power point for Business Research Method

CHAPTER: ONE
AN INTRODUCTION
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Business Research Method

Introduction Cont’d…
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What is science?
What is Scientific method?
What is research?
What is scientific research?
What is business Research?

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1. Science
Science is a methodical approach to studying the natural world.
 Science asks basic questions, such as how does the world work,
How did the world come to be, What was the world looks like in the
past, what is it like now, & what will it be like in the future?
 These questions are answered using observation, testing, &
interpretation through logic

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 Science is characterized by logic or rationality and observation
of empirical facts.
 Research is scientific because it is just a search for knowledge
through recognized methods of data collection, analysis, and
interpretation.
 The purpose of research is to discover answers to
questions through the application of scientific procedures.

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2. Scientific Method
Scientific method is the pursuit of truth as determined by logical
considerations.
 The ideal of science is to achieve a systematic interrelation of
facts.
 Scientific method attempts to achieve “this ideal by
experimentation, observation, logical arguments from accepted
postulates and a combination of these three in varying proportions.

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 The three major differences between scientific and non scientific
methods that affect the reliability and validity of the results are: -
i. Objectivity of the researcher: researchers must base their
judgment on facts not preconceived notions or intuitions.
ii. Accuracy of measurement: scientific method attempts to obtain
the most accurate measurements possible.
iii. Continuing and exhaustive nature of investigation: - it is the
aggressive searching for additional evidence to support or confused,
the existing conclusion.

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Characteristics of Scientific Method
Relies on empirical evidence
Utilizes relevant concepts
Committed to only objective considerations
Presupposes ethical neutrality
Results in to probabilistic predictions
Its methodology is made known to all concerned for critical
scrutiny

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3. Methods of acquiring Knowledge
 There are five methods of acquiring knowledge:
i. Authority: When knowledge is obtained from individuals who are
qualified producers of knowledge.
ii.Tradition: When we accept without questioning the traditions,
customs or culture of our forefathers or the society.
iii.Experience: Our personal experience or our observation of the
experiences of other people.
iv. Reasoning: Knowledge can be obtained based on some rules and
forms of logic.

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a. Deductive reasoning:- is a thinking process in which one will
go from general statements to specific statements.
b. Inductive reasoning:- is to go from specific to general ideas.
v. Scientific Method: integrates the most important aspects of the
inductive and deductive methods.
 In scientific method, Knowledge is obtained through a step by
step process of collecting data, analyzing it and reaching at a
conclusion.

Introduction
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4. Meaning of Research
Is a quest (search) driven by a specific question that needs an
answer.
 Is a systematic attempt to obtain answers to questions about
events
Is a systematic inquiry aimed at providing information to solve
problems.
 Research is a scientific and systematic search for new and useful
information on a particular topic.
 It is a scientific method of finding solutions for different
problems through objective and systematic analysis.

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 It is a search for knowledge, that is, a discovery of hidden
truths.
 Scientific research focuses on solving problems and pursues a
step by step logical, organized and rigorous method to identify
the problems, gather data, analyze them and draw valid
conclusions there from.

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5. Business Research
-is a systematic inquiry aimed at providing information to solve
managerial problems.
- is a formalized means of designing, gathering, analyzing and
reporting information that can be used to solve specific managerial
problems.
- Provides the needed information that guides managers to make
informed decisions to successfully deal with problems.

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The Role of Business Research
 To identify and define market opportunities
To generate and refine management actions
 To monitor strategies and plans
 Business research helps managers to make better and informed
decisions.

An Introduction
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4. Categories of Research
 Generally, there are three Categories of research:
i. Pure Vs Applied Research
a. Pure/Basic/fundamental Research: adds new knowledge to the
existing body of knowledge. It is end it self.
- Concerned with generalizations and formulation of theories
- Aims to solve perplexing questions of theoretical nature
- It does not necessarily provide results of immediate, practical use
b. Applied Research: Carried out to solve an immediate and practical
problem. It is oriented to a specific problem.
- It has a practical problem solving emphasis.
- Conducted to solve socio-economic or industrial problems and thus
have policy implication. It is the means to an end.
- Business research is almost always applied research

An Introduction
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- The purpose of applied research is to find out solution for an
immediate or practical problems.
ii. Quantitative Vs Qualitative Research
a. Quantitative Research: - Research based on the measurement of
quantity or amount.
-is the systematic and scientific investigation of quantitative
properties and phenomena and their relationships.
b. Qualitative Research: Research concerned with qualitative
phenomenon.
-Qualitative research involves studies that do not attempt to
quantify their results through statistical analysis.
- Especially important in behavioral sciences

An Introduction
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iii. Conceptual Vs Empirical Research
a. Conceptual Research: - related to abstract ideas or theories.
- Philosophers and thinkers generally use it to develop new concepts.
b. Empirical Research: - relies on experience or observation alone.
-It is data based research coming up with conclusions
- considered that evidences gathered through experiments or empirical studies
provides the most powerful support.
- The researcher should collect enough data to prove or disprove
Both applied and pure research are basically problem solving based but in
different senses.

An Introduction
iv. Descriptive Vs Analytical Research
a. Descriptive: - is describing the state of affairs as it prevails at the
time of study.
- The researcher can only report what is happenning or what has
happened.
b. Analytical research: -use facts or information already available
& analyze these to make a critical evaluation of the subject.

An Introduction
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5. Research Process
 The basic steps in the research process are:
1. Establish the need for research
2. Define the research problem
3.Review of literatures (both concepts and theories and previous
research findings)
4. Formulation of working hypothesis
5. Determine research design
6. Identify information types and sources
7. Determine methods of data collection

An Introduction
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8. Design data collection forms (Questionnaire)
9. Determine sampling design and size
10. Collect data
11. Analyze data
12. Draw conclusions and report the final result
13. Reporting the result
1. Establish the need for research
Research may not be needed if: -
-Information is already available
- There is insufficient time for Research
-Resources are not available
- Cost Outweigh the value of Research

An Introduction
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2. Define the Research Problem: reflects a gap in understanding
-a felt difficulty which requires solution
-when there is a problematic situation and a need to solve the
problem. “A well defined problem is half solved”
-Can be identified from personal observation, theory, professional
literatures and discussion with experts.
3. Review of Literatures: is to undertake extensive literature
survey connected with the problem.
Two types of literatures can be reviewed:-
i. Conceptual: - is concerned with related concepts and theories.
ii. Empirical: - are studies made earlier which are similar to the
one proposed.

