Research Design: Quantitative, Qualitative and Mixed Methods Design

Research Design
1
K.THIYAGU,
Assistant Professor, Department of Education,
Central University of Kerala, Kasaragod

• Blueprint
• Strategic plan
• Structural framework
Research Design
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• Research Design is a kind of blueprint that
we prepare before actually carrying out
research.
• Research Design is the strategic plan of the
project that sets out the broad structure of
the research.
• Research design is the framework of
research methods and techniques chosen by
a researcher.
• A Research Design is simply a structural
framework of various research methods as
well as techniques that are utilised by a
researcher.
Research Design
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Function and Purpose of
Research Design
Black and Champion (1976) have pointed
out the following three functions of
research design.
• A research design provides a blueprint
of operationalizing the research
activity.
• It defines the limit and scope of the
research
• It provides an opportunity to the
researcher to foresee possible areas of
problems in the process, carrying out
the research.
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10 Steps in Research Design
Reviewing the
Literature
Formulating
Hypothesis and
Identifying
Variables
Standardisation of
Research
Collection of
Data
Pilot Study/ use
of Statistical
other methods
Selection of
Research
techniques and
methods
Identifying the
Universe and
Unit of Study
Defining
Research
Problem
Analysing
Data
Interpretation
and report
writing
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Types of research
(Fundamental,
Applied and Action)
Criteria
(On the basis)
Types
Objectives Fundamental Research Applied Research Action Research
Nature of Data Qualitative Research Quantitative Research
Nature of Findings Explanatory Research Exploratory Research Descriptive Research
Experimental Manipulations Experimental Non-Experimental
Approach involved Longitudinal Research Cross sectional research
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Types of Scientific Method (Exploratory, Explanatory and Descriptive)
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Three Approaches of Research
Quantitative
Research
Quantitative research is an
approach for testing objective
theories by examining the
relationship among variables.
02
60%
Mixed Methods
Research
Mixed methods research is an
approach to inquiry involving
collecting both quantitative and
qualitative data, integrating the two
forms of data, and using distinct
designs that may involve
philosophical assumptions and
theoretical frameworks.
03
Good +60%
Qualitative
Research
Qualitative research is an approach
for exploring and understanding the
meaning individuals or groups
ascribe to a social or human
problem.
01
Good

QUANTITATIVE RESEARCH DESIGN
The use of graphics, figures, pie charts is the main form of
data collection measurement and meta-analysis (it is
information about the data by the data).
Design
In Quantitative Research Design, a
researcher examines the various
variables while including numbers as
well as statistics in a project to
analyze its findings.
85%

Case studies are mainly used in Qualitative Research Design
in order to understand various social complexities.
Example
This type of research is quite contrary to quantitative
research design. It is explanatory in nature and always seeks
answers to “What’s” and “How’s”. It mainly focuses on why a
specific theory exists and what would be the respondent’s
answer to it. This allows a researcher to draw a conclusion
with proper findings.
Design
QUALITATIVE RESEARCH DESIGN

VS
Analysis
Expression
Sample
Questions
Key Terms
Example
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02
03
04
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06
07
01
02
03
04
05
06
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Focuses on putting ideas and hypothesis
to the test
Concentrate on generating ideas
and developing a theory or
hypothesis
Math and statistical analysis were used to
examine the situation.
Numbers, graphs, and tables are the
most common forms of expression.
It necessitates the participation of a large
number of people
Closed Questions (Multiple Choices)
Key Terms: Testing, Measurement,
Objectivity, Replicability
Descriptive Survey, Experimental
Summarizing, Classifying and analyzing data
were used to conduct the analysis.
Mostly represented with words.
Only a few people are required to
answer
Open ended inquiries
Key Terms: Understanding, Context,
Complexity, Subjectivity.
Case Study
Quantitative Research (Fixed Design) Qualitative Research (Flexible Design)
Meaning

