An Overview Of Data Analysis And Interpretations In Research

Review
An Overview of Data Analysis and Interpretations in
Research
Dawit Dibekulu Alem
Lecturer at Mekdela Amba University, College Social Scinces and Humanities , Departement of English
Languge and Literature. Email: dawitdibekulu7@gmail.com
Accepted 16 March 2020
Research is a scientific field which helps to generate new knowledge and solve the existing problem.
So, data analysis is the crucial part of research which makes the result of the study more effective. It is
a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the
required information. In a research it supports the researcher to reach to a conclusion. Therefore,
simply stating that data analysis is important for a research will be an understatement rather no
research can survive without data analysis. It can be applied in two ways which is qualitatively and
quantitative. Both are beneficial because it helps in structuring the findings from different sources of
data collection like survey research, again very helpful in breaking a macro problem into micro parts,
and acts like a filter when it comes to acquiring meaningful insights out of huge data-set. Furthermore,
every researcher has sort out huge pile of data that he/she has collected, before reaching to a
conclusion of the research question. Mere data collection is of no use to the researcher. Data analysis
proves to be crucial in this process, provides a meaningful base to critical decisions, and helps to
create a complete dissertation proposal. So, after analyzing the data the result will provide by
qualitative and quantitative method of data results. Quantitative data analysis is mainly use numbers,
graphs, charts, equations, statistics (inferential and descriptive). Data that is represented either in a
verbal or narrative format is qualitative data which is collected through focus groups, interviews,
opened ended questionnaire items, and other less structured situations.
Key Words: Data,data analysis, qualitative and quantitative data analysis
Cite This Article As: Dawit DA (2020). An Overview of Data Analysis and Interpretations in Research. Inter. J.
Acad. Res. Educ. Rev. 8(1): 1-27
INTRODUCTION
Research can be considered as an area of
investigation to solve a problem within a short period of
time or in the coming long future. As explained by Kothari
(2004), research in common parlance refers to a search
for knowledge. It can also be defined as a scientific and
systematic search for pertinent information on a specific
topic. In fact, research is an art of scientific investigation.
The Advanced Learner’s Dictionary of Current English
Oxford, (1952, p. 1069) cited in Kothari (2004), lays down
the meaning of research as “a careful investigation or
inquiry especially through search for new facts in any
branch of knowledge.” Moreover, Redman and Mory
(1923) cited in Kothari (2004), define research as a
“systematized effort to gain new knowledge.”
In research getting relevant data and using these data
properly is mandatory. The task of data collection begins
after a research problem has been defined and research
design/ plan chalked out. While deciding about the
International Journal of
Academic Research in
Education and Review
Vol. 8(1), pp. 1-27, March 2020
DOI: 10.14662/IJARER2020.015
Copy © right 2020
Author(s) retain the copyright of this article
ISSN: 2360-7866
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2 Inter. J. Acad. Res. Educ. Rev.
method of data collection to be used for the study, the
researcher should keep in mind two types of data viz.,
primary and secondary. The primary data are those
which are collected afresh and for the first time, and thus
happen to be original in character. The secondary data,
on the other hand, are those which have already been
collected by someone else and which have already been
passed through the statistical process. The researcher
would have to decide which sort of data he would be
using (thus collecting) for his study and accordingly he
will have to select one or the other method of data
collection. The methods of collecting primary and
secondary data differ since primary data are to be
originally collected, while in case of secondary data the
nature of data collection work is merely that of
compilation. Whatever it is the data used in any research
should be analyzed properly either qualitatively or
quantitatively based on the nature of the data collected.
Data collected from various sources can gathered,
reviewed, and then analyzed to form some sort of finding
or conclusion.
There are a variety of specific data analysis method,
some of which include data mining, text analytics,
business intelligence, and data visualizations.
Patton(1990) stated that data analysis is a process of
inspecting, cleansing, transforming, and modeling data
with the goal of discovering useful information,
suggesting conclusions, and supporting decision-making.
Data analysis has multiple facets and approaches,
encompassing diverse techniques under a variety of
names, in different business, science, and social science
domains. It can be done qualitatively or quantitatively.
Data analysis is the central step in both qualitative and
qualitative research.
Whatever the data are, it is their analysis that, in a
decisive way, forms the outcomes of the research. The
purpose of analyzing data is to obtain usable and useful
information. The analysis, irrespective of whether the
data is qualitative or quantitative, may: describe and
summarize the data, identify relationships between
variables, compare variables, identify the difference
between variables and forecast outcomes Sometimes,
data collection is limited to recording and documenting
naturally occurring phenomena, for example by recording
interactions which may be taken as qualitative type.
Qualitative analysis is concentrated on analyzing such
recordings. On the other hand data may be collected
numerically using questionnaires and some rating scales
and these data mostly analyzed using quantitative
techniques.
With this introduction, this paper focuses on data
analysis, concepts, techniques, expected assumptions,
advantages and some limitations of selected data
analysis techniques. That is, the concept of data analysis
and processing steps are treated in the first part of the
paper. In second part, concepts of qualitative and
quantitative data analysis methods are explained in
detail. More over emphasize is given to descriptive and
inferential statistics methods of data analysis. Finally, the
how of writing summary, conclusion and
recommendations based on the findings gained
qualitatively as well as quantitatively are included.
Data Analysis
Concept of Data Analysis
What do we mean when we say data in the first place?
The 1973 Webster’s New Collegiate Dictionary defines
data as “factual information (as measurements or
statistics) used as a basis for reasoning, discussion, or
calculation.” The 1996 Webster’s II New Riverside
Dictionary Revised Edition defines data as “information,
especially information organized for analysis.” Merriam
Webster Online Dictionary defines data” as: factual
information (as measurements or statistics) used as a
basis for reasoning, discussion, or calculation;
information output by a sensing device or organ that
includes both useful and irrelevant or redundant
information and must be processed to be meaningful or
information in numerical form that can be digitally
transmitted or processed.
Taking from the above definitions, a practical approach
to defining data is that it is numbers, characters, images,
or other method of recording, in a form which can be
assessed to make a determination or decision about a
specific action. Many believe that data on its own has no
meaning, only when interpreted does it take on meaning
and become information. By closely examining data (data
analysis) we can find patterns to perceive information,
and then information can be used to enhance knowledge
(The Free On-line Dictionary of Computing, 1993-2005
Denis Howe).
Simply, data analysis is changing the collected row
data into meaningful facts and ideas to be understood
either qualitatively or quantitatively. It is studying the
tabulated material in order to determine inherent facts or
meanings. It involves breaking down existing complex
factors into simpler parts and putting the parts together in
new arrangements for the purpose of interpretation. As to
Kothari (2004) data analysis includes comparison of the
outcomes of the various treatments upon the several
groups and the making of a decision as to the
achievement of the goals of research. The analysis,
irrespective of whether the data is qualitative or
quantitative, may be to describe and summarize the
data, identify relationships between variables, compare
variables, identify the difference between variables and
forecast outcomes as mentioned in the introduction.
According to Ackoff (1961), a plan of analysis can and
should be prepared in advance before the actual

collection of material. A preliminary analysis on the
skeleton plan show as the investigation proceeds,
develop into a complete final analysis enlarged and
reworked as and when necessary. This process requires
an alert, flexible and open mind. Caution is necessary at
every step.
In the process of data analysis, statistical method has
contributed a great deal. Simple statistical calculation
finds a place in almost any study dealing with large or
even small groups of individuals, while complex statistical
computations form the basis of many types of research. It
may not be out of place, therefore to enumerate some
statistical methods of analysis used in educational
research. The analysis and interpretation of data
represent the application of deductive and inductive logic
to the research process.
Technically speaking, processing implies editing,
coding, classification and tabulation of collected data so
that they are amenable to analysis. The term analysis
refers to the computation of certain measures along with
Dawit 3
searching for patterns of relationship that exist among
data-groups (Kothari,2004). Thus, “in the process of
analysis, relationships or differences supporting or
conflicting with original or new hypotheses should be
subjected to statistical tests of significance to determine
with what validity data can be said to indicate any
conclusions”. But persons like (Selltiz, et.al., 1959) do
not like to make difference between processing and
analysis. They opine that analysis of data in a general
way involves a number of closely related operations
which are performed with the purpose of summarizing the
collected data and organizing these in such a manner
that they answer the research question(s). We, however,
shall prefer to observe the difference between the two
terms as stated here in order to understand their
implications more clearly.
Generally, data analysis in research divided into
qualitative and quantitative data analysis. The data, after
collection, has to be processed and analyzed in
accordance with the outline laid down for the purpose at
the time of developing the research plan. This is essential
for a scientific study and for ensuring that we have all
relevant data for making contemplated comparisons and
analysis.
Data Processing Operations
In the data analysis process we need to focus on the
following data analysis process operation stages. Kothari
(2004) suggested the following data analysis operation
stages.
1. Editing: Editing of data is a process of examining the
collected raw data (especially in surveys) to detect errors
and omissions and to correct these when possible. As a
matter of fact, editing involves a careful scrutiny of the
completed questionnaires and/or schedules. Editing is
done to assure that the data are accurate, consistent with
other facts gathered, uniformly entered, as completed as
possible and have been well arranged to facilitate coding
and tabulation (Kothari, 2004). So, this indicates that
editing is the process of data correction.
According to Kothari (2004) editing should be done, one
can talk of field editing and central editing.
Field editing consists in the review of the reporting forms
by the investigator for completing (translating or rewriting)
what the latter has written in abbreviated and/or in
illegible form at the time of recording the respondents’
responses. This type of editing is necessary in view of the
fact that individual writing styles often can be difficult for
others to decipher. On the other hand,
central editing should take place when all forms or
schedules have been completed and returned to the
office. It implies that all forms should get a thorough
editing by a single editor in a small study and by a team
of editors in case of a large inquiry. Editor(s) may correct
the obvious errors such as an entry in the wrong place,
entry recorded in months when it should have been
recorded in weeks, and the like.
2. Coding: Coding refers to the process of assigning
numerals or other symbols to answers so that responses
can be put into a limited number of categories or classes.
Such classes should be appropriate to the research
problem under consideration. They must also possess
the characteristic of exhaustiveness (i.e., there must be a
class for every data item) and also that of mutual
exclusively which means that a specific answer can be
placed in one and only one cell in a given category set
(Kothari ,2004). In addition, Coding is necessary for
efficient analysis and through it the several replies may
be reduced to a small number of classes which contain
the critical information required for analysis. Coding
decisions should usually be taken at the designing stage
of the questionnaire. This makes it possible to pre-code
the questionnaire choices and which in turn is helpful for
computer tabulation as one can straight forward key
punch from the original questionnaires (Neuman, 2000).
3. Classification: Most research studies result in a large
volume of raw data which must be reduced into
homogeneous groups if we are to get meaningful
relationships. This fact necessitates classification of data
which happens to be the process of arranging data in

