PRACTICAL RESEARCH 2 Modular Approach

(QUANTITATIVE RESEARCH)
Compiled by:
EVELYN C. BIAY,Ed.D.
SHIAHARI I. CORTEZ,R.N., M.Ed.
Module
in
PRACTICAL
RESEARCH 2

Introduction
As a researcher and a human being we have always asked ourselves questions, as much
about the phenomena we observe on a daily basis as the deepest mysteries of nature. When curiosity
and intuition are applied in a systematic approach to find the answers to questions like these, when
we draw on experience and the knowledge we‘ve already acquired, then we‘re doing research. All
of us in our daily lives explore, investigate, invent, solving problems at work, trying out new recipes
in the kitchen, finding the best way to prune a plant, or simply playing with the kids. Dedicating our
lives to research means making study and experiment our profession, and leads these activities to
the acquisition of new knowledge.
In this module, all the information was gathered through the use of the different internet
websites including different books in order to get the information needed to give an essential
knowledge and skills of the young researcher like you!
Unlock your imaginations and creativity, spread your eyes around you, and make research as
your baseline in making decision. You can change the world by your own simple discovery. Come
on! Join us in this adventure and let us see the treasure that we discover.

Acknowledgment
“In everything, Give Thanks…” 1 Thes. 5:18
The researcher wishes to express profound gratitude and sincere on the following persons
who were behind the realization to made this compilation of this module made possible.
To their beloved Parents, for undying love they have given them, also for the full support
and guidance. They never left them; they were always there to encourage and never stop believing
in them.
To Dr. Evelyn Corpuz-Biay, thank Prof. for all the support and sharing your expertise
regarding research and being one of the best contributor of this compiled module.
To all the students serves as inspirations of this module, thank you so much!
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TABLE OF CONTENTS
Page
Introduction…………………………………………………………. i
Acknowledgment……………………………………………………. ii
Table of Contents……………………………………………………. iii
Module 1: Nature of Inquiry and Research
Lesson 1: The characteristics, Strengths,
Weaknesses, and kinds of
Quantitative Research……………................... 1
Inquiry-Based Learning…………………….. 2
The Nature of Research…………………….. 2
Characteristics of Quantitative
Research……………………………………. 2
Strengths and Weaknesses…………………. 4
Lesson 2: The Nature of Variables…………………… 5
Variables…………………………………… 5
Types of Variables…………………………. 6
Categorical and Continuous Variables…….. 8
Module 2: Identifying the Inquiry and stating the problem…… 13
Lesson 3: Research in our daily life…………………… 14
Quantitative vs Qualitative…………………. 15
Sources of Research Problems……………… 15
Guidelines in choosing a Research
Topic………………………………………... 16

Research topic to be avoided………………… 16
Writing a Research Title…………………….. 17
Scope and Delimitation……………………… 18
Lesson 4: Hypothesis…………………………………… 19
Module 3: Learning from other and Reviewing the Literature…. 25
Lesson 5: Review of Related Literature (RRL)………… 26
Purpose of Review of Related Literature…..... 26
Styles or approaches of RRL or Review
of Related Literature………………………… 27
Lesson 6: Referencing…………………………………. 28
Lesson 7: Research Ethics…………………………….. 39
Lesson 8: Conceptual Framework…………………….. 41
Module 4: Understanding Data and ways to systematically
Collect data…………………………………………… 50
Lesson 9: Quantitative Data Research Design……….. 51
Lesson 10: Instrument Development…………………... 55
Usability…………………………………… 57
Validity……………………………………. 58
Reliability…………………………………. 58
Lesson 11: Guidelines in Writing Research
Methodology……………………………… 59

Module 5: Finding Answers through Data Collection…….. 66
Lesson 12: Quantitative Data Analysis………………... 66
Using Software for statistical analysis…….. 68
Sampling…………………………………… 68
Steps in Quantitative data analysis………… 69
Lesson 13: Statistical Methods………………………… 73
Statistical Methodologies………………….. 74
Types of Statistical Data Analysis………… 74
Measure of Correlations………………….. 76
Lesson 14: Sampling Procedure……………………… 93
Sampling techniques……………………... 97
Sample size………………………………. 97
Under-sized samples……………………… 99
Module 6: Report and Sharing Findings………………………. 104
Lesson 15: Draws Conclusions……………………….. 104
Suggestions Based Upon the Conclusions... 106
Summary-The Strengths of the Results…… 106
Formulates Recommendation……………… 107
List References……………………………. 107
Finalizes and present best research design…. 111
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Introduction
An inquiry and research are two terms are almost the same in meaning. Both of them
involved investigative work and any process that has the aim of augmenting knowledge, resolving
doubt, or solving a problem. A theory of inquiry is an account of the various types of inquiry and a
treatment of the ways that each type of inquiry achieves its aim while research is to discover truths
by investigating on your chosen topic scientifically.
Intended Learning Outcomes
After this lesson, you should be able to:
1. describes characteristics, strengths, weaknesses, and kinds of quantitative research;
2. use some new terms you have learned in expressing their world views freely;
3. understanding the kinds of quantitative research;
4. infer about the strengths and weaknesses of quantitative research;
5. illustrate the importance of quantitative research across fields; and
6. differentiates kinds of variables and their uses.
PERFORMANCE STANDARD
The learner is able to;
decide on suitable quantitative research in different areas of interest.
INQUIRY-BASED LEARNING
What is Inquiry?
Inquiry is a learning process that motivates you to obtain knowledge or information about
people, things, places, or events. (Baraceros 2016) It requires you to collect data, meaning, facts,
and information about the object of your inquiry, and examine such data carefully. On other hand,
in your analysis, you execute varied thinking strategies that range from lower-order to higher-order
thinking skills such as inferential, critical, integrative, creative thinking.
Module
1 NATURE OF INQUIRY AND RESEARCH
THE CHARACTERISTICS, STRENGTHS, WEAKNESSES, AND
KINDS OF QUANTITATIVE RESEARCH
LESSON
1

Furthermore, according to Badke cited by Baraceros, solving a problem, especially social
issues, does not only involved yourself but other members of the society too. Whatever knowledge
you have about world bears the influence of your cultural, sociological, institutional, or ideological
understanding of the world. (Badke 2012)
THE NATURE OF RESEARCH
The research process is, for many of us, just the way we do things. We research the best
buys in cars and appliances, we research book reviews before shopping for books, we research the
best schools for our children and ourselves, and we probably perform some kind of research in our
jobs. Our search for information may lead us to interview friends or other knowledgeable people;
read articles in magazines, journals, or newspapers; listen to the radio; search an encyclopedia on
CD-ROM; and even explore the Internet and World Wide Web for information. We use our local
public libraries and our school libraries.
Research can be a way of life; it is the basis for many of the important decisions in our lives.
Without it, we are deluged with information, subjected to the claims of advertisers, or influenced by
hearsay in making sense of the world around us. This informal, experiential research helps us
decipher the flood of information we encounter daily.
Formal academic research differs from experiential research and may be more investigative
in nature. For example, it may require us to learn about an area in which we have little knowledge
or inclination to learn. It may be library-oriented or field-oriented, depending on the nature of the
research.
CHARACTERISTICS OF QUANTITATIVE RESEARCH
Your goal in conducting quantitative research study is to determine the relationship between
one thing (an independent variable) and another (a dependent or outcome variable) within a
population. Quantitative research designs are either descriptive (subjects usually measured once)
or experimental (subjects measured before and after a treatment). A descriptive study establishes
only associations between variables; an experimental study establishes causality.
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
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reasoning (i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-
flowing manner).
Its main characteristics are:
1. The data is usually gathered using structured research instruments.
2. The results are based on larger sample sizes that are representative of the population.
3. The research study can usually be replicated or repeated, given its high reliability.
4. Researcher has a clearly defined research question to which objective answers are sought.
5. All aspects of the study are carefully designed before data is collected.
6. Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or
other non-textual forms.
7. Project can be used to generalize concepts more widely, predict future results, or investigate
causal relationships.
8. Researcher uses tools, such as questionnaires or computer software, to collect numerical
data.
The overarching aim of a quantitative research study is to classify features, count them, and
construct statistical models in an attempt to explain what is observed.
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.
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 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.
STRENGTHS AND WEAKNESSES
Quantitative method
Quantitative data are pieces of information that can be counted and which are usually
gathered by surveys from large numbers of respondents randomly selected for inclusion. Secondary
data such as census data, government statistics, health system metrics, etc. are often included in
quantitative research. Quantitative data is analyzed using statistical methods. Quantitative
approaches are best used to answer what, when and who questions and are not well suited to how
and why questions.
Strengths Weaknesses
Findings can be generalized if selection process
is well-designed and sample is representative of
study population
Related secondary data is sometimes not
available or accessing available data is
difficult/impossible
Relatively easy to analyze Difficult to understand context of a
phenomenon
Data can be very consistent, precise and reliable Data may not be robust enough to explain
complex issues
IMPORTANCE OF QUANTITATIVE RESEARCH
1. More reliable and objective
2. More reliable and objective
3. Can use statistics to generalize a finding
4. Often reduces and restructures a complex problem to a limited number of variables
4

5. Looks at relationships between variables and can establish cause and effect in highly
controlled circumstances
6. Tests theories or hypotheses
7. Assumes sample is representative of the population
8. Subjectivity of researcher in methodology is recognized less
9. Less detailed than qualitative data and may miss a desired response from the participant
10.
11.
All experiments examine some kind of variable(s). A variable is not only something that we
measure, but also something that we can manipulate and something we can control for. To
understand the characteristics of variables and how we use them in research, this guide is divided
into three main sections. First, we illustrate the role of dependent and independent variables.
Second, we discuss the difference between experimental and non-experimental research. Finally, we
explain how variables can be characterized as either categorical or continuous.
VARIABLES
– A variable is a label of name that represents a concept or characteristic that varies
(e.g., gender, weight, achievement, attitudes toward inclusion, etc.)
– Conceptual and operational definitions of variables
Conceptual and operational definitions of variables
– Conceptual (i.e., constitutive) definition: the use of words or concepts to define a
variable
Achievement: what one has learned from formal instruction
Aptitude: one‘s capability for performing a particular task or skill
– Operational definition: an indication of the meaning of a variable through the
specification of the manner by which it is measured, categorized, or controlled
A test score
Income levels above and below $45,000 per year
The use of holistic or phonetic language instruction
THE NATURE OF VARIABLES
LESSON
2
5

TYPES OF VARIABLE
Dependent and Independent Variables
An independent variable, sometimes called an experimental or predictor variable, is a
variable that is being manipulated in an experiment in order to observe the effect on
a dependent variable, sometimes called an outcome variable.
Imagine that a tutor asks 100 students to complete a math test. The tutor wants to know why
some students perform better than others. Whilst the tutor does not know the answer to this, she
thinks that it might be because of two reasons: (1) some students spend more time revising for their
test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to
investigate the effect of revision time and intelligence on the test performance of the 100 students.
The dependent and independent variables for the study are:
Dependent Variable: Test Mark (measured from 0 to 100)
Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)
The dependent variable is simply that, a variable that is dependent on an independent
variable(s). For example, in our case the test mark that a student achieves is dependent on revision
time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may
not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other
words, whilst the number of hours a student spends revising and the higher a student's IQ score may
(or may not) change the test mark that a student achieves, a change in a student's test mark has no
bearing on whether a student revises more or is more intelligent (this simply doesn't make sense).
Therefore, the aim of the tutor's investigation is to examine whether these independent
variables - revision time and IQ - result in a change in the dependent variable, the students' test
scores. However, it is also worth noting that whilst this is the main aim of the experiment, the tutor
may also be interested to know if the independent variables - revision time and IQ - are also
connected in some way.
In the section on experimental and non-experimental research that follows, we find out a
little more about the nature of independent and dependent variables.
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Three types of variables defined by the context within which the variable is discussed
– Independent and dependent variables
– Extraneous and confounding variables
– Continuous and categorical variables
1. Independent and dependent (i.e., cause and effect)
– Independent variables act as the ―cause‖ in that they precede, influence, and predict
the dependent variable
– Dependent variables act as the effect in that they change as a result of being
influenced by an independent variable
– Examples
The effect of two instructional approaches (independent variable) on student
achievement (dependent variable)
The use of SAT scores (independent variable) to predict freshman grade point
averages (dependent variable)
2. Extraneous and confounding variables
– Extraneous variables are those that affect the dependent variable but are not
controlled adequately by the researcher
Not controlling for the key-boarding skills of students in a study of computer-
assisted instruction
– Confounding variables are those that vary systematically with the independent
variable and exert influence of the dependent variable
Not using counselors with similar levels of experience in a study comparing
the effectiveness of two counseling approaches
3. Continuous and categorical variables
– Continuous variables are measured on a scale that theoretically can take on an
infinite number of values
Test scores range from a low of 0 to a high of 100
Attitude scales that range from very negative at 0 to very positive at 5
Students‘ ages
– Categorical variables are measured and assigned to groups on the basis of specific
characteristics
Examples
 Gender: male and female
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 Socio-economic status: low middle, and high
The term level is used to discuss the groups or categories
 Gender has two levels - male and female
 Socio-economic status has three levels - low, middle, and high.
– Continuous variables can be converted to categorical variables, but categorical
variables cannot be converted to continuous variables
IQ is a continuous variable, but the researcher can choose to group students
into three levels based on IQ scores - low is below a score of 84, middle is
between 85 and 115, and high is above 116
Test scores are continuous, but teachers typically assign letter grades on a ten point scale
(i.e., at or below 59 is an F, 60 to 69 is a D, 70 to 79 is a C, 80-89 is a B, and 90 to 100 is an A
Categorical and Continuous Variables
Categorical variables are also known as discrete or qualitative variables. Categorical variables can
be further categorized as nominal, ordinal or dichotomous.
1. Nominal variables are variables that have two or more categories, but which do not have an
intrinsic order. For example, a real estate agent could classify their types of property into
distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a
nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note,
the different categories of a nominal variable can also be referred to as groups or levels of
the nominal variable. Another example of a nominal variable would be classifying where
people live in the USA by state. In this case there will be many more levels of the nominal
variable (50 in fact).
2. Dichotomous variables are nominal variables which have only two categories or levels. For
example, if we were looking at gender, we would most probably categorize somebody as
either "male" or "female". This is an example of a dichotomous variable (and also a nominal
variable). Another example might be if we asked a person if they owned a mobile phone.
Here, we may categorize mobile phone ownership as either "Yes" or "No". In the real estate
agent example, if type of property had been classified as either residential or commercial
then "type of property" would be a dichotomous variable.
8

3. Ordinal variables are variables that have two or more categories just like nominal variables
only the categories can also be ordered or ranked. So if you asked someone if they liked the
policies of the Democratic Party and they could answer either "Not very much", "They are
OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories,
namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the
most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not
very much). However, whilst we can rank the levels, we cannot place a "value" to them; we
cannot say that "They are OK" is twice as positive as "Not very much" for example.
Continuous variables are also known as quantitative variables. Continuous variables can be
further categorized as either interval or ratio variables.
o Interval variables are variables for which their central characteristic is that they can be
measured along a continuum and they have a numerical value (for example, temperature
measured in degrees Celsius or Fahrenheit). So the difference between 20C and 30C is the
same as 30C to 40C. However, temperature measured in degrees Celsius or Fahrenheit is
NOT a ratio variable.
o Ratio variables are interval variables, but with the added condition that 0 (zero) of the
measurement indicates that there is none of that variable. So, temperature measured in
degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no
temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often
called absolute zero) indicates that there is no temperature whatsoever. Other examples of
ratio variables include height, mass, distance and many more. The name "ratio" reflects the
fact that you can use the ratio of measurements. So, for example, a distance of ten meters is
twice the distance of 5 meters.
Ambiguities in classifying a type of variable
In some cases, the measurement scale for data is ordinal, but the variable is treated as
continuous. For example, a Likert scale that contains five values - strongly agree, agree, neither
agree nor disagree, disagree, and strongly disagree - is ordinal. However, where a Likert scale
contains seven or more value - strongly agree, moderately agree, agree, neither agree nor disagree,
disagree, moderately disagree, and strongly disagree - the underlying scale is sometimes treated as
continuous (although where you should do this is a cause of great dispute).
9

Name: ____________________________________ Score: _____________
Strand/Section/Grade: ______________________ Date: ______________
CHECK YOUR KNOWLEDGE (Short Answer Question)
(2 POINTS EACH)
DIRECTIONS: Read the question carefully. Write your answer on the space provided.
_______________________1. Is a learning process that motivates you to obtain knowledge or
information about people, things, places, or events?
_______________________2. Can be a way of life; it is the basis for many of the important
decisions in our lives.
_______________________3. 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).
_______________________4. This data are the pieces of information that can be counted and
which are usually gathered by surveys from large numbers of
respondents randomly selected for inclusion.
_______________________5. Sometimes called an experimental or predictor variable.
_______________________6. The aim is to manipulate an independent variable(s) and then
examine the effect that this change has on a dependent variable(s).
_______________________7. The relationships between two variables.
_______________________8. Design involves selecting groups, upon which a variable is tested
without any random pre-selection process.
_______________________9. Statement to be proven or disproved.
_______________________10. Uses interviews, questionnaires, and sampling polls to get a sense
of behavior with intense precision.
_______________________11. Variables that have two or more categories, but which do not have
an intrinsic order.
_______________________12. Nominal variables which have only two categories or levels.
_______________________13. Variables for which their central characteristic is that they can be
measured along a continuum and they have a numerical value
_______________________14. Interval variables, but with the added condition that 0 (zero) of the
measurement indicates that there is none of that variable.
_______________________15. The researcher does not manipulate the independent variable(s).
10

Name: ____________________________________ Score: _____________
Directions: INDIVIDUAL WORK. Complete the concept map by writing words associated with the
middle word. Be guided by the clues in the sentence below each graph.
The detectives need more time to inquire about the case.
The witness‘ statement is crucial to the solution of the case.
INQUIRE
CRUCIAL
GUARANTEEE
11

