Roger Millsap's Home Page

This website contains links to various pages, some related to my professional work, and others related to personal interests.

Professional Stuff

On the professional side, I am a faculty member in the Department of Psychology at Arizona State University in Tempe, Arizona. I am a quantitative psychologist, which means that I develop and/or apply statistical and mathematical methods to help resolve research questions in psychology. I am currently teaching coursework in psychological measurement and statistical methods at the doctoral level as an active member of the Quantitative Psychology Doctoral Program in the Department. I am also working as the Co-Director of the Methodology Core group at the Prevention Research Center on campus. This Center conducts research on programs to help children and families cope with stressful experiences, with a focus on parental divorce, poverty, bereavement, and parental job loss.

You can find a list of my publications here. I am now serving as the Editor of Psychometrika, the journal of the Psychometric Society. Psychometrika is a journal that publishes papers on all aspects of quantitative psychology. I am the former Editor of Multivariate Behavioral Research, (MBR) a quarterly journal devoted to multivariate research methods in the behavioral sciences. MBR is the journal of the Society of Multivariate Experimental Psychology(SMEP), a group of researchers with common interests in multivariate research methods and their use in behavioral science. I am on the Editorial Boards of two other journals, Applied Psychological Measurement and Structural Equation Modeling.

I am active in several professional organizations. The SMEP group is one of these; their annual meeting is one that I enjoy attending every year. I also attend meetings of the Psychometric Society, a group that meets internationally on alternate years. The most recent meeting in July of 2007 was in Tokyo. I am also active in Division 5 (Evaluation, Measurement, and Statistics) of the American Psychological Association. Division 5 is devoted to the development and dissemination of quantitative methods within the psychological community. In previous years, I have been active in the Federation of Behavioral, Psychological, and Cognitive Sciences, a coalition of many professional groups within the behavioral sciences. If you are interested in funding issues or legislation in Washington D.C. related to behavioral science, you will want to know more about the Federation.

Research Interests

My central research interest at present concerns methods for detecting bias in psychological measures. The bias of interest concerns systematic errors of measurement that are associated with group membership, with groups usually defined demographically (although other definitions of "group", such as randomized groups in experiments, are potentially interesting as well). Technically, we say that a measure is "biased" if systematic group differences in observed scores exist among individuals who are identical on the attribute(s) being measured. This latter requirement is the difficult part, as there is ordinarily no sure way to completely "match" individuals on attributes that cannot be directly measured. Nearly all psychological attributes fall in this category; we cannot directly measure emotions, attitudes, or abilities. The challenge of this research area is to develop methods for studying bias in the absence of simple, direct measures on which to match individuals from different groups.

Related to the above, I have an ongoing interest in statistical methods associated with latent variable models , especially factor analysis and item response theory. Simply put, latent variable models attempt to model observed states or conditions as outcomes of unobserved processes or conditions. A latent variable model imposes some structure in the relationship between the observed and unobserved variables, but the structure is not generally fully amenable to empirical tests. Factor analysis is one of the older models (see FA100 for a recent conference on 100 years of factor analysis); item response theory is a bit younger, but hardly new. Aside from applications of latent variable models to the measurement bias problem, I have done work in longitudinal models, factor models for multitrait-multimethod data(MTMM), and identification problems in latent variable models.

For a list of resources related to the above research interests, see the following:

Current Teaching

I am currently teaching three courses on a rotating basis. The first course, Psych 534 Psychometric Methods, is a doctoral course in the theory and practice of psychological measurement. The course presumes no prior coursework in measurement, but does assume some prior coursework in statistics, especially in linear models and regression. You can find the latest version of the syllabus here. Please note that syllabi are periodically updated. Psych 534 emphasizes an understanding of measurement models, especially classical test theory and generalizability theory, as a way of motivating reliability concepts. The course also emphasizes test validity and validation, focusing on empirical strategies for validation and their basis in theory. The second course, Psych 591 Advanced Psychometrics, is a course in item response theory (IRT), but also dwells on the use of confirmatory factor analysis to test measurement hypotheses. The previous course Psych 534 (or its equivalent) is a prerequisite for Psych 591. Psych 591 requires the use of software for IRT, but no prior knowledge of this software is assumed. The current syllabus for Psych 591 is available here. The course begins by presenting a variety of IRT models, their assumptions, methods of estimation, and model-fitting. We then study various applications of IRT, including computer adaptive testing, the study of item bias, and the use of IRT in longitudinal measurement. The third course, Psych 533 Structural Equation Modeling is a course in structural equation modeling (SEM). The course assumes no prior exposure to this topic, but does require a previous course in multivariate statistics. The current syllabus for this course is available here. We use the LISREL and Mplus software programs in this course. We begin with path analyses in observed variables, and then move to confirmatory factor models. These two topics merge at the end of the course with the full structural equation model.

Personal Interests

Last Updated June 9, 2008
Web Page by Roger E. Millsap
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