, C.
(2004). Implementing the ecological approach in tobacco control programs:
Results of a case study. Evaluation and Program Planning, 27, 409-421.
Rog, D. J., & Huebner, R. B. (1992). Using research and theory in developing
innovative programs for homeless individuals. In H. T. Chen & P. Rossi (Eds.),
Using theory to improve program and policy evaluations (pp. 129-144). New
York: Greenwood.
Rogers, E. M., & Larsen, J. K. (1984). Silicon Valley fever: Growth of high-
technology culture. New York: Basic Books.
Rosenbaum, D. P. (Ed.). (1986). Community crime prevention: Does it work?
Thousand Oaks, CA: Sage.
Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York: Springer.
Rosenthal, R. (1966). Experimenter effects in behavioral research. New York:
Appleton-Century-Crofts.
Rubin, A., & Babbie, E. (1993). Research methods for social work. Pacific
Grove, CA: Brooks/Cole.
Rubin, H. J., & Rubin, I. S. (1995). Qualitative interviewing: The art of hearing
data. Thousand Oaks, CA: Sage.
Schatzman, L., & Strauss, A. (1973). Field research. Englewood Cliffs, NJ:
Prentice Hall.
Schein, E. (2003). DEC is dead, long live DEC: Lessons on innovation,
technology, and the business gene. San Francisco: Berrett-Koehler.
Schorr, L. B. (1997). Common purpose: Strengthening families and
neighborhoods to rebuild America. New York: Anchor.
Schramm, W. (1971, December). Notes on case studies of instructional media
projects. Working paper for the Academy for Educational Development,
Washington, DC.
Selznick, P. (1980). TVA and the grass roots: A study of politics and
organization. Berkeley: University of California Press. (Original work published
1949)
Shavelson, R., & Townes, L. (Eds.). (2002). Scientific research in education.
Washington, DC: National Academy Press.
Sidowski, J. B. (Ed.). (1966). Experimental methods and instrumentation in
psychology. New York: Holt, Rinehart & Winston.
Silverman, D. (2000). Doing qualitative research: A practical handbook.
Thousand Oaks, CA: Sage.
Spilerman, S. (1971). The causes of racial disturbances: Tests of an explanation.
American Sociological Review, 36, 427-442.
Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.
Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin & Y. S. Lincoln
(Eds.), The Sage handbook of qualitative research (3rd ed., pp. 443-466).
Thousand Oaks, CA: Sage.
Stake, R. E. (2006). Multiple case study analysis. New York: Guilford.
Standerfer, N. R., & Rider, J. (1983). The politics of automating a planning
office. Planning, 49, 18-21.
Stein, H. (1952). Case method and the analysis of public administration. In H.
Stein (Ed.), Public administration and policy development (pp. xx-xxx). New
York: Harcourt Brace Jovanovich.
Stoecker, R. (1991). Evaluating and rethinking the case study. The Sociological
Review, 39, 88-112.
Sudman, S., & Bradburn, N. M. (1982). Asking questions: A practical guide to
questionnaire design. San Francisco: Jossey-Bass.
Supovitz, J. A., & Taylor, B.S. (2005). Systemic education evaluation:
Evaluating the impact of systemwide reform in education. American Journal of
Evaluation, 26, 204-230.
Sutton, R. I., & Staw, B. M. (1995). What theory is not. Administrative Science
Quarterly, 40, 371-384.
Szanton, P. (1981). Not well advised. New York: Russell Sage Foundation and
The Ford Foundation.
Thacher, D. (2006). The normative case study. American Journal of Sociology,
111, 1631-1676.
Towl, A. R. (1969). To study administrations by cases. Boston: Harvard
University Business School.
Trochim, W. (1989). Outcome pattern matching and program theory. Evaluation
and Program Planning, 12, 355-366.
U.S. Government Accountability Office, Program Evaluation and Methodology
Division. (1990). Case study evaluations. Washington, DC: Government Printing
Office.
U.S. National Commission on Neighborhoods. (1979). People, building
neighborhoods. Washington, DC: Government Printing Office.
U.S. Office of Technology Assessment. (1980-1981). The implications of cost-
effectiveness analysis of medical technology: Case studies of medical
technologies. Washington, DC: Government Printing Office.
Van Maanen, J. (1988). Tales of the field: On writing ethnography. Chicago:
University of Chicago Press.
Veerman, J. W., & van Yperen, T. A. (2007). Degrees of freedom and degrees of
certainty: A developmental model for the establishment of evidence-based youth
care. Evaluation and Program Planning, 30, 212-221.
Voelpel, S., Leibold, M., Tekie, E., & von Krogh, G. (2005). Escaping the red
queen effect in competitive strategy: Sense-testing business models. European
Management Journal, 23, 37-49.
Warner, W. L., & Lunt, P. S. (1941). The social life of a modern community. New
Haven, CT: Yale University Press.