groups or classes on the basis of common
characteristics. Data having a common characteristic are
placed in one class and in this way the entire data get
divided into a number of groups or classes. Classification
can be one of the following two types, depending upon
the nature of the phenomenon involved:
(a) Classification according to attributes: data are
classified on the basis of common characteristics which
can either be descriptive (such as literacy, sex, honesty,
etc.) or numerical (such as weight, height, income, etc.).
Descriptive characteristics refer to qualitative
phenomenon which cannot be measured quantitatively;
only their presence or absence in an individual item can
be noticed. Data obtained this way on the basis of certain
attributes are known as statistics of attributes and their
classification is said to be classification according to
attributes. Such classification can be simple classification
or manifold classification (Kothari, 2004).
(b) Classification according to class-intervals: unlike
descriptive characteristics, the numerical characteristics
refer to quantitative phenomenon which can be measured
through some statistical units. Data relating to income,
production, age, weight, etc. come under this category.
Such data are known as statistics of variables and are
classified on the basis of class intervals. All the classes
or groups, with the irrespective frequencies taken
together and put in the form of a table, are described as
group frequency distribution or simply frequency
distribution. Classification according to class intervals
usually involves the following three main problems:
(i) How may classes should be there? What should
be their magnitudes?
There can be no specific answer with regard to the
number of classes. The decision about this calls for skill
and experience of the researcher. However, the objective
should be to display the data in such a way as to make it
meaningful for the analyst. Typically, we may have 5 to
15 classes. With regard to the second part of the
question, we can say that, to the extent possible, class-
intervals should be of equal magnitudes, but in some
cases unequal magnitudes may result in better
classification.
Hence the researcher’s objective judgment plays an
important part in this connection. Multiples of 2, 5 and 10
are generally preferred while determining class
magnitudes. Some statisticians adopt the following
formula, suggested by Sturges as cited in Kothari,( 2004),
determining the size of class interval:
i = R/(1 + 3.3 log N)
Where i = size of class interval;
R = Range (i.e., difference between the values of the
largest item and smallest item among the given items);
N = Number of items to be grouped.
It should also be kept in mind that in case one or two or
very few items have very high or very low values, one
may use what are known as open-ended intervals in the
overall frequency distribution.
(ii) How to choose class limits?
While choosing class limits, the researcher must take into
consideration the criterion that the mid-point (generally
worked out first by taking the sum of the upper limit and
lower limit of a class and then divide this sum by 2) of a
class-interval and the actual average of items of that
class interval should remain as close to each other as
possible. Consistent with this, the class limits should be
located at multiples of 2, 5, 10, 20, 100 and such other
figures. Class limits may generally be stated in any of the
following forms:
Exclusive type class intervals: They are usually stated
as follows:
10–20 read as above 10 and under 20
Thus, under the exclusive type class intervals, the items
whose values are equal to the upper limit of a class are
grouped in the next higher class. For example, an item
whose value is exactly 30 would be put in 30–40 class
intervals and not in 20–30 class intervals.
In simple words, we can say that under exclusive type
class intervals, the upper limit of a class interval is
excluded and items with values less than the upper limit
(but not less than the lower limit) are put in the given
class interval.
Inclusive type class intervals: They are usually stated
as follows:
11–20
21–30
31–40
41–50
In inclusive type class intervals the upper limit of a class
interval is also included in the concerning class interval.
Thus, an item whose value is 20 will be put in 11–20
class intervals. The stated upper limit of the class interval
11–20 is 20 but the real limit is 20.99999 and as such
11–20 class interval really means 11 and under 21.

When the phenomenon under consideration happens to
be a discrete one (i.e., can be measured and stated only
in integers), then we should adopt inclusive type
classification. But when the phenomenon happens to be
a continuous one capable of being measured in fractions
as well, we can use exclusive type class intervals.
4. Tabulation: When a mass of data has been
assembled, it becomes necessary for the researcher to
arrange the same in some kind of concise and logical
order. This procedure is referred to as tabulation. Thus,
tabulation is the process of summarizing raw data and
displaying the same in compact form (i.e., in the form of
statistical tables) for further analysis. In a broader sense,
tabulation is an orderly arrangement of data in columns
and rows. As Kothari (2004) stated tabulation is essential
because of the following reasons;
it conserves space and reduces explanatory and
descriptive statement to a minimum ,
it facilitates the process of comparison,
it facilitates the summation of items and the
detection of errors and omissions, and
it provides a basis for various statistical
computations.
Generally, in the process of data analysis the above four
steps need to be critical applied. Because, without
applying the above process operation of data one cannot
do good data analysis.
Qualitative Data Analysis
Concept of Qualitative Data Analysis
Data that is represented either in a verbal or narrative
format is qualitative data. These types of data are
collected through focus groups, interviews, opened
ended questionnaire items, and other less structured
situations. A simple way to look at qualitative data is to
think of qualitative data in the form of words Migrant&
Seasonal Head Start, (2006) stated as:
Qualitative data analysis is the classification and
interpretation of linguistic (or visual) material to
make statements about implicit and explicit
dimensions and structures of meaning-making in
the material and what is represented in it.
Meaning-making can refer to subjective or social
meanings.
From the above explanation we can understand that
qualitative data analysis is one way of data analysis
which helps to describe or interpret the data through
words which transfer information through different
Dawit 5
dimensions.
Qualitative data analysis is the range of processes and
procedures whereby we move from the qualitative data
that have been collected, into some form of explanation,
understanding or interpretation of the people and
situations we are investigating (Cohen, et.al. 2007).It is
usually based on an interpretative philosophy. The idea is
to examine the meaningful and symbolic content of
qualitative data. It refers to non-numeric information such
as interview transcripts, notes, video and audio
recordings, images and text documents.
Qualitative data analysis can be divided into the
following five categories:
1. Content analysis: This refers to the process of
categorizing verbal or behavioral data to classify,
summarize and tabulate the data. According to Cohen,
et.al. (2007), content analysis is the procedure for the
categorization of verbal or behavioral data for the
purpose of classification, summarization and tabulation.
Content analysis can be done on two levels:
a. Descriptive: What is the data? And
b. Interpretative: what was meant by the data?
2. Narrative analysis: This method involves the
reformulation of stories presented by
respondents taking into account context of each
case and different experiences of each
respondent. In other words, narrative analysis is
the revisions of primary qualitative data by
researcher. Narratives are transcribed
experiences. Every interview/observation has
narrative aspect in which the researcher has to
sort-out and reflects up on them, enhance them
and present them in a revised shape to the
reader. The core activity in narrative analysis is to
reformulate stories presented by people in
different contexts and based on their different
experiences.
3. Discourse analysis: A method of analysis of
naturally occurring talk and all types of written
text. This is a method of analyzing a naturally
occurring talk (spoken interaction) and all types
of written texts. It focuses on how people express
themselves verbally in their everyday social life
i.e. how language is used in everyday situations?
a. Sometimes people express themselves in a
simple and straightforward way
b. Sometimes people express themselves vaguely
and indirectly
c. Analyst must refer to the context when
interpreting the message because the same
phenomenon can be described in a number of
different ways depending on context.

4. Framework analysis: This is more advanced
method that consists of several stages such as
familiarization (Transcribing & reading the data),
identifying a thematic framework (Initial coding
framework which is developed both from a priori
issues and from emergent issues), coding
(Using numerical or textual codes to identify
specific piece of data which correspond to
different themes) , charting(Charts created using
headings from thematic framework), and
mapping and interpretation (Searching for
patterns, associations, concepts and
explanations in the data).
5. Grounded theory: as Corbin and Nicholas
(2005) this method of qualitative data analysis
starts with an analysis of a single case to
formulate a theory. Then, additional cases are
examined to see if they contribute to the theory.
This theory starts with an examination of a single
case from a ‘pre-defined’ population in order to
formulate a general statement (concept or a
hypothesis) about a population. Afterwards the
analyst examines another case to see whether
the hypothesis fits the statement. If it does, a
further case is selected but if it doesn’t fit there
are two options: Either the statement is changed
to fit both cases or the definition of the population
is changed in such a way that the case is no
longer a member of the newly defined population.
Then another case is selected and the process
continues. In such a way one should be able to
arrive at a statement that fits all cases of a
population-as-defined. This method is only for
limited set of analytic problems: those that can be
solved with some general overall statement
(Cohen, et.al. 2007).
Aims of Qualitative Data Analysis
The analysis of qualitative data can have several aims.
Neuman (2000) explained that: The first aim may be to
describe a phenomenon in some or greater detail. The
phenomenon can be the subjective experiences of a
specific individual or group (e.g. the way people continue
to live after a fatal diagnosis). This can focus on the case
(individual or group) and its special features and the links
between them. The analysis can also focus on comparing
several cases (individuals or groups) and on what they
have in common or on the differences between them.
The second aim may be to identify the conditions on
which such differences are based. This means to look for
explanations for such differences (e.g. circumstances
which make it more likely that the coping with a specific
illness situation is more successful than in other cases).
And the third aim may be to develop a theory of the
phenomenon under study from the analysis of empirical
material (e.g. a theory of illness trajectories).
Advantages and disadvantages of qualitative
analysis
Advantages of Qualitative Analysis
Qualitative data analysis has different advantages.
Denscombe (2007), stated there are a number of
advantages such as:
The first is the data and the analyses are ‘grounded. A
particular strength associated with qualitative research is
that the descriptions and theories such research
generates are ‘grounded in reality’. This is not to suggest
that they depict realityin some simplistic sense, as though
social reality were ‘out there’ waiting to be ‘discovered’.
But it does suggest that the data and the analysis have
their roots in the conditions of social existence. There is
little scope for ‘armchair theorizing’ or ‘ideas plucked out
of thin air’.
The second, there is a richness and detail to the data.
The in-depth study of relatively focused areas, the
tendency towards small-scale research and the
generation of ‘thick descriptions’ mean that qualitative
research scores well in terms of the way it deals with
complex social situations. It is better able to deal with the
intricacies of a situation and do justice to the subtleties of
social life.
The third, there is tolerance of ambiguity and
contradictions. To the extent that social existence
involves uncertainty, accounts of that existence ought to
be able to tolerate ambiguities and contradictions, and
qualitative research is better able to do this than
quantitative research (Maykut and Morehouse, 1994 as
cited in Denscombe, 2007). This is not a reflection of a
weak analysis. It is a reflection of the social reality being
investigated.
Lastly, there is the prospect of alternative
explanations. Qualitative analysis, because it draws on
the interpretive skills of the researcher, opens up the
possibility of more than one explanation being valid.
Rather than a presumption that there must be, in theory
at least, one correct explanation, it allows for the
possibility that different researchers might reach different
conclusions, despite using broadly the same methods.
A. Disadvantages of Qualitative Analysis
In relation to the disadvantages Denscombe (2007),

