Publications

PUBLISHED PAPERS (REFEREED)

                                    2024

  1. Mulder, Friel, Leifeld (2024). Bayesian testing of scientific expectations under exponential random graph models. Social Networks, 78, 40–53. org/10.1016/j.socnet.2023.11.004
  2. Vieira Generoso, Leenders, McFarland, & Mulder (2024). A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures. Behaviormetrika, 51, 37–74. org/10.1007/s41237-023-00203-4

                                    2023

  1. Mulder (2023). Bayesian testing of linear versus nonlinear effects using Gaussian process priors. The American Statistician, 77(1), 1-11. org/10.1080/00031305.2022.2028675
  2. Meijerink, Back, Geukes, Leenders, & Mulder. (2023). Discovering trends of social interaction behavior over time: An introduction to relational event modeling. Behavior Research Methods, 55, 997–1023. org/10.3758/s13428-022-01821-8
  3. Shafiee-Kamalabad, Leenders & Mulder. What’s the point of change? Changepoint relational event modeling. Social Networks, 74, 166–181. doi.org/10.1016/j.socnet.2023.03.004
  1. Karimova, Leenders, Meijerink, & Mulder. Separating the Wheat from the Chaff: Bayesian regularization in Dynamic Social Networks. Social Networks, 74, 139–155. doi.org/10.1016/j.socnet.2023.02.006
  1. Arena, Leenders, & