INTRODUCTION TO RESEACH METHODS

Dr. Abood Mohammed Jameel

Cihan University
Accounting Department

2019-2020

1
 INTRODUCTION RESEADCH METHODS

A. Course Description

This course is designed to introduce students to the art and science of

conducting research in the field of Accounting and Finance. It is designed to
build a solid background for the students to enable them successfully
conduct empirical studies, employing both quantitative and qualitative
techniques of data analysis.

B. Course Objectives
On the completion of this course, students should be able to:
(i) Select a good research topic amenable to investigation.
(ii) Write a good research proposal.
(iii) Conduct a good sampling of a population.
(iv) Undertake a good review of related literature.
(V) Conduct a successful research work.
(Vi) Write a good research report.

2
C. Grading Formula
1. Continuous Assessment 10%
2. Mid-Term 10%
3. Mid-Term 10%
4. Write a good research report. 10%
2. Final Examination 60%
100%
The continuous assessment marks are to be absorbed through attendance,
test(s), assignments and paper presentation.
D. Course Contents
1. Introduction to Research Method/The Concept of Research
2. Selecting a Good Research Topic
3. Statement of the Research Problem and Objectives
4. Formulation of Research Hypotheses/Questions
5. Timing and Outline of Research Project
6. Literature Searching and Critical Review
7. Theoretical Framework
8. Population, Sample Size and Sampling Techniques
9. Sources and Methods of Data Collection
10.Data Editing, Tabulation and Presentation
11.Methods of Data Analysis and Interpretation
12.Variables and their Measurement
13.Summary of Research Work
14.Reaching Conclusion and Making Recommendations
15.References/Bibliography
16.Research Ethics

3
 CHAPTER ONE

1-Introduction
In many ways, your introduction is a longer version of your abstract.
In the introduction you want to accomplish several very important things:
1. Statement of Topics.
2. Description of your Data.
3. Outline of the Structure of your Paper.
The concluding section of your introduction should tell the reader what to
expect in the pages that follow. If the body of your text is divided into
sections, say so, and tell the reader what those sections are. You should
also tell the reader what you are going to talk about in your conclusions.

2-Research definition: According to Kinney (1986), the general definition

of research is: the development and testing of new theories about “how the
world works”
3-What is the purpose of research?
The idea of research is of course, is to make discoveries, understand things
better, and in long run to improve things.
It’s useful to make this a little more definite by thinking of what the
outputs from a research project might be. I can think of four possibilities:
1 Discovering the truth about something
2 Creating, modifying or justifying a theory or model of something
3 Finding a good, or better, way of doing or implementing
something
4 Creating something like a computer program for stock control,
or a training course.
4
This is a rather muddled list, it's probably not complete, and the categories
may overlap. The outputs may be in the form of a report for the audience
to read (about the truth for example), or a computer program, or some
teaching materials, or a combination of several of these.
What is definitely worth noting is that there are two, apparently very
different, criteria for evaluating these outputs. The first is "Is it true?" And
the second is "Is it useful?" The relationship between these two is subtle
and need not concern us unduly here, except to remember that, ultimately,
the aim of a research project is usually to improve the future by finding
out how to manage better. This might be directly by 3 or 4 above. Or it
might be indirectly by a better understanding of the present situation (1),
or better theories about how things work (2). So in this sense at least,
usefulness is the primary aim.
The usefulness criterion begs the question: useful for what? What is it that
we value - happiness, money, or whatever? And whose happiness, or whose
money matters? These value judgments needs very careful consideration.

4- Research Objectives

• The objectives of a research project summarize what is to be

achieved by the study. These objectives should be closely related to
the research problem.
• The general objective of a study states what researchers expect
to achieve by the study in general terms.

5
5- Research Importance.

1-Research adds to our knowledge.

2-Addresses gaps in knowledge
3-Expands knowledge
4-Replicates knowledge
5-Research helps improve practice.
6-new ideas,
7-new insights into approaches.

6-Hypothesis definition:

In its simplest form, a hypothesis is a guess. Hypothesis is a formal

statement of an unproven proposition that is empirically testable.
(Conjectured relationship between variables)

Null and alternative hypothesis:

Null: is defined as the prediction that there is no interaction between
variables or the correlation between them is equal to zero. The symbol for
the null hypothesis is ‘H0’.
Alternative: is opposite to null and defined as the prediction that there
is a measurable interaction between variables. The symbol for the
alternative hypothesis is ‘H1’.

6
7- Characteristics of good topics.
1. Interesting – keeps the researcher interested in it throughout the
research process

2. Researchable – can be investigated through the collection and

analysis of data

3. Significant – contributes to the improvement and understanding of

educational theory and practice

4-Manageable – fits the level of researcher’s level of research skills

8-Choosing research topic

The main things to
consider are:
 Usefulness and interest. Will the results be useful? Will they make
the world a better place? Or will readers just be left asking “So
what?” or “I know this already.” Will acquiring a state-of-the-art
expertise in the topic help your career?
 Feasibility. Are you likely to be able to get the data, etc to
research the topic properly? The project should be reasonably
challenging.

9- Applications of Research in Accounting

Research can be applied to any field of accounting. In Auditing,
for example, research can be applied in assessing the adequacy or
otherwise of internal control system in safeguarding against errors
and fraud or in ensuring/enhancing efficient management of
financial resources. Similarly, research can be applied to
determined/assessed/appraised the role of accounting in
7
enhancing financial accountability, the role of internal control or
internal auditors in enhancing compliance with due process, or
the relevance of audit reports in investment decisions, among
others.
In taxation, research can be applied to investigate the revenue
generation and tax evasion at the three tiers of government, the
federal, state and local government; or to assess the cost of
governance and revenue assurance mechanism at federal, state or
local government level; or to assess the contributions of IGR to
revenue generation at states and local government level; or to
assess prudence, transparency and accountability in revenue
administration; or to carry out an impact assessment of PITA
2011 on IGR at states level, among others. Similarly, in finance,
research can be extensively used in financial planning and
forecasting, cost-benefit-analysis, capital budgeting, credit
collection policies and their impact on cash flows, etc.
Research can therefore, be applied in every aspect of accounting,
including public sector accounting, forensic accounting,
environmental accounting, oil and gas accounting, corporate
finance, investment and capital market studies, accounting ethics,
accounting theory and GAAPs, corporate governance and
corporate social responsibility accounting, financial reporting and
accounting regulations, entrepreneurship accounting, accounting
for the extractive industry, among others. Thus, it can be said that,
invariably, every accountant is a potential researcher and every
problem in accounting call for research.

8
 CHAPTER TWO

2.1-Literature review

Why we are doing a Literature review ?

Shows the reader that you are a scholar in the field that you know what
the important issues are, what has gone on before you that you know
and understand the issues and that you have given them critical
thought.

What can you learn from what is already out there?

How people think about the issues you are interested in.

What techniques have been used to gather information on the issues you
are interested in.

The following steps help you writing literature review:

Step 1: identify the topics you will search

In this project you are given the place to start but you will need to refine
according to your own interests (what are your subjects)

Step 2: Find the documents, Electronic sources in library

Based on methodical collection of evidence to back up argument

Step 3: summarize the documents Write in your own words what the
main argument is. Make a critical comment

Step 4: Write a lit review synthesizing the documents you have

prepared

9
2.2-Research sequence:
Figure 2.1 specifies the typical research sequence described by Howard and
Sharp (1983) as a series of stages we would expect to progress through in
most forms of Accounting Research, while moving from original idea to
eventual publication.

