QUEENS COLLEGE BUSINESS RESEARCH METHODS.pptx

Queens’ College
WELCOME TO:
BUSINESS RESEARCH METHODS &
TECHNIQUES
Credit hours: 2

Course Chapters
1. Chapter One: Research Introduction
2. Chapter Two-Research proposal and Research
Process
3. Chapter Three-Sampling and Sample Design
4. Chapter Four-Data Collection
5. Chapter Five-Data Analysis Methods
6. Chapter Six-What statistical analysis should We use?

CHAPTER ONE:
RESEARCH INTRODUCTION

Literally, Research (Re-Search) means “To
Search Again”
 Research is a structured inquiry that utilizes
acceptable scientific methodology to solve
problems and creates new knowledge that is
generally applicable.
 Research is a process for collecting, analyzing
and interpreting information to answer
questions.
 Is the application of the scientific method in
searching for the truth about a particular
1.1 Definitions of Research

Research is:
 Refers to the systematic and rational method
consisting of identifying the problem,
formulating the hypothesis, collecting relevant
data, analyzing the data, and reaching certain
conclusions either in the form of solutions
towards the concerned problem or in certain
generalizations for some theoretical
formulation.
Research is a movement from the unknown to
the known

Research is:
 Hence, Business Research is designed to
facilitate the managerial-decision making
process for all aspects of the business such as
finance, marketing, HRM, production,
performance.
Scientific Method-the way researchers go
about using knowledge and evidence to reach
objective conclusions about the real world.

Why Research?
 To discover/seek answers to questions through the applications of
scientific procedures
 To gain familiarity with a phenomenon or to achieve new insights
into it (exploratory research studies)
 To describe accurately the characteristics of a particular
individual, situation or a group (descriptive research);
 To determine the frequency with which something occurs
(diagnostic research);
 To test a hypothesis of a causal relationship between variables
(Explanatory).
1.2. OBJECTIVES OF
RESEARCH

What makes people to undertake research?
1. Desire to get a research degree along with its
consequential benefits;
2. Desire to face the challenge in solving the
unsolved problems,
3. Desire to get intellectual joy of doing some
creative work;
4. Desire to serve the society;
5. Desire to get respectability.
1.3. MOTIVATION IN RESEARCH

Research methods refer to all those
methods/techniques that are used for conduction
of research
-research methods can be put into the following
three groups:
1. Methods which are concerned with the
collection of data.;
2. Statistical techniques which are used for
establishing relationships between the data
3. Methods which are used to evaluate the
accuracy of the results obtained.
1.4. Research methods vs Research methodology

Research methodology
 Research methodology is a way to
systematically solve the research problem.
 A science of studying how research is done
scientifically.
 The strategy one chooses to answer
research questions
 It is the various steps that are generally
adopted by a researcher in studying his
research problem
Research method is a part of Research
Conti…

1 Business Research may be classified into two based on its Purpose
A. Applied Research: Is also called Action Research
 It is a research conducted to address a specific business decisions/problem for a
specific firm or organization.
 A research undertaken to answer questions about specific problems or to make
decisions about a particular courses of actions or policies.
 Is aimed at certain conclusions (say a solution) facing a concrete business problem.
 Focused on immediate solutions
B. Basic or Pure or Fundamental Research
 A type of research conducted without a specific decision in mind that usually does
not address the needs of a specific organization.
 It attempts to expand the limits of knowledge in general and is not aimed at solving
a particular pragmatic problem.
 Focused on formulating knowledge (theory)
Types of Research

2. Business Research may be classified into Three on the basis of
Specific Objective of the Research
A. Descriptive Research
 The major purpose of descriptive research is to describes characteristics of objects,
people, groups, organizations, or environment.
 Tries to paint a picture of a given situation by addressing who, what, when, where,
and how questions like the current economic and employment situation of a
country, survey on consumer decisions (demand and supply situations)
 Description of the state of affairs as it exists at present.
 Describes what is happened or what is happening
 Its goal is to describe the current status of a given phenomenon.
B. Explanatory Research
 Is also called causal research.
 Seeks to identify cause-and-effect relationships like how will implementing a new
employee training program change job performance?
Types of Research

C. Exploratory Research
 Is conducted to clarity ambiguous situations or discover ideas that may be potential
business opportunities
 is not intended to provide conclusive evidence from which to determine a
particular course of action
 Is conducted because a problem has not been clearly defined
 Is conducted when there are few or no earlier studies to which references can be
made for information
 Its purpose is to gain background information and better understand and clarify a
problem
 Can give some indication as to the why, how, and when something occurs.
 The results of exploratory research are not usually useful for decision-making by
themselves, but they can provide significant insight into a given situation.
 In business exploratory research is particularly useful in “new product
development”
Types of Research

3. Business Research may be classified into Two based
on “Approaches” of research
A. Quantitative Research
 Quantitative research generates statistics through the use of
large-scale survey research, using methods such as
questionnaires or structured interviews.
 is based on the measurement of quantity or amount.
 It is applicable to phenomena that can be expressed in
terms of quantity.
Quantitative research helps:
1. Precise measurement
2. Knowing trends or changes overtime
3. Comparing trends or individual units

2. Qualitative Research
 Qualitative research explores attitudes, behaviour and experiences
through such methods as interviews or focus groups.
 There is typically a high level of researcher involvement with
subjects; strategies of participant observation and in-depth,
unstructured interviews are often used.
 The data produced provide a description, usually narrative, of
people living through events in situations.
 This type of research aims at discovering the underlying motives
and desires,
 Attitude or opinion research i.e., research designed to find out how
people feel or what they think about a particular subject or
institution.

Quantitative versus Qualitative
Quantitative Research Strategy
 Investigation aims to assess a pre-
stated theory (Deductive Reasoning)
 Often involves hypothesis testing
 Attempts to minimise the influence
of the researcher on the outcome
 Quantitative data infers statistics
 Data collection therefore requires
‘closed’ responses
Qualitative Research Strategy
 Investigation aims to create a novel
theory (Inductive Reasoning)
 Researcher becomes an inherent
part of the study
 Qualitative data infers complex
statements or opinions
 Data collection therefore permits
‘open’ responses

QUEENS COLLEGE BUSINESS RESEARCH METHODS.pptx

Research proposal and
Research Process
Chapter Two

What is a research proposal?
 The research proposal is a systematic plan, which brings to focus the
preliminary planning that will be needed to accomplish the purpose
of the proposed study.
 The research proposal is the detailed plan of study
 Is a document which sets out your ideas in an easily accessible way.
 The objective in writing a proposal is to describe what you will do,
why it should be done, how you will do it and what you expect will
result.
 The written proposal forces the students/researchers to clarify
their thoughts and to think about all aspects of the study.
The Research Proposal

A well-thought out and well-written proposal can be judged according
to three main criteria.
 Is it adequate to answer the research question(s), and achieve the
study objective?
 Is it feasible in the particular set-up for the study?
 Does it provide enough detail that can allow another investigator to
do the study and arrive at comparable results?
Importance of Research Proposal:
 It serves as a basis for determining the feasibility of the project.
 It gives the research supervisor a basis for guiding the researcher
while conducting the study.
 It reduces the probability of costly mistakes.
The Research Proposal

• What do you want to do? – research question
• Why do you want to do it? – Any information gap
• Why is it important? – any practical importance or
knowledge advancement
• Who has done similar work? - background
• How are you going to do it? -methodology
• How long will it take? – plan of work
What a proposal should contain? It is based on your clear
research question.