QUANTITATIVE METHODS MIXED METHODS QUALITATIVE METHODS
Pre-determined Both predetermined and emerging
methods
Emerging Methods
Closed Questions Both open and closed ended
Questions
Open ended questions
Attitude data
Census data
Observational data
Performance data
Multiple forms of data drawing on all
possibilities
Interview data
Observation data
Document data and
Audio visual data
Statistical Analysis Statistical and text analysis Text and image analysis
Statistical Interpretation Across databases interpretation Themes, Patterns interpretation
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Alternative Research Designs
Quantitative
Experimental designs
Non-experimental
Design (Survey)
Longitudinal Designs
Qualitative
Narrative Research
Phenomenology
Grounded Theory
Ethnographies
Case Study
Mixed
Convergent
Explanatory Sequential
Exploratory Sequential
Complex designs with
embedded core design
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Framework for Research – The Interconnection of worldviews, Design and Methods
Philosophical
Worldviews
Design
Research
Methods
Postpositivist
Constructivist
Transformative
Pragmatic
Quantitative
Qualitative
Mixed Method
Questions
Date Collection
Data Analysis
Interpretation
Research
Approaches
Qualitative
Quantitative
Mixed Method
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Research Design - Five Subtypes
01 02 03 04 05
Descriptive Research
Design
Descriptive research refers
to the methods that
describe the
characteristics of the
variables under study.
Experimental Research
Design
Experimental research
establishes a relationship
between the cause and effect
of a situation. It is a causal
design where one observes
the impact caused by the
independent variable on the
dependent variable.
Correlational Research
Design
A correlation refers to an
association or a
relationship between two
entities.
Diagnostic Research
Design
In a diagnostic research
design, the researcher is
trying to evaluate the
cause of a specific
problem or phenomenon.
Explanatory Research
Design
Explanatory design uses a
researcher’s ideas and
thoughts on a subject to
further explore their
theories. The research
explains unexplored aspects
of a subject and details about
what, how, and why of
research questions.

Characteristics of Research Design
The results projected in the research
should be free from bias and neutral.
With regularly conducted research,
the researcher involved expects
similar results every time. Your
design should indicate how to form
research questions to ensure the
standard of results.
There are multiple measuring tools
available. However, the only correct
measuring tools are those which help
a researcher in gauging results
according to the objective of the
research. The questionnaire
developed from this design will then
be valid.
The outcome of your design should
apply to a population and not just a
restricted sample. A generalized
design implies that your survey can
be conducted on any part of a
population with similar accuracy.
VALIDITY
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Research Design
Characteristics
NEUTRALITY
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Quantitative
Research
Design
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Survey Research
A survey design provides a quantitative or
numeric description of trends, attitudes, or
opinions of a population by studying a
sample of that population.
21

22
Creswell, J. W., & Creswell, J. D. (2018). Research
design (5th ed.). SAGE Publications.

Research Design Elements
Variables
Sampling
Techniques
Instruments
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Experimental
Research
Experimental research is
a scientific approach to
research, where one or
more independent
variables are
manipulated and applied
to one or more
dependent variables to
measure their effect on
the latter.

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Qualitative
Research
Designs
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Creswell, J. W., & Creswell, J. D. (2018). Research design (5th ed.). SAGE
Publications.

Variables
The quantity or condition
that can change
(anything that has a quantity or
quality that varies)
50

Independent
(A variable that we can control)
Cause
X Y
Independent Variable (IV)
also called
exposure,
explanatory,
manipulated variables
51

Dependent
(A variable that we can observe or measure)
Effects
X Y
Dependent Variable (IV)
also called
outcome,
explained,
response variable 52

X Y
Cause Effect
Manipulated Measured
Independent Variable Dependent Variable
53

CONTROLLED VARIABLE
A controlled variable is one
which the investigator
holds constant (controls)
during an experiment. Thus
we also know the
controlled variable as a
constant variable or
sometimes as a “control”
only.
54