Wax, R. (1971). Doing field work. Chicago: University of Chicago Press.
Webb, E., Campbell, D. T., Schwartz, R. D., Sechrest, L., & Grove, J. B. (1981).
Nonreactive measures in the social sciences (2nd ed.). Boston: Houghton
Mifflin.
Wholey, J. (1979). Evaluation: Performance and promise. Washington, DC: The
Urban Institute.
Whyte, W. F. (1955). Street corner society: The social structure of an Italian
slum. Chicago: University of Chicago Press. (Original work published 1943)
Wilford, J. N. (1992). The mysterious history of Columbus. New York: Vintage.
Windsor, D., & Greanias, G. (1983). The public policy and management
program for case/course development. Public Administration Review, 26, 370-
378.
Wolcott, H. F. (1990). Writing up qualitative research. Newbury Park, CA: Sage.
Yin, R. K. (1970). Face recognition by brain-injured patients: A dissociable
ability? Neuropsychologia, 8, 395-402.
Yin, R. K. (1978). Face perception: A review of experiments with infants,
normal adults, and brain-injured persons. In R. Held, H. W. Leibowitz, & H.-L.
Teuber (Eds.), Handbook of sensory physiology: Vol. 8. Perception (pp. 593-
608). New York: Springer-Verlag.
Yin, R. K. (1980). Creeping federalism: The federal impact on the structure and
function of local government. In N. J. Glickman (Ed.), The urban impacts of
federal policies (pp. 595-618). Baltimore: Johns Hopkins University Press.
Yin, R. K. (1981a). The case study as a serious research strategy. Knowledge:
Creation, Diffusion, Utilization, 3, 97-114.
Yin, R. K. (1981b). The case study crisis: Some answers. Administrative Science
Quarterly, 26, 58-65.
Yin, R. K. (1981c). Life histories of innovations: How new practices become
routinized. Public Administration Review, 41, 21-28.
Yin, R. K. (1982a). Conserving America’s neighborhoods. New York: Plenum.
Yin, R. K. (1982b). Studying the implementation of public programs. In W.
Williams, R. F. Elmore, J. S. Hall, R. Jung, M. Kirst, S. A. MacManus, et al.
(Eds.), Studying implementation: Methodological and administrative issues (pp.
36-72). Chatham, NJ: Chatham House.
Yin, R. K. (1986). Community crime prevention: A synthesis of eleven
evaluations. In D. P. Rosenbaum (Ed.), Community crime prevention: Does it
work? (pp. 294-308). Thousand Oaks, CA: Sage.
Yin, R. K. (1994a). Discovering the future of the case study method in
evaluation research. Evaluation Practice, 15, 283-290.
Yin, R. K. (1994b). Evaluation: A singular craft. In C. Reichardt & S. Rallis
(Eds.), New directions in program evaluation (pp. 71-84). San Francisco: Jossey-
Bass.
Yin, R. K. (1997). Case study evaluations: A decade of progress? New
Directions for Evaluation, 76, 69-78.
Yin, R. K. (1999). Enhancing the quality of case studies in health services
research. Health Services Research, 34, 1209-1224.
Yin, R. K. (2000). Rival explanations as an alternative to “reforms as
experiments.” In L. Bickman (Ed.), Validity & social experimentation: Donald
Campbell’s legacy (pp. 239-266). Thousand Oaks, CA: Sage.
Yin, R. K. (2003). Applications of case study research (2nd ed.). Thousand Oaks,
CA: Sage.
Yin, R. K. (Ed.). (2004). The case study anthology. Thousand Oaks, CA: Sage.
Yin, R. K. (Ed.). (2005). Introducing the world of education: A case study
reader. Thousand Oaks, CA: Sage.
Yin, R. K. (2006a). Case study methods. In J. Green, G. Camilli, & P. Elmore
(Eds.), Handbook of complementary methods in education research (3rd ed., pp.
111-122). Washington, DC: American Educational Research Association.
Yin, R. K. (2006b). Mixed methods research: Are the methods genuinely
integrated or merely parallel? Research in the Schools, 13, 41-47.
Yin, R. K., & Davis, D. (2006). State-level education reform: Putting all the
pieces together. In K. Wong & S. Rutledge (Eds.), Systemwide efforts to improve
student achievement (pp. 1-33). Greenwich, CT: Information Age Publishing.
Yin, R. K., & Davis, D. (2007). Adding new dimensions to case study
evaluations: The case of evaluating comprehensive reforms. In G. Julnes & D. J.
Rog (Eds.), Informing federal policies for evaluation methodology (New
Directions in Program Evaluation, No. 113, pp. 75-93). San Francisco: Jossey-
Bass.
Yin, R. K., & Oldsman, E. (1995). Logic model for evaluating changes in
manufacturing firms. Unpublished paper prepared for the National Institute of
Standards and Technology, U.S. Department of Commerce, Gaithersburg, MD.
Yin, R. K., Schmidt, R. J., & Besag, F. (2006). Aggregating student achievement
trends across states with different tests: Using standardized slopes as effect sizes.
Peabody Journal of Education, 81(2), 47-61.
Zigler, E., & Muenchow, S. (1992). Head Start: The inside story of America’s
most successful educational experiment. New York: Basic Books.
 Author Index