Components of the Research Proposal
The basic components of a proposal are described in the order in which they most logically appear in a proposal.
1. Preliminary Part/Section
o Title Page
o Abstract
o Table of Contents
o List of Abbreviations
o List of Figures and Tables
2. Introduction
o Background of the Study
o Statement of the Problem
o Research Objectives
o Research Questions
o Significance of the Study
o Scope and Delimitations of the Study
o Organization of the Paper
3. Review of Related Literature
o Conceptual Definitions
o Empirical Findings
o Conceptual Framework and Conclusion
The Research Proposal…

4. Research Design and Methods
o Research Design
o Sampling Techniques and Procedure
o Sources of Data and Collection Methods
o Operational Definition of Variables
o Method of Data Analysis
5. Bibliography/References
6. Work Plan
7. Budget Schedule
8. Appendices/Annexes
The Research Proposal…

1. The Title
 It should give sufficient information about the nature of study
 The title should not be too lengthy. It should be specific to the area
of study.
 The title should not be burdened by pompous words and the language
in the title should be professional
 The title of your research proposal should state your topic exactly in
the smallest possible number of words.
 All words in the title should be chosen with great care, and
association with one another must be carefully managed.
 Put your name, the name of your department/faculty/college, the name of your
advisor(s) and date of delivery under the title.
2. Abstract: is a one page brief summary of the proposal.
Components of the Research Proposal…

2. Introduction
Background of the Study
The background of the research proposal should address the following
points:
 Sufficient background information to allow the reader to understand
the context and significance of the question you are trying to address
 Proper acknowledgement of the previous work on which you are
building
 The introduction should be focused on the research question(s).
 Explain the scope of your work, what will and will not be included.
 It gives a background information about the issue under study

2. Introduction
Statement of the Problem
 Concerns the reason why you are going to conduct a research on this issue
 Effective problem statements answer the question “Why does this research need to
be conducted.”
 The purpose of the problem statement is to identify the issue that is a concern and
focus it in a way that allows it to be studied in a systematic way.
 Identifying the research gap (Time, Methodological, Conceptual)
Objectives and Research Questions
 Consists clear research goal (what to attain if the problem is solved)
 Develops key research questions that show the major building blocks of the
problem statement that need answer in your research
 General and Specific (tells one thing at a time)

Delimitations of the Study/Scope of the study
 Delimiting research is giving full disclosure of
what the researcher intends to do or does not
intend to do.
 When a researcher is able to set the scope of
the study, one can make the research
manageable. At the same time, this can lead to
the choice of research method to employ.
Significance of the study
 The justification for the need of the research.
 The benefit you get as a researcher
 How the results of the study will be useful to

Review of Related Literature
 Discusses the theoretical and empirical framework
 provides information about what was done, how it was
done, and what results were gathered.
 Defines the technical terms and phrases that operationalize
the concepts and that have special meanings.
 Summary of the literature that contains the major issues
that you will adapt to your research or that you will use in
your research.

Researchers ask the following questions before any
research
 what types of research has been done in the area?
 What has been found in previous studies?
 What suggestions do other researchers make for
further study?
 What has not been investigated?
 How can the proposed study add to our knowledge of
the area?
 What research methods were used in previous
studies?
Answers to these questions will usually help develop

Research Design and Methods
1. A Research Design:-
 Is a master plan that specifies the methods and procedures for collecting and
analyzing the needed information.
 It constitutes:- where will the study be carried out? What type of data is required?
Where can the required data be found? What techniques of data collection will be
used? How will the data be analyzed?
2. Sampling Techniques and Procedures:-
 A sample design is a definite plan for obtaining a sample from a given population.
 Probability Vs Non-probability Sampling Techniques
 Steps contain Clearly define the population, List down the sample frame,
Determine the optimum “sample Size”, Decide the sampling
techniques/procedures.

3. Sources of Data and Collection Methods:-
 Is the process of gathering or collecting data from sample units
 Sources od data may be “Primary Vs Secondary”
 Data Collection Methods: Questionnaire, Interview, Direct Observation, Focus Group
Discussion
4. Methods of Data Analysis:
 After the data have been collected, the researcher turn to the task of editing, coding,
and analyzing the data.
 Is the application of reasoning to understand the data that have been gathered

Bibliography/References: You should include a short
list of references used in the proposal.
Work Plan-Time Schedule
Budget Schedule/financial breakdown/

Assignment -1
1. Prepare a Research
Proposal

2.4. Research Process
 Research process consists of series of actions or
steps necessary to effectively carry out research
A brief description of these steps is as follows:
1. Formulating the Research Problem
2. Extensive Literature Review
3. Developing Working Hypothesis
4. Preparing the Research Design
5. Determining Sampling Design
6. Collecting the data
7. Execution of the project
8. Analysis of Data
9. Hypothesis Testing
10. Generalization and Interpretation
11. Preparation of the Research Report

1. Formulating the Research Problem
The first and most important step in the research
process.
It means defining the problem precisely
It is like determination of the destination before
undertaking a journey.
A problem defined is half solved- Formulation of
problem is often more essential than its solution
 It refers to some difficulty which a researcher experiences in the
context of either a theoretical or practical situation and wants to
obtain a solution for the same
There are two types of research problems:
A. Those which relate to states of nature and;
B. Those which relate to relationships between

Conti…
Formulation of a problem involves the
following steps:
a) Statement of the problem in a general way
b) Understanding the nature of the problem
c) Surveying the available literature
d) Developing the idea through discussion
e) Rephrasing the research problem into a
working proposition.
NB- it should answers “ why you are
conducting research on the given topic”

Criteria for selecting a problem
Internal
• researcher’s
interest
• researcher’s
competence
• researcher’s
own
resources
i.e., finance,
time, etc.
External
• Researchability i.e., (problems having
solutions)
• Importance, urgency, usefulness and social
relevance, i.e., relative importance and
significance of problem visa -a -vis utility of
expected findings
• Novelty or originality
• Feasibility
•Availability of data
•Suitable methodology
•Cooperation of organizations and
individuals
•Available time
• Facilities /infrastructure
• Reason validity
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Some important sources for selecting a problem:
• Professional Experience,
• Contact and Discussion with People,
• Inference from theory,
• Professional Literature, and
• Technological and Social Changes.
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Evaluation of the Problem
• Before the final decision is passed on the
investigation of the problem, the feasibility of the
problem has to be tested in terms of personal
suitability of the researcher and social value of
the problem.
• In short, the research problem should be
evaluated in terms of the following criteria:
•Is the problem researchable?
•Is the problem new?
•Is the problem significant?
•Is the problem feasible for the particular
researcher?
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A research problem is explained in the
form of:
• Objective of the study
• Basic Research questions
• Hypothesis
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2. Extensive Literature Review
Once the problem is formulated, a brief
summery of it should be written down.
 Literature Review gives an overview of the
problem
 Provide indication of why the problem is
worth considering
 Explain what contribution the study will
make
 Cite one or more studies that are directly
relevant to the proposed study or lead to
the theoretical justification.

Literature review:
 It is a body of text that aims to review the critical
points of current knowledge including substantive
findings as well as theoretical and methodological
contributions to a particular topic.
Reasons for Reviewing Literature
a) Bringing clarity and focus to the research
problem
b) Improving the methodology
c) Broadening the researcher knowledge in the
research area.
d). Contextualize your findings.
Reviewing a literature is a continuous process.
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Four main reasons of reviewing the
Literature
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Conti…
Procedures in reviewing the literature
There are four steps involved in conducting a
literature review:
 a) Search for existing literature in your area of
study
 b) Review the literature selected
 c) Develop a theoretical framework
 d) Develop a conceptual framework.
 1. Distinguish theoretical framework and
conceptual framework?