The continuous presence of your name on the Dean‘s list guarantee a good future for you.
EDD-904 Understanding & Using Data: Characteristics of Quantitative Research
http://spalding.libguides.com/c.php?g=461133&p=3153088
https://coursedev.umuc.edu/WRTG999A/chapter4/ch4-01.html
What is the nature of research? | Insights Association
www.insightsassociation.org/faq/what-nature-research
http://betterthesis.dk/research-methods/lesson-1different-approaches-to-research/strengths-and-
limitations
Baraceros, Esther L., PRACTICAL RESEARCH 1,First Edition 2016, Rex Book Store, 856
Nicanor, Sr. St., Manila, Philippines.
12

Introduction
This module discusses the topics that will help the learners to develop the ability to
formulate a research problem and find answers towards these inquiries or questions.
Inquiry or research pushes you to a thorough or a detailed investigation of a certain subject
matter. This kind of study involves several stages that require much time and effort. The learners
need more time to think in finalizing its decision about a particular topic to research on or in
determining the appropriateness of such topic by obtaining the background information of the study,
and formulating some questions that you want to answer.
After this module, the learner demonstrates understanding of:
1. the range of research topics in the area of inquiry;
2. the value of research in the area of interest;
3. the specificity of the problem posed;
4. distinguish a researchable from a non-researchable research problem;
5. narrow down a general topic into a smaller one;
6. explain the meaning of a quantitative research problem;
7. use prose and non-prose means of comparing-contrasting the approaches and types of
research question; and
8. apply the guidelines in stating a quantitative research problem and research question.
PERFORMANCE standard
The learner is able to:
formulate clearly the statement of the problem.
Module
2
IDENTIFYING THE INQUIRY AND
STATING THE PROBLEM
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RESEARCH IN OUR DAILY LIFE
Guidelines in making a Research Problems
1. One or more sentences indicating the goal, purpose, or overall direction of the study
2. General characteristics
– Implies the possibility of empirical investigation
– Identifies a need for the research
– Provides focus
– Provides a concise overview of the research
3. Two ways of stating the problem
 Research problems: typically a rather general overview of the problem with just
enough information about the scope and purpose of the study to provide an initial
understanding of the research
 Research statements and/or questions: more specific, focused statements and
questions that communicate in greater detail the nature of the study
4. A general research problem
 (e.g.) The purpose of this study is to investigate the attitudes of high school students
to mandated drug testing programs
5. Specific statements and questions
 (e.g.) This study examines the differences between males‘ and females‘ attitudes
toward mandated high school drug testing programs.
 (e.g.) What are the differences between freshmen, sophomore, junior, and senior
students‘ attitudes toward mandated high school drug testing programs?
6. Researchable and non-researchable problems
 Researchable problems imply the possibility of empirical investigation
QUANTITATIVE RESEARCH PROBLEMLESSON
3
14

 What are the achievement and social skill differences between children
attending an academically or socially oriented pre-school program?
 What is the relationship between teachers‘ knowledge of assessment methods
and their use of them?
7. Researchable and non-researchable problems
 Non-researchable problems include explanations of how to do something, vague
propositions, and value-based concerns
- Is democracy a good form of government?
- Should values clarification be taught in public schools?
- Can crime be prevented?
- Should physical education classes be dropped from the high school
curriculum?
QUANTITATIVE VS QUALITATIVE
Quantitative problems Qualitative problems
– Specific - General
– Closed - Open
– Static - Evolving
– Outcome oriented - Process oriented
– Use of specific variables
(Copyright, Allyn & Bacon 2008)
SOURCES OF RESEARCH PROBLEMS
– Personal interests and experiences
 The use of formative tests in a statistics class
 The use of technology in a research class
– Deductions from theory
 The effectiveness of math manipulative
 The effectiveness of a mastery approach to learning research
– Replication of studies
 Checking the findings of a major study
 Checking the validity of research findings with different subjects
 Checking trends or changes over time
15

 Checking important findings using different methodologies
 Clarification of contradictory results
Quantitative Research Problems
Identifies three specific elements
– The type of research design
– The variables of interest and the relationships between or among these variables
– The subjects involved in the study
Guidelines in Choosing a Research Topic
1. Interest in the Subject Matter
2. Availability of information
3. Timeliness and relevance of the topic
4. Limitation on the subject
5. Personal resources
Research Topics to be avoided
1. Controversial topics
- These are topics that depend greatly on the writer‘s opinion, which tend to be biased or
prejudicial. Facts cannot support topics like these.
2. Highly technical subjects
- For a beginner, researching on topics that require an advance study, technical
knowledge, and vast experience is a very difficult.
3. Hard-to-investigate subjects
- A topic or a subject is hard to investigate if there is no available data or reading materials
about it and if such materials are not-up-date or obsolete.
4. Too broad subjects
- A subject or a topic that are too broad will prevent the researcher from giving a
concentrated or in –depth analysis of the subject matter of the research paper.
5. Too narrow subjects
- The subjects are so limited or specific that an extensive or thorough searching or reading
for information about the subject is necessary.
6. Vague subjects
- Choosing topics like these will prevent you from having a clear insights or focus on your
study. For instance, titles beginning with indefinite adjectives such as several, many,
some, etc., as in ―Some Remarkable Traits of a Ilocano‖ Several People‘s Comments on
16

the Extra Judicial Killings,‖ are vague enough to decrease the readers‘ interest and
curiosity.
WRITING A RESEARCH TITLE
When writing a research paper title, authors should realize that despite being repeatedly
warned against it, most people do indeed fall prey to ―judging a book by its cover.‖ This cognitive
bias tends to make readers considerably susceptible to allowing the research paper title to function
as the sole factor influencing their decision of whether to read or skip a particular paper. Although
seeking the professional assistance of a research paper writing service could help the cause, the
author of the paper stands as the best judge for setting the right tone of his/her research paper.
Readers come across research paper titles in searches through databases and reference
sections of research papers. They deduce what a paper is about and its relevance to them based on
the title. Considering this, it is clear that the title of your paper is the most important determinant of
how many people will read it.
A good research paper title:
 Condenses the paper‘s content in a few words
 Captures the readers‘ attention
 Differentiates the paper from other papers of the same subject area
Three basic tips to keep in mind while writing a title:
o Keep it simple, brief and attractive: The primary function of a title is to provide a precise
summary of the paper‘s content. So keep the title brief and clear. Use active verbs instead of
complex noun-based phrases, and avoid unnecessary details. Moreover, a good title for a
research paper is typically around 10 to 12 words long. A lengthy title may seem unfocused
and take the readers‘ attention away from an important point.
Avoid: Drug XYZ has an effect of muscular contraction for an hour in snails of Achatina
fulcia species
Better: Drug XYZ induces muscular contraction in Achatina fulcia snails
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o Use appropriate descriptive words: A good research paper title should contain key words
used in the manuscript and should define the nature of the study. Think about terms people
would use to search for your study and include them in your title.
Avoid: Effects of drug A on schizophrenia patients: study of a multicenter mixed group
Better: Psychosocial effects of drug A on schizophrenia patients: a multicenter randomized
controlled trial
o Avoid abbreviations and jargon: Known abbreviations such as AIDS, NATO, and so on
can be used in the title. However, other lesser-known or specific abbreviations and jargon
that would not be immediately familiar to the readers should be left out.
Avoid: MMP expression profiles cannot distinguish between normal and early osteoarthritic
synovial fluid
Better: Matrix metalloproteinase protein expression profiles cannot distinguish between
normal and early osteoarthritic synovial fluid
Always write down the hypothesis and then take into consideration these simple tips. This would
help you in composing the best title for your research paper.
SCOPE AND DELIMITATIONS
It is important to narrow down your thesis topic and limit the scope of your study. The
researcher should inform the reader about limits or coverage of the study. The scope identifies the
boundaries of the study in term of subjects, objectives, facilities, area, time frame, and the issues to
which the research is focused.
Sample phrases that help express the scope of the study:
The coverage of this study……….
The study consists of ……..
The study covers the ……….
This study is focus on……..
18

The delimitation of the study is delimiting a study by geographic location, age, sex,
population traits, population size, or other similar considerations. Delimitation is used to make
study better and more feasible and not just for the interest of the researcher. It also identifies the
constraints or weaknesses of your study which are not within the control of the researcher.
Sample phrases that expressed the delimitations of the study
The study does not cover the……
The researcher limited this research to……
This study is limited to………
A hypothesis is a specific statement of prediction. It describes in concrete (rather than
theoretical) terms what you expect will happen in your study. Not all studies have hypotheses.
Sometimes a study is designed to be exploratory. There is no formal hypothesis, and perhaps the
purpose of the study is to explore some area more thoroughly in order to develop some specific
hypothesis or prediction that can be tested in future research. A single study may have one or many
hypotheses.
Actually, whenever the researcher talks about hypothesis, the researcher really thinking
simultaneously about two hypotheses. Let's say that you predict that there will be a relationship
between two variables in your study. The way we would formally set up the hypothesis test is to
formulate two hypothesis statements, one that describes your prediction and one that describes all
the other possible outcomes with respect to the hypothesized relationship. Your prediction is that
variable A and variable B will be related (you don't care whether it's a positive or negative
relationship). Then the only other possible outcome would be that variable A and variable B
are not related. Usually, we call the hypothesis that you support (your prediction)
the alternative hypothesis, and we call the hypothesis that describes the remaining possible
outcomes the null hypothesis. Sometimes we use a notation like HA or H1 to represent the
alternative hypothesis or your prediction, and HO or H0 to represent the null case. You have to be
careful here, though. In some studies, your prediction might very well be that there will be no
HYPOTHESESLESSON
4
19

difference or change. In this case, you are essentially trying to find support for the null hypothesis
and you are opposed to the alternative.
If your prediction specifies a direction, and the null therefore is the no difference prediction
and the prediction of the opposite direction, we call this a one-tailed hypothesis. For instance, let's
imagine that you are investigating the effects of a new employee training program and that you
believe one of the outcomes will be that there will be less employee absenteeism. Your two
hypotheses might be stated something like this:
The null hypothesis for this study is:
HO: As a result of the XYZ company employee training program, there will either be no significant
difference in employee absenteeism or there will be a significant increase.
which is tested against the alternative hypothesis:
HA: As a result of the XYZ company employee training program, there will be a
significant decrease in employee absenteeism.
In the figure on the left, we see this situation
illustrated graphically. The alternative hypothesis --
your prediction that the program will decrease
absenteeism -- is shown there. The null must account
for the other two possible conditions: no difference,
or an increase in absenteeism. The figure shows a
hypothetical distribution of absenteeism differences.
We can see that the term "one-tailed" refers to the tail of the distribution on the outcome variable.
When your prediction does not specify a direction, we say you have a two-tailed hypothesis. For
instance, let's assume you are studying a new drug treatment for depression. The drug has gone
through some initial animal trials, but has not yet been tested on humans. You believe (based on
theory and the previous research) that the drug will have an effect, but you are not confident enough
to hypothesize a direction and say the drug will reduce depression (after all, you've seen more than
enough promising drug treatments come along that eventually were shown to have severe side
effects that actually worsened symptoms). In this case, you might state the two hypotheses like this:
The null hypothesis for this study is:
20

HO: As a result of 300mg./day of the ABC drug, there will be no significant difference in
depression.
which is tested against the alternative hypothesis:
HA: As a result of 300mg./day of the ABC drug, there will be a significant difference in depression.
The figure on the right illustrates this two-tailed
prediction for this case. Again, notice that the term
"two-tailed" refers to the tails of the distribution for
your outcome variable.
The important thing to remember about stating
hypotheses is that you formulate your prediction
(directional or not), and then you formulate a second hypothesis that is mutually exclusive of the
first and incorporates all possible alternative outcomes for that case. When your study analysis is
completed, the idea is that you will have to choose between the two hypotheses. If your prediction
was correct, then you would (usually) reject the null hypothesis and accept the alternative. If your
original prediction was not supported in the data, then you will accept the null hypothesis and reject
the alternative. The logic of hypothesis testing is based on these two basic principles:
the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible
outcomes the testing of these so that one is necessarily accepted and the other rejected
(https://www.socialresearchmethods.net/kb/hypothes.php)
21

Name: ____________________________________ Score: _____________
CHECK YOUR KNOWLEDGE (Short Answer Question) (2 POINTS EACH)
_______________________1. typically a rather general overview of the problem with just
enough information about the scope and purpose of the study
to provide an initial understanding of the research
_______________________2. more specific, focused statements and questions that
communicate in greater detail the nature of the study
_______________________3. include explanations of how to do something, vague
propositions, and value-based concerns.
_______________________4. These are topics that depend greatly on the writer‘s opinion,
which tend to be biased or prejudicial. Facts cannot support
topics like these..
_______________________5. For a beginner, researching on topics that require an advance
study, technical knowledge, and vast experience is a very
difficult.
_______________________6. A topic or a subject is hard to investigate if there is no
available data or reading materials about it and if such
materials are not-up-date or obsolete.
_______________________7. A subject or a topic that are too broad will prevent the
researcher from giving a concentrated or in –depth analysis of
the subject matter of the research paper.
_______________________8. Choosing topics like these will prevent you from having a
clear insights or focus on your study.
_______________________9. It describes in concrete (rather than theoretical) terms what
you expect will happen in your study.
_______________________10. If your prediction specifies a direction, and the null therefore
is the no difference prediction and the prediction of the
opposite direction, we call this a .
Guidelines in Choosing a Research Topic
_______________________1.
_______________________2.
_______________________3.
_______________________4.
22

Name: ____________________________________ Score: _____________
GROUP WORK
List down at least three major problems and with the statement of the problems.
(Discus it within the group)
Write down the reason behind why you choose that research topic.
INDIVIDUAL WORK: Let you imagination do it!
What immediately comes to your mind the moment you hear these two words: PROBLEM
and QUESTION? How would you compare and contrast the two? In the space below, make an
appropriate diagram to show their similarities and differences.
23

http://universalteacher.com/1/criteria-for-selecting-a-research-problem/
https://www.socialresearchmethods.net/kb/hypothes.php
http://www.editage.com/insights/3-basic-tips-on-writing-a-good-research-paper-title
limitations
24

Introduction
A literature review is an evaluative report of information found in the literature related to
your selected area of study. The review should describe, summarize, evaluate and clarify this
literature. It should give a theoretical base for the research and help you (the author) determine the
nature of your research. Works which are irrelevant should be discarded and those which are
peripheral should be looked at critically.
A literature review is more than the search for information, and goes beyond being a
descriptive annotated bibliography. All works included in the review must be read, evaluated and
analyzed (which you would do for an annotated bibliography), but relationships between the
literature must also be identified and articulated, in relation to your field of research.
"In writing the literature review, the purpose is to convey to the reader what knowledge and ideas
have been established on a topic, and what their strengths and weaknesses are. The literature review
must be defined by a guiding concept (e.g. your research objective, the problem or issue you are
discussing or your argumentative thesis). It is not just a descriptive list of the material available, or a
set of summaries.
1. Enumerate the purposes of review of related literature;
2. Familiarize themselves with the review or related literature in a quantitative research;
3. Make a graphical presentation of the systematic review of related literature;
4. Trace the steps of systematic review of literature;
5. Differentiate meta-analysis from other Literature-review methods;
6. Compare and contrast these two referencing styles: APA and MLA;
7. Document their research paper with their chosen referencing style; and
8. Practice the ethical standards in writing their literature-review results.
Module
3
LEARNING FROM OTHERS AND
REVIEWING THE LITERATURE
25

The learner demonstrates understanding to:
1. Select, cite, and synthesize judiciously related literature and use sources according to ethical
standards.
2. Formulate clearly conceptual framework, research hypotheses (if appropriate), and define
terms used in study.
3. Present objectively written review of related literature and conceptual framework.
4.
5.
6.
What is Review of Related Literature?
While the research problem is still being conceptualized, the researcher must already start
reviewing literature. In identifying and defining the research problem, the researcher must be able to
show evidences that the problem really exists and is worth investigating. It is important that the
researcher knows what is already known about the problem or what earlier researchers have found
about it and what questions still need to be answered before the research questions or objectives are
finalized.
Theories which the researchers use to explain the existence of a research problem and used
as bases in analyzing relationships between variables can be generated from reference books on
theories or from related studies. The researcher therefore, must have already read adequate literature
at the start of the research activity.
Purpose of Review of Related Literature (RRL)
1. It helps the researcher identify and define a research problem
2. It helps justify the need for studying a problem.
3. It prevents unnecessary duplication of a study
4. It can be a source of a theoretical basis for the study
5. It enables the researcher to learn how to conceptualize a research problem and properly
identify and operationally define study variables
6. It helps formulate and refine research instruments
7. It provides lesson for data analysis and interpretation.
REVIEW OF RELATED LITERATURE (RRL)LESSON
5
26

Styles or Approaches of RRL or Review of Related Literature
1. Traditional Review of Literature
A "traditional" literature review provides an overview of the research findings on
particular topics. A traditional literature is written by examining a body of published
work, then writing a critical summary (an impressionistic overview) of the body of
literature. The purpose of a literature review is making clear for a reader what the research
collectively indicates with regard to a particular issue or question.
Traditional review is of different types that are as follows:
1. Conceptual review – analysis of concepts or ideas to give meaning to some national
or world issues.
2. Critical review – focuses on theories or hypotheses and examines meanings and
results of their application to situation.
3. State-of-the-Art review – makes the researcher deal with the latest research studies
on the subject.
4. Expert review – encourages a well-known expert to do the RRL because of the
influence of certain ideology, paradigm, or belief on him/her.
5. Scoping review – prepares a situation for a future research work in the form of
project making about community development, government policies, and health
services, among others.
2. Systematic Review of Literature
Systematic reviews aim to find as much as possible of the research relevant to the particular
research questions, and use explicit methods to identify what can reliably be said on the basis of
these studies. Methods should not only be explicit but systematic with the aim of producing
varied and reliable results. Such reviews then go on to synthesize research findings in a form
which is easily accessible to those who have to make policy or practice decisions. In this way,
systematic reviews reduce the bias which can occur in other approaches to reviewing research
evidence.
The following table shows the way several books on RRL. Compare and contrast the two
styles of RRL.
Standards Traditional Review Systematic Review
Purpose To have a thorough and clear
understanding of the field
To meet a certain objective based on
specific research questions
Scope Comprehensive, wide picture Restricted focus
Review Design Indefinite plan, permits creative and
exploratory plan
Viewable process and paper trail
Choice of studies Purposeful selection by the reviewer Prepared standards for studies selection
27

