BUSINESS RESEARCH METHODS

Contents
Ch.1) Introduction to Research .............................................................................................. 2
Ch.2) Literature Review ........................................................................................................ 10
Ch.3) Problem definition and hypothesis formulation .......................................................... 15
Ch.4) Research Design.......................................................................................................... 21
Ch.5) Measurement, Scaling and Sampling .......................................................................... 27
Ch.6) Data Collection and Analysis ....................................................................................... 42
Ch.7) Writing Proposals and Project Reports ........................................................................ 59
Ch.1) Introduction to Research

Research:
The etymological meaning of word ‘research’ is ‘to search again and again’ which comprises
of two words “re” and “search” where “re” means ‘again’. The Advanced Learner’s
Dictionary of Current English defines research as “A new facts in any branch of knowledge
through careful investigation or inquiry is research.”
➢ the systematic investigation into and study of materials and sources in order to establish
facts and reach new conclusions.
➢ the creation of new knowledge and/or the use of existing knowledge in a new and
creative way so as to generate new concepts, methodologies and understandings.
Motivation of Research:
• To get certificate of degree along with its significant benefits
• To resolve the practical problems people initiate research
• To do creative work and get intellectual enjoyment
• To get respect, responsibility and service to the society
• Directive of government, jobs and desire to know new thing
Nature of Research:
1. Systematic in Nature:
Usually, research is the sorted out and systematic method for discovering answers to
questions. It implies that research is organized with specified steps to be taken in a
predetermined as per the well-defined set of rules.
2. Logical in Nature:
Logic in research refers that research is guided by the guidelines of logical thinking and
logical process. Good research is logical as the research is guided by the standards of
logical reasoning and the logical process of acceptance and reasoning, which are of
extraordinary incentive in completing research.
3. Empirical in Nature:
Empirical methods depend on verified observation and experience as opposed to theory
or pure research. Empirical research is related essentially to at least one part of a real
situation and manages concerned data that gives basis to external legitimacy to research
result. Therefore, it is ought to be the way toward testing a hypothesis.
4. Reductive in Nature:
Scientific research is partitioned into discovery and verification. Reductive research
depends on dividing a large problem into a few sub-problems that are solved sparely using
the bottom-up approach, inductive speculation, and additionally some other strategies for
disclosure.
5. Replicable in Research:
Replicability in research indicates the philosophy of replicating others’ work or result to
establish its validity. It is one of the fundamental characteristics of research. Scientific
study, therefore, ought to be replicable.
Types of Research:
On the Basis of Application:
o Pure Research:
Pure research (also known as “basic” or “fundamental” research) is exploratory in nature
and is conducted without any practical end-use in mind. It is driven by gut instinct,
interest, curiosity or intuition, and simply aims to advance knowledge and to
identify/explain relationships between variables.
o Applied Research:
Applied research is a non-systematic way of finding solutions to specific research
problems or issues. These problems or issues can be on an individual, group, or societal
level. It is called “non-systematic” because it goes straight to finding solutions.
On the Basis of Objectives:
o Exploratory Research:
Exploratory research is a methodology approach that explores research questions that
have not previously been studied in depth. It is often used when the issue you're studying
is new, or the data collection process is challenging in some way.
o Descriptive Research:
Descriptive research aims to accurately and systematically describe a population,
situation or phenomenon. It can answer what, where, when and how questions, but not
why questions. A descriptive research design can use a wide variety of research methods
to investigate one or more variables.
o Correlational Research:
A correlational research design investigates relationships between variables without the
researcher controlling or manipulating any of them. A correlation reflects the strength
and/or direction of the relationship between two (or more) variables. The direction of a
correlation can be either positive or negative.
o Explanatory Research:
Explanatory research is a research method that explores why something occurs when
limited information is available. It can help you increase your understanding of a given
topic, ascertain how or why a particular phenomenon is occurring, and predict future
occurrences.
On the Basis of Information Sought:
o Quantitative Research:
o Qualitative Research:
o Mixed Methods Research:
Scientific Research:
Scientific research refers to the process of following standard rules and principles of
conducting research. It often involves establishing a cause-and-effect relationship between
different variables. All researches, whether basic or applied, involve the scientific method.
The scientific method is the way researchers go about using knowledge and evidence to
reach objective conclusions about the real world.
Scientific Research Process:
1) Identify the problem:
The first step in the process is to identify a problem or develop a research question. The
research problem may be something the agency identifies as a problem, some
knowledge or information that is needed by the agency, or the desire to identify a
recreation trend nationally.
2) Review the literature:
Now that the problem has been identified, the researcher must learn more about the
topic under investigation. To do this, the researcher must review the literature related to
the research problem. This step provides foundational knowledge about the problem
area. The review of literature also educates the researcher about what studies have been
conducted in the past, how these studies were conducted, and the conclusions in the
problem area.
3) Clarify the problem:
Many times, the initial problem identified in the first step of the process is too large or
broad in scope. In step 3 of the process, the researcher clarifies the problem and
narrows the scope of the study. This can only be done after the literature has been
reviewed. The knowledge gained through the review of literature guides the researcher
in clarifying and narrowing the research project.
4) Clearly define terms and concepts:
Terms and concepts are words or phrases used in the purpose statement of the study or
the description of the study. These items need to be specifically defined as they apply to
the study. Terms or concepts often have different definitions depending on who is
reading the study. To minimize confusion about what the terms and phrases mean, the
researcher must specifically define them for the study.
5) Define the population:
Research projects can focus on a specific group of people, facilities, park development,
employee evaluations, programs, financial status, marketing efforts, or the integration of
technology into the operations. For example, if a researcher wants to examine a specific
group of people in the community, the study could examine a specific age group, males
or females, people living in a specific geographic area, or a specific ethnic group. Literally
thousands of options are available to the researcher to specifically identify the group to
study. The research problem and the purpose of the study assist the researcher in
identifying the group to involve in the study. In research terms, the group to involve in
the study is always called the population. Defining the population assists the researcher
in several ways. First, it narrows the scope of the study from a very large population to
one that is manageable. Second, the population identifies the group that the
researcher's efforts will be focused on within the study. This helps ensure that the
researcher stays on the right path during the study. Finally, by defining the population,
the researcher identifies the group that the results will apply to at the conclusion of the
study.
6) Develop the instrumentation plan:
The plan for the study is referred to as the instrumentation plan. The instrumentation
plan serves as the road map for the entire study, specifying who will participate in the
study; how, when, and where data will be collected; and the content of the program.
This plan is composed of numerous decisions and considerations.
7) Collect data:
Once the instrumentation plan is completed, the actual study begins with the collection
of data. The collection of data is a critical step in providing the information needed to
answer the research question. Every study includes the collection of some type of data—
whether it is from the literature or from subjects—to answer the research question. Data
can be collected in the form of words on a survey, with a questionnaire, through
observations, or from the literature.
8) Analyze the data:
All the time, effort, and resources dedicated to steps 1 through 7 of the research process
culminate in this final step. The researcher finally has data to analyze so that the
research question can be answered. In the instrumentation plan, the researcher
specified how the data will be analyzed. The researcher now analyzes the data according
to the plan. The results of this analysis are then reviewed and summarized in a manner
directly related to the research questions.
Characteristics of Scientific Research:
1) Purposive:
With a clear goal in mind, research should begin. Research is meaningless if it is
undertaken without any goals. The process of choosing a sample, gathering data, and
interpreting that data are all governed by the goal of the research. Therefore, you can
prevent mistakes and reach a true conclusion if you are able to create a clear and
practical target.
2) Testability:
Researchers develop hypotheses logically in scientific research. A hypothesis is an
estimation of the relationship between variables. Such a relationship is established
logically. Relationships are tested using various statistical and mathematical tools. Thus,
testability is one of the important qualities of scientific research.
3) Replicability:
The result gained is considered reproducible if the same result is discovered when the
investigation is done once more. Such excellence is needed for scientific research. Our
conclusions or findings are deemed untrustworthy if observations cannot be replicated.
The study ought should be repeatable as a result.
4) Objective:
It should make it possible for us to impartially and accurately categorize facts. The
conclusions drawn from our data should not be based on our subjective or emotional
values but rather on the facts established by actual data. Non-data-based decisions lead
the organization astray and encourage performance decline.
5) Rigorous:
Rigorous research is defined as using a strong theoretical foundation and methodological
framework. It refers to the carefulness and degree of exactness in research investigation.
6) Precision:
Precision reflects the degree of exactness of the results. The conclusion drawn from the
research must be nearer to the actual result as it is based on evidence and objectivity.
7) Generalizability:
It speaks to the extent to which the research’s conclusions can be applied to other
organizations as well. The greater the scope of applicability of the research’s answers,
the more beneficial the research is to users.
Emerging Paradigms in Research:
Emerging paradigms seek to promote and encourage the serious work being put into
researching this fascinating spectrum of study by both scientific and non-scientists alike.
While the scientific community has much to offer in the methods employed to study the
phenomenon of consciousness, we cannot deny that understanding consciousness is within
each person’s grasp through our own direct experience, and that important experiences and
insights can come from only one of us truth has a strange way of presenting itself. Emerging
paradigms is also one way of delving into the area of research that one involved, with the
intention of furthering his/her own understanding of mind and self.
Some of the emerging paradigms in different social science research are:
1) Design-based Research:
Design-based Researchers’ innovations embody specific theoretical claims about
teaching and learning, and help us understand the relationship among educational
theory, designed artifact, and practice. Design is central in efforts to foster learning,
create usable knowledge, and advance theories of learning and teaching in complex
settings. Design-based research also may contribute to the growth of human capacity for
subsequent educational reform.
2) Innovation Management:
The innovation management is the key activity for company, and the innovation synergy
mechanism and pattern behavior technology element and non-technology elements
(mainly including strategy, culture, organization and institution) remain the core issues
for innovation-based organization. This new paradigm combines the insights and
coherence of the traditional innovation management view with the more relevant
portfolio innovation management, and draws on three distinct areas of research, namely,
the innovation theory of the firm, the resource-based view (RBV), as well as the
complexity theory.
3) International Entrepreneurship:
International entrepreneurship, an emergent field of study represents a particular set of
challenges for researchers who are intent on explaining its nature, its scope, its content
and its process. They further argued that international entrepreneurship is a new way of
thinking about cross-border business activity which builds on the extant of theories of
 international business, through integration with theories from the fields of strategic
management, entrepreneurship and innovation, inter alia, to explain the complexities of
enterprise in an international and sometimes global environment.
Research Approaches:
Research approach is defined as plans and the procedures for research. It is the full
extension of the steps from broad assumptions to detail methods of data collection, analysis
and interpretation. It helps researcher to understand on individuals cautiously examining
encounters occasions, and realities in social reality.
1) Quantitative Research:
Quantitative research is generally associated with the positivist paradigm. It usually
involves collecting and converting data into numerical form so that statistical calculations
can be made and conclusions drawn. In natural and social sciences, quantitative research
is the systematical empirical investigation of observable phenomena via statistical,
mathematical, or computational techniques. Mostly, positivist depend on qualitative
data. They seek facts or causes of social phenomena. The subjective state of individual is
the less concern side of quantitative approach.
2) Qualitative Research:
Qualitative research is the approach usually associated with the social constructivist
paradigm which emphasizes the socially constructed nature of reality. It is about
recording, analyzing and attempting to uncover the deeper meaning and significance of
human behavior and experience, including contradictory beliefs, behaviors and
emotions. Researchers are interested in gaining a rich and complex understanding of
people’s experience and not in obtaining information which can be generalized to other
larger groups. It uses written or spoken words.
3) Mixed Method Research:
As the term ‘mixed’ refers to the combination, this approach is the combination of both
quantitative and qualitative approaches. It is being popular worldwide due to its feature
of drawing best practices of both quantitative and qualitative approaches. The pragmatic
approach to science involves using the method which appears best suited to the
research problem and not getting caught up in philosophical debates about which is the
best approach. Pragmatic researchers therefore caught up in philosophical debates
about which is the best approach. Pragmatic researchers therefore grant themselves the
freedom to use any of the methods, techniques and procedures typically associated with
quantitate or qualitative research. They recognize that every method has its limitations
and that the different approaches can be complementary.
Business Research:
➢ Business research is an organized, systematic, data based, critical, objective, scientific
inquiry or investigation into a specific problem, undertaken with the purpose of finding
answers or solutions to it.
➢ Business research is a process of acquiring detailed information on all the areas of
business and using such information to maximize the sales and profit of the business.
Role of business research as decision making tool:
1) Problem or Opportunity Identification:
The success of any organization depends on its ability to diagnose the problem and solve
it immediately. Success also lies in identifying the opportunities and grabbing them on
time. Any delay in the problem identification and solution implementation, and the
opportunity identification and encashment become harmful to the organization. Hence,
timely action is always required. The company needs to seek reasonable scientific
information before investigating in diversification.
2) Diagnosing the Problem or Opportunity:
Organization presents these problems or opportunity scenario to the business
researchers. Business researcher actually diagnoses the problem or opportunity.
Diagnosing involves exploring the situation to have a better insight about the situation.
3) To Explore the Solution:
After identification and diagnosis of the problems, business searcher systematically
conducts research to present a solution. A theoretical model is developed with the help
of extensive literature survey. Hypotheses are formulated, sample size and sampling
procedure are determined, data are collected through a well-developed design
questionnaire, statistical analysis is executed and findings are noted.
4) Implemented Present Solution:
Business researcher conducts research in consolation with the decision matters of the
concerned organization. The findings are presented to the decision makers and he or she
analyzes these findings in the light of his or her decisions’ range. Decision makers have
also got some limitation in terms of their own constraints. A decision maker analyzes all
these constraints and takes the appropriate decision based on the solutions presented by
the business research.
5) Evaluate the Effectiveness of Decision Making:
A decision can click or it can fail miserably. The decision maker takes the decisions in an
uncertain environment and it’s after effects are examined later. After taking the decision,
its effectiveness is examined. This is sometimes referred to as the evaluation research.
Decision may go right or wrong. The company’s management systematically examines the
after effects of any decisions. This examination is also very systematic and explores all
dimensions affectively in terms of time, cost and other resources.
Types of Business Research:
1) Policy Research:
Policy research is defined as the process of conducting research on, or analysis of, a
fundamental social problem in order to provide policymakers with pragmatic, action-
oriented recommendations for alleviating the problem.
2) Action Research:
Action research also known as participatory research is learning by doing in the sense
that a group of workers identify a problem, develop resolution, implement the solution
 and then analyze the final result. It is a philosophy and methodology of research generally
applied in the social sciences.
3) Evaluating Research:
Evaluation research, also known as program evaluation, refers to research purpose
instead of a specific method. It is defined as a form of disciplined and systematic inquiry
that is carried out to arrive at an assessment or appraisal of an object, program, practice,
activity, or system with the purpose of providing information that will be of use in
decision making.
4) Basic/Pure Research:
Basic (aka fundamental or pure) research is driven by a scientist's curiosity or interest in a
scientific question. The main motivation is to expand man's knowledge, not to create or
invent something. There is no obvious commercial value to the discoveries that result
from basic research. It aims to improve scientific theories for improved understanding or
prediction of natural or other phenomena.
5) Objective Research:
Objective research can be defined as research that is unbiased or impartial. Thus, an
objective researcher (or project, report or study) is like a judge who attempts to give a fair
hearing to both sides of a legal dispute. Therefore, objective research can be defined as
unbiased or impartial, value free, reliable and factual. We also think of objective research
as value-free because research is unaffected by moral., economic, social, political and
religious values.
Ethical Consideration in Business Research:
The ethical issues in management research can be:
1. Business ethics
2. Moral standards
3. Ethical dilemma
4. Relativism
5. Idealism
Ethics refers to the principle of behavior that distinguishes as what is good, bad, right or
wrong. Hence, research ethics is especially interested in the analysis of ethical issues that
are raised when people are involved as participants in research. To be ethical, a researcher
should not do the following work:
• Discrimination
• Deception of participants
• Fabricating the data
• Put pressure to participants for selling information
• Illegal
• Dishonesty
• Violation of participants’ right
• Disrespect of social culture, norms and values
• Disrespect to the intellectual property
By: rahulkanwaredu@gmail.com
Ch.2) Literature Review

Literature: written works, especially those considered of superior or lasting artistic merit.
Literature Review:
➢ A literature review is a piece of academic writing demonstrating knowledge and
understanding of the academic literature on a specific topic placed in context.
➢ Literature review is an inclusive summary of previous researches on a topic where
scholars survey various articles, books and other sources relevant to a particular area of
research.
The purpose of a literature review is to collect relevant, timely research on your chosen
topic, and synthesize it into a cohesive summary of existing knowledge in the field. This then
prepares you for making your own argument on that topic, or for conducting your own
original research.
Steps in Literature Review:
1) Identify Your Area of Research (Question):
Identifying your research question is the initial step to any research project. Research
should use a strategy when constructing their research question. A good question will
enable you to the research for better study and meaningful research.
2) Review Discipline Style:
Different disciplines have different ways to have their own styles in writing literature
review. Review style for writing a literature review in social science may look different
than those from the biological sciences or engineering.
3) Search the Literature:
Once the review discipline is identified, the next step is to search the relevant literature.
Professor or faculty member may recommend articles to help you begin your reading of
the literature. You can then use these as a jumping off point to locate additional sources.
4) Manage your Reference:
You will need to provide a detail list of references that your professor wants to know where
you found your information. Your professor will also require that you use a specific format
(“style") for citing your references (such as APA, MLA or any relevant and prescribed one)
as a part of literature review. Hence, managing reference is an important part of literature
review.
5) Critically Analyze and Evaluate:
This step requires critically analyzing and evaluating the information by using a chosen
approach to combine and analyze studies. While reviewing, researchers must ask
themselves several questions about each book or article they include.
6) Synthesize (or Organize the Literature):
Once you have located the literature, assessed its quality, and checked it for its relevance
the next step is to organize it for a literature review. This process involves photocopying
and filling the literature. At this time, you might quickly read it, take notes on it, and
determine how it fits into overall literature- a literature map, which helps to organize it.
7) Write Literature Review:
A literature review is not only a list of summarizing one piece of literature after another
but also a piece of discursive prose. Organize the literature review into sections that
present themes or identify trends, including relevant theory. You not only try to make a
list of all the material but also synthesize and evaluate it as per the guiding concept of
your thesis or research question.
APA format of referencing:
The APA referencing style is an "author-date" style, so the citation in the text consists of the
author(s) and the year of publication given wholly or partly in round brackets. Use only the
surname of the author(s) followed by a comma and the year of publication.
For example: (Field, 2005).
Literature Search Through the Internet:
The Internet is a huge research resource that offers access to various materials on almost
any subject. Scholarly articles, books, and other information sources are more accessible
than ever because of the growth of internet databases and digital libraries. Additionally, the
Internet is vital for researcher collaboration and communication because it allows them to
exchange thoughts, information, and findings instantly. However, other people contend that
the Internet is not a trustworthy source of information since not all of the data found there
is dependable or accurate. It is crucial to assess the material attentively and keep the
sources' reliability in mind. Furthermore, there are concerns about plagiarism and how
simple it is to copy and paste text from the Internet without giving proper credit also exist.
There are security and privacy issues when using the Internet for research. As a result, it is
crucial to use critical thinking while assessing the reliability of sources discovered online.
When employing sources for research, it is equally crucial to correctly reference them to
acknowledge the original writers and guarantee that your work is appropriately credited.
There are many ways in which the Internet can aid research, some of which include:
• Providing access to vast information
• Facilitating communication and collaboration
• Enabling the use of online research tools
• Allowing for the sharing of research results
• Facilitating the publication of research
Guidelines for literature review:
1) Review University Guidelines:
First and foremost, important part is to go through your university guidelines. As
universities can have their own formats and styles for the preparation of literature review
and overall document, students must follow the instruction given in the guidelines. Paper
use, font size, space and justification, margins, page numbers, headings and sub headings,
layout of report and chapters, instruction of writing and citation is important to
understand before conducting review.
2) Decide on a Topic:
 The second step is to decide/ choose a topic of your interest. Though students can pick
any scholarly topic on their research purpose, they should decide a particular topic which
is of their interest and feasible to complete their research work. It will help them
considerably for their literature review.
3) Identify a Literature that you will Review:
The third step is to identify the literature. In this step, you have to collect and gather
papers, books and any other materials you want to review. It can be hardcopy or softcopy
receiving form the help of internet. Once you have identified and located the articles for
review, you need to collect, save and organize them in a proper way.
4) Analyze the Literature:
It is another important step here you have to overview the entire document that you
have collected for review. It can be done into three phases. First one is to overview the
article, here you have to skim the article and need to decide whether it is suitable you to
review or not. Second, you have to group the article into categories, into topic and
headings as per your plan of literature review. Third, you have to take note to understand
what type of literature is available and what their main messages are.
5) Summarize the literature:
It is an important step. Summarize means to look back to your not and prepare your
summary table and concept map. It is common form of literature review process. If
students are new in research, they can write their note as per order prescribed by their
guidelines.
6) Synthesize the literature:
The next step is the synthesis of the literature that you have reviewed. Synthesis is
defined as combining a number of different parts and ideas to come up with a new idea
or theory. Here you can plan how you will organize your findings into a unique analysis of
a picture that you have captured in your note and summarized in your literature.
7) Writing the Review:
This is the most critical section of your literature review process. While writing the review,
first identify the broad problem area; indicate why this topic being reviewed is important
and distinguishable between research findings. The most important thing to understand
here is that you must discuss other reviews avoiding jargons and sentences.
8) Developing a Coherent Essary:
This is the last and crucial part of the review. Once your write up finishes, you have to
check their logical flow, coherence and sufficiency of review. Linking the arguments in a
clear and concise way is important in literature review. Therefore, you have to check the
flow of your coherence and maintain flow of arguments.
Theoretical Framework:
➢ the structure that can hold or support a theory of a research study.
➢ diagrammatical or logical presentation of interrelated concept.
It manages your research, figures out what things you will quantify and what factual
relationship you will search for. Theoretical framework comprises of concepts and along
with their definitions and reference to important insightful literature, existing theory that is
utilized for your specific study.
Need of Theoretical Framework for Research:
1. Strengthening the study
2. Permitting the reader to evaluate them critically
3. Making basis for hypothesis and choice of research method
4. Formulating generalization
5. Helping to identify variables necessary for the study
6. Helping to limit of the study
Theory:
Theory is a set of interrelated constructs or concepts, definitions and prepositions that
present a systematic view of a phenomenon by specifying the relationship among variables,
with the purpose of predicting and explaining the phenomenon. Theories are formulated to
explain, predict, and understand phenomena and, in many cases, to challenge and extend
existing knowledge within the limits of critical bounded assumptions or predictions of
behavior. The theoretical framework is the structure that can hold or support a theory of a
research study.
Research Approaches: Induction and Deduction
Inductive reasoning involves starting from specific premises and forming a general
conclusion, while deductive reasoning involves using general premises to form a specific
conclusion.
Inductive Reasoning:
Inductive approach to research (also known as inductive reasoning) begins with specific
observations and ends with broad generalizations. Inductive reasoning is also referred to as
a "bottom-up" approach to knowing. When there is little or no existing literature on a topic,
it is general practice to perform inductive research because there is no theory to test.
Theories are proposed to be built as a result of observations or on the basis of
generalization formulated at the end of research. Hence, in inductive reasoning, we start
our activity with specific observations and measures, work to detect patterns and
regularities, formulate some tentative hypotheses that we can explore, and finally end up
developing some general conclusions or theories.
Deductive Reasoning:
Deductive reasoning works from the general to the more specific. That means, when you
conduct deductive research, you always start with a theory (the result of inductive research)
and reasoning deductively means testing these theories. In other words, this approach to
research is concerned with developing a hypothesis based on existing theory followed by
designing a research strategy to test the hypothesis. It clearly states that if there is no
theory developed yet, you cannot conduct deductive research. Since this approach works
from the more general to the more specific, sometimes this is called a "top-down"
approach.
Steps in Inductive and deductive Reasoning:
Inductive: Observation→Pattern→Tentative hypothesis→Theorization
Deductive: Theorization→Hypothesis→Observation→Confirmation
Sources of Literature:
Sources of information or evidence are often categorized as primary, secondary, or tertiary
material. These classifications are based on the originality of the material and the proximity
of the source or origin.
• Primary Sources:
These sources are records of events or evidence as they are first described or actually
happened without any interpretation or commentary. It is information that is shown for
the first time or original materials on which another research is based. Primary sources
display original thinking, report on new discoveries, or share fresh information.
Examples of Primary sources:
Theses, dissertations, scholarly journal articles (research based), some government
reports, symposia and conference proceedings, original artwork, poems, photographs,
speeches, letters, memos, personal narratives, diaries, interviews, autobiographies, and
correspondence.
• Secondary Sources:
These sources offer an analysis or restatement of primary sources. They often try to
describe or explain primary sources. They tend to be works which summarize, interpret,
reorganize, or otherwise provide an added value to a primary source.
Examples of secondary sources:
Textbooks, edited works, books and articles that interpret or review research works,
histories, biographies, literary criticism and interpretation, reviews of law and legislation,
political analyses and commentaries.
• Tertiary Sources:
These are sources that index, abstract, organize, compile, or digest other sources. Some
reference materials and textbooks are considered tertiary sources when their chief
purpose is to list, summarize or simply repackage ideas or other information. Tertiary
sources are usually not credited to a particular author.
Examples of Tertiary Sources:
Dictionaries/encyclopedias (may also be secondary), almanacs, fact books, Wikipedia,
bibliographies (may also be secondary), directories, guidebooks, manuals, handbooks,
and textbooks (may be secondary), indexing and abstracting sources.

By: rahulkanwaredu@gmail.com
Ch.3) Problem definition and hypothesis formulation

Problem:
➢ a matter or situation regarded as unwelcome or harmful and needing to be dealt with
and overcome.
➢ Problem is the obstacle in the path of satisfying our needs.
Research Problem:
➢ A research problem is a statement about an area of concern, a condition to be improved,
a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in
theory, or in practice that points to the need for meaningful understanding and
deliberate investigation.
➢ A research problem is a declaration about an area of interest, a situation to be enhanced
on a problem to be removed, or a problematic question that occurs in academic
literature, in theory or in practice, pointing to the need for fuller insight and deliberate
analysis.
➢ In general terms, the research problem is a condition that requires a solution. This is also
often referred to as a difference between current and defined state.
Steps in Problem Formulation:
1. Identifying a Broad Field of Subject Area of Interest to You:
This will help us to find an interesting topic, and one which may be of use to us in the
future it is a good idea to think about the field in which we would like to work after
graduation. This will help us to find an interesting topic, and one which may be of use to
us in the future.
2. Dissect the broad area into subareas:
In preparing this list of subareas we should also consult others who have some
knowledge of the area and the literature in our subject area. Once we have developed an
exhaustive list of the subareas from various sources, we proceed to the next stage where
we select what will become the basis of your enquiry.
3. Select what is most interest to you:
One way to decide what interests us most is to start with the process of elimination. We
should go through out list and delete all those subareas in which we are not very
interested. We need to continue until we are left with something that is manageable
considering the time available to us, our level of expertise and other resources needed to
undertake the study. Once we are confident that we have selected an issue we are
passionate about and can manage, we are ready to the next step.
4. Raise Research Question:
We should make a list of whatever questions come to our mind relating to our chosen
subarea and if we think there are too many to be manageable, go through the process of
elimination, as we did in step 3.
5. Formulation of Objectives:
 Once the research question is ready, formulate your main objectives and sub-objectives
from your questions. The main difference between objective and research questions is
the way in which they are written. Research objective begins with verbs like to examine,
to determine, to confirm etc.
6. Assess Your Objectives:
Once research objective is ready, now observe your objectives for feasibility of attaining
them via our research endeavor. That means, whether one can collect the data, analyze
them and draw the conclusion based on these objectives should be determined.
7. Double Check:
Finally, go back to all the systema and give final consideration whether you are sufficiently
interested in the study area or not. Also check whether you get ample resources to justify
it or not.
Research Question:
A research question is a question that a study or research project aims to answer. This
question often addresses an issue or a problem, which, through analysis and interpretation
of data, is answered in the study's conclusion.
Types of Research Questions:
1. Descriptive Research Questions:
Descriptive survey questions are designed to uncover a respondent's response towards a
particular question or variable. Put simply, it's the easiest way to quantify the particular
variable(s) you're interested in on a large scale. Common descriptive questions begin with
“how many?”, “What number?”, “What time?”, “What’s up?”.
2. Comparative Research Questions:
Comparative research questions help examine the difference between two or more
groups based on one or more dependable variables. The number of variables depends on
the need of market research. The question asks the respondents, “what is the difference
in” a dependent variable between two or more groups?
3. Relationship-based Research Questions:
Relationship-based research questions are also known as correlational research questions
because they seek to clearly identify the link between 2 variables. Examples of
relationship-based research questions include: What is the relationship between
purchasing power and the business site?
Hypothesis:
➢ a supposition or proposed explanation made on the basis of limited evidence as a starting
point for further investigation.
➢ Hypothesis is an assumption whose testability is to be tested on the basis of the
compatibility of its implications with empirical evidence and previous knowledge.
A hypothesis states your predictions about what your research will find. It is a
tentative answer to your research question that has not yet been tested. For some
research projects, you might have to write several hypotheses that address different
aspects of your research question.
Functions of Research Hypothesis:
1) The formulation of a hypothesis compels you to exactly specify what you want to explore
about and helps you in bringing the clarity to your study.
2) The clarification and obviousness that you need to consider in the process of hypothesis
formulation leads you only to collect the information you require by your study.
3) Hypothesis also helps you to maintain the validity of your study as it ensures to measure
what you set out to measure.
4) Construction of hypothesis also helps in strengthening the objectivity of the study.
5) Hypothesis testing makes you to confirm what is true or what is false, which, in turn,
helps you to build up theory.
6) Hence, hypothesis creates a ground for future knowledge, that is, after its verification
hypothesis emerges as a new body of knowledge.
7) In its broad sense, hypothesis provides proper direction to the researcher.

Types of Research Hypothesis:

1) Descriptive Hypothesis:
Descriptive hypothesis presents the existence, size, type, and distribution of variables.
The descriptive hypothesis contains only one variable. So, it does not present the
relationship between or among the variables. Thus, researchers often use a research
question rather than a descriptive hypothesis.
For example: Group study helps to achieve high marks in examination. The rate of
unemployment among arts students are high.
2) Directional Hypothesis:
A directional hypothesis is when someone makes a specific prediction about an effect or
relationship between two variables. For example, "caffeine is linked to an increase in
activity" is a directional hypothesis because it predicts a specific effect.
3) Explanatory Hypothesis:
Explanatory hypotheses are often used to explain the mechanisms behind the patterns
that generalizing hypotheses describe. The hypothesis statement can be followed by the
specific, measurable prediction you can make if the hypothesis is valid.
Explanatory Hypothesis: Plants need nitrogen as a nutrient for growth.
Prediction: Tomato plants with grown nitrogen fertilizer will grow faster and taller and
have more leaves than tomato plants grown without nitrogen fertilizer.
4) Relational Hypothesis:
A relational hypothesis is a statement about the relationship between two variables with
respect to some case. Relational hypothesis may be correlational or casual. A
correlational hypothesis is a statement indicating that variables occur together in some
specified manner without implying that one causes the other. A causal hypothesis is a
statement that describes a relationship between two variables in which one variable
leads to a specified effect on the other variable. An example of a relational hypothesis is
that a significant relationship exists between smoking and obesity, GPA and study hours.
5) Null Hypothesis:
As the most the traditional form of writing a hypothesis, null hypothesis makes
predictions that there is no relationship between no independent and dependent
variables. To use and test this hypothesis you need to select a sample of all possible
elements and draw conclusion from the statistical analysis of this sample for the
population.
6) Alternative Hypothesis:
This hypothesis makes the prediction that there is a measurable interaction between
variables. Alternative hypothesis is also called "Maintained hypothesis" or "Research
hypothesis". The formulation of an alternative hypothesis is commonplace in scientific
circles. Regarding the relationship between null hypothesis and alternative hypothesis,
when null hypothesis is rejected, alternative hypothesis is accepted and vice- versa.
Example:
• Null Hypothesis: H0: There is no difference in the salary of factory workers based on
gender.
Alternative Hypothesis: Ha: Male factory workers have a higher salary than female
factory workers.
• Null Hypothesis: H0: There is no relationship between height and shoe size.
Alternative Hypothesis: Ha: There is a positive relationship between height and shoe
size.

Criteria of Good Hypothesis Statement:

1. Conceptually Clear:
Hypothesis must be thoughtfully clear. The idea used in the hypothesis ought to be
obviously characterized by clear statement. Hypothesis must clearly identify facts that are
relevant and those that are not. The wording used in it must be clear and adequate to
everybody. It must clearly state the condition, size or distribution of some variables in
terms of values and meaningfulness to the research problem.
2. Capable of Being Tested:
Hypothesis must be capable for being tested at the end of the research. It is fundamental
that the analyst may embrace some earlier investigation to guarantee that his speculation
can be tried or tested. For this, the hypothesis should use acceptable techniques. By a
testable hypothesis, we simply imply that hypothesis should reveal consequences or
derivatives that can be deduced for testing purposes. Also, it must set up some
connection between factors or variables.
3. Have Empirical References:
A hypothesis should have empirical reference. No usable hypothesis can have moral
decisions. The ideas in the hypothesis must have exact correlation. All the ideas which
express perspectives can be examined. A hypothesis should be expressed to such an
 extent that it is conceivable to conclude consistently. That is, the hypothesis should have
empirical references.
4. Easily Understandable:
A hypothesis must be effectively reasonable. Words used in hypothesis must be easy to
understand. Utilizing of basic phrasing makes speculation less noteworthy is not right. It is
such that use of complex and specialized languages cannot improve estimation of
speculation. Researcher should state hypothesis as conceivable as in most basic term.
5. Specific in Naure:
A good hypothesis is explicit in nature, implying that the idea is deliberately
characterized. As such, all the tasks and prediction showed in the hypothesis must be
plainly illuminated. It must be figured for a specific and explicit issue.
6. Related to Available Techniques:
A hypothesis must be identified with accessible strategies. It is on the grounds that theory
and methods can't be inversed to one another. The researcher who doesn't manner have
the idea what procedures are accessible to test his/ her hypothesis is in helpless manner
to define usable inquiry. Along these lines, hypothesis must be applicable to the
procedures which are accessible for testing. Further, a scientist must think about the
useful strategies before formulating a hypothesis.
7. Related to the Body of Theory:
The hypothesis must be identified with a group of hypotheses. This prerequisite concerns
theoretical method of reasoning of speculation, for example, what will be the theoretical
ground of testing the hypothesis. No subject can create without expanding on existing
assortment of realities and theory. It can never create, if each examination is a segregated
overview or it is just thematic explanation.
8. Closest to Observable Things:
A hypothesis must have close contact with observable things. Sometimes, its acceptance
depends on perception. Those things and subject which can't be observed, they can't be
figured with prediction. The confirmation of recognizable hypothesis depends on
recognizable things. Henceforth, theory must be close to things recognizable and
observable in nature.
9. Simple and Brief:
A good hypothesis ought to be basic and brief, that is, less complex the hypothesis, better
it will be for the researcher for the understanding and examination. It must be brief with
the goal that it is conceivable. A hypothesis can get brief on that it is expressed in a logical
term.