also explained the following are some powerful
disadvantages:
First, the data might be less representative. The flip-
side of qualitative research’s attention to thick description
and the grounded approach is that it becomes more
difficult to establish how far the findings from the detailed,
in-depth study of a small number of instances may be
generalized to other similar instances. Provided sufficient
detail is given about the circumstances of the research,
however, it is still possible to gauge how far the findings
relate to other instances, but such generalizability is still
more open to doubt than it is with well conducted
quantitative research.
Second, interpretation is bound up with the ‘self’ of
the researcher. Qualitative research recognizes more
openly than does quantitative research that the
researcher’s own identity, background and beliefs have a
role in the creation of data and the analysis of data. The
research is ‘self-aware’. This means that the findings are
necessarily more cautious and tentative, because it
operates on the basic assumption that the findings are a
creation of the researcher rather than a discovery of fact.
Although it may be argued that quantitative research is
guilty of trying to gloss over the point – which equally well
applies – the greater exposure of the intrusion of the ‘self’
in qualitative research inevitably means more cautious
approaches to the findings (Denscombe, 2007).
Third, there is a possibility of de-contextualizing the
meaning. In the process of coding and categorizing the
field notes, texts or transcripts there is a possibility that
the word (or images for that matter) get taken literally out
of context. The context is an integral part of the
qualitative data, and the context refers to both events
surrounding the production of the data, and events and
words that precede and follow the actual extracted pieces
of data that are used to form the units for analysis. There
is a very real danger for the researcher that in coding and
categorizing of the data the meaning of the data is lost or
transformed by wrenching it from its location (a) within a
sequence of data (e.g. interview talk), or (b) within
surrounding circumstances which have a bearing on the
meaning of the unit as it was originally conceived at the
time of data collection (Denscombe, 2007).
Fourth, there is the danger of oversimplifying the
explanation. In the quest to identify themes in the data
and to develop generalizations the researcher can feel
pressured to underplay, possibly disregard data that
‘doesn’t fit’. Inconsistencies, ambiguities and alternative
explanations can be frustrating in the way they inhibit a
nice clear generalization – but they are an inherent
feature of social life. Social phenomena are complex, and
the analysis of qualitative data needs to acknowledge this
Dawit 7
and avoid attempts to oversimplify matters (Denscombe,
2007).
Fifth, the analysis takes longer. The volume of data
that a researcher collects will depend on the time and
resources available for the research project. When it
comes to the analysis of that data, however, it is almost
guaranteed that it will seem like a daunting task
(Denscombe, 2007).
Quantitative Data Analysis
Quantitative data is expressed in numerical terms, in
which the numeric values could be large or small.
Numerical values may correspond to a specific category
or label. Quantitative analysis is statistically reliable and
generalizable results. In quantitative research we classify
features, count them, and even construct more complex
statistical models in an attempt to explain what is
observed. Findings can be generalized to a larger
population, and direct comparisons can be made
between two corpora, so long as valid sampling and
significance techniques have been used (Bryman and
Cramer, 2005). Thus, quantitative analysis allows us to
discover which phenomena are likely to be genuine
reflections of the behavior of a language or variety, and
which are merely chance occurrences. The more basic
task of just looking at a single language variety allows
one to get a precise picture of the frequency and rarity of
particular phenomena, and thus their relative normality or
abnormality.
However, the picture of the data which emerges from
quantitative analysis is less rich than that obtained from
qualitative analysis. For statistical purposes,
classifications have to be of the hard-and-fast (so-called
"Aristotelian" type). An item either belongs to class x or it
doesn't. So in the above example about the phrase "the
red flag" we would have to decide whether to classify
"red" as "politics" or "color". As can be seen, many
linguistic terms and phenomena do not therefore belong
to simple, single categories: rather they are more
consistent with the recent notion of "fuzzy sets" as in the
red example. Quantitative analysis is therefore an
idealization of the data in some cases. Also, quantitative
analysis tends to sideline rare occurrences. To ensure
that certain statistical tests (such as chi-squared) provide
reliable results, it is essential that minimum frequencies
are obtained - meaning that categories may have to be
collapsed into one another resulting in a loss of data
richness (Dawson, 2002). So, in generally concept
quantitative data analysis is mainly use numbers,
graphs, charts, equations, statistics( inferential and
descriptive), ANOVA, ANCOVA, regression, and
correlation etc.

Statistical Analysis of Data
Statistics is the body of mathematical techniques or
processes for gathering, describing organizing and
interpreting numerical data. Since research often yields
such quantitative data, statistics is a basic tool of
measurement and research. The researcher who uses
statistics is concerned with more than the manipulation of
data, statistical methods goes back to fundamental
purposes of analysis. Research in education may deal
with two types of statistical data application: Descriptive
Statistical Analysis, and Inferential Statistical Analysis.
To understand the difference between descriptive and
inferential statistics, you must first understand the
difference between populations and samples.
A population is the entire collection of a carefully
defined set of people, objects, or events(Celine,
2017).
So, population is the broader group of people to whom
your results will apply. For example, if the a researcher
wants to conduct a research in education (eg. Grade 8
Students language skills in Debre Markos Administration
town primary schools) all grade 8 students in that specific
area are considered to be population in which the
samples will be taken.
A sample is a subset of the people, objects, or events
selected from that population (Celine, 2017).
So, sample is the group of individuals who participate in
your study. For example, selected grade 8 students from
the total population can be the sample for the research.
A. Descriptive Statistics
Descriptive statistics is the type of statistics that
probably springs to most people’s minds when they hear
the word “statistics.” In this branch of statistics, the goal is
to describe. As Weiss (1999) stated that numerical
measures are used to tell about features of a set of data.
There are a number of items that belong in this portion of
statistics, such as:
The average, or measure of the center of a data
set, consisting of the mean, median, mode, or
midrange, The spread of a data set, which can
be measured with the range or standard
deviation, Overall descriptions of data such as
the five number summary, Measurements such
as skewness and kurtosis.
The exploration of relationships
and correlation between paired data, and the
presentation of statistical results in graphical form. These
measures are important and useful because they allow
scientists to see patterns among data, and thus to make
sense of that data. Descriptive statistics consist of
methods for organizing and summarizing information
(Weiss, 1999)
A parameter is a descriptive characteristic of a
population (Hinkle, Wiersma, & Jurs, 2003). For example,
if we found the average of language skills all grade 8
students mentioned above in the town be it population,
the resulting average (also called the mean) would be a
population parameter. To obtain this average, we first
need to tabulate the amount of numerated skills of every
student. When calculating this mean, we are engaging in
descriptive statistical analysis.
As, Weiss (1999) explained that descriptive statistical
analysis focuses on the exhaustive measurement of
population characteristics. You define a population,
assess each member of that population, and compute a
summary value (such as a mean or standard deviation)
based on those values. It is concerned with numerical
description of a particular group observed and any
similarity to those outside the group cannot be taken for
granted. The data describe one group and that one group
only. Much simple educational research involves
descriptive statistics and provides valuable information
about the nature of a particular group or class.
Data collected from tests and experiments often have
little meaning or significance until they have been
classified or rearranged in a systematic way. This
procedure leads to the organization of materials into few
heads:
(i) Determination of range of the interval between the
largest and smallest scores.
(ii) Decision as to the number and size of the group to be
used in classification.
Class interval is therefore, helpful for grouping the data
in suitable units and the number and size of these class
intervals will depend upon the range of scores and the
kinds of measures with which one is dealing. The number
of class intervals which a given range will yield can be
determined approximately by dividing the range by the
interval tentatively chosen.
According to Agresti, & Finlay (1997), the most
commonly used methods of analysis data statistically are:
Calculating frequency distribution usually in percentages
of items under study, testing data for normality of
distribution skewness and kurtosis, calculating
percentiles and percentile ranks, calculating measures of
central tendency-mean, median and mode and
establishing norms, calculating measures of dispersion-
standard deviation mean deviation, quartile deviation and
range, calculating measures of relationship-coefficient of

correlation, reliability and validity by the Rank-difference
and Product moment methods, and graphical
presentation of data-Frequency polygon curve,
Histogram, Cumulative frequency polygon and Ogive etc.
While analyzing data investigations usually make use
of as many of the above simple statistical devices as
necessary for the purpose of their study. There are two
kinds of descriptive statistics that social scientists use:
Measures of central tendency -mean, median,
and mode are included under this category.
Measures of central tendency capture general
trends within the data and are calculated and
expressed as the mean, median, and mode. A
mean tells scientists the mathematical average
of all of a data set, such as the average age at
first marriage; the median represents the middle
of the data distribution, like the age that sits in
the middle of the range of ages at which people
first marry; and, the mode might be the most
common age at which people first marry (Huck,
2004).
The above explanation indicates that the central
tendency of a distribution is an estimate of the "center" of
a distribution of values. A measure of central tendency is
a central or typical value for a probability distribution. Let
us see the following examples:
Example one: consider the test score values: 15, 20,
21, 20, 36, 15, 25, 15. The sum of these
8 values is 167, so the mean is 167/8 =
20.875.
Example two: if there are 500 scores in the list, score
250 would be the median. If we order the
8 scores shown above, we would get:
15,15,15,20,20,21,25,36. There are 8
scores and score 4 and 5 represent the
halfway point. Since both of these scores
are 20, the median is 20. If the two middle
scores had different values, you would
have to interpolate to determine the
median.
Example three: in a bimodal distribution there are two
values that occur most frequently.
Notice that for the same set of 8 scores
we got three different values -- 20.875,
20, and 15 -- for the mean, median and
mode respectively. If the distribution is
truly normal (i.e., bell-shaped), the
mean, median and mode are all equal to
each other.
Dawit 9
Measures of spread- variance, standard deviation,
quartiles and others are included under this. As Strauss
and Corbin (1990) stated that:
Measures of spread describe how the data are
distributed and relate to each other, including:
The range, the entire range of values present in
a data set, The frequency distribution, which
defines how many times a particular value
occurs within a data set, Quartiles, subgroups
formed within a data set when all values are
divided into four equal parts across the range,
Mean absolute deviation, the average of how
much each value deviates from the mean,
Variance, which illustrates how much of a spread
exists in the data, Standard deviation, which
illustrates the spread of data relative to the
mean.
So, the above explanation shows that, Measures of
spread are often visually represented in tables, pie and
bar charts, and histograms to aid in the understanding of
the trends within the data. These are ways of
summarizing a group of data by describing how spreads
out the scores are. It describes how similar or varied the
set of observed values are for a particular variable (data
item). For example, the mean score of our 100 students
may be 65 out of 100. However, not all students will have
scored 65 marks. Rather, their scores will be spread out.
Some will be lower and others higher. Measures of
spread help us to summarize how spreads out these
scores are. To describe this spread, a number of
statistics are available to us, including the range,
quartiles, absolute deviation, variance and standard
deviation.
Generally, Descriptive statistics includes the
construction of graphs, charts, and tables, and the
calculation of various descriptive measures such as
averages, measures of variation, and percentiles. In fact,
the most part of this course deals with descriptive
statistics.
B. Inferential Statistics
The second type of statistics is inferential statistics.
Inferential statistical analysis involves the process of
sampling, the selection for study of a small group that is
assumed to be related to the large group from which it is
drawn. Agresti & Finlay (1997) stated the small group is
known as the sample; the large group, the population or
universe. A statistics is a measure based on a sample. A
statistic computed from a sample may be used to
estimate a parameter, the corresponding value in the
population which it is selected. This is a set of methods
used to make a generalization, estimate, prediction or
decision. Inferential statistics is the mathematics and