Styles of Referencing
 Styles of referencing differ.
 Currently two referencing styles are
commonly used:
• the Harvard style and
• the American Psychological
Association (APA) style, both of which
are author-date systems.
 It is important to apply an adopted style
strictly and consistently
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1. The Harvard style
 Referencing in the text (in-text citation)
 The Harvard style is an author-date system.
 It usually uses the author’s name and year of
publication to identify cited documents within
the text.
Referencing in the references or
bibliography
-AUTHOR(S) (Year) Title. Edition. Place of
publication: Publisher.
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2) APA Style
 Referencing in the text (in-text citation)
The APA style is also an author-date
system.
It usually uses the author’s name and
year of publication with punctuation to
identify cited documents within the text.
Referencing in the references or
bibliography
-AUTHOR(S),Title. Edition. Place of
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3. Developing Working Hypothesis
 After extensive literature survey, researcher should
state the working hypothesis in clear terms.
It is tentative assumption made in order to draw out
and test its logical or empirical consequences.
Hypotheses affect the manner in which tests must be
conducted in the analysis of data and indirectly the
quality of data which is required for the analysis.
Hypotheses should be very specific, limited,
operationalisable, conceptually clear and should be
related to the body of knowledge.
It should also be stated in precise and clearly defined
terms and also it has to be tested.

Conti…
 It indicates the type of data, the type of
methods of data analysis to be used and it
guides the researcher by delimiting the area
of research and keep him on the right track
there are two common categories of
hypothesis:
 1. Research Hypothesis
 2. Alternate Hypothesis

Steps involved in hypothesis
testing
The various steps involved in hypothesis
testing are stated below:
1. Making a formal statement
2. Selecting a significance level
3. Deciding the distribution to use
4. Selecting a random sample and computing
an appropriate value
5. Calculation of the probability
6. Comparing the probability

Errors in Hypothesis Testing
In hypothesis testing, there are four possible
outcomes:
 The hypothesis is true but our test leads to its
rejection
 The hypothesis is false but our test leads to
acceptance
 The hypothesis is true and our test leads to
acceptance
 The hypothesis is false and our test leads to its
rejection
The first two lead to an erroneous decision. The first

Deduction & Induction
A. Deductive reasoning- works from the more
general to the more specific. Sometimes this is
informally called a "top-down" approach.
It begins with thinking up a theory about a topic of
interest; then narrow to more specific hypotheses

B. Inductive reasoning
Inductive reasoning- works the other way, moving from
specific observations to broader generalizations and theories.
Informally, sometimes called as "bottom up" approach.
It begins with specific observations and measures; then
formulate some tentative hypotheses, and finally end up
with developing some general theories.

4. Preparing the Research Design
 Research Design- Decisions regarding
what, where, when, how much, by what
means concerning an inquiry or a research
study constitute
 A research design is the arrangement of
conditions or the blueprint for collection,
measurement and analysis of data.
 Research design is a plan, structure and
strategy of investigation so conceived as to
obtain answers to research questions or
problems

Characteristics of a Good Design
• The design that clearly stated the
objective of the problem to be studied,
the nature of the problem to be studied,
• The design which is characterized by
adjectives like flexible, appropriate,
efficient, economical, etc. ;
• The design which minimizes bias and
maximizes the reliability of the data
collected and analyzed;
• The design which gives the smallest
experimental error;
57

• The design which yields relevant
information and provides an
opportunity for considering many
aspects of a problem;
• The design that assumes the
availability and skills of the researcher
• The design that consider the
availability of time and money for the
research work
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decisions happen to be in respect
of:
(i) What is the study about?
(ii) Why is the study being made?
(iii) Where will the study be carried out?
(iv) What type of data is required?
(v) Where can the required data be found?
(vi) What periods of time will the study
include?
(vii) What will be the sample design?
(viii) What techniques of data collection will be
used?
(ix) How will the data be analyzed?

Research designs can be broadly
categorized into three:
• Exploratory
• Descriptive
• Experimental
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Exploratory Design
The main purpose is to discover
new ideas and insights
A very flexible, open-ended
process.
 Require qualitative or mixed
approaches
61

Descriptive Design
• Describes attitudes, perceptions,
characteristics, activities and
situations.
• Examines who, what, when,
where, why, & how questions
• Most of the social researches fall
under this category
• Require quantitative, qualitative
and mixed method approach
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Experimental Design
• Provides evidence that a cause-
and-effect relationship exists or
does not exist.
• Premise is that something (and
independent variable) directly
influences the behavior of something
else (the dependent variable).
• Most practical to talk about
associations or impact of one
variable on another.
• Require quantitative or mixed
approaches
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5. Determining Sampling Design
 A sample design is a definite plan determined
before any data are actually collected for obtaining
a sample from a given population.
 Sample design consists about
1. Sample size and
2. Sampling techniques
Sampling techniques can be either probability
sampling or non-probability sampling.

6. Collecting the data
 Some times data at hand are inadequate, and
hence, it becomes necessary to collect appropriate
data
There are two major sources of data
A. Primary Data
B. Secondary Data
 Primary data can be collected either through
experiments or through survey.
 If the researcher conducts experiment, he
observes some quantitative measurements or data
with examines or test the truth

Conti…
But in the case of a survey, data can
be collected by any one of the following
ways:
a. Observation
b. Interview
c. Questionnaire

7. Execution of the project
 The project should be executed in a systematic manner
and in time.
8. Analysis of Data
 The analysis of data requires a number of closely
related operations such as:
a). Establishment of categories- the application of
these categories to raw data through coding,
classification, tabulation, and graphing;
b). Data description (measure of central tendencies,
dispersion, and relation) and data interpretation
c). Drawing statistical inferences.

9. Hypothesis Testing
 After analyzing the data, test the hypothesis.
 Various tests of hypotheses, such as Chi square test, t-
test, F-test may be applied.
10. Generalization & Interpretation
 If a hypotheses is tested, it is possible for the researcher
to arrive at generalization, i.e., to build a theory.
 If the researcher had no hypotheses, the findings on the
basis of some theory is known as interpretation.
11. Preparation of the Research Report
 Finally, the researcher has to prepare the report in
appropriate formats and appropriate language.

Chapter Three
Sampling and
Sample Design

How and why sampling relate to
business research?
 The world is large and full of people
 We wanted to find out things about
people
 Sampling is a practical way of studying
people and their activities, thoughts,
attitudes, abilities, relationships in
relation to business through taking small
bit
 Note that sample must be
representative of the population from

Why we need sampling?
Sampling makes possible the study
of a large, heterogeneous
population.
Sampling is for economy.
 Sampling is for speed .
 Sometimes,Sampling is for
accuracy.
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Sampling: Important terms
 Population: is the total set of units in which a researcher is
interested; Can be finite or infinite population
 Examples: All employees of an organization to study the reasons
of employee turnover
 Element/case: a single member of the population.
 Census: includes all the elements in the population
 Two conditions are appropriate for census study:
 when the population is small ( for populations under 50 it is
usually more sensible to collect data from the entire population)
 When the variability is high (when the elements are quite
different from each other) and when the size is manageable
 Sampling: is the process of selecting units into a sample from a
larger set of the same units (Population)

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 Sampling frame: a listing of all the elements in the
population from which the sample is drawn
 For example the list of employees found in
personnel department to get information on
employee turnover
 Unit of analysis: the type of object whose
characteristics the researcher wants to measure and
study.
 For example: If data are collected on Employees,
the unit of analysis is employee.
 Is the object that the hypothesis describes.
 All variables in a hypothesis must be
operationalized for the same unit of analysis.