An Introduction
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4. Formulation of Working Hypothesis: is tentative assumption
made in order to draw out and test its empirical consequences.
-Hypothesis formulation starts with a well stated research problem.
- There are two types of hypothesis:
i. Null Hypothesis: - is a default position.
- It hypothesizes that there is no relationship between two variables.
ii. Alternative Hypothesis: - is the opposite of null hypothesis
- Hypothesize s that there is r/ship between two variables .

An Introduction
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5. Determine the Research Design: is the methods and approaches
which will best achieve the research objectives.
6. Identify information types and sources: two types of
information are available to a researcher i.e. primary and secondary.
7. Determine Method of data Collection: includes telephone
surveys, mail surveys, personal interviews, observations, mail
intercept studies and etc.
8. Design Data collection forms: is a form that is used to ask and
record this information.

An Introduction
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9. Determine sample design and size: is decision on the way of
selecting a sample for a study.
-Samples can be either probability samples or non-probability
samples.
10. Collecting the Data (Execution): Collecting the required data
to solve the research problem.
-Primary data can be collected either through experiment or through
survey. Survey data collection methods are: -
 Observation
 Personal interview
 Telephone interviews
 Questionnaires
 Through schedules

An Introduction
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11. Analysis of Data (Including Test of Hypothesis): - involves:
- Entering data in to computer
- Inspecting it for errors
- Running tabulation and various statistical tests.
12. Drawing Conclusions and Report the final result: is
making generalizations
-The conclusions must be based on the data and the analysis
within the framework of the research study
- Communicating the result or solution of the problem to the
intended audience

An Introduction
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6. Characteristics of a Good Research
 Clearly defined research purpose and problem
 The procedures should be described in sufficient detail for
replication.
 Research design thoroughly planned
 Limitation frankly revealed
 High ethical standards applied
 Adequate analysis and method of analysis for decision makers
 Conclusions should be confined to those justified by the data
 Good experience of the researcher
 Unambiguous findings

CHAPTER TWO
DEFINING THE RESEARCH PROBLEM
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2.1. Problem definition
-is the first activity of the research process and is the most
difficult phase
- general statement of why the research is to be undertaken
- is the most critical step in scientific research
 A research problem: reflects a gap in understanding.
-is some difficulty which a researcher experiences in the context
of either a practical or theoretical situation
 A problem is said be occurred if: -
-The individual or the organization does not know what course of
action is best

Defining the Research Problem
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-When the individual or organization is in doubt about the
solution.
- Problem definition involves:
-Specifying the symptoms
- Itemizing the possible causes
 Formulating research problem entails asking a previously
unanswered questions for which an answer is sought.
2.2. Sources of Research Problem
-A research problem is a set of conditions that need solution
through data collection and analysis
- A good research problem should be one that is socially and
scientifically important.

Sources of Research Problem
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 Research problem may arise from the following sources:
a. Personal Experience of Practical Situation: - day to day
experience and observation of the sounding real phenomena
- Unresolved issues in the surrounding environment
b. Previous Research and Related Literature: - articles and
journals, books, magazines and other research outputs and
literature bodies on the area of interest gives an insight
- Identify research gaps which appear to justify additional
research.
c. Current Social and Political Issues: - Contemporary debates,
concerns or movements.
- Changes in social, economic, political, cultural aspects …etc .
Their impacts or influence can be studied.

Sources of Research Problem
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d. From Discussions with Peoples: -discussions with fellows,
students, neighbors, workers of different organizations, and with
different members of the society on various socio-economic,
political issues.
e. Personal Motivation: - an interest to know certain phenomena
or events from different perspectives
f. Lack of Information: - Policy-makers and decision makers need
relevant information on various issues in order to formulate policies
and make various decisions.

The Components of the Research Problem
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2.3. The Components of the Research Problem
1. There must be an individual or a group which has some difficulty
or problem.
2. There must be some objective(s) to be attained.
3. There must be alternative means (or courses of action)
4. There must remain some doubt in the mind of a researcher with
regard to the selection of alternatives.
5. There must be some environment(s) to which the difficulty
pertains.

Selection of Research Problem
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2.4. Selection of Research Problem
- The first step in the problem identification involves selecting a
research topic.
- Statement of the problem is generated from a research topic
- Problem must spring from the researcher’s mind
 The researcher should consider the following points in selecting
research problem
i. Subject which is overdone should not be normally chosen
ii. Controversial subject should not be chosen
iii. Too narrow or too vague problems should be avoided
iv. The subject selected for research should be familiar and
feasible

Selection of Research Problem
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v. The importance of the subject, the costs involved and the time
factor
 The research topic should be: -
i. The one in which the researcher has an interest
ii. The one that the researcher has the background knowledge
iii. The one that address felt need
iv. The one on which enough materials could be obtained
v. The one whose selection is justifiable on social, scientific or
policy grounds.

Techniques of Defining a Research Problem
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2.5. Techniques of Defining a Research Problem
- Defining the research problem properly and clearly is a crucial
part of a research study
 The technique for defining a research problem involves the
following general steps
i. Statement of the problem in a general way
ii. Understanding the nature of the problem
iii. Surveying the available literature
iv. Developing ideas through discussions
v. Rephrasing the research problem into a working proposition.