Roles of Hypothesis in Research:

1) The formulation of a hypothesis compels you to exactly specify what you want to explore
about and helps you in bringing the clarity to your study.
2) The clarification and obviousness that you need to consider in the process of hypothesis
formulation leads you only to collect the information you require by your study.
3) Hypothesis also helps you to maintain the validity of your study as it ensures to measure
what you set out to measure.
4) Construction of hypothesis also helps in strengthening the objectivity of the study.
5) Hypothesis testing makes you to confirm what is true or what is false, which, in turn,
helps you to build up theory.
6) Hence, hypothesis creates a ground for future knowledge, that is, after its verification
hypothesis emerges as a new body of knowledge.
7) In its broad sense, hypothesis provides proper direction to the researcher.

By: rahulkanwaredu@gmail.com
Ch.4) Research Design

Research Design:
➢ Research design refers to the overall strategy utilized to carry out research that defines a
succinct and logical plan to tackle established research question through the collection,
interpretation, analysis, and discussion of data.
➢ Research design is a master plan of specifying the methods and procedures for collecting
and analyzing the needed information.
Research design is a blueprint of a scientific study. It includes research methodologies, tools,
and techniques to conduct the research. It helps to identify and address the problem that
may rise during the process of research and analysis.
Features of Research Design:
1) Objectivity 2) Reliability 3) Validity 4) Generalizability 5) Adequate Information
Elements of Research Design:
1. Dependent and Independent Variables:
A concept which can take different quantitative values is known as variable. For an
example, the concept like height, weight and income are variables. If one variable and
significantly depends upon other variable, then it is known as dependent variable and the
variable that is antecedents to the dependent variable is termed as an independent
variable.
2. Extraneous Variable:
Extraneous variables are those independent variables which are not linked to the study
purpose however they may affect the dependent variable.
3. Control:
The technical term ‘control' means constant and unchanged that is needed when we
design the study to minimize the effects of extraneous independent variables. In
experimental research the term control' is used to refer to restrict experimental
conditions.
4. Confound Relationship:
When the extraneous variable(s) have an impact on dependent variable then the
relationship between the dependent and independent variable is termed to be
confounded by an extraneous variable(s)
5. Research Hypothesis:
Research hypothesis is required when a predicted or hypothesized relationship is to be
tested through scientific methods. It is a predictive statement that associates an
independent variable to a dependent variable.
6. Experimental and Non-experimental Hypothesis Testing Research:
When the aim of research is to test a hypothesis then it is termed as hypothesis testing
research.
7. Experimental and Control Group:
 While doing an experimental hypothesis testing when a selected group is exposed to
usual conditions in research it is termed as a 'control' group but if the group is exposed to
unusual conditions, then it is termed as experimental group.
8. Treatments:
The different conditions under which experimental and control groups are put are usually
referred to as treatments.
9. Experiments:
Experiments are the process of investigating the truth of a statistical hypothesis,
connecting to some research problem.
10.Experimental Units(s):
It is the pre-determined plots or the blocks, where different actions are used, which are
known as experimental. Such experimental units need to be selected (defined) very
carefully.
Classification of Research Design:
1. Quantitative Research Design:
Qualitative research is applied in cases where a relationship between collected data and
observation is established on the basis of mathematical calculation. Theories related to
logically existing phenomenon could be proved or disproved by using mathematical
calculations. Researchers rely on qualitative research deign where they are predicted to
conclude 'why' a particular theory exits along with 'what' respondents have to say about
it.
2. Qualitative Research Design:
Quantitative research is applicable in those cases where it is necessary for a researcher to
have statistical conclusion in order to gather practical insights. To make important
business decisions numbers play an important role. Quantitative research design is
necessary for the progress of any organization as any conclusion made within the
foundation of numbers and analysis will only prove to be helpful for the business.
Quantitative Research Qualitative Research
Deals with numbers and statistics. Deals with words and meanings.
Primary focus is on testing theories and Focus is on exploring ideas and formulating
hypothesis. theories.
Requires many respondents Requires few respondents
Close multiple choice questions Open ended questions
Data collection method include surveys, Data collection method include interviews,
experiments, and observations focus groups, case studies and literature review.
Data analysis method include finding Data analysis methods include thematic
common patterns in the data using analysis, discourse analysis and content
tools such as R, SPSS, Excel, etc. analysis.
Types of Research Design:
1) Descriptive Research Design:
 Descriptive research design uses a range of both qualitative research and quantitative
data (although quantitative research is the primary research method) to gather
information to make accurate predictions about a particular problem or hypothesis.
Descriptive research design is the fact finding operational or action plan for searching
adequate information or fact to establish certain relationship between variables.
Developmental Study:
Developmental research is the study of change brought by the time being in all the
overall social, economic, cultural and so forth aspects of life and community. It identifies
the rate and direction of change with the help of crucial analysis of variables along with
some interviewing factors.
Case Study:
Case study is a detailed and intensive analysis of a single case. Case-study research
examines many features of a case. The cases can be individuals, groups, organizations,
movements, events, or geographic units. The data on the case are detailed, varied, and
extensive.
2) Exploratory Research Design:
Just as the word implies, it explores, that is to find out about something by answering the
question in ‘what’ or ‘how’ manners. Exploratory study is also known as ‘inductive’ or
‘theory building’ and it is also called a formulated or formulative research design. In this
kind of study, researchers don’t begin with a theory. Instead, we collect data that, after
analysis, we will use to develop a theory. After developing the theory, researchers might
then design a study to test the study.
3) Correlational Research Design:
A correlational research design investigates relationships between two variables (or
more) without the researcher controlling or manipulating any of them. It's a non-
experimental type of quantitative research. A correlation reflects the strength and/or
direction of the relationship between two (or more) variables. The direction of a
correlation can be either positive or negative. For example, correlational research may
reveal the statistical relationship between high-income earners and relocation.
4) Casual-Comparative Research Design:
Causal-comparative research also known as “ex post fact” research (Latin for “after the
fact”) is a methodology used to identify cause-effect relationships between independent
and dependent variables. Researchers can study cause and effect in retrospect. This can
help determine the consequences or causes of differences already existing among or
between different groups of people. For example, to find out the difference in wages
between men and women, researchers have to make a comparative study of wages
earned by both genders across various professions, hierarchies and locations.
5) Experimental Research Design:
The experimental research design is a research method used to investigate the interaction
between independent and dependent variables, which can be used to determine a cause-
and-effect relationship. Experimental research is commonly used within the framework of
 the scientific method. An example of an experimental design would be randomly selecting
all of the schools participating in the hand washing poster campaign. The schools would
then randomly be assigned to either the poster-group or the control group, which would
receive no posters in their bathroom.
Qualitative Research:
Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding
of social phenomena within their natural setting. It focuses on the "why" rather than the
"what" of social phenomena and relies on the direct experiences of human beings as
meaning-making agents in their everyday lives. Rather than by logical and statistical
procedures, qualitative researchers use multiple systems of inquiry for the study of human
phenomena including biography, case study, historical analysis, discourse analysis,
ethnography, grounded theory, and phenomenology.
Basic Assumptions of Qualitative Research:
1) Researcher Him/Herself Functions as Key Instrument for Data Collection:
Qualitative researchers do not try to use or depend on questionnaires or instruments
developed by other researchers. They follow the process of collecting the data
themselves through examining documents, observing behavior of the people, or
interviewing participants.
2) Research is Mostly Conducted in Natural Setting:
Qualitative researchers are inclined to collect data in the field where participants
(respondents) experience the issue or problem under study. That is, participants real-life
experiences are observed and recorded for the analysis. For Such participatory
observation researchers do not send out instruments for individuals to complete. So, the
information is gathered by talking directly to people and observing their behaviors and
conducts within their context.
3) Researcher Uses Inductive Data Analysis Method:
Mostly, qualitative researchers use inductive method for data collection and analysis.
That is, they build patterns, categories, and themes from the bottom-up principle (i.e.,
collecting information in the field from people) and they organize the data accordingly.
This inductive process works as back and forth between the themes and the database
until the researchers establish a comprehensive set of themes.
4) Researcher Gathers Data from Multiple Sources:
Qualitative researchers mostly collect data from multiple sources, such as interviews,
observations, documents, and audiovisual information. These are all open-ended forms
of stimulus/response in which the participants share their ideas freely, which are not
constrained by predetermined scales or instruments.
5) Researcher Follows a Holistic Account of Study:
Qualitative researchers tend to develop a comprehensive picture of the problem or issue
under study. That is, holistic way includes collecting information from multiple
perspectives, identifying many factors involved in a situation, and developing the larger
picture of behavior or attributes of the population or community under study.
6) Researcher Gives Importance to Participants’ Meanings:
In the entire qualitative research process, the researchers always try to learn the meaning
that the participants perceive and interpret about the problem or issue, not the meaning
that the researchers interpret and presents about the community being studied.
7) Researcher Applies Emergent Design:
As the research process for qualitative researchers is emergent, the initial plan for
research cannot be predetermined. Almost all phases of the process may change after the
researcher enters the field and begins to collect data. For example, the questions may
change, the forms of data collection may shift, and the individuals studied and the sites
visited may be modified.
8) Researcher Have Reflexivity:
In qualitative research, researchers' role is reflected in study. That means their personal
background, culture, and experiences play for potential source for shaping their
interpretations, such as the themes they advance and the meanings they ascribe to the
data. That is, researchers' background actually may shape the direction of the study. So,
their interpretations cannot be separated from own backgrounds.
Features of Qualitative Research:
1. Based on Exploration:
Exploration is the essential features of qualitative research. Exploration means to discover
new area, people and their lifestyles and learning from them. Exploration helps
researchers to understand the behavior, perception and actions of the participants.
2. Follows Inductive Approach:
Inductive and deductive approaches are two approaches in research. Deductive approach
is commonly used to test the hypothesis whereas inductive approach is more towards
gathering information to develop concept and generate hypothesis. Qualitative research
generally follows inductive approach as it allows them to develop their idea by listening
and observing people.
3. Interactive in Nature:
Another feature of qualitative research is that it is interactive. Interactive means actions
and communication between two people or two things, such as two-communication.
Rather following pre-set questions in mindset, researcher use interaction between them
and participants to get closure to the topic under study. Therefore, interactive is a
research process characterized by joint learning by participants through their involvement
in that process from the beginning.
4. Reflexivity:
Reflexivity is the process of becoming self-aware. It is an attitude of attending
systematically to the context of knowledge construction. In quantitative research, the role
of researcher depends upon the type of questions they develop. But in qualitative research,
researchers should make regular efforts to consider their own thoughts and actions in light
of different contexts as there is no uniform format for collecting information.
5. Flexibility:
 Flexibility means the ability to be easily modified. Qualitative research is different from
quantitative research as it allows for flexibility throughout the research process. In
qualitative research, researcher can change his/her idea and design as per the situation.
6. Follows Holistic Approach:
The term holistic is relating to the idea that things should be studied as a whole and not
just as a sum of their parts. Qualitative research follows holistic approach as it is reflexive
and process driven that ultimately producing culturally situated and theory enmeshed
knowledge through an ongoing interplay between researcher and the respondents.
Qualitative Research Design:
1) Goals:
Goals explain about what your study is going to do. Goals also relate to issues you want to
clarify. In addition, this component of the qualitative research design tries to elaborate
about practices and policies that your study will influence upon. Moreover, goals will tell
you why do you want to conduct this study, and why you should care about the results.
2) Conceptual Framework:
Conceptual framework accounts for your thinking on the issues, settings, or people you
plan to study. It also describes about theories, beliefs, and prior research findings that
guide or inform your research. Furthermore, conceptual framework also explains about
the literature, preliminary studies, and personal experiences you have to draw on for
understanding the people or issues you are studying.
3) Research Questions:
Research questions, specifically, tell what do you want in order to better understand
about the settings or participants that you are studying. It also clarifies what you do not
know about these that you want to lean. Also, it tries to reveal what questions best
capture these learning and understandings. Moreover, it also deals with how these
questions are related to one another.
4) Methods:
Method explains about what you actually have to do in conducting this study and more
specifically, it tells on what approaches and techniques you will have to use to collect and
analyze your data. For this, four parts of this component of your design have been
identified: (a) the relationships that you establish with the participants in your study: (b)
your selection of settings, participants, times and places of data collection, (c) your
methods for collecting your data; and (d) your data analysis strategies and techniques.
5) Validity:
In this component, the description is about how your results and conclusions might be
wrong. It also accounts for the plausible alternative interpretations and validity threats to
these results and conclusions. Also, validity tells you about how you will have to deal with
these. With the help of validity test, you can speak about how the data that you can have,
or that you could potentially collect, support or challenge your ideas about what’s going
on. It also answers about why should we believe your results.

