.......Final worksheet manual synopsis

Students’ Worksheets. 1
DEVELOPING A RESEARCH PROPOSAL

TABLE OF CONTENTS
Contents: Page
TITLE PAGE…………………………………………………………………………
ABSTRACT………………………………………………………………………………
KEYWORDS……………………………………………………………………………………
INTRODUCTION…………………………………………………………………………………………………
1.INTRODUCTION……………………………………………………
1.1.A Brief Introduction to the Study…………………………………......
The Problem Statement……………………………………………….
The Purpose Statement………………………………………………..
1.2.Research Questions/ Hypotheses……………………………………...
1.3.Research Objectives……………..…………………………………….
1.4.Operational Definitions……………………………………..................
1.5.Rationale of the Study…………………………....................................
Theoretical Framework ……………………………………………….
Delimitations of the Study…………………………………………….
1.6.Significance of the Study……………………………………………...
1.7.

REVIEW OF THE RELATED LITERATURE…………………………...
CRITICAL FRAMEWORK AND METHODOLOGY…………………...
Strategy of Inquiry/ Approaches to Inquiry (Qualitative/ Quantitative/ Mixed
Method…………………………………………………………………………
Research Method (e.g. Survey, Case Studies, Ethnography,
Experimental)…………………..........................................................................
Theoretical Perspective…………………………………………………….......
Sampling Techniques…………………………………………………………..
Instrumentation………………………………………………………………....
Procedural Details concerning Data collection and Data Analysis……………
ETHICAL STATEMENT……………………………………………………
TIME SCHEDULE…………………………………………………………...
BIBLIOGRAPHY AND REFERENCES…………………………………....

ELABORATION OF IMPORTANT COMPONENTSIN A RESEARCH
PROPOSAL
Title Page
Placement
As the first major section of the document, the title page appears at the top of the first page.
Components
The title page is comprised of a few key elements:
 Running head (or shortened title) and label
 Page number
 Full title of the paper
 Author byline: first name(s), middle initial(s), and last name(s)
 Affiliated Institution(s) or Organization(s)
 Author note (optional)
Creating a Title Page
1) The Running Head
This is a shortened version of the title, and is the header that you will use at the top of each page.
This needs to be left justified.
2) The Page Number
This needs to be made using the header function available in word processing papers. The
running header should be separated from the page number by 5 or 7 spaces, and it will appear on
every page throughout the document. It must be right aligned.
3) The Title
This is the most important part, and should be center aligned, about halfway down the page. This
is the full title of the research paper, dissertation or thesis.
2) Personal Details
At the bottom of the page, center aligned, should be your name, your institution and the date of
submission.This is the most variable part of the title page, and you may need to include the name
of your supervisor and also the level of paper - dissertation, thesis, and term paper. Check your
departmental recommendations.

Apa Title Page(Sample)
Running Head: A SHORT TITLE (Note: Manuscript page header does not need to be the
same as Running Head)
The Title of the Paper
(It Is Centered About Here)
Your Name
Your University Affiliation
Note: This paper can serve as a guideline for your own paper. It reflects current APA format for research
papers. It should give you a sense of the kinds of things you should mention, and b) what should go
where. In any case:Clarity is key!

Abstract
Writing An Abstract for a ResearchProposal
Salient Features of an Abstract:
i. The problem being addressed
ii. The central purpose/focus of study
iii. Information about theoretical perspective, research design, sampling technique,
instrumentation, and procedural detail pertaining to data collection and data analysis
iv. The key results
v. Possible implication of research in future as connected with the findings
Writing an Abstract (APA)
1. The title of an abstract should be centered at the top of the page.
2. There is no extra space between the title and the paragraph.
3. Do not format the title of abstract with bold, italic, underlining, or quotation marks.
4. Avoid capitalizing all letters of the abstract’s title.
5. Do not indent the first line of the abstract’s paragraph.
6. An abstract’s length should be a minimum of 150 words, and a maximum of 250 words.
7. The abstract should be confined within a single paragraph.
8. Like the rest of the document, the abstract should be double spaced, and use Times New
Roman 12pt.
9. Define any abbreviations or acronyms that appear in your project in the abstract’s
paragraph.
10. Depending on your professor’s directives, you may choose to include a short list of
keywords to enable researches and databases to locate your work more effectively.
11. “key words” as the heading should be italicized and indented.
12. Do not place period at the end of a list of keywords.
13. The margins around abstracts are set at “1” on all sides.

Keywords in a Research Proposal
Listing the keywords help researchers find their work in databases. Keywords are words or
phrases that capture the most important aspects of a research project. The keywords line should
begin indented like a paragraph. (In typeset APA journal articles, the keywords line is aligned
under the abstract.) The term “Keywords” should be italicized, followed by a space. The words
themselves, mentioned under keywords, should not be italicized.
Introduction
An Introduction is the first passage in a journal article, dissertation, or a scholarly research study.
It sets the stage for the entire study. According to Wilkinson (1991)
The introduction is the part of the paper that provides readers with the background information
for the research reported in the paper. Its purpose is to establish a framework for the research,
so that readers can understand how it is related to other research. (p. 96).
Qualitative, quantitative and Mixed Method Introduction
A general review of all introductions shows that they follow a similar pattern: the author
announces a problem and justifies why it needs to be studied. The type of problem presented in
an introduction will vary depending on the approach. As qualitative research is primarily
exploratory in its nature, in a qualitative project, the author will describe a research problem that
can best be understood by exploring a concept or phenomenon. Qualitative introductions may
begin with a personal statement of experiences from the author, such as those found in
phenomenological studies (Moustakas, 1994). The written expression may manifest subjectivity
whereby a researcher may position himself/herself in a narrative.
Less variation is seen in quantitative introductions. In a quantitative project, the problem is best
addressed by understanding what factors or variables influence an outcome. For example, in
response to worker cutbacks (a problem for all employees), an investigator may seek to discover
what factors influence businesses to downsize. Another researcher may need to understand the
high divorce rate among married couples (a problem) and examine whether financial issues
contribute to divorce. In both of these situations, the research problem is one in which
understanding the factors that explain or relate to an outcome helps the investigator best
understand and explain the problem. A quantitative introduction may be written from the
impersonal point of view, to convey objectivity.