Figure 2.1. Research sequence

1. Identify broad area: Narrow the focus from

accounting in general to a stream associated with
financial accounting, management accounting,
auditing, accounting education or accounting
information systems.
2. Select topic: Specification of a sub-area to provide
a tighter focus, and one for which supervision
capacity is available, but one which may be modified in the light of
subsequent developments.
3. Decide approach: Early thoughts regarding the approach to be
adopted will revolve around the resources available, and in particular access
to the necessary data sources. A detailed specification of research methods
to be adopted must wait until the literature review has been conducted and
theoretical foundations and outline hypotheses have been established.
4. Formulate plan: Milestones and targets should be established at the
outset so that it is clear how the research will progress over an extended

10
period. This is particularly important for part-time researchers who may be
contemplating study over six or seven years.
5. Collect information: Data collection can safely proceed only when
we recognize exactly what we want to know, and for what purpose. The
planning stage should highlight the period over which we want to collect
data; this usually effectively precludes most longitudinal studies, partly
because it takes too long to collect data and partly because of the increased
vulnerability associated with extended site access.
6. Analyze data: Methods of data analysis and software requirements
should be apparent early in the research process.
7. Present findings: Preliminary findings will normally be presented at
university workshops and seminars, and then at specialist conferences. These
provide the precursor to publication in the refereed literature, which may
take place before completion of any associated doctoral dissertation.
Publication in the professional literature will bring important findings to the
attention of interested practitioners.

2.3-Research proposal outline

1. Cover page: Name, Title ,10 key words

2.Abstract (100- 500 words quickly )

11
3.Aims of the research ,1/2 page 

4.Rationale (why is this an important topic practically 1/2 page 

5.Literature Review (framework of the research) 5 pages 

6.Research Questions (similar to aims but more specific. ) 1/2 page

Interpretations of key terms.7 

8.Research design( what is a methodology?

9. Data Collection.

10. Data Analysis.

11. Bibliography- References .

The standard layout (Plan) for a project report is:

The cover page needs to provide key information, you need to include:

▪ University’s logo or name.

▪ Indication of what type of project (seminar BA).

▪ Title of your project.

▪ Name of your supervisor.

▪ Your name and due date.

▪ Note that cover page has no page number.

The abstract (short summary of project including conclusions)

An abstract is a short (100-200 words) descriptive passage that
summarizes your paper in a highly specific way. Its purpose is to provide
12
the reader with a key to your paper's contents, and to allow the reader to
judge whether your paper is related to her or his own research
interests. It is important that students learn how to write abstracts early
in their academic careers.
Basically, an abstract contains the following information:

1. A statement of the topic or issue under discussion.

2. A brief description of the data or information used.

3. An indication of the theoretical or methodological focus of the paper.

4. A brief statement of the conclusions reached.

 Background and aims (what you’re trying to find out and why it’s
important)
 Literature review (of relevant previous research which you will
build on, modify or extend; should be firmly focused on your
research area, and be “critical” in the sense of evaluating the
strengths and weaknesses of the work discussed.
 Research design and methods – plan and justification (what you
did to meet the aims, and why it was a sensible approach)
 Analysis (should show how you derived your conclusions and
recommendations to convince skeptical readers and impress
examiners; important tables, diagrams etc must be in the text,
details in appendix)
 Results, conclusions, recommendations, limitations, further
research. Make sure these match your aims.
 References (list only works cited in text in alphabetical order)
13
 Appendices – Ethics form.

2.5-Sources of Data:

1-Experiments,

2-Sport.

3-Internet,

4-University Database

5-Banks, Companies, Other privet sector.

6-Governmental documents

7-Libraries

2.6-Data Collection:

1-Registeration :- A register is a depository ( Store, Pool) of information

on Companies, Banks, or Individuals. It can be used to obtain a complete
Information through a legal requirement. Registers are usually very useful in
the design of statistical system.

2-Questionnaires. Questionnaires refer to form filled in by respondents

alone, and it can be used to collect regular data.

3-Interviews. Face to Face meeting ,in it information is obtained through

inquiry and recorded by persons.