logic of how this generalization from sample to population
can be made. The fundamental question is: can we infer
the population’s characteristics from the sample’s
characteristics?
Ex. Of 350 (grade 8) randomly selected students in the
town of Debre Markos out of 2000 students and their
average listening skills tests is calculated to be (75%),
this is sample result that we can make generalization
about the total population (2000 students) which is
inferential statistics.
The major use of inferential statistics is to use
information from a sample to infer something about
a population. Inferential statistics consist of methods for
drawing and measuring the reliability of conclusions
about population based on information obtained from a
sample of the population (Weiss, 1999).
Inferential statistics are produced through complex
mathematical calculations that allow scientists to infer
trends about a larger population based on a study of a
sample taken from it. Scientists use inferential statistics
to examine the relationships between variables within a
sample and then make generalizations or predictions
about how those variables will relate to a larger
population.
A measured value based upon sample data is statistic.
A population value estimated from a statistic is a
parameter. A sample is a small proportion of a population
selected for analysis. By observing the sample, certain
inferences may be made about the population. Samples
are not selected haphazardly, but are chosen in a
deliberate way so that the influence of chance or
probability can be estimated. The basic ideas of inference
are to estimate the parameters with the help of sample
statistics which play an extremely important role in
educational research. These basic ideals, of which the
concept of underlying distribution is a part, comprise the
foundation for testing hypotheses using statistical
techniques.
The parameters are never known for certain unless the
entire population is measured and then there is no
inference. We look at the statistics and their underlying
distributions and from them we reason to tenable
conclusions about the parameters.
It is usually impossible to examine each member of the
population individually. So scientists choose a
representative subset of the population, called
a statistical sample, and from this analysis, they are able
to say something about the population from which the
sample came. There are two major divisions of inferential
statistics (Agresti, & Finlay, (1997) stated as follow:
A confidence interval gives a range of values
for an unknown parameter of the population by
measuring a statistical sample. This is expressed
in terms of an interval and the degree of
confidence that the parameter is within
the interval.
Tests of significance or hypothesis testing where
scientists make a claim about the population by
analyzing a statistical sample. By design, there is
some uncertainty in this process. This can be
expressed in terms of a level of significance.
The above explanation shows us, in statistics, a
confidence interval (CI) is a type of interval estimate (of a
population parameter) that is computed from the
observed data. The confidence level is the frequency
(i.e., the proportion) of possible confidence intervals that
contain the true value of their corresponding parameter.
And once sample data has been gathered through an
observational study or experiment, statistical inference
allows analysts to assess evidence in favor or some
claim about the population from which the sample has
been drawn. The methods of inference used to support or
reject claims based on sample data are known as tests of
significance.
Furthermore, Howell, (2002) stated that a statistic is a
numerical value that is computed from a sample,
describes some characteristic of that sample such as the
mean, and can be used to make inferences about the
population from which the sample is drawn. For example,
if you were to compute the average amount of insurance
sold by your sample of 100 agents, that average would
be a statistic because it summarizes a specific
characteristic of the sample. Remember that the word
“statistic” is generally associated with samples, while
“parameter” is generally associated with populations. In
similar taken, Weiss, (1999) in contrast to descriptive
statistics, inferential statistical analysis involves using
information from a sample to make inferences, or
estimates, about the population.
Inferential statistics consist of methods for drawing and
measuring the reliability of conclusions about population
based on information obtained from a sample of the
population (Weiss, 1999). In short, inferential statistics
includes methods like point estimation, interval estimation
and hypothesis testing which are all based on probability
theory.
Example (Descriptive and Inferential Statistics).
Consider event of tossing dice. The dice is rolled 100
times and the results are forming the sample data.
Descriptive statistics is used to grouping the sample data
to the following table.

Dawit 11
Outcome of the roll Frequencies in the sample data
Outcome of the roll Frequencies in the sample data
1 10
2 20
3 18
4 16
5 11
6 25
Inferential statistics can now be used to verify whether the dice is a fair or not.
Generally, descriptive and inferential statistics are
interrelated. It is almost always necessary to use
methods of descriptive statistics to organize and
summarize the information obtained from a sample
before methods of inferential statistics can be used to
make more thorough analysis of the subject under
investigation. Furthermore, the preliminary descriptive
analysis of a sample often reveals features that lead to
the choice of the appropriate inferential method to be
later used. Sometimes it is possible to collect the data
from the whole population. In that case it is possible to
perform a descriptive study on the population as well as
usually on the sample. Only when an inference is made
about the population based on information obtained from
the sample does the study become inferential.
Analysis of Variance (ANOVA)
Concept of ANOVA
One of the methods for quantitative data analysis is
analysis of variance. According to Kothari (2004),
Professor R.A. Fisher was the first man to use the term
‘Variance’ and, in fact, it was he who developed a very
elaborate theory concerning ANOVA, explaining its
usefulness in practical field. Later on Professor Snedecor
and many others contributed to the development of this
technique.
ANOVA is essentially a procedure for testing the
difference among different groups of data for
homogeneity. “The essence of ANOVA is that the total
amount of variation in a set of data is broken down into
two types, that amount which can be attributed to chance
and that amount which can be attributed to specified
causes ”(Bryman, and Cramer, 2005).There may be
variation between samples and also within sample items.
Cramer, (2005) stated that the specific analysis of
variance test that we will study is often referred to as the
one way ANOVA. It consists in splitting the variance for
analytical purposes. Hence, it is a method of analyzing
the variance to which a response is subject into its
various components corresponding to various sources of
variation. Through this technique one can explain
whether various varieties of seeds or fertilizers or soils
differ significantly so that a policy decision could be taken
accordingly, concerning a particular variety in the context
of agriculture researches(Cramer, 2005). Similarly, the
differences in various types of feed prepared for a
particular class of animal or various types of drugs
manufactured for curing a specific disease may be
studied and judged to be significant or not through the
application of ANOVA technique. Likewise, a manager of
a big concern can analyze the performance of various
salesmen of his concern in order to know whether their
performances differ significantly (Neuman, 2006).
ANOVA can be one way or two way ANOVA.
One way (single –factor) ANOVA- Under the one-way
ANOVA, we consider only one factor and then observe
that the reason for said factor to be important is that
several possible types of samples can occur within that
factor (Neuman, 2006); Armstrong, Eperjesi and
Gilmartin, 2002). We then determine if there are
differences within that factor.
Two-Way ANOVA- this technique is used when the
data are classified on the basis of two factors. For
example, the agricultural output may be classified on the
basis of different varieties of seeds and also on the basis
of different varieties of fertilizers used. A business firm
may have its sales data classified on the basis of different
salesmen and also on the basis of sales in different
regions. In a factory, the various units of a product
produced during a certain period may be classified on the
basis of different varieties of machines used and also on
the basis of different grades of labour (Neuman, 2006);.
Such a two-way design may have repeated
measurements of each factor or may not have repeated
values.
Assumptions of ANOVA
Like so many of our inference procedures, ANOVA has
some underlying assumptions which should be in place in

order to make the results of calculations completely
trustworthy. In relation to the assumption of ANOVA,
Huck (2004) stated that: Subjects are chosen via a
simple random sample, Within each
group/population, the response variable is normally
distributed, While the population means may be
different from one group to the next, the population
standard deviation is the same for all groups.
Fortunately, ANOVA is somewhat robust (i.e., results
remain fairly trustworthy despite mild violations of these
assumptions). Assumptions (ii) and (iii) are close enough
to being true if, after gathering simple random samples
from each group, we: Look at normal quintile plots for
each group and, in each case, see that the data points
fall close to a line, and Compute the standard deviations
for each group sample, and see that the ratio of the
largest to the smallest group sample standard deviation is
no more than two( Cramer, (2005); Neuman, (2006) and
Armstrong, Eperjesi and Gilmartin, 2002).
Uses of ANOVA
The one-way analysis of variance for independent
groups applies to an experimental situation where there
might be more than two groups. The t-test was limited to
two groups, but the ANOVA can analyze as many groups
as you want. Examine the relationship between variables
when there is a nominal level independent variable has 3
or more categories and a normally distributed interval/
ratio level of dependent variable produces an F-ratio,
which determines the statistical significance of the result.
Reduces the probability of a Type I error (which would
occur if we did multiple t-tests rather than one single
ANOVA)(Singh, 2007).
In relation to the use of ANOVA, Mordkoff (2016) stated
that One-way ANOVA is used to test for significant
differences among sample means, differs from t-test
since more than 2 groups are tested, simultaneously, one
factor (independent variable) is analyzed, also called the
“grouping” variable, and dependent variable should be
interval or ratio but independent variable is usually
nominal.
A Two Way ANOVA is an extension of the One Way
ANOVA. With a One Way, you have one independent
variable affecting a dependent variable. With a Two Way
ANOVA, there are two independents. Use a two way
ANOVA when you have one measurement variable (i.e. a
quantitative variable) and two nominal variables. In other
words, if your experiment has a quantitative outcome and
you have two categorical explanatory variables, a two
way ANOVA is appropriate. Assumptions for Two Way
ANOVA: The population must be close to a normal
distribution, Samples must be independent, Population
variances must be equal, and Groups must have equal
sample sizes (Mordkoff (2016).
Generally, An ANOVA tests whether one or more
samples means are significantly different from each
other. To determine which or how many sample means
are different requires post hoc testing. Two samples
where means are significantly different. These two
sample means are NOT significantly different due to
smaller difference and high variability. Even with same
difference between means, if variances are reduced the
means can be significantly different.
Analysis of Co-variance (ANCOVA)
The Analysis of Covariance (generally known as
ANCOVA) is a technique that sits between analysis of
variance and regression analysis. It has a number of
purposes but the two that are, perhaps, of most
importance are: to increase the precision of comparisons
between groups by accounting to variation on important
prognostic variables, and to "adjust" comparisons
between groups for imbalances in important prognostic
variables between these groups.
When we measure covariates and include them in an
analysis of variance we call it analysis of covariance (or
ANCOVA for short). There are two reasons for including
covariates in ANOVA:
1. To reduce within-group error variance: In the
discussion of ANOVA and t-tests we got used to the idea
that we assess the effect of an experiment by comparing
the amount of variability in the data that the experiment
can explain against the variability that it cannot explain. If
we can explain some of this ‘unexplained’ variance (SSR)
in terms of other variables (covariates), then we reduce
the error variance, allowing us to more accurately assess
the effect of the independent variable (SSM) (Hinkle,
Wiersma, &Jurs, 2003).
2. Elimination of confounds: In any experiment,
there may be unmeasured variables that confound the
results (i.e. variables that vary systematically with the
experimental manipulation). If any variables are known to
influence the dependent variable being measured, then
ANCOVA is ideally suited to remove the bias of these
variables. Once a possible confounding variable has
been identified, it can be measured and entered into the
analysis as a covariate Hinkle, Wiersma, &Jurs, 2003).
The above two explanations indicates that the reason
for including covariates is that covariates are a variable
that a researcher seeks to control for (statistically

subtract the effects of) by using such techniques as
multiple regression analysis (MRA) or analysis of
covariance (ANCOVA).
But, there are other reasons for including covariates in
ANOVA but because I do not intend to describe the
computation of ANCOVA in any detail I recommend that
the interested reader consult my favorite sources on the
topic (Stevens, 2002; Wildt & Ahtola, 1978). Imagine that
the researcher who conducted the Viagra study in the
previous chapter suddenly realized that the libido of the
participants’ sexual partners would affect the participants’
own libido (especially because the measure of libido was
behavioral). Therefore, they repeated the study on a
different set of participants, but this time took a measure
of the partner’s libido. The partner’s libido was measured
in terms of how often they tried to initiate sexual contact.
Analysis of Covariance (ANCOVA) is an extension of
ANOVA that provides a way of statistically controlling the
(linear) effect of variables one does not want to examine
in a study. These extraneous variables are called
covariates, or control variables. (Covariates should be
measured on an interval or ratio scale.) (Vogt, 1999).It
allows you to remove covariates from the list of possible
explanations of variance in the dependent variable.
ANCOVA does this by using statistical techniques (such
as regression to partial out the effects of covariates)
rather than direct experimental methods to control
extraneous variables. ANCOVA is used in experimental
studies when researchers want to remove the effects of
some antecedent variable. For example, pretest scores
are used as covariates in pretest posttest experimental
designs. ANCOVA is also used in non-experimental
research, such as surveys or nonrandom samples, or in
quasi-experiments when subjects cannot be assigned
randomly to control and experimental groups. Although
fairly common, the use of ANCOVA for non-experimental
research is controversial (Vogt, 1999).
Assumptions and Issues in ANCOVA
In addition to the assumptions underlying the ANOVA,
there are two major assumptions that underlie the use of
ANCOVA; both concern the nature of the relationship
between the dependent variable and the
covariate(Howell, 2002; Huck, 2004; and Vogt, 1999).
They stated the assumptions as follow:
The first is that the relationship is linear. If the
relationship is nonlinear, the adjustments made in the
ANCOVA will be biased; the magnitude of this bias
depends on the degree of departure from linearity,
especially when there are substantial differences
between the groups on the covariate. Thus it is important
Dawit 13
for the researcher, in preliminary analyses, to investigate
the nature of the relationship between the dependent
variable and the covariate (by looking at a scatter plot of
the data points), in addition to conducting an ANOVA on
the covariate (Howell (2002); Huck (2004) and Vogt
,1999).
The second assumption has to do with the
regression lines within each of the groups, (Howell
(2002); Huck, S. W. (2004) and Vogt, W. P. (1999). We
assume the relationship to be linear. Additionally,
however, the regression lines for these individual groups
are assumed to be parallel; in other words, they have the
same slope. This assumption is often called
homogeneity of regression slopes or parallelism and
is necessary in order to use the pooled within-groups
regression coefficient for adjusting the sample means
and is one of the most important assumptions for the
ANCOVA.
Failure to meet this assumption implies that there is an
interaction between the covariate and the treatment. This
assumption can be checked with an F test on the
interaction of the independent variable(s) with the
covariate(s). If the F test is significant (i.e., significant
interaction) then this assumption has been violated and
the covariate should not be used as is. A possible
solution is converting the continuous scale of the
covariate to a categorical (discrete) variable and making
it a subsequent independent variable, and then use a
factorial ANOVA to analyze the data.
Moreover, the assumptions underlying the ANCOVA had
a slight modification from those for the ANOVA, however,
conceptually, they are the same. According to Hinkle,
Wiersma, &Jurs, (2003) ANCOVA has the following
assumptions:
Assumption 1: The cases represent a random sample
from the population, and the scores on the dependent
variable are independent of each other, known as the
assumption of independence.
The test will yield inaccurate results if the independence
assumption is violated. This is a design issue that should
be addressed prior to data collection. Using random
sampling is the best way of ensuring that the
observations are independent; however, this is not
always possible. The most important thing to avoid is
having known relationships among participants in the
study.
Assumption 2: The dependent variable is normally
distributed in the population for any specific value of the
covariate and for any one level of a factor (independent
variable), known as the assumption of normality.