By: rahulkanwaredu@gmail.com
Ch.5) Measurement, Scaling and Sampling

Variables:
A variable is a property that takes on different values. The word ‘variable’ comes from a
Latin word, variabilis (i.e., changeable). In Latin ‘vari’ means ‘various’ and ‘abilis’ means
‘able’ which collectively means capable of changing. In mathematics, variable is a letter of
symbol that represents any of a set of values. Variables are commonly used to represent
vectors, metrics and functions.
Similarly, in research, a variable is defined as anything that has a quantity or quality that
varies. It is any feature or aspect of an event, function or process that, by its presence and
nature, affects some other event of process that are being studied. In research, variable is a
factor that may be subject to change.
Variable in research simply refers to a person, place, thing or phenomenon that you are
trying to measure in some way.
Types of Variables:
1. Independent Variable:
An independent variable is a variable in an experiment that remains unchanged or
influenced by another variable in the experiment. Age is an example of an independent
variable, the place they live or their diet doesn't change their age. Independent variables
can, though, influence and have an effect on other variables. The independent variable is
often the one in an experiment that a researcher changes and manipulates to discover if
it's responsible for an observed change in other variables and how it accomplishes this
change.
2. Dependent Variable:
A dependent variable is one that other factors, or variables, can change and influence.
The dependent variable is often the one being observed and measured in an experiment.
Researchers often look to explain what makes the dependent variable change and the
mechanism for this change. A score on an exam may be a dependent variable, for
example, with study time or teaching methods being independent variables.
3. Moderating Variable:
A moderating variable is one that can strengthen, negate, diminish, or in some other way,
change the relationship between independent and dependent variables. It typically does
that by influencing the effect of an intervening variable. For example, a study may look at
the relationship of economic status and lifestyle, with income as the independent
variable and time spent exercising per week as the dependent. Age may be a moderating
variable in this instance, with the relationship between income and exercise possibly
being stronger in younger, more physically healthy individuals and weaker in older
individuals.
4. Intervening Variable:
An intervening variable, sometimes called a mediator variable, is a hypothetical variable
used to explain the reason and cause of the link between other variables in a study. These
 are typically the independent and dependent variables. For example, a study may show a
correlation between household income and lifespan, showing that those with a greater
income live longer. A researcher may hypothesize that the intervening variable
responsible for this link is diet and the ability to access better quality foods and maintain
a healthy diet, for instance.
Measurement:
In common sense measurement is an action of measuring something, Measurement is
observed by people in their everyday life knowingly or unknowingly. People purchase many
goods with the help of weight, they judge new songs as per its pleasant hearing, they judge
their surroundings with one or other objects they are previously conscious with. Either
people measuring size, weight, length, amount of something or a unit of measuring, is
called measurement.
Measurement Scales:
1) Nominal Scale:
The nominal scale of measurement defines the identity property of data. This scale has
certain characteristics, but doesn’t have any form of numerical meaning. The data can be
placed into categories but can’t be multiplied, divided, added or subtracted from one
another. It’s also not possible to measure the difference between data points. Examples
of nominal data include eye color and country of birth.
2) Ordinal Scale:
The ordinal scale defines data that is placed in a specific order. While each value is
ranked, there’s no information that specifies what differentiates the categories from each
other. These values can’t be added to or subtracted from. An example of this kind of data
would include satisfaction data points in a survey, where ‘ two = neutral, and
three = unhappy.
3) Interval Scale:
The interval scale contains properties of nominal and ordered data, but the difference
between data points can be quantified. This type of data shows both the order of the
variables and the exact differences between the variables. They can be added to or
subtracted from each other, but not multiplied or divided. For example, 40 degrees is not
20 degrees multiplied by two. This scale is also characterized by the fact that the number
zero is an existing variable. In the ordinal scale, zero means that the data does not exist.
In the interval scale, zero has meaning – for example, if you measure degrees, zero has a
temperature.
4) Ratio Scale:
Ratio scales of measurement include properties from all four scales of measurement. The
data is nominal and defined by an identity, can be classified in order, contains intervals
and can be broken down into exact value. Weight, height and distance are all examples of
ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied.
Ratio scales also differ from interval scales in that the scale has a ‘true zero’. The number
zero means that the data has no value point. An example of this is height or weight, as
 someone cannot be zero centimeters tall or weigh zero kilos – or be negative centimeters
or negative kilos. Examples of the use of this scale are calculating shares or sales.
Attitude Measurement:
A reaction made by a person over any event, product or other thing that is known as
attitude. Social science researcher has developed various scales for the measurement of
attitude of the people. Such measurement is known as attitude measurement.
Scale Construction for Attitude Measurement/Measuring the attitudes of customer’s
interest and satisfaction:
1) Arbitrary Approach:
It is an approach where scale is developed on ad hoc (task based and temporary) basis.
This is the most widely used approach. It is presumed that such scales measure the
concepts for which they have been designed, although there is little evidence to support
such an assumption. Arbitrary scale falls under this approach.
2) Consensus Approach:
Here a panel of judges evaluate the items chosen for inclusion in the instrument in terms
of whether they are relevant to the topic area and unambiguous in implication.
Differential scales such as Thurstone Differential Scale falls under this approach.
3) Item Analysis Approach:
Under it a number of individual items are developed into a test which is given to a group
of respondents. After administering the test, the total scores are calculated for everyone.
Individual items are then analyzed to determine which items discriminate between
person or objects with high total scores and those with low scores. Summated scales such
as Likert Scale falls under this approach.
4) Cumulative Scales Approach:
They are chosen on the basis of their confirming to some ranking of items with ascending
and descending discriminating power. For instance, in such a scale the endorsement of an
item representing an extreme position should also result in the endorsement of all items
including a less extreme position. Cumulative scales such as Guttman’s Scalogram falls
under this approach.
5) Factor Scales Approach:
They may be constructed on the basis of inter-correlations of items which indicate that a
common factor accounts for the relationship between items. This relationship is typically
measured through factor analysis method, such as Osgood's Semantic Differential, Multi-
dimensional scaling, etc. falls under this approach.
Measurement Scales:
1) Nominal Scale:
Nominal scale is simply a system of assigning number symbols to event in order to label
them. Such number cannot be considered to be associated with an ordered scale, for
their order is of no consequence. The numbers are just convenient labels for the
particular class of events and as such have no quantitative value. Nominal scales provide
convenient ways keeping track of people, objects and events. One cannot do much with
 the numbers involved. Example, one cannot usefully average the numbers on the back of
a group of football players and come up with a meaningful value. Neither can one usefully
compare the numbers assigned to one group with the numbers assigned to another.
2) Ordinal Scale:
The ordinal scale places event in order, but there is no attempt to make the intervals of
scale equal in terms of some rule. Rank orders represent ordinal scales and are frequently
used in research relating to qualitative phenomena. A students rank in has graduation
class involves the use of ordinal scale. One has to be very careful in making statement
about scores based on ordinal scales. For example, if Ram's position in his class is 10th
and Mohan's position 40th, it cannot be said that Ram's position is four times as good as
that of Mohan. The statement would make no sense at all ordinal scales only permit the
ranking of items from highest to lowest. Ordinal measures have no absolute values and
the real differences between adjacent ranks may not be equal. All that can be said one
person is higher or lower on the scale than another, but more precise comparisons
cannot be made
3) Interval Scale:
In case of interval scale, the intervals are adjusted in terms of some rule that has been
established as a basis of a making the units equal. The unit are equal only in so far as one
accepts the assumptions on which the rule is based. Interval scales can have an arbitrary
zero, but it is not possible to determine for them what may be called an absolute zero or
the unique origin. The primary limitation of the interval scale is the lack of a true zeros, it
does not have the capacity to measure the complete absence of a trait or characteristics.
The Fahrenheit scale is an example of an interval scale and shows similarities in what one
can and cannot do write it. One can say that an increase in temperature from 30o to 40o
involves the same in temperature as an increase from 60o to 70o; but one cannot say that
the temperature of 60o is twice as warm as the temperature of 30o because both
numbers are dependent on the fact that the zero on the scale is set arbitrarily at the
temperature of freezing point of water. The ratio of the two temperatures, 30o and 60o,
means nothing because zero is an arbitrary point. Interval scale provides a more powerful
measurement than ordinal scale for interval scale also incorporates the concept of
equality of interval.
4) Ratio Scale:
Ratio scales have an absolute or true zero of measurement. The term 'absolute zero' is
not as precise as it was once believed to be. We can conceive of an absolute zero of
length and similarly we can conceive of an absolute zero of time. With ratio scale involved
one can make statements like "X's" typing performance was twice as good as that of Y's.
The ratio involved does have significance and facilities a kind of comparison which is not
possible in case of interval scale. Ratio scale represents the actual amounts of variables.
Generally, all statistically techniques are usable with ratio scales and all manipulations
that one can carry out with real numbers can also be carried out with ratio scale values.
Arbitrary Scales:
Arbitrary has been derived from the Latin word ‘arbitrarius, the source of arbiter, which
means someone who is requested to judge something. In other word, arbitrary refers to
random choice or personal whim, rather than any reason or system. So, an arbitrary legal
judgment is a decision made at the choice of the judge, not one that is fixed by law. In its
wider concept, arbitrariness is the quality of being "determined by chance, whim, or
impulse, and not by necessity, reason, or principle". It is also used to refer to a choice made
without any specific criterion or restraint. Arbitrary decisions are not necessarily the Same
as random decision. If you describe an action, rule and decision as arbitrary, you think that
is not based on any principle, plan or system.
Differential Scales (or Thurstone-type Scales):
Thurstone scale is defined as a unidimensional scale that is used to track respondent’s
behavior, attitude or feeling towards a subject. This scale consists of statements about a
particular issue or topic where each statement has a numerical value that indicates the
respondent’s attitude towards the topic as favorable or unfavorable. Respondents indicate
the statements that they agree with, and an average is computed. A mean score of the
agreements or disagreements is calculated as the attitude of the respondent towards the
topic.
Likert-type Scale (Summated Scale):
A Likert scale is a rating scale used to measure opinions, attitudes, or behaviors. It consists
of a statement or a question, followed by a series of five or seven answer statements.
Respondents choose the option that best corresponds with how they feel about the
statement or question. Because respondents are presented with a range of possible
answers, Likert scales are great for capturing the level of agreement or their feelings
regarding the topic in a more nuanced way. However, Likert scales are prone to response
bias, where respondents either agree or disagree with all the statements due to fatigue
or social desirability or have a tendency toward extreme responding or other demand
characteristics.
Cumulative Scales (Guttman Scale):
The Guttman scale is also known as cumulative scaling or scalogram analysis. It is an ordinal
scale with a number of statements placed in a hierarchical order. The order is arranged so
that if a respondent agrees with a statement, they will also agree with all of the statements
that fall below it in extremity. The first statement that indicates disagreement shows the
respondent’s position on the subject. To truly understand how the respondent feels about a
topic, statements should be formulated and ordered to represent an increasingly extreme
stance on an issue (least to most supportive of the topic). It’s best to engage a group to help
create and place statements in order to reduce the risk of bias in both the statements and
the order.
Measurement Scales and Techniques Commonly Used in Business Research:
1) Dichotomous Scale:
The dichotomous scale is used to elicit a Yes or No answer, as in the example below. Note
that a nominal scale is used to elicit the response.
Example: Do you own a car?
Yes No
2) Category Scale:
The Category scale uses multiple items to elicit a single response, as per the following
example. This also uses the nominal scale.
Example: Where do you reside in Kathmandu Valley?
- Lalitpur - Bhaktapur - Kathmandu
3) Semantic Differential Scale:
Several bipolar attributes are identified at the extremes of the scale, and respondents are
asked to indicate their attitudes, on what may be called a semantic space, toward a
particular individual, object, or event on each of the attributes. The bipolar adjectives
used might employ such terms as Good-Bad; Strong-Weak; Hot-Cold. The semantic
differential scale is used to assess respondents’ attitudes towards a particular brand,
advertisement, object and individual. The responses can be plotted to obtain a good idea
of their perceptions. A semantic differential scale is ordinal in nature. However, it is often
treated as an interval scale. An example of the semantic difference scale follows:
Example: Responsive Unresponsive
Beautiful Ugly
Courageous Timid
4) Numerical Scales:
The numerical scale is similar to the semantic differential scale, with the difference that
numbers on a five-point or seven-point scale are provided, with bipolar adjectives at both
ends, as illustrate below. This scale is also often treated as an interval scale although it is
formally ordinal in nature.
Example:
How pleases are you with your new estate agent?
Extremely Pleased 7 6 5 4 3 2 1 Extremely Displeased
5) Itemized Rating Scale:
A five-point of seven-point scale with anchors, as needed, is provided for each item and
the respondent states the appropriate number on the side of each item, or circles the
relevant number against each item, as per the examples that follow. The responses to the
items are then summed. This uses an interval scale.
Example 1:
Respond to each item using the scale below, and indicate your response number on the
line by each item.
1 2 3 4 5 6
Very Unlikely Unlikely Neither Unlikely Likely Very Likely Nor Likely
1. I will be changing my job within the next 12 months.
 2. It will take on new assignments in the near future.
3. It will be possible that I will be out of this organization within next 12 months.
It is notable that the above is a balanced rating scale with a neutral point.
Example 2:
Not at all Somewhat interested Moderately Very much
interested interested interested
1 2 3 4
How would you rate your interest in changing current organizational policies?
This is an unbalanced rating scale which does not have a neutral point.
6) Fixed or Constant Sum Rating Scale:
The respondents are here asked to distribute a given number of points across various
items as per the example below. This is more in the natural of an ordinal scale.
Example:
In choosing body soap, indicate the importance you attach to each of the flowing five
aspects by allotting points for each to total 100 in all.
Fragrance -
Color -
Shape -
Size -
Texture of leather -
Total point 100
7) Stapel Scale:
This scale simultaneously measures both the direction and intensity of the attitude
toward the items under study. The characteristics of interest to the study is placed at the
center with a numerical scale ranging, say, from +3 to -3, on either side of the item, as
illustrated in the example below. This gives an idea of how close or distant the individual
response to the stimulus is. Since this does not have an absolute zero point, this is an
interval scale.
Example:
State how you would rate your supervisor's abilities with respect to each of the
characteristics mentioned below, by circling the appropriate number.
+3 +3 +3
+2 +2 +2
+1 +1 +1
Adopting modern technology Product Innovation Interpersonal Skill
-1 -1 -1
-2 -2 -2
-3 -3 -3
8) Graphic Rating Scale:
A graphical representation helps the respondents to indicate on this scale their answers
to a particular question by placing a mark at the appropriate point on the line, as in the
 following example. This is an ordinal scale, through the following example might make it
look like an interval scale.
Example:
On a scale of 1 to 10, how would you rate your supervisor?
- 10 Excellent
-
-
-
- 5 Adequate
-
-
-
-
- 1 Very bad
This scale is easy to respond to. The brief descriptions on the scale points are meant to
serve as a guide in locating the rating rather than representing discrete categories. The
faces scale, which depicts faces ranging from smiling to sad, is also a graphic rating scale
used to obtain responses regarding people’s feeling with respect to some aspect – say,
how they fell about their jobs.
9) Consensus Scale:
Consensus is also a basis for developing scales, where a panel of judges selects certain
items, which in its view measure the relevant concept. The items are chose particularly
based on their pertinence or relevance to the concept. Such a consensus scale is
developed after the selected items have been examined and tested for their validity and
reliability.
10) Paired Comparison Scale:
A paired comparison scale presents the respondent with two choices and calls for a
preference. For example, the respondent is asked which color he or she likes better, red
or blue, and a similar process is repeated throughout the scale items. Note that there are
no scale properties within each item; that is, respondents are not provided with any scale