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i. Statement of the problem in a general way: - the problem
should be stated in a broad general way and narrow it down and
phrase the problem in operational terms
ii. Understanding the Nature of the Problem: - understanding
its origin and nature clearly. The best way of understanding the
problem is to discuss it with those who first raised it.
iii. Surveying the Available Literatures: - All available literatures
concerning the problem at hand must be surveyed and
examined.
- It helps a researcher to know if there are certain gaps in the
theories, or whether the existing theories applicable to the
problem under study.
- Studies on related problems are useful for indicating the type of
difficulties that may be encountered in the present study

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iv. Developing the Ideas through discussions: - a researcher must
discuss his problem with his colleagues and others who have
enough experience in the same area.
v. Rephrasing the Research Problem: - is putting the research
problem in as specific terms as possible.
- In rephrasing research problem, a researcher has to develop: -
i. Statement of the problem: conveys the specific research
problem, usually in a few paragraphs. It should indicate:
- The focus area of the research
- Indicates researches done by other researchers in the area
including their findings
- Indicate issues that are not yet assessed by other researcher in
the area

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- Show difference in the findings of various researchers in the
area
- Indicate the study area and the issues or subjects of research
- Indicate the purpose of the study
- A good research problem should be capable of being stated in
one major research question.
ii. Statement of Research Purpose: - conveys the focus of the
study and should be spelt out in clear, direct and unambiguous
manner
iii. Research Objective: specify the issues or area of knowledge
that the researcher wants to examine or explore.

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 A well-defined research problem leads naturally to the
statement of the research objectives, research hypotheses, the key
variables & a selection of methodology for measuring the
variables.
 A poorly defined research problem leads to confusion.

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iv. Research Questions: -describe the ideas contained in a research
objective in the form of questions
v. Formulation of Research Hypothesis: - is a tentative prediction
of the expected relationships between two or more variables.
- is a tentative statement or an educated guess suggesting possible
relationship, difference between two or more variables that can be
investigated and tested.
-Problem statement and hypothesis are similar in substance, except
that hypothesis is declarative statements.
- A hypothesis can be formed either in descriptive or relational
form.

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 Characteristics of Good Hypothesis
- Capable of being expressed as a question
- It may be stated in a negative way
- Capable of being answered with a “yes” and “no” or “may be.”
- Should clearly show the relationship between the variables under
consideration
- Should be in line with the prevailing research findings and
theories.
- Should be testable
- Should be in agreement with research topic, problem statement,
research questions and research objectives

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 Types of Research Hypothesis
1. Research Hypothesis (H1) – is a researcher’s proposition about
some social fact
2. Null Hypothesis (Ho) - is the reverse of research hypothesis. It is
a hypothesis of no relationship.


DEVELOPING A RESEARCH PROPOSAL
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 Research proposal is a written document of research plan
meant to convince specific readers
 is also known as a work plan, prospectus, outline, statement of
intent, or draft plan.
 It tells us: -
-What will be done?
- Why it will be done?
- How it will be done?
- Where it will be done?
- To whom it will be done?
- What is the benefit of doing it?

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 is essentially a road map, showing clearly the location from
which a journey begins, the destination to be reached, and the
method of getting there.
The Purpose of Proposal
-To present the problem to be researched and its importance.
-To discuss the research efforts of others who have worked on
related problems.
-To suggest the data necessary for solving the problem
 Proposals are also useful both for sponsors and for researchers.

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Types of a Research proposal
 Research proposals can be generally classified into internal and
external.
1. Internal proposals: - are done for corporations by staff specialist
or by the research department of the firm.
2. External proposals: - are done by external parties such as
consultant organizations or individuals.
- Can be either solicited or unsolicited. Solicited when the proposal
is competing , where as unsolicited proposal has no competition.

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Functions of a research proposal
A research proposal serves as: -
a. A means of communication: used to communicate the
researcher’s plan to those who give consultations.
b. A plan: - helps the researcher to organize his idea in a systematic
manner.
c. A contract : - constitutes a bond of agreement between the parties
(Between a sponsor and a researcher or a supervisor and a
researcher).

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 STRUCTURE/OUTLINE/ OF A RESEARCH PROPOSAL
- There is no single way of writing research proposals.
- The following proposal components are usually important.
1. Cover page
2. Abstract
3. Background of the Study
4. Statement of the research problems
5. Research objectives
6. Significance of the study
7. Scope and Limitation of the study
8. Preliminary survey of literature
9. Research methods
10. Timetable/time schedule
11. Budget
12. Reference/ Bibliography

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1. Cover page
The cover page contains: -
Title: tells what the research proposal is all about. It should be
catchy, concise and descriptive.
- Capture and reflect the content of the proposal
- The name of the researcher /author of the proposal
- The name of the department/ institution
- Submission date
N.B. The title should use the fewest possible words that adequately
describe the content of the paper.
2. Abstract: - is a short summary of the research proposal.
- is a brief summary of approximately 250 words.

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 Abstract should include the research problem, research questions,
the rationale for the study, the objective, the method and expected
value addition.
 It allows a busy managers or sponsors to understand quickly the
thrust of the proposal.
3. Background of the study: presents the background of what the
researcher wants to investigate and say briefly why he/she thinks
the works should be done and relate it to what is already known
about the problem.
- A description of the background of the study topic.
4. Statement of the problem: - reflects the gap and justifies that
the issue is worth researching.

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-Should provide clear description of the problem to be investigated
by the research
A problem statement should reflect: -
-The problem area and major research gaps
- The social, scientific and development relevance of the problem
- A brief and preliminary overview of the literature
5. Research Objectives: - address the purpose of investigation
a.General objective: is the overall objective of the research project
b.Specific objectives: are elements of the general objectives which
are directly addressable by the methodology.

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6. Significance of the study
- Is the importance or contribution that the study will have for
different bodies.
- States why the research has to be done.
7. Scope and Limitations of the study
- Scope: - refers to the coverage of the study in terms of area as
well as to the treatment of the variables.
- Limitation: - includes constraints or difficulties that will have
influence on the results of the study.

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8. Preliminary literature Review
- is to locate literatures from a variety of sources, reading it
carefully and organizing it in to ideas (themes).
- Examining recent or historically significant research results,
company data, or industry reports.
- Aims to review the critical points of current knowledge about the
research topic.
 The major purpose and importance of literature review are: -
 To identify the potential difficulty in the investigation
 To avoid duplication (maintain originality)
 To improve the methodological and procedural matters
 To identify the gap in the specific study topic

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9. Research Methodology
- Answers the “how” questions since it provides the work plan and
describes the activities necessary for the completion of the project.
- Gives details of how the research activity is going to be carried out.
- This section has to include: -
i. Study design
ii. Data type and source
iii. Population
iv. Sample size and sampling techniques
v. Data source and method of data collection
vi. Data collection instrument and procedure
vii. Data analysis techniques and computer program to be used.