Standards Traditional Review Systematic Review
Nature of studies Inquiry-based techniques involving
several studies
Wide and thorough search for all
studies
Quality appraisal Reviewers views Assessment checklists
Summary Narrative Graphical and short summary
answers
Referencing is important
1. It shows where you got information from (you are not making up)
2. It acknowledges the contribution of other people.
3. It helps other people find source you found if they want more detail.
4. It stops you being accused of plagiarism
5. It allows people to check the accuracy of your interpretation of other people‘s work
It is not just referencing that is important it is also the accuracy of the referencing and the
consistent use of a style. There are two places in research chapter where referencing is placed: as
cited in Chapter I and in the Reference List or Bibliography.
REFERENCINGLESSON
6
28

References Cited or Reference List
29

Reference List: Basic Rules
Your references should begin on a new page separate from the text of the essay; label this
page References (with no quotation marks, underlining, etc.), centered at the top of the page. It
should be double-spaced just like the rest of your essay.
Basic Rules
1. All lines after the first line of each entry in your reference list should be indented or
make hanging 0.5 inch from the left margin.
2. Authors' names are inverted (last name first); give the last name and initials for all
authors of a particular work unless the work has more than six authors. If the work has
more than six authors, list the first six authors and then use et al. after the sixth author's
name to indicate the rest of the authors.
3. Reference list entries should be alphabetized by the last name of the first author of each
work.
4. If you have more than one article by the same author, single- author references or
multiple-author references with the exact same authors in the exact same order are listed in
order by the year of publication, starting with the earliest.
5. When referring to any work that is NOT a journal, such as a book, article, or Web
page, capitalize only the first letter of the first word of a title and subtitle, the first word after
a colon or a dash in the title, and proper nouns. Do not capitalize the first letter
of the second word in a hyphenated compound word.
6. Capitalize all major words in journal titles.
7. Italicize titles of longer works such as books and journals.
8. Do not italicize, underline, or put quotes around the titles of shorter works, such as
journal articles or essays in edited collections.
The following rules for handling works by a single author or multiple authors apply to all APA-
style references in your reference list, regardless of the type of work (book, article, electronic
resource, etc.)
Single Author
Last name first, followed by author initials.
Berndt, T. J. (2002). Friendship quality and social development. Current
Directions in Psychological Science, 11, 7-10.
33

Two Authors
List by their last names and initials. Use the ampersand instead of "and."
Wegener, D. T., & Petty, R. E. (1994). Mood management across affective
states: The hedonic contingency hypothesis. Journal of Personality & Social
Psychology, 66, 1034-1048.
Three to Six Authors
List by last names and initials; commas separate author names, while the last author name is
preceded again by ampersand.
Kernis, M. H., Cornell, D. P., Sun, C. R., Berry, A., & Harlow, T. (1993).
There's more to self-esteem than whether it is high or low: The importance of stability of
self-esteem. Journal of Personality and Social Psychology, 65, 1190-1204.
More Than Six Authors
If there are more than six authors, list the first six as above and then "et al.," which stands for "and
others." Remember not to place a period after "et" in "et al."
Harris, M., Karper, E., Stacks, G., Hoffman, D., DeNiro, R., Cruz, P., et al.
(2001). Writing labs and the Hollywood connection. Journal of Film
and Writing, 44(3), 213-245.
Two or More Works by the Same Author in the Same Year
If you are using more than one reference by the same author (or the same group of authors listed in
the same order) published in the same year, organize them in the reference list alphabetically by the
title of the article or chapter. Then assign letter suffixes to the year. Refer to these sources in your
essay as they appear in your reference list, e.g.: "Berdnt (1981a) makes similar claims...―
Berndt, T. J. (1981a). Age changes and changes over time in prosocial intentions
and behavior between friends. Developmental Psychology, 17, 408-416.
Berndt, T. J. (1981b). Effects of friendship on prosocial intentions and
behavior. Child Development, 52, 636-643.
Reference List: Articles in Periodicals
Basic Form
APA style dictates that authors are named last name followed by initials; publication year goes
between parentheses, followed by a period. The title of the article is in sentence-case, meaning only
34

the first word and proper nouns in the title are capitalized. The periodical title is run in title case,
and is followed by the volume number which, with the title, is also italicized or underlined.
Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of
Periodical, volume number (issue number), pages.
Article in Journal Paginated by Volume
Journals that are paginated by volume begin with page one in issue one, and continue numbering
issue two where issue one ended, etc.
Harlow, H. F. (1983). Fundamentals for preparing psychology journal articles.
Journal of Comparative and Physiological Psychology, 55, 893-896.
Article in Journal Paginated by Issue
Journals paginated by issue begin with page one every issue; therefore, the issue number gets
indicated in parentheses after the volume. The parentheses and issue number are not italicized or
underlined.
Scruton, R. (1996). The eclipse of listening. The New Criterion, 15(30), 5-13.
Article in a Magazine
Henry, W. A., III. (1990, April 9). Making the grade in today's schools. Time,
135, 28-31.
Article in a Newspaper
Unlike other periodicals, p. or pp. precedes page numbers for a newspaper reference in APA style.
Single pages take p., e.g., p. B2; multiple pages take pp., e.g., pp. B2, B4 or pp. C1, C3-C4.
Schultz, S. (2005, December 28). Calls made to strengthen state energy policies.
The Country Today, pp. 1A, 2A.
Letter to the Editor
Moller, G. (2002, August). Ripples versus rumbles [Letter to the editor].
Scientific American, 287(2), 12.
Review
Baumeister, R. F. (1993). Exposing the self-knowledge myth [Review of the book The self-
knower: A hero under control ]. Contemporary Psychology, 38, 466-467.
35

Multivolume Work
Wiener, P. (Ed.). (1973). Dictionary of the history of ideas (Vols. 1-4). New
York: Scribner's.
Encyclopedia Americana (2008) Electricity (Vol. 3) New York: Phoenix Pub.
An Entry in An Encyclopedia with author
Bergmann, P. G. (1993). Relativity. In The New Encyclopedia Britannica (Vol.
26, pp. 501-508). Chicago: Encyclopedia Britannica.
Thesis / Dissertation Abstract
Yoshida, Y. (2001). Essays in urban transportation (Doctoral dissertation,
Boston College, 2001). Dissertation Abstracts International, 62, 7741A.
Government Document
National Institute of Mental Health. (1990). Clinical training in serious mental
illness (DHHS Publication No. ADM 90-1679). Washington, DC: U.S.
Government Printing Office.
Report From a Private Organization
American Psychiatric Association. (2000). Practice guidelines for the treatment
of patients with eating disorders (2nd ed.). Washington, D.C.: Author.
Conference Proceedings
Schnase, J.L., & Cunnius, E.L. (Eds.). (1995). Proceedings from CSCL '95: The
First International Conference on Computer Support for Collaborative
Learning. Mahwah, NJ: Erlbaum.
Reference List: Electronic Sources
Article From an Online Periodical
Online articles follow the same guidelines for printed articles. Include all information the
online host makes available, including an issue number in parentheses.
36

Author, A. A., & Author, B. B. (Date of publication). Title of article. Title of
Online Periodical, volume number(issue number if available). Retrieved month day, year,
from http://www.someaddress.com/full/url/
Bernstein, M. (2002). 10 tips on writing the living Web. A List Apart: For People
Who Make Websites, 149. Retrieved May 2, 2006, from
http://www.alistapart.com/articles/writeliving
Online Scholarly Journal Article
Author, A. A., & Author, B. B. (Date of publication). Title of article. Title of
Journal, volume number. Retrieved month day, year, from
http://www.someaddress.com/full/url/
Kenneth, I. A. (2000). A Buddhist response to the nature of human rights. Journal
of Buddhist Ethics, 8. Retrieved February 20, 2001, from
http://www.cac.psu.edu/jbe/twocont.html
Reference List: Other Non-Print Sources
Interviews, Email, and Other Personal Communication
No personal communication is included in your reference list; instead, parenthetically cite the
communicators name, the fact that it was personal communication, and the date of the
communication in your main text only.
(E. Robbins, pers. comm., January 4, 2001).
A. P. Smith also claimed that many of her students had difficulties with APA style (pers. comm.,
November 3, 2002).
Motion Picture
Basic reference list format:
Producer, P. P. (Producer) & Director, D.D. (Director). (Date of publication). Title
of motion picture [Motion picture]. Country of origin: Studio or distributor.
Note: If a movie or video tape is not available in wide distribution, add the following to your
citation after the country of origin: (Available from Distributor name, full address and zip code).
A Motion Picture or Video Tape with International or National Availability
Smith, J.D. (Producer) & Smithee, A.F. (Director). (2001). Really big disaster
movie [Motion picture]. United States: Paramount Pictures.
37

A Motion Picture or Video Tape with Limited Availability
Harris, M. (Producer), & Turley, M. J. (Director). (2002). Writing labs: A history
[Motion picture]. (Available from Purdue University Pictures, 500 Oval
Drive, West Lafayette, IN 47907)
Television Broadcast or Series Episode
Producer, P. P. (Producer). (Date of broadcast or copyright). Title of broadcast
[Television broadcast or Television series]. City of origin: Studio or
distributor.
Single Episode of a Television Series
Writer, W. W. (Writer), & Director, D.D. (Director). (Date of publication). Title
of episode [Television series episode]. In P. Producer (Producer), Series
title. City of origin: Studio or distributor.
A Television Series
Bellisario, D.L. (Producer). (1992). Exciting action show [Television series].
Hollywood: American Broadcasting Company.
Music Recording
Songwriter, W. W. (Date of copyright). Title of song [Recorded by artist if
different from song writer]. On Title of album [Medium of recording].
Location: Label. (Recording date if different from copyright date).
Taupin, B. (1975). Someone saved my life tonight [Recorded by Elton John]. On
Captain fantastic and the brown dirt cowboy [CD]. London: Big Pig
Music Limited.
38

1.
Introduction
Research Ethics is the highest ethical standards shall be applied to basic education research.
Whether or not human subjects are involved, researchers must ensure that the study will not cause
people harm. Research participants should have informed consent, must be cognizant about the
general purpose of the study and should not be exposed to unusual risk. Consistent with the
principle of excellence, integrity also requires honesty and accuracy in the collection, analysis and
reporting of data.
How do you know if it’s ethical or unethical?
Webster‘s New World Dictionary defines ‗ethical‘ (behavior) as ‗conforming to the
standards of conduct of a given profession or group.’ What researchers consider to be ethical,
therefore, is largely a matter of agreement among them.
Three very important research ethical issues
(1) Protecting participants from harm
Meaning: Participants in a research study are protected from physical or psychological harm,
discomfort, or danger that may arise
Logic: Any sort of study that is likely to cause lasting, or even serious harm or discomfort to any
participant should not be conducted unless it has great benefits
Tip: Obtain the consent of the participants if they may be exposed to any risk through a form
Role of DO: ‗Almost all educational research involves activities that are within the customary, usual
procedures of schools or other agencies and as such involve little or no risk‘
(2) Ensuring confidentiality of data
Meaning: Researchers should make sure that no one else (other than perhaps a few key research
assistants) has access to the data
RESEARCH ETHICSLESSON
7
39

Logic: All subjects should be assured that any data collected from or about them will be held in
confidence
Tips:
(a) Whenever possible, remove all names from all data collection forms. How? Assign numbers
to forms, or answer anonymously.
(b) Do not use the names of the participants from any publications that describe the research.
(c) Allow the participants to withdraw, or information about them not be used.
Warning: ‗Sometimes, however, it is important for a study to identify individual subjects.‘ Role of
DO: ‗Almost all educational research involves activities that are within the customary, usual
procedures of schools or other agencies and as such involve little or no risk‘
(3) Subject deception
Meaning: ‗no full or erroneous information‘
Logic: It is often difficult to find naturalistic situations in which certain behaviors occur frequently
Warning: Many studies cannot be carried out unless some deception of subjects take place; but it
would bring questions on the reputation of the scientific community, or to the researcher himself.
Tip:
a. Whenever possible, do not deceive.
b. If no alternatives are possible, weigh the study‘s benefits to prospective scientific,
educational and applied value
c. If participants are deceived, ensure sufficient explanation as soon as possible.
Other unethical activities in research
1. Publishing an article in two different journals without informing the editor
2. Failing to inform your collaborator that your are filing a patent of the research
40

3. Writing the name of your colleague as one of the writers even though he did not participate
in any part of the conduct of the research
4. Discussing with your colleagues data from the paper that you are reviewing for a journal
5. Trimming outlines from a data set without providing sufficient justification
6. Using inappropriate statistical techniques in order to obtain favorable results
7. Making the results of a study publicly known without first giving the peers the opportunity
to review the work
8. Failing to acknowledge the contributions of other people in the field (RRL)
9. Making derogatory comments and personal attacks in your review of author‘s submission
10.
A conceptual framework is an analytical tool with several variations and contexts. It is
used to make conceptual distinctions and organize ideas. Strong conceptual frameworks capture
something real and do this in a way that is easy to remember and apply.
• Present a schematic diagram of the paradigm of the research and discuss the relationships of
the elements/variables therein
• Identify and discuss the variables related to the problem
• Can use the Input-Process-Output (IPO) Model or the Dependent-Independent-Moderator
Model
• The conceptual framework serves as basis for the research paradigm and objectives of the
project
CONCEPTUAL FRAMEWORKLESSON
8
41

In other words, the conceptual framework is the researcher‘s understanding of how the
particular variables in his study connect with each other. Thus, it identifies the variables required in
the research investigation. It is the researcher‘s ―map‖ in pursuing the investigation.
As McGaghie et al. (2001) put it: The conceptual framework ―sets the stage‖ for the
presentation of the particular research question that drives the investigation being reported based on
the problem statement. The problem statement of a thesis presents the context and the issues that
caused the researcher to conduct the study.
The conceptual framework lies within a much broader framework called theoretical
framework. The latter draws support from time-tested theories that embody the findings of many
researchers on why and how a particular phenomenon occurs.
Step by Step Guide on How to Make the Conceptual Framework
Before you prepare your conceptual framework, you need to do the following things:
1. Choose your topic. Decide on what will be your research topic. The topic should be within
your field of specialization.
42

2. Do a literature review. Review relevant and updated research on the theme that you decide
to work on after scrutiny of the issue at hand. Preferably use peer-reviewed and well-known
scientific journals as these are reliable sources of information.
3. Isolate the important variables. Identify the specific variables described in the literature
and figure out how these are related. Some abstracts contain the variables and the salient
findings thus may serve the purpose. If these are not available, find the research paper‘s
summary. If the variables are not explicit in the summary, get back to the methodology or
the results and discussion section and quickly identify the variables of the study and the
significant findings. Read the TSPU Technique on how to skim efficiently articles and get to
the important points without much fuss.
4. Generate the conceptual framework. Build your conceptual framework using your mix of
the variables from the scientific articles you have read. Your problem statement serves as a
reference in constructing the conceptual framework. In effect, your study will attempt to
answer a question that other researchers have not explained yet. Your research should
address a knowledge gap.
Example
Fig. 1: The research paradigm illustrating the researcher‘s conceptual framework.
Notice that the variables of the study are explicit in the paradigm presented in Figure 1. In the
illustration, the two variables are 1) number of hours devoted in front of the computer, and 2)
43

number of hours slept at night. The former is the independent variable while the latter is the
dependent variable. Both of these variables are easy to measure. It is just counting the number of
hours spent in front of the computer and the number of hours slept by the subjects of the study.
Assuming that other things are constant during the performance of the study, it will be
possible to relate these two variables and confirm that indeed, blue light emanated from computer
screens can affect one‘s sleeping patterns. (Please read the article titled ―Do you know that the
computer can disturb your sleeping patterns?‖ To find out more about this phenomenon) A
correlation analysis will show whether the relationship is significant or not.
Again, review the abstracts carefully. Keep careful notes so that you may track you‘re thought
processes during the research process.
44

Name: ____________________________________ Score: _____________
_______________________1. A literature review is more than the search for information, and
goes beyond being a descriptive _____________.
_______________________2. review provides an overview of the research findings on
particular topics.
_______________________3. analysis of concepts or ideas to give meaning to some national
or world issues.
_______________________4. focuses on theories or hypotheses and examines meanings and
results of their application to situation.
_______________________5. makes the researcher deal with the latest research studies on the
subject.
_______________________6. encourages a well-known expert to do the RRL because of the
influence of certain ideology, paradigm, or belief on him/her.
_______________________7. prepares a situation for a future research work in the form of
project making about community development, government
policies, and health services, among others.
_______________________8. It aim to find as much as possible of the research relevant to
the particular research questions, and use explicit methods to
identify what can reliably be said on the basis of these studies.
_______________________9. The highest ethical standards shall be applied to basic
education research.
_______________________10. Research participants should have informed _______, must be
cognizant about the general _______, of the study and should
not be exposed to unusual _______.
45