other than the extreme choices they must make (e.g., red or blue) on each item.
11) Forced Choice Scale:
The forced choice enables respondents to rank objects relative to one another, among
the alternatives provided. This is easier for the respondents, particularly, if the number of
choices to be ranked is limited in number.
Example:
Rank the following magazines that you would like to subscribe to in the order of
preference, assigning 1 to the most preferred choice and 5 to the least preferred.
Fortune -
Playboy -
Time -
People --
 Prevention
12) The Comparative Scale:
The comparative scale provides a benchmark or a point of reference to assess attitudes
toward the current object, event, or situation under study. An example of the use of the
comparative scale follows.
Example:
In a volatile financial environment, compared to stocks, how wise of useful is it to invest
in Treasury bonds? Please circle the appropriate response.
More useful About the same Less useful
Validity:
Validity refers to how accurately a method measures what it is intended to measure. If
research has high validity, that means it produces results that correspond to real properties,
characteristics, and variations in the physical or social world. If the thermometer shows
different temperatures each time, even though you have carefully controlled conditions to
ensure the sample’s temperature stays the same, the thermometer is probably
malfunctioning, and therefore its measurements are not valid.
Types of Validity:
1) Content Validity:
Content validity is whether or not the measure used in the research covers all of the
content in the underlying construct (the thing you are trying to measure). Content validity
is considered a subjective form of measurement because it still relies on people’s
perceptions for measuring constructs that would otherwise be difficult to measure.
2) Criterion Related Validity:
Criterion-related validity (also called instrumental validity) is a measure of the quality of
your measurement methods. The accuracy of a measure is demonstrated by comparing it
with a measure that is already known to be valid. In other words – if your measure has a
high correlation with other measures that are known to be valid because of previous
research.
3) Construct Validity:
A construct represents a collection of behaviors that are associated in a meaningful way
to create an image or an idea invented for a research purpose. Construct validity is the
degree to which your research measures the construct (as compared to things outside
the construct). Depression is a construct that represents a personality trait that manifests
itself in behaviors such as oversleeping, loss of appetite, difficulty concentrating, etc.
Reliability:
Reliability refers to how consistently a method measures something. If the same result can
be consistently achieved by using the same methods under the same circumstances, the
measurement is considered reliable. You measure the temperature of a liquid sample
several times under identical conditions. The thermometer displays the same temperature
every time, so the results are reliable.
Types of Reliability:
1) Test-Rest Reliability:
We estimate test-retest reliability when we administer the same test to the same sample
on two different occasions. This approach assumes that there is no substantial change in
the construct being measured between the two occasions. The amount of time allowed
between measures is critical. We know that if we measure the same thing twice that the
correlation between the two observations will depend in part by how much time elapses
between the two measurement occasions. The shorter the time gap, the higher the
correlation; the longer the time gap, the lower the correlation. This is because the two
observations are related over time.
2) Parallel-Forms Reliability:
When responses of two comparable sets of measures tapping the same construct are
highly correlated, we have parallel-form reliability, it is also called equivalent-form
reliability. Both forms have similar items and same response format, the only changes
being the wording and the order or sequence of the questions. What we try to establish
here is the error variability resulting from wording and ordering of the questions. If two
such Comparable forms are highly correlated, we may be fairly certain that the measures
are reasonably reliable, with minimal error variance caused by wording, ordering, or
other factors.
3) Spit-half Reliability:
In split-half reliability we randomly divide all items that purport to measure the same
construct into two sets. We administer the entire instrument to a sample of people and
calculate the total score for each randomly divided half.
4) Inter Item Consistency Reliability:
This is test of consistency of respondents answer to all the items in measure. To the
degree that items are independent of measures of the same concept, they will be
correlated with each other.
Sampling:
Sampling is a process in statistical analysis where researchers take a predetermined number
of observations from a larger population. Sampling allows researchers to conduct studies
about a large group by using a small portion of the population.
Sampling Terminologies:
1. Population: a group of individual persons, objects, or items from which samples are taken
for statistical measurement. The related target group of the study refers to population.
2. Census: It is usually complete enumeration of a population, sample of entire population.
3. Sample: A representative part or a single item from a larger whole or group especially
when presented for inspection or shown as evidence of quality refers to sample. Sample
is subset of the target population selected to represent the whole population
4. Sample Frame/Sampling Frame: A list of the items or people forming a population from
which sample is taken indicates sampling frame. It is a list or other way of identifying
units from which sample is to be drawn.
 5. Sample Design: A sample design is the framework, or road map, that serves as the basis
for the selection of a survey sample and affects many other important aspects of a survey
as well.
6. Sampling: The act, process, or techniques of selecting a representative part of a population
for the purpose of determining parameters or characteristics of the whole population.
7. Sampling Unit: The term sampling unit refers to a singular value within a sample
database. It is individuals, households or other target population “elements” available for
selection during sampling process.
8. Sample Size:
The sample size of a survey most typically refers to the number of units that were chosen
from which data were gathered.
9. Extent: The point, degree, or limit to which something extends. It refers to the
geographical boundaries.
10.Time: Time refers to the point or period when something occurs. It is time period under
consideration for job.
Process of Sampling:
Sampling is an important function of research. Right sampling helps to draw right
conclusions and such conclusions can only be applied in practice. Thus, sample should not
be selected in hunches but it should be selected following certain process. Generally, the
following procedures are pursued while selecting sample.
1) Identify the Target Population (Population of Interest):
Target population refers to the group of individuals or objects to which researchers are
interested in generalizing their findings. The target population is the group of individuals
or objects, from which the sample might be taken. A well - defined population reduces
the probability of including the participants who all are not suitable for the research
objective. For example, researcher wants to study the behavior of toddlers in playgroup.
He has to select a particular area (e.g., all playgroups in his city) for his study because it is
impossible to study the whole world toddlers.
2) Selecting a Sample Frame:
While selecting sample units from the population, it is sometimes desirable to choose a
list of the population from which the researcher select units. Sampling frame is the group
of individuals or objects (e.g., list of all playgroups in researcher’s city) from which
researcher will draw his/her sample. It is the list of all units in a study population from
which the sample is taken. For example, researcher takes 3 playgroups near to his house
in his sampling frame to conduct his study.
3) Specify the Sampling Technique:
Sampling can be done through probability (random selection) or non-probability (non-
random) technique. Now, if the sampling frame is approximately the same as the target
population, random selection may be used to select sample. On the other hand, if the
sampling frame does not really represent the target population, the researcher may
 choose non-random selection which can give at least an idea about the population in his
nearby area.
4) Determine the Sample Size:
The sample size is simply the number of units in the sample. Sample size determination
depends on many factors such as time, cost, facility. In general, larger samples are better,
but they also require more resources.
5) Executing the Sampling Plan:
Once population, sampling frame, sampling technique and sample size are identified,
researcher can use all that information to choose his/her sample.
Types of Sampling:
A. Probability Sampling:
A probability sampling is a sample in which every unit in the population has a chance
(greater than zero) of being selected in the sample, and this probability can be accurately
determined. In other word, probability sampling is a sampling technique in which sample
from larger population are chosen using a method based on the theory of probability. For
example, if you have a population of 100 people every person would have adds of 1 in 100
for getting selected. Probability sampling gives you the best chance to create a sample that
is truly representative of the population.
Types of Probability Sampling
1. Simple Random Sampling:
In a simple random sample, every member of the population has an equal chance of
being selected. Your sampling frame should include the whole population. To conduct this
type of sampling, you can use tools like random number generators or other techniques
that are based entirely on chance. For example: You want to select a simple random
sample of 1000 employees of a social media marketing company. You assign a number to
every employee in the company database from 1 to 1000, and use a random number
generator to select 100 numbers.
2. Systematic (quasi-random) sampling:
Systematic sampling is similar to simple random sampling, but it is usually slightly easier
to conduct. Every member of the population is listed with a number, but instead of
randomly generating numbers, individuals are chosen at regular intervals. For example:
All employees of the company are listed in alphabetical order. From the first 10 numbers,
you randomly select a starting point: number 6. From number 6 onwards, every 10th
person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of
100 people.
3. Stratified Random Sampling:
Stratified random sampling involves a method where a larger population can be divided
into smaller groups, that usually don’t overlap but represent the entire population
together. While sampling these groups can be categorized and then a sample is drawn
from each group separately. A common method is to arrange or classify by age, sex,
ethnicity and similar ways splitting subjects into mutually exclusive groups and then using
 simple random sampling to choose members from group. Members in each of these
groups should be distinct so that every member of all groups gets equal opportunity to be
selected using simple probability. For example, say you want to investigate how income
differs based on educational attainment, but you know that this relationship can vary
based on race. Using stratified sampling, you can ensure you obtain a large enough
sample from each racial group, allowing you to draw more precise conclusions.
4. Cluster (area) Sampling:
Cluster sampling also involves dividing the population into subgroups, but each subgroup
should have similar characteristics to the whole sample. Instead of sampling individuals
from each subgroup, you randomly select entire subgroups. If it is practically possible,
you might include every individual from each sampled cluster. If the clusters themselves
are large, you can also sample individuals from within each cluster using one of the
techniques above. This is called multistage sampling. For Example: The company has
offices in 10 cities across the country (all with roughly the same number of employees in
similar roles). You don’t have the capacity to travel to every office to collect your data, so
you use random sampling to select 3 offices – these are your clusters.
B. Non-Probability Sampling:
Non-probability sampling is defined as a sampling technique in which the researcher selects
samples based on the subjective judgment of the researcher rather than random selection.
It is a less stringent method. This sampling method depends heavily on the expertise of the
researchers. It is carried out by observation, and researchers use it widely for qualitative
research.
Types of Non-Probability Sampling:
1) Quota Sampling:
Quota sampling relies on the non-random selection of a predetermined number or
proportion of units. This is called a quota. You first divide the population into mutually
exclusive subgroups (called strata) and then recruit sample units until you reach your
quota. These units share specific characteristics, determined by you prior to forming your
strata. The aim of quota sampling is to control what or who makes up your sample. For
example, a cigarette company wants to find out what age group prefers what brand of
cigarettes in a particular city. They apply survey quota on the age groups of 21-30, 31-40,
41-50, and 51+. From this information, the researcher gauges the smoking trend among
the population of the city.
2) Convenience Sampling:
A convenience sample simply includes the individuals who happen to be most accessible
to the researcher. This is an easy and inexpensive way to gather initial data, but there is
no way to tell if the sample is representative of the population, so it can’t
produce generalizable results. Convenience samples are at risk for both sampling
bias and selection bias. A basic example of a convenience sampling method is
when companies distribute their promotional pamphlets and ask questions at a mall or
 on a crowded street with randomly selected participants. Businesses use this sampling
technique to gather information to address critical issues arising from the market.
3) Purposive Sampling:
This type of sampling, also known as judgement sampling, involves the researcher using
their expertise to select a sample that is most useful to the purposes of the research. It is
often used in qualitative research, where the researcher wants to gain detailed
knowledge about a specific phenomenon rather than make statistical inferences, or
where the population is very small and specific. An effective purposive sample must have
clear criteria and rationale for inclusion. Always make sure to describe your inclusion and
exclusion criteria and beware of observer bias affecting your arguments. For example: You
want to know more about the opinions and experiences of disabled students at your
university, so you purposefully select a number of students with different support needs
in order to gather a varied range of data on their experiences with student services.
4) Snow ball Sampling:
If the population is hard to access, snowball sampling can be used to recruit participants
via other participants. The number of people you have access to “snowballs” as you get in
contact with more people. The downside here is also representativeness, as you have no
way of knowing how representative your sample is due to the reliance on participants
recruiting others. This can lead to sampling bias. For example: You are researching
experiences of homelessness in your city. Since there is no list of all homeless people in
the city, probability sampling isn’t possible. You meet one person who agrees to
participate in the research, and she puts you in contact with other homeless people that
she knows in the area.
Sampling Error:
A sampling error is a statistical error that occurs when an analyst does not select a sample
that represents the entire population of data. As a result, the results found in the sample do
not represent the results that would be obtained from the entire population.
In other word, a sampling error is a deviation in sampled values versus the true population
value due to the fact that the sample is not representative of the population or biased in
same sampling error since it is only an approximation, of the population from which it is
drawn.
Types of Sampling Error:
i. Bias Error:
sampling bias is a possible source of sampling errors, wherein the sample is chosen in a
way that makes some individuals less likely to be included in the sample than others. It
leads to sampling errors which either have a prevalence to be positive or negative such as
errors can be considered to be systematic errors sampling errors and biases can be
summarized as below:
• Selection Bias: When the true selection probabilities differ from those assumed in
calculating the result.
 • Random Sampling Error: Random variation in the results due to the elements in the
sample being selected at random.
ii. Unbiased Error:
Unbiased sampling errors or compensating errors are the errors in which the ultimate
result would be neutralized. If the observations are large in number, then these unbiased
errors will not affect the final results. For example, the chance of making an
overestimation is almost same as the chance of making an underestimation. Like the
value 275, 325, 345 are round to nearest, number 300. In this case, 325 and 345 are
overestimated and 275 is underestimated. Therefore, these unbiased sampling errors are
also known as compensatory errors.
Non-Sampling Error:
A non-sampling error is a statistical term that refers to an error that results during data
collection, causing the data to differ from the true values.
Types of Non-Sampling Error:
i. Response Error:
In survey sampling, respondents' errors refer to any error introduced into the survey
results due to respondent providing untrue or incorrect information. It is a type of
systematic bias. Several factors can lead to respondents' errors. Language and
educational issues can lead to a misunderstanding of the question by the respondent or
similarly, a misunderstanding of the response by the surveyor.
ii. Non-response Bias:
Non-response error occurs when sampling unit selected for a sample unit are not
interviewed. Sampled units typically do not respond because they are unable,
unavailable, or unwilling to do. In a web-based survey, for example, survey link may be
incompatible with a respondent’s browser, leaving the respondents may be a vacation for
the duration of the data collection period. Respondents may also be unwilling to take
survey because they don't want to take survey, or don't trust researchers.
iii. Coverage Bias:
A coverage error would occur, for example, if a person was counted twice in survey or
their answers were counted twice in a survey or their answers were duplicated on the
survey. Further, coverage error is a type of non- sampling error that occurs when there is
not a one-to-one correspondence between the target population and the sampling frame
from which sample is drawn. This can bias estimates calculated using survey data. For
example, a researcher may wish to study the opinions of registered voters (target
population) by calling residences (listed in a telephone directory (sampling frame). Under
coverage may occur if not all voters are listed in the phone directory. Over coverage may
occur if some voters have more than one listed phone. Bias could also occur if some
phone numbers listed in the directory do not belong to registered voters.