Abelmann. H.

Accordino, J.
Agranoff, R.

Alkin, M.
Allison, G. T.


Babbie, E.

Barlow. H.
Barzun, J.

Basu, O. N.
Batsche.

Bebring, P. B.
Becker, H. S.

Benbasat.
Bennett, A.

Berends, M.
Berman, P.

Bernstein, M.
Besag, F.

Bickman.
Blau, P.

Bonoma, T. V.
Boruch, R.

Bouchard, R.
Boyatzis, R. E.

Bradburn, N. M.
 Bradshaw, T. K.

Brinton.
Britton, E. D.

Bryk, A. S.


Campbell. T.

Campbell, J. P.
Carr, P. J.

Caulley. N.
Chaskin, R. J.

Cochran, W. G.
Coleman, J.

Coles. M.
Cook, T. D.

Cooper, H. M.
Corbin, J.

Cox, G. M.
Crabtree, B. F.

Crane, J.
Creswell, J. W.

Crewe, K.
Cronbach. J.


Dabbs, J. M., Jr.

Daft, R. L.
Daillak, R.

Datta.
Davis.

Denis, J.-L.
Denzin, N. K.

Derthick, M.
Dion.

Dirsmith, M. W.
Dowdy.

Drucker, P.
Dyer.


Easton, J. Q.

Eckstein, J.
Eilbert, K. W.

Einsenhardt, K. M.
Elmore, R. F.

Ericksen, J.
Eronen, P. J.


Feagin, J. R.

Fetterman.
Fielder, J.

Fielding, N. G.
Fineberg, H.

Firestone, W. A.
Fisher, R. A.

Fiske, M.
Fitz-Gibbon. T.

Flippen.
Foley, E.

Fowler, F. J., Jr.
Freeman, M. E.

Fuhrman, S. H.


Galster, G.

Gans, H.
Garet, M. S.

Garvin. A.
George, A. L.