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 Sampling unit: a unit or set of units considered for selection at
a stage of sampling.
 Sampling unit may or may not be the same as a unit of
analysis. It is possible to include several units of analysis.
 For example, if the researcher wants to interview senior
managers in the public sector, the senior managers become
the unit of analysis and the public organisations across the
country become sampling unit.
 Parameter: is a characteristics of the population about
which researchers are interested to find out.
 Example: The average income of all families in a city or the
age distribution of the city’s population.

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 Statistics: characteristics of a sample and is
developed from information about the member of the
sample,
 are used to make estimates of population
parameters
 Example: The mean income computed from a
sample or the age distribution of that sample are
statistics.
 Sampling errors: the difference between population
parameter and the statistical estimate.
 sampling error can be expressed through the use of
confidence levels and confidence intervals.
Example: being 95% confident that the population

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 Sample bias: misrepresentation of the
population by the sample. Caused by the flaw
in the design or in the implementation of
sampling procedures.
 Sampling fraction: % of population selected
for the sample
 Sample size: the number of elements selected
for the sample to represent the population.

Determination of Sample Size
Sample size determination is influenced by:
 The purpose of the study,
 Population size,
 The risk of selecting a "bad“ sample,
 Nature of the population- The degree of
Heterogeneous or homogenous
 Nature of study (qualitative or quantitative)
 Sampling design or type of sample
 The resource availability
78

Characteristics of a Good Sampling
Design
• Truly representative
• Having small sampling error
• Economically viable
• Systematic bias is controlled (in a better way)
• Results can be applied to the population in
general with a reasonable level of confidence
• Optimum size (adequately large)
• Similar to population-should have all the
characteristics that are present in the population
79

Steps in sampling Design
Defining the population – target
population
Listing the population – sampling
frame
Obtaining an adequate sample size
–
Based on different approaches
Selecting a representative sample
– based on appropriate method
80

 STRATEGIES FOR DETERMINING SAMPLE SIZE
 using a census for small populations (e.g., 50 or less).
 imitating a sample size of similar studies,
 using published tables, and
 applying formulas to calculate a sample size
 For continuous values
 Where n is sample size, Z is the abscissa at specific confidence level,
s standard deviation of the sample and d is the precision level.
Sample size
2









d
Zs
n

82
Sample Design
 Sample design: the set of procedures for
selecting the units from the population that are
to be in the sample.
Two major types of sample design
 Probability/Random sampling:
 Non probability/None random sampling

83
A. Probability/ Random sampling
Four types of probability sampling
I. Simple random sampling:
II. Systematic random sampling
III. Stratified random sampling
IV. Cluster sampling
I. Simple Random Sampling
 Each unit in the population has equal chance of
being selected.
 Can be lottery method or a random number table
 It requires a complete list of the study
population. The researcher assigns each member
of sampling frame a number before selecting

84
Simple random sampling
 Helps to eliminate the inadvertent
introduction of sample bias.
Example: assume there are 150 employees
(with BA degree and above) in the
organization with the problem of high
employee turnover. If the sample size is 35
employees. Use lottery and random number
table. to select the sample elements.

85
Simple random sampling
Procedures:
1. Identify the population: All employees
with BA degree and above in the
organization
2. The sampling frame: The list of
employees with BA degree and above,
names are sequentially numbered from
001 to 150
3. Prepare numbered balls equal to the
number of the population
4. Thoroughly mix up the balls and then
5. Draw 35 balls from the 150 balls.

86
II. Systematic Random sampling
 It requires the complete list of population
 It reduces the amount of effort required to draw a sample
and provides adequate results. But it does not result in a truly
random sample
 Applicable when the researcher wants to pick households in
the sample from the population of consecutive households
found along a street/road.
Procedures:
 Population has N units. Plan to sample n units and then
 The sampling interval/skip= N/n------K
 Line-up all N units and Randomly select a number between 1 and K
 Select the randomly selected unit and every kth unit after that
Example: the list contains 10,000 element and you want a sample of 1,000:
 Sampling interval = Population size/Sample size=10
 Randomly select a number between 1 and 10. Assume the first element
in the sample is number 7, then the selection of elements continue as
7, 17, 27 …, 9987, 9997

87
III. Stratified sampling
 Involves a process of stratification or segregation,
followed by random/purposive/sample from each
stratum.
Procedures
1st: divide or classify the population into strata, or
groups, on the basis of some common characteristics
such as sex, race, or institutional affiliation, level of
management, or income, etc.
-Mutually exclusive groups: the classification should
be done so that every member of the population is
found in one and only one stratum.
2nd Determine the size to be taken from each
stratum (proportionately or disproportionately)
rd

 It ensures homogeneity within each stratum, but
heterogeneity between strata
 Stratified sampling can be further classified as:
A). Proportional:- take different sample from each
stratum based on the proportionality of the stratum size
B). Disproportional:- not consider the size of
prospective respondents, but will take the same sample
from each stratum
Stratified sampling

89
IV. Cluster Sampling
 It involves division of elements of a population into
geographical groups-the groups are termed clusters
Recommended when:
 it is necessary to study a large geographical area
and
 It is difficult to identify the sampling frame
 The geographical distribution of the members is
scattered
Stages in cluster sampling
1. The sampling frame is the complete list of clusters
rather than individuals
2. Select a few clusters, normally using simple random
sampling technique.
3. Collect data from the selected clusters either using
census or by taking sample.

 It is different from stratified sampling,
because Every cluster is not sampled; where
as every stratum is sampled in the case of
stratified sampling.
 It saves time and money
 it may requires larger sample than other
methods for the same level of accuracy
 It may losses key information as a result of
random selection and re-selection process of
groups.
Cluster sampling

V. Multi-stage Sampling
 This method is used in large-scale surveys.
A sample of first—stage sampling units is
chosen, each of the selected units is divided
into second- stage units, samples of second-
stage units are selected, and so on
 Different methods (simple random,
stratified, systematic or cluster sampling)
may be used at any stage
 The first-stage units may be Country →
regions → Woredas → kebeles →
91

92
B. Non probability/ None random sampling
 Four types
1. Convenient sampling
2. Purposive sampling (Expert sampling)
3. Quota sampling
4. Snow ball (referral Sampling)
Non-probability sampling designs
 Can work well for exploratory studies
 Useful if it is not important to obtain accurate
estimates of population characteristics
 The units are selected at the discretion of the
researcher
 Cheaper and easier to carry out than probability
designs
 one cannot estimate parameters from sample
statistics

93
I. Convenience sampling
 Also called haphazard or Accidental sampling
 It involves collecting information from members of the
population who are conveniently available to provide
it.
 For example: collecting information from Volunteers
It is get and talk approach
 Criteria: The availability/ the ease of obtaining/ and
willingness to respond
 Convenient and Economical to sample employees in
a nearby area
 During election times TV channels often present man-
on-the-street interviews to reflect public opinion.

