Education research using Stan
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.
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 (email@example.com).