Publications & Resources

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• Papers
• Chapters
• Software

Papers

• Kaplan, D., Jude, N., Harra, K., & Stampka, J. (2025). On the development of probabilistic projections of country-level progress to the UN SDG indicator of minimum proficiency in reading and mathematics. https://arxiv.org/abs/2511.06107.
• Kaplan, D. (2025) Commentary on Journal of School Psychology Special Issue: Bayesian Statistics. Journal of School Psychology. https://doi.org/10.1016/j.jsp.2025.101515.
• Harra, K. & Kaplan, D. (2025). Incorporating sparsity into Bayesian stacking Procedures. Proceedings of the 89th Annual International Meeting of the Psychometric Society, 2024, Prague, Czech Republic.
• Kaplan, D., Harra, K., Stampka, J., & Jude, N. (2025). Stacking models of growth: A methodology for predicting the pace of progress to the education sustainable development targets using international large-scale sssessments. Psychometrika, https://doi.org/10.1017/psy.2025.2
• Huang, M. & Kaplan, D. (2024). Predictive performance of Bayesian stacking in multilevel education data. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986241255969
• Kaplan, D. & Harra, K. (2024). A Bayesian workflow for the analysis and reporting of international large-scale assessments: A case study using the OECD Teaching and Learning International Survey. Large-Scale Assessments in Education. https://doi.org/10.1186/s40536-023-00189-1 | Email ORSComms@education.wisc.edu to request the R code and data
• Harra, K., & Kaplan, D. (2023). On the performance of horseshoe priors for inducing sparsity in structural equation models. Structural Equation Modeling. https://doi.org/10.1080/10705511.2023.2280895 | Email ORSComms@education.wisc.edu to request the R code and data
• Kaplan, D., Chen, J., Lyu, W., & Yavuz, S. (2023). Bayesian historical borrowing with longitudinal large-scale assessments. Large-Scale Assessments in Education, https://doi.org/10.1186/s40536-022-00140-w (Supplementary materials PDF)
• Kaplan, D. & Chen, J. & Yavuz, S. & Lyu, W. (2022). Bayesian dynamic borrowing of historical information with applications to the analysis of large-scale assessments. Psychometrika. https://doi.org/10.1007/s11336-022-09869-3 (Supplementary materials PDF)
• Kaplan, D. & Huang, M. (2021). Bayesian Probabilistic Forecasting with Large-Scale Educational Trend Data: A Case Study Using NAEP. Large-Scale Assessments in Education, 9, https://doi.org/10.1186/s40536-021-00108-2 | Email ORSComms@education.wisc.edu to request the R code and data
• Kaplan, D. (2021). On the quantification of model uncertainty: A Bayesian perspective. Psychometrika, 86, 215-238.  https://doi.org/10.1007/s11336-021-09754-5 | Email ORSComms@education.wisc.edu to request the R code and data
• Kaplan, D. & Yavuz, S. (2019). An approach to addressing multiple imputation model uncertainty using Bayesian model averaging. Multivariate Behavioral Research, https://doi.org/10.1080/00273171.2019.1657790
• Kaplan, D. & Lee, C. (2018). Optimizing Prediction Using Bayesian Model Averaging:  Examples Using Large-Scale Educational Assessments. Evaluation Review, https://doi.org/10.1177/0193841X18761421 | Email ORSComms@education.wisc.edu to request the R code and data
• Lee, Y. & Kaplan, D. (2018). Generating multivariate ordinal data via entropy principles. Psychometrika. https://doi.org/10.1007/s11336-018-9603-3
• Kaplan, D. (2016). Causal inference with large-scale assessments in education: A Bayesian perspective. Large-Scale Assessments in Education, 4, https://doi.org/10.1186/s40536-016-0022-6
• Kaplan, D. & Lee, C. (2015). Bayesian Model Averaging Over Directed Acyclic Graphs With Implications for the Predictive Performance of Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, https://doi.org/10.1080/10705511.2015.1092088
• Park, S. & Kaplan, D (2015). Bayesian causal mediation analysis for group randomized designs with homogenous and heterogenous effects:  Simulation and case study. Multivariate Behavioral Research, 50, 316-333. https://doi.org/10.1080/00273171.2014.1003770 | Email ORSComms@education.wisc.edu to request the R code
• Chen, J. & Kaplan, D. (2015). Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies. Journal of Research on Educational Effectiveness, 8: 280–302, 2015. https://doi.org/10.1080/19345747.2014.911396
• Kaplan, D. & Chen, J. (2014). Bayesian model averaging for propensity score analysis. Multivariate Behavioral Research, 49, 505-517. https://doi.org/10.1080/00273171.2014.928492
• 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. https://doi.org/10.1111/cdev.12169
• Kaplan, D. & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika. Published online. https://doi.org/10.1007/S11336-012-9262-8 (Erratum PDF)

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Chapters

• Depaoli, S., Kaplan, D. & Winter, S. D. (2023). Foundations and extensions of Bayesian structural equation modeling. In Hoyle, R. (ed.), Handbook of Structural Equation Modeling, 2nd Edition. New York, Guilford Publications, Inc.
• Kaplan, D. & Park, S. (2013). Analyzing international large-scale assessment data within a Bayesian framework. In L. Rutkowski, M. Von Davier, and D. Rutkowski (eds.), A Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis. (pp 547-581). London: Chapman Hall/CRC Press. (PDF).
• Kaplan, D. & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (ed.), Oxford Handbook of Quantitative Methods. (pp 407-437) Oxford: Oxford University Press. (PDF). 
  Email ORSComms@education.wisc.edu to request the data for the chapter.
• Kaplan, D. & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling (pp. 650-673). New York: Guilford Publications, Inc. (PDF).