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10. Time Schedule/ Work Plan
-A research project has a beginning and an ending time.
-Is a realistic time schedule for completing the study.
- is dividing the study in to specific activities and assigning dates
/reasonable time for completing each activity.
11.Budget plan
- A plan for expending the resources required to carry out the
research in monetary terms such as personal costs, secretarial
services, fieldwork costs, overheads, Books/journal costs ,
equipments, etc.
12. Reference/ Bibliography
- Is a list of books, journals, and other documents that a researcher has
consulted and used while the study is conducted.

CHAPTER: FOUR
SAMPLING DESIGN
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CENSUS AND SAMPLE SURVEY
Population: is the set of people or entities to which findings are to be
generalized.
 Sample: is a group of units selected from a population.
 Census: is collections of data from every person or entity in the
population or the study of all individual members of a population.
 Sampling Technique: is the process of selecting samples from a
population
Sample design: - is the technique or procedure the researcher adopts
for selecting items for the sample from the population.
The Need for Sampling
i. There could be resource (time, finance, manpower, etc.) limitations
ii. Tests may be destructive

SAMPLING DESIGN
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iii. Sampling provides much quicker results than does a census.
iv. If the population is infinite.
v. The quality of a study with sampling is often better than with a
census.
Two major principles underlie all sample design:-
 The desire to avoid bias in the selection procedure
 To achieve the maximum precision

SAMPLING DESIGN
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Bias in selection can arise:
- If the selection of the sample is done by some non-random method
- If the sample frame doesn’t adequately cover the target population
- If some sections of the population are impossible to find or refuse to
co-operate.
STEPS IN SAMPLING DESIGN
 While developing a sample design, consider the following points.
1. Type of universe/population: - define the population explicitly before
a sample is taken and realize whether the universe is finite or infinite.
2. Sampling Units: is the unit of analysis or case in a population.

SAMPLING DESIGN
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- Sampling unit may be a geographical area such as district, kebele,
village, or a social unit such as family, school, or it may be an
individual.
3. Source list/sampling frame: - is a list that contains the names of all
elements in the population.
4. Sample size: is the number of items to be selected from the
population to constitute a sample.
5. Parameters of interest: - In determining the sample design, one
must consider the question of the specific population parameters
which are of interest.
6. Budgetary constraint
7. Sampling procedure: - Deciding on the type of sample the
researcher will use.

SAMPLING DESIGN
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Types and Causes of Incorrect Inferences in Sampling
 There are two causes of incorrect inferences: -
i. Systematic bias (Non- sampling error): results from errors in
the sampling procedures and it cannot be reduced by increasing
the sample size which may include field work problems,
interviewer bias, clerical problems, etc. This bias could arise due
to: -
 Inappropriate sampling frame: - is a biased representation of
the population. Non-coverage error or wrong population sampled.
 Defective measuring device: - if the questionnaire or the
interviewer is biased or if the physical measuring device is
defective . Interviewer error or instrument error.
 Non-respondents: if we are unable to sample all the individuals

SAMPLING DESIGN
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 Indeterminacy principle: individuals act differently when kept
under observation than what they are not.
Natural bias in the reporting of data:
ii. Sampling errors: - are errors attributable to sampling not to
census.
- are the random variations in the sample estimates around the true
population parameters. It decreases with the increase in the size of
the sample.
TYPES OF SAMPLE DESIGNS
 Sample designs are basically of two types based on the principles
of randomness and those that are not
i. Probability sampling
ii. Non-probability sampling.

SAMPLING DESIGN
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i. Probability sampling designs: is a sampling technique in which
each member of a population has a known non-zero probability of
being chosen.
- Is also known as random sampling or chance sampling.
 The advantage of probability sampling over the non-probability
sampling are:
 Sampling bias will be minimized
 Relies on random process
 Helps to get consistent and unbiased estimates of the population
parameters
 One can calculate confidence interval within which the true
population parameter lies.

SAMPLING DESIGN
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 The common types of probability sampling are:
1. Simple random sampling
2. Systematic random sampling
3. Stratified random sampling
4. Cluster random sampling
1. Simple random sampling: - each unit in the population has equal
chance to be included in the sample.
-Easy to accomplish and to explain to others.
- Fair way to select a sample
- Reasonable to generalize the results from the sample back to
population.
- Appropriate for homogenous population

SAMPLING DESIGN
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- Is the best technique for selecting a representative sample.
- Uses lottery method and random number table method to
take a random sample.
2. Systematic random sampling: - is a technique of selecting
sampling units using a fixed interval on a randomly ordered list of
the sampling frame.
- It involves selection of every kth element in the sampling frame.
Here are the steps to use systematic random sampling: -
i.Number the units in the population from 1 to N
ii. Decide on the sample size “n”
iii. Determine interval size “k” using k = N/n

SAMPLING DESIGN
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iv. Randomly select starting point 1 to k
v. Take every kth element until the sample size reach to n
3. Stratified random sampling: - involves dividing the population
in to homogenous subgroups/strata and then taking a sample from
each subgroup.
- Each of the homogenous subgroup is known as strata.
- Used when the population is heterogeneous
 The basic steps for stratified sampling are: -
i. Divide the population in to subgroups or strata
ii. Determine sample size of the whole population
iii. Compute sample proportion for each stratum based on their
population proportion.

SAMPLING DESIGN
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iv. Make a separate and complete list of each stratum
v. Select sample from each stratum using simple random sampling
or systematic sampling technique to constitute the sample
The strata are formed on the basis of common characteristic(s) of
the items
 The number of items be selected from each stratum is done using
proportional allocation method.
4. Cluster (Area) Sampling:- is a probability sampling technique
whose sampling units are homogeneous sub-groups (usually
geographic areas) called clusters.
- The elements within each cluster are heterogeneous.