A mixed methods study can employ either the qualitative or the quantitative approach (or some
combination) to writing an introduction. In any given mixed methods study, the emphasis might
tip in the direction of either quantitative or qualitative research, and the introduction will mirror
that
emphasis. For other mixed methods projects, the emphasis will be equal between qualitative and
quantitative research. In this case, the problem may be one in which a need exists to both
understand the relationship among variables in a situation and explore the topic in further depth.
The deficiencies model of an introduction
The deficiencies model of an introduction is an approach to writing an introduction to a research
study that builds on gaps existing in the literature. It includes the elements of stating the research
problem, reviewing past studies about the problem, indicating deficiencies in these studies, and
advancing the significance of the study. It is a general template for writing a good introduction. It
is a popular approach used in the social sciences, and once its structure is elucidated, the reader
will find it appearing repeatedly in many published research studies (not always in the order
presented here). It consists of five parts, and a separate paragraph can be devoted to each part, for
an introduction of about two pages in length:
1. State the research problem.
2. Review studies that have addressed the problem.
3. Indicate deficiencies in the studies.
4. Advance the significance of the study for particular audiences.
5. State the purpose statement.
The Problem Statement
A problem statement is a clear concise description of the issue(s) that need(s) to be addressed by a
researcher. The problem statement revolves around 5 ‘W’s
Who - Who does the problem affect?
What - What are the boundaries of the problem?
When - When does the issue occur? - When does it need to be fixed?
Where - Where is the issue occurring?
Why - Why is it important that we fix the problem?

The Purpose Statement
According to Locke, Spirduso, and Silverman (2013), the purpose statement indicates why a
researcher wants to conduct a study and what does he/she intends to accomplish. This passage is
called the purpose statement because it conveys the overall intent of a proposed study in a
sentence or several sentences. The purpose statement sets the objectives, the intent, or the major
idea of a proposal or a study. This idea builds on a need (the problem) and is refined into specific
questions (the research questions).
Design features for writing a Qualitative Purpose Statement
• Use words such as purpose, intent, or objective to signal attention to this statement as the
central controlling idea.
• Focus on a single phenomenon (or concept or idea). Narrow the study to one idea to be
explored or understood. This focus means that a purpose does not convey relating two or more
variables or comparing two or more groups, as is typically found in quantitative research.
Instead, advance a single phenomenon, recognizing that the study may evolve into an exploration
of relationships or comparisons among ideas. None of these related explorations could be
anticipated at the beginning.
• Use action verbs to convey how learning will take place. Action verbs and phrases, such as,
understand, develop, explore, examine the meaning of, or discover, keep the inquiry open and
convey an emerging design.
• Use neutral words and phrases—nondirectional language—such as, exploring the “self-
expression experiences of individuals” rather than the “successful self-expression of
individuals.”
• Provide a general working definition of the central phenomenon or idea, especially if the
phenomenon is a term that is not typically understood by a broad audience.
• Include words denoting the strategy of inquiry to be used in data collection, analysis, and the
process of research, such as whether the study will use an ethnographic, grounded theory, case
study, phenomenological, narrative approach, or some other strategy.
• Mention the participants in the study, such as one or more individuals, a group of people, or an
entire organization.

• Identify the site for the research, such as homes, classrooms, organizations, programs, or
events. Describe this site in enough detail so that the reader knows exactly where a study will
take place.
• As a final thought in the purpose statement, include some language that delimits the scope of
participation or research sites in the study.
Design features for writing a Quantitative Purpose Statement
Quantitative purpose statements differ considerably from the qualitative models in terms of then
language and a focus on relating or comparing variables or constructs. A quantitative purpose
statement begins with identifying the proposed major variables in a study (independent,
intervening, dependent), accompanied by a visual model to clearly identify this sequence, and
locating and specifying how the variables will be measured or observed. Finally, the intent of
using the variables quantitatively will typically be either to relate variables, as one usually finds
in a survey, or to compare samples or groups in terms of an outcome, as commonly found in
experiments.
The major components of a good quantitative purpose statement include the following:
• Include words to signal the major intent of the study, such as purpose, intent, or objective. Start
with “The purpose (or objective or intent) of this study will be …”
• Identify the independent and dependent variables, as well as any mediating, moderating, or
control variables used in the study.
• Use words that connect the independent and dependent variables to indicate that they are
related, such as “the relationship between” two or more variables or a “comparison of” two or
more groups.
• Position or order the variables from left to right in the purpose statement—with the independent
variable followed by the dependent variable. Place intervening variables between the
independent and dependent variables. Many researchers also place the moderating variables
between the independent and dependent variables.
• Mention the specific type of strategy of inquiry (such as survey or experimental research) used
in the study.
• Make reference to the participants (or the unit of analysis) in the study, and mention the
research site.

• Define each key variable, preferably using set and accepted established definitions
found in the literature.
Design features for writing a Mixed Method Purpose Statement
Mixed methods purpose statements contain the overall intent of the study, information about
both the quantitative and qualitative strands of the study, and a rationale of incorporating both
strands to study the research problem. These statements need to be identified early, in the
introduction, and they provide major signposts for the reader to understand the quantitative and
qualitative parts of a study. Several guidelines might direct the organization and presentation of
the mixed methods purpose statement:
• Begin with words that signal intent, such as “The purpose of” or “The intent of.”
• Indicate the type of mixed methods design, such as an exploratory sequential design, or an
embedded sequential design, or transformational or multiphase, or others.
• Discuss the reasons for combining both quantitative and qualitative data. Some of the possible
reasons can be as follows:
i. To develop a complete understanding of a research problem by converging quantitative
and qualitative data and comparing the two databases (a convergent design).
ii. To understand the data at a more detailed level by using qualitative follow-up data to help
explain a quantitative database, such as a survey.
iii. To develop measurement instruments that actually fit a sample by first exploring
qualitatively (e.g., through interviews) and using the information to design an instrument
that then can be tested with a large sample (an exploratory sequential design).
iv. To incorporate these reasons into a larger design, such as an experiment (an embedded
design),to frame them within a paradigm of social justice for a marginalized group (a
transformative design), or to connect them to a single overall purpose in a multiphase,
longitudinal program of research (a multiphase design).
ResearchObjectives
 The objectives of the study should be clearly stated and specific in nature. Each sub-
objective should delineate only one issue. Use action-oriented verbs such as ‘to
determine’, ‘to find out’ and ‘to ascertain’ in formulating sub-objectives, which should be

numerically listed. If the objective is to test a hypothesis, you must follow the convention
of hypothesis formulation with specific reference to the wording as used in the type of
methodology selected by you. More detailed objectives, if need be, can be developed
after a study is complete.
 According to some researchers, objectives must be set after having formulated good
research questions so as to develop coherence between research questions and objectives.
Objectives are usually headed by infinitive verbs such as:
 To identify
 To establish
 To describe
 To determine
 To estimate
 To develop
 To compare
 To analyze
 To collect
Example
Main objective:
To examine the relationship between academic achievement and social environment.
Sub-objectives:
1. To find out the relationship, if any, between self-esteem and a student’s academic achievement
at school.
2. To ascertain the association between parental involvement in a student’s studies and his/her
academic achievement at school.
3. To examine the links between a student’s peer group and academic achievement.
4. To explore the relationship between academic achievement and the attitude of students
towards teachers