By: rahulkanwaredu@gmail.com
Ch.6) Data Collection and Analysis

Data:
➢ The information of facts collected through record, observation and measurement is
known as data.
➢ Data can be defined as facts, observations or experiences which make the ground for
testing or construction of theory and argument.
Types of Data:
1. Observational Data:
Observational data are caught through the observations of a human conduct or action. It
is gathered utilizing strategy, for example, human observation, open-ended interviews, or
the utilization of an instrument or senso to screen and record data. As observational
information is caught progressively, it would be hard to reproduce whenever lost.
2. Experimental Data:
Active intervention by the researcher helps to collect experimental data, which produces
and measures change and creates difference when a variable is appeared. Such data
characteristically makes the researcher convenient to determine a causal relationship.
Besides, such data is naturally projectable to a layer of population.
3. Simulation Data:
Simulation data is a type of data, which are gathered by imitating the operation of a real-
life process or system overtime using computer test models. Examples are to predict
weather conditions, economic models, chemical reactions or seismic activity. This method
tells or explains what would, or could happen under certain conditions. The test model
used in simulation data is often considered more important than information accumulated.
4. Derived Data:
Derived data are such data, which use existing data points obtained from different data
sources and create new data through some sort of transformation such as an arithmetic
formula or aggregation. Normally, this data can usually be replaced if lost, it may be very
time consuming.
Source of Data:
A. Primary Data:
Primary data are the data that has been generated by the researcher himself/herself,
surveys, interviews, experiments, specially designed for understanding and solving the
research problem at hand.
Advantages of Using Primary Data:
❖ They have their advantages and applications in data management and storage.
❖ Data can be collected specifically for the problem under study.
❖ The quality of the data collected by the investigator cannot be distrusted.
Disadvantages of Using Primary Data:
❖ Since it requires a lengthy study procedure including field work, it is more expensive to
carry out.
❖ Primary data is limited to the specific time, place or number of participants and etc.
❖ It is time consuming and data collection may not be possible always.
Methods of Collecting Primary Data/Primary Sources of Data:
1. Observation Method:
In this method, researchers observe a situation around them and record the findings. It
can be used to evaluate the behavior of different people in controlled (everyone knows
they are being observed) and uncontrolled (no one knows they are being observed)
situations. This method is highly effective because it is straightforward and not directly
dependent on other participants.
2. Interview Method:
Interviews are a direct method of data collection. It is simply a process in which the
interviewer asks questions and the interviewee responds to them. It provides a high
degree of flexibility because questions can be adjusted and changed anytime according to
the situation.
3. Survey and Questionnaire Method:
Surveys and questionnaires provide a broad perspective from large groups of people.
They can be conducted face-to-face, mailed, or even posted on the Internet to get
respondents from anywhere in the world. The answers can be yes or no, true or false,
multiple choice, and even open-ended questions. However, a drawback of surveys and
questionnaires is delayed response and the possibility of ambiguous answers.
4. Schedule Method:
Schedule method is utilized to gather information by the enumerators through
questionnaire design. Enumerators are people delegated by the researcher for the
purpose of collecting information. They are instructed to directly meet the
sources/respondents with the questions. They clarify the degree and goal of the inquiry
to the respondents and requests for their collaboration. The enumerators pose the
questions to the respondents and record their answers in the survey and gather required
information. The accomplishment of this technique relies upon the genuineness and
proficiency of the enumerators.
5. Focus Group:
A focus group is similar to an interview, but it is conducted with a group of people who all
have something in common. The data collected is similar to in-person interviews, but they
offer a better understanding of why a certain group of people thinks in a particular way.
However, some drawbacks of this method are lack of privacy and domination of the
interview by one or two participants. Focus groups can also be time-consuming and
challenging, but they help reveal some of the best information for complex situations.
6. Panels:
Panel like focus groups is another source of collecting primary data for research purpose.
It is such that members in focus group meet for a one-time group discussion, panels (of
members). On the other hand, meet more than once. In the study of the effects of certain
inventions or changes over a period of time, panel studies are very useful and
 appropriate. As a method of study, individuals are randomly selected to serve as panel
members. For example, if the effects of a proposed advertisement and their intensions of
purchasing the brand are assessed this can be taken as the response from the consumer
who were exposed to the advertisement.
B. Secondary Data:
Secondary data refers to data that is collected by someone other than the primary user.
Secondary data sources are extremely useful. They allow researchers and data analysts to
build large, high-quality databases that help solve business problems. By expanding their
datasets with secondary data, analysts can enhance the quality and accuracy of their
insights.
Advantages of Using Secondary Data:
1. Secondary data saves cost and time in comparison to primary data.
2. Secondary data is main sources of data for time series related study.
3. Secondary data sources will be the main census data when the whole population of any
nations is required.
4. In a few cases, secondary data can be more precise than essential information. For
instance, if an organization needs data on the sales, benefit, etc. of different
organizations, it can get more solid data from government delivered sources than
organizational themselves.
Disadvantages of Using Secondary Data:
1. The analyst has no information on how the information was gathered, nor do they have
any authority over it. Along these lines, they know nothing about its exactness of its limits
of control.
2. It is additionally hard to assess the exactness of information that was previously gathered.
3. In numerous cases the optional information may not be adequate to meet the
information necessity for the current research.
4. Secondary data can be outdated and thus can’t be utilized in moment research.
Sources of Secondary Data:
1) Published Sources:
Various agencies like government, non-government and private and individuals collect
primary data for their research and publish such data for the knowledge of stakeholders.
The researchers obtain such data from the following forms: -
a) Government reports and publications:
Reports from ministers, departments, Nepal Rastra Bank, Central Bureau of Statistics,
Planning Commission, etc. are used by the researchers in their research activities.
b) Publication of semi-government organizations:
Various semi-government organizations like industrial service Centre, Trade Promotion
Centre, Nepal Airlines Corporation, Nepal Food Corporation, etc. publish books and
reports time and again. Other researcher can collect data for their research from these
books and reports.
c) Reports and publication of international organizations:
 International Organizations like World Bank, International Monetary fund, World Health
Organization, UN Mission and Agencies, Asian Development Bank, International Labor
Organization and Commercial Organizations publish progress report and conduct research
time and again. As well they publish bulletins and books for the knowledge of public.
Other organizations and individuals use such data to conduct further research in the
same field.
d) Private publications:
Various individuals and business houses establish organizations like FNCCI, Chamber of
Commerce, Confederation of industry etc. publish reports for the knowledge of public.
The research reports and progress reports of NGO’s and INGO’s are also the prime source
of secondary data.
2) Unpublished Sources:
Some data and information are not published after the completion of the work. Such
sources of data are known as unpublished sources of data. Reports of private offices and
organizations, records, some secret information of government and non-government
Organizations, records of hospitals, schools and dissertations of students are some
unpublished sources of data. It is also a major source of secondary data.
3) Computerized Database:
Computerized database consists of information that has been made available in computer
for electronic distribution. Computerized database may be classified as online, internet of
offline. Online database consists of a Central data bank which is accessed with the
computer through telecommunication network. Internet database can be accessed,
searched and analyzed on the internet and off-line database that make the information
available on Hard disks drive or CD-ROM disks.
Questionnaire:
A questionnaire is a medium of communication between the researcher and the respondent.
It is likely a play where the researcher is following the rules to the reading script while
respondents reply to the questions as per their understanding and will. In order to enhance
the desired response rate, questionnaire should be crispy and attractive. If researchers want
the survey questions to provide valid and reliable measures, then the respondents should
quickly grasp each question. The questions should not be confused and frustrate
respondents. This means that researchers must exercise extra care in preparing their
questionnaires. Though it is easy to write a questionnaire, respondents need a lot of skills and
experience to write a good questionnaire: one in which every question is clear and can be
answered accurately and has usable results. Mainly two key strategies guide writing good
survey questions: avoid possible confusion and keep the respondents’ perspective in mind.
Principles of the Questionnaire:
1) Avoid Jargon, Slang and Abbreviations:
Jargon and technical terms come in many forms. The plumber talks about ‘snakes’, a
lawyer about a contractor of ‘uberrima fides’, and a psychologist about the ‘Oedipus
 complex’. Slang is a kind of jargon with a subculture. For example, people who are
homeless talk about a ‘snow bird’ and ‘snowboarders’ talk about ‘goofy foot’.
2) Avoid Ambiguity:
Make sure it is absolutely clear what you are asking and how you want it answered. For
example, if you just ask "What is your income?" The respondent doesn't know whether
you mean weekly or monthly or annual, pretax or after tax, household or individual, this
year or last year, from salary only or including dividends, interest, etc. Avoid indefinite
words or response categories. For example, "Do you jog regularly?" What does
"regularly" mean?
3) Avoid Emotional Language and Prestige Bias:
Word has implicit connotative as well as explicit denotative meanings. Likewise, title or
positions in society (e.g., president, expert) carry prestige and status words with strong
emotional connotations and issues connected to high status. People can color how
respondents answer survey questions. It is best to use neutral language. Switch
emotional language because respondents may be reacting to the emotional words rather
than the substantive issues, for example, the question “what do you think about paying
murderous terrorists who threaten to steal the freedom of peace-loving people?” They
are full of emotional words as ‘murderous’, ‘freedoms’, ‘steal’ and ‘peace’. As in another
example, you ask, “most doctors say that cigarette smoke causes lung disease for those
how are near a smoker. Do you agree?” People who think it best to agree with doctors
might agree even if they personally disagree.
4) Avoid Double Barreled Questions:
Make each question about one and only one topic. For example, don't ask "Does your
company have pension and health insurance benefits?" because if their company has only
one of those benefits, it is unclear whether the respondent will say "yes" or "no".
5) Avoid Leading Questions:
You always want respondents to believe that all response choices are equally legitimate
and never want them to become aware of an answer that you expect or want. A leading
(or loaded) query is one which leads the respondent by its language to one response over
another. There are plenty of leading queries. For examples, “You don’t snoke, do you?”
query leads respondents to claim they don’t smoke. Loaded questions can lead
respondents to either positive or negative answers. For example, “should the mayor
spend even more tax money to keep the city’s excellent street in super shape?” leads
respondents to answering “no”. A question phrased, “should the mayor allocate funds to
fix streets with large potholes that have become dangerous and are forcing drivers to
make costly repairs” leads respondents to say “yes.”
6) Avoid Questions Beyond Respondents’ Capabilities:
Asking something that respondents do not know creates confusion, and frustration and
draws inaccurate responses. Respondents cannot always recall past details and may not
know specific information. For example, asking a 40-year-old, “How did you feel about
your brother when you were 6 years old?” is probably worthless, as is asking about an
 issue respondent recalls nothing about. Respondents may give you an answer but an
unreliable and meaningless one. When many respondents are unlikely to know about an
issue, use special question formats. Clear and relevant questions increase accuracy and
validity.
7) Avoid False Premises:
If you start a query with an assumption that the respondents disagree with and give an
option about it, respondents become confused and may not answer.
8) Avoid Asking about Distant Future Intentions:
Avoid asking people about what they might do under hypothetical circumstances. A
question such as “suppose a new grocery store opens down the road. Would you shop at
it?” is usually a waste of time. It is better to ask about current or recent attitudes and
behavior now.
9) Consider Exhaustive Response:
Exhaustive means that every respondent has a choice - a place to go. For example, asking
respondents, “Are you working or unemployed” omits respondents who are not working
and who are not unemployed, such as full-time homemakers, people on vacation, full-
time students, people who are permanently disabled and cannot work, and people who
are retired to avoid such problems. First, think seriously about what you really want to
measure and consider the circumstance of all possible respondents.
10) Consider Balanced Response Categories:
Further, keep response categories balanced. Unbalanced response categories create a
type of leading question. An unbalanced choice is “what kind of job is the major doing:
outstanding, excellent, very good or satisfactory?” The way that you ask a question could
give you very different pictures of what people think.
11) Avoid Double Negative:
Double negatives in ordinary for Example, “I haven’t got no job” grammatically and
logically means that I have a job. Some people use the second negatives for emphasis.
Such errors are rare but subtle forms of the double that negative is also confusing. They
can arise when we ask respondents to agree or disagree with a statement. For example,
you ask “Do you agree or disagree that students should not be required to take a
compressive exam to graduate?” This is confusing to disagree in a double negative.
12) Avoid Overlapping and Unbalanced Response Categories:
Make response categories or choice mutually exclusive, exhaustive, and balanced.
Mutually exclusive means that the response categories do not overlap. It is easy to fix
overlapping categories that are numerical ranges (e.g., 5-10, 10-20, 20-30 become 5-9,
10-19, 20-29). Ambiguous verbal choices can be overlapping response categories for
example, “Are you satisfied with your job or are there things you do not like about it?”
Components of Questionnaire/Steps in the Construction of a Questionnaire:
1. Invitation/Salutation:
Invitation involves identifying how you are going to invite your respondents to answer the
survey questionnaire. The invitation section consists of (i) Greetings (ii) Introduction of
 the researcher and research (iii) Why the respondents have been selected to respond?
(iv) How long time does the survey questionnaire take to fill up? (v) What benefits will
they get for responding? (vi) How will their response be used (i.e. confidentiality)? and
(vii) seeking their permission to proceed.
2. Question Types:
With the permission granted from the respondent, next part is to come up with question
with the main objective of the researcher. To begin with the questionnaire for question
types, two basic subsections can be made.
a) Socio-demographic Information Section:
In socio-demographic information section, questions related to personal characteristics
such as name, address, contact number, age, sex, marital status etc. are incorporated.
Also, respondents’ social characteristics of living in the society, their social attributes and
other socio-demographic issues are also included. Further, some economic related
questions also often come within this domain. This type of question helps enumerators to
establish courtesy with the respondents.
b) Objective Based Questions Section:
The questionnaire should concentrate on objectives and targets. Questionnaire should
gather the correct information and ensure that each query is accurate, logical and
understandable. Though questionnaire development based on their objective may not be
feasible for some research, it is applicable for student-based research. Student based
research clearly defines its objective and specific objective. Some of their specific
objectives may seek secondary data, while other area based on primary data.
3. Closing Statements of Questionnaire:
In Closing statement, researcher can put final opinion of the respondent, often open
question nature, which can bring positive change of solution to their study. It also can
include the stakeholders’ role and responsibility to uplift/manage the research problem
research investigates. It gives positive feelings to the respondent for their involvement in
the questionnaire participation. Finally, greet and thanks should be given to the
respondents for their support, in written form. So, even those respondents who read
questionnaire feel their satisfaction to involve in survey.
Research Interview:
A research interview is typically a two-person meeting conducted to collect information on
a certain topic. It is a qualitative data collection method to gain primary information.
Types of Interviews:
1. Face to Face Interview:
The fact to face interview, also called an in-person interview, is popular and the oldest
form of survey data collection. It is a data collection method when the interviewer
directly communicates with the respondent in accordance with the prepared
questionnaire. It is survey method that is utilized when a specific target population is
involved. Face to face interviews can be conducted by the researchers with the target
respondents in the streets, homes, offices and meeting place. The purpose of conducting
 a personal interview study is to explore the responses of the people to gather more and
deeper information. This method enables to acquire factual information, consumer
evaluations, attitudes, preferences and other information coming out during the
conversation with the respondent.
2. Telephone Interview:
Telephone interview is a data collection method when the interviewer communicates
with the respondents on the telephone in accordance with the prepared questionnaire. It
is a quantitative research tool practiced in public opinion or other target group surveys. A
telephone interview allows interpersonal communication without a face-to-face meeting.
It is one of the cost-effective alternatives than face-to-face interview. However, telephone
interviews lack visual contact between interviewers and interviewees. Telephone
interview is a short and focused on a collection of concentrated information. Therefore,
by closing telephone surveys researchers gets prompt answers to the questions of their
interest.
3. Computer Assisted Personal Interview:
Computer-assisted personal interviewing (CAPI) is an interviewing technique in which the
respondent or interviewer uses an electronic device to answer the questions. It is similar
to computer-assisted telephone interviewing, except that the interview takes place in
person instead of over the telephone. This method is usually preferred over a telephone
interview when the questionnaire is long and complex. It has been classified as a personal
interviewing technique because an interviewer is usually present to serve as a host and to
guide the respondent. If no interviewer is present, the term Computer-Assisted Self
Interviewing (CASI) may be used.
Sources of Qualitative Data:
1. Observation:
Observation is a systematic data collection approach. It is one way to collect primary data.
It is a way to collect data by way of watching and listening to an interaction or
phenomenon as it takes place in the specific area that the researcher has selected for
his/her study. As it is a way of collecting data through observing, it involves prolonged
engagement in a natural setting or social situation. Therefore, to get accurate
information, the researcher has to emerge himself/herself in the setting where his/her
respondents are carrying out their day-to-day activities. Observational research is
prevalent in all types of research including social sciences and management research.
Observation involves going into “the field” – that may be village, market of any ordinary
society – watching how people live, how they work, what they work, how they manage
their society, how decision works or any other similar activities.
2. Focus-Group Discussion:
A focus group discussion (FGD) is a qualitative research method widely used in the social
sciences and management research. It is a discussion for 90 minutes to 2 hours among a
group of participants (typically 6 to 8). FGD involves people from similar background or
experience as it intends to discuss a specific topic of interest. It is a pre-determined semi-
 structured interview lead by a skilled moderator. FGD allows the participants to agree or
disagree with each other so that it provides an insight into how a group thinks about an
issue, about the range of opinion and ideas, and the inconsistencies and variation that
exists in a particular community in terms of belief and their experiences and practices.
Data Analysis:
