Econ 2063: Research Methods

Chapter-5: Data Colleciton and Management

Lemi D.

Department of Economics
Addis Ababa University
School of Commerce

January 11, 2021

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Introduction

Once an appropriate research topic is determined, proper data

collection, retention, and sharing are vital to the research
enterprise.
Data are the foundation of economic research since every
study is a search for information about the given topic.

Definition: Data refers to any group of facts, measurements, or

observations used to make inferences about the problem of
investigation.

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Introduction

Data can range form material created in a laboratory, to

information obtained in social-science research, such as a
filled-out questionnaire, video and audio recordings, or
photographs, etc.
No research project has unlimited funds, so selection of the
most promising data usually depends on the priorities of cost
and convenience.
I Collection of data should be feasible and the data should
be sufficient to test the hypotheses.
The first step in good data management is designing your
experiment in a way that will create meaningful and
unbiased data, that will not waste resources, and that will
appropriately protect human and animal subjects.

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Introduction

We also need to consider the replicability of findings in data

management.
I If data are not recorded in a fashion that allows others to
validate findings, results can be called into question.
Data selection should precede actual data collection.
I Clear data selection standards set in advance help prevent
selective data reporting—selectively excluding data that
are not supportive of a research hypothesis—later in the
research.
There are a number of methodological issues researchers
should be aware of when selecting data:
I Data types (e.g., nominal, ordinal, interval, or ratio
measures).
I Sampling frame and sample size, instruments.
I Methodologies, etc.

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Data Collection

Data collection is the process of gathering and measuring

information on variables of interest in an accepted systematic
fashion.
Rigorous collection methodologies, when based on a foundation
of solid data selection, enable researchers to answer the
research question(s), test hypotheses, and evaluate outcomes.
Data collection methods vary by discipline and data types; but
the emphasis on ensuring accurate collection remains the same.
Both the selection of appropriate data collection instruments
and clearly delineated instructions for their use reduce the
likelihood of error.

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Data Collection

Consequences from improperly collected data include:

I Inability to answer research questions accurately;
I Inability to repeat and validate the study;
I Distorted, inaccurate findings;
I Wasted resources;
I Misleading other researchers to pursue fruitless avenues of
investigation;
I Compromised decisions for public policy or private
decision-making;
I Harm to human participants and animal subjects.

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Data Collection

While the impact of faulty data collection varies by discipline

and the nature of investigation, poor collection may cause
disproportionate harm when the results of the flawed research
are used to support public policy recommendations.
Effective methods make the detection of errors
easier—whether the errors are intentional and deliberate
falsification or inadvertent systematic or random errors.
As with data selection, it is crucial that researchers have
sufficient skills to ensure the integrity of their data collection
efforts.
I For instance, quality data collection requires a rigorous
and detailed recruitment and training plan for data
collectors.

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Data Management Issues
Storage and Protection

Research data must be stored securely both during a research

project and after it ends.
Reliable security policies and procedure are essential to
safeguard data stored electronically or in the physical form.
Everyday risks like fire, water or other environmental damage,
or common technical failure like hard disk crashes, must be
considered.
It is essential to make backup copies of your data periodically
and store the copies in a secure location.

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Data Management Issues
Confidentiality

Confidentiality refers to limiting information access and

disclosure to authorized users and preventing access by or
disclosures to unauthorized persons.
If research data are maintained on a personal computer, it is
essential to keep the PC physically secured, update software
regularly, particularly anti-malware utilities, and using access
protections.

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Data Management Issues
Integrity

Integrity refers to the trustworthiness of the information

collected.
It postulates that data have not been modified inappropriately,
whether accidentally or deliberately.
Integrity includes the notion that the person or entity in
question gave the right information,
I i.e., the information reflects the actual circumstances
(validity) and under the same circumstances would
generate identical data (reliability).

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Data Collection Techniques

The critical question here is from where and how to get the
data.
Data can be acquired from secondary or primary sources, or
from both.
Secondary Sources of Data
Secondary data are those which have been collected by other
individuals or agencies.
As much as possible, secondary data should always be
considered first, if available.

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Secondary Data

When dealing with secondary data you should ask:

I Is the owner of the data making them available to you?
I Is it free of charge? If not, how will you pay?
I Are the data in a format that you can work with?
I A description of the sampling technique, i.e., how the
sample was collected is also necessary.
Secondary data may be acquired from various sources:
I Reports of various kinds, books, periodicals, reference
books (encyclopedia), university publications (thesis,
dissertations, etc.), policy documents, statistical
compilations, proceedings, personal documents (historical
studies), the Internet, etc.

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Secondary Data

Advantages of secondary data:

I can be found more quickly and cheaply,
I most researches on past events or distant places have to
rely on secondary data sources.
Limitations of secondary data:
I The information often does not meet one’s specific needs.

For example, definitions might differ, units of measurements

may be different and different time periods may be involved.
I Difficult to assess the accuracy of the information
I Data could also be out of date.

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Primary Sources of Data

Primary data refers to data that came into being for the first
time by the people directly involved in the research.
There are two approaches to primary data collection:
I the qualitative approach
I the quantitative approach
Qualitative data collection approaches
Qualitative data can be acquired from:
I case studies;
I Rapid appraisal methods;
I Focus group discussions (FGDs); and
I Key informant interviews

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Qualitative Data

i. Case studies
A case study research involves a detailed investigation of a
particular case through interviews or through a direct
observation (field visits).
ii. Rapid Rural Appraisal (RRA)
RRA is a systematic expert observation but semi-structured
activity often carried out by a multidisciplinary team.
The RRA method:
I takes only a short time to complete,
I tends to be relatively cheap, and
I makes use of more ’informal’ data collection procedures.
Includes interviews with individuals, households, and key
informants as well as group interview techniques.