Name: ____________________________________ Score: _____________
APA Citation Activity
Directions : If you are unfamiliar with APA citation styles, you may find it helpful to review the
material inside the "Citing sources using APA citation style" folder before beginning this
assessment.
Question 1
Choose the citation that is in proper APA citation format for a book.
a. Jenkins, Henry. Fans, bloggers, and gamers: exploring participatory cultures. New
York: New York University Press, 2006.
b. Jenkins, H. Fans, bloggers, and gamers: exploring participatory cultures. New York
University Press, New York. 2006.
c. Jenkins, H. (2006). Fans, bloggers, and gamers: exploring participatory culture.
New York: New York University Press.
d. Jenkins, Henry. (2006). Fans, Bloggers, and Gamers: Exploring Participatory Culture.
New York UP: New York.
Question 2
Choose the citation that is in proper APA citation for a chapter from a book (no named author of
chapter).
a. Cook, V.J.(2004). "Flava'N Gorillaz: Pop Group Names." In Accomodating Brocolli in
the Cemetary, (pp. 21-22). Simon and Schuster: New York.
b. Flava 'n Gorillaz: Pop group names. (2004). In V.J. Cook, Accomodating Brocolli in the
Cemetary (pp. 21-22). New York: Simon and Schuster.
c. Flava 'n Gorillaz: Pop group names. In Cook, V.J. Accomodating Brocolli in the
Cemetary (pp. 21-22). New York: Simon and Schuster, 2004.
d. V.J. Cook. 2004. "Flava'n Gorillaz: Pop group names." In Accomodating Brocolli in the
Cemetary, pp. 21-22. Simon and Schuster: New York.
46

Question 3
Choose the correct APA citation for a newspaper article.
a. Yonke, D. (2008, September 13). Monks on the road for peace: Tibetan Buddhists bring
message that 'happiness is an internal event'. The Blade (Toledo, OH), p. B7.
b. Yonke, David. (2008). "Monks on the road for peace: Tibetan Buddhists bring message that
'happiness is an internal event'." The Blade (Toledo, OH), pp. B7.
c. Yonke, David. Monks on the road for peace: Tibetan Buddhists bring message that
'happiness is an internal event'. The Blade, September 13, 2008. p. B7.
d. Yonke, David. "Monks on the road for peace: Tibetan Buddhists bring message that
'happiness is an internal event'." The Blade 13 Sept. 2008: B7.
Question 4
Choose the correct APA citation for an article from a library research database.
a. Weickgenannt, Nicole. (2008). The Nation's Monstrous Women: Wives, Widows and
Witches in Salman Rushdie's Midnight's Children. In Journal of Commonwealth
Literature. 43.2, pp. 65-83. Retrieved October 31, 2008, from Humanities
International Complete http:// 0-search.ebscohost.com.maurice.bgsu.edu/
login.aspx?direct=true&db=hlh&AN=32541323&loginpage=login.asp&site=ehost-
live&scope=site
b. Weickgenannt, Nicole. "The nation's monstrous women: Wives, widows and witches in
Salman Rushdie's Midnight's Children." Journal of Commonwealth Literature 43.2
(June 2008): 65-83. Humanities International Complete. EBSCO. Bowling Green
State University Libraries, Bowling Green, Oh.. 31 Oct. 2008 <http://
0-search.ebscohost.com.maurice.bgsu.edu/
login.aspx?direct=true&db=hlh&AN=32541323&loginpage=login.asp&site=ehost-
live&scope=site>.
c. Weickgenannt, N. The Nation's Monstrous Women: Wives, Widows and Witches in Salman
Rushdie's Midnight's Children. Journal of Commonwealth Literature. 43.2: pp.65-
83. Retrieved October 31, 2008, from Humanities International Complete. (2008,
June).
d. Weickgenannt, N. (2008, June). The nation's monstrous women: Wives, widows and
witches in Salman Rushdie's Midnight's Children. Journal of Commonwealth
Literature, 43(2), 65-83. Retrieved October 31, 2008, from Humanities International
Complete.
47

Question 5
Create an APA citation for this publication:
Article Title: Truly, Madly, Depp-ly
Author: Frank DeCaro
Publication: Advocate
Volume number: 906
Date: January 20, 2004
Pages: 76-77
Source: Gender Studies Database
Date of access: October 31, 2008
hyperlink: <http://0-search.ebscohost.com.maurice.bgsu.edu/
login.aspx?direct=true&db=fmh&AN=GSD0048
Developed by Amy Fyn, Bowling Green State University Libraries, 2008, for LIB225: Information Seeking and Management in Contemporary Society
48

http://libguides.uwf.edu/c.php?g=215199&p=1420520
http://simplyeducate.me/2015/01/05/conceptual-framework-guide/
http://universalteacher.com/1/criteria-for-selecting-a-research-problem/
https://www.socialresearchmethods.net/kb/hypothes.php
http://www.editage.com/insights/3-basic-tips-on-writing-a-good-research-paper-title
limitations
Teaching ACRL‘s 5th Information Literacy Competency Standard: APA Citation Practice Activity
http://libguides.bgsu.edu/c.php?g=227185&p=1507882
49

Introduction
These information‘s are a compiled, resources gathered from an extensive literature review;
much of the information is verbatim from the various web sites. The objective is to familiarize the
readers in terms with the data collection tools, methodology, and sampling. It is important to note
that while quantitative and qualitative data collection methods are different (cost, time, sample size,
etc.), each has value. Most often uses deductive logic, in which researchers start with hypotheses
and then collect data which can be used to determine whether empirical evidence to support that
hypothesis exists.
1. Choose appropriate quantitative research design;
2. Describes sampling procedure and the sample;
3. Plans data collection procedure;
4. Plans data analysis using statistics and hypothesis testing ;
5. Presents written research methodology; and
6. Implements design principles to produce creative work.
1. Describes adequately quantitative research designs, sample, instrument used,
intervention, data collection, and analysis procedures.
2. Apply imaginatively art/design principles to create artwork.
Module
4
UNDERSTANDING DATA AND WAYS TO
SYSTEMATICALLY COLLECT DATA
50

7.
8.
9.
QUANTITATIVE RESEARCH
If the researcher views quantitative design as a continuum, one end of the range represents a
design where the variables are not controlled at all and only observed. Connections amongst
variable are only described. At the other end of the spectrum, however, are designs which include a
very close control of variables, and relationships amongst those variables are clearly established. In
the middle, with experiment design moving from one type to the other, is a range which blends
those two extremes together.
TYPES OF QUANTITATIVE RESEARCH
Quantitative research is a type of empirical investigation. That means the
research focuses on verifiable observation as opposed to theory or logic. Most often
this type of research is expressed in numbers. A researcher will represent and
manipulate certain observations that they are studying. They will attempt to explain
what it is they are seeing and what affect it has on the subject. They will also determine
and what the changes may reflect. The overall goal is to convey numerically what is
being seen in the research and to arrive at specific and observable conclusions.
(Klazema 2014)
Non-Experimental Research Design
Non-experimental research means there is a predictor variable or group of subjects that
cannot be manipulated by the experimenter. Typically, this means that other routes must be used to
draw conclusions, such as correlation, survey or case study. (Kowalczyk 2015)
QUANTITATIVE DATA RESEARCH DESIGNLESSON
9
51

Types of Non-Experimental Research
1. Survey Research
Survey research uses interviews, questionnaires, and sampling polls to get a sense
of behavior with intense precision. It allows researchers to judge behavior and then present
the findings in an accurate way. This is usually expressed in a percentage. Survey research
can be conducted around one group specifically or used to compare several groups. When
conducting survey research it is important that the people questioned are sampled at
random. This allows for more accurate findings across a greater spectrum of respondents.
Remember!
 It is very important when conducting survey research that you work with
statisticians and field service agents who are reputable. Since there is a high level
of personal interaction in survey scenarios as well as a greater chance for
unexpected circumstances to occur, it is possible for the data to be affected. This
can heavily influence the outcome of the survey.
 There are several ways to conduct survey research. They can be done in person,
over the phone, or through mail or email. In the last instance they can be self-
administered. When conducted on a single group survey research is its own
category.
2. Correlational Research
Correlational research tests for the relationships between two variables. Performing
correlational research is done to establish what the effect of one on the other might be and
how that affects the relationship.
Remember!
 Correlational research is conducted in order to explain a noticed occurrence. In
correlational research the survey is conducted on a minimum of two groups. In
most correlational research there is a level of manipulation involved with the
specific variables being researched. Once the information is compiled it is then
52

analyzed mathematically to draw conclusions about the effect that one has on the
other.
 Correlation does not always mean causation. For example, just because two data
points sync doesn‘t mean that there is a direct cause and effect relationship.
Typically, you should not make assumptions from correlational research alone.
3. Descriptive
As stated by Good and Scates as cited by Sevilla (1998), the descriptive method is
oftentimes as a survey or a normative approach to study prevailing conditions.
Remember!
 Descriptive method involves the discretion, recognition, analysis and interpretation
of condition that currently exist. Moreover, according to Gay (2007) Descriptive
research design involves the collection of the data in order to test hypotheses or to
answer questions concerning the current status of the subject of the study. It
determines and reports the way things are.
4. Comparative
Comparative researchers examine patterns of similarities and differences across a
moderate number of cases. The typical comparative study has anywhere from a handful to
fifty or more cases. The number of cases is limited because one of the concerns of
comparative research is to establish familiarity with each case included in a study. (Ragin,
Charles 2015)
 Like qualitative researchers, comparative researchers consider how the different
parts of each case - those aspects that are relevant to the investigation - fit together;
they try to make sense of each case. Thus, knowledge of cases is considered an
important goal of comparative research, independent of any other goal.
53

5. Ex Post Facto
According to Devin Kowalczyk, that Ex post facto design is a quasi-experimental
study examining how an independent variable, present prior to the study, affects a dependent
variable.
Remember!
 A true experiment and ex post facto both are attempting to say: this independent variable is
causing changes in a dependent variable. This is the basis of any experiment - one variable is
hypothesized to be influencing another. This is done by having an experimental group and a
control group. So if you're testing a new type of medication, the experimental group gets the
new medication, while the control group gets the old medication. This allows you to test the
efficacy of the new medication. . (Kowalczyk 2015)
Experimental Research
Though questions may be posed in the other forms of research, experimental research is
guided specifically by a hypothesis. Sometimes experimental research can have several
hypotheses. A hypothesis is a statement to be proven or disproved. Once that statement is made
experiments are begun to find out whether the statement is true or not. This type of research is the
bedrock of most sciences, in particular the natural sciences. Quantitative research can be exciting
and highly informative. It can be used to help explain all sorts of phenomena. The best
quantitative research gathers precise empirical data and can be applied to gain a better
understanding of several fields of study. (Williams 2015)
Types of Experimental research
1. Quasi-experimental Research
Design involves selecting groups, upon which a variable is tested without any
random pre-selection process. For example, to perform an educational experiment, a class
might be arbitrarily divided by alphabetical selection or by seating arrangement. The
division is often convenient especially in an educational situations cause a little disruption
as possible.
54

2. True Experimental Design
According to Yolanda Williams (2015) that a true experiment is a type of
experimental design and is thought to be the most accurate type of experimental research.
This is because a true experiment supports or refutes a hypothesis using statistical analysis.
A true experiment is also thought to be the only experimental design that can establish cause
and effect relationships. So, what makes a true experiment?
There are three criteria that must be met in a true experiment
1. Control group and experimental group
2. Researcher-manipulated variable
3. Random assignment
4.
5.
Developing a research instruments
Before the researchers collect any data from the respondents, the young researchers will need to
design or devised new research instruments or they may adopt it into the other researches (the tools
they will use to collect the data).
If the researcher/s is planning to carry out interviews or focus groups, the young researchers will
need to plan an interview schedule or topic guide. This is a list of questions or topic areas that all
the interviewers will use. Asking everyone the same questions means that the data you collect will
be much more focused and easier to analyze.
If the group wants to carry out a survey, the young researchers will need to design a questionnaire.
This could be on paper or online (using free software such as Survey Monkey). Both approaches
have advantages and disadvantages.
If the group is collecting data from more than one ‗type‘ of person (such as young people and
teachers, for example), it may well need to design more than one interview schedule or
INSTRUMENT DEVELOPMENTLESSON
10
55

questionnaire. This should not be too difficult as the young researchers can adapt additional
schedules or questionnaires from the original.
When designing the research instruments ensure that:
 they start with a statement about.
 the focus and aims of the research project
 how the person‘s data will be used (to feed into a report?)
 confidentiality
 how long the interview or survey will take to complete.
 Usage of appropriate language
 every question must be brief and concise.
 any questionnaires use appropriate scales. For young people ‗smiley face‘ scales can work
well
REMEMBER!
Any questionnaires ask people for any relevant information about themselves, such as their
gender or age, if relevant. Don‘t ask for so much detail that it would be possible to identify
individuals though, if you have said that the survey will be anonymous.
The Instrument
Instrument is the generic term that researchers use for a measurement device (survey, test,
questionnaire, etc.). To help distinguish between instrument and instrumentation, consider that
the instrument is the device and instrumentation is the course of action (the process of developing,
testing, and using the device).
Instruments fall into two broad categories, researcher-completed and subject-completed,
distinguished by those instruments that researchers administer versus those that are completed by
participants. Researchers chose which type of instrument, or instruments, to use based on the
research question. Examples are listed below:
Researcher-completed Instruments Subject-completed Instruments
Rating scales Questionnaires
56

Interview schedules/guides Self-checklists
Tally sheets Attitude scales
Flowcharts Personality inventories
Performance checklists Achievement/aptitude tests
Time-and-motion logs Projective devices
Observation forms Sociometric devices
Usability
Usability refers to the ease with which an instrument can be administered, interpreted by the
participant, and scored/interpreted by the researcher. Example usability problems include:
Students are asked to rate a lesson immediately after class, but there are only a few minutes before
the next class begins (problem with administration).
Students are asked to keep self-checklists of their after school activities, but the directions are
complicated and the item descriptions confusing (problem with interpretation).
Teachers are asked about their attitudes regarding school policy, but some questions are worded
poorly which results in low completion rates (problem with scoring/interpretation).
Validity and reliability concerns (discussed below) will help alleviate usability issues. For now, we
can identify five usability considerations:
How long will it take to administer?
Are the directions clear?
How easy is it to score?
Do equivalent forms exist?
Have any problems been reported by others who used it?
57

Validity
Validity is the extent to which an instrument measures what it is supposed to measure and performs
as it is designed to perform. It is rare, if nearly impossible, that an instrument be 100% valid, so
validity is generally measured in degrees. As a process, validation involves collecting and analyzing
data to assess the accuracy of an instrument. There are numerous statistical tests and measures to
assess the validity of quantitative instruments, which generally involves pilot testing. The remainder
of this discussion focuses on external validity and content validity.
External validity is the extent to which the results of a study can be generalized from a sample to a
population. Establishing eternal validity for an instrument, then, follows directly from sampling.
Recall that a sample should be an accurate representation of a population, because the total
population may not be available. An instrument that is externally valid helps obtain population
generalizability, or the degree to which a sample represents the population.
Content validity refers to the appropriateness of the content of an instrument. In other words, do the
measures (questions, observation logs, etc.) accurately assess what you want to know? This is
particularly important with achievement tests. Consider that a test developer wants to maximize the
validity of a unit test for 7th grade mathematics. This would involve taking representative questions
from each of the sections of the unit and evaluating them against the desired outcomes.
Reliability
Reliability can be thought of as consistency. Does the instrument consistently measure what it is
intended to measure? It is not possible to calculate reliability; however, there are four general
estimators that you may encounter in reading research:
Inter-Rater/Observer Reliability: The degree to which different raters/observers give consistent
answers or estimates.
Test-Retest Reliability: The consistency of a measure evaluated over time.
Parallel-Forms Reliability: The reliability of two tests constructed the same way, from the same
content.
Internal Consistency Reliability: The consistency of results across items, often measured with
Cronbach‘s Alpha.
58

1.
2.
Methodology is the systematic, theoretical analysis of the methods applied to a field of
study. It comprises the theoretical analysis of the body of methods and principles associated with a
branch of knowledge.
Methodology section is one of the parts of a research paper. This part is the core of your
paper as it is a proof that you use the scientific method. Through this section, your study‘s validity
is judged. So, it is very important. Your methodology answers two main questions:
Guided Question to start writing a research methodology:
 How did you collect or generate the data?
 How did you analyze the data?
While writing this section, be direct and precise. Write it in the past tense. Include enough
information so that others could repeat the experiment and evaluate whether the results are
reproducible the audience can judge whether the results and conclusions are valid.
The explanation of the collection and the analysis of your data are very important because;
 Readers need to know the reasons why you chose a particular method or procedure instead
of others.
 Readers need to know that the collection or the generation of the data is valid in the field of
study.
 Discuss the anticipated problems in the process of the data collection and the steps you took
to prevent them.
 Present the rationale for why you chose specific experimental procedures.
 Provide sufficient information of the whole process so that others could replicate your study.
You can do this by: giving a completely accurate description of the data collection equipment
and the techniques. Explaining how you collected the data and analyzed them.
GUIDELINES IN WRITING RESEARCH
METHODOLOGY
LESSON
11
59

Specifically;
 Present the basic demographic profile of the sample population like age, gender, and the
racial composition of the sample. When animals are the subjects of a study, you list their
species, weight, strain, sex, and age.
 Explain how you gathered the samples/ subjects by answering these questions:
- Did you use any randomization techniques?
- How did you prepare the samples?
 Explain how you made the measurements by answering this question.
 What calculations did you make?
 Describe the materials and equipment that you used in the research.
 Describe the statistical techniques that you used upon the data.
The order of the methods section;
1. Describing the samples/ participants.
2. Describing the materials you used in the study
3. Explaining how you prepared the materials
4. Describing the research design
5. Explaining how you made measurements and what calculations you performed
6. Stating which statistical tests you did to analyze the data.
60