SAMPLING DESIGN
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 Steps we follow in cluster sampling are: -
1. Divide the population in to clusters
2. Randomly sample clusters
3. Measure all units within sampled clusters
 Multi-Stage Random Sampling: is a two or more stage cluster
random sampling.
-It is a further development of the principle of cluster sampling.
ii. Non-probability sampling designs: - doesn’t involve random
selection of sampling units.
-Items for the sample are selected deliberately by the researcher
- Personal element has a great chance of entering into the selection
of the sample.

SAMPLING DESIGN
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 Non-probability sampling techniques are classified into: -
a. Judgment (purposive) sampling: - takes sample with a purpose
in mind.
- A researcher uses his judgment to select people that he feels are
representative of the population
b. Convenience (accidental) sampling: -is where the researcher
selects subjects on the basis of availability.
- The most convenience population is chosen, which may be the
researchers friends, work colleagues, etc.
c. Quota sampling: - is selecting a predetermined number of
individuals from different group (i.e. based on age, gender, etc).

SAMPLING DESIGN
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d. Referral Sampling: - utilizes some form of referral, wherein
respondents who are initially contacted are asked to recommend
others.

OF DATA
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 TYPES AND SOURCES OF DATA
Data sources can be generally classified into:
i. Primary sources: - are original or first hand data pertaining to
people, objects, or events.
ii. Secondary source: - are those which have already been collected by
someone else and passed through the statistical process.
 Consider the following points when choosing whether to collect
quantitative or qualitative data,
The purpose for which the data is required
 The subject matter
 The method of data collection: observation, interview, etc
 The method of data presentation

TYPES AND SOURCES OF DATA
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The selection of data sources depends on:
 Purpose and scope of inquiry
Availability of time
 Availability of finance
Accuracy required
 Methods of data collection

SOURCES AND COLLECTION OF DATA
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 Collection of Primary Data
-Primary data can be collected through experimentation or
through surveys.
Experiment: - is examining the relationship between two factors
by manipulating one whilst measuring changes in the other.
 Survey: - refers to the method of securing information
concerning a phenomenon under study from all or a selected
number of respondents.
 Survey designs can be broadly divided into: -
i. Cross-sectional design
ii. Longitudinal design

Survey Design
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i. Cross-sectional survey: - collects data at one time.
- Findings are generalized from such one-shot studies to the
sampled population
ii. A longitudinal survey: - takes place over time with two or more
data collections.
-The following are the types of longitudinal surveys:
a. A trend survey: is a longitudinal survey in which a general
population is studied over time.
-Different samples are selected from a general population and
studied at different time.
- The members of the population will change overtime of study.
- Can be carried out using secondary data

Survey Design
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b. A cohort survey: - is a longitudinal survey in which a specific
population is studied over time.
- It focuses on the same specific population each time data are
collected.
- Data are collected from different samples that are drawn from the
same specific population.
- Can be carried out using secondary data
c. A panel survey: - is a longitudinal survey in which the same
sample is measured two or more times.
- The samples can represent either a specific or a general
population.
- Cannot be carried out through secondary analysis of previously
collected data.

Survey Data collection Methods
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 Survey Data collection Techniques
1. Observation Method
- is watching and recording behaviour and non- behaviour
characteristics of living beings, objects or phenomena.
- useful also when studying subjects who are not capable of giving
verbal reports of their feelings.
- Observation method of data collection can be designed as either
participant or non-participant.
 Participant Observation: - when the researcher actually
participates in the event or phenomenon being observed.

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 Non-participant observation: - where the observer is outside the
social setting of the observed.
2. Interview Method - involves oral questioning of respondents
either individually or as a group.
a. Personal interviews: - requires a person (interviewer) asking
questions in a face-to-face contact to the interviewee.
b. Telephone interviews: This method of collecting information
consists of contacting respondents on telephone itself.
 Interviews can be: -
 Structured: - uses of a set of predetermined questions and highly
standardized techniques of recording.
- Interview questionnaires are used in structured interviews.

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 Unstructured: - uses a list of topics rather than fixed questions.
-is more flexible and allows much greater freedom to ask in case of
need.
3. Questionnaire
- is a data collection tool in which written questions are presented and
given to respondents.
- It can be administered to respondents in different ways
 Sending questionnaires by mail
 Gathering all or part of the respondents in one place at one time
 Hand-delivering questionnaires to respondents and collecting them
later.

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4. Focus Group Discussion /FGD/Method
- is typically composed of 7 to 12 participants who are unfamiliar each
other and conducted by a trained facilitator.
- Eight is a popular size
- The facilitator sets the agenda and lets the participants brainstorm on
the agenda.
 Case Study Method
- is an intensive study of a case which may be an individual,
institutions, a system, a community, an organization, an event, or
culture.
- It is a method of in depth study rather than in breadth.
- is an intensive and comprehensive investigation of the particular
unit under consideration.

Collection of Secondary Data
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- Secondary data are originally collected for a task other than the task
at hand.
- Most scientific papers are secondary data sources.
- Sources of secondary data can be:
 Central Statistical Authority Publications
 Publications by Regional Governments
 Publications by the different Ministries
 Publications of the National Bank of Ethiopia
 On-line and Electronic Data Bases
 Reports and publications of various associations

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 Before using secondary data, one should consider: -
 Reliability of data: - by answering questions of who collected
them? What were the sources of data? What methods were used? At
what time were they collected? How they were analyzed?
 Suitability of data: Data must be evaluated whether they could
serve for another purpose other than the one for which they were
collected.
 Adequacy of data: This should be done in terms of area
coverage, level of accuracy, number of respondents

Chapter Six: Data Processing, Analysis and
Interpretation
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Data Processing
 Processing implies editing, coding, classification and tabulation
of collected data
Editing: - is a process of examining the collected raw data to
detect errors and omissions
 Coding: - refers to the process of assigning numerals or other
symbols to answers or coding using numerical values to make it
ready for analysis.
 Classification: - is the process of arranging data in groups or
classes on the basis of common characteristics
 Tabulation: - is the process of summarizing raw data and
displaying the same in compact form for further analysis.

