Using SPSS and AMOS for Research in Marketing and Behavioral Science
Ph. D. Level course on
Applying Statistics and SEM in Research
Faculty: Sonjaya S. Gaur, Ph. D.
Associate Professor of Marketing @ SJM SOM, IIT Bombay, India &
Visiting Research Professor, Marketing & Logistics Research Group,
LIU School of Management, Linköping University, Linköping, Sweden
Objectives
The principal objective of this course (seminar) is to develop the skills necessary to
design a publishable research and apply the statistical techniques found in contemporary
management research. The course (seminar) is organised around the themes of survey
based data analysis and linear modelling techniques. By the end of the course (seminar),
course participants will have learnt the method for a publishable research design, refined
the skills necessary to formulate an effective instrument (questionnaire) for survey based
research and implement the data analyses that form a fundamental part of conducting
good, rigorous research. As an offshoot of this, participants should also develop a better
appreciation for what constitutes a "good" research. This better appreciation will
translate into an improved ability to constructively critique, make use of research done by
others and publish their own work.
This course will not focus on statistical theories. Instead, the class will focus on how to
apply quantitative tools namely, Exploratory Factor Analysis (EFA), Confirmatory Factor
Analysis (CFA), Structural Equation Modelling (SEM) and Regression analysis for
hypotheses testing to research projects in fields of management sciences. Participants will
get hands-on experience in using statistical software namely, SPSS and AMOS to analyze
real data sets in this course. Topics include multivariate regression, factor analysis &
structure equation modelling. Target participants are Post graduate and Doctoral research
students.
Approach
Each session is structured as a seminar. Participants will be expected to have read and
critically analysed all the readings assigned for the session. The number of readings for
discussion in each session will range from several to none, depending on the focus of the
session. In addition to having prepared the readings and other required materials for the
session, participants will be expected to participate actively in the class session by
discussing the topics being explored. In some sessions, a series of questions might be
used to guide the discussion, in other sessions; participants might be asked to prepare a
presentation on one of the topics that we will cover.
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Using SPSS and AMOS for Research in Marketing and Behavioral Science
The latter statement reflects one of the key intents of the credit course (seminar) –
learning how to apply various techniques to make the research work publishable. To that
end, a strong emphasis will be placed upon the implementation and interpretation of
various research techniques. What this means in practice is that participants will be using
various statistical software packages on real data, and then presenting the results of these
data runs in the seminar, or in the form of a written report.
Grading
There will be three components for grades:
Data analysis exercises participation
Participation in discussion
Final analytical report and paper
Participants will be provided with the datasets for most of the assignments. However, for
the final report, participants should have their own data. Any data collected in the past
can be used in this course. Participants are encouraged to discuss the final project at the
beginning so as to avoid any disappointment at the end.
Content and Session Details
The course is in the format of a workshop, so a minimum of 3 hours should be allotted to
it each day for 6 days.
This course has two sections that cover the following topics:
Part 1: Linear Regression Models:
Simple regression models: the basic linear model; correlation and causation; OLS
estimation; residuals and assumptions regarding error terms; standardized regression
coefficients and correlations; R-squared; statistical inference (confidence intervals and
hypothesis tests).
Multiple regression: examples of models; partial effects; causation; assumptions about
variables; testing individual regression coefficients; hypothesis tests concerning several
regression coefficients; standardized regression coefficients; stepwise regression.
Diagnosing multicollinearity: variance inflation factors; tolerances.
Part 2: Latent Variable Models: Exploratory and Confirmatory Factor analysis,
Structural Equation Modeling, reliability and validity considerations etc
Assessing model fit: goodness of fit statistics; model building strategies.
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Using SPSS and AMOS for Research in Marketing and Behavioral Science
Software
Participants will be exposed to and made to work on two major software used presently
in academic research. These are SPSS and AMOS.
Recommended reference books
Gaur A. S. & Gaur, Sanjaya S. (2006). Statistical Methods for Practice and Research: A Guide to
Data Analysis Using SPSS. New Delhi: Sage.
Barbara M. Byrne (2001). Structural Equation Modeling With AMOS: Basic Concepts,
Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum.
Tebachnick, B, G., & Fidell, L. 2001. Using Multivariate Statistics. Allyn and Bacon.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/
correlation analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
Kline, Rex B. (1998). Principles and Practice of Structural Equation Modeling. New
York: Guilford Press.
Tentative Session Plan (Each session is for 90 Minutes)
Session
Topics
Session 1
Introduction: Getting familiar, course expectations, requirements
etc., Over view of Research and Journal Publishing
Designing a publishable research,
Designing an effective instrument (questionnaire)
Simple Linear regression; Multiple Linear Regression
Testing moderating and mediating effects
Project proposal Presentation
Exploratory Factor Analysis
Confirmatory Factor Analysis
Reliability and Validity Considerations
Structural Equation Modeling
Summing up
Final Project Presentations
Session 2
Session 3
Session 4
Session 5
Session 6
Session 7
Session 8
Session 9
Session 10
Session 11-12
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