Publications

BOOK

Kaplan, D. (2023). Bayesian Statistics for the Social Sciences (2nd Edition). New York: Guilford Press. (Order online)

Companion Resources: R code and data are here (.zip)

Supplementary materials

PAPERS

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 
(PDF)

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 
(PDF) (zip file of 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
(PDF) (zip file of 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. (PDF)  (Supplementary materials)

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. (PDF)  (Supplementary materials)

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/s405 021-00108-2. (PDF) (zip file of R code and data)

Kaplan, D. (2021).  On the quantification of model uncertainty: A Bayesian perspective.  Psychometrika, 86, 215-238.  DOI: 10.1007/s11336-021-09754-5. (PDF)  (zip file of R code and data)

Kaplan, D. & Yavuz, S. (2019).  An approach to addressing multiple imputation model uncertainty using Bayesian model averaging.  Multivariate Behavioral Research, DOI: 10.1080/00273171.2019.1657790. (PDF)

Kaplan, D. & Lee, C. (2018).  Optimizing Prediction Using Bayesian Model Averaging:  Examples Using Large-Scale Educational Assessments.  Evaluation Review, DOI: 10.1177/0193841X18761421. (PDF)  (zip file of 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 (PDF)

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 (PDF)

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, DOI: 10.1080/10705511.2015.1092088 (PDF)

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.  (PDF)  (zip file of 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. (PDF)

Kaplan, D. & Chen, J. (2014).  Bayesian model averaging for propensity score analysis.  Multivariate Behavioral Research, 49, 505-517. (PDF)

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 (PDF).

Kaplan, D. & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika.  Published online.
DOI: 10.1007/S11336-012-9262-8 (PDF). Erratum

CHAPTERS

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). Data for chapter (zip).

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).

SOFTWARE

Package “ShinyBHB” is a Shiny based software program that can conduct Bayesian historical borrowing for single level, multilevel, and longitudinal data.  Two general types of historical borrowing methods are allowed: static and dynamic borrowing.  Under static borrowing methods include no borrowing and power priors.  For dynamic borrowing, the program presently only allows for  Bayesian dynamic borrowing.  ShinyBHB runs the Stan programming language in the background and provides a full range of MCMC diagnostics and output.  Results can be saved as .csv, .xls, or .tex files.  Theoretical details can be found here and the ShinyBHB program can be found here.

Package “miBMA”: An R program to conduct multiple imputation using Bayesian model averaging. The package makes use of the “mice” syntax (van Buuren, S. & Groothuis-Oudshoorn, K., 2011, Journal of Statistical Software, 45, 1-67) and conducts fully chained equations imputation under the normal model, where each cycle consists of an additional Bayesian model averaging step. Theoretical details can be found here and the R package can be found here.

Package “BMASEM”: An R program that expands the work of Madigan and his colleagues (Madigan & Raftery, 1994; Raftery, Madigan, & Hoeting, 1997) by considering a structural equation model as a special case of a directed acyclic graph (Pearl, 2009). BMASEM searches the model space for sub-models and obtains a weighted average of the sub-models using posterior model probabilities. BMASEM version 2.1 is limited to the 24 paths in the model for the 4GM Ram computer system and continuous mediator and outcome variables. The manual is here and the package is here.

Package 'BayesPSA': An R program to conduct Bayesian propensity score analysis and covariate balance tests via MCMC or Bayesian model averaging. This package is not on the CRAN. Please read the description file first. The program can be downloaded and installed using "install.packages() from local zip files". The description file is here and the package is here.