Measurement Scales
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Measurement Scales
 Measurement is the precision by which a variable is measured.
- There are four basic measurement scales: -
1. Nominal: - describes variables that are categorical in nature or
deals with classifying a variable into different distinct groups.
2. Ordinal: - describes variables that can be ordered or ranked in
some order of importance e.g. (Likert scale for level of satisfaction
Or small, medium, large).
3. Interval : - are those in which the distance between each
descriptor is known or it describes variables that have more or
less equal intervals.
- Has no true zero or has an arbitrary origin.

Measurement Scales
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4. Ratio: - describes variables that have equal intervals and a fixed
reference point.
- Represents the actual amounts of a variable.
- Are ones in which a true origin exists. That is, a zero income is
meaningful in real life.
Data Analysis: - is the process of cleaning, transforming, and
modeling data with a goal of extracting useful information in the
form of conclusion.
- Data analysis can be categorized as: -
1. Descriptive Analysis: - describes the basic features of a survey
data.
2. Inferential analysis: - give results beyond description of the data.

Frequency Distribution
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1. Descriptive analysis: - are used to describe the basic features of a
survey data.
-Describe and summarize data using descriptive statistics.
-It includes frequency distributions, mean, median, mode; range,
variance, and standard deviations.
a. Frequency Distribution- is shows the distribution (or the count)
of individual scores in a sample for a specific variables.
- Frequency refers to the number of times each score or response
occurs.
-Ungrouped Frequency distribution
- Grouped Frequency distribution

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Ungrouped Data: - consists of single values along with their
frequencies.
Example: a survey of 16 micro and small businesses engaged in sale
of the products
Sales Amount Frequency Percentages Cumulative Percentages
30 3 18.75 18.75
35 4 25.00 43.75
40 5 31.25 75.00
45 2 12.50 87.50
50 2 12.50 100.00
N = 16 100%

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Group Frequency Distribution- when values are grouped into
categories or intervals when the samples is very big.
- Class interval normally starts with the lowest score
- W =
W= width, K= No. of classes, R= range, Width should be the same.
Class-Interval Frequency Class Boundaries Mid Points
1 – 5 5 0.5 – 5.5 3
6 - 10 5 5.5 – 10.5 8
11 - 15 10 10.5 – 15.5 13
16 – 20 15 15.5 – 20.5 18
21 – 25 10 20.5 – 25.5 23
26 – 30 5 25.5 – 30.5 28
N = 50
K
R

Measures of Central Tendencies
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b. Measures of Central Tendencies - are used to compute summary
statistics of variables being studied.
i. Mode: - is the most frequent value in the data
ii. Median: - is the middle value that divides ranked data into two equal
parts
iii. Mean: - is the average of a value of a data distribution
c. Measures of Dispersion/ Variation
i. Range: - Is the difference between the highest and the lowest value
ii. Standard Deviation: - shows the extent to which values in a
distributions deviate from their mean.
n
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Correlation Analysis
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Correlation analysis: - measures the degree of linear relationship
between two variables in a dataset.
- Shows the presence of association and degree of relation between
two and/or more variables in the data.
Correlation entails:
 Whether or not the correlation exists or not
 Degree of correlation
 Is it positive or negativities, i.e. direct or indirect
 Is the relationship strong or weak
 Correlation coefficient varies between +1 and -1.
- If the value is +1, there is a perfect positive relationship
- If the value is -1, a perfect negative relationship

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- If the value is zero, the two variables are not related to each other
 The most common methods of correlation coefficient calculation
are Pearson’s and Spearman’s coefficient of correlation.
a. Pearson’s coefficient of correlation (Simple Correlation): - is
used for variables measured at interval or ratio scale and normally
distributed. It can be computed as: -
Where, X = sample mean of X, Sx = Standard deviation of X
Y= sample mean of Y, Sy = Standard deviation of Y
r = Pearson’s correlation coefficient.
n = sample size
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Student X Y X2 Y2 XY
1 5 10 25 100 50
2 3 7 9 49 21
3 1 4 1 16 4
4 6 5 36 25 30
5 7 3 49 9 21
6 2 8 4 64 16
N=16 X = 24 Y= 37 X2 = 124 Y2 = 263 XY= 142

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r =
r =
- The value indicates low/weak negative correlation between
variable X and Y.
b. Spearman’s coefficient of correlation (Rank correlation): - is
used for data that have ordered categories.
- When the data are sufficient to rank the data as first, second, third,
and so forth
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For calculating rank correlation coefficient:
1. Rank the observations by giving 1 for the highest value, 2 to the
next highest value, and so forth.
2. Record the difference between ranks (‘d’) for each pair of
observations
3. Square these differences to obtain a total of such differences
4. Then, spearman’s correlation coefficient is computed as: -
Where, d2 is the sum of the squared difference of the ranks
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Data Analysis
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Example: - let us take one example of two sets of categories of 15
students with rank orders. One category is “ popularity ranking” (
i.e., the level of popularity of these 15 students among say 120
second year students of Accounting department, H U, and other
category of “exam-mark ranking” ( i.e., percentage of marks
secured in, say research methods course)

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Students’ Exam Marks Popularity D ( difference D2
Name (1) Result (2) ranking (3) in ranking ( 2-3)
L 15 13 2 4
M 7 8 -1 1
N 2 1 1 1
O 5 7 -2 4
P 6 4 2 4
Q 13 15 -2 4
R 9 14 -5 25
S 11 9 2 4
T 8 5 3 9
U 10 10 0 0
V 4 6 -2 4
W 12 11 1 1
X 14 12 2 4
Y 1 2 -1 1

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Computing rr, for data given above, we get:
rr = 1 - 6 x 66 = 1 - 396 = 1 – 396 = 1- 0.117 = 0.88
15(152-1) 15 (225-1) 3360
- The obtained value of (rr) shows that:
 There is an association between popularity and exam results
(since coefficient is not zero)
 Association is very strong, and
 Association is positive.
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Regression Analysis
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Regression Analysis: - deals with prediction of values for a
dependent variable using known values of an independent variable.
-Is technique that establishes a functional relationship between the
dependent and one or more independent variables.
- Regression analysis explains the change in the dependent variable
as one of the independent variables change by one unit.
Simple regression analysis: - is predicting the value of one
dependent variable based on the value of one independent variable.
Multiple regression analysis: - is predicting the value of one
dependent variable using many independent variables.

