Michel Meulders

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Publications

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International journal articles

• Gelman, A., Van Mechelen, I., Verbeke, G., Heitjan, D. F., & Meulders, M. (2005). Multiple imputation for model checking: Completed-data plots with missing and latent data. Biometrics, 61, 74-85. doi:10.1111/j.0006-341X.2005.031010.x
• Ip, E. H., Wang, Y. J., De Boeck, P., & Meulders, M. (2004). Locally dependent latent trait models for polytomous responses with application to inventory of hostility. Psychometrika, 69, 191-216. doi:10.1007/BF02295940
• Janssen, R., Tuerlinckx, F., Meulders, M., & De Boeck, P. (2000). A hierarchical IRT model for criterion-referenced measurement. Journal of Educational and Behavioral Statistics, 25, 285-306. doi:10.3102/10769986025003285
• Kuppens, P., Van Mechelen, I., & Meulders, M. (2004). Every cloud has a silver lining: Interpersonal and individual differences determinants of anger-related behaviors. Personality and Social Psychology Bulletin, 30, 1550-1564. doi:10.1177/0146167204271176
• Meulders, M., De Boeck, P., Kuppens, P., & Van Mechelen, I. (2002). Constrained latent class analysis of three-way three-mode data. Journal of Classification, 19, 277-302. doi:10.1007/s00357-001-0046-6
• Meulders, M., De Boeck, P., & Van Mechelen, I. (2001). Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations. Computational Statistics & Data Analysis, 38, 217-233. doi:10.1016/S0167-9473(01)00029-9
• Meulders, M., De Boeck, P., & Van Mechelen, I. (2003). A taxonomy of latent structure assumptions for probability matrix decomposition models. Psychometrika, 68, 61-77. doi:10.1007/BF02296653
• Meulders, M., De Boeck, P., Van Mechelen, I., & Gelman, A. (2005). Probabilistic feature analysis of facial perception of emotions. Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 781-793. doi:10.1111/j.1467-9876.2005.00515.x
• Meulders, M., De Boeck, P., Van Mechelen, I., Gelman, A., & Maris, E. (2001). Bayesian inference with probability matrix decomposition models. Journal of Educational and Behavioral Statistics, 26, 153-179. doi:10.3102/10769986026002153
• Meulders, M., Ip, E. H., & De Boeck, P. (2005). Latent variable models for partially ordered responses and trajectory analysis of anger-related feelings. British Journal of Mathematical & Statistical Psychology, 58, 117-143. doi:10.1348/000711005X38555
• Rijmen, F., Tuerlinckx, F., Meulders, M., Smits, D. J. M., & Balázs, K. (2005). Mixed model estimation methods for the Rasch model. Journal of Applied Measurement, 6, 273-288.
• Van den Noortgate, W., De Boeck, P., & Meulders, M. (2003). Cross-classification multilevel logistic models in psychometrics. Journal of Educational and Behavioral Statistics, 28, 369-386. doi:10.3102/10769986028004369

Chapters in books

• Meulders, M., De Boeck, P., & Van Mechelen, I. (2002). Rater classification on the basis of latent features in responding to situations. In W. Gaul & G. Ritter (Eds.), Classification, automation, and new media (pp. 453-461). Berlin: Springer.
• Meulders, M., Gelman, A., Van Mechelen, I., & De Boeck, P. (1998). Generalizing the probabibility matrix decomposition model: An example of Bayesian model checking and model expansion. In J. Hox (Ed.), Assumptions, robustness, and estimation methods in multivariate modeling (pp. 1-19). Amsterdam: TT-publikaties.
• Meulders, M., & Xie, Y. (2004). Person-by-item predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 213-240). New York: Springer.
• Rijmen, F., Tuerlinckx, F., Meulders, M., Smits, D. J. M., & Balázs, K. (2006). Mixed model estimation methods for item response models. In E. V. Smith & R. M. Smith (Eds.), Rasch Measurement: Advanced and Specialized Applications. Chicago: University of Illinois.
• Tuerlinckx, F., Rijmen, F., Molenberghs, G., Verbeke, G., Briggs, D., Van den Noortgate, W., Meulders, M., & De Boeck, P. (2004). Estimation and software. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 343-373). New York: Springer.