Gerring, J.
Ghauri, P.

Giacquinta, J. B.
Gibbert, M.

Gilgun, J. F.
Glaser, B.

Goldstein. K.
Gottschalk.

Graebner, M. E.
Graff, H.

Greanias, G.
Grönhaug, K.

Gross, N.
Grove, J. B.

Guba, E. G.
 Gupta, P. P.


Hamel, J.

Hammond, P. E.
Hanna, K. S.

Hedges. V.
Hedrick, T.

Hernandez, M.
Herriott, R. E.

Hersen, M.
Hipp, J. R.

Hoaglin. C.
Hooks, G.

Huberman, A. M.
Huebner, R. B.

Hulin. L.


Jacobs, J.

Jacobs, R. N.
Jadad, A.

Johnson, R. B.
Jorgensen.

Judd. M.


Kaufman, H.

Keating, W. D.
Kelling, G. L.

Kelly, A. E.
Kendall.

Kennedy, M. M.
Kerbow.

Kidder.
Kidder, T.

Kraemer, K. L.
Kratochwill, T. R.

Krumholz, N.


Lafronza.

Larsen, J. K.
Lauber, H.

Lavrakas, P. J.
Lee, R. M.

Leibold, M.
Liebow, E.

Light, R. J.
Lijphart, A.

Lincoln, Y. S.
Lipset, S. M.

Lipsey, M. W.
Llewellyn, K. N.

Lunt, P. S.
Lynd, H.

Lynd, R.


McAdams.

McClintock.
McLaughlin, M.

McPeek, B.
Magaziner. C.

Magnuson, W. G.
Mannoni.

Markus, M. L.
Marshall.

Mead, M.
Merton, R. K.

Miles, M. B.
Miller, W. L.

Moore, B.
Morris. L.

Mosteller, F.
Muenchow, S.

Mulroy, E. A.
Murphy, J. T.


Nachmias.

Nachmias.
Naumes, M. J.

Naumes, W.
Nesman, T. M.

Neustadt, R. E.


Onwuegbuzie, A. J.

Orum, A. M.


Patinkin, M.

Patton, M. Q.
Payne, M. R.

Pelletier, J.
Perry, J. M.

Peters, T. J.
Peterson, K. A.

Philliber, S. G.
Platt, J.

Pluye, P.
Potvin.

Pressman, J. L.


Radin, B. A.

Ragin. C.
Raizen, S. A.

Randolph, J. J.
Redman, E.

Richard.
Rider, J.

Rog. J.
Rogers, E. M.

Rollow, S.
Rosenbaum.

Rosenbaum, P. R.
Rosenthal, R.

Rossman, G. B.
Rubin, A.

Rubin, H. J.
Rubin. S.

Ruigrok, W.


Samsloss, G.

Schatzman.
Schein, E.

Schmidt, R. J.
Schorr.

Schramm, W.
Schwa [box], M. R.

Schwartz, R. D.
Sechrest.

Selznick, P.
Shavelson, R.

Sidowski, J. B.
Silverman.

Sjoberg, G.
Standerfer, N. R.

Stanley, J.
Staw, B. M.

Stein, H.
Stoecker, R.

Stoto, M. A.
 Strauss, A.

Sudman, S.
Supovitz, J. A.

Sutton, R. I.
Szanton, P.


Tatian, P.

Taylor, B. S.
Towl, A. R.

Townes.
Trow, M.


Van Maanen, J.

van Yperen, T. A.
Veerman, J. W.

Voelpel, S.
von Krogh, G.


Warner, W. L.

Waterman, R. H., Jr.
Wax, R.

Web [box], E.
White, P.

Wholey, J.
Whyte, W. F.

Wildavsky, A.
Wilford, J. N.

Windsor.
Wolcott, H. F.


Yin, R. K.


Zelikow, P.

Zigler, E.