Regression Analysis
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To predict the value of dependent variable from the known value of
independent variable, one should follow two steps.
1.Determine linear regression line by using mathematical equation
2. Predict the value of dependent variable by using the mathematical
equation.
Y = a + bx
Y= Predicted value (Dependent variable)
b = slope of the line
a = Y-axis intercept ( when X value is zero).
X = Independent variable
- The slope of the line shows how the value of dependent variable
(Y) changes with the changing value of independent variable(X).

Regression Analysis
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- The slope of the line (b) and the Y-intercept ( a) are determined as:
b = nXY -XY ; where n = represent number of observation
nX2- (X)2 b = Slope of the regression line
a = Y -bX ; where a = Y-intercept
n
a = Y- bx; Y and X = Mean value of Y, and X respectively
Example:- Suppose that a researcher conducted investigation on n
=20 business men with respect to two variables i.e. ability to
generate ideas (independent variable-X), and ability to create or
innovate (dependent variable-Y). Accordingly, he/she has collected
the following data from the 20 study units in relation with the
variables under consideration.

Regression Analysis
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Individual
Cases ( n)
X Y X2 Y2 XY
1 15 12 225 144 180
2 10 13 100 169 130
3 7 9 49 81 63
4 18 18 324 324 324
5 5 7 25 49 35
6 10 9 100 81 90
7 7 14 49 196 98
8 17 16 289 256 272
9 15 10 225 100 150
10 9 12 81 144 108

Regression Analysis
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11 8 7 64 49 56
12 15 13 225 169 195
13 11 14 121 196 154
14 17 19 289 361 324
15 8 10 64 100 80
16 11 16 121 256 176
17 12 12 144 144 144
18 13 16 169 256 208
19 18 19 324 361 342
20 7 11 49 121 77
X = 233 Y = 257 X2 = 3037 Y2 = 3557 XY = 3205
X = 11.65; Y = 12.85; Sx = 4.120 ; Sy = 3.66, where Sx and Sy are standard deviation of X and Y
observations respectively

Regression Analysis
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i. Determine the slope of the line (b)
ii. Determine the Y-intercept (a)
iii. Determine the regression equation
iv. Predict the value of Y when the value of X is 12.
v. b = nXY -XY = b = (20) (3205)-(233)(257) = 0.654
nX2- (X)2 ( 20)(3037)- (233)2
vi. a = Y -bX = 257 – (0.654)(233) = 5.2309
n 20
vii. The regression line equation will therefore be:
Y = 0.65 X + 5.2309
viii. The value of Y when the value X of is 12, will be
Y = 0.654 (12) + 5.2309 = 13.08

Inferential Analysis
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2. Inferential analysis: - includes estimating of population values
and testing statistical hypothesis.
Test of Hypothesis
- Hypothesis is a tentative statement about the expected
relationship.
- Hypothesis testing is the procedure of determining the validity or
otherwise of inferences about population based on results obtained
from samples.
-There are various types of inferential statistical procedures that are
used in testing hypotheses.
Characteristics of Hypothesis
-It should be clear and precise.
-It should be capable of being tested.

Hypothesis Testing
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 It should state relationship between variables.
 It should be limited in scope and must be specific,
 Hypothesis should be stated as far as possible in most simple
terms
 Hypothesis should be consistent with most known facts
 Hypothesis should be amenable to testing within a reasonable
time.
-The null hypothesis is termed as Ho, while the alternative
hypothesis is termed as H1.
-Alternative hypothesis is usually to prove and null hypothesis is
usually to disprove
Business Research Method
Chapter: One
An Introduction
1
Abebaw W., 2017

Hypothesis Testing
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Steps to be Followed in Hypothesis Testing
i. Formulate the Hypothesis ( Making a formal statement)
Null Hypothesis (Ho) M1 = M1
Alternative Hypothesis (H1) M1 # M1
Example, to investigate the relationship between educational
achievement and income level, a researcher may formulate the
following null and alternative hypothesis.
Null hypothesis (Ho): There is no significant relationship between
educational achievement and income level.
Alternative hypothesis (H1): Income level is significantly related to
one’s educational achievement.

Hypothesis Testing
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ii. Spell Out the Assumptions and Characteristics of the Model
(data and sample). This include:
The type of variables and measurement scale;
Sampling method and sample size; and
Sampling distribution ( normal or non-normal distribution)
iii. Establishing the Significance Level or the Confidence Level
-is a measure of how strong the evidence must be before the Ho is
rejected.
- A researcher would be 95% confident that the difference found
between or among groups are due to treatment.
- The hypothesis is tested on a predetermined level of significance.

Hypothesis Testing
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Type-I Error and Type-II Error
- Type I error : - Rejecting Ho when it is True
-Type II error: - Accepting Ho when it is False
iv. Select a Random Sample and Computing an Appropriate Value
or Test of Statistic
DECISION DECISION
ACCEPT H O REJECT HO
HO TRUE CORRECT DECISION TYPE I ERROR
( α error)
HO FALSE TYPE II ERROR
( β error)
CORRECT DECISION

Hypothesis Testing
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v. Making the Decision (Decision Rule).
- In making decision, the computed statistic is compared with the
theoretical or critical value of the statistic for the significance
level set in advance.
As a rule, compare the calculated value with specified value of , the
significance level, then
 If Sc > Se , Ho is rejected ( H1 is inferred)
 If Sc < Se , Ho is accepted( Fail to reject Ho)
Where, Sc = Computed statistic
Se = Expected or critical statistic
- In terms of probability, Ho is rejected if the probability for the test
statistic is less than the critical significance level set, eg = 0.05 or
0.01.