This assumption describes multiple conditional
distributions of the dependent variable, one for every
combination of values of the covariate and levels of the
factor, and requires them all to be normally distributed.
To the extent that population distributions are not normal
and sample sizes are small, p values may be invalid. In
addition, the power of ANCOVA tests may be reduced
considerably if the population distributions are non-
normal and, more specifically, thick-tailed or heavily
skewed. The assumption of normality can be checked
with skewness values (e.g., within +3.29 standard
deviations).
Assumption 3: The variances of the dependent variable
for the conditional distributions are equal, known as the
assumption of homogeneity of variance. To the extent
that this assumption is violated and the group sample
sizes differ, the validity of the results of the one-way
ANCOVA should be questioned. Even with equal sample
sizes, the results of the standard post hoc tests should be
mistrusted if the population variances differ. The
assumption of homogeneity of variance can be checked
with the Levine’s F-test.
It happens when they’re trying to run an analysis of
covariance (ANCOVA) model because they have a
categorical independent variable and a continuous
covariate. The problem arises when a coauthor,
committee member, or reviewer insists that ANCOVA is
inappropriate in this situation because one of the
following ANCOVA assumptions is not met: The
independent variable and the covariate are independent
of each other, and there is no interaction between
independent variable and the covariate (Helwig, 2017).
Regression and Correlation
A. Regression
Regression analysis is used in statistics to find trends in
data. For example, we might guess that there’s a
connection between how much we eat and how much we
weigh; regression analysis can help us quantify that.
Regression analysis will provide us with an equation for a
graph so that we can make predictions about our data.
For example, if we’ve been putting on weight over the last
few years, it can predict how much we’ll weigh in ten
years’ time if we continue to put on weight at the same
rate. It will also give us a slew of statistics (including a p-
value and a correlation coefficient) to tell us how accurate
your model is. Most elementary statistics courses cover
very basic techniques, like making scatter plots and
performing linear regression. However, we may come
across more advanced techniques like multiple
regressions Gogtay, Deshpande ,and Thatte 2017).
Furthermore, Huck (2004) stated that Regression
analysis is a way of predicting an outcome variable from
one predictor variable(simple regression) or several
predictor variables (multiple regressions).
This tool is incredibly useful because it allows us to go a
step beyond the data that we collected. So, this indicates
that Regression analysis is a statistical technique for
investigating the relationship among variables. O’Brien,
and Scott (2012) stated that the concept of regression as
follow:
Regression is particularly useful to understand
the predictive power of the independent
variables on the dependent variable once a
causal relationship has been confirmed. To be
precise, regression helps a researcher
understand to what extent the change of the
value of the dependent variable causes the
change in the value of the independent
variables, while other independent variables are
held unchanged (p. 3).
Form the above explanation we can understand that,
regression is one of a tool for quantitative analysis which
is used to understand which among the independent
variables are related to the dependent variable, and to
explore the forms of these relationships used to infer
causal relationships between the independent and
dependent variables.
In regression analysis, the problem of interest is the
nature of the relationship itself between the dependent
variable (response) and the (explanatory) independent
variable. The regression equation describes the
relationship between two variables and is given by the
general format:
FY = a + bX + ε
Where: Y = dependent variable;
X = independent variable,
a = intercept of regression line;
b = slope of regression line, and
ε = error term
In this format, given that Y is dependent on X, the slope b
indicates the unit changes in Y for every unit change in X.
If b = 0.66, it means that every time X increases (or
decreases) by a certain amount, Y increases (or
decreases) by 0.66 that amount. The intercept a indicates
the value of Y at the point where X = 0. Thus if X
indicated market returns, the intercept would show how
the dependent variable performs when the market has a
flat quarter where returns are 0. In investment parlance, a
manager has a positive alpha because a linear

regression between the manager's performance and the
performance of the market has an intercept number a
greater than
Assumptions for regression: there are assumptions to
be taken in to consideration to use regression as a tool of
data analysis. Gogtay, Deshpande, and Thatte (2017)
stated the following assumptions:
Assumption 1: The relationship between the
independent variables and the dependent variables is
linear. The first assumption of Multiple Regression is that
the relationship between the IVs and the DV can be
characterized by a straight line. A simple way to check
this is by producing scatter plots of the relationship
between each of our IVs and our DV.
Assumption 2: There is no multi co linearity in your
data. This is essentially the assumption that your
predictors are not too highly correlated with one another.
Assumption 3: The values of the residuals are
independent. This is basically the same as saying that
we need our observations (or individual data points) to be
independent from one another (or uncorrelated). We can
test this assumption using the Durbin-Watson statistic.
Assumption 4: The variance of the residuals is
constant. This is called homoscedasticity, and is the
assumption that the variation in the residuals (or amount
of error in the model) is similar at each point across the
model. In other words, the spread of the residuals should
be fairly constant at each point of the predictor variables
(or across the linear model). We can get an idea of this
by looking at our original scatter plot, but to properly test
this, we need to ask SPSS to produce a special scatter
plot for us that includes the whole model (and not just the
individual predictors).
To test the 4th assumption, we need to plot the
standardized values our model would predict, against the
standardized residuals obtained.
Assumption 5: The values of the residuals are
normally distributed. This assumption can be tested by
looking at the distribution of residuals.
Assumption 6: There are no influential cases biasing
your model. Significant outliers and influential data
points can place undue influence on your model, making
it less representative of your data as a whole.
B. Correlation
Correlation is a measure of association between two
variables. The variables are not designated as dependent
or independent. As O’Brien and Scott (2012), explained
Dawit 15
that the two most popular correlation coefficients are:
Spearman's correlation coefficient rho and Pearson's
product-moment correlation coefficient. When calculating
a correlation coefficient for ordinal data, select
Spearman's technique. For interval or ratio-type data, use
Pearson's technique.
Ott, (1993) stated that:
Correlation is a measure of the strength of a
relationship between two variables. Correlations
do not indicate causality and are not used to
make predictions; rather they help identify how
strongly and in what direction two variables co-
vary in an environment.
So, form the above definition we can deduce that
Correlation analysis is useful when researchers are
attempting to establish if a relationship exists between
two variables. The correlation coefficient is a measure of
the degree of linear association between two continuous
variables.
Pearson r correlation: Pearson r correlation is widely
used in statistics to measure the degree of the
relationship between linear related variables (Gogtay,
Deshpande, and Thatte, 2017). For example, in the stock
market, if we want to measure how two commodities are
related to each other, Pearson r correlation is used to
measure the degree of relationship between the two
commodities.
Assumption For the Pearson r correlation, both
variables should be normally distributed. Other
assumptions include linearity and homoscedasticity.
Linearity assumes a straight line relationship between
each of the variables in the analysis and
homoscedasticity assumes that data is normally
distributed about the regression line (Gogtay,
Deshpande, and Thatte, 2017).
Spearman rank correlation: Spearman rank correlation
is a non-parametric test that is used to measure the
degree of association between two variables. It was
developed by Spearman, thus it is called the Spearman
rank correlation (Gogtay, Deshpande, and Thatte ,2017).
Spearman rank correlation test does not assume any
assumptions about the distribution of the data and is the
appropriate correlation analysis when the variables are
measured on a scale that is at least ordinal.
Assumptions: Spearman rank correlation test does not
make any assumptions about the distribution. The
assumptions of Spearman rho correlation are that data
must be at least ordinal and scores on one variable must
be monotonically related to the other variable.

The value of a correlation coefficient can vary from -1 to
1. -1 indicates a perfect negative correlation, while a +1
indicates a perfect positive correlation. A correlation of
zero means there is no relationship between the two
variables. When there is a negative correlation between
two variables, as the value of one variable increases, the
value of the other variable decreases, and vise versa
(Gogtay, Deshpande, and Thatte ,2017). In other words,
for a negative correlation, the variables work opposite
each other. When there is a positive correlation between
two variables, as the value of one variable increases, the
value of the other variable also increases. The variables
move together.
|-----------------------------|------------------------------|--------------------------|------------------------|
-1.00 -.5 0 0 +.50 +1.00
strong negative relationship weak or none strong positive relationship
The standard error of a correlation coefficient is used to
determine the confidence intervals around a true
correlation of zero. If your correlation coefficient falls
outside of this range, then it is significantly different than
zero. The standard error can be calculated for interval or
ratio-type data (i.e., only for Pearson's product-moment
correlation).
Example: A company wanted to know if there is a
significant relationship between the total number of
salespeople and the total number of sales. They collect
data for five months.
Variable 1 Variable 2
207 6907
180 5991
220 6810
205 6553
190 6190
--------------------------------
Correlation coefficient = .921
Standard error of the coefficient = ..068
t-test for the significance of the coefficient = 4.100
Degrees of freedom = 3
Two-tailed probability = .0263
Generally, as Hinkle, Wiersma, &Jurs, (2003) stated that
the goal of a correlation analysis is to see whether two
measurement variables co vary, and to quantify the
strength of the relationship between the variables,
whereas regression expresses the relationship in the
form of an equation.
Types of Regression
Gogtay, Deshpande, and Thatte (2017) stated that
essentially in research, there are three common types of
regression analyses that are used viz., linear, logistic
regression and Cox regression. These are chosen
depending on the type of variables that we are dealing
with. Cox regression is a special type of regression
analysis that is applied to survival or “time to event “data
and will be discussed in detail in the next article in the
series. Linear regression can be simple linear or multiple
linear regressions while Logistic regression could be
Polynomial in certain cases.
The type of regression analysis to be used in a given
situation is primarily driven by the following three metrics:
Number and nature of independent variable/s , Number
and nature of the dependent variable/s, and Shape of the
regression line.
A. Linear regression: Linear regression is the most
basic and commonly used regression technique and is of
two type’s viz. simple and multiple regressions. You can
use Simple linear regression when there is a single
dependent and a single independent variable. Both the
variables must be continuous and the line describing the
relationship is a straight line (linear). Multiple linear
regression on the other hand can be used when we have
one continuous dependent variable and two or more
independent variables. Importantly, the independent
variables could be quantitative or qualitative (O’Brien and
Scott, 2012). They added that, in simple linear
regression, the outcome or dependent variable Y is
predicted by only one independent or predictive variable.
Multiple regression is not just a technique on its own. It is,
in fact, a family of techniques that can be used to explore
the relationship between one continuous dependent
variable and a number of independent variables or
predictors. Although multiple regression is based on
correlation, it enables a more sophisticated exploration of
the interrelationships among variables.
Both the independent variables here could be expressed
either as continuous data or qualitative data. A linear
relationship should exist between the dependent and
independent variables.