2.8-Analyzing data
It is difficult to generalize about analyzing your data and deriving
conclusions from it because the best way to do it depends on your
14
particular research. However there are two common types of analysis.
First, there is statistical analysis. This ranges from tables of averages and
bar charts to complex methods such as factor analysis and multiple
regressions.

Second, you may have “qualitative” data (words or speech) from

interviews or from open-ended questions in questionnaires. You will find
various suggestions in the textbooks. On a simple level, I would suggest:
If you have a lot (say 20+) of cases, it may be worth coding your data,
and then analyzing it statistically. For example, suppose you have asked
college students who have left their course about the reasons for leaving.
You could then categories them into categories like “didn’t like course”,
“found a job”, etc, and analyze the frequency of each category. If you do
this you should check the reliability of your categorization by getting
another judge to check how you have categorized a sample of your
sample.
If you have two more cases, it may be worth making a data table with
each case on a separate row, and each question or other bit of
information in a separate column. Then in each cell you can make a note
about how Case X answered Question Y. This should help you to spot
and analyze any patterns.
You can also treat each interview, or other bit of data of whatever kind, as
a case to help you explore particular possibilities. Suppose you are
researching the impact of mobile technology on work-life balance, and
find that one of your interviewees has reduced their time in the office by
80%. This might be an interesting case worth exploring in detail for what

15
it might teach others about the possibilities offered by the technology. Tell
the story using direct quotes when possible – because this makes it clear
that you are simply reporting what your interviewee said, not putting your
own interpretation on it. Obviously this person may not be typical, and
statistics are irrelevant with a sample of one, but the information could
still be very useful.
(A distinction is sometimes made between “quantitative” (statistical)
and “qualitative” research, as opposed to types of analysis. In my view,
this, and similar distinctions are not helpful and best avoided – see
Wood and Welch, 2010 for the reasons.)
Whatever you do it is important that you include full details of the
evidence and its analysis in the main text of your project report – e.g. key
quotes from interviewees, tables and diagrams showing the statistical
analysis. The idea is to explain how you analysed your data to arrive at
your conclusions, in sufficient detail to convince the reader. Extra details
can go in the appendix, but the main story must be in a chapter called
Analysis or something similar. Otherwise your readers may not believe
your conclusions.

2.9-Bibliography

The purpose of a bibliography is to let the reader know

exactly where to find the information you have discussed in your
paper. A bibliography must include publication information on
every source you used in your paper. It includes:

1- Last name, first name.

16
2- Year of publishing.

3- Title.

4- Publishing city or country.

5-Name of the publisher

17
 CHAPTER FOUR

Types of Research:

• Applied vs. Fundamental (Pure) Research

• Quantitative vs. Qualitative Research

• Descriptive vs. Analytical Research

• Conceptual vs. Empirical Research

. Mixed Methods:

Qualitative vs. Quantitative Methods

Quantitative method: addresses research objectives through empirical

assessments that involve numerical measurement and analysis.
Qualitative method: addresses business objectives through techniques
that allow the researcher to provide elaborate interpretations of
phenomena without depending on numerical measurement.
Mixed method: includes the mixing of quantitative & qualitative methods.

18
Differences between

19
 CHAPTER FIVE
Population, Sample Size and Sampling Techniques

5.1-Population refers to the totality of all conceivable elements or subjects

relating to a particular phenomenon of interest to the researcher. The subjects or
elements are the individual items that make up the population, which may be
observed or physically counted, e.g. a population of banks in Nigeria, a
population of companies listed on the Nigerian Stock Exchange, the number of
audit staff in a company, etc.

5.2-A sample, on the other hand, is a part drawing from the population to serve
as its representative. The procedure of drawing a sample is known as sampling.
A sampling frame is a list of all the elements of the population under study and
is used to select the sample.