ResearchQuestions
a. Qualitative Research Questions
In a qualitative study, inquirers generally state research questions, not hypotheses (i.e.,
predictions that involve variables and statistical tests). These research questions assume
two forms: (a) a central question and (b) associated sub-questions.
The central question is a broad question that asks for an exploration of the central
phenomenon or concept in a study. There can be one or two central questions in a
qualitative research.
Sub questions are the questions that follow each general central question; they narrow the
focus of the study but leave open the questioning.
According to Creswell (2014), as qualitative research is exploratory in its essence, one
may use exploratory verbs that convey the language of emerging design in a qualitative
research. Mentioned below are some of the examples in this regard:
Report (or reflect) the stories (e.g., narrative research)
Describe the essence of the experience (e.g., phenomenology)
Discover (e.g., grounded theory)
Seek to understand (e.g., ethnography)
Explore a process (e.g., case study)
b. Quantitative Research Questions
Quantitative research questions inquire about the relationships among variables that the
investigator seeks to know. Quantitative hypotheses, on the other hand, are predictions
the researcher makes about the expected outcomes of relationships among variables.
Types of Quantitative Research Questions:
Descriptive Research Questions:
Descriptive research questions simply aim to describe the variables you are measuring.
When we use the word describe, we mean that these research questions aim to quantify
the variables you are interested in. Think of research questions that start with words such
as "How much?", "How often?", "What percentage?", and "What proportion?",
but also sometimes questions starting "What is?" and "What are?". Often, descriptive
research questions focus on only one variable and one group, but they can include
multiple variables and groups. We provide some examples below:
Question: How many calories do American men and women

consume per day?
Variable: Daily calorific intake
Group:
1. American men
2. American women
Question:
How often do British university students use
Facebook each week?
Variable: Weekly Facebook usage
Group: British university students
Comparative Research Questions:
Comparative research questions aim to examine the differences between two or more
groups on one or more dependent variables (although often just a single dependent
variable). Such questions typically start by asking "What is the difference in?" a
particular dependent variable (e.g., daily calorific intake) between two or more groups
(e.g., American men and American women). Examples of comparative research questions
include:
Question: What is the difference in the weekly photo uploads on Facebook between British male
and female university students?
Dependent variable: Weekly photo uploads on Facebook
Groups:
1. Male, British university students
2. Female, British university students
Relationship based Research Questions
Whilst we refer to this type of quantitative research question as a relationship-based research
question, the word relationship should be treated simply as a useful way of describing the fact
that these types of quantitative research question are interested in the causal relationships,
associations, trends and/or interactions amongst two or more variables on one or more groups.
We have to be careful when using the word relationship because in statistics, it refers to a
particular type of research design, namely experimental research designs where it is possible to
measure the cause and effect between two or more variables; that is, it is possible to say that
variable A (e.g., study time) was responsible for an increase in variable B (e.g., exam scores).
However, at the undergraduate and even master's level, dissertations rarely involve
experimental research designs, but rather quasi-experimental and relationship-based
research designs .This means that you cannot often find causal relationships between
variables, but only associations or trends.

However, when we write a relationship-based research question, we do not have to make this
distinction between causal relationships, associations, trends and interactions (i.e., it is just
something that you should keep in the back of your mind). Instead, we typically start a
relationship-based quantitative research question, "What is the relationship?", usually
followed by the words, "between or amongst", then list the independent variables (e.g., gender)
and dependent variables (e.g., attitudes towards music piracy), "amongst or between" the
group(s) you are focusing on. Examples of relationship-based research questions are:
Question:
What is the relationship between gender and attitudes towards music piracy amongst
adolescents?
Dependent variable: Attitudes towards music piracy
Independent variable: Gender
Group: Adolescents
How to Structure Quantitative Research Questions?
The following steps are involved in structuring quantitative research questions:
(1) Choosing the type of quantitative research question you are trying to create (i.e.,
descriptive, comparative or relationship-based); (2) Identifying the different types of
variables you are trying to measure, manipulate and/or control, as well as any groups you
may be interested in; (3) Selecting the appropriate structure for the chosen type of
quantitative research question, based on the variables and/or groups involved; and (4)
Writing out the problem or issues you are trying to address in the form of a complete
research question.
Question:
What is the relationship between study time and exam scores amongst university
students?
Dependent variable: Exam scores
Independent variable: Study time
Group: University students

c. Mixed Method ResearchQuestions
A strong mixed methods study should contain the qualitative question, the quantitative
question or hypothesis, and a mixed methods question. This configuration is necessary
because mixed methods does not rely exclusively on either qualitative or quantitative
research but on both forms of inquiry. Researchers should consider what types of
questions should be presented and when and what information is most needed to convey
the nature of the study.
• Both qualitative and quantitative research questions (or hypotheses) need to be
advanced in a mixed methods study in order to narrow and focus the purpose statement.
These questions or hypotheses can be advanced at the beginning or when they emerge
during a later phase of the research. For example, if the study begins with a quantitative
phase, the investigator might introduce hypotheses. Later in the study, when the
qualitative phase is addressed, the qualitative research questions appear.
The mixed methods question can be written in different ways. This can assume one of
three forms.
The first is to write it in a way that conveys the methods or procedures in a study (e.g.,
Does the qualitative data help explain the results from the initial quantitative phase of the
study?). The second form is to write it in a way that conveys the content of the study
(e.g., Does the theme of social support help to explain why some students become bullies
in schools?) (see Tashakkori & Creswell, 2007). The third approach is to combine the
methods and content (e.g., How does the qualitative interview data on student bullying
further explain why social support, as measured quantitatively, tends to discourage
bullying as measured on a bullying scale?).
An Example of Mixed Method ResearchQuestion
To what extent and in what ways do qualitative interviews with students and faculty
members serve to contribute to a more comprehensive and nuanced understanding of this
predicting relationship between CEEPT scores and student academic performance, via
integrative mixed methods analysis?
(Lee & Greene, 2007, p. 369).