Z: Confounding, Mediating and Moderating Variables
X Y
Z ?
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Confounding Variables
X Y
Z
Confounder Both X and Y are affected by Z
Subject
Interest
Hours
of Study
Exam
Score
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Mediating Variable
X Y
Z
Mediator A part of the association between X and Y goes through Z
Practice
Problems
Hours
of Study
Exam
Score
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Moderating Variable
X Y
Z
Moderator Z affects the association between X and Y
Tiredness
Hours
of Study
Exam
Score
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X Y
Z
Level of Education
Intervening Variable
Spending
Income
X Y
Z
Higher Education Higher Income
Better Occupation
X Y
Z
Poverty Shorter Longevity
Lack of access to
healthcare
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Active Variable
Variables which can be
manipulated
(The variables that the researcher creates
are the active variables.)
Active variables can also be
independent variables.
E.g. Effectiveness of Flipped Classroom
Strategies on achievement.
Attribute Variable
Variables which cannot be
manipulated
(variable where we do not alter the variable
during the study)
It can also be the
independent variable
Eg: age, gender, blood group, color of eyes, etc.
We might want to study the effect of age on weight.
We cannot change a person's age,
but we can study people of different ages and weights.
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Demographic Variables
Demographic variables are characteristics or
attributes of subjects that are collected to
describe the sample.
They are also called sample characteristics.
It means these variables describe study sample
and determine if samples are representative of
the population of interest.
Eg: age, gender, occupation, marital status,
income etc.
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Dichotomous
Variable
Gender: Male and female
Locality: Rural and Urban
Pregnant and non pregnant
Alive and dead
Literate and illiterate
Trichotomous
Variable
Residence:
Urban, semi urban and
rural
Religion:
Hindu, Muslim, and
Christianity
Multiple
Variables
Blood groups: A,B,AB and O
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Extraneous Variables
An extraneous variable refers to any variables that you
are not intentionally studying (or cannot study, perhaps
because of reasons of cost or difficulty)
Any variable that you are not intentionally studying in
your dissertation is an extraneous variable that could
threaten the internal validity of your results
When an extraneous variable changes systematically along
with the variables that you are studying, this is called
a confounding variable.
63

Dependent Variable
Task performance
(a continuous variable, measured in terms of the number of tasks
employees perform correctly per hour)
Independent Variable
Background music
(a nominal variable because employees are either
provided with or without background music)
Intentional Variables
The intentional variables in this study are the variables that the
researcher wants to examine.
These include one independent variable and one dependent variable.
Dependent Variable
Employee Tiredness
Employee Motivation
Job Satisfaction
Type of background music (chart, dance, classical music, etc.)
Loudness of background music (low, medium, high volumes, etc.)
Time of day morning, afternoon, night)
The extraneous variables in this study are those variables that
could also be measured, which may also affect the results.
Study: The relationship between background music and task performance amongst employees at a packing facility
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Dependent Variable
Exam performance
(statistics exam ranging from 0-100 marks)
Learning Format/Teaching Style
(either lectures or seminars)
Intentional Variables
The intentional variables in this study are the variables that the
researcher wants to examine.
These include one independent variable and one dependent variable.
Dependent Variable
Student Tiredness
Quality of lecturer vs. seminars; teacher
The extraneous variables in this study are those variables that
could also be measured, which may also affect the results.
Study: The impact of learning format/teaching style (lectures/seminars) on exam performance
65

Types of Extraneous Variables
• Environmental clues which tell the participant how
to behave, like features in the surrounding or
researcher’s non-verbal behavior.
Demand
characteristics
• where the researcher unintentionally affects the
outcome by giving clues to the participants about
how they should behave.
Experimenter /
Investigator Effects
• like prior knowledge, health status or any other
individual characteristic that could affect the
outcome.
Participant
variables
• noise, lighting or temperature in the environment.
Situational
variables
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Concept of Universe and Sample
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Characteristics
of a
Good Sample
A true representative of the population
Free from error or bias
Accuracy to the degree to which bias is
absent from the sample
Sample size adequate in size and reliable
Free from random sampling error
Each unit of the population should be
independent and relevant.
69