Name: ____________________________________ Score: _____________
_______________________1. there is a predictor variable or group of subjects that cannot be
manipulated by the experimenter.
_______________________2. the research focuses on verifiable observation as opposed to
theory or logic.
_______________________3. uses interviews, questionnaires, and sampling polls to get a
sense of behavior with intense precision.
_______________________4. tests for the relationships between two variables. Performing
correlational research is done to establish what the effect of
one on the other might be and how that affects the
relationship.
_______________________5. It is conducted in order to explain a noticed occurrence. In
correlational research the survey is conducted on a minimum
of two groups.
_______________________6. This research method involves the discretion, recognition,
analysis and interpretation of condition that currently exist.
_______________________7. This research examine patterns of similarities and differences
across a moderate number of cases
_______________________8. Though questions may be posed in the other forms of
research, experimental research is guided specifically by a
hypothesis. Sometimes experimental research can have
several hypotheses.
_______________________9. It is a statement to be proven or disproved. Once that
statement is made experiments are begun to find out whether
the statement is true or not.
_______________________10. This research can be exciting and highly informative.
_______________________11. This research design that can establish cause and effect
relationships.
_______________________12. the extent to which an instrument measures what it is
supposed to measure and performs as it is designed to
perform.
_______________________13. refers to the appropriateness of the content of an instrument.
61

Name: ____________________________________ Score: _____________
DIRECTIONS: Make a reflection Relating Reliability and Validity at least 250 words. (25 poits)
Relating Reliability and Validity
Reliability is directly related to the validity of the measure. There are several important
principles. First, a test can be considered reliable, but not valid. Consider the SAT, used as a
predictor of success in college. It is a reliable test (high scores relate to high GPA), though only a
moderately valid indicator of success (due to the lack of structured environment – class attendance,
parent-regulated study, and sleeping habits – each holistically related to success).
Second, validity is more important than reliability. Using the above example, college
admissions may consider the SAT a reliable test, but not necessarily a valid measure of other
quantities colleges seek, such as leadership capability, altruism, and civic involvement. The
combination of these aspects, alongside the SAT, is a more valid measure of the applicant‘s
potential for graduation, later social involvement, and generosity (alumni giving) toward the alma
mater.
Finally, the most useful instrument is both valid and reliable. Proponents of the SAT argue that it is
both. It is a moderately reliable predictor of future success and a moderately valid measure of a
student‘s knowledge in Mathematics, Critical Reading, and Writing.
62

RUBRIC
Criteria Superior (54-60
points)
Sufficient (48-53 points) Minimal (1-47
points)
Unacceptable (0
points)
Depth of
Reflection
(25% of TTL
Points)
___/15
Response demonstrates
an in-depth reflection
on, and personalization
of, the theories,
concepts, and/or
strategies presented in
the course materials to
date. Viewpoints and
interpretations are
insightful and well
supported. Clear,
detailed examples are
provided, as applicable.
Response demonstrates a
general reflection on, and
personalization of, the
theories, concepts, and/or
strategies presented in
the course materials to
interpretations are
supported. Appropriate
examples are provided,
as applicable.
Response
demonstrates a
minimal reflection
on, and
personalization of,
the theories,
concepts, and/or
strategies presented
in the course
materials to date.
Viewpoints and
interpretations are
unsupported or
supported with
flawed arguments.
Examples, when
applicable, are not
provided or are
irrelevant to the
assignment.
Response
demonstrates a lack of
reflection on, or
personalization of, the
theories, concepts,
and/or strategies
presented in the
course materials to
interpretations are
missing,
inappropriate, and/or
unsupported.
Examples, when
applicable, are not
provided.
Required
Components
(25% of TTL
Points)
___/15
Response includes all
components and meets
or exceeds all
requirements indicated
in the instructions. Each
question or part of the
assignment is addressed
thoroughly. All
attachments and/or
additional documents
are included, as
required.
Response includes all
components and meets
all requirements
indicated in the
instructions. Each
question or part of the
assignment is addressed.
All attachments and/or
additional documents are
included, as required.
Response is missing
some components
and/or does not fully
meet the
requirements
indicated in the
instructions. Some
questions or parts of
the assignment are
not addressed. Some
attachments and
additional
documents, if
required, are missing
or unsuitable for the
purpose of the
assignment.
Response excludes
essential components
and/or does not
address the
requirements
indicated in the
instructions. Many
parts of the
assignment are
addressed minimally,
inadequately, and/or
not at all.
Structure
(25% of TTL
Points)
___/15
Writing is clear,
concise, and well
organized with
excellent
sentence/paragraph
construction. Thoughts
are expressed in a
coherent and logical
manner. There are no
more than three
spelling, grammar, or
syntax errors per page
of writing.
Writing is mostly clear,
concise, and well
organized with good
sentence/paragraph
construction. Thoughts
are expressed in a
coherent and logical
manner. There are no
more than five spelling,
grammar, or syntax
errors per page of
writing.
Writing is unclear
and/or disorganized.
Thoughts are not
expressed in a logical
manner. There are
more than five
syntax errors per
page of writing.
Writing is unclear and
disorganized.
Thoughts ramble and
make little sense.
There are numerous
syntax errors
throughout the
response.
63

Evidence and
Practice
(25% of TTL
Points)
___/15
Response shows strong
evidence of synthesis of
ideas presented and
insights gained
throughout the entire
course. The
implications of these
insights for the
respondent's overall
teaching practice are
thoroughly detailed, as
applicable.
Response shows
evidence of synthesis of
ideas presented and
insights gained
course. The implications
of these insights for the
presented, as applicable.
Response shows little
evidence of synthesis
of ideas presented
and insights gained
course. Few
implications of these
insights for the
presented, as
applicable.
Response shows no
evidence of synthesis
of ideas presented and
insights gained
course. No
implications for the
presented, as
applicable.
64

Yadipe University Writing Center School of Foreign Languages
https://yuwritingcenter.wikispaces.com/How+to+Write+the+Methodology+of+a+Research+
Paper
http://people.uwec.edu/piercech/researchmethods/data%20collection%20methods/data%20collectio
n%20methods.htm
http://www.socialresearchmethods.net/kb/sampprob.php
http://www.stat.ncsu.edu/info/srms/survpamphlet.html
http://www.statcan.ca/english/edu/power/ch2/methods/methods.htm
http://www.statisticssolutions.com/quantitative-research-approach/
http://study.com/academy/lesson/true-experiment-definition-examples.html
http://study.com/academy/lesson/non-experimental-and-experimental-research-differences-
advantages-disadvantages.html
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Introduction
Data collection is the process of gathering and measuring information on variables of interest,
in an established systematic fashion that enables one to answer stated research questions, test
hypotheses, and evaluate outcomes. The data collection component of research is common to all
fields of study including physical and social sciences, humanities, business, etc. While methods
vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.
Craddick et.al (2003)
1. collects data using appropriate instruments.
2. presents and interprets data in tabular and graphical forms.
3. uses statistical techniques to analyze data— study of differences and relationships
limited for bivariate analysis.
4. Use descriptive statistics in analyzing data.
The learner is able to;
Gather and analyze data with intellectual honesty, using suitable techniques.
3.
Quantitative Data Analysis
It is a systematic approach to investigations during which numerical data is collected and/or
the researcher transforms what is collected or observed into numerical data. It often describes a
Module
5
FINDING ANSWERS THROUGH DATA
COLLECTION
QUANTITATIVE DATA ANALYSISLESSON
12
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situation or event; answering the 'what' and 'how many' questions you may have about something.
This is research which involves measuring or counting attributes (i.e. quantities)
A quantitative approach is often concerned with finding evidence to either support or
contradict an idea or hypothesis you might have. A hypothesis is where a predicted answer to a
research question is proposed, for example, you might propose that if you give a student training
in how to use a search engine it will improve their success in finding information on the Internet.
You could then go on to explain why a particular answer is expected - you put forward
a theory.
We can gather quantitative data in a variety of ways and from a number of different sources.
Many of these are similar to sources of qualitative data, for example:
 Questionnaires - a series of questions and other prompts for the purpose of gathering
information from respondents
 Interviews - a conversation between two or more people (the interviewer and the
interviewee) where questions are asked by the interviewer to obtain information from the
interviewee - a more structured approach would be used to gather quantitative data
 Observation - a group or single participants are manipulated by the researcher, for example,
asked to perform a specific task or action. Observations are then made of their user behavior,
user processes, workflows etc, either in a controlled situation (e.g. lab based) or in a real-
world situation (e.g. the workplace)
 Transaction logs - recordings or logs of system or website activity
 Documentary research - analysis of documents belonging to an organization
Why do we do quantitative data analysis?
Once you have collected your data you need to make sense of the responses you have got
back. Quantitative data analysis enables you to make sense of data by:
 organizing them
 summarizing them
 doing exploratory analysis
And to communicate the meaning to others by presenting data as:
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 tables
 graphical displays
 summary statistics
We can also use quantitative data analysis to see:
 where responses are similar , for example, we might find that the majority of students all go
to the university library twice a week
 if there are differences between the things we have studied, for example, 1st year students
might go once a week to the library, 2 nd year students twice a week and 3 rd year students
three times a week
 if there is a relationship between the things we have studied. So, is there a relationship
between the number of times a student goes to the library and their year of study?
Using software for statistical analysis
Some key concepts
Before we look at types of analysis and tools we need to be familiar with a few concepts first:
 Population - the whole units of analysis that might be investigated, this could be students,
cats, house prices etc.
 Sample - the actual set of units selected for investigation and who participate in the research
 Variable - characteristics of the units/participants
 Value - the score/label/value of a variable, not the frequency of occurrence. For example, if
age is a characteristic of a participant then the value label would be the actual age, eg. 21,
22, 25, 30, 18, not how many participants are 21, 22, 25, 30, 18.
 Case/subject - the individual unit/participant of the study/research.
Sampling
Sampling is complex and can be done in many ways dependent on 1) what you want to
achieve from your research, 2) practical considerations of who is available to participate!
The type of statistical analysis you do will depend on the sample type you have. Most
importantly, you cannot generalize your findings to the population as a whole if you do not have
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a random sample. You can still undertake some inferential statistical analysis but you should
report these as results of your sample, not as applicable to the population at large.
Common sampling approaches include:
 Random sampling
 Stratified sampling
 Cluster sampling
 Convenience sampling
 Accidental sampling
Steps in Quantitative Data Analysis
According to Baraceros (2016), she identified the different steps in Quantitative data
analysis and she quoted that no ―data organization means no sound data analysis‖.
1. Coding system – to analyzed data means to quantify of change the verbally
expressed data into numerical information. Converting the words, images, or
pictures into numbers, they become fit for any analytical procedures requiring
knowledge of arithmetic and mathematical computations. But it is not possible for
the researcher to do the mathematical operations such as division, multiplication, or
subtraction in the word level, unless you code the verbal responses and observation
categories.
For example:
As regards gender variable, give number 1 as the code or value for Male and
number 2 for Female. As to educational attainment as another variable, give the
value of 2 for elementary; 4 for high school, 6 for college, 9 for M.A., and 12 for
PhD level. By coding each item with a certain number in a data set, you are able to
add the points or values of the respondent answers to a particular interview
questionnaire item.
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Total Sample size: 24
Gender Male: 11 (46%)
Female: 13 (54%)
Program Fine Arts: 9 (37%)
Architecture: 6(25%)
Journalism: 4 (17%)
Com. Arts: 5 (20%)
School FEU: 3 (12%)
MLQU: 4 (17%)
UCU: 3 (12%)
PUNP: 5 (20%)
LNL: 4 (17%)
PSU: (5 %)
Attending in 2017 Summer Arts
Seminar-Workshop on Arts
Yes: 18 (75%)
No: 6 (25%)
Role in the 2017 Seminar-Workshop on
Arts
Speaker: 2 (17%)
Organizer: 3 (12%)
Demonstrator: 5 (20%)
Participant: 12 (50%)
Satisfaction with the demonstration and
practice exercises
Strongly agree: 11 (46%)
Agree: 3 (12%)
Neutral: 2 (8%)
Disagree: 4 (14%)
Strongly disagree: 2 (8%)
Source: Baraceros 2016 Practical Research 2, RexBookstore pp-110
Step 2: Analyzing the Data
Data coding and tabulation are both essential in preparing the data analysis. Before you interpret
every component of the data, the researcher decides first what kind of quantitative analysis to
use whether to use a simple descriptive statistical technique or an advance analytical method.
The first one that college students often use tells some aspects of categories of data such as:
frequency of distribution, measure of central tendency (mean, median and mode), and standard
70

deviation. However, this does not give information about population from where the sample
came. The second one, on the other hand, fits graduate-level studies because this involves
complex statistical analysis requiring a good foundation and thorough knowledge the data-
gathering instrument used. The results of the analysis reveal the following aspects of an item in
a set of data (Mogan 2014; Punch 2014; Walsh 2010) cited by Baraceros (2016):
 Frequency distribution – gives you the frequency of distribution and
percentage of the occurrence of an item in asset of data. In other words, it
gives you the number of responses given repeatedly for one question.
Question: By and large, do you find the Senators‘ attendance
in 2015 legislative session awful
Measurement
Scale
Code
Frequency
Distribution
Percent
Distribution
Strongly agree 1 14 58%
Agree 2 3 12%
Neutral 3 2 8%
Disagree 4 1 4%
Strongly disagree 5 4 17%
Source: Baraceros 2016 Practical Research 2, RexBookstore pp-111
 Measure of Central Tendency – indicates the different positions or values
of the items, such that in a category of data, you find an item or items
serving as the:
Mean – average of all the items or scores
Example: 3+8+9+2+3+10+3 = 38
38 ÷ 7 = 5.43 (Mean)
Median – the score in the middle of the set of items that cuts or divides the
set into two groups
Example: The number in the example for the Mean has 2 as the Median.
Mode – refers to the item or score in the data set that has the most repeated
appearance in the set.
Example: Again, in the example above for the Mean, 3 is the Mode.
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 Standard Deviation – shows the extent of the difference of the data from the mean. An
examination of this gap between the mean and the data gives you an idea about the extent of
the similarities and differences between the respondents. There are mathematical operations
that you have to determine the standard deviation.
Step 1: Compute the Mean.
Step 2: Compute the deviation (difference) between each respondent‘s answer (data item) and
the mean. The positive sign (+) appears before the number if the difference is higher; negative
sign (-), if the difference is lower.
Step 3: Compute the square of each deviation.
Step 4: Compute the sum of squares by adding the squared figures.
Step 5: Divide the sum of squares by the number of data items to get the variance.
Step 6: Compute the square root of variance figure to get standard deviation.
Example:
Standard Deviation of the category of data collected from selected faculty
members of one university.
(Step 1) Mean: 7
(Step 2) (Step 3)
Data Item Deviation Square of Deviation
1 -8 68
2 -5 25
6 -1 1
6 -1 1
8 +8 1
6 -1 1
6 -1 1
14 +7 49
16 +9 81
Total: 321
(Step 4) Sum of Squares: 321
(Step 5) Variance = 36 (321 ÷ 9)
(Step 6) Standard Deviation -6 (square root of 6)
2. Advanced Quantitative Analytical Methods – An analysis of quantitative data that involves the
use of more complex statistical methods needing computer software like the SPSS, STATA, or
MINITAB, among others, occurs graduate-level students taking their MA or PhD degrees.
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Some of the advanced method of quantitative data analysis are the following (Argyous 2011;
Levin & Fox 2014; Godwin 2014; as cited by Baraceros 2016)
a) Correlation – uses statistical analysis to yield results that describes
the relationship of two variables. The results, however are incapable
of establishing casual relationships.
b) Analysis of Variance (ANOVA) - is a statistical method used to test
differences between two or more means. It may seem odd that the
technique is called "Analysis of Variance" rather than "Analysis of
Means." As you will see, the name is appropriate because inferences
about means are made by analyzing variance.
c) Regression - In statistical modeling, regression analysis is a statistical
process for estimating the relationships among variables. It includes many
techniques for modeling and analyzing several variables, when the focus is
on the relationship between a dependent variable and one or more
independent variables (or 'predictors').