94
II. Quota sampling
 It classifies the population into group and then
select a quota of individual units with defined
characteristics in given the population
 It is not a representative of the population
 It is a type of stratified sample in which selection of cases
within strata is entirely non-random.
 Is called Dimensional sampling If all dimensions of the
population are considered in quota sample
 It can also be administered proportionally or
disproportionally
 Example: A researcher is interested to assess the attitudes of
employees towards working condition. male are 60 percent and
female are 40% in the organizations: If Sample size is 30 employees,
then 18 conveniently available male and 12 female workers will be
sampled

95
III. Purposive sampling
 Is judgmental/ deliberate sampling
 Identify and target individuals who are believed to
be typical of the population being studied.
 The researcher uses his own judgment about
which respondents to choose, and
 Picks only those best meet the purposes of the
study.
Expert sampling: involves selecting persons with
known experience or expertise in an area.
 With purposive sampling the sample is ‘hand
picked’ for the research

96
IV. Snowball /referral sampling
 Snowball: Individuals are discovered initially,
and then each individual is used to locate others
(the names & addresses) who possess similar
characteristics and who, in turn, identify others.
 Used when members of a population cannot be
located easily by other methods and where the
members of a population know each other.
 Example: we may want to sample very small
populations who are not easily distinguishable from
the general population or who do not want to be
identified, example drug users, homeless people

Questions about sample design?

Chapter Four:
DATA COLLECTION METHODS

1. What are the Types of Data?
2. What are the ethical issues in Data collection?
3. What are the methods of primary data collection?
4. Questionnaire:
a. Closed ended: Advantages and disadvantages
b. Open ended Advantages and disadvantages
c. What are the do’s and don'ts in developing questionnaires
5. Interview
a. Types of interview
b. Advantages and disadvantages
6. Observation
a. Types of observations
7. Focus group discussion
a. Moderator
8. Secondary data
a. Sources
Discussion Pints

 Ethical issues: protection from harm, informed consent, right to
privacy and honesty with professional colleagues
 Data can be primary or secondary
1.1. The primary data:
 are data collected for the first time and hence they are original
 Major sources of primary data are diaries of eyewitness, tape-
records, films, letters and autobiographies
1.2. The secondary data:
 are those which have already been gathered by someone else and
which have already been passed through the statistical process
 These include historical studies based on the actual data, statistical
research based on census data, newspaper reports of an event,
company records, government publications
1. Data collection methods

11/15/2012
1.1. The primary data

 The questionnaire is usually mailed, administered
personally or electronically
 Preparation of questionnaire can be tedious, involving
several drafts and more than one pretests
Questions can be asked to gather
information on:
 Facts: help to get objective information from
respondents. Examples are gender, age, marital status,
education, income, etc.
 Behaviour: behaviour questions help to get information
about what people do. Examples: “Have you ever
attended any public lecture”
1.1.1. Questionnaire

 Opinions: asking people what they think about
specific issue or event
 Attitudes: help to get information on the
underlying belief of the respondent or the way
people look at things.
 Motives: asking people why people behave in a
particular manner or hold certain opinions or
attitudes.
 Knowledge: It helps to obtain information about
the extent of knowledge an individual has and to
what extent the information is accurate.
Questionnaire

General Merits of Questionnaire
1. There is low cost even when the universe is large
and is widely spread geographically.
2. It is free from the bias of the interviewer;
answers are in respondents’ own words.
3. Respondents have adequate time to give well
thought out answers.
4. Respondents, who are not easily approachable,
can also be reached conveniently.
5. Large samples can be made use of and thus the
results can be made more dependable and
reliable.

General Demerits of questionnaire:
1. Low rate of return; bias due to no-response is
often indeterminate.
2. It can be used only when respondents are
educated and cooperating.
3. The control over questionnaire may be lost once it
is sent.
4. There is inbuilt inflexibility because of the
difficulty of amending the approach once
questionnaires have been dispatched.
5. There is also the possibility of ambiguous replies
or omission of replies altogether to certain
questions; interpretation of omissions is difficult.
6. It is difficult to know whether willing respondents
are truly representative.
7. This method is likely to be the slowest of all.

Types of Questionnaires: open-ended or closed-ended
Closed ended questions allow only answers which fit into
categories that have been established in advance by the researcher.
 Open-ended - unrestricted type of questionnaire, free response in
the respondent's own words; leave the respondent to decide the
wording and the length of the answer and the kind of matters to be
raised in the answer.
Advantages of closed- ended questions:
 The answers are standards, and can be compared from person to
person.
 The answers are much easier to code and analyze
 They are easier for a respondent to answer as he or she merely
choose a category,
Conti...

Disadvantages of closed-ended questions:
 guesses or randomly answers if a respondent does not
know the answer or has no opinion
 There is no opportunity for the respondent to clarify or
qualify his or her answer
 Differences in interpretation of what was meant by the
question may go undetected
 Variations in answers among the different respondents may
be eliminated artificially by forced- choice responses
 A likelihood of a clerical error as the respondent circles
Conti...

The advantages of open-ended questions:
 They can be used when not all of the possible answer categories
are known and to see what the respondent views as appropriate
answer categories
 To allow the respondent to answer adequately, in all detail he or
she likes, and to clarify and qualify his or her answer
 They can be used when there are too many potential answer
categories to list on the questionnaire – you can not list too many
questions in a given questionnaire
 They allow the respondent to have more opportunity for creativity
or self-expression
Conti…

Disadvantages of open-ended questions
 The possibility of collecting worthless and irrelevant
information
 Data are often not standardized from person to person,
making comparison or statistical analysis difficult
 Coding is often very difficult and subjective
 require a lot of time for analysis
 may require more of the respondent's time and effort, and
may engender a high refusal rate /reduce respondents’
willingness to take part in the research.
 possibly discouraging some respondents who do not wish to
answer a lengthy questionnaire.
Conti…

 make each question brief and the wording clear and concise with
minimal use of jargon
 keep the length of the questionnaire to a minimum: a maximum of
around 20 questions is probably a good guide for most surveys.
 Make all definitions, assumptions, and qualifiers clearly understood
 Avoid making significant memory demands
 make the questions simple to answer
 Keep it interesting – don’t make it monotonous, do have a logical
sequence to the question
 Avoid biased, loaded, leading, or sensitive questions.
◦ Example: ‘are you a heavy smoker?’ Instead use ranges:. Please
indicate your smoking habit: less than 10 cigarettes a day,
between 10 and 20, over 20
◦ Leading: what are your views on the level of VAT in Ethiopia? Is
better than ‘ do you agree that the level of VAT in Ethiopia is too
Questionnaire: a list of some dos and don’ts

 start with simple questions such as gender, leaving more
complicated questions to be answered late in the
questionnaire.
 avoid asking personal questions
 avoid asking the same questions in a different fashion
◦ Abortion should be legalized - agree / disagree) at one
point and Abortion should not be legalized (agree/disagree)
 Don’t ask two questions in one/ double barrelled
questions- with two purposes or interpretations/
◦ Example: Is your job interesting and well paid? Is unlikely
to be answered with a simple yes or no
 Don’t ask hypothetical question such as winning the
National Lottery).
Question; a list of some dos and don’ts

I. Introductory statement of a Questionnaire
 state the purpose of the study and its significance;
 explain who the data collector is, the basis of its authority/the
sponsor-under whose auspices is the research being
undertaken?, and why it is conducting the study;
 tell how and why the respondents were selected;
 explain why their answers are important;
 tell how to complete the format and list the person to call if
help is needed to complete the form;
 provide assurance of confidentiality and anonymity when
appropriate;
 explain how the data will be used;
 explain who will have access to the information; and
 present the response efforts as a favor and thank the
respondents for their cooperation.
Questionnaire: Important points to note

II. Format of the questionnaire
 Cleanly format your questionnaire: margins,
spacing, font, etc.
 Proofread your questionnaire carefully for grammar
and spelling errors
 conduct a pilot survey on a small sample so that to
test the reliability and validity of your measure.
Schedule vs questionnaire method
 Schedule is a device consisting of a set of
questions, which are asked and filled in by the
enumerators who are specially trained for the
purpose
Questionnaire: Important…