Hypotheses in a research
1. Hypothesis in research refers to a tentative, testable assertion regarding the
occurrence of certain behaviors, phenomena, or events. It is a prediction regarding the
outcomes of a study.
2. Hypothesis in a research refers to a prediction of the possible outcomes of a study.
3. Hypothesis is a tentative proposition suggested as solution to / an explanation of some
phenomenon.
4. A hypothesis is a conjectural statement of the relation between two or more variables.
(Note: A hypothesis must be testable; it must be stated as simply and as concisely as
possible. A hypothesis provides direction to research. A hypothesis can be deduced
from a theory or from other hypotheses.)
Some of the Important Types of Hypotheses
Null Hypothesis (Ho)
A null hypothesis is a statement that there is no actual relationship between variables. Not
rejecting a null hypothesis (Ho) does not really mean that Ho is true. There might not be enough
evidence against Ho.
The null hypothesis is the statement of equivalence. It usually states that there is no difference
and no relationship between the two variables or between the means of two samples from the
same population.
In the language of research the null hypothesis is written as:
H0: X1 = X2 (There is no difference between the means, both are equal)
Example “There is no significant difference in the anxiety level of children of High IQ and those
of low IQ.”
Alternative Hypothesis (H1 or HA)
An alternative hypothesis is a statement that suggests a potential outcome that the researcher may
expect. It is established only when a null hypothesis is rejected. There are two types of
alternative hypothesis:
1. Directional Hypothesis

2. Non-Directional Hypothesis
Directional hypothesis is one tailed. You assume that by manipulating the independent variable
the dependent variable will change in a specific direction. You can predict if this change will be
positive or negative. In case of directional hypothesis, the expressions like “is more
than”………”will be lesser” are generally mentioned in the statements.
Example
“Children with high IQ will exhibit more anxiety than children with low IQ”
Non-directional research hypothesis is two tailed. You assume that by manipulating the
independent variable there will be a change in the dependent variable. You cannot predict if this
change will be positive or negative. In case of Non directional hypothesis, no definite direction
of the expected findings is specified. It can be presented through the expressions like “There is a
difference between…”
Example
“There is a difference in the anxiety level of the children of high IQ and those of low IQ.”
Note: A hypothesis can be derived deductively or inductively. Hypotheses or research
questions may be used to:
 Compare the variables
 Relate the variables or
 Describe the variables
Variables in Research
A variable is an element, feature, or factor that is liable to change. When all members of a
class are identical, we do not have a variable. Such characteristics are called constants.
We can distinguish between two types of variables according to the level of measurement:
1. Continuous or Quantitative Variables.
2. Discrete or Qualitative Variables.

Continuous or Quantitative Variables
 Interval-scale variable
Interval scale data has order and equal intervals. Interval scale variables are measured
on a linear scale, and can take on positive or negative values. It is assumed that the
intervals keep the same importance throughout the scale. They allow us not only to
rank order the items that are measured but also to quantify and compare the
magnitudes of differences between them. We can say that the temperature of 40°C is
higher than 30°C, and an increase from 20°C to 40°C is twice as much as the increase
from 30°C to 40°C.
 Ratio-scale variable
Ratio- scale variables are the interval variables that meet an additional condition”a
measurement value of zero”, which must mean that there is none of that variable>
some examples of ratio variables are temperature measured in Kelvin. 0Kelvin,
oftern called “absolute zero” indicates that there is no temperature whatsoever. Some
of the other examples in this regard are mass, height, weight, distance etc.
Diacrete/Categorical orQualitative Variable
Discrete variables are also called categorical variables. A discrete variable, X, can take on a
finite number of numerical values, categories or codes. Discrete variables can be classified
into the following categories:
1. Nominal variables
2. Dichotomous variables
3. Ordinal variables
4. Dummy variables from quantitative variable
5. Multiple response variables
6. Preference Variable
 Nominal Variables are the kinds of categorical variable. Nominal variables can have
two or more categories without having any kind of natural order. They are variables
with no numeric values e. g. occupation, political party affiliation, types of pet, eye
colors etc.
 Dichotomous or Binary Variables are nominal variables which have only two
categories or levels e.g. Gender (male/ female). Similarly, yes/ no answers are
examples of nominal variables.

 Ordinal Variables are the variables having two or more categories that can be
ordered or ranked e.g. “Do you like policies of government”? (Yes, A lot, Not very
much, they are satisfactory etc.).
 Dummy Variables A quantitative variable can be transformed into a categorical
variable, called a dummy variable by recoding the values. Consider the following
example: the quantitative variable Age can be classified into five intervals. The values
of the associated categorical variable, called dummy variables, are 1, 2,3,4,5:
[Up to 25] 1
[25, 40 ] 2
[40, 50] 3
[50, 60] 4
[Above 60] 5
 Multiple Response Variable
Multiple response variables are those, which can assume more than one value. A typical
example is a survey questionnaire about the use of computers in research. The
respondents were asked to indicate the purpose(s) for which they use computers in their
research work. The respondents could score more than one category.
1. Statistical analysis
2. Lab automation/ process control
3. Data base management, storage and retrieval
4. Modeling and simulation
5. Scientific and engineering calculations
6. Computer aided design (CAD)
7. Communication and networking
8. Graphics
 Preference Variable
Preference variables are specific discrete variables, whose values are either in a
decreasing or increasing order. For example, in a survey, a respondent may be
asked to indicate the importance of the following 5 sources of information in his
research and development work, by using the code [1] for the most important
source and [5] for the least important source:
1. Literature published in the country
2. Literature published abroad
3. Scientific abstracts

4. Unpublished reports, material, etc.
5. Discussions with colleagues within the research unit
Some other Kinds of Variables
1. Dependent Vs. Independent Variable
Dependent variable is a variable affected or expected to be affected by the
independent variable. It is also called criterion or outcome variable.
Independent variable is a variable that affects (or is presumed to affect) the
dependent variable under study and is included in the research design so that its effect
can be determined; sometimes called the experimental or treatment variable. A
manipulated variable is an independent variable created by a researcher. A selected
variable is the independent variable that already exists that a researcher locates and
then chooses to study.
2. Control Variable
A variable that is held constant in order to assess or clarify the relationship between two
other variables. Control variable should not be confused with controlled variable, which
is an alternative term for independent variable.
3. Extraneous variable
Extraneous Variables are undesirable variables that influence the relationship between the
variables that an experimenter is examining. Another way to think of this, is that these are
variables that influence the outcome of an experiment, though they are not the variables that
are actually of interest. These variables are undesirable because they add error to an
experiment. A major goal in research design is to decrease or control the influence of
extraneous variables as much as possible. A Confounding Variable is an extraneous variable
whose presence affects the variables being studied so that the results you get do not reflect
the actual relationship between the variables under investigation