 Subject Index


The Administrative Behavior of Federal Bureau Chiefs (Kaufman)
Alternative perspectives
example of adding
exemplary case study inclusion of
See also Rival theories
The Anatomy of a Revolution (Brinton)
Anonymous case identities
Archival analysis method
Archival records
Atlas


Bias avoidance
Bibliography (case study report)
Bold Ventures (Raizen & Britton)


CAQDAS
Case studies
defining an exemplary
pilot
preparation and training for specific
protocol for
See also Case study method; Research design
Case study database
case study documents as part of
case study notes as part of
creating a
exercise on practicing development of
narratives as part of
reliability through use of
tabular materials as part of
Case study documents
See also Documentation
Case study investigators
asking good questions
avoiding bias
being a good “listener,”
case study protocol followed by
desired skills of
exercising adaptiveness and flexibility
preparation and training of
screening the candidate “cases,”
understanding the issues being studied
See also Evidence
Case study method
abstract overview of
compared to other research methods
definition as research method

 as linear and iterative process
relevant situations for
things to understand about
traditional prejudices against
variations and applications of
See also Case studies; Social science research methods
Case study notes
Case study preparation
deciding problems to be addressed
human subjects protection
protocol development and review
screening candidate “cases,”
seminar training as
Case study propositions
data analysis based on theoretical
design linking data to
example of exploratory case study
as research design component
Case study protocol
case study questions
description and purpose of
detailed and illustrative protocol question
development and review of
exercise on developing a
field procedures
guide for case study report
letter of introduction
overview of the case study project
reliability through use of
table of contents of
Case study question exercises
defining a case study question
identifying when other research methods are used
Case study questions
asking good
detailed and illustrative protocol
 developing substantial
general orientation of
levels of
protocol on
as research design component
types of
unit of data collection versus unit of analysis
See also Data collection
Case study report composition
chronological structures
comparative structures
issues to consider for
linear-analytic structures
summary of six structures for
suspense structures
theory-building structures
unsequenced structures
Case study report procedures
real versus anonymous case identities
reviewing draft case study
when and how to start composing
Case study report sections
bibliography of
methodological section of
preliminary literature review section of
Case study reports
audience and required tasks of
as communication device
engaging or seductive writing of
examples of
exercise for reducing barriers to composing
on field observations
formats for written
illustrative structures for composition used in
issues related to
using metaphor in
 as part of mixed method studies
from pilot cases
procedures in doing
protocol guiding the
qualities of an exemplary
sections of
tip on completing the
See also Findings
Case study team
Case study tips
completing the case study report
how to know when to use it
how to select cases for case study
readiness to start collecting data
starting the data analysis process
time and effort of data collection
Case study training
preparatory readings for
reviewing case study tools and methods used
as seminar experience
Case study vignettes
alternative perspectives
analytic quality
case studies containing multiple “cases,”
case study database use of narratives
case study report examples
cross-case analysis
data collection examples
defining unit of analysis
Essence of Decision: (Allison & Zelikow)
explanation building
“exploration” analogy for exploratory case study
field research logistics (1924-1925)
flexibility in designing case study
generalizing case studies to theory
multiple-case study examples
 pattern matching examples
quantifying descriptive elements
reviewing case study examples
single-case study examples
Street Corner Society (Whyte)
The Swine Flu Affair: (Neustadt & Fineberg)
testing logic model
time-series analysis examples
Chain of evidence
data collection and
exercise on establishing
increasing construct validity/reliability using
maintaining a
“The Changed World Economy” (Drucker)
Chronological events technique
Chronological structures
Closed research designs
Comparative case method
Comparative structures
Complex time-series analysis
“The Complexity of Joint Action” (Pressman & Wildavsky)
Concepts
example of “neighboring,”
examples of more or less concrete
See also Unit of analysis
Confidentiality issues
Construct validity
data triangulation to address
research design quality and
reviewing case study draft to increase
“Cornerville” case study
COSMOS Corporation
Critical case rationale
Cross-case synthesis
COSMOS Corporation use of
data analysis using
 of HIV/AIDS Prevention study
Hooks’ testing policy-oriented theory using
Rosenbaum’s program evaluations using
Cuban Missile Crisis case study
The Dance of Legislation (Redman)