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Qualitative Data

iii. Focus Group Discussions (FGDs)

A FGD is a group discussion guided by a facilitator, during
which group members talk freely and spontaneously about a
certain topic.
The group of individuals are expected to have experience or
opinion on the topic and are selected by the researcher.
Its purpose is to obtain in-depth information on concepts,
perceptions, and ideas of a group.
It is more than a question-answer interaction.
I The idea is that group members discuss the topic and
interact among themselves with guidance from the
facilitator.

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Qualitative Data

iv. Key Informant Interview

The key informant interview technique is an interviewing
process for gathering information from opinion leaders such as
elected officials, government officials, and business leaders, etc.
This technique is particularly useful for:
I Raising community awareness about socio-economic
issues.
I Learning minority viewpoints.
I Gaining a deeper understanding of opinions and
perceptions, etc.

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Qualitative Data

v. Triangulation
Triangulation refers to the use of more than one approach to
the investigation of a research question in order to enhance
confidence in the findings.
The purpose of triangulation is to obtain confirmation of
findings through convergence of different perspectives.
By combining multiple methods, and empirical materials,
researchers can hope to overcome the weakness or biases and
problems that are associated with a single method.

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Quantitative Primary Data Collection Methods

A quantitative research method involves a numeric or

statistical approach to research design.
It involves the collection of data so that information can be
quantified and subjected to statistical treatment.
Primary data may be collected through:
I Direct personal observation method,
I Survey or questioning other persons,
I From a literature search, or
I Combining the above.

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Quantitative Primary Data Collection Methods

i. The Observation Method

Observation includes the full range of monitoring behavioral
and non-behavioral activities.
Advantages:
I It is less difficult and has less bias.
I One can collect data at the time it occurs and need not
depend on reports by others.
I With this method one can capture the whole event as it
occurs.

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Quantitative Primary Data Collection Methods
The Observation Method

Weaknesses:
I The observer normally must be at the scene of the event
when it takes place. But it is often difficult or impossible
to predict when and where an event will occur.
I Observation is also a slow and an expensive process since
it requires either human observers or some type of costly
surveillance equipment.
I Its most reliable results are restricted to data that can be
determined by an open or deliberate action or surface
indicator.
I Limited as a way to learn about the past, or difficult to
gather information on such topics as intentions, attitudes,
opinions, and preferences.

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Quantitative Primary Data Collection Methods

ii. The Survey Method

This is the most commonly used method in economic research.
To survey is to ask people questions in a questionnaire, mailed
or handled by interviewers.
Advantages:
I It is a versatile/flexible method, capable of many different
uses.
I It does not require there to be a visual or other objective
perception of the sought information by a researcher.
I Questioning might be the best way to learn about opinion
and attitudes of people.
I Surveys tend to be more efficient and economical than
observations—surveying using telephone or mail is less
expensive.

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Quantitative Primary Data Collection Methods
The Survey Method

Weaknesses:
I The quality of information secured depends heavily on the
ability and willingness of the respondents.
I A respondent may interpret questions or concepts
differently from what was intended by the researcher.
I A respondent may deliberately mislead the researcher by
giving false information.
Surveys could be carried out through:
I Face to face personal interview;
I By telephone interview;
I By mail or e-mail, or;
I By a combination of all these.

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Designing a Questionnaire

In developing a survey instrument, the following issues need to

be considered carefully:
I Question content
I Question wording
I Response form
I Question sequence
In other words, both the subject and wording of each question
as well as the psychological order of the question needs to be
considered.
Questions that are more interesting, easier to answer, and less
threatening usually are placed early in the sequence to
encourage response.

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Designing a Questionnaire
Main Components of a Questionnaire

Covering letter: brief purpose of the survey, who is doing it,

time involved, etc. The cover letter provides your best chance
to persuade the respondent to complete the survey.
Identification Data: respondent’s name, address, time and
date of interview, code of interviewer, etc.
Instruction: Include clear and concise instructions on how to
complete the questionnaire. These must be very easy to
understand, so use short sentences and basic vocabulary.
Information sought: major portion of the questionnaire.

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Designing a Questionnaire
General Considerations

A questionnaire is developed to directly address the goals of

the study.
Well-defined goals are the best ways to assure a good
questionnaire design.
Hence, ask only questions that directly address the study
goals. Avoid the temptation to ask questions because it would
be "interesting to know".
As a general rule, with only a few exceptions, long
questionnaires get less response than short questionnaires.
Response rate is the single most important indicator of how
much confidence you can place in the results.
I You must do everything possible to maximize the
response rate.
I Hence, keep your questionnaire short to maximize
response rate.
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Designing a Questionnaire

There are good and bad questions. Qualities of a good

question are as follows:
I Evokes the truth: Questions must be non-threatening.
I Asks for an answer on only one dimension.
I Can accommodate all possible answers.
I Has mutually exclusive options.
I Produces variability of responses.
I Follows comfortably from the previous question.
I Does not presuppose a certain state of affairs.
I Does not imply a desired answer.
I Does not use emotionally loaded or vaguely defined words.
I Does not use unfamiliar words or abbreviations.

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************* End of Chapter Five *************

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