4. Intervening or Mediating Variables
An intervening variable (sometimes called a mediating variable) is a hypothetical
variable used to explain causal links between other variables. Intervening variables
cannot be observed in an experiment (that’s why they are hypothetical). For example,
there is an association between being poor and having a shorter life span. Just because
someone is poor doesn’t mean that will lead to an early death, so other hypothetical
variables are used to explain the phenomenon. These intervening variables could include:
lack of access to healthcare or poor nutrition.
Samples and Population in Research
Researchers make the distinction between a population , the universe of people to which the
study could be generalized, and a sample , the subset of people from the population who will
participate in the current study. The sampling frame refers to the eligible members of the
population
In general, there are two ways to select members for a study: randomly or non - randomly. A
random sample , sometimes called a probabilistic sample , is a sample in which each member of
the sampling frame has an equal chance of being selected as a study participant. A non - random
sample is a sample in which each member of the sampling frame does not have an equal chance
of being selected as a participant in the study.
Random Sampling (Probability sampling)
In probability sampling, every individual in the population has a non-zero chance to be included
in the sample of the study. It provides the researcher with a more representative sample, so it is a
more reliable sampling procedure.
Following are the commonly used kinds of random sampling:
1. Simple Random Sampling
2. Stratified Sampling
4. Systematic Sampling

Simple Random Sampling
A simple random sample is a subset of a statistical population in which each member of the subset has an
equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a
group. An example of a simple random sample would be a group of 25 employees chosen out of 250
employees. In this case,the population is all 250 employees, and the sample is random because each
employee has an equal chance of being chosen.
Stratified Sampling
Stratified random sampling is a method of sampling that involves the division of a population
into smaller groups known as strata. In stratified random sampling, the strata are formed based
on members' shared attributes or characteristics. A random sample from each stratum is taken in
a number proportional to the stratum's size when compared to the population. These subsets of
the strata are then pooled to form a random sample.
Systemic Sampling
Systematic sampling is a type of probability sampling method in which sample members from a larger
population are selected according to a random starting point and a fixed, periodic interval. This interval,
called the sampling interval, is calculated by dividing the population size by the desired sample size.
Despite the sample population being selected in advance,systematic sampling is still thought of as being
random, provided the periodic interval is determined beforehand and the starting point is random.
Non Random Sampling (Non-probability sampling)
In non probability sampling the sample is not selected randomly. It is used where the
population is widely spread and it is difficult to use any sampling frame for such a great
population.
Following are the most commonly used techniques of non-probability sampling:
1. Purposive Sampling
2. Quota Sampling
3. Convenience Sampling
4. Snowball Sampling

Purposive Sampling
Purposive sampling represents a group of different non-probability sampling techniques. Also known
as judgmental, selective or subjective sampling, purposive sampling relies on the judgement of the
researcher when it comes to selecting the units (e.g., people, cases/organisations, events, pieces of data)
that are to be studied. Usually, the sample being investigated is quite small, especially when compared
with probability sampling techniques. The main goal of purposive sampling is to focus on particular
characteristics of a population that are of interest, which will best enable you to answer your research
questions. The sample being studied is not representative of the population, but for researchers pursuing
qualitative or mixed methods research designs, this is not considered to be a weakness. Rather, it is a
choice, the purpose of which varies depending on the type of purposing sampling technique that is used.
For example, in homogeneous sampling, units are selected based on their having similar characteristics
because such characteristics are of particular interest to the researcher.
Quota Sampling
A sampling method of gathering representative data from a group. As opposed to random sampling, quota
sampling requires that representative individuals are chosen out of a specific subgroup. For example, a
researcher might ask for a sample of 100 females, or 100 individuals between the ages of 20-30.
Convenience Sampling
A statistical method of drawing representative data by selecting people because of the ease of their
volunteering or selecting units because of their availability or easy access. The advantages of this type of
sampling are the availability and the quickness with which data can be gathered. The disadvantages are
the risk that the sample might not represent the population as a whole, and it might be biased by
volunteers.
Snowball Sampling
Snowball sampling is where research participants recruit other participants for a test or study. It
is used where potential participants are hard to find. It’s called snowball sampling because (in
theory) once you have the ball rolling, it picks up more “snow” along the way and becomes
larger and larger. Snowball sampling is a non-probability sampling technique. It doesn’t have the
probability involved, with say, simple random sampling (where the odds are the same for any
particular participant being chosen). Rather, the researchers used their own judgment to choose
participants.
Snowball sampling consists of two steps:
1. Identify potential subjects in the population. Often, only one or two subjects can be found
initially.
2. Ask those subjects to recruit other people (and then ask those people to recruit.

How to Write a Rationale for Research
Rationale refers to a set of reasons or a logical basis for a course of action or belief. A rationale for
research includes an account of the logical basis behind the selection of the following:
 a specific area of inquiry, (i.e the research problem),
 research design, (i.e. qualitative, quantitative, or mixed method approach)
 a specific research method within the preferred research design
 theoretical framework
 Procedural details (data collection (justifying the selection of specific sampling techniques and
instrumentation), and data analysis
How to Write Significance of the Study?
The Significance of the Study describes what contribution your study will make to the broad literature
or set of broad educational problems upon completion.
As you draft your Significance of the Study, consider the following:
 Broadly, a component of the significance of the study is to illustrate the contribution that
your study will make to the existing literature on the problem or broad problem areas of
education.
 Generally, a component of the significance of the study is to elucidate exactly how you
will extend the current literature.
 Specifically, a component of the significance of the study is to determine who
benefits from the study and how that specific audience will benefit from its findings.
How to Write TheoreticalFramework ofthe study?
A theoretical framework refers to the theory that a researcher chooses to guide him/her in his/her
research. Thus, a theoretical framework is the application of a theory, or a set of concepts drawn
from the selected theoretical perspective, to offer an explanation of an event, or shed some light
on a particular phenomenon or research problem.
Theoretical framework provides a theoretical foundation to your study. Guided by theoretical
framework, a researcher seeks to answer the questions raised by him/ her in his research. While
selecting any theory to substantiate the overall research, a researcher is required to examine:
 the title of study
 the research questions
 the research design

 the variables of study
According to Creswell (2014) “In quantitative research, researchers often test theories as an
explanation for answers to their questions. In qualitative research, the use of theory is much
more varied. The inquirer may generate a theory as the final outcome of a study and place it at
the end of a project, such as in grounded theory. In other qualitative studies, it comes at the
beginning and provides a lens that shapes what is looked at and the questions asked, such as in
ethnographies or in transformative research. In mixed methods research, researchers may both
test theories and generate them. Moreover, mixed methods research may contain a theoretical
framework within which both quantitative and qualitative data are collected” (p. 83).
(The theoretical framework preferred by a researcher must not be superficially stated (providing
merely the details concerning a theory/ theoretical foundation), it must rather be deeply
integrated within the very design of an overall research project).
What is meant by Qualitative, Quantitative, and Mixed Method Research?
(Source: Creswell, 2014)
QUANTITATIVE RESEARCH
Quantitative research is ‘Explaining phenomena by collecting numerical data that are
analyzed using mathematically based methods (in particular statistics)’.
Quantitative approaches to research
1. Experimental design
Experimental design seeks to determine if a specific treatment influences an outcome. The
researcher assesses this by providing a specific treatment to one group (experimental group)
and withholding it from another group (Control group) and then determining how both
groups scored on an outcome.
What is the difference between a Control group and a Treatment group in experimental
research?
A control group in a generally performed experimental research refers to a group that does not
receive the treatment or the experimental manipulation that the treatment group receives.
Types of Experimental design