An interview is a process of
interaction in which the interviewee
gives the needed information verbally
in a face-to-face situation or through
telephone (koal 1996).
 Involves presentation of oral-verbal
stimuli and reply in terms of oral-
verbal responses.
114
1.1.2. Interview

• Answers are recorded by:
– Writing the response
– Tape recording or
– A combination of writing and tape recording
• Interview can be conducted through:
– personal / face-to-face (individually or in
group) or
– telephone
– through internet/online
115

Purpose of interview
 It is the principal means of collecting information
about what an interviewee:
 Knows (i.e., getting knowledge or information)
 likes or dislikes (i.e., values & preferences)
and
 Thinks (i.e., attitudes and beliefs)
 It may be used to test hypotheses or to suggest
new
 It could be used in conjunction with other
methods in a research undertaking
116

 Types of Interview: structured , unstructured and semi-
structured
A) Structured interview
 Set of predetermined questions and of highly
standardized recording technique (audio or digital
recorders)
 The same types of questions are presented in the same
order to each interviewee
 The interviewer has no freedom to rephrase or change the
order of questions to be presented
 quite often used in the case of descriptive studies
Interview

B. Unstructured interview
 The interviewer has a general plan of inquiry but not a
specific set of questions that must be asked in particular
words and in a particular order
 characterized by flexibility
 The interviewer is largely free to arrange, rephrase, modify,
and add some new questions
 Very important for exploratory research studies
C. Semi-structured interview
 Shares the nature of both structured and unstructured
interview
Interview tech…

 Prepare for interview, self-presentation: objective
of the study, its background, how the respondent
was selected, the confidential nature of the
interview, the beneficial values of the research
findings
 Conduct the interview – use your communication
skills here (attentive, non-judgmental), ask the
questions properly, probe meaningfully
 Recording of the interview; record the responses
accurately, record response as they occur; use
some shorthand system (abbreviating words, key
words)
Interview: Techniques of Interviewing

Advantages:
 More and in-depth information can be obtained
 There is greater flexibility
 Personal information can be obtained easily
 high response rate
 The language of the interview can be adapted to the
ability the person interviewed
Disadvantages
 Expensive including Cost of selecting, training and
supervising the field-staff
 Bias of interviewer and the respondent - presence of
the interviewer on the spot may over-stimulate the
respondent - may give imaginary information
 Important officials or executives may not be easily
approachable
 More-time-consuming, when calling the
respondents
Interview…

 observing what is occurring in some real - life situation , without
asking questions of respondents
 It is valuable instrument in a wide range of research studies.
◦ Examples: Cultural study, traffic counts, direction of traffic
flows
Planning and execution of observation
 Selecting an appropriate group of subjects to observe
 Identifying the specific activities or units of behavior to be
observed and focusing attention on same at the time of
implementation
 Proper arrangement of specific conditions for the subject(s) to be
observed
 Assuming the proper role or physical positions for observing
 Handling well the recording instruments to be used
1.1.3. Observation Methods

A) Direct versus Indirect observation
Direct observation: the observer is physically
present and personally monitors what takes
place
 Very flexible - the observer can react to
events
 The observer is free to shift places, change
the focus of observation, or concentrate on
unexpected events
 weakness - the observers' perception may
become overloaded as events move quickly;
they must later try to reconstruct what they
are not be able to record
Classification of observation methods

Indirect observation
 The recording is done by mechanical/adjusted
instruments
◦ Example: a special camera that takes one
frame every second is mounted in a
department of a large store to study customer
and employee movement
 Less flexible but much less biasing, less
unpredictable or erratic in accuracy
 The permanent record can be analyzed to
include any different aspects of an event
Observation

B. ) Disguised (Covert) Vs undisguised (overt)
observation
 The role of the observers should be disguised in
situations where people behave differently if they
know they were being observed
 Often technical means are used such as one-way
mirrors, hidden cameras, or microphones
 Reduce the risk of observer bias but bring up a
question of ethics
◦ Hidden observation is a form of intelligence work
 A modified approach - the presence of the
observer is not concealed, but the observer´s real
purpose and subject of interest are hidden
Observation…

c) Participant Vs non-participant
observation
 Participant observation: The observer becomes one of
the groups under observation
 Non-participant observation: Observer takes position
where his presence is not disturbing the group.
d) Structured Vs. unstructured observation
 Structured observation is systematic and has a high
level of predetermined steps
 Objective: To quantify behavior (your focus is to
determine how often things happen rather than why they
happen. Ex: Time and motion study
 Unstructured observation: The observer has no
definite ideas of the particular aspects that need focus.
 Observing events that are happening may also be a part
of the plan as in many qualitative studies.
Observation…

 the observer must take utmost care to minimize the
influence of his biases, attitudes and values on the
observation report
Advantages:
 Useful in locating data about non-readers, young
children, people with mental disorders, and laboratory
animals
 The data obtained through observation of events as
they normally occur are generally more reliable and
free from respondent bias.
Disadvantages:
 It is time consuming
 It is costly to collect data.
 The data may reflect observers’ bias
Observation: Recording and
interpreting the observation

 a special type of interview that offers opportunity to
interview a number of people at the same time.
 Made by a panel of 8 to 12 respondents led by a trained
moderator
 The moderator uses group dynamics principles to focus or
guide the group in an exchange of ideas, feelings, and
experiences on a clearly understood topic
 Good for exploratory research
 Qualities of a moderator: (Kindness with firmness,
Tolerance, Involvement, understanding, Encouragement,
Flexibility, Sensitivity / emotional response)
 Benefits of FGD : (Synergism , Snowballing, Stimulation,
Security, Spontaneity—/natural behavior/,
Serendipity/discovery of something fortunate,
Specialization, Scientific Scrutiny, Structure, Speed)
1.1.4. Focus group discussion

Collection of Secondary Data
Secondary data means data that are
already available i.e., the data which
have already been collected and
analyzed by someone else.
128

 Secondary data may either be published data or
unpublished data.
 Usually published data are available in:
various publications of the central, state or
local governments;
various publications of foreign governments
or of international organizations;
technical and trade journals.
129

books, magazines and newspapers;
reports and publications of various
associations connected with business and
industry, banks, stock exchanges, etc.;
reports prepared by research scholars,
universities, economists, etc. in different
fields; and
public records and statistics, historical
documents, and other sources of published
information.
130

The sources of unpublished data are
many; they may be found in diaries,
letters, unpublished biographies and
autobiographies and also may be
available with scholars and research
workers, trade associations, labor
bureaus and other public/ private
individuals and organizations.
131

 Researcher must be very careful in using secondary
data. He must see that they possess following
characteristics:
 Reliability of data
 Suitability of data
 Adequacy of data
 From all this we can say that it is very risky to use the
already available data. The already available data
should be used by the researcher only when he finds
them reliable, suitable and adequate.
132

Strengths
 Enable researchers to study past events or issues
 Usually most secondary documents are readily
available
 It is more economical
 Give an easy way of obtaining other peoples perception
133

Limitations
 Some secondary sources may be unreliable and
inaccurate
 Some sources could be confidential/secret/private
 Some documents may not be up to date and
complete
 Documents may be biased to some extent since they
represent the views of the authors
134

Contents
 Data Processing: Editing, Coding, Classification,
Tabulation, and presentation
 Employing Statistical Tools for Data Analysis
 Overview of descriptive and inferential statistics
 Parametric and non-parametric tests
 Interpretation of Data
 Utilizing Computers for Data Processing (using STATA or
SPSS): An Overview
137