Hypothesis Testing
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Techniques to test a Hypothesis
i. Non-parametric Test: - utilizes variables measured in categorical
scale and do not make assumptions about the data.
a. Chi- Square (χ 2) Test: - used to establish relationship between
two variables, both of which are categorical in nature.
-Compares the theoretical population and actual data when
categories are used.
Characteristics of chi-square test
It is based on frequencies and not on the parameters like mean and
S.D.
It is used for testing the hypothesis and not for estimation.
 It has less mathematical details are involved.
 No rigid assumptions are necessary regarding the type of
population

Chi- Square (χ 2) Test
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Types of chi-square Test
a. Test for goodness of fit.
b. Test of independence.
c. Test of equality of several population proportions
Example: suppose we want to test the hypothesis that there is a
relationship between educational achievement and income level i.e.
test of independence. The variables “Educational achievement”, can
be categorized as “primary”, “secondary”, and “college”. The
variable “income level” can be categorized as “low income”,
“middle income”, and “high income”.
- If the observed frequency greatly departs from what is expected,
then we reject the null hypothesis that the two variables are
independent of each other.

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i. Research Problem: What is the relationship between Educational
achievement and Income level?
ii. Null hypothesis ( Ho): - There is no relationship between educational
achievement and income level
iii. Alternative Hypothesis (Ha): - There is significant relationship between
educational achievement and income level
iv. Model Specification and Assumptions
Categorical variables; educational achievement and income level
χ 2 distributions
Random Samples
vi. Significance level: = 0.05
Degree of freedom ( r-1) ( c-1) , where ‘r’- refers to no. of raw, and ‘c’-refers
to no. of columns in the observation.
Critical Value of χ 2 = 9.488

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vi) Computing χ 2 Statistic
Table: The Relationship Between Income Levels and Educational Achievement
NB. The numbers in the bracket are computed or expected values ( fe); while
the numbers outside brackets are observed values ( fo).
Income
Level
Educational Achievements
Total
Primary Secondary College
Low
Income
135
(97)
125
(110)
40
(93)
300
Middle
Income
80
(87)
110
(99)
80
(84)
270
High
Income
75
(106)
95
(121)
160
(103)
330
Total 290 330 280 900

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Expected value( fe) = CT x RT where, CT- Column Total; RT – Raw Total, a
GT GT- Grand Total
Accordingly, fe = 290 x 300 = 97
900
Computed χ 2 Statistic =  (fo – fe)2/fe
χ 2
c = (135-97)2 /97 + (125-110)2/110 + (40-93)2/93 + …+(106-
103)2/103
= 14.88+ 2.04 + 30.20 + 0.56 + 1.22 +0.19 + 9.07 +5.59 +31.54 =
95.27
Decision χ 2
c = 95.27 > χ 2 expected, which is 9.488
- Hence, Ho is rejected, i.e. income level is significantly related to
one’s educational achievement.

Parametric Test
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ii. Parametric Test: - utilizes variables measured in interval and
ration scale and make assumptions such as normal distributions and
linearity.
- Parametric techniques are more powerful than non-parametric
techniques when making inferences.
i.T-Test: - is used to test whether there are significant differences
between two means derived from two samples or groups at a
specified probability level.
a. T-test for Independent Samples: used when there are two
experimental conditions and different participants at each condition.
The members of one group are not related to members of the other
but they are selected from the same population.
- Hence, T-test is used to determine whether there is a significant
difference between the means of the two independent samples

T-Test
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Example: - If the researcher wants to compare the IQ performance of
rural and urban children, he may select a sample of 30 subjects from a
rural school and another 30 subjects from an urban school and
administers the IQ test to both.
- A t-test is then used to compare the mean scores obtained by these
two groups.
General Rule: If calculated ‘t’ > critical ‘t’, reject Ho and then we
conclude that the two groups are significantly different.
Mean Std. Dev. Std error Case
Rural 90 5 0.93 30
Urban 120 8 1.49 30
Se =
n
SD

T-Test
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112
i. Null hypothesis (Ho): There is no significant difference between
the IQ performance of rural and urban children
ii. Alternative Hypothesis (H1): IQ performance varies significantly
between rural and urban children.
iii. Model Specification and assumptions
Random sample n1 = 30 ; n2 = 30
Normal distribution of the dependent variable
T-distribution ( two tailed)
The dependent variable is measured at either interval or ration
scale.
iii. Significance Level = 0.05
Degree of freedom = ( n1 + n2)
Critical value of t = 1.67 (from t-table).

T-Test
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113
iv. Computing the t-test
tc =
Se = Standard error
V. Decision: t computed 19.29 > t expected 1.67, thus, Ho is rejected
which says ‘the means of the two groups are not statistically
different. Standard error means the standard deviation of sample
means.
b.T-Test for Non- Independent Samples - the same group is pre-
tested on some dependent variable and then post-tested at a later
date. Used when there are two experimental condition and the same
participants took part in both conditions.
- T-test is used to determine whether there is a significant difference
between the means for one sample at two different times.
29
.
19
42
.
2
30
49
.
1
93
.
0
120
90
ban
x Ur
Se
+
rural
x
Se
y
-
x






Analysis of Variance (ANOVA)
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114
Analysis of Variance (ANOVA): - used when the means of more
than two groups are compared.
-It tells us whether three or more means are the same or not.
- An ANOVA produces an F-statistic or F-ratio.
- F-ratio tells us only that the experimental manipulation has had
some effect but it doesn’t show what the effect was.
- If only one factor is taken and investigate the differences among
its various categories, it is one-way ANOVA.
- If two factors are taken at the same time, then we have to use two-
way ANOVA.
- Then we have to make two estimate of population variance i.e. one
based on between samples variance and other based on within
sample variance.

Analysis of Variance (ANOVA)
Derbew B., 2024
115
F= Estimate of population variance based on between sample variance
Estimate of population variance based on within sample variance
F-ratio = MS between
MS within
- If the calculated F-value is less than the F- table value, there is no
difference between sample mean or Fail to reject Ho.

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