B. Logistic regression: This type of regression analysis
is used when the dependent variable is binary in nature.
For example, if the outcome of interest is death in a
cancer study, any patient in the study can have only one
of two possible outcomes- dead or alive. The impact of
one or more predictor variables on this binary variable is
assessed. The predictor variables can be either
quantitative or qualitative. Unlike linear regression, this
type of regression does not require a linear relationship
between the predictor and dependent variables.
Dawit 17
For logistic regression to be meaningful, the following
criteria must be met/satisfied: The independent variables
must not be correlated amongst each other and the
sample size should be adequate. If the dependent
variable is non-binary and has more than two
possibilities, we use the multinomial or polynomial logistic
regression.
Table 1: Types of regression adopted from Gogtay, Deshpande, andThatte (2017)
The following table will summarize the basic types of regression:
Type of
regression
Dependent variable
and its nature
Independent variable
and its nature
Relationship
between
variables
Simple linear One, continuous,
normally distributed
One, continuous, normally
distributed
Linear
Multiple linear One, continuous Two or more, may be
continuous or categorical
Linear
Logistic One, binary Two or more, may be
Need not be linear
Polynomial
(logistic)
[multinomial]
Non-binary Two or more, may be
Need not be linear
Cox or
proportional
hazards
regression
Time to an event Two or more, may be
Is rarely linear
Multiple Correlation and Regression
When there are two or more than two independent
variables, the analysis concerning relationship is known
as multiple correlations and the equation describing such
relationship as the multiple regression equation. We here
explain multiple correlation and regression taking only
two independent variables and one dependent variable
(Convenient computer programs exist for dealing with a
great number of variables).
In correlation, the two variables are treated as equals.
In regression, one variable is considered independent
(=predictor) variable (X) and the other the dependent
(=outcome) variable Y(Quirk,2007).Prediction: If you
know something about X, this knowledge helps you
predict something about Y.
In simple linear regression, the outcome or dependent
variable Y is predicted by only one independent or
predictive variable. It should be stressed that in very rare
cases, the dependent variable can only be explained by
one independent variable.
Assumptions behind Multiple Regressions
Multiple regressions make a number of assumptions
about the data, and are important that these are met. The
assumptions are: Sample Size, Multi co linearity of IVs,
Linearity, Absence of outliers, Homo scedasticit, and
Normality.so, Tests of these assumptions are numerous
so we will only look at a few of the more important ones.
a. Sample size: You will encounter a number of
recommendations for a suitable sample size for multiple
regression analysis (Tabachinick & Fidell, 2007). As a
simple rule, you can calculate the following two values:
104 + m
50 + 8m where m is the number of independent variables,
and take whichever is the largest as the minimum
number of cases required.
For example, with 4 independent variables, we would

18
require at least 108 cases: [104+4=108] [50+8*4=82]
With 8 independent variables we would require at least
114 cases: [104+8=112] [50+8*8=114] With Stepwise
regression, we need at least 40 cases for every
independent variable (Pallant, 2007). However, when any
of the following assumptions is violated, larger samples
are required.
b. Multicollinearity of Independent Variables: Any two
independent variables with a Pearson correlation
coefficient greater than .9 between them will cause
problems. Remove independent variables with a
tolerance value less than 0.1. A tolerance value is
calculated as 1, which is reported in SPSS (Tabachinick
& Fidell, 2007).
c. Linearity: Standard multiple regressions only looks at
linear relationships. You can check this roughly using
bivariate scatterplots of the dependent variable and each
of the independent variables (Tabachinick & Fidell,
2007).
d. Absence of outliers: Outliers, such as extreme cases
can have a very strong effect on a regression equation.
They can be spotted on scatter plots in early stages of
your analysis. There are also a number of more
advanced techniques for identifying problematic points.
These are very important in multiple regression analysis
where you are not only interested in extreme values but
in unusual combinations of independent values.
e. Homoscedasticity: This assumption is similar to the
assumption of homogeneity of variance with ANOVAs. 1
More advanced methods include examining residuals. It
requires that there be equality of variance in the
independent variables for each value of the dependent
variable. We can do this in a crude way with the scatter
plots for each independent variable against the
dependent variable (Tabachinick &Fidell, 2007). If there
is equality of variance, then the points of the scatter plot
should form an evenly balanced cylinder around the
regression line.
f. Normality: The dependent and independent variables
should be normally distributed.
When we talk about Multiple Regressions it can be:
Standard Multiple Regressions (All of the independent (or
predictor) variables are entered into the equation
simultaneously0, Hierarchical Multiple Regressions (The
independent variables are entered into the equation in
the order specified by the researcher based on their
theoretical approach) , and Stepwise Multiple Regression
(The researcher provides SPSS with a list of independent
variables and then allows the program to select which
variables it will use and in which order they can go into
the equation, based on statistical criteria.
Uses of Correlation and Regression
There are three main uses for correlation and regression.
Cohen, (1988) stated that,
One is to test hypotheses about cause-and-effect
relationships. In this case, the experimenter
determines the values of the X-variable and sees
whether variation in X causes variation in Y. For
example, giving people different amounts of a
drug and measuring their blood pressure.
The second main use for correlation and
regression is to see whether two variables are
associated, without necessarily inferring a cause-
and-effect relationship. In this case, neither
variable is determined by the experimenter; both
are naturally variable. If an association is found,
the inference is that variation in X may cause
variation in Y, or variation in Y may cause
variation in X, or variation in some other factor
may affect both X and Y.
The third common use of linear regression is
estimating the value of one variable
corresponding to a particular value of the other
variable.
Advantages and Disadvantages of Quantitative
Analysis
Advantages of Quantitative Analysis
Denscombe, (2007)stated the following advantages of
quantitative analysis:
First, it is Scientific: Quantitative data lend themselves
to various forms of statistical techniques based on the
principles of mathematics and probability. Such statistics
provide the analyses with an aura of scientific
respectability. The analyses appear to be based on
objective laws rather than the values of the researcher.
Second, Confidence: Statistical tests of significance give
researchers additional credibility in terms of the
interpretations they make and the confidence they have
in their findings. Third, Measurement: The analysis of
quantitative data provides a solid foundation for
description and analysis. Interpretations and findings are
based on measured quantities rather than impressions,
and these are, at least in principle, quantities that can be
checked by others for authenticity. Fourth, Analysis.
Large volumes of quantitative data can be analyzed
relatively quickly, provided adequate preparation and
planning has occurred in advance. Once the procedures
are ‘up and running’, researchers can interrogate their

19
results relatively quickly. Fifth, Presentation. Tables and
charts provide a succinct and effective way of organizing
quantitative data and communicating the findings to
others. Widely available computer software aids the
design of tables and charts, and takes most of the hard
labor out of statistical analysis.
Disadvantages of Quantitative Analysis
According to Denscombe (2007) the following are some
limitations of quantitative data analysis.
First, quality of data: The quantitative data are only as
good as the methods used to collect them and the
questions that are asked. As with computers, it is a
matter of ‘garbage in, garbage out’. Second, Technicist:
There is a danger of researchers becoming obsessed
with the techniques of analysis at the expense of the
broader issues underlying the research. Particularly with
the power of computers at researchers’ fingertips,
attention can sway from the real purpose of the research
towards an overbearing concern with the technical
aspects of analysis. Third, Data overload: Large
volumes of data can be strength of quantitative analysis
but, without care, it can start to overload the researcher.
Too many cases, too many variables, too many factors to
consider – the analysis can be driven towards too much
complexity. The researcher can get swamped. Fourth,
false promise: Decisions made during the analysis of
quantitative data can have far-reaching effects on the
kinds of findings that emerge. In fact, the analysis of
quantitative data, in some respects, is no more neutral or
objective than the analysis of qualitative data. For
example, the manipulation of categories and the
boundaries of grouped frequencies can be used to
achieve a data fix, to show significance where other
combinations of the data do not. Quantitative analysis is
not as scientifically objective as it might seem on the
surface.
DISCUSSION OF RESULTS
Qualitative Data Result
Research Gateway shows us how to discuss the
results that we have found in relation to both our research
questions and existing knowledge. This is our opportunity
to highlight how our research reflects, differs from and
extends current knowledge of the area in which we have
chosen to carry out research. This section is our chance
to demonstrate exactly what we know about this topic by
interpreting our findings and outlining what they mean. At
the end of our discussion we should have discussed all of
the results that we found and provided an explanation for
our findings.
Dawit 19
Discussion section should not be simply a summary of
the results we have found and at this stage we will have
to demonstrate original thinking. First, we should highlight
and discuss how our research has reinforced what is
already known about the area. Many students make the
mistake of thinking that they should have found
something new; in fact, very few research projects have
findings that are unique. Instead, we are likely to have a
number of findings that reinforce what is already known
about the field and we need to highlight these, explaining
why we think this has occurred.
Second, we may have discovered something different
and if this is the case, we will have plenty to discuss. We
should outline what is new and how this compares to
what is already known. We should also attempt to provide
an explanation as to why our research identified these
differences. Third, we need to consider how our results
extend knowledge about the field. Even if we found
similarities between our results and the existing work of
others, our research extends knowledge of the area, by
reinforcing current thinking. We should state how it does
this as this is a legitimate finding. It is important that this
section is comprehensive and well structured; making
clear links back to the literature we reviewed earlier in the
project. This will allow us the opportunity to demonstrate
the value of our research and it is therefore very
important to discuss our work thoroughly.
The resources in this section of the gateway should help
us to:
Interpret the research: the key to a good discussion is a
clear understanding of what the research means. This
can only be done if the results are interpreted correctly.
Discuss coherently: a good discussion presents a
coherent, well-structured explanation that accounts for
the findings of the research, making links between the
evidence obtained and existing knowledge.
As always, use the Gateway resources appropriately.
As usual, the resources have been included because we
believe they provide accessible, practical and helpful
information on how to discuss our work. On the other
hand, don’t forget that our institution will have
requirements of us and our project that override any
information that we get from this Gateway. For example,
we might not have to produce a separate discussion
section (it depends on different institutions and research
types) as this may need to be included with the
presentation of results. This is often the case for
qualitative research, so we must be sure what is needed.
Find out, and then use the Gateway accordingly.
When crafting our findings, the first thing we want to
think about is how we will organize our findings. Our