Plan for processing and analysis:
Quantitative data
• Data Processing: Editing, Coding,
Classification, Tabulation, and presentation
• Level of measurements [Nominal, Ordinal,
Interval and Ratio]
• Employing Statistical Tools for Data Analysis
 Descriptive Vs inferential statistics
 Parametric and non-parametric tests
• Interpretation of Data
• Utilizing Computers for Data Processing
(using SPSS, STATA, etc)
138

Qualitative Data Analysis
 Best used when for in-depth understanding of the
intervention
 Used for any non-numerical data collected:
– unstructured observations
– open-ended interviews
– analysis of written documents
– focus groups transcripts
– diaries, observations
 Analysis challenging
 Take care for accuracy (validity concern)
140

Computer help for qualitative data
analysis
 Software packages to help you organize data [example,
Qualpro, Hyperqual, Anthropax, Atlas-ti, Envivo, etc]
 Search, organize, categorize, and annotate textual and
visual data
 Help you visualize the relationships among data
141

Data Processing:
 Once the data have been collected, the next step is data
processing, generally consisting of:
 Editing,
 Coding/recoding
 Classification and
 Tabulations including producing tables, graphs,
coefficients etc.
 Data processing requires careful attention and
understandings. Else it results in what is known as
GIGO: Garbage in Garbage out.
142

Questionnaire Editing
 Editing of data is a process of examining the collected
raw data (especially in surveys) to detect errors and
omissions and to correct these.
 It involves a careful scrutiny of the completed
questionnaires and /or schedules. It is done to assure
that information received are complete as much as
possible and have been well arranged to facilitate
coding and tabulation.
143

 Editing requires checking for the following:
a. Completeness: Whether every questions has
answers or not. Incomplete questions can be
imputed (if possible).
b. Accuracy: Check if every questions has an appropriate answer.
Inaccuracy often arises out of carelessness on the part of enumerator,
deliberate misleading, and ticking wrong boxes or circling wrong
codes.
c. Uniformity: Failure to give explicit instructions or
clear understanding of the questions could lead
to recording the same answer in different
ways. A check on uniformity is believed to
eradicate this source of error.
144

Data Coding
 Coding is the process of converting answers to
numbers and classifying answers accordingly so that
responses can be put into a limited number of
categories or classes. .
 Coding is the primary task in reduction of
qualitative data.
 Coding decision should usually be taken at the
designing stage of the questionnaire.
145

Six main steps in Coding and
Classifying quantitative data:
a. Classifying responses
b. Allocating codes to each variable
c. Allocating column numbers to each
variables
d. Producing a codebook
e. Checking from coding errors
f. Entering data into computer
146

Data Entry
Requirements for Data Entry
1. Definition of Data Dictionary – Giving names and
explanations for each of the variables to be entered
into the database.
2. Defining range : In order to regulate the magnitude
of answers to be entered for each of the questions on
the questionnaires, the researcher needs to limit the
scope of answers and their flow patterns.
147

Data editing and cleaning after data entry
 Data editing and cleaning after data entry is
tantamount to drying and ironing washed clothes
before putting on.
 Wrong entries either in the field or during data
coding and entry need to be checked and removed
before the commencement of data analysis.
 Cleaning can be done by looking at patterns of the
data via identification of outliers and unexpected
responses through running frequencies and cross
tabulating related variables.
148

Four broad considerations of data
analysis
 Identification of Level of measurement of each
variable
 Number of variables that each of the particular
pieces of analysis requires.
 Types of analysis required: descriptive vs analytic
 Application of ethical principles of full, fair,
appropriate and challenging analysis to the selection
of data to be analyzed and reported.
149

Tabulation and data analysis
 Tabulation starts with production of simple
frequency and contingency tables to construction of
complex and multi-dimensional tables
 Tabulation is often known as a skeleton form of the
survey research.
 A researcher shall assume some knowledge of
quantitative data analysis procedures to assume the
sense of skeleton.
 Even if the researcher does not have sufficient
knowledge of data analysis, he/she can consult
someone who has sufficient knowledge of data
processing.
150

Graphical methods of displaying data
 Pie Charts
Categories represented as percentages of total
 Bar Graphs
Heights of rectangles represent group frequencies
Bars do not touch each other
 Frequency Polygons
Height of line represents frequency
 Histogram
 A histogram is a chart made of bars of different
heights but interconnected.
 Time Plots
Represents values over time
155

Pie Chart
33.0%
23.0%
19.0%
19.0%
6.0%
Category
Happy with career
Don't like my job but it is on my career path
Job is OK, but it is not on my career path
Enjoy job, but it is not on my career path
My job just pays the bills
Figure 1-1: Extent of job satisfication
My job just pays the bills
Happy with career
Enjoy job, but it is not on my career path
Job OK, but it is not on my career path
Do not like my job, but it is on my career path
NB: Use different colors for each of the slices to
distinguish between categories
156

Bar Chart
C4
1Q
4Q
3Q
2Q
1Q
1.5
1.2
0.9
0.6
0.3
0.0
Figure 1-2
2003 2004
Quartely net income for General Motors (in billions)
Bar chart is advantageous to make presentations for
those who are not familiar with statistical materials
157

Frequency Histogram
Histogram Example
158

Relative Frequency Polygon
Frequency Polygon
5 0
4 0
3 0
2 0
1 0
0
0 . 3
0 . 2
0 . 1
0 . 0
Sales
It visualizes gradual shifts in frequency from one
category to another
159

O
S
A
J
J
M
A
M
F
J
D
N
O
S
A
J
J
M
A
M
F
J
D
N
O
S
A
J
J
M
A
M
F
J
8 .5
7 .5
6 .5
5 .5
M o n th
Milli
ons
o
f
Tons
M o n th ly S te e l P r o d u c tio n
Time Plot/Line Graph
160

What are the determinants to say
qualitative and/or quantitative analysis
approach?
Data nature
Analysis techniques
 if not, Mixed approach

Measurement of Shape of
Distribution: Skewness and Kurtosis
 Skewness
 Measure of asymmetry of a frequency distribution
 Skewed to left
 Symmetric or unskewed
 Skewed to right
 Kurtosis
 Measure of flatness or peakedness of a
frequency distribution
 Platykurtic (relatively flat)
 Mesokurtic (normal)
 Leptokurtic (relatively peaked)
174

Kurtosis
Platykurtic - flat distribution
178

Kurtosis
Mesokurtic – neither too flat nor too peaked
179

Kurtosis
Leptokurtic - peaked distribution
180

Chapter Six
What statistical analysis should We
use?

6.1. introduction
6.1.1. Levels of Measurement of Data
 There are four levels of measurement: Nominal, Ordinal,
Interval, and Ratio.
A. Nominal Data: are categorical or qualitative data
that are converted into numerical data by coding the
various categories. These are numerical in name
only; because the numbers assigned are more
symbols and hence cannot have any numerical
meaning in the real sense. There is no any
mathematical difference between categories.
Examples:
 Sex , Ethnic group, and Marital status

Conti…
B. Ordinal Data: are nominal data, which
have order and consensus. Measurements
with ordinal scales are ordered in the
sense that higher numbers represent
higher values, i.e., they can have
meaningful inequalities (< or >). In such
kind of data, only counting and ranking are
possible but it is not likely to find exact
differences.
Examples:
 Military ranks, Graduates , likert scal

Conti…
C. Interval Data: are ordinal data in which the
differences between units have meaning.
These data do not have a’ true’ zero point
and therefore it is not possible to make
statements about how many times higher one
score is than other. In other words, the ratios of
different values are meaningless.
Examples:
 Number of votes in election.
 Exam scores of students.
 Data on shoe size of individuals.