Dashed-line feedback loop
Data
using both qualitative and quantitative
embedded single-case design
readiness to start collecting
report section description of the
research design linking propositions to
See also Evidence
Data analysis
computer-assisted tools used for
exercise on analyzing the
five techniques for
four general strategies for
need for analytic strategy for
pressing for a high-quality
tip on starting the process of
See also Evidence
Data analysis strategies
using both qualitative and quantitative data
developing a case description
examining rival explanations
exercise on creating general
relying on theoretical propositions
Data analysis techniques
cross-case synthesis
explanation building
logic models
pattern matching
time-series analysis
Data collection
ensuring quality control during
ethnography method of

 participant-observation
protocol on
textbook resources on
three principles of
tips on time and effort to spend on
unit of analysis questions versus
See also Case study questions
Data collection principles
1: using multiple sources of evidence
2: creating a case study database
3: maintaining a chain of evidence
Data General Corporation
Data triangulation
The Death and Life of Great American Cities (Jacobs)
Decision-making theory
Descriptive theory
Digital Equipment Corporation
Direct observation
Documentation
collecting evidence using
examples of using
strengths and weaknesses of using
See also Case study documents
Duval County School District case study
The Dynamics of Bureaucracy (Blau)


Embedded designs
complex time-series analysis in single-case
multiple-case holistic
single-case holistic versus
Type 2 single-case
Type 4 multiple-case
The Epidemic That Never Was (Neustadt & Fineberg)
Essence of Decision: Explaining the
Cuban Missile Crisis (Allison &
Zelikow)
The Ethnograph
Ethnography, description of
Evidence
case study display of sufficient
case study protocol for gathering
chain of
creating converging lines of inquiry for
multiple sources of
principles for working with sources of
six sources of
textbook resources on collecting
triangulation of
See also Case study investigators; Data; Data analysis
Evidence sources
archival records
convergence and nonconvergence of multiple
direct observation
documentation
interviews
participant-observation
physical artifacts
prerequisites for using multiple

 strengths and weaknesses of each
triangulation rationale for using multiple
Exemplary case studies
“completeness” characteristic of
consideration of alternative perspectives by
engaging manner characteristic of
exercise on defining a
significance as characteristic of
sufficient evidence displayed by
Exercises
1.1: defining a case study question
1.2: identifying research questions with other research methods are used
1.3: examining case studies used for teaching purposes
1.4: finding/analyzing existing case study form literature
1.5: defining different types of case studies
2.1: defining boundaries of case study
2.2: defining unit of analysis for case study
2.3: defining criteria for judging quality of research designs
2.4: defining a case study research design
2.5: establishing rationale for multiple-case study
3.1: identifying skills for doing case studies
3.2: analyzing your own skills for doing case studies
3.3: conducting training for doing case study
3.4: developing case study protocol
3.5: selecting case for doing pilot study
4.1: using evidence
4.2: identifying specific types of evidence
4.3: seeking converging evidence
4.4: practicing the development of a database
4.5: establishing chain of evidence
5.1: using quantitative data in case study
5.2: creating general analytic strategy
5.3: constructing an explanation
5.4: analyzing time-series trends
5.5: analyzing the analytic process
6.1: reducing the barriers to composition
 6.2: defining the audience
6.3: maintaining anonymity in case studies
6.4: anticipating difficulties of review process
6.5: defining a good case study
Experiment research method
description of
situations appropriate for
Explanation building
as data analysis technique
elements of
exercise on constructing
internal validity through
iterative nature of
plausible or rival explanations and
potential problems in
in single-case and multiple-case studies
External validity
analytic techniques related to
research design quality and
Extreme case rationale


FBI’s Uniform Crime Reports
Feedback loop (dashed-line)
Field (or social) experiment
Field research
case study protocol on
combining with other types of evidence
combining personal experience with
example of logistics (1924-1925)
reporting field observations and
Findings
criteria for interpreting
integrating case study and survey evidence in
See also Case study reports
Flexible research design
Focused interview
Formal survey


