Website: Bayesian Methods for Education Research
David Kaplan is the Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin – Madison. Dr. Kaplan holds affiliate appointments in the University of Wisconsin’s Department of Population Health Sciences and the Center for Demography and Ecology. Dr. Kaplan is a Research Fellow of the German Institute for International Educational Research and is also an Honorary Research Fellow in the Department of Education at the University of Oxford. He is an elected member of the National Academy of Education, a recipient of the Samuel J. Messick Distinguished Scientific Contributions Award from Division 5 of the American Psychological Association, Fellow of the American Psychological Association (Division 5), a recipient of the Humboldt Research Award, and was a Jeanne Griffith Fellow at the National Center for Education Statistics. Dr. Kaplan received his Ph.D. in education from UCLA in 1987.
My current program of research focuses on the development of Bayesian methods applied to a wide range of education research settings. My specific interests include: Bayesian model averaging; objective versus subjective Bayesian modeling; and Bayesian approaches to problems in large-scale survey methodology. My collaborative research involves applications of advanced quantitative methodologies to substantive and methodological problems in international large-scale assessments in education. I have been actively involved in the OECD Program for International Student Assessment (PISA) where I served on its Technical Advisory Group from 2005-2009 and its Questionnaire Expert Group from 2004-present where I served as the Chair of the Questionnaire Expert Group for PISA 2015 and remain a member of the Questionnaire Expert Group for PISA 2018. I also sit on the Questionnaire Expert Group for the OECD Teaching and Learning International Survey (TALIS) as well as the Design and Analysis Committee and the Questionnaire Standing Committee for the National Assessment of Educational Progress (NAEP).
Structural equation modeling, Bayesian statistical methods.
Selected Grants and Sponsorships
- 2011-2016 – Amount: $3,496,812.00, “Longitudinal Study Of Vocabulary Growth And Phonological Development,” Awarded By: National Institute of Deafness and other Communicative Disorders, David Kaplan, Co-Principal; Jan Edwards, Principal; Mary Beckman, Co-Principal; Benjamin Munson, Co-Principal.
- 2011-2014 – Amount: $566,397.00, “Bayesian Inference For Experimental And Observational Studies In Education,” Awarded By: Institute of Educational Sciences, David Kaplan, Principal.
- 2010-2014 – Amount: $1,600,000.00, “Validating Universal Screening And Progress Monitoring Instruments For Use With Ells In Response-To- Intervention Models.,” Awarded By: Institute of Education Sciences, Sponsor Type: Federal, David Kaplan, Co-Principal; Craig A. Albers, Principal; Thomas R. Kratochwill, Co-Principal.
- Kaplan, D. (2016). Causal inference with large-scale assessments in education: A Bayesian perspective.Large-Scale Assessments in Education, 4, doi; 10.1186/s40536-016-0022-6
- Kaplan, D., & Lee, C. (2016). Bayesian model averaging over directed acyclic graphs with implications for the predictive performance of structural equation models. Structural Equation Modeling: A Multidisciplinary Journal.
- Kaplan, D., & Su, D. (2016). On matrix sampling and imputation of context questionnaires with implications for the generation of plausible values in large-scale assessments. Journal of Educational and Behavioral Statistics. 41, 57-80.
- Park, S., & Kaplan, D. (2015). Bayesian causal mediation analysis for group randomized designs with homogenous and heterogenous treatment effects: Simulation and Case Study. Multivariate Behavioral Research. 50, 316-333.
- Chen, J., & Kaplan, D. (2015). Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies. Journal of Research on Educational Effectiveness. 8, 280-302.
- Kaplan, D., & Chen, J. (2014). Bayesian model averaging for propensity score analysis. Multivariate Behavioral Research. 49, 505-517.
- Kaplan, D. (2014). Bayesian statistics for the social sciences. New York: Guilford Press.
- van de Schoot, R., Kaplan, D., Dennisen, J., Asndorpf, J.B., Neyer, F.J., & van AKen, M. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. 85, 842-860.
- Kaplan, D., & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (Eds.), Oxford Handbook of Quantitative Methods, (pp. 407-437). Oxford: Oxford University Press.
- Kaplan, D., & McCarty, A.T. (2013). Data fusion with international large scale assessments: A case study using the OECD PISA and TALIS surveys. Large-scale Assessments in Education. 1(6), doi: 10.1186/2196-0739-1-6.
- Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika. 77, 581-609.
- Kaplan, D., & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (Eds.), Handbook of Structural Equation Modeling, (pp. 650-673). Guilford Publications Inc.
- Kaplan, D., & Keller, B. (2011). A note on cluster effects in latent class analysis. Structural Equation Modeling. 18, 526-536.
- Kaplan, D., & Depaoli, S. (2011). Two studies of specification error in models for categorical latent variables. Structural Equation Modeling. 18, 397-418.
- Kaplan, D. (2009). Structural Equation Modeling: Foundations and Extensions. Newbury Park, CA: SAGE Publications.
Books and Edited Volumes
- Kuger, S., Klieme, E., Jude, N. & Kaplan, D. (Eds.) (2016). Assessing Contexts of Learning: An International Perspective. Heidelberg, Springer.
- Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press.
- Kaplan, D. (2009). Structural Equation Modeling: Foundations and Extensions (2nd Edition). Newbury Park, CA: SAGE Publications.
- Kaplan, D. (Ed.) (2004). The SAGE Handbook of Quantitative Methodology in the Social Sciences. Newbury Park, CA: SAGE Publications.
- Kaplan, D. (2000). Structural Equation Modeling: Foundations and Extensions. Newbury Park, CA: Sage Publications.
- Kaplan, D., & Chen, J. (2013). Bayesian model averaging for propensity score analysis, 78th Annual Meeting of the Psychometric Society, Arnhem, The Netherlands.
- Kaplan, D., & Turner, A. (2011). Statistical Matching of Large-Scale Assessments: A Case Study of PISA and TALIS., National Council on Measurement in Education, Vancouver, BC.
- Kaplan, D. (2011, August 7). Propensity score analysis from a Bayesian perspective., Annual Meeting of the American Psychological Association, Washington, DC.
- Kaplan, D. (2011, July 22). A two-step appraoch for Bayesian propensity score analysi, 76th Annual and 17th International Meeting of the Psychometric Society., Hong Kong.
- Kaplan, D. (2011, April 11). Bayesian Multilevel SEM for Predicting Student Achievement: An Application to PISA, AERA, New Orleans.
- Kaplan, D. (2011, March 6). Bayesian propensity score analysis, Society for Research on Educational Effectiveness, Washington DC.
- Kaplan, D., & Chen, J. (2010). A comparative study of Bayesian and frequentist propensity score analysis., Society for Multivariate Experimental Psychology, Atlanta, Georgia.
- Kaplan, D., & Depaoli, S. (2010). Bayesian growth mixture modeling: Theory and applica- tion., International Meeting of the Psychometric Society, Athens, Georgia.
- Kaplan, D. (2010). A Bayesian Perspective on Methodologies for Drawing Causal Inferences in Experimental and Non-Experimental Settings., Society for Research on Educational Effectiveness, Washington, DC.
Awards and Honors
- PhD, Education, Quantitative Methods, University of California – Los Angeles
- MA, Education, Quantitative Methods, University of California – Los Angeles
- BA, Psychology, California State University – Northridge