Basic Concept
Statistics is a form of mathematical analysis that uses quantified models, representations and
synopses for a given set of experimental data or real-life studies. Statistics studies methodologies to
gather, review, analyze and draw conclusions from data. Statistical methods analyze large volumes
of data and their properties. Statistics is used in various disciplines such as psychology, business,
physical and social sciences, humanities, government and manufacturing. Statistical data is gathered
using a sample procedure or other method. Two types of statistical methods are used in analyzing
data: descriptive statistics and inferential statistics. Descriptive statistics are used to synopsize data
from a sample exercising the mean or standard deviation. Inferential statistics are used when data is
viewed as a subclass of a specific population.
STATISTICAL METHODSLESSON
13
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Statistical Methodologies
1. Descriptive Statistics- Descriptive statistics are brief descriptive coefficients that
summarize a given data set, which can be either a representation of the entire population or
a sample of it. Descriptive statistics are broken down into measures of central tendency and
measures of variability, or spread. Measures of central tendency include the mean, median
and mode, while measures of variability include the standard deviation or variance, and the
minimum and maximum variables.
2. Inferential Statistics - Now, suppose you need to collect data on a very large population.
For example, suppose you want to know the average height of all the men in a city with a
population of so many million residents. It isn't very practical to try and get the height of
each man. This is where inferential statistics comes into play. Inferential statistics makes
inferences about populations using data drawn from the population. Instead of using the
entire population to gather the data, the statistician will collect a sample or samples from
the millions of residents and make inferences about the entire population using the sample.
The sample is a set of data taken from the population to represent the population.
Probability distributions, hypothesis testing, correlation testing and regression analysis are
all fall under the category of inferential statistics.
Types of Statistical Data Analysis
1. Univariate Analysis – analysis of one variable.
2. Bivariate Analysis – analysis of two variables (independent and dependent)
3. Multivariate Analysis – analysis of multiple relations between multiple variables.
Statistical Methods of Bivariate Analysis
According to the book of Baraceros (2016) bivariate analysis happens by means of the
following methods (Argyrous 2011; Babbie 2013; Punch 2014)
1. Correlation or Covariation (correlated variation) – describes the relationship between two
variables and also tests the strengths or significance of their linear relation.
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Covariance is the statistical term to measure the extent of the change in the relationship of
two random variables. Random variables are data with varied values like those ones in the
interval level or scale (Strongly disagree, disagree, neutral, agree, strongly agree) whose
values depend on the arbitrariness of the respondents.
2. Cross Tabulation – is also called ―crosstab or students-contingency table‖ that follows the
format of a matrix that is made up of lines of numbers, symbols, and other expressions.
Similar to one type of graph called table, matrix arranges data in rows and columns. If the
table compares data on only two variables, such table is called Bivariate Table.
Example:
Secondary School Participants who attend the 1st
UCNHS Research Conference
School MALE FEMALE Row Total
QMA
152
(18.7%)
127
(15.4%) 279
UNCNHS
120
(14.8%)
98
(11.9%) 218
PUNP
59
(7.2%)
48
(5.8%) 107
UCU
61
(7.5%)
58
(7%) 119
LNL
81
(10%)
79
(9.5%) 159
U-Pang.
79
(9.7%)
99
(12%) 178
CLLC
102
(12.6%)
120
(14.5%) 222
ABE
69
(8.5%)
93
(11.3%) 162
STI
83
(10.2%)
101
(12.2%) 184
Column Total 806
(100%)
823
(100%) 1,629
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Measure of Correlations
Correlation is a bivariate analysis that measures the strengths of association between two variables
and the direction of the relationship. In terms of the strength of relationship, the value of the
correlation coefficient varies between +1 and -1. When the value of the correlation coefficient lies
around ± 1, then it is said to be a perfect degree of association between the two variables. As the
correlation coefficient value goes towards 0, the relationship between the two variables will be
weaker. The direction of the relationship is simply the + (indicating a positive relationship between
the variables) or - (indicating a negative relationship between the variables) sign of the
correlation. Usually, in statistics, we measure four types of correlations: Pearson correlation,
Kendall rank correlation, Spearman correlation, and the Point-Biserial
 PEARSON R CORRELATION
Pearson r correlation is the most widely used correlation statistic to measure the degree of the
relationship between linearly related variables. For example, in the stock market, if we want to
measure how two stocks are related to each other, Pearson rcorrelation is used to measure the
degree of relationship between the two. The Point-biserial correlation is conducted with the
Pearson correlation formula except that one of the variables is dichotomous. The following
formula is used to calculate the Pearson r correlation:
r = Pearson r correlation coefficient
N = number of value in each data set
∑xy = sum of the products of paired scores
∑x = sum of x scores
∑y = sum of y scores
∑x2= sum of squared x scores
∑y2= sum of squared y scores
Types of research questions a Pearson correlation can examine:
Is there a statistically significant relationship between age, as measured in years, and height,
measured in inches?
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Is there a relationship between temperature, measure in degree Fahrenheit, and ice cream sales,
measured by income?
Is there a relationship among job satisfaction, as measured by the JSS, and income, measured in
dollars?
Assumptions
For the Pearson r correlation, both variables should be normally distributed (normally distributed
variables have a bell-shaped curve). 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.
CONDUCT AND INTERPRET A PEARSON CORRELATION
KEY TERMS
Effect size: Cohen‘s standard will be used to evaluate the correlation coefficient to determine the
strength of the relationship, or the effect size, where correlation coefficients between .10 and .29
represent a small association, coefficients between .30 and .49 represent a medium association, and
coefficients of .50 and above represent a large association or relationship.
Continuous data: Data that is interval or ratio level. This type of data possesses the properties of
magnitude and equal interval between adjacent units. Equal intervals between adjacent units‘
means that there are equal amounts of the variable being measured between adjacent units on the
scale. An example would be age. An increase in age from 21 to 22 would be the same as an
increase in age from 60 to 61.
 KENDALL RANK CORRELATION
Kendall rank correlation is a non-parametric test that measures the strength of dependence
between two variables. If we consider two samples, a and b, where each sample size is n, we know
that the total number of pairings with a b is n(n-1)/2. The following formula is used to calculate the
value of Kendall rank correlation:
Nc= number of concordant
Nd= Number of discordant
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CONDUCT AND INTERPRET A KENDALL CORRELATION
KEY TERMS
Concordant: Ordered in the same way.
Discordant: Ordered differently.
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. 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.
The following formula is used to calculate the Spearman rank correlation:
P= Spearman rank correlation
di= the difference between the ranks of corresponding values Xi and Yi
n= number of value in each data set
Questions Spearman Correlation Answers
Is there a statistically significant relationship between participants' responses to two Likert scales
questions?
Is there a statistically significant relationship between how the horses rank in the race and the
horses‘ ages?
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 montonically related to the other variable.
CONDUCT AND INTERPRET A SPEARMAN CORRELATION
KEY TERMS
Effect size: Cohen‘s standard will be used to evaluate the correlation coefficient to determine the
strength of the relationship, or the effect size, where coefficients between .10 and .29 represent a
small association; coefficients between .30 and .49 represent a medium association; and coefficients
of .50 and above represent a large association or relationship.
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Ordinal data: Ordinal scales rank order the items that are being measured to indicate if they possess
more, less, or the same amount of the variable being measured. An ordinal scale allows us to
determine if X > Y, Y > X, or if X = Y. An example would be rank ordering the participants in a
dance contest. The dancer who was ranked one was a better dancer than the dancer who was ranked
two. The dancer ranked two was a better dancer than the dancer who was ranked three, and so
on. Although this scale allows us to determine greater than, less than, or equal to, it still does not
define the magnitude of the relationship between units.
 Chi-square
is the statistical test for bivariate analysis of nominal variables, specifically, to test the null
hypothesis. It tests whether or not a relationship exists between or among variables and tells
the probability that the relationship is caused by chance. This cannot in any way show extent
of the association between two variables.
Types of Data:
There are basically two types of random variables and they yield two types of data:
numerical and categorical. A chi square (X2
) statistic is used to investigate whether distributions of
categorical variables differ from one another. Basically categorical variable yield data in the
categories and numerical variables yield data in numerical form. Responses to such questions as
"What is your major?" or Do you own a car?" are categorical because they yield data such as
"biology" or "no." In contrast, responses to such questions as "How tall are you?" or "What is your
G.P.A.?" are numerical. Numerical data can be either discrete or continuous. The table below may
help you see the differences between these two variables.
Data Type Question Type
Possible
Responses
Categorical What is your sex? male or female
Numerical
Discrete- How many cars do you
own?
two or three
Numerical Continuous - How tall are you? 72 inches
Notice that discrete data arise from a counting process, while continuous data arise from a
measuring process.
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The Chi Square statistic compares the tallies or counts of categorical responses between two (or
more) independent groups. (Note: Chi square tests can only be used on actual numbers and not on
percentages, proportions, means, etc.)
2 x 2 Contingency Table
There are several types of chi square tests depending on the way the data was collected and the
hypothesis being tested. We'll begin with the simplest case: a 2 x 2 contingency table. If we set the 2
x 2 table to the general notation shown below in Table 1, using the letters a, b, c, and d to denote the
contents of the cells, then we would have the following table:
Table 1. General notation for a 2 x 2 contingency table.
Variable 1
Variable 2 Data type 1 Data type 2 Totals
Category 1 A B a + b
Category 2 C D c + d
Total a + c b + d a + b + c + d = N
For a 2 x 2 contingency table the Chi Square statistic is calculated by the formula:
Note: notice that the four components of the denominator are the four totals from the table columns
and rows.
Suppose you conducted a drug trial on a group of animals and you hypothesized that the animals
receiving the drug would show increased heart rates compared to those that did not receive the drug.
You conduct the study and collect the following data:
Ho: The proportion of animals whose heart rate increased is independent of drug treatment.
Ha: The proportion of animals whose heart rate increased is associated with drug treatment.
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Table 2. Hypothetical drug trial results.
Heart Rate
Increased
No Heart Rate
Increase
Total
Treated 36 14 50
Not treated 30 25 55
Total 66 39 105
Applying the formula above we get:
Chi square = 105 [ (36) (25) - (14) (30) ]2
/ (50) (55) (39) (66) = 3.418
Before we can proceed we need to know how many degrees of freedom we have. When a
comparison is made between one sample and another, a simple rule is that the degrees of freedom
equal (number of columns minus one) x (number of rows minus one) not counting the totals for
rows or columns. For our data this gives (2-1) x (2-1) = 1.
We now have our chi square statistic (x2
= 3.418), our predetermined alpha level of
significance (0.05), and our degrees of freedom (df = 1). Entering the Chi square distribution table
with 1 degree of freedom and reading along the row we find our value of x2
(3.418) lies between
2.706 and 3.841. The corresponding probability is between the 0.10 and 0.05 probability levels.
That means that the p-value is above 0.05 (it is actually 0.065). Since a p-value of 0.65 is greater
than the conventionally accepted significance level of 0.05 (i.e. p > 0.05) we fail to reject the null
hypothesis. In other words, there is no statistically significant difference in the proportion of
animals whose heart rate increased.
What would happen if the number of control animals whose heart rate increased dropped to
29 instead of 30 and, consequently, the number of controls whose hear rate did not increase changed
from 25 to 26? Try it. Notice that the new x2
value is 4.125 and this value exceeds the table value of
3.841 (at 1 degree of freedom and an alpha level of 0.05). This means that p < 0.05 (it is now0.04)
and we reject the null hypothesis in favor of the alternative hypothesis - the heart rate of animals is
different between the treatment groups. When p < 0.05 we generally refer to this as a significant
difference.
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Table 3. Chi Square distribution table.
Probability level (alpha)
Df 0.5 0.10 0.05 0.02 0.01 0.001
1 0.455 2.706 3.841 5.412 6.635 10.827
2 1.386 4.605 5.991 7.824 9.210 13.815
3 2.366 6.251 7.815 9.837 11.345 16.268
4 3.357 7.779 9.488 11.668 13.277 18.465
5 4.351 9.236 11.070 13.388 15.086 20.517
To make the chi square calculations a bit easier, plug you‘re observed and expected values into the
following applet. Click on the cell and then enter the value. Click the compute button on the lower
right corner to see the chi square value printed in the lower left hand corner.
Chi Square Goodness of Fit (One Sample Test)
This test allows us to compare a collection of categorical data with some theoretical expected
distribution. This test is often used in genetics to compare the results of a cross with the theoretical
distribution based on genetic theory. Suppose you preformed a simpe monohybrid cross between
two individuals that were heterozygous for the trait of interest.
Aa x Aa
The results of your cross are shown in Table 4.
Table 4. Results of a monohybrid cross between two heterozygotes for the 'a' gene.
A a Totals
A 10 42 52
A 33 15 48
Totals 43 57 100
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The phenotypic ratio 85 of the ―A‖ type and 15 of the a-type (homozygous recessive). In a
monohybrid cross between two heterozygotes, however, we would have predicted a 3:1 ratio of
phenotypes. In other words, we would have expected to get 75 A-type and 25 a-type. Are or results
different?
Calculate the chi square statistic x2
by completing the following steps:
1. For each observed number in the table subtract the corresponding expected number (O — E).
2. Square the difference [ (O —E)2
].
3. Divide the squares obtained for each cell in the table by the expected number for that cell [
(O - E)2
/ E ].
4. Sum all the values for (O - E)2
/ E. This is the chi square statistic.
For our example, the calculation would be: x2
= 5.33
Observed Expected (O — E) (O — E)2 (O — E)2/ E
A-type 85 75 10 100 1.33
a-type 15 25 10 100 4.0
Total 100 100 5.33
We now have our chi square statistic (x2
= 5.33), our predetermined alpha level of
significance (0.05), and our degrees of freedom (df =1). Entering the Chi square distribution table
with 1 degree of freedom and reading along the row we find our value of x2
5.33) lies between
3.841 and 5.412. The corresponding probability is 0.05<P<0.02. This is smaller than the
conventionally accepted significance level of 0.05 or 5%, so the null hypothesis that the two
distributions are the same is rejected. In other words, when the computed x2
statistic exceeds the
critical value in the table for a 0.05 probability level, then we can reject the null hypothesis of equal
distributions. Since our x2
statistic (5.33) exceeded the critical value for 0.05 probability level
83

