While on the marketing faculty at Arizona State University, I had the privilege of teaching a doctoral seminar in multivariate statistics. That I was teaching the course probably surprised me more than anyone. Why? Because I've never considered myself a 'quant jock.' For reasons probably due to genetic ancestry, I find it difficult to get excited about the inner workings of optimization algorithms or exploring the sensitivity of MANOVA to violations in the independence assumption, for example. Rather, my interest in the various multivariate tools arises from their usefulness as a means for examining phenomena that do interest me. Now, if an analysis tool allows me to identify the direct and indirect effects of mass media on identity vs. the direct and indirect effects of possessions on identity, you've got my attention! (can you identify an analysis tool that can do just this?).

As a number of folks have asked for the seminar's syllabus and/or reading lists, I've published them here for easy access by all (well, at least all with ready web access).

If you have any questions, please drop me a note.

A Caveat

I am not a quant jock, nor do I play one on TV. My strength with multivariate statistics is in my ability to explain multivariate analysis tools in a way that makes sense to the statistics-challenged. I emphasize the issues scholars face in trying to divine whether the data support their a priori hypotheses.

An Emphasis

In my experience, few multivariate statistics courses emphasize, or even address, the practical issues associated with doing the tasks fundamental to the success of any research project:

  • designing a study with full consideration given to the analysis methods used to test the hypotheses,
  • applying the analysis tools to evaluate measurement quality, and, ultimately,
  • applying the analysis tools to your data and interpreting the results in light of your hypotheses.

I designed this seminar to address head-on those weaknesses.


This semester-long seminar was designed originally for marketing doctoral students who had completed all other program course work and were rapidly approaching their comprehensive exams and the task of developing their dissertation proposal. Students were assumed to bring with them familiarity with the research process, experimental design, ANOVA, and multiple regression. The content of the course proved popular with students in not only marketing, but also management, operations research, and political science.

Seminar Overview

The seminar flows like this:

We begin with an overview of the research process and issues unique to the dissertation process.

Measure Quality Assessment then receives extensive coverage. We begin exploring measure reliability and validity with simple bi-variate correlations, factor analysis, and ultimately confirmatory factor analysis.

Testing Construct Relations is then covered extensively. We first build on our knowledge gained via confirmatory factor analysis to explore hypothesis testing using the full covariance structure analysis framework. We then tour the more conventional multivariate analysis tools (e.g., MANOVA, multivariate regression, etc. with an emphasis on how to use them to test theoretically driven construct relations.



The Seminar's Full Agenda Follows:

NOTE: The Syllabus and reading lists are in Adobe Acrobat Format. You need the Adobe Acrobat Reader to view these files. Acrobat Reader is probably already installed on your machine.

If you don't have Acrobat Reader, Click the logo below to get your free copy:Click here to get the Acrobat Reader from Adobe.   It's Free!

  1. The syllabus (from 1996)
  2. What's my style?
  3. Colloquium on Doing a Dissertation and Rob's Thoughts on Doing Exemplary Research
  4. Is another study necessary?  Literature reviews, meta-analysis, and replication. 
  5. Finding, using, and creating measures.
  6. Measure quality assessment (MQA) I:  Exploratory factor analysis (and principle components analysis).
  7. MQA II:  Introducing . . . confirmatory factor analysis.
  8. MQA III:  CFA cont'd and MTMM madness.
  9. Testing construct relations:  Let's get structural . . . structural . . .
  10. The full CSA model cont'd. and concluded.
  11. Canonical correlation analysis.
  12. Multiple regression and discriminant analysis.
  13. Multivariate analysis of variance.
  14. ANOVA, ANCOVA, interpretation of interaction effects, pairwise comparisons.
  15. Grab Bag:  Categorical data analysis, multidimensional scaling and cluster analysis
  16. Putting it all together.

Click Here!