20
findings represent the story we are going to tell in
response to the research questions we have answered.
Thus, we will want to organize that story in a way that
makes sense to us and will make sense to our reader.
We want to think about how we will present the findings
so that they are compelling and responsive to the
research question(s) we answered. These questions may
not be the questions we set out to answer but they will
definitely be the questions we answered. We may
discover that the best way to organize the findings is first
by research question and second by theme. There may
be other formats that are better for telling our story. Once
we have decided how we want to organize the findings,
we will start the chapter by reminding our reader of the
research questions. We will need to differentiate between
is presenting raw data and using data as evidence or
examples to support the findings we have identified
(Cohen et.al.,2007).Here are some points to consider:
Our findings should provide sufficient evidence from our
data to support the conclusions we have made. Evidence
takes the form of quotations from interviews and excerpts
from observations and documents, ethically we have to
make sure we have confidence in our findings and
account for counter-evidence (evidence that contradicts
our primary finding) and not report something that does
not have sufficient evidence to back it up, our findings
should be related back to our conceptual framework, our
findings should be in response to the problem presented
(as defined by the research questions) and should be the
“solution” or “answer” to those questions ,and We should
focus on data that enables us to answer your research
questions, not simply on offering raw data (Neuman,
2000).
Qualitative research presents “best examples” of raw
data to demonstrate an analytic point, not simply to
display data. Numbers (descriptive statistics) help our
reader understand how prevalent or typical a finding is.
Numbers are helpful and should not be avoided simply
because this is a qualitative dissertation.
Quantitative Data Result
Quantitative data result is on type of result in research
which is presented in quantitative way like numbers,
statistics. As Creswell, (2013); Neuman, and Robson,
(2004); and Neuman, and Neuman, (2006) stated that
Quantitative methods are used to examine the
relationship between variables with the primary goal
being to analyze and represent that relationship
mathematically through statistical analysis. This is the
type of research approach most commonly used in
scientific research problems.
The finding of your study should be written objectively
and in a succinct and precise format. In quantitative
studies, it is common to use graphs, tables, charts, and
other non-textual elements to help the reader understand
the data. Make sure that non-textual elements do not
stand in isolation from the text but are being used to
supplement the overall description of the results and to
help clarify key points being made (Agresti, and Finlay,
1997). Further information about how to effectively
present data using charts and graphs can be found here.
Quantitative Research is used to quantify the problem
by way of generating numerical data or data that can be
transformed into usable statistics. It is used to quantify
attitudes, opinions, behaviors, and other defined
variables and generalize results from a larger sample
population. Quantitative Research uses measurable data
to formulate facts and uncover patterns in research Huck
(2004). So, for quantitative data you will need to decide in
what format to present your findings i.e. bar charts, pie
charts, histograms etc. You will need to label each table
and figure accurately and include a list of tables and a list
of figures with corresponding page numbers in your
Contents page or Appendices.
Following is a list of characteristics and advantages of
using quantitative methods: The data collected is
numeric, allowing for collection of data from a large
sample size, Statistical analysis allows for greater
objectivity when reviewing results and therefore, results
are independent of the researcher, Numerical results can
be displayed in graphs, charts, tables and other formats
that allow for better interpretation, Data analysis is less
time-consuming and can often be done using statistical
software, Results can be generalized if the data are
based on random samples and the sample size was
sufficient, Data collection methods can be relatively
quick, depending on the type of data being collected, and
Numerical quantitative data may be viewed as more
credible and reliable, especially to policy makers,
decision makers, and administrators (Neuman, &
Robson, 2004).
For qualitative data you may want to include quotes
from interviews. Any sample questionnaires or transcripts
can be included in your Appendices. Qualitative analysis
and discussion will often demand a higher level of writing
/ authoring skill to clearly present the emergent themes
from the research. It is easy to become lost in a detailed
presentation of the narrative and lose sight of the need to
give priority to the broader themes. Creswell (2013)
stated that Quantitative research deals in numbers, logic,
and an objective stance. Quantitative research focuses
on numeric and unchanging data and detailed,
convergent reasoning rather than divergent reasoning
[i.e., the generation of a variety of ideas about a research
problem in a spontaneous, free-flowing manner]. So,
quantitative data results are presented in the same way.
Singh, (2007) stated that quantitative data result has its
main characteristics: The data is usually gathered using
structured research instruments, the results are based on

21
larger sample sizes that are representative of the
population, The research study can usually be replicated
or repeated, given its high reliability, Researcher has a
clearly defined research question to which objective
answers are sought, All aspects of the study are carefully
designed before data is collected, Data are in the form of
numbers and statistics, often arranged in tables, charts,
figures, or other non-textual forms, Project can be used to
generalize concepts more widely, predict future results,
or investigate causal relationships, Researcher uses
tools, such as questionnaires or computer software, to
collect numerical data.
In quantitative data result presentation, Bryman and
Cramer (2005) explained the following things to keep in
mind when reporting the results of a study using
quantitative methods: Explain the data collected and their
statistical treatment as well as all relevant results in
relation to the research problem you are investigating.
Interpretation of results is not appropriate in this section,
Report unanticipated events that occurred during your
data collection. Explain how the actual analysis differs
from the planned analysis. Explain your handling of
missing data and why any missing data does not
undermine the validity of your analysis, Explain the
techniques you used to "clean" your data set, choose a
minimally sufficient statistical procedure; provide a
rationale for its use and a reference for it. Specify any
computer programs used, Describe the assumptions for
each procedure and the steps you took to ensure that
they were not violated, When using inferential statistics,
provide the descriptive statistics, confidence intervals,
and sample sizes for each variable as well as the value of
the test statistic, its direction, the degrees of freedom,
and the significance level [report the actual p value],
Avoid inferring causality, particularly in nonrandomized
designs or without further experimentation, Use tables to
provide exact values; use figures to convey global
effects. Keep figures small in size; include graphic
representations of confidence intervals whenever
possible, Always tell the reader what to look for in tables
and figures.
Generally, Quantitative methods emphasize objective
measurements and the statistical, mathematical, or
numerical analysis of data collected through polls,
questionnaires, and surveys, or by manipulating pre-
existing statistical data using computational techniques.
Quantitative research focuses on gathering numerical
data and generalizing it across groups of people or to
explain a particular phenomenon
Summary, Conclusion, And Recommendations
Summary
A research summary is a professional piece of writing
Dawit 21
that describes your research to some prospective
audience. Main priority of a research summary is to
provide the reader with a brief overview of the whole
study. To write a quality summary, it is vital to identify the
important information in a study, and condense it for the
reader. Having a clear knowledge of your topic or subject
matter enables you to easily comprehend the contents of
your research summary (Philip, 1986).
As Globio (2017), stated that guidelines in writing the
summary of findings are the following.
1. There should be brief statement about the main
purpose of the study, the population or
respondents, the period of the study, method of
research used, the research instrument, and the
sampling design.
Example, a research conducted study of
teaching science in the high schools of Province
may be explained as: This study was conducted
for the purpose of determining the status of
teaching science in the high schools of Province
A. The descriptive method of research was
utilized and the normative survey technique was
used for gathering data. The questionnaire
served as the instrument for collecting data. All
the teachers handling science and a 20%
representative sample of the students were the
respondents. The inquiry was conducted during
the school year 1989-’90.
2. The findings may be lumped up all together but
clarity demands that each specific question under
the statement of the problem must be written first
to be followed by the findings that would answer
it. The specific questions should follow the order
they are given under the statement of the
problem.
Example. How qualified are the teachers
handling science in the high schools of province
A?
Of the 59 teachers, 31 or 53.54 % were BSC
graduates and three or 5.08% were MA degree
holders. The rest, 25 or 42.37%, were non-BSC
baccalaureate degree holders with at least 18
education units. Less than half of all the
teachers, only 27 or 45.76% were science
majors and the majority, 32 or 54.24% were non-
science majors.
3. The findings should be textual generalizations,
that is, a summary of the important data
consisting of text and numbers. Every statement

22
of fact should consist of words, numbers, or
statistical measures woven into a meaningful
statement. No deductions, nor inference, nor
interpretation should be made otherwise it will
only be duplicated in the conclusion.
Only the important findings, the highlights of the data,
should be included in the summary, especially those
upon which the conclusions should be based. Findings
are not explained nor elaborated upon anymore. They
should be stated as concisely as possible.
The summary actually is found at the beginning of the
written piece and will often lead to a concise abstract of
the work which will aid with search engine searches
(Erwin,2013). The summary of any written paper that
delves into a research related topic will provide the
reader with a high level snap shot of the entire written
work. The summary will give a brief background of the
topic, highlight the research that was done, significant
details in the work and finalize the work’s results all in
one paragraph. Only top level information should be
provided in this section and it should make the reader
want to read more after they see the summary.
Conclusions
Conclusion is one part of research. Girma Tadess (2014)
stated that the conclusion may be the most important part
of the research. The writer must not merely repeat the
introduction, but explain in expert like detail what has
been learned, explained, decided, proven, etc. The writer
must reveal the way in which the paper’s thesis might
have significance in society. It should strive to answer
questions that the readers logically, raise. The writer
should point out the importance or implication of the
research on the area of the societal concern.
Guidelines in writing the conclusions. The following
should be the characteristics of the conclusions Philip
(1986):
1. Conclusions are inferences, deductions,
abstractions, implications, interpretations, general
statements, and/or generalizations based upon
the findings. Conclusions are the logical and valid
outgrowths upon the findings. They should not
contain any numeral because numerals generally
limit the forceful effect or impact and scope of a
generalization. No conclusions should be made
that are not based upon the findings.
Example: The conclusion that can be drawn from
the findings in No. 2 under the summary of
findings is this: All the teachers were qualified to
teach in the high school but the majority of them
were not qualified to teach science.
2. Conclusions should appropriately answer the
specific questions raised at the beginning of the
investigation in the order they are given under the
statement of the problem. The study becomes
almost meaningless if the questions raised are
not properly answered by the conclusions.
Example. If the question raised at the beginning of the
research is:
“How adequate are the facilities for the
teaching of science?” and the findings show
that the facilities are less than the needs of
the students, the answer and the conclusion
should be: “The facilities for the teaching of
science are inadequate”.
3. Conclusions should point out what were factually
learned from the inquiry. However, no
conclusions should be drawn from the implied or
indirect effects of the findings.
Example: From the findings that the majority
of the teachers were non-science majors and
the facilities were less than the needs of the
students, what have been factually learned
are that the majority of the teachers were not
qualified to teach science and the science
facilities were inadequate?
It cannot be concluded that science teaching in the high
schools of Province A was weak because there are no
data telling that the science instruction was weak. The
weakness of science teaching is an indirect or implied
effect of the non-qualification of the teachers and the
inadequacy of the facilities. This is better placed under
the summary of implications.
If there is a specific question which runs this way “How
strong science instruction in the high schools of Province
A as is perceived by the teachers and students?”, then a
conclusion to answer this question should be drawn.
However, the respondents should have been asked how
they perceived the degree of strength of the science
instruction whether it is very strong, strong, fairly strong,
weak or very weak. The conclusion should be based
upon the responses to the question.
4. Conclusions should be formulated concisely, that
is, brief and short, ye they convey all the
necessary information resulting from the study as
required by the specific questions.
Without any strong evidence to the contrary,