5.3- Reasons for Sampling

The following are some of the reasons for sampling.
(i) Similarities among the elements of a population thereby making the study
of a part there-of sufficient.
(ii) Sometimes, it is practically impossible to study the entire population
because of their number or nature and pattern of dispersion.
(iii) It is cheaper to study a sample than the entire population.
(iv) Sampling enables more thoroughness and affords more effective
supervision that studying the whole population, hence the possibility of
greater accuracy.

20
 (v) Sampling enables quicker results than the whole population and enables
the conduct of large-scale studies.

5.4-Why do we use sampling?

• Researchers most often have a population that is too large to test, so

have to draw a sample from the population and generalize from the
known characteristics of the sample to the unknown population.
• Sampling enables us to:

– Reduced costs

– Reduced time

– Reduced effort

– Increased accuracy

5.5- Characteristics of a Good Sample

The following are some of the characteristics of a good sample.
(i) Representativeness of the various groups/components/segments of a
population.
(ii) Sampling precision which minimizes sampling error.
(iii) Absence of sampling bias.
5.6- Sampling Designs
There are two types of sampling designs. These are probability sampling and
non-probability sampling designs.

21
1 - Probability Sampling
This is a sampling design which ensures randomness in the selection of
sampling by given population element an equal and independent chance of being
included in the sample. Methods of probability sampling may include the
following:
(i) Simple Random Sampling: This can be done through a raffle draw or
through the use of table of random numbers.
(ii) Systematic Random Sampling: This involves the selection of nth
subjects from serially listed population. Where “n” is any number usually
determined by the division of the population by the required sample size.
The researcher can randomly select any number from 1 to 7 on the
population list and then select every 7th subject after the first.
(iii) Stratified Random Sampling: In this method the population is grouped
into some definite characteristics, called strata, and then sample is drawn
from each stratum in proportion to the stratum share of the total
population. This is superior to other two methods explained above
because it uses extra method of representativeness.
(iv) Area Sampling or Cluster Sampling: This is used mainly in
geographically distributed population, and it is as effective as stratified
sampling method. If the population represented by each cluster is known,
the sample is drawn proportionately.

2- Non-Probability Sampling
Unlike probability sampling, non-probability sampling does not guarantee
randomness, although randomness may occur by chance. However, the
existence of randomness by chance does not matter since every element is not
deliberately given an equal and independent chance of being included in the
22
 sample. The following are some of the types on non-probability sampling
designs:
(i) Convenience/Accidental Sampling: This is sampling done at the
convenience of the researcher, when the researcher is only interested in
having a smattering idea of the phenomenon or where he is operating
with little or no budget.
(ii) Quota Sampling: This is done when there are certain characteristics of
the population e.g. sex, that needs to be included in the sample. Its
application gives a semblance of representativeness.
(iii) Judgment Sampling: In choosing some sample elements the researcher
may be guided by what he/she considers as typical cases which are most
likely to provide him/her with the requisite data or information. In such
a case judgment sampling may be used.

3-When to Use Non-Probability Sampling Technique

(i) When the researcher finds that he/she is dealing with an infinite
population where most sample subjects cannot be reached or the
population elements can only be imagined.
(ii) Where random sampling technique is not likely to guarantee the
inclusion of typical cases or subjects.
(iii) The type of statistical analysis envisaged by the researcher may
determine the sampling method to be used.
(iv) Research situation where generalization of result is not necessary or
may not be intended.
(v) Cost and time required, since probability sampling is more expensive
and time-consuming that non-probability sampling.
23
4- Other Sampling Techniques
Situations may arise where none of these methods discussed will be
adequate. The researcher may discover that the following techniques may be
resorted to. These are:
(i) Multi-Stage Sampling: This entails the selection of sample in stages
until the desired sample size is reached.
(ii) Panel-Sampling: This is a defined as a sampling system where
members of a permanent sample are used repeatedly for successive
interviewing.
(iii) Double Sampling: This is a situation where a researcher uses any of
the sampling methods discussed earlier to choose a sample size larger
than the size actually needed. From this extra-large sample, the
researcher then chooses the actual sample size by applying any of the
sampling methods discussed earlier.
(iv) Snowball Sampling: This is often used to obtain a sample when there
is no adequate list which could be used as a sampling frame. The
approach involves contacting a member of the population of interest
and asking whether they know any one else with the required
characteristics, for example micro enterprises own by a particular
tribe, or foreigners.