➢ Data analysis is the process of collecting and organizing data to draw useful conclusion
from it.
➢ Data analysis is the process of gathering, arranging, classifying, modeling and analyzing
the data with the purpose of generating useful information.
A simple example of data analysis can be seen whenever we make a decision in our daily
lives by evaluating what has happened in the past or what will happen if we make that
decision. Basically, this is the process of analyzing the past or future and making a decision
based on that analysis.
Data Preparation and Description:
1) Data Processing:
Data processing is the transformation of information into usable and desired structure.
The change or handling is done utilizing a predefined sequence of tasks either physically
or automatically. The vast majority of the data processing is done by utilizing computers,
along these lines done consequently extricated from information and could be utilized in
various computational structures like picture, diagram, table or some other wanted
configuration relying upon the software of techniques of data processing used.
2) Data Coding:
Coding is the procedure/activity by which information/reactions are composed into the
class/classifications and numerals or different images, which are given to everything as
indicated by the class in which it falls. In other words, coding includes two significant
activities: 1) Deciding the classifications to be used and 2) Allocating individual response
to them.
3) Data Editing:
Data editing is the process, which involves the assessment and adjustment of collected
raw data. The goal of data editing is to control the quality of the collected data. Data
editing can be performed manually with the assistance of a computer of combination of
both. As far as you collect the data from various primary/secondary sources, you get it in
RAW form. This means there are chances of data encountering with errors and
inconsistencies. Hence, data editing is the process carried out to maximize its usefulness
for the purpose of which it was collected which includes (i) assessment of the data to
check for consistency, adequacy, and the detection of error and outlines and (ii) the task
of correcting the errors within the data set.
4) Data Entry:
Data entry is the process of inputting data or information into the computer. It is the
computerized way of filling in a large table of codes. Even today data collection through
paper-based questionnaire is popular among the researcher. When questionnaire have
 been used to collect data, it must enter into the computer for its further analysis process.
Data entry is often thought of as time consuming process. Once the data entry completes
researchers can start their data analysis as per their objective of nature of the data.
Statistical Analysis of Data:
A. Descriptive Analysis:
Descriptive analysis (statistics) is a sort of data research that aids in describing,
demonstrating, or helpfully summarizing data points so those patterns may develop that
satisfy all of the conditions of the data. It is the technique of identifying patterns and links
by utilizing recent and historical data. There are two kinds of descriptive statistics: -
1) Measures of Central Tendency:
These are methods of portraying the central position of a frequency distribution for a
gathering of information. It portrays the central situation of frequency distribution for
large amount of data. These measures include many different statistics such as mean,
median and mode.
a. Mean:
Mean is a measure of central tendency that offers a general picture of data calculating
average of series of data. The mean can be derived by adding the value together and
dividing by the number of items. It is often used in the study of social and economic
sector where quantitative measurement is possible.
b. Median:
Median is the size of the middle item when the items are arranged in their order of
magnitude. Median is that value which divides to the series of values in two equal parts.
It is used to divide original data into two equal parts.
c. Mode:
The mode of a data set is the most frequently displayed value. In other words, it is the
most likely value to be sampled. Mode is easily spotted in the distribution. The
distribution that occurs mostly in the data set is considered to be mode. It is the category
of value that has highest frequency.
2) Measures of Dispersion/Spread:
Measures of spread depict how spread out the appropriation is for a specific group of
information. Measures of spread give a though of the range and variety in a given
arrangement of information. This builds up a superior comprehension of the idea of
information. Measures of spread incorporate things like variance, standard deviation,
range and quartiles.
a. Range:
Range refers to the difference between the size of the largest and smallest items.
𝑅𝑎𝑛𝑔𝑒 = 𝐿𝑎𝑟𝑔𝑒𝑠𝑡 𝑉𝑎𝑙𝑢𝑒 − 𝑆𝑚𝑎𝑙𝑙𝑒𝑠𝑡 𝑉𝑎𝑙𝑢𝑒
b. Quartile Deviation:
Quartile Deviation is one half the distance from the third Quartile (Q3) and first Quartile
(Q1). Quartile divides the distribution into four equal parts.
c. Standard Deviation:
 Standard deviation is the positive square root of the arithmetic mean of the squares of
the deviations of the given observation from their arithmetic mean.
Σf(𝑥 − 𝑥)2
𝜎= √ 𝑓𝑜𝑟 𝑑𝑖𝑠𝑐𝑟𝑒𝑡𝑒 𝑎𝑛𝑑 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠 𝑒𝑟𝑖𝑒𝑠
𝑛
B. Inferential Analysis:
Inferential statistics are often used to compare the differences between the treatment
groups. Inferential statistics use measurements from the sample of subjects in the
experiment to compare the treatment groups and make generalizations about the larger
population of subjects.
Illustrative list of questions that are covered under inferential statistics:
 Is there a significant difference between the average population age and 35?
 Does population average income considerably exceed Rs.25000/- a month?
 Are there significant variations of age among users and non-users of a brand?
 Is statistically significant growth in the company’s sales?
 Are household consumption and disposable income significantly correlated?
Tools of Inferential Analysis:
1) T-test:
A t test is a statistical test that is used to compare the means of two groups. It is often
used in hypothesis testing to determine whether a process or treatment actually has an
effect on the population of interest, or whether two groups are different from one
another.
2) Z-test:
A z-test is a statistical test to determine whether two population means are different
when the variances are known and the sample size is large (n>30). A z-test is a hypothesis
test in which the z-statistic follows a normal distribution. A z-statistic, or z-score, is a
number representing the result from the z-test.
3) F-test:
An F-test is any statistical test in which the test statistic has an F-distribution under the
null hypothesis. It is most often used when comparing statistical models that have been
fitted to a data set, in order to identify the model that best fits the population from which
the data were sampled.
4) Analysis of Variance (ANOVA):
ANOVA is a statistical method used to analyze the differences between the means of two
or more groups or treatments. It is often used to determine whether there are any
statistically significant differences between the means of different groups. ANOVA
compares the variation between group means to the variation within the groups. If the
variation between group means is significantly larger than the variation within groups, it
suggests a significant difference between the means of the groups.
5) Chi Square test:
Chi square test is rigorously used as non-parametric test in the research. Generally, chi
square test is used to check the dependency of two or more than two groups. Chi square
 test is used when the data is collected in nominal scale, the sample size is more than 50,
expected frequency is not less than 5 or if it so then frequency is made more than 5
adding existing frequencies, and the individuals or events are divided into two or more
than two nominal groups.
6) Correlation:
Correlation is a reality that measures how much two factors move corresponding to one
another. In the broadest sense correlation is a measure of an interaction of variables.
Range between -1.0 to 1.0. A relationship of -1.0 shows an ideal negative connection,
while a relationship of 1.0 shows and ideal positive relationship. The connection of 0.0
doesn’t show any connection between the moves of the two factors.
7) Regression:
Regression analysis is a powerful statistical method that allows you to examine the
relationship between two or more variables of interest. While there are many types of
regression analysis, at their core they all examine the influence of one or more
independent variables on a dependent variable.
8) Multivariate Analysis:
Multivariate analysis takes a whole host of variables into consideration. This makes it a
complicated as well as essential tool. The greatest virtue of such a model is that it
considers as many factors into consideration as possible. This results in tremendous
reduction of bias and gives a result closest to reality.
Descriptive Statistics Inferential Statistics
Descriptive Statistics gives information Inferential statistics, on the other hand,
about raw data regarding its description or draw inferences about the population by
features. using data extracted from the population.
We use descriptive statistics to describe a while we use inferential statistics to explain
situation. the probability of occurrence of an event.
As for descriptive statistics, it helps to Inferential statistics helps to compare data,
organize, analyze and present data in a make hypotheses and predictions.
meaningful manner.
Descriptive statistics explains already Inferential statistics, however, aims to draw
known data related to a particular sample inferences or conclusions about a whole
or population of a small size. population.
We use charts, graphs, and tables to while we use probability methods for
represent descriptive statistics. inferential statistics.
Hypothesis Testing:
Hypothesis testing refers to the formal procedures used by statisticians to accept or reject
statistical hypothesis. In statistics, hypothesis testing is a way for you to test the result of a
survey or experiment to see if you have meaningful results. In hypothesis testing, you are
basically testing whether your result is valid by figuring out the odds that your result has
happened by chance. If your result has happened by chance, the experiment won’t be
repeatable and so has little use.
Types of Hypothesis testing:
1. Null Hypothesis:
A null hypothesis is a statement, in which there is no significant relationship between two
variables. It is what the researcher tries to disprove. It is denoted by H0. A null hypothesis
represents, no observed effect. In null hypothesis, the observations are the outcome of
chance.
2. Alternative Hypothesis:
An alternative hypothesis is statement in which there is some statistical significance
between two measured phenomena. It is what the researcher tries to prove. It is denoted
by H1. An alternative hypothesis reflects, some observed effect. In the alternative
hypothesis, the observations are an outcome of real effect.
Procedures for Testing Hypothesis:
1) State the Null and Alternative Choosing a Suitable Test Statistic:
In the process of hypothesis testing, in first step we begin by stating the value of a
population mean in a null hypothesis, which we presume is true. The null hypothesis (H0),
is a statement about a population parameter, such as the population mean, that is
assumed to be true. The null hypothesis is a starting point. We will test whether the value
stated in the null hypothesis is likely to be true. Keep in mind that the only reason we are
testing the null hypothesis is because we think it is wrong. We state what we think is
wrong about the null hypothesis in an alternative hypothesis. Statement in alternative
hypothesis (H1) directly contradicts a null hypothesis by stating that the actual value of
population parameter is less than, greater than or equal to the value stated in the null
hypothesis.
2) Establish a Level of Significance:
The second step in hypothesis testing is to set the criteria for decision. To set the criteria
for decision, we state the level of significance for a test. Level of significance refers to the
criteria of judgement upon which a decision is made regarding the value stated in a null
hypothesis. The criteria are based on the probability of obtaining a statistic measured in a
sample if the value state in the null hypothesis were true. In social and behavioral
science, the criteria or level of significance is typically set a 5%. When the probability of
obtaining, a sample, mean is less than 5%., i.e., if the null hypotheses were true, then we
reject the value stated in the null hypothesis.
3) Choosing a Suitable Test Statistics:
The test statistics is a mathematical formula that allows researchers to determine the
likelihood of obtaining sample outcomes if the null hypothesis was true. The value of the
test statistics is used to make a decision regarding the null hypothesis. Specially, the test
statistics tells us how far, or how many standard deviations, a sample mean is from the
population mean.
4) Obtain the Critical Value:
Researcher has to follow the next step to establish a critical region, which is the area
under the normal curve divided into two mutually exclusive regions. These regions are
 termed as acceptance region (where the null hypothesis is accepted) and the rejection
region or critical region (when the null hypothesis is rejected). If the computed value of
the test statistics falls in the acceptance region, the null hypothesis is accepted otherwise
it is rejected. For making a decision regarding the acceptance or rejection of null
hypothesis, a researcher has to determine the critical values which separate the rejection
region from the acceptance region. The determination of critical value depends on the
size of the rejection region, which is directly related to the risk involved in decision
making.
5) Conclusion:
Researcher needs to make decision in this stage of hypothesis testing. We use the value
of the test statistics to make a decision about the null hypothesis. The decision is based
on the probability of obtaining a sample mean, given that the value stated in the null
hypothesis is true. If the probability of obtaining a sample mean is less than 5%, when the
null hypothesis is true, then the decision is to reject the null hypothesis. If the value is
greater than 5%, then the decisions is to retain the null hypothesis.
Type I Error: Rejecting true null hypothesis(H0)
A type I error appears when the null hypothesis (H0) of an experiment is true, but still, it is
rejected. It is stating something which is not present or a false hit. A type I error is often
called a false positive (an event that shows that a given condition is present when it is
absent). In words of community tales, a person may see the bear when there is none
(raising a false alarm) where the null hypothesis (H0) contains the statement: “There is no
bear”. The type I error significance level or rate level is the probability of refusing the null
hypothesis given that it is true. It is represented by Greek letter α (alpha) and is also known
as alpha level. Usually, the significance level or the probability of type I error is set to 0.05
(5%), assuming that it is satisfactory to have a 5% probability of inaccurately rejecting the
null hypothesis.
Type II Error: Accepting false null hypothesis(H0):
A type II error appears when the null hypothesis is false but mistakenly fails to be refused. It
is losing to state what is present and a miss. A type II error is also known as false negative
(where a real hit was rejected by the test and is observed as a miss), in an experiment
checking for a condition with a final outcome of true or false. A type II error is assigned
when a true alternative hypothesis is not acknowledged. In other words, an examiner may
miss discovering the bear when in fact a bear is present (hence fails in raising the alarm).
Again, H0, the null hypothesis, consists of the statement that, “There is no bear”, wherein, if
a wolf is indeed present, is a type II error on the part of the investigator. Here, the bear
either exists or does not exist within given circumstances, the question arises here is if it is
correctly identified or not, either missing detecting it when it is present, or identifying it
when it is not present. The rate level of the type II error is represented by the Greek letter β
(beta) and linked to the power of a test (which equals 1−β).
Methods of Analyzing Quantitative Data/Different types of data analysis tool:
1) Excel:
Excel is a basic, popular and widely used analytical tool almost in all industries. Whether
you are an expert in Sas, R or Tableau, you will still need to use Excel. Excel becomes
important when there is a requirement of analytics on the client’s internal data. It
analyzes the complex task that summarizes the data with a preview of pivot tables that
helps in filtering the data as per client requirement. Excel has the advance business
analytics option which helps in modeling capabilities which have a prebuilt options like
automatic relationship direction, a creation of DAX measures and time grouping.
2) STATA:
STATA is a general-purpose statistical software package developed by STATA Corp for data
manipulation, visualization, statistics, and automated reporting. It is used by researchers
in many fields, including economics, sociology, political science, biomedicine, and
epidemiology. Stata is a powerful statistical software that enables users to analyze,
manage, and produce graphical visualizations of data. It is primarily used by researchers
in the field of economics, biomedicine, and political science to examine data patterns.
Stata has a topical index built into the online help system. Stata has an easy-to-use point
and click interface providing access to its commands. Stata also has a simple, consistent
command syntax.
3) EViews:
EViews is a Statistical package for Windows, used mainly for time-series oriented
economic analysis. It is developed by Quantitative Micro Software, now a part of HIS.
Version 1.0 was released in March 1994, and replaced Micro Tsp. Using EViews, you can
quickly and efficiently manage your data, perform econometric and statistical analysis,
generate forecasts of model simulations, and produce high quality graphs and tables for
publication or inclusion in other applications. EViews is designed with your workflow in
mind. EViews is a good software for statistical or any other business analysis. In
professional level people can hand data easily using EViews. A s a researcher EViews can
greatly help in analyzing and visualizing data. One can clean data for error and check
consistency using EViews.
4) SPSS:
SPSS Statistics is a software package used for interactive or batch, statistical analysis. Long
ago produce by SPSS Inc, it was acquired by IBM in 2009. Current versions have the brand
name. IBM survey companies, government entities, education researchers, marketing
organizations, data miners, and many more for processing and analyzing survey data,
such as you collect with an online survey platform like Alchemer. SPSS statistics is a
software package used for logical batched and non-batched statistical analysis. SPSS is
user friendly software & easy to use for the beginners and also helps in analysis even
when the data set goes larger.
5) R Programming:
 R is the leading analytics tool in the industry and widely used for statistics and data
modeling. It can easily manipulate your data and present in different ways. It has
exceeded SAS in many ways like capacity of data, performance and outcome. R compiles
and runs on a wide variety of platforms viz- UNIX, Windows and MacOS. It has 11,556
packages and allows you to browse the packages by categories. R also provides tools to
automatically install all packages as per user requirement, which can also be well
assembled with big data.
6) Tableau Public:
Tableau Public is a free software that connects any data source be it corporate Data
Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps,
dashboards, etc. with real-time updates presenting on web. They can also be shared
through social media or with the client. If you want to see the power of tableau, then we
must have very good data source. Tableau’s Big Data capabilities makes them important
and one can analyze and visualize data better than any other data visualization software
in the market.
7) Python:
Python is an object-oriented scripting language which is easy to read, write, maintain and
is a free open-source tool. It was developed by Guido Van Rossum in late 1980’s which
supports both functional and structured programming methods. Python is easy to learn
as it is very similar to JavaScript, Ruby and PHP. Also, Python has very good machine
learning libraries viz. Scikit Learn, Theano, Tensorflow and Keras. Another important
feature of python is that it can be assembled on any platform like SQL server, a MongoDB
database of JSON. Python can also handle text data very well.
Methods of Analyzing Qualitative Data:
1) Content Analysis:
Content analysis is a research tool used to determine the presence of certain words,
themes, or concepts within some given qualitative data (i.e. text). Using content analysis,
researchers can quantify and analyze the presence, meanings, and relationships of such
certain words, themes, or concepts. As an example, researchers can evaluate language
used within a news article to search for bias or partiality. Researchers can then make
inferences about the messages within the texts, the writer(s), the audience, and even the
culture and time of surrounding the text.
2) Narrative Analysis:
Narrative analysis is a type of qualitative data analysis that focuses on interpreting the
core narratives from a study group's personal stories. Using first-person narrative, data is
acquired and organized to allow the researcher to understand how the individuals
experienced something. Instead of focusing on just the actual words used during an
interview, the narrative analysis also allows for a compilation of data on how the person
expressed themselves, what language they used when describing a particular event or
feeling, and the thoughts and motivations they experienced. A narrative analysis will also
consider how the research participants constructed their narratives
3) Thematic Analysis:
Thematic analysis is a method for analyzing qualitative data that involves reading through
a set of data and looking for patterns in the meaning of the data to find themes. It is an
active process of reflexivity in which the researcher’s subjective experience is at the
center of making sense of the data. Thematic analysis is typical in qualitative research. It
emphasizes identifying, analyzing, and interpreting qualitative data patterns. With this
analysis, you can look at qualitative data in a certain way. It is usually used to describe a
group of texts, like an interview or a set of transcripts. The researcher looks closely at the
data to find common themes: repeated ideas, topics, or ways of putting things.