23
conclusions should be stated categorically. They should
be worded as if they are 100 percent true and correct.
They should not give any hint that the researcher has
some doubts about their validity and reliability. The use of
qualifiers such as probably, perhaps, may be, and the like
should be avoided as much as possible.
5. Conclusions should refer only to the population,
area, or subject of the study. Take for instance,
he hypothetical teaching of science in the high
schools of Province A, all conclusions about the
faculty, facilities, methods, problems, etc. refer
only to the teaching of science in the high
schools of Province A.
Conclusions should not be repetitions of any
statements anywhere in the thesis. They may be
recapitulations if necessary but they should be worded
differently and they should convey the same information
as the statements recapitulated.
In drawing the conclusion, we should aware of Some
Dangers to Avoid in Drawing up Conclusions (Bacani,
et.al., pp. 48-52) avoid Bias, Incorrect generalization (An
incorrect generalization is made when there is a limited
body of information or when the sample is not
representative of the population), Incorrect deduction
(This happens when a general rule is applied to a specific
case), Incorrect comparison (A basic error in statistical
work is to compare two things that are not really
comparable), Abuse of correlation data( A correlation
study may show a high degree of association between
two variables), Limited information furnished by any one
ratio, and Misleading impression concerning magnitude
of base variable. So, Conclusions should not be
repetitions of any statements anywhere in the thesis.
They may be recapitulations if necessary but they should
be worded differently and they should convey the same
information as the statements recapitulated.
The conclusion will be towards the end of the work and
will be the logical closure to all the work found at the end
of the document. As Erwin (2013) the conclusion will
have more detailed information than the summary but it
should not be a repeat of the entire body of the work. The
conclusion should revisit the main points of the research
and the results of the investigation. This section should
be where all the research is pulled together and all open
topics be closed. The final results and a call to action
should be included in this phase of the writing.
RECOMMENDATIONS
In recommendation section, it should be in concluded in
the a report part when the results and conclusions
indicate that further work must be done or when the writer
needs to discuss several possible options to best remedy
Dawit 23
a problem. The writer should not introduce new ideas in
the recommendations section, but relay on the evidence
presented in the result and conclusions sections. Via the
recommendations section, the writer is able to
demonstrate that he or she fully understands the
importance and implication of his or her research by
suggesting ways in which it may be further developed
(Berk , Hart , Boerema ,and Hands, 1998).
Furthermore, Erwin, (2013) described that for
recommending similar researches to be conducted, the
recommendation should be: It is recommended that
similar researches should be conducted in other places.
Other provinces should also make inquiries into the
status of the teaching of science in their own high schools
so that if similar problems and deficiencies are found,
concerted efforts may be exerted to improve science
teaching in all high schools in the country.
Research Report Writing
Research report is a condensed form or a brief
description of the research work done by the researcher.
It involves several steps to present the report in the form
of thesis or dissertation. A research paper can be used
for exploring and identifying scientific, technical and
social issues. If it's your first time writing a research
paper, it may seem daunting, but with good organization
and focus of mind, you can make the process easier on
yourself (Berk, Hart, Boerema , and Hands, 1998). In
addition to this Erwin,(2013) stated that writing a research
paper involves four main stages: choosing a topic,
researching your topic, making an outline, and doing the
actual writing. The paper won't write itself, but by
planning and preparing well, the writing practically falls
into place. Also, try to avoid plagiarism. So, in each
section of the paper we will need to be critically writing
the paper. Most of the consideration in each section
explained as follow:
a. Introduction: The introduction is a critical part of
your paper because it introduces the reasons behind your
paper’s existence. It must state the objectives and scope
of your work, present what problem or question you
address, and describe why this is an interesting or
important challenge(Erwin, 2013). In addition, it is
important to introduce appropriate and sufficient
references to prior works so that readers can understand
the context and background of the research and the
specific reason for your research work. Having explored
those, the objectives and scope of your work must be
clearly stated. The introduction may explain the approach
that is characteristic to your work, and mention the
essence of the conclusion of the paper.
b. Methods: The Methods section provides

24
sufficient detail of theoretical and experimental methods
and materials used in your research work so that any
reader would be able to repeat your research work and
reproduce the results. Be precise, complete and concise:
include only relevant information. For example, provide a
reference for a particular technique instead of describing
all the details.
c. Results: The Results section presents the facts,
findings of the study, by effectively using figures and
tables. Wilkinson (1991) explained that this section must
present the results clearly and logically to highlight
potential implications. Combine the use of text, tables,
and figures to digest and condense the data, and
highlight important trends and extract relationships
among different data items. Figures must be well
designed, clear, and easy to read. Figure captions should
be succinct yet provide sufficient information to
understand the figures without reference to the text.
d. Discussion: In the Discussion section, present
your interpretation and conclusions gained from your
findings. You can discuss how your findings compare
with other experimental observations or theoretical
expectations. Refer to your characteristic results
described in the Results section to support your
discussion, since your interpretation and conclusion must
be based on evidence. By properly structuring this
discussion, you can show how your results can solve the
current problems and how they relate to the research
objectives that you have described in the Introduction
section. This is your chance to clearly demonstrate the
novelty and importance of your research work (Wilkinson,
1991).
e. Conclusions: The Conclusion section
summarizes the important results and impact of the
research work. Future work plans may be included if they
are beneficial to readers (Singh, 2007).
f. Acknowledgments: The Acknowledgments
section is to recognize financial support from funding
bodies and scientific and technical contributions that you
have received during your research work.
g. References: The References section lists prior
works referred to in the other sections. It is vitally
important from an ethical viewpoint, to fully acknowledge
all previously published works that are relevant to your
research. Whenever we use previous knowledge, we
must acknowledge the source. Readers benefit from
complete references as it enables them to position our
work in the context of current research. Ensure that the
references given are sufficient as well as current, and
accessible by the readers (Singh, 2007).
Writing a research paper need critical attention; in the
area different expertise stated that research report writing
need critical rewriting, editing, revising and etc. stages to
make it more effective, accurate and acceptable. Among
them Philip (1986) stated the following tips may be useful
in writing the paper:
You need not start writing the text from the Introduction.
Many authors actually choose to begin with the results
section since all the materials that must be described are
available. This may provide good motivation for carrying
out the procedure most effectively. And Your paper must
be interesting and relevant to your readers. Consider
what your readers want to know rather than what you
want to write. Describe your new ideas precisely in an
early part of your paper so that your results are readily
understood. Otherwise, do not use lengthy descriptions of
the details. For example, writing too many equations and
showing resembling figures or too much detailed tables
should be avoided. Clarity and conciseness are
extremely important.
He also added that during and after writing your draft,
you must edit your writing by reconsidering your starting
plan or original outline. You may decide to rewrite
portions of your paper to improve logical sequence,
clarity, and conciseness. This process may have to be
repeated over and over. When editing is completed, you
can send the paper to your co-authors for improvement.
When all the co-authors agree on your draft, it is ready to
be submitted to the journal. It is worth performing one
final check of grammatical and typographical errors.
English correction of the manuscript by a native speaker
is highly recommended before your submission if you are
not a native speaker. Unclear description prohibits
constructive feedback in the review process.
In relation to Writing and Editing Philip (1986);Singh
(2007); Wilkinson(1991) and (Erwin, 2013). and the
following tips may be useful in writing the paper.
The first, we need not start writing the text from the
Introduction. Many authors actually choose to begin with
the results section since all the materials that must be
described are available. This may provide good
motivation for carrying out the procedure most effectively.
The Second, Our paper must be interesting and
relevant to our readers. Consider what our readers want
to know rather than what we want to write. Describe our
new ideas precisely in an early part of our paper so that
our results are readily understood. Otherwise, we do not
use lengthy descriptions of the details. For example,
writing too many equations and showing resembling
figures or too much detailed tables should be avoided.
Clarity and conciseness are extremely important.
The third, during and after writing our draft, we must

25
edit our writing by reconsidering our starting plan or
original outline. We may decide to rewrite portions of our
paper to improve logical sequence, clarity, and
conciseness. This process may have to be repeated over
and over.
The fourth, when editing is completed, we can send
the paper to our co-authors for improvement. When all
the co-authors agree on the draft, it is ready to be
submitted to the journal (if journal publication is needed).
It is worth performing one final check of grammatical
and typographical errors.
The fifth, English correction of the manuscript by a
native speaker is highly recommended before our
submission if we are not a native speaker. Unclear
description prohibits constructive feedback in the review
process.
Research report has its own format or organization
which is accepted by in different field of study John
(1970) stated that research has the following Format:
Preliminary Section
This part includes: Title Page (Be specific. Tell what,
when, where, etc. In one main title and a subtitle, give a
clear idea of what the paper investigated),
Acknowledgments (if any) (Include only if special help
was received from an individual or group), Table of
Contents (Summarizes the report including the
hypotheses, procedures, and major findings), List of
Tables (if any), List of Figures (if any) and Abstract.
Main Body
CHAPTER ONE: INTRODUCTION
This part of the paper includes: Background of the study (
overview of the study and This is a general introduction to
the topic), Statement of the problem (This is a short
reiteration of the problem), Objectives of the study (What
is the goal to be gained from a better understanding of
this question?), Scope of the study, Limitation of the
study (Explain the limitations that may invalidate the
study or make it less than accurate), Significance of the
study (Comment on why this question merits
investigation), Origination of the study, and Definition of
Terms (Define or clarify any term or concept that is used
in the study in a non-traditional manner or in only one of
many interpretations).
CHAPTER TWO: REVIEW OF RELATED LITERATURE
This part of the thesis states analysis of previous
research and It Gives the reader the necessary
background to understand the study by citing the
investigations and findings of previous researchers and
Dawit 25
documents the researcher's knowledge and preparation
to investigate the problem.
CHAPTER THREE: METHODOLOGY
The methodology part includes: Design of the study
(Description of Research Design and Procedures Used),
Sample method and size/ Sampling Procedures, Sources
of Data (Give complete information about who, what,
when, where, and how the data were collected), Methods
and Instruments of Data Gathering (Explain how the data
were limited to the amount which was gathered. If all of
the available data were not utilized, how was a
representative sample) achieved? , and Gives the reader
the information necessary to exactly replicate (repeat) the
study with new data or if the same raw data were
available, the reader should be able to duplicate the
results. This is written in past tense but without reference
to or inclusion of the results determined from the
analysis.
CHAPTER FOUR: DATA ANALYSIS
It contains: text with appropriate, tables and figures.
Describe the patterns observed in the data. Use tables
and figures to help clarify the material when possible.
CHAPTER FIVE: SUMMARY, CONCLUSION AND
RECOMMENDATION
Under this part of our paper we will include: Restatement
of the Problem , Description of Procedures, Major
Findings (reject or fail to reject Ho) , Conclusions,
Recommendations for Further Investigation, and This
section condenses the previous sections, succinctly
presents the results concerning the hypotheses, and
suggests what else can be done.
A. Reference Section: includes: End Notes (if in
that format of citation), Bibliography or Literature Cited
and Appendix.
SUMMARY
In summing up, research is a scientific field which helps
to generate new knowledge and solve the existing
problem. So, to get this function we need to pass
deferent stages. Among this data analysis is the crucial
part of research which makes the result of the study more
effective. Data Analysis is a process of collecting,
transforming, cleaning, and modeling data with the goal
of discovering the required information. The results so
obtained are communicated, suggesting conclusions, and
supporting decision-making. Data analysis is to verify our
results whether it is valid, reproducible and

26
unquestionable and it is a process used to transform,
remodel and revise certain information (data) with a view
to reach to a certain conclusion for a given situation or
problem.
It can be applied in two ways which is qualitatively and
quantitative. Whatever the research apply one of the two
in his or her research applying the most effective data
analysis in research work is essential. Due to this, data
analysis is beneficial because it helps in structuring the
findings from different sources of data collection like
survey research, is again very helpful in breaking a
macro problem into micro parts, and acts like a filter
when it comes to acquiring meaningful insights out of
huge data-set. Furthermore, every researcher has sort
out huge pile of data that he/she has collected, before
reaching to a conclusion of the research question. Mere
data collection is of no use to the researcher. Data
analysis proves to be crucial in this process, provides a
meaningful base to critical decisions, and helps to create
a complete dissertation proposal. So, after analyzing the
data the result will provide by qualitative and quantitative
method of data results.
In research work, summary, conclusion and
recommendation are the most important part which is
need to be write in effective and efficient to make the
paper more convincible and reputable. And in writing
research report needs a critical attention to make the
report more academic and effective.
Generally, one of the most important uses of data
analysis is that it helps in keeping human bias away from
research conclusion with the help of proper statistical
treatment. With the help of data analysis a researcher
can filter both qualitative and quantitative data for an
assignment writing projects. Thus, it can be said that data
analysis is of utmost importance for both the research
and the researcher.
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