Techniques of Sampling
(Probability and Non-probability Sampling)
70

Tools & Techniques of
Research
71

Tool Constructions - Steps
Final Draft
Reliability & Validity
Reliability: Test retest, Split Half, Cronbach Validity: Face, Content, etc.
Pilot Study
Item Analysis Accept / Reject Statements
Preliminary Draft
Overlapping items – modified (Guide & investigator) Number of statement, scales, (Preliminary Draft)
Item Writing
Positive & Negative Statements Cover the content / dimensions
Planning
Study of books, articles & Experts Discussion List out the Statement
72

Tools of Research in Education
Inquiry
Forms
Questionnaire
Checklist
Score-card
Schedule
Rating scale
Opinionnaire
Attitude scale
Observation Interview Sociometry
Psychological
Test
Achievement
Aptitude
Intelligence
Interest
inventory
Personality
Measures
73

Rating Scales
Rating scales record judgment or opinions
and indicates the degree or amount of
different degrees of quality which are
arranged along a line is the scale.
75

Common wordings for Category Scales
Quality
Very Good Fairly Good Neither Good nor Bad Not Very Good Not good at all
Excellent Good Fair Poor -
Importance
Very Important Fairly Important Neutral Not so important Not at all important
Satisfaction
Very Satisfied Somewhat
Satisfied
Neither Satisfied nor
dissatisfied
Somewhat
dissatisfied
Very dissatisfied
Very Satisfied Quite Satisfied - Somewhat
satisfied
Not at all satisfied
Interest
Very Interested Somewhat Interested Not very Interested
76

Category Scales
Frequency
All of the time Very often Often Sometimes Hardly ever
Very often Often Sometimes Rarely never
All of the time Most of the time - Some of the time Just now and then
Attitude Scale
Strongly Agree Agree Neutral Disagree Strongly Disagree
Truth
Very True Somewhat True Not very true Not at all true
Definitely true More true than false More false than true Definitely not true
Performance
Distinguished Excellent Commendable Adequate Poor
Outstanding Satisfactory Unsatisfactory 77

Ladder Scale
Worst Possible Life
Best Possible Life
80

Item Analysis
Item analysis is a statistical technique which is used for selecting and rejecting the
items of the test on the basis of their difficulty value and discriminated power.
Objectives of Item Analysis
• To select appropriate items for the final
draft
• To obtain the information about the
difficulty value (D.V) of all the items
• To provide discriminatory power (D.I)
to differentiate between capable and
less capable examinees for the items
• To provide modification to be made in
some of the items
• To prepare the final draft properly (
easy to difficult items)
Steps of Item analysis
• Arrange the scores in
descending order
• Separate two sub groups of the
test papers
• Take 27% of the scores out of
the highest scores and 27% of
the scores falling at bottom
• Count the number of right
answer in highest group (R.H)
and count the no of right answer
in lowest group (R.L)
• Count the non-response (N.R)
examinees
82

Cronbach’s Alpha Value
Cronbach's alpha is a measure of internal consistency, that is, how closely related a set of
items are as a group. It is considered to be a measure of scale reliability. A "high" value
for alpha does not imply that the measure is unidimensional.
MSE = Mean Score Error; MSB= Mean Score Between group
Spss demo
Excel demo
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‘t’ Test
Independent sample ‘t’ test
Comparing low and high group
Spss demo
Excel demo
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Correlation
Karl Pearson's Coefficient of Correlation
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r
The item total correlation is a correlation between the question score and the overall
assessment score. It is expected that if a participant gets a question correct they should, in
general, have higher overall assessment scores than participants who get a question wrong.
This relationship in psychometrics is called ‘discrimination’ referring to how well a question
differentiates between participants who know the material and those that do not know the
material.
Values for an item-total correlation (point-biserial) between 0 and 0.19 may indicate that the
question is not discriminating well, values between 0.2 and 0.39 indicate good discrimination,
and values 0.4 and above indicate very good discrimination.
Spss demo
Excel demo 85