By: rahulkanwaredu@gmail.com
Ch.7) Writing Proposals and Project Reports

Project Work:
A project work is a planned and definitely formulated piece of study involving a task or
problem taken up by the learner, either individually or in a group, to supplement and apply
classroom and laboratory transactions. It follows the approach of Learning by Doing and
Learning by Living.
Purpose of Project Work:
The word 'purpose' means the reason for which something is done. In academic research
the purpose of project work indicates the reason for doing particular research by the
students to fulfill particular requirements or consideration under given guidelines. Project
work challenges students to think beyond the boundaries of the classroom and help them
develop their skills, behaviors and confidence.
Methods of Project Work:
1. Case Study Method:
Case Study is a research method that is commonly used in social sciences. It is a detailed
study often descriptive and exploratory, of a specific subject, person, group, place, event,
organization, community and phenomena. A case study method is appropriate research is
the researcher wants to get in depth knowledge about a specific real-world subject. It is
helpful for looking at unique situations and allows researchers to gather detailed
information about a specific individual, groups of people or organizations. Case studies
are good for describing, comparing, evaluating and understanding different aspects of
real problems. Case studies in Management research are generally used to explore the
key characteristics, meaning and implications of a firm, market and human beings related
to management domain.
2. Small-Scale Survey Method:
It is most common method of project work in undergraduate level. In recent time small-
scale survey has emerged as one of the most popular and common approaches to social
sciences research. When researcher wants information about people’s attitude and
behavior regarding objects and phenomenon, small-scale survey method is used. In
general, what people do, what they think, what they want about their business and
surroundings are the prime issues in small-scale survey. Small-scale survey follows
research methodology that can be conducted by using qualitative research design,
quantitative research design and mixed of both called mixed research design.
3. Feasibility Study:
Feasibility study is another research method of project work. As it is complex compared
to previous two methods, it is less preferred research method for project work by
undergraduate students. Feasibility study is an assessment of the practically of a purpose,
plan, project and system. Often feasibility study is done as a project under the name of
Business Development Plan or Project Master Plan. The goal of feasibility study is to
understand throughout all aspects of project or product that the researcher selects for
 his/her study. Conducting feasibility study of any project or product helps researchers to
identify the strength, weakness, opportunities and threats of the project and product.
Research Proposals:
➢ A research proposal is a concise and coherent summary of your proposed research. It
sets out the central issues or questions that you intend to address. It outlines the general
area of study within which your research falls, referring to the current state of
knowledge and any recent debates on the topic.
➢ A research proposal is a structured, formal document that explains what you plan to
research (your research topic), why it’s worth researching (your justification),
and how you plan to investigate it (your methodology).
Functions of Research Proposal:
1) As a Means of Communication:
The document is an essential resource on which the committee base the elements of
review, consultation and increasingly significant to endorse the implementation of the
research project. Moreover, it serves a comparative function for people holding the
various funding agencies. There is also a direct dependency among the nature of
assistance, the economy of consultation and the likelihood of financial support on the
cleanness and meticulousness of the proposal.
2) As a Plan:
The proposal fills in as an overall structure of the research project. Its empirical research
comprises very cautious and efficient interpretations of some confined arrangement. The
relevancies of results are evaluated in terms of strategies that are utilized in making
records and interpretations. Accordingly, the basis on which the thesis or report will be
judged on the basis of its supporting arguments and explications. The research report can
be no superior to the investigation plan. Hence, an adequate proposal helps to set forth
the plan in detailed form.
3) As a contract:
A complete proposal constitutes an agreement between the student and their advisors,
which needs to be approved for its execution and signed by all the members of the
sponsoring committee. The approved proposal results in agreement between investigators
and funding sources. It further depicts the study that, whenever conducted with
competency and completeness, the proposal ought to provide a basis to a report that
would satisfy all guidelines for acceptability. Similarly, once the agreement has been made,
everything but minor changes should occur only when arguments can be made
immediately. The proposal needs to be in final form before data are collected. Substantial
corrections should be made uniquely with the presence of full committee. When the report
is endorsed in a conclusive structure, neither the student nor the sponsoring faculty
members are allowed to adjust the guidelines of agreement through a one-sided decision.
Types of Research Proposal:
1. Internal Proposal:
An internal proposal is composed for making some learning within the organization. They
are short, ranging from one to three pages, laying out the statement of the problem, study
objectives, research design and schedule which is sufficient for exploratory study. In
internal proposals, frequently the literature review and reference index aren’t pushed
because it would be expressed in the research design section. For instance, while writing a
proposal to someone within the organization, it is called an internal proposal. An internal
proposal can be both an academic proposal and non-academic research proposal.
2. External Proposal:
An external proposal is generally composed for the audience, beyond the organization. It
can be either solicited or unsolicited.
a. Solicited Proposal: It is such a proposal which is prepared only after the request of
individuals/organizations. A client may request a proposal either verbally, or in a written
form. Thus, a solicited proposal furnishes researchers with a portrayal on the basis of
client needs.
b. Unsolicited Proposal: An unsolicited proposal is a composed application for a unique idea,
submitted to an agency based on the agreement. Such proposals are not prepared upon
the request rather by the researcher’s intense professional will activity.
3. Academic Proposal:
An academic proposal is the initial phase of creating a thesis or a major project. Its aim is
to persuade a supervisor or academic committee so that a researcher’s topic and
approach are rigorous in order to gain endorsement to genuine research.
Components of Research Proposal:
1. Title Page (or Title):
This is only a conditional title for the expected research. Over a span of proposed
research, a researcher will have the option to reconsider their title. Title needs to be brief
however it should be far reaching enough to highlight the value of proposed work.
2. Introduction/Background of the study:
In this segment, a researcher ought to clarify the extensive foundation against the
research. Researchers needs to show a concise review of the general area of study inside
which the proposed research falls, summing up the present status of information and
recent discussions on the subject. This will permit the researcher to present a similarity
with relevant fields as well as the capability to communicate effectively. More specifically,
a background segment should provide enough foundation to empower readers and by
showing how its solutions will advance the field or becomes significant with similar kinds
of work.
3. Statement of the Problem:
Statement of the problem refers to the brief portrayal of the issues that should be
addressed by the researcher. It is then utilized to focus and center the researcher towards
 the beginning, map the effort, and use it to approve that the efforts and results address
the statement of the problem.
4. Research Objective:
The research objectives are the outcome viewed by the researcher towards the end of
research. Likewise, at the end of the study, these objectives are what the research needs
to achieve. A research objective generally falls on two categories which are the main
objective and the specific objective(s)
5. Research Hypothesis:
Hypothesis refers to a particular explanation of forecast. It describes, as opposed to
theoretical terms, regarding what the researcher expects will occur in the further study.
There are two types of research hypothesis: Null hypothesis (H0) and alternative
hypothesis (H1). However, not all studies consist of hypotheses whereas a single study
may comprise one or numerous hypotheses.
6. Need and Significance of the Study:
This incorporates the meaning of the research based on the researcher's personal view
and ought to incorporate how the research impacts others either to some degree or
entirely. It examines what individuals or groups of people may benefit by pursuing the
research.
7. Limitation of the Study:
It refers to such an impact which a researcher can't control. They are the pitfalls,
conditions or impacts that can't be within the control of a researcher. It tends to place
restrictions on the methodology and conclusion part. Such limitations which may impact
the outcomes need to be mentioned.
8. Literature Review:
A literature review is an assessment of information, in a report form, identified in those
literatures of the author's chosen region of the study. Such review portrays, sums up,
assesses and provides lucidity on available literature. Likewise, it gives a hypothetical base
for the research and helps the researcher to decide the nature of their research.
9. Research Methodology:
It is the study of concentrating how research is to be done. Basically, it is the strategy of
describing, clarifying and anticipating in regards to how the research is done. It is also
characterized as the review of methods by which the information is taken. The
methodology section should cover (i) research design (ii) population and sampling
procedure (iii) nature and sources of data collection (iv) research framework and definition
of variables, (V) methods of analysis. It should be written in future tense in the proposal.
10.References:
A reference is the arrangements of all the sources that a researcher utilizes during the
research. It provides formatted details about the sources thus the reader can discover
and confirm it. Reference should follow APA, MLA or other prescribed styles of guidelines.
11. Appendix (optional):
 Appendix are the extra parts of research which entail detailed information and look
better to put independently. It might incorporate letter seeking participants, moral
endorsement, tables and figures, which generate additional support. Some of them are
KII, FGDs and Questionnaire related forms. Given the proposal is composed for
sponsored/funded project, the following parts ought to be included.
12. Gantt Chart (Optional):
Gantt chart is a type of bar chart that illustrates the timeline of the research that the
researcher purposed. Though it is not compulsory for all types of researcher student in
proposal writing phase, some institution demand such chart to show the actual timeline
of the overall research. It helps researcher to meet their timeline.
13. Budget (Optional):
Budget preparation is less in practice where the students are self-financed for his/her
degree. But it is often required by the funding agencies to fund any types of academic
research. While preparing budget, the spending plan ought to be introduced as the sponsor
demands. Some organizations/institutions may have their own budget templates too.
Features/Characteristics of a Research Proposal:
1. Is Innovative:
A common understanding in academic research is that to be a successful proposal it must
be innovative. Innovative means it should have sense of creativity and originality. That
means research proposal should be unique, relevant and not the mere repetition of
already published work.
2. Includes Specific Aims:
Another feature of a successful research proposal is that it must indicate specific aims. It
means research proposal should set the specific objectives appropriately. Such objectives
must be achievable and measurable in the given time frame.
3. Includes Primary Data:
Research can be conducted by using primary and secondary data. But, as Harvard Medical
School, working with primary data should be aim of proposal. Primary data related work
allows researcher to learn much more practical and behavioral aspects compared to
secondary data. Hence, a successful proposal, at undergraduate level, should include
primary data related work.
4. Describe Approach:
Another important feature of a successful research proposal is to describe approach of
research. Researcher should make clear concept whether they are following qualitative,
quantitative or mixed research approach. Clear in research approach helps researcher to
set their study design and complete the work as per their plan.
5. Indicates the Significance of the Proposal:
Indicating significance of the study is another important feature of a successful research
proposal. It shows worth of carrying out the research. The proposal must explain how the
study could benefit targeted people, society, concerned organizations and government
body. It also expresses the impact on science and the researchers’ personal growth.
Research Report:
➢ A research report is a well-crafted document that outlines the processes, data, and
findings of a systematic investigation.
➢ Research report is an oral presentation and written statement. It is the last stage of each
research wherein research methodology, analysis, its findings, etc. are presented and
sorted in the most deliberate way.
According to William G. Zikmund, the purposes of research report are: -
a) To communicate research results
b) To make strategic recommendations
c) To provide conclusions to management or other specific audience
Process of Research Report:
1) Initial Preparation:
First of all, a researcher needs to be intellectually and mentally prepared to compose the
report. It is very important to comprehend the reason and objective of a report.
Researcher should be able to answer what information is required for logical inference of
the study. Researchers should also be familiar about the study, its methodology. planning
and implementation of the report. Thus, it is valuable to prepare a time schedule so that
researchers follow the schedule to be on the right track and similarly complete the report
within the time.
2) Gathering the Material:
In this phase, researcher should gather all the necessary documents like reading materials,
data, facts, figures and other important materials from the sources they are available and
accessible. To gather the materials, researcher can use several methods such as
observation, reading published and unpublished document, interview, discussions with key
informants and supervisor, surveys, secondary sources, internet surfing and experiments.
3) Sorting, Selecting, Arranging, and Recording the Material:
After the materials are gathered, the researcher needs to check in order to guarantee
that all the required arguments of the research are collected. Then, it should be arranged
in a logical sequence so that it ensures a natural flow while writing. Similarly, the
questionnaires, surveys and other documents need to be clarified appropriately. In order
to guarantee the clearness, appropriate notation should be used for visual outlines i.e.,
diagrams, maps, charts, measurable tables and so forth
4) Writing the Introduction:
After completion of material collection and recording the materials, researcher can write
the introduction. While writing the introductory note for research report, researcher
must take the following points into consideration:
• Create the background of the work and workplace for which the research is being carried
out.
• Describe the purpose behind the study.
• Explain all the troubles/challenges experienced during the planning of report.
• Being a researcher, give an introduction of yourself and the institution briefly.
• Ensure progress in between all the areas.
5) Reading Inferences:
The flow of conclusion or findings should come from the outcome of the investigation
carried out. Then, it should lead to a prompt recommendation and managerial
implications. During the conclusion part, the objectives of the study should be kept in
mind so that it encourages the understanding of the research quality and simultaneously
measures the work progress. Similarly, an outline of alternative solutions to the problem
should be expressed. Besides, the reason behind dismissing the possible alternative
should be clarified very logically.
6) Writing Recommendations:
while writing recommendations, the researcher should possess a productive and positive
attitude. Those focuses which need to be portrayed in recommendation should be
enlisted first. However, if there is a chance of recommending financial figures, it should be
included in corresponding areas of the content.
7) Finishing Strongly on a Positive Note:
During this phase, the report is nearly completed and the researcher ought to proceed for
further stages of the presentation.
Types of Research Report:
1. Formal and Informal Reports:
A formal report refers to an official report that contains detailed information, research
and data important in order to settle business decisions. Generally, this report is
composed with the view of solving a problem. On the other hand, informal reports are
used to share important information with individuals or a small group of people too. It is
generally composed of brief information and can be delivered in email or memo format.
2. Written and Oral Reports:
An oral report refers to a report given in a verbal form. Oral reports are now and again
utilized in various sorts of verbally spoken presentations and in business meetings as well.
Whereas a written report is composed of writing and afterwards, transmitting to the
readers. It is also called as formal means of communication, where the intended message
needs to be carefully drafted and formulated in a well-written form. It also acts as a
source for future reference.
3. Internal and External Reports:
Internal reporting is an indispensable segment of every corporation, yet in most
organizations, it's full of complexities as it may include both the versioning overlap and
manual processes. These barriers result in inaccuracy, inefficiency and a great deal of
disappointment. On the other hand, an external report has the company information in it.
It is meant for the general public and its associated stakeholders to get the useful
information.
4. Long and Short Reports:
A short report is also called informal report while a long report is sometimes referred to as
formal report. A short report is often no more than a single page of statement containing
 facts and figures in the most concise manner. This style of report is often casual and relaxed.
Long report always has a title, introduction, body and then conclusion. It is always more
than one page in length. It also contains a covering letter that maintains all the details that
are included in the long report. At the end of the long report, there is bibliography and
appendix. It is common to have a long report printed and binded with hard cover. The tone
in a long report is restrained and somber in contrast to a short letter.
5. Technical and Popular Reports:
A technical report is a document that describes the process, progress or results of technical
or scientific research or the state of a technical report. It is also called scientific report. This
report provides the state of a technical or scientific research problem. It is used whenever
a full written report of the study is required either for record keeping or for public
dissemination purposes. On the other hand, A popular research report is one for a general
audience; that is, for individuals who do not necessarily have any knowledge in the field of
study. A popular research report aims to make information accessible to everyone.
6. Descriptive and Analysis Reports:
Analytical reports tend to offer both the informative and analytical parts. It includes
recommendations at the end after the vivid observation of the data set. On the other
side, Descriptive report includes surveys and fact-finding inquiries of different kinds. The
major purpose of descriptive research is to provide the description of the state of affairs,
as they exist at present.
Procedure for Writing Research Reports:
1) Analyzing Subject Matter Logically:
Logical analysis of the subject matter is the first step of any report writing. The subject
matter can be analyzed either logically or chronologically. Logical development requires
mental work to construct/develop the association between study subject matters. Logical
treatment consists developing subject matter from simple to complex. On the other hand,
chronological development of subject matter deals with the time frame and the changes
that have been observed in the subject matter in the period of time.
2) Final Outline Preparation:
Once a subject matter is developed, next step is to prepare final outline. Outline
preparation is important task to the researcher as it gives details of the to do list. Outline
preparation helps researcher to construct their study and helps to organize material in a
logical way. It also reminds researcher the part (or points) to be stressed in the report. As
outline preparation takes time and effort, researcher should prepare outline in a
comprehensive way and should not change the final outline frequently.
3) Rough Draft Preparation:
Rough draft preparation is an important aspect in the process of research report. It
provides hints to the researcher whether the outlines prepared are well constructed,
practical and doable. By preparation of the rough draft, researcher is now ready to start
writing what he/she has done in the context of his/ her study. The researcher should start
writing rough draft immediately after the completion of the outline. Because the idea
 developed in the outline may be forgotten by the researcher if he/she delays in writing
the draft. While writing rough draft, researcher has to arrange all the materials required
for his/her report that has been already prepared in the final outline.
4) Take Rest for Few Days:
Once the rough draft is completed, researcher should leave such draft for some days to
forget what he/she has done to prepare his/her rough draft.
5) Revising, Rewriting and Polishing Rough Draft:
After the break, a careful revision should be done to bring rough draft into final draft.
Revising whole document thoroughly is mandatory in this stage. If researcher missed to ill
up any gap, later on s/he should follow the same process, which is equally time
consuming. In many areas re-writing the previous document may be required to make it
complete. At the end, policing the report is a must. All typos, grammatical errors,
coherence and other communication aspects should be taken care of. While developing
final report, researcher should keep in mind the readers of the report.
6) Prepare the Final References/Bibliography:
The next step is to prepare a final list of references/ bibliography. References are the list
of the referred sources while preparing the report.
7) Writing the Final Draft:
The final step is to write the final draft. It must follow a concise and objective style using
simple language. Jargon, vague words and abstract terminologies should be avoided
while writing final draft report. A research report should reflect authors originality and
able to reflect their work in terms of what they purposed in their research objective. The
report must contribute to the scholarly field and add the knowledge of both the reader
and the researcher
Conventions of Academic Writing:
1. Style Conventions:
Style describes the manner in which authors write. Style includes elements such word
usage, sentence structure, tone, voice and many other aspects.
• Numbers and dates
• Capitals
• Print enhancement
• Abbreviations
• Typing and Spelling
2. Tone Conventions:
Tone conventions in academic writing often suggest to:
• Follow formal writing
• Jargon and cliché-free
• Impersonal writer
3. Formatting Conventions:
 Format convention turns the information gathered into a well-written essay or report.
Formatting conventions follows specific guidelines provided by the university or
institution. Some of the guidelines for formatting conventions are:
• Standard font size
• Margins
• Format of paragraph
• Use of quotations
• Use of abbreviations
• Format for table and figure
• Page numbering and style
• Title page
• Prefatory section requirement
• Body section requirement
• Supplementary section requirement
Components of the Project Report:
1) The Preliminary Section:
The preliminary section makes report more formal and useful for variety of purposes. It
adds formality in report, emphasizes report content and allocates information that make
reports more comprehensive, quick and authenticate. The prefatory section includes:
a) Title Page
b) Declaration
c) Certificate from the Supervisor
d) Acknowledgements
e) Table of Contents
f) List of Tables
g) List of Figures
h) Abbreviations
2) Main Body of the Report:
The body is the main part of any research work. It contains supportive evidence that
researcher collect and arrange to make readers more understandable. The body should
have logical organization. The body contains 3 main headings.
i. Chapter I: Introduction
ii. Chapter II: Descriptive Analysis
iii. Chapter III: Summary and conclusion
Research Methodology:
A research methodology describes the techniques and procedures used to identify and
analyze information regarding a specific research topic. It is a process by which researchers
design their study so that they can achieve their objectives using the selected research
instruments.
1) Research Design:
 Research design is a basic plan of methods and procedures for providing guideline to
research for collecting data analyzing the research. The research design ensures the data
to meet the objective of the study. It explains the action and movement of researcher
towards his/her goods. Researcher therefore needs to explain which research design
(such as exploratory, descriptive, comparative) their study falls on and why?
2) Population and Sample, and Sampling Design:
Population in research represents what the researcher aims to study for and sample
represents how many from the population researcher wants to investigate during his/her
study. Researcher should clearly identify the population of his/her study and select
sample representatives to the population. While planning sampling, researcher must be
sure which sampling technique - probability or non-probability he/she is selecting. Be
sure that, data generated through probability sampling techniques is considered good for
generalization of their findings from the study.
3) Nature and Sources of Data, and the Instrument of Data Collection:
Data is important part of the research. Thesis and dissertation of humanities and social
sciences stream often require work done with data analysis. Data can be of different
types as quantitative data, qualitative data and mixed data. Whatever the nature of data
is, its collection is important part. The collection of data differs as per its nature. It can be
therefore sub-categorized into:
• Collection of Secondary data: Data collection in secondary data requires sources to
recognize. First researcher needs to identify their variables and the year they plan to
collect data for. Then the student requires to verify where the data is located. It leads
them to understand the availability of their data and possible result analysis.
• Collection of Primary data: In primary data students need to do a lot as they have to
identify their study area and population, sample the population, prepare questionnaire
and collect the data required to fulfill their quest. In both data collection process,
researcher should follow the method of data collection.
4) Methods of Analysis:
The most important part is method of data analysis. In this part researcher needs to
stipulate what is their plan for data analysis and how they are planning to execute their
data analysis part in order to bring the desirable results. It should maintain which type of
analysis is prepared to get the result that has set for the objectives in their study. What
types of tools they are planning should also be maintained. Regression and many other
data analysis models can be developed and illustrated in this section. Finally, summary
table showing objective-wise data analysis can be prepared to understand and follow the
actual process to analyze their data.
5) Research Framework and Definition of Variables:
In the method section research framework discussed about how the existing theories fit
in the present study and from which perspective. Research framework is logically
developed argument that the researcher tries to justify in his/her work. It can be in
mathematical form, functional form, graph and chart and in a logical order written form.
 Research with a sound research framework means the work is meaningful and can
generalize in a bigger context. The main idea of this framework is to show how research
has been planned to complete by the research in a logical sequence. The development of
research framework must be guided by the review of theory and empirical studies in
literature review section of the study. The research framework must show all the
variables that the researchers are undertaking in his/her study. Hence, after the
framework development, the researcher should define each of the variables and establish
their relationship with their study. It is better to explain how such variables are used in
other similar studies.
Documenting Sources:
Documenting means showing where you got source information that's not your own.
Remember, a research paper blends your ideas with ideas and information from other
sources. Documentation shows the reader what ideas are yours and what information and
ideas you've taken from a source to support your point of view.
APA Style of Citation and Referencing:
APA style is a writing style and format for academic documents such as journal, articles and
books. American Psychological Association (APA) has prepared and distributed a style for
writing which is commonly known as APA style. APA is the commonly used to cite sources in
Psychology, Education, Management and Social Sciences.
One Authored Article:
Structure:
Author, A. A. (Year). Title of article. Title of Journal, Volume (issue), page-page
Reference:
Moody, M. S. (2019). If instructional coaching really works, why isn’t it working? Educational
Leadership 77(3), 30-35
Two Authored Book:
Structure:
Author A. A. & Author B. B. (Year). Title of Book. Location: Publisher
Reference:
Stoneman, R., & Pattanayak, S. (2008). Alexander the Great: A life in legend. Yale University
Press
Essentials of Good Research Reports:
1. Factual Accuracy:
A research report should be precise, extra careful in quoting facts and statistical data,
specific and accurate. Supply of too much information might lead to confusion. Since, the
information in the report cover all significant facts, findings, analysis and conclusion
which are used to make crucial decisions. Research is based on relevant accurate data
and information gathered from valid source and scholarly avoiding incorrect and
unnecessary data.
2. Precision and Objectivity:
 Drafting a report also terms as mapping report. First of all, academic topic should keep in
our mind before drafting a report. The report should be objective in nature. It should be
free from the writer's personal view point. The professional report conveys technical
information encompassed by precision writing including direction. The valuable research
paper turns report into clarity and conciseness because it brings trust and credibility.
3. Relevance:
A report includes relevant information that adds quality in report writing. Report writer
must concern about statistics, empirical evidences and facts which are relevant to
objective and actual subject of report. Irrelevant sources and information might mislead
report and can make the research weak and make it unrealistic. The journals, books,
published reports etc. are relevant pieces of information form scholarly sources. The
proper reference makes report more impressive, accurate and relevant where data and
facts are inevitable.
4. Clarity:
A rough outline creates mind mapping diagram among students which is essential for
academic report. The mind mapping diagrams will help to address main points in the
report achieving objectives of clarity. Formal, academic, simple and clear language should
be used in academic report. The students should try to avoid long and complicated
sentences and terms as far as possible
5. Standard Format:
The standard report writing follows structure, which organizes content in a systematic
order. The standard format includes abstract, introduction, research or analysis,
results/findings, conclusion and references.

By: rahulkanwaredu@gmail.com