Conti…
D. Ratio Data: are interval data, which also
have true zero point. With these data, one
can perform addition, subtraction, division
and multiplication.
Examples:
 1. Income is a ratio data because zero
dollars is truly “no income”
 2. Measurement data like height, weight,
volume and area.
 N.B. Both Nominal Data and Ordinal Data
are categorical data

Type of Tests
 Parametric tests are statistical tests which
make certain assumptions about the parameters
of the full population from which the sample is
taken.
 These tests normally involve data expressed in
absolute numbers (interval or ratio) rather
than ranks and categories (nominal or ordinal).
 Such tests include analysis of variance (ANOVA), t-
tests, Z-test, etc.
199

Non parametric test
• Non-parametric tests are used to test
hypotheses with nominal and ordinal data.
• The use of non-parametric methods may be
necessary when data have a ranking but no
clear numerical interpretation, such as when
assessing preferences; in terms of levels of
measurement, for data on an ordinal scale.
• Such tests are like Chi-Square (X2), Mann-
Whitney Test, kruskal wallis, etc
200

6.1.2. Types of Variable
A. Independent Variable
B. Dependent Variable
C. Mediator variable

Conti…
C. Mediator variable
 A mediator variable influences the
strength and/or direction of the relation
between the independent and
dependent variables; mediators are
often called intervening (Baron & Kenny,
1986). It is used to explain the causal
relation between dependent and
independent variables (Hair et al, 2006).

Conti…
A. Qualitative variable: variables in which
the characteristic or variable being studied
is non-numeric. A qualitative variable is a
variable that can be described only in
words.
 Example: gender, color, religion, ethnic
group etc.
B. Quantitative variable: variables that can
be expressed numerically or are
variables that are numeric in nature.

Conti…
i. Discrete variables: A Variable that
assumes a finite or countable number of
possible values is called a discrete variable.
There are finite or countable numbers of
choices available with discrete data. You
cannot have 2.63 people in the room.
Discrete variable is usually obtained by
counting.
-E.g., number of children’s in a family, number
of cars at a traffic light is usually obtained
by counting.
ii. Continuous variables: A variable that can

Latent variable Vs observed
variables
I. Latent variable- are a central
concept and abstract phenomena
which are of hidden or unobserved
and theoretical (Bowen and Guo,
2012), and typically hypothetically
existing constructs of interests in a
study (Raykov and Marcoulides,
2006).
-Latent variables are measured
indirectly by their respective

II. observed variables
-are variables that can be directly measured and are
indicators of a latent variables (Wang and Wang ,
2012).
. Thus, the observed variables can be categorical,
ordinary, and continuous, but all latent variables
are continuous (Kline, 2011).
 A latent variable with three indictors are considered as
acceptable,
 Four or more is recommendable, but, a latent
variable with 5 to 7 indicators are considered as to
be maximum (Hair et al., 2006).
-When a latent variable has only one observed variables,

Practical examples
Customer Loyalty is a latent variable can be
measured though a rapid loyalty approach.
It is measured indirectly by their respective
indicators (observed variables) such as
Customer attraction
Customer retention
Customers’ advocacy
Customer’s repeat purchase
Customer’s bulk purchase

6.2. Part one: Categorical
Variables/data
What is categorical variable?
- A variable that can be studied in providing
categorized alternatives, or can be answered or
described only in categories.
What is categorical data?
 The data (whether it is expressed numerically or in
word, Discrete variables or Continuous variables)
that can be offered in terms of categories.

Likert-Type Versus Likert Scales
Likert-type items
 Likert-type items- as single questions that use some
aspect of the original Likert response alternatives.
 Likert items- are used to measure respondents'
attitudes to a particular question or statement.
A Likert Scales
 Likert Scales on the other hand, is composed of a
series of four or more Likert-type items that are
combined into a single composite score/variable
during the data analysis process.
 Combined, the items are used to provide a quantitative
measure of a character

Conti…
With likert type data we cannot use
the mean as a measure of central
tendency
Likert-type items fall into the ordinal
measurement scale
For Likert-type items - mode or
median for central tendency,
frequencies for variability, chi-square
measure of association, Kendall Tau
B, and Kendall Tau C

Conti…
Likert scale data, on the other hand, are
analyzed at the interval measurement scale.
B/C it is created by calculating a composite
score (sum or mean)
 Descriptive statistics recommended for
interval scale items include the ‘mean’ for
central tendency and standard deviations
for variability.
 Additional data analysis procedures
appropriate for interval scale items would
include the Pearson's r, t-test, ANOVA, and

Nature of Likert-Type Items
I. Items measuring degree of
acceptance
-this type of Likert items are used to measure
respondents' attitudes towards to accepting a particular
statement.
it is usually coded as follows.
 1 = Strongly disagree
 2 = Disagree
 3 = Neutral
 4 = Agree

Conti…
II. Items measuring degree of
extent
-Likert items are used to measure respondents'
believe towards the extent of a particular question.
it is usually coded as follows.
 1 = very low extent
 2 = low extent
 3 = medium extent
 4 = high extent
 5 = very high extent

I. How to analysis Categorical data?
A. Univariate
Variables With two outcomes- Binomial Probability
Test
Variables With more than two outcomes- Chi-
square Goodness of fit test

B. Bivariate-when there are two
categorical variables

i. when there are one categorical
dependant variable and one predictor
variable
- Simple logistic regression
(Simple Binary outcome logistic regression &
Simple Ordered logistic regression)
- Spearman’s rank correlation

C. Multivariate- when there are one
categorical dependant variable but more than
one predictor variable
-Multiple logistic regression-
(Multiple binary outcome logistic regression & Multiple
ordered logistic regression)
-Multinomial (polytomous) logistic
regression
-Correlation- (Partial and semi-partial
correlations, and Multiple correlation)

6.3. Part two:
interval/continuous
Variables/data

How to analysis interval variables
A. Univariate
-one-sample t-test
B. Bivariate
i. when there are two interval variables
-Paired t test (Two sample, paired)
ii. Group Difference test-when there are one
Continuous dependant variable and one
categorical independent variable
ANOVA- analysis of variance and ANCOVA-analysis of
covariance
iii. Causality test-(Simple linear
regression)
-when there are one interval dependant variable
and one predictor variable

C. Multivariate-
i. Group Difference test- when there are two or
more dependent continuous and independent
categorical variable/s
MANOVA- Multiple analysis of variance
MANCOVA-Multiple analysis of covariance
ii. Multiple linear regression- when there are
one Continuous dependant variable and two or more
predictors

Other statistical tests
i. Multivariate regression
-It is a type of regression in which there are two or
more dependent and two or more predictor
(independent) variables
It can serve to compute the coefficient of
regression when you have;
A. Two or more categorical dependent variables
B. Two or more continuous dependent variables
C. The combination of categorical and continuous
dependent variables

ii. Factor analysis
Steps
i. Exploratory factor analysis (EFA) or principal component
analysis (PCA)
ii. Correlation matrix and then merging and rejection
variables
iii. Confirmatory factor analysis
- It is developing or building model for each latent variable,
determine path coefficient and then model fit Index
iii. Path analysis
 To indicate the direct and indirect effects of predictor variables
on the DV?
 It needs one or more independent/predictor variables, one or
more intermediate variables, and one or more DV.
iv. Structural equation modeling (SEM):
Add covariance, connect latent variables and then
estimate the coefficient to indicate the direct and indirect

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