(3.841) we can reject the null hypothesis that the observed values of our cross are the same as the
theoretical distribution of a 3:1 ratio.
Probability level (alpha)
Df 0.5 0.10 0.05 0.02 0.01 0.001
1 0.455 2.706 3.841 5.412 6.635 10.827
2 1.386 4.605 5.991 7.824 9.210 13.815
3 2.366 6.251 7.815 9.837 11.345 16.268
4 3.357 7.779 9.488 11.668 13.277 18.465
5 4.351 9.236 11.070 13.388 15.086 20.517
To put this into context, it means that we do not have a 3:1 ratio of A_ to aa offspring.
To make the chi square calculations a bit easier, plug your observed and expected values into the
following java applet.
Click on the cell and then enter the value. Click the compute button on the lower right corner to see
the chi square value printed in the lower left hand coner.
Chi Square Test of Independence
For a contingency table that has r rows and c columns, the chi square test can be thought of as a test
of independence. In a test of independence the null and alternative hypotheses are:
Ho: The two categorical variables are independent.
Ha: The two categorical variables are related.
fo - fe)2
/ fe
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Here fo denotes the frequency of the observed data and fe is the frequency of the expected values.
The general table would look something like the one below:
Category
I
Category
II
Category
III
Row Totals
Sample A a b C a+b+c
Sample B d e F d+e+f
Sample C g h I g+h+i
Column
Totals
a+d+g b+e+h c+f+i a+b+c+d+e+f+g+h+i=N
Now we need to calculate the expected values for each cell in the table and we can do that using the
the row total times the column total divided by the grand total (N). For example, for cell a the
expected value would be (a+b+c)(a+d+g)/N.
Once the expected values have been calculated for each cell, we can use the same procedure are
before for a simple 2 x 2 table.
Observed Expected |O - E| (O — E)2 (O — E)2/ E
Suppose you have the following categorical data set.
Table . Incidence of three types of malaria in three tropical regions.
Asia Africa
South
America
Totals
Malaria A 31 14 45 90
Malaria B 2 5 53 60
Malaria C 53 45 2 100
Totals 86 64 100 250
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We could now set up the following table:
Observed Expected |O -E| (O — E)2 (O — E)2/ E
31 30.96 0.04 0.0016 0.0000516
14 23.04 9.04 81.72 3.546
45 36.00 9.00 81.00 2.25
2 20.64 18.64 347.45 16.83
5 15.36 10.36 107.33 6.99
53 24.00 29.00 841.00 35.04
53 34.40 18.60 345.96 10.06
45 25.60 19.40 376.36 14.70
2 40.00 38.00 1444.00 36.10
Chi Square = 125.516
Degrees of Freedom = (c - 1) (r - 1) = 2(2) = 4
probability level (alpha)
Df 0.5 0.10 0.05 0.02 0.01 0.001
1 0.455 2.706 3.841 5.412 6.635 10.827
2 1.386 4.605 5.991 7.824 9.210 13.815
3 2.366 6.251 7.815 9.837 11.345 16.268
4 3.357 7.779 9.488 11.668 13.277 18.465
5 4.351 9.236 11.070 13.388 15.086 20.517
Reject Ho because 125.516 is greater than 9.488
Thus, we would reject the null hypothesis that there is no relationship between location and type of
malaria. Our data tell us there is a relationship between type of malaria and location, but that's all it
says.
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The T-Test
The t-test assesses whether the means of two groups are statistically different from each
other. This analysis is appropriate whenever you want to compare the means of two groups, and
especially appropriate as the analysis for the posttest-only two-group randomized experimental
design.
Figure 1 shows the distributions for the treated (blue) and control (green) groups in a study.
Actually, the figure shows the idealized distribution -- the actual distribution would usually be
depicted with a histogram or bar graph. The figure indicates where the control and treatment group
means are located. The question the t-test addresses is whether the means are statistically different.
What does it mean to say that the averages for two groups are statistically different?
Consider the three situations shown in Figure 2. The first thing to notice about the three situations is
that the difference between the means is the same in all three. But, you should also notice that the
three situations don't look the same -- they tell very different stories. The top example shows a case
with moderate variability of scores within each group. The second situation shows the high
variability case. the third shows the case with low variability. Clearly, we would conclude that the
two groups appear most different or distinct in the bottom or low-variability case. Why? because,
there is relatively little overlap between the two bell-shaped curves. In the high variability case, the
group difference appears least striking because the two bell-shaped distributions overlap so much.
Figure 2. Three scenarios for differences between means.
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This leads us to a very important conclusion: when we are looking at the differences between scores
for two groups, we have to judge the difference between their means relative to the spread or
variability of their scores. The t-test does just this.
Statistical Analysis of the t-test
The formula for the t-test is a ratio. The top part of the ratio is just the difference between the two
means or averages. The bottom part is a measure of the variability or dispersion of the scores. This
formula is essentially another example of the signal-to-noise metaphor in research: the difference
between the means is the signal that, in this case, we think our program or treatment introduced into
the data; the bottom part of the formula is a measure of variability that is essentially noise that may
make it harder to see the group difference. Figure 3 shows the formula for the t-test and how the
numerator and denominator are related to the distributions.
Figure 3. Formula for the t-test.
The top part of the formula is easy to compute -- just find the difference between the means. The
bottom part is called the standard error of the difference. To compute it, we take the variance for
each group and divide it by the number of people in that group. We add these two values and then
take their square root. The specific formula is given in Figure 4:
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Figure 4. Formula for the Standard error of the difference between the means.
Remember, that the variance is simply the square of the standard deviation.
The final formula for the t-test is shown in Figure 5:
Figure 5. Formula for the t-test.
The t-value will be positive if the first mean is larger than the second and negative if it is
smaller. Once you compute the t-value you have to look it up in a table of significance to test
whether the ratio is large enough to say that the difference between the groups is not likely to have
been a chance finding. To test the significance, you need to set a risk level (called the alpha level).
In most social research, the "rule of thumb" is to set the alpha level at .05. This means that five
times out of a hundred you would find a statistically significant difference between the means even
if there was none (i.e., by "chance"). You also need to determine the degrees of freedom (df) for the
test. In the t-test, the degree of freedom is the sum of the persons in both groups minus 2. Given the
alpha level, the df, and the t-value, you can look the t-value up in a standard table of significance
(available as an appendix in the back of most statistics texts) to determine whether the t-value is
large enough to be significant. If it is, you can conclude that the difference between the means for
the two groups is different (even given the variability). Fortunately, statistical computer programs
routinely print the significance test results and save you the trouble of looking them up in a table.
The t-test, one-way Analysis of Variance (ANOVA) and a form of regression analysis are
mathematically equivalent (see the statistical analysis of the posttest-only randomized experimental
design) and would yield identical results.
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 ANALYSIS OF VARIANCE (ANOVA)
Analysis of Variance
 One Way (one factor, fixed effects)
 Two Way (two factors, randomized blocks)
 Two Way with Repeated Observations (two factors, randomized block)
 Fully Nested (hierarchical factors)
 Latin Square (one primary and two secondary factors)
 Crossover (two factors, fixed effects, treatment crossover)
 Kruskal-Wallis (nonparametric one way)
 Friedman (nonparametric two way)
Related:
 Homogeneity of Variance (examine the ANOVA assumption of equal variance)
 Normality (examine the ANOVA assumption of normality)
 Agreement (examine agreement of two or more samples)
Basics Concepts
ANOVA is a set of statistical methods used mainly to compare the means of two or more
samples. Estimates of variance are the key intermediate statistics calculated, hence the reference to
variance in the title ANOVA. The different types of ANOVA reflect the different experimental
designs and situations for which they have been developed.
Excellent accounts of ANOVA are given by Armitage & Berry (1994) and Kleinbaum et. al
(1998). Nonparametric alternatives to ANOVA are discussed by Conover (1999) and Hollander and
Wolfe (1999).
ANOVA and regression
ANOVA can be treated as a special case of general linear regression where independent/predicator
variables are the nominal categories or factors. Each value that can be taken by a factor is referred
to as a level. k different levels (e.g. three different types of diet in a study of diet on weight gain) are
coded not as a single column (e.g. of diet 1 to 3) but as k-1 dummy variables. The
dependent/outcome variable in the regression consists of the study observations.
General linear regression can be used in this way to build more complex ANOVA models
than those described in this section; this is best done under expert statistical guidance.
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Fixed vs. random effects
A fixed factor has only the levels used in the analysis (e.g. sex, age, blood group). A random
factor has many possible levels and some are used in the analysis (e.g. time periods, subjects,
observers). Some factors that are usually treated as fixed may also be treated as random if the study
is looking at them as part of a larger group (e.g. treatments, locations, tests).
Most general statistical texts arrange data for ANOVA into tables where columns represent fixed
factors and the one and two way analyses described are fixed factor methods.
Multiple comparisons
ANOVA gives an overall test for the difference between the means of k groups. StatsDirect
enables you to compare all k(k-1)/2 possible pairs of means using methods that are designed to
avoid the type I error that would be seen if you used two sample methods such as t test for these
comparisons. The multiple comparison/contrast methods offered by StatsDirect are Tukey(-
Kramer), Scheffé, Newman-Keuls, Dunnett and Bonferroni (Armitage and Berry, 1994;
Wallenstein, 1980; Liddell, 1983; Miller, 1981; Hsu, 1996; Kleinbaum et al., 1998). See multiple
comparisons for more information.
Further methods
There are many possible ANOVA designs. StatsDirect covers the common designs in its
ANOVA section and provides general tools (see general linear regression and dummy variables) for
building more complex designs.
Other software such as SAS and Genstat provide further specific ANOVA designs. For example,
balanced incomplete block design:
- with complete missing blocks you should consider a balanced incomplete block design provided
the number of missing blocks does not exceed the number of treatments.
Treatments
1 2 3 4
Blocks:
A X X x
B X X X
C X x X
D X x X
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Complex ANOVA should not be attempted without expert statistical guidance. Beware situations
where over complex analysis is used in order to compensate for poor experimental design. There is
no substitute for good experimental design.
 Regression
Regression is a statistical measure used in finance, investing and other disciplines that attempts
to determine the strength of the relationship between one dependent variable (usually denoted
by Y) and a series of other changing variables (known as independent variables). Regression
helps investment and financial managers to value assets and understand the relationships
between variables, such as commodity prices and the stocks of businesses dealing in those
commodities.
The two basic types of regression are linear regression and multiple linear regression, although
there are non-linear regression methods for more complicated data and analysis. Linear
regression uses one independent variable to explain or predict the outcome of the dependent
variable Y, while multiple regressions use two or more independent variables to predict the
outcome.
Regression can help finance and investment professionals as well as professionals in other
businesses. Regression can help predict sales for a company based on weather, previous sales,
GDP growth or other conditions. The capital asset pricing model (CAPM) is an often-used
regression model in finance for pricing assets and discovering costs of capital. The general
form of each type of regression is:
Linear Regression: Y = a + bX + u
Multiple Regression: Y = a + b1X1
+
b2X2 + b3X3 + ... + btXt + u
Where:
Y = the variable that you are trying to predict (dependent variable)
X = the variable that you are using to predict Y (independent variable)
a = the intercept
b = the slope
u = the regression residual
Regression takes a group of random variables, thought to be predicting Y, and tries to find a
mathematical relationship between them. This relationship is typically in the form of a straight
line (linear regression) that best approximates all the individual data points. In multiple
regression, the separate variables are differentiated by using numbers with subscript.
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Regression in Investing
Regression is often used to determine how many specific factors such as the price of a
commodity, interest rates, particular industries or sectors influence the price movement of an asset.
The aforementioned CAPM is based on regression, and it is utilized to project the expected returns
for stocks and to generate costs of capital. A stock's returns are regressed against the returns of a
broader index, such as the S&P 500, to generate a beta for the particular stock. Beta is the stock's
risk in relation to the market or index and is reflected as the slope in the CAPM model. The
expected return for the stock in question would be the dependent variable Y, while the independent
variable X would be the market risk premium.
Additional variables such as the market capitalization of a stock, valuation ratios and recent
returns can be added to the CAPM model to get better estimates for returns. These additional factors
are known as the Fama-French factors, named after the professors who developed the multiple
linear regression model to better explain asset returns.
4.
Sampling Procedures
Sampling is a process or technique of choosing a sub-group from a population to participate
in the study; it is the process of selecting a number of individuals for a study in such a way that the
individuals selected represent the large group from which they were selected (Ogula, 2005). There
are two major sampling procedures in research. These include probability and non-probability
sampling.
Probability Sampling Procedures
In probability sampling, everyone has an equal chance of being selected. This scheme is one
in which every unit in the population has a chance (greater than zero) of being selected in the
sample. There are four basic types of sampling procedures associated with probability samples.
These include simple random, systematic sampling, stratified and cluster.
SAMPLING PROCEDURELESSON
14
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Simple Random Sampling Procedure
Simple random sampling provides the base from which the other more complex sampling
methodologies are derived. To conduct a simple random sample, the researcher must first prepare
an exhaustive list (sampling frame) of all members of the population of interest. From this list, the
sample is drawn so that each person or item has an equal chance of being drawn during each
selection round (Kanupriya, 2012).
To draw a simple random sample without introducing researcher bias, computerized
sampling programs and random number tables are used to impartially select the members of the
population to be sampled. Subjects in the population are sampled by a random process, using either
a random number generator or a random number table, so that each person remaining in the
population has the same probability of being selected for the sample (Friedrichs, 2008).
Systematic Sampling Procedure
Systematic sampling procedure often used in place of simple random sampling. In
systematic sampling, the researcher selects every nth member after randomly selecting the first
through nth element as the starting point. For example, if the researcher decides to sample 20
respondents from a sample of 100, every 5th member of the population will systematically be
selected.
A researcher may choose to conduct a systematic sample instead of a simple random sample
for several reasons. Firstly, systematic samples tend to be easier to draw and execute, secondly, the
researcher does not have to go back and forth through the sampling frame to draw the members to
be sampled, thirdly, a systematic sample may spread the members selected for measurement more
evenly across the entire population than simple random sampling. Therefore, in some cases,
systematic sampling may be more representative of the population and more precise (Groves et al.,
2006).
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Stratified Sampling Procedure
Stratified sampling procedure is the most effective method of sampling when a researcher
wants to get a representative sample of a population. It involves categorizing the members of the
population into mutually exclusive and collectively exhaustive groups. An independent simple
random sample is then drawn from each group. Stratified sampling techniques can provide more
precise estimates if the population is surveyed is more heterogeneous than the categorized groups.
This technique can enable the researcher to determine desired levels of sampling precision for each
group, and can provide administrative efficiency. The main advantage of the approach is that it‘s
able to give the most representative sample of a population (Hunt & Tyrrell, 2001).
Cluster Sampling Procedure
In cluster sampling, a cluster (a group of population elements), constitutes the sampling unit,
instead of a single element of the population. The sampling in this technique is mainly
geographically driven. The main reason for cluster sampling is cost efficiency (economy and
feasibility). The sampling frame is also often readily available at cluster level and takes short time
for listing and implementation. The technique is also suitable for survey of institutions (Ahmed,
2009) or households within a given geographical area.
But the design is not without disadvantages, some of the challenges that stand out are: it may
not reflect the diversity of the community; other elements in the same cluster may share similar
characteristics; provides less information per observation than an SRS of the same size (redundant
information: similar information from the others in the cluster); standard errors of the estimates are
high, compared to other sampling designs with the same sample size.
Non Probability Sampling Procedures
Non probability sampling is used in some situations, where the population may not be well
defined. In other situations, there may not be great interest in drawing inferences from the sample to
the population. The most common reason for using non probability sampling procedure is that it is
less expensive than probability sampling procedure and can often be implemented more quickly
(Michael, 2011). It includes purposive, convenience and quota sampling procedures.
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Purposive/Judgmental Sampling Procedure
In purposive sampling procedure, the researcher chooses the sample based on who he/she
thinks would be appropriate for the study. The main objective of purposive sampling is to arrive as
at a sample that can adequately answer the research objectives. The selection of a purposive sample
is often accomplished by applying expert knowledge of the target population to select in a non-
random manner a sample that represents a cross-section of the population (Henry, 1990).
A major disadvantage of this method is subjectivity since another researcher is likely to
come up with a different sample when identifying important characteristics and picking typical
elements to be in the sample. Given the subjectivity of the selection mechanism, purposive
sampling is generally considered most appropriate for the selection of small samples often from a
limited geographic area or from a restricted population definition. The knowledge and experience of
the researcher making the selections is a key aspect of the ‗‗success‘‘ of the resulting sample
(Michael, 2011). A case study research design for instance, employs purposive sampling procedure
to arrive at a particular ‗case‘ of study and a given group of respondents. Key informants are also
selected using this procedure.
Convenience Sampling Procedure
Convenience sampling is sometimes known as opportunity, accidental or haphazard
sampling. It is a type of nonprobability sampling which involves the sample being drawn from that
part of the population which is close to hand, that is, a population which is readily available and
convenient. The researcher using such a sample cannot scientifically make generalizations about the
total population from this sample because it would not be representative enough (Michael, 2011).
This type of sampling is most useful for pilot testing.
Convenience sampling differs from purposive sampling in that expert judgment is not used
to select a representative sample. The primary selection criterion relates to the ease of obtaining a
sample. Ease of obtaining the sample relates to the cost of locating elements of the population, the
geographic distribution of the sample, and obtaining the interview data from the selected elements
(de Leeuw, Hox & Huisman, 2003).
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Sampling Techniques
When sampling, you need to decide what units (i.e., what people, organizations, data, etc.)
to include in your sample and which ones to exclude. As you'll know by now, sampling techniques
act as a guide to help you select these units, and you will have chosen a
specific probability or non-probability sampling technique:
 If you are following a probability sampling technique, you'll know that you require a list
of the population from which you select units for your sample. This raises potential data
protection and confidentiality issues because units in the list (i.e., when people are your
units) will not necessarily have given you permission to access the list with their details.
Therefore, you need to check that you have the right to access the list in the first place.
 If using a non-probability sampling technique, you need to ask yourself whether you are
including or excluding units for theoretical or practical reasons. In the case of purposive
sampling, the choice of which units to include and exclude is theoretically-driven. In such
cases, there are few ethical concerns. However, where units are included or excluded
for practical reasons, such as ease of access or personal preferences (e.g., convenience
sampling), there is a danger that units will be excluded unnecessarily. For example, it is not
uncommon when select units using convenience sampling that researchers' natural
preferences (and even prejudices) will influence the selection process. For example, maybe
the researcher would avoid approaching certain groups (e.g., socially marginalized
individuals, people who speak little English, disabled people, etc.). Where this happens, it
raises ethical issues because the picture being built through the research can be excessively
narrow, and arguably, unethically narrow. This highlights the importance of using theory to
determine the creation of samples when using non-probability sampling techniques rather
than practical reasons, whenever possible.
Sample size
Whether you are using a probability sampling or non-probability sampling technique to help you
create your sample, you will need to decide how large your sample should be (i.e., your sample
size). Your sample size becomes an ethical issue for two reasons: (a) over-sized samples
and (b) under-sized samples.
 Over-sized samples
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A sample is over-sized when there are more units (e.g., people, organizations) in the
sample than are needed to achieve you goals (i.e., to answer your research questions
robustly). An over-sized sample is considered to be an ethical issue because it potentially
exposes an excessive number of people (or other units) to your research. Let's look at
where this may or may not be a problem:
 Not an ethical issue
Imagine that you were interested in the career choices of students at your university, and
you were only asking students to complete a questionnaire taking no more than 10 minutes,
all an over-sized sample would have done was waste a little of the students' time. Whilst
you don't want to be wasting peoples' time, and should try and avoid doing so, this is not a
major ethical issue.
 A potential ethical issue
Imagine that you were interested in the effect of a carbohydrate free diet on the
concentration levels of female university students in the classroom. You know that
carbohydrate free diets (i.e., no breads, pasta, rice, etc.) are a new fad amongst female
university students because some female students feel that it helps them loose weight (or not
put weight on). However, you have read some research showing that such diets can make
people feel lethargic (i.e., low on energy). Therefore, you want to know whether this is
affecting students' performance; or more specifically, the concentration levels of female
students in the classroom.
You decide to conduct an experiment where you measure concentration levels
amongst 40 female students that are not on any specific diet. First, you measure their
concentration levels. Then, you ask 20 of the students to go on a carbohydrate free diet and
whilst the remaining 20 continue with the normal food consumption. After a period of time
(e.g., 14 days), you measure the concentration levels of all 40 students to compare any
differences between the two groups (i.e., the normal group and the group on the
carbohydrate free diet). You find that the carbohydrate free diet did significantly impact on
the concentration levels of the 20 students.
So here comes the ethical issue: What if you could have come to the same conclusion
with fewer students? What if you only needed to ask 10 students to go on the carbohydrate
free diet rather than 20? Would this have meant that the performance of 10 students would
not have been negatively for a 14 day period as a result? The important point is that you do
not want to expose individuals to distress or harm unnecessarily.
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Under-sized samples
A sample is under-sized when you are unable to achieve your goals (i.e., to answer your
research questions robustly) because you insufficient units in your sample. The important point is
that you fail to answer your research questions not because a potential answer did not exist, but
because your sample size was too small for such an answer to be discovered (or interpreted). Let's
look where this may or may not be a problem:
 Not an ethical issue
let‘s take the example of the career choices of students at your university. If you
did not collect sufficient data; that is, you did not ask enough students to complete your
questionnaire, the answers you get back from your sample may not be representative of
the population of all students at your university. This is bad from two perspectives, but only
one is arguably a potential ethical issue: First, it is bad because your dissertation findings will
be of a lower quality; they will not reflect the population of all students at the university that
you are interested in, which will most likely lead to a lower mark (i.e., external validity is an
important goal of quantitative research). This is bad for you, but not necessarily unethical.
However, if the findings from your research are incorrectly taken to reflect the views of all
students at your university, and somehow wrongly influence policy within the university
(e.g., amongst the Career Advisory Service), your dissertation research could have negatively
impacted other students. This is a potential ethical issue. Despite this, we would expect that
the likelihood of this happening is fairly low.
 A potential ethical issue
Going back to the example of the effect of a carbohydrate free diet on the
concentration levels of female university students in the classroom, an under-sized
sample does pose potential ethical issues. After all, with the exception of students that just
want to help you out, it is likely that most students are taking part voluntarily because they
want to the effect of such a diet on their potential classroom performance. Perhaps they have
used the diet before or are thinking about using the diet. Alternately, perhaps they are
worried about the effects of such diets, and what to further research in this area. In either
case, if no conclusions can be made or the findings are not statistically significant because
99