Difficulty Value (D.V)
“The difficulty value of an item is defined as the proportion or percentage
of the examinees who have answered the item correctly” - J.P. Guilford
The formula for difficulty value (D.V)
D.V = (R.H + R.L)/ (N.H + N.L)
R.H – rightly answered in highest group
R.L - rightly answered in lowest group
N.H – no of examinees in highest group
N.L - no of examinees in lowest group
In case non-response examinees available means,
The formula for difficulty value (D.V)
D.V = (R.H + R.L)/ [(N.H + N.L)- N.R]
N.R – no of non-response examinees 86

Discrimination Index (D.I)
“Index of discrimination is that ability
of an item on the basis of which the
discrimination is made between
superiors and inferiors”
- Blood and Budd (1972)
Zero discrimination or No discrimination
The item of the test is answered correctly or know the answer by all the examinee’s.
An item is not answered correctly any of the examinee.
Positive discrimination index
An item is correctly answered by superiors and is
not answered correctly by inferiors. The
discriminative power range from +1 to -1.
Negative discrimination index
An item is correctly answered by inferiors and is
not answered correctly by superiors.
Types of Discrimination Index (D.I)
The formula for discrimination index(D.I)
D.I = (R.H - R.L)/ (N.H or N.L)
87

Range of
Difficulty Index
Interpretation Action
0 – 0.25 Difficult Revise or
discard
0.26 – 0.75 Right difficulty Retain
0.76 - above Easy Revise or
discard
Discrimination
Index
Item Evaluation
≥0.40 Very good items
0.30 - 0.39 Reasonably good but subject to
improvement
0.20 – 0.29 Marginal items , need improvement
<0.19 Poor items . Rejected or revised
88

Reliability Interpretation
.90 and above Excellent reliability; at the level of the best standardized tests.
.80 - .90 Very good for a classroom test
.70 - .80 Good for a classroom test; in the range of most. There are probably a few
items which could be improved.
.60 - .70 Somewhat low. This test should be supplemented by other measures (e.g.,
more test) for grading.
.50 - .60 Suggests need for revision of test, unless it is quite short (ten or fewer
items). The test definitely needs to be supplemented by other measures
(e.g., more tests) for grading.
.50 or below Questionable reliability. This test should not contribute heavily to the
course grade, and it needs revision.
Reliability Interpretation
89

Instruments /
Tools of Research
Validity, Reliability and
Standardisation of a Tool
90

Respondent
Test
Environment
Affect Reliability 94

Reliability
• The consistency of your
measurement instrument
• The degree to which an instrument
measures the same way each time it
is used under the same condition
with the same subjects
Reliability
is
95

Validity
• if an instrument measures what it
is supposed to
• how “true” or accurate the
measurement is
Validity
asks
96

Test-Retest
Reliability
Same Test
Same Sample
Different Times
98
Test-Retest Reliability:
Used to assess the consistency of a measure from one time to another.
(or) The consistency of a measure evaluated over time.

Parallel Forms Reliability
One major problem
with this approach is
that you have to be
able to generate lots of
items that reflect the
same construct.
Furthermore, this
approach makes the
assumption that the
randomly divided
halves are parallel or
equivalent. Even by
chance this will
sometimes not be the
case.
99
Parallel-Forms Reliability:
• Used to assess the consistency of the results of two tests constructed in the same way from the same
content domain.
• The reliability of two tests constructed the same way, from the same content.
Two Tests
Same Sample
At a same Times

Split-Half Reliability
• The parallel forms approach is
very similar to the split-half
reliability described below.
• The major difference is that
parallel forms are constructed
so that the two forms can be
used independent of each
other and considered
equivalent measures.
• With split-half reliability we
have an instrument that we
wish to use as a single
measurement instrument and
only develop randomly split
halves for purposes of
estimating reliability.
100
Internal Consistency Reliability:
• Used to assess the consistency of results across items within a test. (or)
• The consistency of results across items, often measured with Cronbach’s Alpha.
Single Test
Split halves

Average Inter-item Correlation
We first compute the
correlation between each
pair of items, as
illustrated in the figure.
In the example, we find an
average inter-item
correlation of .90 with the
individual correlations
ranging from .84 to .95.
101