 Pre-experimental design
 Quasi experiments
 True experiments
 Single subject design
With pre-experimental designs, the researcher studies a single group and provides an
intervention during the experiment. This design does not have a control group to compare with
the experimental group. In quasi-experiments, the investigator uses control and experimental
groups but does not randomly assign participants to groups (e.g., they may be intact groups
available to the researcher). In a true experiment, the investigator randomly assigns the
participants to treatment groups. A single-subject design involves observing the behavior of a
single individual (or a small number of individuals) over time.
Examples of Pre-Experimental Designs
One-Shot Case Study
This design involves an exposure of a group to a treatment followed by a measure.
One-Group Pretest-Posttest Design
This design includes a pretest measure followed by a treatment and a posttest for a single group.
Static Group Comparison or Posttest-Only With Nonequivalent Groups
Experimenters use this design after implementing a treatment. After the treatment, the researcher
selects a comparison group and provides a posttest to both the experimental group(s) and the
comparison group(s).
Examples of Quasi-Experimental Designs
Nonequivalent (Pretest and Posttest) Control-Group Design
In this design, a popular approach to quasi-experiments, the experimental Group A and the
control Group B are selected without random assignment. Both groups take a pretest and
posttest. Only the experimental group receives the treatment.
Single-Group Interrupted Time-Series Design
In this design, the researcher records measures for a single group both before and after a
treatment.

Control-Group Interrupted Time-Series Design
This design is a modification of the Single-Group Interrupted Time-Series design in which two
groups of participants, not randomly assigned, are observed over time. A treatment is
administered to only one of the groups (i.e., Group A).
Examples of True Experimental Designs
Pretest-Posttest Control-Group Design
A traditional, classical design, this procedure involves random assignment of participants to two
groups. Both groups are administered both a pretest and a posttest, but the treatment is provided
only to experimental group.
Posttest-Only Control-Group Design
This design controls for any confounding effects of a pretest and is a popular experimental
design. The participants are randomly assigned to groups, a treatment is given only to the
experimental group, and both groups are measured on the posttest.
Solomon Four-Group Design
A special case of a 2 X 2 factorial design, this procedure involves the random assignment of
participants to four groups. Pretests and treatments are varied for the four groups. All groups
receive a posttest.
A-B-A Single-Subject Design
This design involves multiple observations of a single individual. The target behavior of a single
individual is established over time and is referred to as a baseline behavior. The baseline
behavior is assessed by providing the treatment and then withdrawing the treatment.
2. Non- Experimental Quantitative Research
Causal Comparative Research (or Ex-Post Facto Research)
Causal-comparative research is also often referred to as ex-post facto (Latin for “after the fact”).
In causal comparative research, the researcher attempts to determine the cause, or reason , for the
pre-existing differences in groups of individuals. In other words, it is observed that groups are
different on some variables and the researcher attempts to identify the main factors that had led
to the difference.

Example
A researcher might hypothesize that participation in pre-school education is the major
contributing factor for differences in the social adjustment of first graders. To examine this
hypothesis, the researcher will select a sample of first graders who had participated in preschool
education and a sample of first graders who had not, and then compare the social adjustment of
the two groups. If the group that did participate in preschool education exhibited a higher level of
social adjustment, the researcher’s hypothesis would be supported. Thus the basic causal
comparative research starts with analyzing an effect, and thereby analyzing possible causes.
Correlational Research
Correlational research is sometimes treated as a type of descriptive research, primarily
because it does describe an existing condition. However, the condition it describes is
distinctively different from the conditions typically described in survey or observational
studies. Correlational research involves collecting data in order to determine whether and to
what degree, a relationship exists between two or more quantifiable variables. The degree of
relationship is expressed as a correlation coefficient. If a relationship exists between two
variables, it means that a score within a certain range on one variable are associated with
scores within a certain range on the other variable.
Example
For example there is a relationship between intelligence and academic achievement;
persons, who score highly on intelligence tests, tend to have high grade point averages, and
persons who score lowly on intelligence tests tend to have low grade point averages.
(The purpose of correlational study is to determine relationships between variables or to use
these relationships to make predictions). Correlational studies provide a numerical estimate
of how related two variables are. The investigator uses correlational statistics to describe and
measure the degree of relationship or association between two or more variables. Clearly, the
higher the correlation, the more the two variables are related and the more accurate are the
predictions based on the relationships.
Survey Research (Descriptive Research)
Survey Research provides a quantitative or numeric description of trends, attitudes, or
opinions of a population by studying a sample of that population. It includes cross-sectional
and longitudinal studies using questionnaires or structured interviews for data collection—
with the intent of generalizing from a sample to a population. Surveys may be used to obtain

descriptive information about a target population, (e.g. to measure levels of literacy or
numeracy in a school or region), or to examine relationships between various factors (e.g. to
explain the differences in mathematics achievement of students in terms of their age, gender,
exposure to the mathematics curriculum, and amount of time spent in class learning
mathematics). It may also seek to explain the nature of an issue.
Examples of Survey ResearchTopics
Descriptive Studies
Q. What is the average length of time teachers use to prepare lessons?
Relationship Studies
Q. Is there a relationship between teachers’ attitudes toward discipline and students’ satisfaction
with the class?
Explanatory Studies
Q. Why are students in one school achieving better than similar students in another school?
Cross Sectional Vs. Longitudinal Surveys in a survey based research.
Cross-sectional surveys are used to gather information about present conditions in a population
at a single point in time or during a single, relatively brief period, and then comparisons are
made across the variables of interest. Generally, the purpose is to describe situations and estimate
frequencies rather than to establish causal patterns.
In longitudinal surveys, the data are collected at more than one point and the researcher is
interested in making comparisons across time. The data can be collected from one or multiple
groups. Repeated observation for at least two points in time is the key characteristic of the
longitudinal method since this enables the educational researcher to study the processes and
patterns of change and stability in the educational field. The logic of longitudinal studies is that
educational researches are concerned with the process of change, and the study of change
requires that observations are made for at least two points in time. Time is significantly related to
causal influence, since earlier events usually influence later events.
QUALITATIVE RESEARCH
Qualitative Research is primarily exploratory research. It is used to gain an understanding of
underlying reasons, opinions, and motivations. It provides insights into the problem or helps to