the sample size was too small, the effort, and potential distress and harm that these
volunteers put themselves through was all in vein (i.e., completely wasted). This is where an
under-sized sample can become an ethical issue.
As a researcher, even when you're an undergraduate or master's level student, you have a
duty not to expose an excessive number of people to unnecessary distress or harm. This is one of
the basic principles of research ethics. At the same time, you have a duty not to fail to achieve what
you set out to achieve. This is not just a duty to yourself or the sponsors of your dissertation (if you
have any), but more importantly, to the people that take part in your research (i.e., your sample). To
try and minimize the potential ethical issues that come with over-sized and under-sized samples,
there are instances where you can make sample size calculations to estimate the required sample
size to achieve your goals.
Gatekeepers
Gatekeepers can often control access to the participants you are interested in (e.g., a
manager's control over access to employees within an organization). This has ethical implications
because of the power that such gatekeepers can exercise over those individuals. For example, they
may control what access is (and is not) granted to which individuals, coerce individuals into taking
part in your research, and influence the nature of responses. This may affect the level of consent that
a participant gives (or is believed to have given) you. Ask yourself: Do I think that participants are
taking part voluntarily? How did the way that I gained access to participants affect not only
the voluntary nature of individuals? participation, and how will it affect the data?
Problems with gatekeepers can also affect the representativeness of the sample. Whilst
qualitative research designs are more likely to use non-probability sampling techniques, even
quantitative research designs that use probability sampling can suffer from issues of reliability
associated with gatekeepers. In the case of quantitative research designs using probability
sampling, are gatekeepers providing an accurate list of the population without missing out
potential participants (e.g., employees that may give a negative view of an organization)? If non-
probability sampling is being used, are gatekeepers coercing participants to take part or
influencing their responses?
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Name: ____________________________________ Score: _____________
_______________________1. It is a systematic approach to investigations during which
numerical data is collected and/or the researcher transforms what is
collected or observed into numerical data.
_______________________2. a series of questions and other prompts for the purpose of gathering
information from respondents.
_______________________3. _________a conversation between two or more people (the
interviewer and the interviewee) where questions are asked by the
interviewer to obtain information from the ________ - a more
structured approach would be used to gather quantitative data
_______________________4. a group or single participants are manipulated by the researcher, for
example, asked to perform a specific task or action. Observations
are then made of their user behavior, user processes, workflows
etc, either in a controlled situation (e.g. lab based) or in a real-
world situation (e.g. the workplace).
_______________________5. recordings or logs of system or website activity.
_______________________6. analysis of documents belonging to an organization.
_______________________7. the whole units of analysis that might be investigated, this could be
students, cats, house prices etc.
_______________________8. the actual set of units selected for investigation and who participate
in the research
_______________________9. characteristics of the units/participants.
_______________________10. the score/label/value of a variable, not the frequency of occurrence.
For example, if age is a characteristic of a participant then the
value label would be the actual age, eg. 21, 22, 25, 30, 18, not how
many participants are 21, 22, 25, 30, 18.
_______________________11. the individual unit/participant of the study/research.
_______________________12. is complex and can be done in many ways dependent on 1) what
you want to achieve from your research, 2) practical considerations
of who is available to participate.
_______________________13. to analyzed data means to quantify of change the verbally
expressed data into numerical information.
_______________________14. uses statistical analysis to yield results that describes the
relationship of two variables. The results, however are incapable of
establishing casual relationships.
____________________15. is a statistical method used to test differences between two or more means. It may
seem odd that the technique is called "Analysis of Variance" rather than "Analysis of
Means
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Name: ____________________________________ Score: _____________
ACTIVIITY 1: SPECULATIVE THINKING (GROUP WORK)
Directions: Question does not only indicate your curiosity about your world but also signal your
desire for clearer explanation about things. Hence, ask one another thought-provoking questions
about quantitative data analysis. For proper question formulation, you may draft your question on
the space below.
ACTIVIITY 2: INDIVIDUAL WORK: Recall two or three most challenging question from your
classmates shared to the class that you wanted to answer but to get the chance to do so. Write and
answer them on the lines provided.
ACTIVIITY 3: MATCHING TYPE
Directions: Match the expression in A with those in B by writing the letter of your answer on the
line before the word.
A B
________1. Mean a. data-set divider
________2. Ratio b. facts or information
________3. Data c. part-by-part examination
________4. Coding d. data-preparation techniques
________5. Analysis e. repetitive appearance of an item
________6. Mode f. sum divided numbers of items
________7. Media g. valuable zero
________8. Standard deviation h. ANOVA
________9. Regression i. shows variable predictor
________10. Table j. data organizer
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David M. Lane, Online Statistics Education: An Interactive Multimedia
Course of Study, Developed by Rice University (Lead Developer),
University of Houston Clear Lake, and Tufts University
http://onlinestatbook.com/2/analysis_of_variance/intro.html
http://www.health.herts.ac.uk/immunology/Web%20programme%20-
%20Researchhealthprofessionals/quantitative_data_analysis.htm
http://www.investopedia.com/terms/s/statistics.asp
Algina, J., & Keselman, H. J. (1999). Comparing squared multiple correlation coefficients:
Examination of a confidence interval and a test significance. Psychological Methods, 4(1),
76-83.
Bobko, P. (2001). Correlation and regression: Applications for industrial organizational
psychology and management (2nd ed.). Thousand Oaks, CA: Sage Publications.
Bonett, D. G. (2008). Meta-analytic interval estimation for bivariate
correlations. Psychological Methods, 13(3), 173-181.
Chen, P. Y., & Popovich, P. M. (2002). Correlation: Parametric and nonparametric measures.
Thousand Oaks, CA: Sage Publications.
Cheung, M. W. -L., & Chan, W. (2004). Testing dependent correlation coefficients via
structural equation modeling. Organizational Research Methods, 7(2), 206-223.
Coffman, D. L., Maydeu-Olivares, A., Arnau, J. (2008). Asymptotic distribution free interval
estimation: For an intraclass correlation coefficient with applications to longitudinal
data. Methodology, 4(1), 4-9.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation
analysis for the behavioral sciences. (3rd ed.). Mahwah, NJ: Lawrence Erlbaum
Associates.
Hatch, J. P., Hearne, E. M., & Clark, G. M. (1982). A method of testing for serial correlation in
univariate repeated-measures analysis of variance. Behavior Research Methods &
Instrumentation, 14(5), 497-498.
Kendall, M. G., & Gibbons, J. D. (1990). Rank Correlation Methods (5th ed.).
London: Edward Arnold.
Krijnen, W. P. (2004). Positive loadings and factor correlations from positive covariance
matrices. Psychometrika, 69(4), 655-660.
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Research adheres to a certain manner of making public its findings. It is incapable of
convincing and readers of the genuineness of the research report, unless it follows the academically
and professionally accepted standards of writing the report in terms of its knowledge responsible for
making the entire research study reputable, genuine, and credible basis for effecting positive
changes in this world. (Baraceros 2016)
1. Draws a conclusion from the research findings;
2. Formulates recommendations;
3. List references;
4. Presents written report;
5. Finalizes and presents best designs; and
6. Presents research workbook.
1. Guidelines in making conclusions and recommendations
2. The techniques in listing references
3. The process of report writing
4. The selection criteria and process of best design.
Drawing Conclusions
For any research project and any scientific discipline, drawing conclusions is the final, and
most important, part of the process. Whichever reasoning processes and research methods were
used, the final conclusion is critical, determining success or failure. If an otherwise excellent
Module
6
REPORT AND SHARING FINDINGS
DRAWS CONCLUSIONS AND RECOMMENDATIONSLESSON
15
104

experiment is summarized by a weak conclusion, the results will not be taken seriously. Success or
failure is not a measure of whether a hypothesis is accepted or refuted, because both results still
advance scientific knowledge. ( Shuttleworth 2014)
Failure is poor experimental design, or flaws in the reasoning processes, which invalidate
the results. As long as the research process is robust and well designed, then the findings are sound,
and the process of drawing conclusions begins. Generally, a researcher will summarize what they
believe has been learned from the research, and will try to assess the strength of the hypothesis.
Even if the null hypothesis is accepted, a strong conclusion will analyze why the results
were not as predicted. In observational research, with no hypothesis, the researcher will analyze the
findings, and establish if any valuable new information has been uncovered.
Generating Leads for Future Research
However, very few experiments give clear-cut results, and most research uncovers more
questions than answers.
The researcher can use these to suggest interesting directions for further study. If, for
example, the null hypothesis was accepted, there may still have been trends apparent within the
results. These could form the basis of further study, or experimental refinement and redesign.
Evaluation - Flaws in the Research Process
The researcher will then evaluate any apparent problems with the experiment. This involves
critically evaluating any weaknesses and errors in the design, which may have influenced
the results.
Even strict, 'true experimental,' designs have to make compromises, and the researcher must
be thorough in pointing these out, justifying the methodology and reasoning.
For example, when drawing conclusions, the researcher may think that another causal
effect influenced the results, and that this variable was not eliminated during the experimental
process. A refined version of the experiment may help to achieve better results, if the new effect is
included in the design process.
In the global warming example, the researcher might establish that carbon dioxide emission
alone cannot be responsible for global warming. They may decide that another effect is
105

contributing, so propose that methane may also be a factor in global warming. A new study would
incorporate methane into the model.
What are the Clear-Cut Benefits of the Research
The next stage is to evaluate the advantages and benefits of the research. In medicine and
psychology, for example, the results may throw out a new way of treating a medical problem, so the
advantages are obvious. However, all well-constructed research is useful, even if it is just adding to
the fount of human knowledge. An accepted null hypothesis has an important meaning to science.
Suggestions Based Upon the Conclusions
The final stage is the researcher's recommendations based upon the results, depending upon
the field of study. This area of the research process can be based around the researcher's personal
opinion, and will integrate previous studies.
For example, a researcher into schizophrenia may recommend a more effective treatment. A
physicist might postulate that our picture of the structure of the atom should be changed. A
researcher could make suggestions for refinement of the experimental design, or highlight
interesting areas for further study. This final piece of the paper is the most critical, and pulls
together all of the findings.
The area in a research paper that causes intense and heated debate amongst scientists is
when drawing conclusions.
It is critical in determining the direction taken by the scientific community, but the researcher will
have to justify their findings.
Summary - The Strength of the Results
The key to drawing a valid conclusion is to ensure that the deductive and inductive
processes are correctly used, and that all steps of the scientific method were followed.
If your research had a robust design, questioning and scrutiny will be devoted to the experiment
conclusion, rather than the methods.
106

Recommendations
Other recommendations may also be appropriate. When preparing this section, remember that in
making your recommendations, you must show how your results support them. A recommendation
for a preferred alternative should include:
1. Specifically stating what should be done, the steps required to implement the policy, and the
resources needed;
2. discussion of the benefits to the organization and what problems would be corrected or
avoided;
3. discussion of the feasibility of the proposed policy; and
4. general statement about the nature and timing of an evaluation plan that would be used to
determine the effectiveness of the proposed policy.
Recommendations for Further Research
In this section, you finally have the opportunity to present and discuss the actions that future
researchers should take as a result of your Project. A well-thought-out set of recommendations
makes it more likely that the organization will take your recommendations seriously. Ideally you
should be able to make a formal recommendation regarding the alternative that is best supported by
the study. Present and discuss the kinds of additional research suggested by your Project. If the
preferred alternative is implemented, what additional research might be needed?
LIST REFERENCES
A bibliography is a list of the sources you used to get information for your report. It is
included at the end of your report, on the last page (or last few pages).
You will find it easier to prepare your final bibliography if you keep track of each book,
encyclopedia, or article you use as you are reading and taking notes. Start a preliminary, or draft,
bibliography by listing on a separate sheet of paper all your sources. Note down the full title, author,
place of publication, publisher, and date of publication for each source.
107

Also, every time a fact gets recorded on a note card, its source should be noted in the top
right corner. When you are finished writing your paper, you can use the information on your note
cards to double-check your bibliography.
When assembling a final bibliography, list your sources (texts, articles, interviews, and so
on) in alphabetical order by authors' last names. Sources that don't have authors (encyclopedias,
movies) should be put into alphabetical order by title. There are different formats for bibliographies,
so be sure to use the one your teacher prefers.
General Guide to Formatting a Bibliography
For a book:
Author (last name first). Title of the book. City: Publisher, Date of publication.
EXAMPLE:
Dahl, Roald. The BFG. New York: Farrar, Straus and Giroux, 1982.
For an encyclopedia:
Encyclopedia Title, Edition Date. Volume Number, "Article Title," page numbers.
EXAMPLE:
The Encyclopedia Brittanica, 1997. Volume 7, "Gorillas," pp. 50-51.
For a magazine:
Author (last name first), "Article Title." Name of magazine. Volume number, (Date): page numbers.
EXAMPLE:
Jordan, Jennifer, "Filming at the Top of the World." Museum of Science Magazine. Volume 47, No.
1, (Winter 1998): p. 11.
For a newspaper:
Author (last name first), "Article Title." Name of newspaper, city, state of publication. (date):
edition if available, section, page number(s).
108

EXAMPLE:
Powers, Ann, "New Tune for the Material Girl." The New York Times, New York, NY. (3/1/98):
Atlantic Region, Section 2, p. 34.
For a person:
Full name (last name first). Occupation. Date of interview.
EXAMPLE:
Smeckleburg, Sweets. Bus driver. April 1, 1996.
For a film:
Title, Director, Distributor, Year.
EXAMPLE:
Braveheart, Dir. Mel Gibson, Icon Productions, 1995
CD-ROM:
Disc title: Version, Date. "Article title," pages if given. Publisher.
EXAMPLE:
Compton's Multimedia Encyclopedia: Macintosh version, 1995. "Civil rights movement," p.3.
Compton's Newsmedia.
Magazine article:
Author (last name first). "Article title." Name of magazine (type of medium). Volume number,
(Date): page numbers. If available: publisher of medium, version, date of issue.
EXAMPLE:
Rollins, Fred. "Snowboard Madness." Sports Stuff (CD-ROM). Number 15, (February 1997): pp.
109

15-19. SIRS, Mac version, Winter 1997.
Newspaper article:
Author (last name first). "Article title." Name of newspaper (Type of medium), city and state of
publication. (Date): If available: Edition, section and page number(s). If available: publisher of
medium, version, date of issue.
EXAMPLE:
Stevenson, Rhoda. "Nerve Sells." Community News (CD-ROM), Nassau, NY. (Feb 1996): pp. A4-
5. SIRS, Mac. version, Spring 1996.
Online Resources
Internet:
Author of message, (Date). Subject of message. Electronic conference or bulletin board (Online).
Available e-mail: LISTSERV@ e-mail address
EXAMPLE:
Ellen Block, (September 15, 1995). New Winners. Teen Booklist (Online). Helen
Smith@wellington.com
World Wide Web:
URL (Uniform Resource Locator or WWW address). author (or item's name, if mentioned), date.
EXAMPLE: (Boston Globe's www address)
http://www.boston.com. Today's News, August 1, 1996.
Research Design and Execution
An understanding of research design and execution is important for enabling graduates to
provide effective service to a wide variety of researchers and to evaluate archival operations from
the perspective of users. It also allows graduates to assess the status of research in their own
110

discipline, to undertake new research, and to blend theoretical and empirical aspects of archival
studies into scholarly investigations.
Finalizes and present best design
As a researcher finalizing your research paper is important in order to: free your paper from
any flaws (grammatical, punctuation, spelling); ensure that all of the parts contains the information
needed; assure that all the part necessary for the research are included; and references are properly
cited in the text and in the bibliography.
General
 the paper follows the order prescribed by the teacher
 the paper had been proofread and all corrections are made.
 The title page contains all necessary information and follows the format specified by the
teacher.
Sources
All the sources used in the paper are properly cited in the list of references.
All ideas and references from the source have been internally cited within the paper Iin text
citation).
Do not use information from unreliable sources (Wikipedia, sparknotes, and clifnotes etc.)
Development
The ideas included in the paper are appropriate for each part.
The paper follows logical order.
Subtopics are supported with examples, quotations, references, description and / or definition.
111

Senior High School Research Presentation Rubric
Undergraduate research is becoming more important in higher education as evidence is
accumulating that clear, inquiry-based learning, scholarship, and creative accomplishments can and
do foster effective, high levels of student learning. This curricular innovation includes identifying a
concrete investigative problem, carrying out the project, and sharing findings with peers. The
following standards describe effective presentations.
Standards 5 - 4
Exemplary
3 - 2
Satisfactory
1-0
Unacceptable
Score Weight Total
Score
Organization Has a clear opening
statement that
catches audience’s
interest; maintains
focus throughout;
summarizes main
points
Has opening
statement relevant
to topic and gives
outline of speech; is
mostly organized;
provides adequate
“road map” for the
listener
Has no opening
statement or has
an irrelevant
statement; gives
listener no focus
or outline of the
presentation
X 2
Content Demonstrates
substance and depth;
is comprehensive;
shows mastery of
material
Covers topic; uses
appropriate sources;
is objective
Does not give
adequate coverage
of topic; lacks
sources
X 2
Quality of
conclusion
Delivers a conclusion
that is well
documented and
persuasive
Summarizes
presentation‘s main
points; draws
conclusions based
upon these points
Has missing or
poor conclusion; is
not tied to
analysis; does not
summarize points
that support the
conclusion
X 2
Delivery Has natural delivery;
modulates voice; is
articulate; projects
enthusiasm, interest,
and confidence; uses
Has appropriate
pace; has no
distracting
mannerisms; is
easily understood;
Is often hard to
understand; has
voice that is too
soft or too loud;
has a pace that is
X 1.5
112

body language
effectively
too quick or too
slow; demonstrates
one or more
distracting
mannerisms
Use of media Uses slides
effortlessly to
enhance presentation;
has an effective
presentation without
media
Looks at slides to
keep on track; uses
an appropriate
number of slides
Relies heavily on
slides and notes;
makes little eye
contact; uses slides
with too much text
X 1.5
Response to
Questions
Demonstrates full
knowledge of topic;
explains and
elaborates on all
questions
Shows ease in
answering questions
but does not
elaborate
Demonstrates little
grasp of
information; has
undeveloped or
unclear answers to
questions
X 1
Grand Total _____________________
Reviewer_________________________________________________
Adopted: 7/16/2017 – Dorothy Mitstifer, Kappa Omicron Nu
113

https://www2.archivists.org/gpas/curriculum/research-design-execution
https://www.teachervision.com/writing-research-papers/research-paper-
how-write-bibliography
Martyn Shuttleworth,How to write a conclusion
https://explorable.com/drawing-conclusions
All Rights Reserved 2017
114

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