Grounded theory strategies
Group theories


Harm issue
Head Start program
History research method
Holistic designs
multiple-case embedded versus
single-case embedded versus
Type 1 single-case
Type 3 multiple-case
How questions
Human subjects protection
HyperRESEARCH


Illustrative theory
Implementation: How Great
Expectations in Washington
Are Dashed in Oakland
(Pressman & Wildavsky),
Implementing Organizational
Innovations (Gross, Bernstein, &
Giacquinta)
In Search of Excellence (Peters & Waterman)
In-depth interview
Individual theories
Individual-level logic models
Inferences
detective role in making
Levels One and Two
Informed consent
Institutional Review Board (IRB)
Internal validity
analytic techniques related to
research design quality and
Interviews
collecting data using
strengths and weaknesses of
three types of
Joint Committee on Standards for Educational Evaluation


Letter of introduction
Level One inference
Level Two inference
Linear-analytic structures
Listening skills
Literal replication logic
Logic models
alternative configuration for organizational-level
as data analysis technique
firm or organizational-level
individual-level
internal validity through use of
See also Replication logic
Longitudinal case rationale


Methodological section (case study report)
Middletown (Lynd & Lynd)
Mixed methods designs
case study reports as part of
description of
Multiple sources of evidence
Multiple-case designs
establishing rationale for
examples of two-case studies
explanation building in
holistic versus embedded
rationale for
replication logic used in
report format for
single-case versus
Type 3 holistic
Type 4 embedded
See also Research design


Narratives
case study database
question-and-answer report format without
Neighborhood revitalization strategy case study
New Towns In-Town: Why a Federal
Program Failed (Derthick),
Not Well Advised (Szanton)
NVivo


“On the Methods Used in This Study” (Gans)
Oral history
Organizational theories
Organizational-level logic models
alternative configuration for
description and examples of


Participant-observation method
emergence of
example of neighborhood study using
strengths and weaknesses of
Pattern matching
on each of multiple outcomes
internal validity through
literal and theoretical replication logic and
nonequivalent dependent variables as a pattern
precision of
rival explanations as patterns
simpler patterns
People, Building Neighborhoods case study
Physical artifacts
Pilot case study
description of
reports from the
scope of inquiry
selection of
“The Politics of Automating a Planning Office” (Standerfer & Rider)
Preliminary literature review
Pretest
Privacy issues
Propositions. See Case study propositions
Prosopagnosia syndrome


Qualitative data
Quantitative data
Question-and-answer report format
Questions. See Case study questions


Randomized field trials
finding alternatives to
new case study emphasis on
practicality of
Real case identities
Reliability
case study database to increase
chain of evidence to increase
research design quality and
Replication logic
external validity and
literal and theoretical
multiple-case design use of
See also Logic models
Reports. See Case study reports
Representative case rationale
Research design
basic types of
components of
criteria for judging the quality of
definition of
general approach to
issues to consider when selecting
maintaining flexibility during process of
role of theory in
selecting
See also Case studies; Multiple-case designs; Single-case designs
Research design components
criteria for interpreting a study’s findings
listed
logic linking data to propositions
study propositions

 study questions
unit of analysis
Research design quality
construct validity for
external validity for
internal validity for
reliability for
validity testing for
Research design selection
closed or flexible designs
mixing case studies with other methods
single-or multiple-case designs
Research design types
illustration of basic
mixed
“two-tail,”
Type 1 single-case (holistic
Type 2 single-case (embedded)
Type 3 multiple-case (holistic)
Type 4 multiple-case (embedded)
Revelatory case rationale
Reviewing draft case study
Rival theories
data analysis strategy of examining
explanation building using
internal validity through explanations of
pattern matching
research design role of
See also Alternative perspectives; Theory
“Rodney King crisis,”