24
 CHAPTER SIX

METHODS OF DATA ANALYSIS AND INTERPRETATION

Data is defined as information in its raw form. Data analysis may be defined as
the breaking and ordering of the quantitative and qualitative data gathered
through research (i.e. involves searching the trends and patterns of associations
and relationships
among these data). Interpretation is the explanation of the associations and
relationships found among data or groups of data, including inferences and
conclusions reached from the relationship discovered among data or groups of
data.
Statistical analysis is the refinement and manipulation of data in order to prepare
them for the application of logica1 inference. Statistical analysis involves the
following steps:
(i) Data preparation;
(ii) Data tabulation;
(iii) Data presentation;
(iv) Data analysis; and
(v) Data interpretation.

Data preparation involves editing (i.e. examination of data to detect error that
may cause inconsistency) and coding (i.e. assigning numbers or symbols to
qualitative data. Data tabulation is the process of treating data for further
analysis by the use of tables (i.e. may be done by computer or manual1y).

25
 Presenting Data

Presenting Numerical Information

1. Frequency Tables
• In this example the number of errors cited when 3 different websites
are run through an html code checker are tabulated.
• It is normal where percentages are given, to quote the actual numbers
involved, or the total number of cases.

2. Cross Tabulations
• The purpose of a Cross-tabulation is to examine the
relationships between two different variables.
• In this example, the number of men and women of different
age groups expressing an interest in Blue Tooth are cross-
tabulated.

26
27
3-Time Series Data

• A Time Series simply logs the value of a particular variable over a

period of seconds, days or weeks.
• Because of amount of numerical information, it is very difficult to
make sense of time series data unless it is summarized or
presented in graphical form.

4-Presenting Graphical Information

1. Simple Bar Charts & Histograms

• Simple graphs can be highly effective in making a point.

• This one shows the fact that Package A clearly has been rated
higher than the other two packages.

Average Rating for Packages

28
10. Divisions of Statistical Analysis
Statistical analysis is divided into two, namely:
(i) Descriptive statistics: This involves bringing the data into order
involving data presentation, tabulation and summarization as in
measures of central tendency, and dispersion, and
(ii) Inferential statistics: This involves inferring certain meanings and
important relationship which lie hidden within, the data.

11. Functions of Inferential Statistics

1. To estimate a population parameter from a sample drawn from the
population
2. To predict the population characteristics from the characteristics of a
randomly selected sample or samples of the population.
3. To test relevant hypothesis for the purposes of drawing value
conclusions for research studies.
11.2 Division of Inferential Statistics
The two dimensions of inferential statistics with reference to statistical testing,
these are: parametric and non-parametric statistic.
Parametric Statistics: Parametric statistics include tests based on any of the
following:
(i) Student t distribution
(ii) Analysis of variance (ANOVA)
(iii) Correlation Analysis, and
(iv) Regression Analysis.

Parametric statistical tests are powerful tests because they are probability test
of significance. The most regular parametric tests used by students in
29
Accounting and management sciences include student t test, pearson product
movement correlation, analysis of variances and regression. The use of
parametric statistics depends on certain basic assumptions. These are:
(i) The samples being drawn from normally distributed population with
equal variance.
(ii) A scale of high precision such as interval and ratio scales being used
in generating the data for the analysis.

Non-parametric Statistic: This include test based on normal distribution i.e.

used probability distribution or any life phenomenon with variables that are
known to be normally distributed e.g. intelligence tests, age, agricultural
produce per acre etc. Non-parametric procedures are not generally concerned
with population parameter. They are distribution — free statistic.

