Introduction to Research Methodology & Designs: from Theory to Practice by Elphistone College Mumbai

Government of Maharashtra’s
ELPHINSTONE COLLEGE
Mumbai – 400 032
NAAC Reaccredited ‘A’ Grade
INTERNAL QUALITY ASSURANCE CELL
AND
DEPARTMENT OF PSYCHOLOGY
Welcomes you in
A Two-Day Webinar
on
Introduction to Research Designs
From Theory to Practice
A Two-Day Webinar on Introduction to Research Designs: From Theory to Practice organized by Elphinstone College, Mumbai. Resource Person: Shivanand Thorat April 29, 2020

DAY 1
Brief introduction to research designs
Choosing an appropriate statistical test
Usefulness of ANOVA
Assumptions of ANOVA
Running one-way ANOVA with post hoc test in SPSS

DAY 2
Running one-way ANOVA in SPSS
Factorial ANOVA (2 x 3) with post hoc test in
SPSS
Running ANCOVA with assumptions in SPSS
Introduction to MANOVA
Running MANOVA with assumptions in SPSS

Research Design
A research design is the set of methods and procedures used in
collecting and analyzing measures of the variables specified in
the problem research.
The design of a study defines the
study type (descriptive, correlational, semi-experimental,
experimental, review, meta-analytic)
and
sub-type (e.g., descriptive-longitudinal case study), research
problem, hypotheses, independent and dependent
variables, experimental design, and, if applicable, data
collection methods and a statistical analysis plan.
A research design is a framework that has been created to find
answers to research questions.

• Observation
• Survey Research
Descriptive Methods:
• Independent Groups Designs
• Repeated Measures Designs
• Complex Designs
Experimental Methods:
• Single-Case Designs and Small-n Research
• Quasi-Experimental Designs and Program Evaluation
Applied Research:

Survey Research Designs:
• Cross-sectional design: One or more sample are
drawn from the population(s) ‘at the same time’.
• Successive independent samples design:
‘Different samples’ of respondents from ‘the
population’ complete the survey ‘over a time’.
• Longitudinal design: ‘The same respondents are
surveyed over time’

Experimental Methods

Complex Designs

Applied
Research
• Quasi-Experimental Designs:
• One-group pretest-posttest design
• Nonequivalent control group design
• Interrupted Time-Series Designs
• Simple interrupted time-series design
• Time series with nonequivalent control group

Applied Research: Program Evaluation

Decision Tree

ANalysis Of VAriance (ANOVA)
The popularity and usefulness of this technique can be attributed to two sources:
• First, the analysis of variance, like t, deals with differences between or among sample means;
unlike t, it imposes no restriction on the number of means.
Instead of asking whether two means differ, we can ask whether three, four, five, or k means differ.
• Second, the analysis of variance also allows us to deal with two or more independent variables
simultaneously, asking not only about the individual effects of each variable separately but also about
the interacting effects of two or more variables.

Assumptions
1. Homogeneity of Variance:
• A basic assumption underlying the analysis of variance is that each of our populations has the same
variance. In other words,
𝜎1
2
= 𝜎2
2
= 𝜎3
2
= 𝜎4
2
= 𝜎5
2
= 𝝈 𝒆
𝟐
where the notation 𝝈 𝒆
𝟐 is used to indicate the common value held by the five population variances.
This assumption is called the assumption of
homogeneity of variance
or if you like long words,
homoscedasticity.

2. Normality
A second assumption of the analysis of variance is that the
scores for each condition are normally distributed around
their mean.
In other words, each of the distributions is normal. Since
represents the variability of each person’s score around the
mean of that condition, our assumption really boils down to
saying that error is normally distributed within conditions.
Thus, you will often see the assumption stated in terms of
“the normal distribution of error.” Moderate departures
from normality are not usually fatal.

3. Independence of Observations
Third important assumption is that the observations are independent of one another.
Technically, this assumption really states that the error components are independent, but that amounts
to the same thing here.

Government of Maharashtra’s
ELPHINSTONE COLLEGE
Mumbai – 400 032
NAAC Reaccredited ‘A’ Grade
INTERNAL QUALITY ASSURANCE CELL
AND
DEPARTMENT OF PSYCHOLOGY
Welcomes you in
A Two-Day Webinar
on

Session 2
Running one-way repeated measures ANOVA in SPSS
Factorial ANOVA (2 x 3) with post hoc test in SPSS
Introduction to MANOVA
Running MANOVA with assumptions in SPSS

Running one-way repeated measures ANOVA in SPSS
Time
Pretest Posttest Posttest after 3 months
Depression Depression Depression

Factorial ANOVA (2 x 3) with post hoc test in SPSS
Location
Gender Urban Suburban Rural
Male Stress Stress Stress
Female Stress Stress Stress

Treatment
6 weeks 12 weeks
Emotional regulation Emotional regulation
Emotional regulation Emotional regulation

Running one-way MANOVA in SPSS
Customers
E-customers Mixed Customers Non E-Customers
Customer Satisfaction
&
Customer Loyalty
&
Customer Loyalty
&
Customer Loyalty
&
Customer Loyalty
&
Customer Loyalty
&
Customer Loyalty

MANOVA
The design is useful when a researcher is interested in studying several DVs together as an effect of one
or more IVs.
Theoretically it is assumed that IV(s) will have an effect not only on a single DV, but in combination of
some other closely associated DVs.
This allows us to study several criterion measures in a single study.
Four good reasons to plan MANOVA study:
1. Fragmented univariate tests lead to inflated type I error.
2. Univariate tests ignore the information like correlation among the variables, particularly among
DVs.
3. Although the groups may not be significantly different on any of the variables individually; but
jointly, the set of variables may differentiate the groups. (Enhances the power of statistics by
reducing the type II errors)
4. Sometimes, groups may not differ in terms of total score comparison, but groups could differ in
some sub-dimensions of a variable. (Reduces the type I error)

ASSUMPTIONS FOR MANOVA
All the assumptions of univariate ANOVA + following 4
1. Multivariate normality
2. Linear relationships
3. Homogeneity of covariance
4. Absence of multicollinearity

Running one-way MANOVA in SPSS
DVs
IV Customer Satisfaction Customer Loyalty
E-Customers
1
2
3
4
5
…
Mixed Customers
Non E-Customers

Introduction to Research Methodology & Designs: from Theory to Practice by Elphistone College Mumbai

Recommended

Recommended

More Related Content

Similar to Introduction to Research Methodology & Designs: from Theory to Practice by Elphistone College Mumbai

Similar to Introduction to Research Methodology & Designs: from Theory to Practice by Elphistone College Mumbai (20)

More from Shailesh Jaiswal

More from Shailesh Jaiswal (13)

Recently uploaded

Recently uploaded (20)

Introduction to Research Methodology & Designs: from Theory to Practice by Elphistone College Mumbai