Education research using Stan
Hierarchical Two-Parameter Logistic Item Response Model (Abstract)
Rasch and Two-Parameter Logistic Item Response Models with Latent Regression (Abstract)
Rating Scale and Generalized Rating Scale Models with Latent Regression (Abstract)
Partial Credit and Generalized Partial Credit Models with Latent Regression (Abstract)
Stan Language/Interfaces
Annis, J., Miller, B. J., & Palmeri, T. J. (2017). Bayesian inference with Stan: A tutorial on adding custom distributions. Behavior Research Methods, 49(3), 863-886.
Gelman, A., Lee, D., & Guo, J. (2015). Stan: A probabilistic programming language for Bayesian inference and optimization. Journal of Educational and Behavioral Statistics, 40, 530-543.
Grant, R. L., Carpenter, C., Furr, D., & Gelman, A. (2017). Introducing the StataStan interface for fast, complex Bayesian modeling using Stan. The Stata Journal. 17(2), 330-342.
Ames, A. J., & Au, C. H. (2018). Using Stan for item response theory Models. Measurement: Interdisciplinary Research and Perspectives, 16(2), 129-134.
Grant, R. L., Furr, D. C., Carpenter, B., & Gelman, A. (2017). Fitting Bayesian item response models in Stata and Stan. The Stata Journal, 17(2), 343-357.
Luo, Y., & Jiao, H. (2017). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement, 77, 1–25.
Mai, Y. & Zhang, Z., (2018). Software packages for Bayesian multilevel modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 650-658.
Sorensen, T., Hohenstein, S. & Vasishth, S. (2016). Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists. Quantitative Methods for Psychology, 12(3), 175-200.
Causal Inference
Feller, A., Grindal, T., Miratrix, L., & Page, L. C. (2016). Compared to what? Variation in the impacts of early childhood education by alternative care type. The Annals of Applied Statistics, 10(3), 1245-1285.
Hollenbach, F. M., Montgomery, J. M., & Crespo-Tenorio, A. (2018). Bayesian versus maximum likelihood estimation of treatment effects in bivariate probit instrumental variable models. Political Science Research and Methods, 00, 1-9.
Hong, H., Rudolph, K. E., & Stuart, E. A. (2017). Bayesian approach for addressing differential covariate measurement error in propensity score methods. Psychometrika, 82(4), 1078-1096.
Bainter, S.A., (2017). Bayesian estimation for item factor analysis models with sparse categorical indicators. Multivariate Behavioral Research, 52(5), 593-615.
Chang, M.I., (2017). A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models. Doctoral Dissertation, Department of Counseling, Quantitative Methods, and Special Education, Southern Illinois University at Carbondale, Carbondale, U.S.A.
Revuelta, J. & Ximénez, C., (2017). Bayesian dimensionality assessment for the multidimensional nominal response Model. Frontiers in Psychology, 8, 961.
Martin-Fernandez, M., & Revuelta, J. (2017). Bayesian estimation of multidimensional item response models. A comparison of analytic and simulation algorithms. Psicologica: International Journal of Methodology and Experimental Psychology, 38(1), 25-55.
Brandt, H., Cambria, J., & Kelava, A. (2018). An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 00, 1-15.
Holtmann, J., Koch, T., Lochner, K., & Eid, M. (2016). A comparison of ML, WLSMV, and Bayesian methods for multilevel structural equation models in small samples: A simulation study. Multivariate Behavioral Research, 51(5), 661-680.
Jacobucci, R., & Grimm, K. J. (2018). Comparison of frequentist and Bayesian regularization in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 639-649.
Furr (2017). Bayesian and Frequentist Cross-validation Methods for Explanatory Item Response Models. Doctoral Dissertation, Graduate School of Education, University of California, Berkeley, Berkeley, U.S.A.
da Silva, M. A., Bazán, J. L., & Huggins-Manley, A. C. (2018). Sensitivity analysis and choosing between alternative polytomous IRT models using Bayesian model comparison criteria. Communications in Statistics-Simulation and Computation, 00, 1-20.
Luo, Y., & Al-Harbi, K. (2017). Performances of LOO and WAIC as IRT model selection methods. Psychological Test and Assessment Modeling, 59(2), 183-205.
Merkle, E., Furr, D. and Rabe-Hesketh, S. (2018). Bayesian model assessment: Use of conditional vs marginal likelihoods. arXiv preprint arXiv:1802.04452.
Yong, L. (2018). LOO and WAIC as Model Selection Methods for Polytomous Items. arXiv preprint arXiv:1806.09996.
Driver, C. C., & Voelkle, M. C. (In Press). Hierarchical Bayesian continuous time dynamic modeling. Psychological methods.
Matta, T., (2016). A Joint Modeling Approach to Studying English Language Proficiency Development and Time-to-Reclassification. Doctoral Dissertation, Department of Educational Methodology, Policy and Leadership, University of Oregon, Eugene, U.S.A.
Okada, K., & Lee, M. D. (2016). A Bayesian approach to modeling group and individual differences in multidimensional scaling. Journal of Mathematical Psychology, 70, 35-44.
Pan, Y. (2016). Essays on Applying Bayesian Data Analysis to Improve Evidence-based Decision-making in Education . Doctoral dissertation, Teachers College, Columbia University, New York, U.S.A.
Tan, J. Y. C. (2013). Mathematical modelling and statistical analysis of school-based student performance data.Doctoral dissertation, School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia.
Vuorre, M., & Bolger, N. (2017). Within-subject mediation analysis for experimental data in cognitive psychology and neuroscience. Behavior Research Methods, 00, 1-19.
Zupanc, K., & Štrumbelj, E. (2018). A Bayesian hierarchical latent trait model for estimating rater bias and reliability in large-scale performance assessment. PloS One, 13(4), e0195297.
Adkins, M., & Noyes, A. (2018). Do advanced mathematics skills predict success in biology and chemistry degrees? International Journal of Science and Mathematics Education, 16(3), 487-502.
Bellettini, C., Lonati, V., Malchiodi, D., Monga, M., Morpurgo, A. & Torelli, M., 2015, June. How Challenging are Bebras Tasks?: an IRT analysis based on the performance of Italian students. In Proceedings of the 2015 ACM conference on innovation and technology in computer science education (pp. 27-32). ACM.
Berggren, R., Nilsson, J., & Lövdén, M. (2018). Education does not affect cognitive decline in aging: A Bayesian assessment of the association between education and change in cognitive performance. Frontiers in Psychology, 00, 1-9.
Claassen, C. (2015). Measuring university quality. Scientometrics, 104(3), 793-807.
Daus, S., Nilsen, T., & Braeken, J. (2018). Exploring content knowledge: country profile of science strengths and weaknesses in TIMSS. possible implications for educational professionals and science research. Scandinavian Journal of Educational Research, 1-19.
Foxcroft, D. R., Callen, H., Davies, E. L., & Okulicz-Kozaryn, K. (2016). Effectiveness of the strengthening families programme 10–14 in Poland: cluster randomized controlled trial. The European Journal of Public Health, 27(3), 494-500.
Gale, J., Ooms, A., Grant, R., Paget, K., & Marks-Maran, D. (2016). Student nurse selection and predictability of academic success: The Multiple Mini Interview project. Nurse Education Today, 40, 123-127.
Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Sage Press.
McElreath, R. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press.
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
If you know further publications, please contact Sophia Rabe-Hesketh (sophiarh@berkeley.edu).