develop ideas, predictions, or theoretical foundations for potential quantitative research.
Qualitative Research is also used to uncover trends in thought and opinions, to dive deeper into
the problem. Qualitative data collection methods vary using unstructured or semi-structured
techniques. Some common methods include focus groups (group discussions), individual
interviews, and observations. In qualitative researches the sample size is typically small. The
overall nature of qualitative research is unstructured and flexible in terms of its procedural
details.
The methods and types of approaches in Qualitative Research (Creswell, 2014)
1. Narrative research is a design of inquiry from the humanities in which the researcher
studies the lives of individuals and asks one or more individuals to provide stories about
their lives (Riessman, 2008). This information is then often retold by the researcher into a
narrative chronology. Often, in the end, the narrative combines views from the
participant’s life with those of the researcher’s life in a collaborative narrative (Clandinin
& Connelly, 2000).
2. Phenomenological research is a design of inquiry coming from philosophy and
psychology in which the researcher describes the lived experiences of individuals about a
phenomenon as described by participants. This description culminates in the essence of
the experiences for several individuals who have all experienced the phenomenon. This
design has strong philosophical underpinnings and typically involves conducting
interviews (Giorgi, 2009; Moustakas, 1994).
3. Grounded theory is a design of inquiry from sociology in which the researcher derives a
general, abstract theory of a process, action, or interaction grounded in the views of
participants. This process involves using multiple stages of data collection and the
refinement and interrelationship of categories of information (Charmaz, 2006; Corbin &
Strauss, 2007).
4. Ethnography is a design of inquiry coming from anthropology and sociology in which
the researcher studies the shared patterns of behaviors, language, and actions of an intact
cultural group in a natural setting over a prolonged period of time. Data collection often
involves observations and interviews
5. Case studies are a design of inquiry found in many fields, especially evaluation, in which
the researcher develops an in-depth analysis of a case, often a program, event, activity,
process, or one or more individuals. Cases are bounded by time and activity, and

researchers collect detailed information using a variety of data collection procedures over
a sustained period of time (Stake, 1995; Yin, 2009, 2012).
MIXED METHOD RESEARCH
Mixed method research involves combining or integrating qualitative and quantitative research
and data in a research study. Qualitative data tends to be open-ended without predetermined
responses while quantitative data usually includes closed-ended responses obtained through
questionnaires or psychological instruments.
The methods and types of approaches in mixed method research in social
sciences
1. Convergent parallel mixed method is a form of mixed methods design in which the
researcher converges or merges quantitative and qualitative data in order to provide a
comprehensive analysis of the research problem. In this design, the investigator typically
collects both forms of data at roughly the same time and then integrates the information in
the interpretation of the overall results.
2. Explanatory sequential mixed method is one in which the researcher first conducts
quantitative research, analyzes the results and then builds on the results to explain them in
more detail with qualitative research. It is considered explanatory because the initial
quantitative data results are explained further with the qualitative data. It is considered
sequential because the initial quantitative phase is followed by the qualitative phase. This
type of design is popular in fields with a strong quantitative orientation.
3. Exploratory sequential mixed method is the reverse sequence from the explanatory
sequential design. In the exploratory sequential approach the researcher first begins with a
qualitative research phase and explores the views of participants. The data are then analyzed,
and the information used to build into a second, quantitative phase. The qualitative phase
may be used to build an instrument that best fits the sample under study, to identify
appropriate instruments to use in the follow-up quantitative phase, or to specify variables
that need to go into a follow-up quantitative study. Particular challenges to this design reside
in focusing in on the appropriate qualitative findings to use and the sample selection for both
phases of research
What is meant by primary and secondary sources of data in research?

Primary sources provide first-hand testimony or direct evidence concerning a topic under
investigation. They are created by witnesses or recorders who experienced the events or conditions
being documented.
Examples
Autobiographies, memoirs, oral histories, etc.
In research activities, secondary source of data refers to information that has been gathered
(and often interpreted) by other researchers and recorded in books, articles, and other
publications.
Example
Textbooks, encyclopedia articles, biographies, etc.
What is meant by external and internal validity in research
External validity refers to how well the data and theories from one setting of research apply to
another in general.
Internal validity refers to whether the effects observed in a study are due to the manipulation of
the independent variable and not some other factor. Internal validity can be improved by
controlling extraneous variables.
The main criterion of external validity is the process of generalization and whether results
obtained from a small group can be extended to make predictions about the entire population.
External validity helps to answer the question: can the research be applied to the “real world”?
If your research is applicable to other experiments, settings, people, and times, then external
validity is high. If the research cannot be replicated in other situations, external validity is low.
(Within the qualitative research paradigm, external validity is replaced by the concept of
transferability. Transferability is the ability of research results to transfer to situations with
similar parameters, populations and characteristics).
ResearchTools forData Collection
 Questionnaires
 Interviews
 Observations
 Tests (achievement, aptitude, personality, vocational and personality etc.)
 Scales (nominal, ordinal, interval, ratio, Likert etc.)

Questionnaires
Questionnaires refer to a written set of questions that are given to people in order to
collect facts or opinions about something. A questionnaire can either be structured or
unstructured.
A structured questionnaire consists of closed ended questions with predefined answers.
(It is used in quantitative research).
An unstructured questionnaire is made up of open ended questions that elicit free
responses. (It is used in qualitative research)
Example of closed ended questions:
Open ended questions:
 What is your favorite memory from childhood?
 What makes the leaves change color?
Closed ended question
 Is English your favorite language?
Options:
a. Yes
b. No
 According to your opinion, what is the most important problem the internet is facing
today?
Options
a. Slow downloading
b. Privacy
c. Navigation/ searching

Guidelines for creating a cover letter for your questionnaire
Definition
In a research project, the cover letter is a formal letter/ source of information, describing the
accompanying documents (or any other items) and the reasons for sending them.
If the study requires that subjects complete a questionnaire begin the questionnaire with a cover
letter which includes the following information:
1. Title of research
2. Purpose of research
3. Rationale, why the research is important, and how information will be used.
4. Confidentiality
5. Return of the survey (a brief mention)
6. Time it will take to complete survey
7. In the case of sensitive questions, adding a statement saying the respondent can omit any question
they prefer not to answer
8. Appreciation for participation
9. Contact details of the sender for further information.
Interviews
Interviewing involves asking questions and getting answers from participants in a study. Interviewing has
a variety of forms including: individual, face-to-face interviews and face-to-face group interviewing. The
asking and answering of questions can be mediated by the telephone or other electronic devices (e.g.
computers). Interviews can be structured, semi-structure or unstructured.
Types of Interviews
Structured: (The interviewer has a control over the questions and answers. He/ she asks each
respondent the same series of questions. The questions are developed before the interview. There
is generally little room for variation in responses and there are few open-ended questions included in the
interview. (These interviews are used in quantitative research)
Unstructured: Interviewee is free to share his/her thoughts and the interviewer can change the
next question in the light of what has been answered by the interviewee. (Such interviews are
used in qualitative research)
Semi-Structured:
Characteristicsof Semi-structured interviews

