Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press. (Order online)
Companion Resources: R code and data are here (.zip)
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. & 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).
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.
This joint effort is housed within the Wisconsin Center for Education Research at the School of Education, University of Wisconsin-Madison. Copyright ©2011, The Board of Regents of the University of Wisconsin System