David Kaplan
After receiving his Ph.D. in education from UCLA in 1987, Dr. Kaplan joined the faculty of the University of Delaware where he remained until 2006. He is presently Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin - Madison, and holds affiliate appointments in the Department of Population Health Sciences, and the Gaylord Nelson Institute for Environmental Studies. 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) where he is currently a member of the Technical Advisory Group and the Questionnaire Expert Group for the OECD/Programme on International Student Assessment (PISA). He 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.
RESEARCH STATEMENT
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 interested Bayesian approaches to causal inference in experimental and quasi-experimental settings. In the experimental setting, I am particularly interested in the use of Bayesian informative hypotheses to guide the testing of causal claims. In the quasi-experimental setting, my current focus of research is on applications and developments in Bayesian propensity score modeling and Bayesian model averaging as a means of improving causal estimands.
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.
REPRESENTATIVE PUBLICATIONS
Kaplan, D., Kim J-S., & Kim, S-Y. (2009). Multilevel latent variable modeling: Current
research and recent developments. In R. E. Millsap and A. Maydeu-Olivares (eds.), The
SAGE Handbook of Quantitative Methods in Psychology. Newbury Park: SAGE Publications.
Kaplan, D. (2009). Causal inference in non-experimental educational policy research. In G. Sykes, B. Schneider., & D. N. Plank (Eds.), Handbook on Education Policy Research. New
York: Taylor and Francis.
Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (2009) Early math matters:
Kindergarten number competence and later mathematics outcomes. Developmental Psychology, 45, 850-867.
Kaplan, D. (2009). Structural Equation Modeling: Foundations and Extensions (2nd Edition). Newbury Park, CA: Sage Publications.
Kaplan, D. (2008). Univariate and multivariate autoregressive time series models of offensive baseball performance: 1901 – 2005. Journal of Quantitative Analysis in Sports.
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://www.bepress.com/jqas/vol2/iss3/2
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.
Dr. Kaplan’s full vita is here: kaplan_cv.pdf