Sample size
Samsung
Screening candidate “cases,”
The Silent War: Inside the Global
Business Battles Shaping
America’s Future (Magaziner &
Patinkin)
Silicon Valley Fever (Rogers & Larsen)
Simple time-series analysis
Single-case designs
complex time-series analysis in embedded
explanation building in
multiple-case versus
rationales for
using time-series analysis in
Type 1 holistic
Type 2 embedded
See also Research design
Social Origins of Dictatorship and Democracy (Moore)
Social science research methods
exploratory, descriptive, and explanatory purposes of
hierarchical view on
relevant situations for different
when to use each specific
See also Case study method
Societal theories
The Soul of a New Machine (Kidder)
Statistical generalization
Street Corner Society (Whyte)
Study questions. See Case study questions
Survey research method
Suspense structures

The Swine Flu Affair: Decision-Making on a Slippery Disease (Neustadt &
Fineberg)


Tabular materials
Tally’s Corner (Liebow)
Theoretical replication logic
Theory
data analysis based on
descriptive
design work and role of
development of
examples on case studies generalized to
generalizing from case study to
grounded theory strategies
illustrative
making inferences using
See also Rival theories
Theory-building structures
ti (sofware)
Time-series analysis
chronologies of
complex
description of
exercise on
simple
summary conditions for
Tips. See Case study tips
Training. See Case study training
Triangulation of evidence
four types of
rationale for
TVA and the Grass Roots (Selznick)
“Two-tail” design
Type 1 single-case (holistic) design
Type 2 single-case (embedded) design

Type 3 multiple-case (holistic) design
Type 4 multiple-case (embedded) design
Typical case rationale


Uniform Crime Reports (FBI)
Union Democracy (Lipset, Trow, & Coleman)
Unique case rationale
Unit of analysis
case study definition and choice of
complex time-series analysis in single-case with embedded
data collection questions versus
examples of more or less concrete topics
exercise on defining case study
as research design component
See also Concepts
The Urban Villagers (Gans)
U.S. census
U.S. Department of Education
U.S. Government Accountability Office
U.S. National Commission on Neighborhoods
U.S. Office of Technology Assessment


Validity
construct
external
internal
overview of
reliability and
Vignettes. See Case study vignettes
Vulnerable groups


What questions
Where questions
Who questions
Why questions
“Windshield survey,”


Yankee City (Warner & Lunt)

 About the Author

Robert K. Yin was born and raised on the upper west side of Manhattan. He
does not remember encountering case studies then, or at Harvard College where
he received his B.A. (magna cum laude) in history, or even at M.I.T., where he
received his Ph.D. by doing laboratory experiments in brain and cognitive
sciences. Nor, to his knowledge, were case studies among the major works
published by The Commercial Press, founded by his grandfather in 1897 (the
publisher’s main line of books consisted of textbooks, journals, and reference
works—a familiar sounding niche). The Commercial Press has been China’s
largest publishing house and has survived to this day, despite two major regime
changes in China (both were called revolutions).
Dr. Yin’s exposure to case studies occurred during his first few years as an
analyst at the New York City-Rand Institute, which conducted applied studies to
improve the quality of then-declining urban living conditions, including life in
city neighborhoods, citizen participation, and the provision of urban services.
The rest, as they say, is history. Thus, for the past thirty years Dr. Yin has
completed numerous qualitative (field-based) and quantitative (statistical)
studies, also serving for many years as the President of COSMOS Corporation.
He has produced another case study book (Applications of Case Study Research)
and two readers containing lengthy excerpts from exemplary case studies (The
Case Study Anthology and Introducing the World of Education). In addition, he
has taught courses related to case study topics at the Department of Urban
Studies and Planning (M.I.T.), the School for International Service (The
American University), and multi-day seminars in the United States and abroad.
During this time, Dr. Yin has published widely on education and urban topics,
also contributing to methodological advances. Among these have been the
various editions of the present book, whose first edition was published in 1984
and which has now been translated into seven languages. The first translation
was into Japanese, followed by multiple Portuguese and Chinese translations of
multiple editions of the book. Korean, Italian, Romanian, and Swedish have
been the most recent translations. Dr. Yin hopes that readers will find this Fourth
Edition to be an improvement over previous editions as well as a book presented
in legible English.