This approach also uses the inter-
item correlations. In addition, we
compute a total score for the six
items and use that as a seventh
variable in the analysis.
The figure shows the six item-to-
total correlations at the bottom of
the correlation matrix. They range
from .82 to .88 in this sample
analysis, with the average of these
at .85.
Average Item total Correlation
102

Cronbach's Alpha (a)
103

Inter-Rater/Observer Reliability
104
Inter-Rater or Inter-Observer Reliability:
• Used to assess the degree to which different raters/observers give consistent estimates of the same
phenomenon. (or)
• The degree to which different raters/observers give consistent answers or estimates

Logical Statistical
Face Content Predictive
Construct
Concurrent
Validity
Consistency
Reliability Objectivity
106

Face Validity
(Logical Validity)
It refers to the transparency or relevance of a test as it
appears to test participants.
In other words, a test can be said to have face validity if it
"looks like" it is going to measure what it is supposed to
measure.
Eg: if a test is prepared to measure whether students can
perform multiplication, and the people to whom it is shown
all agree that it looks like a good test of multiplication
ability, this demonstrates face validity of the test.
E.g. A test of Mathematics should have numerical questions,
and
107

Content validity (also known as logical validity) refers to the extent to which a measure
represents all facets of a given construct.
For example, a depression scale may lack content validity if it only assesses
the affective dimension of depression but fails to take into account
the behavioral dimension.
Content validity is important primarily for measures of achievement
The test maker first determines the widely accepted goals of instruction in the subject
and then prepares a blueprint for the test. Test content is drawn from the course
content and weighted according to the weightage of the objectives of the course and the
course content.
Content Validity
(Logical Validity)
108

Infers that the test produces similar results
to a previously validated test
e.g.
VO2
max
Incremental
Treadmill Protocol
with expired gas
analysis
Multi-Stage Fitness
(Beep) Test
Concurrent Validity
(Statistical Validity)
109
Concurrent validity is evaluated by showing how
well the test scores correspond to already
accepted measure of performance or status made
at the same time.
Example
• Scores of a test of knowledge of basic
concepts in Geography can be validated
against the teachers' ratings of the students
on this aspect.
• Intelligence test were first validated against
school grades, teacher’s rating etc.
• A newly constructed test of intelligence may
be validated by finding its correlation with
another already existing well accepted test in
this area.
• In this cases, a correlation coefficient
between the two sets of measures is
calculated as an index of validity.

Infers that the test provides a valid
reflection of future performance using
a similar test
Can performance during
test A be used to predict
future performance in
test B?
A B
Predictive Validity
(Statistical Validity)
110
We may be interested in using a test
to predict some future outcome.
Example:
• A test of aptitude for teaching may
be used to admit students to
teacher’s training college and be
expected to predict success at the
job as teachers
• A clerical aptitude test may be used
to predict success on the job as
clerks.

Infers not only that the test is
measuring what it is supposed to, but
also that it is capable of detecting
what should exist, theoretically
Therefore relates to hypothetical or
intangible constructs
e.g.
Team Rivalry
Sportsmanship.
Construct Validity
(Logical / Statistical Validity)
111
Sometimes questions like the following
are asked
§ What does this test mean or signify?
§ What does the score tell us about the
individual?
§ These questions are related with
construct validity of the test.
The terms ‘construct’ is used in
psychology to refer to something that is
not observable but is literally
‘constructed’ by the investigator to
summarize or account for the regularities
or relationships that he observes in
behaviour.

Factorial Validity
Factorial validity is, in a way, extension of the construct
validity.
The intercorrelations of a large number of tests are
examined and if possible accounted for in terms of a much
smaller number of more general ‘factors’ or trait categories.
Sometimes 3 or 4 factors may account for the
intercorrelations among 15 to 20 test.
The factorial validity of a test is defined by its correlation
with a factor, called factor loading.
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Classification Data
Quantitative
Discrete
Counts are Discrete
(It is integer numbers)
Number of Children in Home
(2 Children)
Continuous
Measured are Continuous
(It may be an integer or
fractional number)
Height of a Student,
Age of person (2 yrs 3 mts 4
days),
weight
Qualitative
Binary
Two mutually
exclusive categories
Right / Wrong
True / False
Yes / No
Nominal
Observations can be
assigned a code in the
form number where the
numbers are simply labels.
Gender
Eye cooler
Roll number
Ordinal
Observations can be
ranked or ordered
Economic status
(low, medium and high)

