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Department of Education

David Kaplan

Honorary Research Fellow

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, and is also an Honorary Fellow in the Department of Education at the University of Oxford. He is actively involved in the OECD Program for International Student Assessment (PISA) where he served on its Technical Advisory Group from 2005-2009 and its Questionnaire Expert Group from 2004-present where he served as the Chair of the Questionnaire Expert Group for PISA 2015 and remains a member of the Questionnaire Expert Group for PISA 2018.  Dr. Kaplan also sits on the Design and Analysis Committee and the Questionnaire Standing Committee for the National Assessment of Educational Progress (NAEP).  Dr. Kaplan is a member of the National Academy of Education, a recipient of the Humboldt Research Award, a fellow of the American Psychological Association (Division 5), 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.


Dr. Kaplan’s current program of research focuses on the development of Bayesian methods applied to a wide range of education research settings. His specific interests include: Bayesian model averaging; objective versus subjective Bayesian modeling; Bayesian posterior predictive causal inference; and Bayesian approaches to problems in large-scale survey methodology. Dr. Kaplan’s collaborative research involves applications of advanced quantitative methodologies to substantive and methodological problems in international comparative education.

Peer reviewed publications
  • Park, S. & Kaplan, D. (in press) Bayesian causal mediation analysis for group randomized designs with homogenous and heterogenous treatment effects: Simulation and Case Study. Multivariate Behavioral Research.
  • Chen, J. & Kaplan, D. (in press). Covariate Balance in a Two-Step Bayesian Propensity Score Approach for Observational Studies. Journal of Research on Education Effectiveness.
  • Kaplan, D. & Chen, J. (2014). Bayesian model averaging for propensity score analysis. Multivariate Behavioral Research, 49, 505-517.
  • Edwards, J., Gross, M., Chen, J., MacDonald, M. C., Kaplan, D., Brown, M. & Seidenberg, M. S. (2014). Comprension of mainstream American English in African American and English-speaking children. Journal of Speech, Language, and Hearing Research, 57, 1883-1895.
  • van de Schoot, R., Kaplan, D., Denissen, J., Asndorpf, J. B., Neyer, F. J. & van Aken, M. A. G. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. DOI: 10.1111/cdev.12169.
  • 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.
  • Hazzah, L., Dolrenry, S., Kaplan, D. & Frank, L. (2013) The influence of park access during drought on attitudes toward wildlife and lion killing behavior in Maasailand, Kenya. Environmental Conservation. doi:10.1017/S0376892913000040.
  • Valdez, C. R., Mills, M. T., Bohlig, A. J., & Kaplan, D. (2012). The Role of Parental Language Acculturation in the Formation of Social Capital: Differential Effects on High-risk Children. Child Psychiatry and Human Development, 44, 334{350. Published online: DOI 10.1007/s10578-012-0328-8.
  • Kaplan, D. & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77, 581{609. Published online. DOI: 10.1007/S11336-012- 9262-8. Erratum pg. 610.
  • Poehlmann, J., Schwichtenberg, AJ Miller, Hahn, E., Miller, K. Dilworth-Bart, J., Kaplan, D. & Maleck, S. (2012). Compliance, opposition, and behavior problems in toddlers born preterm or lower birthweight. Infant Mental Health Journal, 33, 34{44.
  • Kaplan, D., & Keller, B. (2011). A note on cluster effects in latent class analysis. Structural Equation Modeling, 18, 525{536.
  • Kaplan, D., & Depaoli, S. (2011). Two studies of specification error in models for categorical latent variables. Structural Equation Modeling, 18, 397{418.
  • 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.
  • Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (2008). Development of number combination skill in the early school years: When do fingers help? Developmental Science, 11, 662-668.
  • Kaplan, D. (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457-467.
  • Kaplan, D. (2008). Univariate and multivariate autoregressive time series models of offensive baseball performance: 1901{2005. Journal of Quantitative Analysis in Sports.
  • 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.
  • Jordan, N. C., Kaplan, D., Nabors-Ol ah, L., & Locuniak, M. N. (2006). Number sense growth in kindergarten: A longitudinal investigation of children at risk for mathematics difficulties. Child Development, 77, 153-175.
  • 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.
  • Archbald, D. A. & Kaplan, D. (2004). Parent choice versus attendance area assignment to schools: Does magnet-based school choice affect NAEP scores? International Journal of Educational Policy, Research & Practice, 5, 3-35.
  • Jordan, N. C., Hanich. L. B., & Kaplan, D. (2003). Arithmetic Fact Mastery in Young Children: A Longitudinal Investigation. Journal of Experimental Child Psychology, 85, 103-119.
  • Jordan, N. C., Hanich, L. B., & Kaplan, D. (2003). A longitudinal study of mathematical competencies in children with specific mathematics difficulties versus children with co-morbid mathematics and reading difficulties. Child Development, 74, 834-850.
