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David Kaplan , Ph.D.

Professor, Quantitative Methods

PhD, 1987, University of California, Los Angeles

David Kaplan received his Ph.D. in education from UCLA in 1987 after which he joined the faculty of the University of Delaware where he remained until 2006. He is currently Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin - Madison. and holds an affiliate faculty appointment in the interdisciplinary Prevention Sciences Program. Dr. Kaplan has been a consultant on numerous projects sponsored by the U.S Department of Education (IES and NCES), the National Science Foundation, and the Organization for Economic Cooperation and Development (OECD). He is currently a member of the Technical Advisory Group and the Questionnaire Expert Group for the OECD/Programme on International Student Assessment (PISA), the Technical Working Group for the Reading First Implementation Evaluation, and chairs the Research Advisory Committee for the American Educational Research Association.  Dr. Kaplan also served on numerous grant review panels, including the National Science Foundation, the National Institutes of Health (NICHD, NIGMS, NIMH), the U.S. Dept. of Education, and The Spencer Foundation.  Dr. Kaplan has been a visiting professor at the Hebrew University of Jerusalem and the University of Milano-Bicocca. Dr. Kaplan was recently named a Vilas Associate at the University of Wisconsin - Madison, and during the 2001-2002 academic year, he was the Jeanne Griffith Fellow at the National Center for Education Statistics.


My current program of research focuses on the development and testing of statistical models for social and behavioral processes that are not necessarily directly observed. Latent variable models, growth curve models, mixture models, and Markov models can be used to study unobserved processes and together constitute statistical methodologies that interest me. I am currently most interested in the application of Bayesian inferential methods applied to these methodologies.

I am also very interested in the problem of causal inference in non-experimental settings within a 'structuralist' perspective. Specifically, I am interested in improving causal inferences drawn from the application of structural models to problems in education and the social and behavioral sciences. Of specific concern is the issue of "exogeneity" in linear statistical models. The weak form of exogeneity concerns the consequences of assuming that marginal information in causal variables can be ignored for the purposes of the estimation of causal relationships. Another form of exogeneity links weak exogeneity and parameter invariance into super-exogeneity, a condition that is crucial for causal inferences.

My collaborative research involves applications of advanced quantitative methodologies to problems in educational psychology, human development, and international comparative education. I am most actively involved in the OECD Program for International Student Assessment (PISA) where I serve on its technical advisory group and questionnaire expert group.

Finally, I have an avid interest in the statistical modeling of Major League Baseball data. The game of baseball represents a complex probabilistic and dynamic structure that lends itself to the application of advanced statistical methods. I am specifically interested in exploring the use dynamic statistical models for the study of change over seasons in baseball performance measures.


Kaplan, D. (in press).  Causal inference in non-experimental educational policy research.  In D. N. Plank, W. E. Schmidt, & G. Sykes (Eds.), AERA Handbook on Education Policy Research.  Washington, D. C.:  AERA

Kaplan, D. (in press).  Univariate and multivariate autoregressive time series models of offensive baseball performance: 1901 – 2005.  Journal of Quantitative Analysis in Sports.

Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (in press).  Development of number combination skill in the early school years:  When do fingers help? Developmental Science

Kaplan, D.  (2008).  An overview of Markov chain methods for the study of stage-sequential developmental processes.  Developmental Psychology, 44, 457-467.

Jordan, N. C., Kaplan, D., Locuniak, M. N. & Ramineni, C. (2007). Predicting first-grade math achievement from developmental number sense trajectories. Learning Disabilities, Research & Practice, 22, 37-47.

Kaplan, D. & Sweetman, H. M. (2006). Finite mixture modeling approaches to the study of growth in academic achievement. In. R. Lissitz (ed.), Longitudinal and Value Added Models of Student Performance, (pp. 130 – 169). Maple Grove , MN . JAM Press.

Kaplan, D. (2006). A variance decomposition of offensive baseball performance. Journal of Quantitative Analysis in Sports. http/

Kaplan, D & Walpole, S. (2005). A stage-sequential model of reading transitions: Evidence from the Early Childhood Longitudinal Study. Journal of Educational Psychology, 97, 551-563.

Kaplan, D. (2005)  Finite Mixture Dynamic Regression Modeling of Panel Data with Implications for Dynamic Response Analysis.  Journal of Educational and Behavioral Statistics, 30, 169-187.

Kaplan, D. (Ed.) (2004).  The Sage Handbook of Quantitative Methodology in the Social Sciences.  Newbury Park, CA:  Sage Publications.

Kaplan, D. (2002).  Modeling Sustained Educational Change With Panel Data:  The Case for Dynamic Multiplier Analysis.  Journal of Educational and Behavioral Statistics, 27, 85-103.

Kaplan, D., Harik, P., & Hotchkiss. (2000). Cross-sectional Estimation of Dynamic Structural Equation Models in Disequilibrium. In R. Cudeck, S. H. C. du Toit & D. Sorbom (Eds.), Structural Equation Modeling: Present and Future. A Festschrift in Honor of Karl G. Joreskog (pp. 315-339). Lincolnville: Scientific Software International.

Dr. Kaplan’s full vita is here: kaplan_cv.pdf



Educational Sciences Building, Room 1061
1025 W. Johnson Street
Madison, WI, 53706

Phone: (608) 262-0836

Email: dkaplan at education dot wisc dot edu

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