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• It is used when the nature of data is grouping or
categorical
• Eg: Gender, Religions, locality etc
Frequencies
• It is used when there are two grouping variables in
the data set
• Eg. Gender with Religion
Crosstabs
• Descriptives command is used only when we deal
with continuous variable.
• Eg: Intelligence, achievement
Descriptives
• The command is used when we want to compare one
continuous variable against one categorical variable.
• Eg: Intelligence vs gender
Explore
Descriptive Statistics in SPSS

Skewness
Right Skewed:
Skewness > +1.0
Normal Probability Curve
Skewness = -1 to +1
Left Skewed:
Skewness < -1.0
Kurtosis
Leptokurtic:
Kurtosis > +1.0
Mesokurtic:
Kurtosis = -1 to +1
Platykurtic:
Kurtosis < -1.0
Measures of the shape of the distribution
Interpretation for the Psychometric Purposes
A Skewness & kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable.
117

Tests of Normality
•Claim:
•H0: The data come from the specified distribution;
•H1: The data do not come from the specified distribution
•It technically can be used to test if the data come from a known,
specific distribution (not just the normal distribution).
Kolmogorov-
Smirnov (K-S)
(Non-Parametric Test)
•Claim:
•Ho: The sample was drawn from a normal distribution.
•H1: The sample was not drawn from a normal distribution
Shapiro-Wilk
(Parametric Test)
The Shapiro-Wilk Test is more appropriate for small sample sizes
(< 50 samples), but can also handle sample sizes as large as 2000.
121

If p < Significant Level of Alpha (a)
Or p < 0.05 / 0.01
Reject the null hypothesis.
There is sufficient evidence
that the data is not normally
distributed.
If p > Significant Level of Alpha (a)
Or p > 0.05 / 0.01
Do not reject the null
hypothesis.
There is not enough evidence
to conclude that the data is
non-normal.
Tests of Normality
Criteria to Reject or Not Reject the Null Hypothesis:
122

Key points to keep in mind for doing a normality test are
as follows:
Skewness and kurtosis z-values should be
somewhere in the span of -1.96 to +1.96
The Shapiro-Wilk test p-value should be above
0.05
Histogram, normal Q-Q plots and box plots should
be visually inspected in order to check the
normality
123

Normal Q-Q Plot
In order to determine normality
graphically, we can use the output of a
normal Q-Q Plot. If the data are
normally distributed, the data points
will be close to the diagonal line
124

Median (Q2/50th Percentile):
The middle value of the dataset.
First Quartile (Q1/25th Percentile):
The middle number between the
smallest number (not the “minimum”)
and the median of the dataset.
Third quartile (Q3/75th Percentile):
The middle value between the median
and the highest value (not the
“maximum”) of the dataset.
Interquartile Range (IQR):
25th to the 75th percentile.
whiskers (shown in blue)
outliers (shown as green circles)
“Maximum”: Q3 + 1.5*IQR
“Minimum”: Q1 -1.5*IQR127

Website Resources
• https://sites.google.com/view/digigogy
• https://sites.google.com/view/digigogy/research-resources/digital-tools-for-research
• https://sites.google.com/view/digigogy/statistics-resources/digital-tools-for-statistics
• https://sites.google.com/view/digigogy/statistics-resources/jamovi-software-tutorial
CC-BY-NC-SA 130

Acknowledgement
Machine
• ICT Tools
• PPT
• Zoom
Human
• Reference Authors and Publishers
• Organisers
• My Teachers / My Students
• Participants
CC-BY-NC-SA 131

Thank You
132
K.THIYAGU,
Assistant Professor, Department of Education,
Central University of Kerala, Kasaragod

Research Design: Quantitative, Qualitative and Mixed Methods Design

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