Publications

Articles in international peer-reviewed journals

  1. Dittrich, D., Leenders, R., & Mulder, J. (2017). Bayesian estimation of the network autocorrelation model. Social Networks, 48, 213-236.
  2. Van Kollenburg, G., Mulder, J., & Vermunt, J. K. (accepted). Posterior calibration of posterior predictive p-values. Psychological Methods.
  3. Böing-Messing, F., Van Assen, M., Hoijtink, H., Hoffman, A., & Mulder, J. (in press). Bayesian evaluation of equality and inequality constrained hypotheses on variances. Psychological Methods.
  4. Flore, P. C., Mulder, J., and Wicherts, J. (accepted registration report). The influence of gender stereotype threat on mathematics test scores of Dutch high school students: A registered report. Comprehensive Results in Social Psychology.
  5. De Jong, J., Rigotti, T., & Mulder, J. About breaks and broken records: How sequences of breached and overfulfilled obligations impact employee attitudes and retrospective psychological contract evaluations. European Journal of Work and Organizational Psychology.
  6. Mulder, J. & Wagenmakers, E.-J. (2016). Editors’ Introduction to the Special Issue “Bayes Factors for Testing Hypotheses in Psychological Research: Practical Relevance and New Developments”. Journal of Mathematical Psychology, 72, 1-5.
  7. Mulder, J. (2016). Bayes Factors for Testing Order-Constrained Hypotheses on Correlations. Journal of Mathematical Psychology, 72, 104-115 
.
  8. Böing-Messing, F. & Mulder J. Automatic Bayes Factors for Comparing Variances of Two Independent Normal Distributions. Journal of Mathematical Psychology. In press.
  9. Gu, X., Hoijtink, H., & Mulder, J. Error probabilities in default Bayesian 
hypothesis testing. Journal of Mathematical Psychology. 
In press.
  10. Van Kollenburg, G., Mulder, J., & Vermunt, J. K. (2015). Bayesian posterior predictive p- 
values for assessing latent class models. Methodology, 11, 65-79.
  11. Braeken, J., Mulder, J., & Wood, S. (2015). Relative effects at work: Bayes factors for order hypotheses. Journal of Management, 41, 511-573.
  12. Gu, X., Mulder, J., Decovic, M., & Hoijtink, H. (2014). Bayesian evaluation of inequality constrained hypotheses. Psychological Methods19, 511-527.
  13. Mulder, J. (2014). Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses. Computational Statistics and Data Analysis, 71, 448-463.
  14. Mulder, J. (2014). Bayes factors for testing inequality constrained hypotheses: Issues with prior specification. British Journal of Mathematical and Statistical Psychology, 67, 153–171.
  15. Fox, J.-P., Marsman, M., Mulder, J., & Verhagen, J. (2014). Complex latent variable 
modeling in educational assessment. Communications in Statistics.
  16. Mulder, J. & Fox, J.-P. (2013). Bayesian tests for variance components in a compound symmetry covariance structure. Statistics and Computing, 21, 109-122.
  17. Mulder, J., Hoijtink, H., & de Leeuw, C. (2012). BIEMS: A Fortran 90 program for calculating Bayes factors for inequality and equality constrained models. Journal of Statistical Software, 46(2).
  18. Kluytmans, A., Van de Schoot, R., Mulder, J., & Hoijtink, H. (2012). Illustrating Bayesian evaluation of informative hypotheses for regression models. Frontiers in Psychology, 3(2).
  19. Van de Schoot, R., Mulder, J., Hoijtink, H., van Aken, M. A. G., Semon Dubas, J., Orobio de Castro, B., Meeuw, W., & Romeijn, J. -W. (2011). An introduction to Bayesian model selection for evaluating informative hypotheses. European Journal of Developmental Psychology, 8(6), 713-729.
  20. Van de Schoot, R., Hoijtink, H., Mulder, J., Aken, M. V., de Castro, B. O., Meeus, W., & Romeijn, J.-W (2011). Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection. Developmental Psychology, 47 (1), 203-212.
  21. Mulder, J., Hoijtink, H., & Klugkist, I. (2010). Equality and inequality constrained multivariate linear models: Objective model selection using constrained posterior priors. Journal of Statistical Planning and Inference, 140, 887-906.
  22. Mulder, J., Klugkist, I., Meeus, W., van de Schoot, A., Selfhout, M., & Hoijtink, H. (2009). Bayesian model selection of informative hypotheses for repeated measurements. Journal of Mathematical Psychology, 53, 530-546.
  23. Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with optimal design criterion for item selection. Psychometrika, 74, 273-296.
  24. Kammers, M. P. M., Mulder, J., De Vignemont, F., & Dijkerman, H. C. (2009). The weight of representing the body: A dynamic approach to investigating multiple body representations in healthy individuals. Experimental Brain Research, 204, 333-342.
  25. Almond, R. G., Mulder, J., Hemat, L. A., & Yan, D. (2009). Bayesian network models for local dependence among observable outcome variables. Journal of Educational and Behavioral Statistics, 34, 491-521.

Book Chapters

  1. Klugkist, I. & Mulder, J. (2008). Bayesian estimation for inequality constrained analysis of variance. In: H. Hoijtink, I. Klugkist, and P. A. Boelen. (Eds.), Bayesian Evaluation of Informative Hypotheses (pp. 27-52). New York: Springer.
  2. Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with Kullback-Leibler information item selection. In W. J. van der Linden & C. A. W. Glas (Eds.), Elements of adaptive testing (pp. 79-104). New York: Springer.
  3. Mulder, J. (2015). Bayesian Testing of Constrained Hypotheses using BIEMS with an Application to Human-Computer Interaction. In M.C. Kaptein & J. Robertson (Eds.), Modern Statistical Methods for HCI.

Miscellaneous works

  1. Mulder, J. (2010). Bayesian Model Selection for Constrained Multivariate Normal Linear Models. PhD thesis, Utrecht University.
  2. Mulder. J. & Hoijtink, H. (2010). Default Bayes factors for Type B Testing Problems. Technical Report Utrecht University.
  3. Almond, R. G., Mulder, J., Hemat, L. A., & Yan, D. (2006). Models for local dependence among observable outcome variables. ETS Research Report RR-06-36, Educational Testing Service.
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