  • Kaplan, D. (2002). Methodological advances in the analysis of individual growth with relevance to education policy. Peabody Journal of Education, 77, 189-215.
  • Jordan, N. C., Kaplan, D., & Hanich. L. B. (2002) Achievement growth in children with learning difficulties in mathematics: Findings of a two-year longitudinal study. Journal of Educational Psychology, 94, 586-597.
  • 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.
  • Hanich, L. B., Jordan, N. C., Kaplan, D., & Dick, J. (2001). Performance across different areas of mathematical cognition in children with learning difficulties. Journal of Educational Psychology, 93, 615-626.
  • Kaplan, D., & Kreisman, M. B. (2000). On the validation of indicators of mathematics education using TIMSS: An application of multilevel covariance structure modeling. International Journal of Educational Policy, Research, and Practice, 1, 217-242.
  • Kaplan, D. (1999). An extension of the propensity score adjustment method for the analysis of group differences in MIMIC models. Multivariate Behavioral Research, 34, 467-492.
  • Kaplan, D. & Ferguson, A. J. (1999). On the utilization of sample weights in latent variable models. Structural Equation Modeling, 6, 305-321.
  • Kaplan, D., & George, R. (1998). Evaluating latent variable growth models through ex post simulation. Journal of Educational and Behavioral Statistics, 23, 216-235.
  • George, R., & Kaplan, D. (1998). A structural model of parent and teacher influences on the science attitudes of eighth graders: Evidence from NELS:88. Science Education, 82, 93-109.
  • Kaplan, D. & Elliott. P. R. (1997). A model-based approach to validating education indicators using multilevel structural equation modeling. Journal of Educational and Behavioral Statistics, 22, 323-348.
  • Kaplan, D., & Elliott, P. R. (1997). A didactic example of multilevel structural equation modeling applicable to the study of organizations. Structural Equation Modeling, 4, 1-24.
  • Kaplan, D. (1995). The impact of BIB-spiralling induced missing data patterns on goodness-of-fit tests in factor analysis. Journal of Educational and Behavioral Statistics, 20, 69-82.
  • Kaplan, D., & George, R. (1995). A study of the power associated with testing factor mean differences under violations of factorial invariance. Structural Equation Modeling, 2, 101-118.
  • Kaplan, D., & Venezky, R. L. (1994). Literacy and voting behavior: A bivariate probit model with sample selection. Social Science Research, 23, 350-367.
  • Kaplan, D. (1994). Estimator conditioning diagnostics for covariance structure models. Sociological Methods and Research, 23, 200-229.
  • Kaplan, D., & Wenger, R. N. (1993). Asymptotic independence and separability in covariance structure models: Implications for specification error, power, and model modification. Multivariate Behavioral Research, 28, 483-498.
  • Fromme, K., Stroot, E., & Kaplan, D. (1993). The comprehensive effects of alcohol: Development and psychometric assessment of a new expectancy questionnaire. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 5, 19-26.
  • Muth en, B., & Kaplan, D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30.
  • Kaplan, D. (1991). The behaviour of three weighted least squares estimators for structured means analysis with non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 44, 333-346.
  • Kaplan, D. (1991). On the modification and predictive validity of covariance structure models. Quality and Quantity, 25, 307-314.
  • Kaplan, D. (1990). Evaluating and modifying covariance structure models: A review and recommendation. Multivariate Behavioral Research, 25, 137-155.
  • Kaplan, D. (1990). Rejoinder on evaluating and modifying covariance structure models. Multivariate Behavioral Research, 25, 197-204.
  • Kaplan, D. (1990). Contributions to structural modeling of mathematics achievement: Application of categorical variable structural equation methodology. International Journal of Educational Research, 14, 175-192.
  • Lapan, R. T., McGrath, E., & Kaplan, D. (1990). Factor structure of the Basic Interest Scales by gender across time. Journal of Counseling Psychology, 37, 216-222.
  • Kaplan, D. (1989). Model modification in covariance structure analysis: Application of the expected parameter change statistic. Multivariate Behavioral Research, 24, 285-305.
  • Kaplan, D. (1989). Power of the likelihood ratio test in multiple group confirmatory factor analysis under partial measurement invariance. Educational and Psychological Measurement, 49, 579-586.
  • Kaplan, D. (1989). The problem of error rate inflation in covariance structure models. Educational and Psychological Measurement, 49, 333-337.
  • Kaplan, D. (1989). A study of the sampling variability and z-values of parameter estimates from misspecified structural equation models. Multivariate Behavioral Research, 24, 41-57.
  • Kaplan, D. (1988). The impact of specification error on the estimation, testing, and improvement of structural equation models. Multivariate Behavioral Research, 23, 69-86.
  • Muthen, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 51, 431-462.
  • Muthen, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
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