 The interviewer and respondents engage in a formal interview.
 The interviewer develops and uses an 'interview guide.' This is a list of questions and
topics that need to be covered during the conversation, usually in a particular order.
 The interviewer follows the guide, but is able to follow topical trajectories in the
conversation that may stray from the guide when he or she feels this is appropriate. (Such
interviews are used in qualitative research)
Focus group A focus group could be defined as a group of interacting individuals having some common
interest or expertise , brought together by a moderator, who uses the group and its interaction as a way to
gain information about a specific or focused issue.
Observations
Observation as a tool of qualitative research implies the recording of the behavior of participants.
Observation can be of two types:
Participant Observation: When you as a researcher become part of some social set-up and
people know that they are being observed.
Non-participant or Systematic Observation: When you do not interfere in the social set-up, or
do not disturb the natural settings of the sample to be observed.
Tests
Test is a measurement tool, which is used to measure the performance of an individual in some
specific areas of interest. There are two main types of tests:
a. Norm Referencedor Standardized Tests
b. Criterion ReferencedTests
Norm-referenced refersto standardized tests that are designed to compare and rank test takers in relation
to one another. Norm-referenced tests report whether test takers performed better or worse than a
hypothetical average student, which is determined by comparing scores against the performance results of
a statistically selected group of test takers, typically of the same age or grade level, who have already
taken the exam.
Achievement tests,personality tests, aptitude tests,intelligence tests, and personality tests in research are
some of the kinds of norm reference tests.
Criterion-referenced tests and assessments are designed to measure student performance against a fixed
set of predetermined criteria or learning standards—i.e.,concise, written descriptions of what students are
expected to know and be able to do at a specific stage of their education. In elementary and secondary

education, criterion-referenced tests are used to evaluate whether students have learned a specific body of
knowledge or acquired a specific skill set. For example, the curriculum taught in a course,academic
program, or content area.
MEASUREMET AND SCALING OF QUANTITATIVE DATA
Measurement refers to the process of describing some property of a phenomenon by assigning numbers.
Scale refers to a set of numbers, amounts, etc.,used to measure or compare the level of something)
Levels ofMeasurements:
Nominal Scale
Nominal scale presents a discrete classification of data, in which data are neither measured nor ordered
but subjects are merely allocated to distinct categories: for example, a record of students' course choices
constitutes nominal data which could be correlated with school results e.g.
Q. What is your hair colour?
1. Black
2. Brown
3. Blonde
4. Grey
5. Other
Ordinal Scale
Ordinal scale is a scale on which data is shown simply in order of magnitude since there is no standard of
measurement of differences e.g.
Q. What do you feel today?
1. Very unhappy
2. Unhappy
3. Normal
4. Happy
5. Very happy
Ratio Scale
Ratio scale is a scale of measurement of data which permits the comparison of differences of values. It is
a scale having a fixed zero value/ an absolute zero (the point where none of the qualities being measured
exists).
Examples
Ruler (Inches or centimeters)
GPA (Grade point averages)

Income (money earned last year)
Interval Scale
Interval scale is scale of measurement of data according to which the differences between values can be
quantified in absolute but not relative terms and for which any zero is merely arbitrary: for instance, dates
are measured on an interval scale since differences can be measured in years, but no sense can be given to
a ratio of times. Interval scales do not have a true zero.
Interval scales are numerical scales in which intervals have the same interpretation throughout. As an
example, consider the Fahrenheit scale of temperature. The difference between 30 degrees and 40 degrees
represents the same temperature difference as the difference between 80 degrees and 90 degrees.
Commonly used types of measurementscales
Likert Scale The Likert scale is a commonly used survey tool that requests an assessment of some
variable from among a range of potential responses. A Likert scale is bipolar scale; it provides options
ranging between two potential extreme positions. This scale typically contains an odd number of options,
usually 5 to 7. Likert scale is named after its inventor Rensis Likert (a Psychologist).The format of a
typical five-level Likert item can be:
1. Strongly disagree
2. Disagree
3. Neither agree nor disagree
4. Agree
5. Strongly agree
Semantic Differential Scale
In a semantic differential scale, several bipolar attributes are identified at the extreme of the
scale, and respondents are asked to indicate their attitudes. Mentioned below is an image of a
semantic differential scale

Thurstone Scale
Thurstone scale is made up of statements about a particular issue and each statement has a
numerical value indicating the respondents’ attitudes towards an issue. The average response of
the respondents is computed. Thurstone scale was developed in 1928 by Louis Leon Thurstone.
Example of a Thurstone Scale
Scale values Items
1.2 I am good at learning speaking skills.
2.2 I feel confident while speaking English.
3.3 I like to speak English even when my friends tease me.
4.5 Sometimes I cannot confidently express the main content of my speech while
speaking English.
6.7 It is difficult to speak English in a formal context.
7.5 Oral presentation in English is a difficult task.

What is Pilot Study?
Pilot study refers to a small- scale study or set of observations undertaken to decide how and
whether to launch a full scale project.
What is meant by triangulation in a researchproject?
Triangulation is the use of two or more methods in a single research to check the reliability and
validity of research evidence. Triangulation offers new insights and ways of looking at the main
themes of research.
Types of Triangulation in a Research
Denzin (1978) identified four basic types of triangulation:
 Data triangulation: (involves using different sources of information)
 Investigator triangulation: (involves multiple researchers in an investigation)
 Theory triangulation: (involves using more than one theoretical scheme in the interpretation of
the phenomenon)
 Methodological triangulation: (involves using more than one method to gather data).
What is the Difference betweenLimitations and Delimitations of
Research?
Limitations are potential weaknesses in a research and are out of the control of researcher.
Delimitations are those characteristics that limit the scope and define the boundaries of your
study. The delimitations are in your control. Delimiting factors include the choice of research
problem, objectives, the research questions, and variables of interest, theoretical perspectives,
and the population you choose to investigate.
What is meant by Hawthrone Effect in a research?
The Hawthorne effect or observer effect is a type of reactivity in which individuals modify or
improve an aspect of their behavior in response to their awareness of being observed.
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