Advantages of Non-Parametric Statistics

1. The non-parametric statistics are used with minimum assumptions, their
use not restricted and their chance of being used improperly is minimal.
2. It is effectively used even when the data are measured on weak
measurement scales (nominal and ordinal scales).
3. They are easy to compute and interpret.

Therefore, non-parametric statistics have become the heaven of researchers

and students, particularly those whose understanding of mathematics and
statistics is limited.

Dis-advantages of Non-Parametric Statistics

30
 (i) Since non parametric statistics are simple to calculate and apply,
they are often abused by researchers and students who do not
bother to understand when each statistic can be used.
(ii) Researchers and students often prefer non-parametric statistics
when it is obvious that parametric statistics are not appropriate. Thus,
some important information is wasted.
(iii) Though their calculations are simple, they are sometimes usually
long and tedious.

When to Use Non-Parametric Statistics

The non- parametric statistics is used:
(i) When the hypothesis to be tested does not involve population
parameters.
(ii) When there is no assumption of normality about the distribution of
the variables.
(iii) When data are generated from weaker measurement scales e.g.
ranking, frequency counts, and some subjective measuring scales.
(iv) When results are needed fast and no statistical elegance is required.
(v) When it is needed in every research situation.

Because they are distribution-free statistics non-parametric procedures fit both

situations that require the use of parametric and non-parametric statistics.

Commonly Used Non-Parametric Statistical Methods

31
Experience with students projects (dissertations, thesis, terms papers etc.)
shows that the three most popular non-parametric statistical methods often
used by students in their projects are:
1. Chi-square (x2)
2. Spearman’s rank order correlation (r)
3. Spearman’s rho (p)

The other non-so popular tests are:

4. Mann-Whitney (u) test
5. Ruska -Wallis test
6. Wilcoxon matched pairs
7. Signed Rank test
8. Wilcoxon Rank sum test
9. Sign tests

Contextualizing the findings of one’s study requires the researcher to very

systematically compare his/her findings with those made by others. The
researcher can quote from these studies to show how his/her findings
contradict, confirm or add to them. It places the researcher’s findings in the
context of what others have found out. This function is undertaken when
writing about one’s findings i.e. after analysis of your data.

CHAPTER SIX
Measurement of Variables

32
 A variable refers to a characteristic or attribute of an individual or an
organization that can be measured or observed and that varies among the
people or organization being studied. A variable typically will vary in two or
more categories or on a range of scores, and it can be measured or assessed on
a scale. Variables often measured in accounting studies include financial
performance, compliance with standard, corporate governance, control
effectiveness, corporate failure, errors and fraud, tax compliance, revenue
generation, corporate social performance, profitability, liquidity, gearing,
capital structure, among others. Variables have both temporal order and
measurement scale.

6.1-Scaling and their types

1-Scaling: is an instrument or mechanism by which individuals are
distinguished as how they differ from one to another on the variables of
interest to the study.
2-Types of scales:
1. Nominal Scale: The simplest level of measurement, which classifies the
individuals and groups into categories without any order or structure.
Example:
what is your gender ; Male Female
Where do you live : North, South

33
2. Ordinal Scale: Ordinal scale indicates the ranking of individuals or
objects in terms of their importance.
Example: rank the following five characteristics in a job in terms of
how important they are for you. Rank the most important item as 1, the
next importance as 2, until you have ranked each of them 1,2,3,4, or 5.

3. Interval Scale: Interval scale is a numeric but standard measurement which

indicates the exact differences or distances between the values or the
ranking of importance.
Example: rate your satisfaction with your laptop on a 5-point scale,
from strongly satisfied to strongly dissatisfied.

34
1) Semantic Differential Scale:

Example: please rate the president of the USA on the following traits:

2) Numerical Scale:

Example:

35
3) Itemized Rating Scale: Example:

4) Likert Scale: Example:

36
5) Staple Scale: Example:

37
4. Ratio Scale:

Usually used in organizational research when exact numbers

on objective (as opposed to subjective) factors are called:

6.2-SUMMARY OF RESEARCH WORK

Summary, conclusions and recommendations chapter is the end of the main
body of the research work. Most readers who have no time to go through the
entire project are usually interested in this chapter because it gives the
summary of the study, the conclusions that are drawn from the findings and
the recommendations that follow the conclusions.

1-The summary referred to here is not the comprehensive summary of

findings (chapter 4) only. It also includes a summary of the entire study from
chapter 1 to chapter 4. It is not a good summary to merely highlight the
subheadings contained in chapter 1 to 4. Rather, the researcher should
endeavor to highlight the very important and revealing aspects of every
chapter. Conciseness, however, should he observed.. Similarly, in itemizing
the summary of findings of the study, there may not be the need for making
reference to every bit of information of the results obtained from the
analyses, or making reference to tables and figures.

2-The Conclusions are deductions from the findings and should be drawn
from both chapter 4 and chapter 2 (Review of literature). Every conclusion
must be relevant to the findings of the study and should not be extraneous to
the study itself. Experience with students has shown that there is always a
tendency by the students to draw conclusions on their study from their

38
personal opinions. Such conclusions can hardly be defended with findings
from any or all parts of the study. This practice can weaken a rather very
strong study and should be avoided by the student. Conclusions, therefore,
are to be drawn from the findings of the study, and there should be
‘marriage’ between the summary of findings and the conclusion of the study,
in such a way that there is one conclusion for every finding.

3-Recommendations on the other hand are drawn based on the conclusion

of the study.
Similarly, just as ‘marriage’ is required between summary and conclusion, it
is also required between conclusion and recommendation, in such a way that
there should be one recommendation for every conclusion. This would help
to ensure that all recommendations have bases from the results obtained in
the study. If a recommendation is policy oriented and not knowledge driven
should in clear and un-ambiguous terms trigger specific individual(s),
group(s), or entity(ies) to action and should mention specifically what to do.
There is also one last sub-section of this section which is titled,
recommendations for further studies. This sub-section usually comes last in
chapter 5 of every thesis or dissertation. Here, the researcher recommends to
potential researchers, some aspects of his study or result of his study which
can lead to further research by any interested researcher. Some researchers
have found this sub-section very useful in selecting a researchable topic. The
advantage of using this recommendation for any study is that the researcher
has a basis already which will guide him in his own study. He can draw from
the literature of the parent study; he may use the study’s method of statistical
analysis; he may even adopt its methodology or if need he, replicate this
study.
39
REFERENCES

1. Asika, N. (2006), Research Methodology in the Behavioural Sciences.

Lagos, Nigeria: Longman Nigeria PLC.
2. Beauchamp, T.L. et al (eds) (1987), Ethical Issues in Social Science
Research. Bestipore: Johns Hopkins University Press.
3. Bell, J. (2000), Doing Your Research Project, Buckingham. Open
University, Press.
4. Bryman, A. and Cramer, D. (1990), Quantitative Data Analysis for
Social Scientists. London, UK: Routledge.
5. Bulmer, M. (ed.) (1982), Social Research Ethics. London: Macmillan.
6. Burgess, R.G. (ed.) (1982), Field Research: A Source Book and Field
Manual. London: Allen and Unwin.
7. Bum, R. B. (1994), Introduction to Research Methods. Melbourne:
Longman Cheshire.
8. Hardy, M. A. (1993), Regression with Dummy Variables. Thousand
Oaks, USA: Sage Publications.
9. Healey, J. F. (2002), Statistics a Tool for Social Research. Belmont,
USA: Wadsworth/Thomson Learning.
10.Ndagi, J.O. (1999), Essentials of Research Methodology for
Educators. Lagos, Nigeria: University Press Plc.
11.Osuala, E.C. (2005), Introduction to Research Methodology. Onitsha,
Nigeria, 3rd Edition: Africana-Fep Publishers Limited.

40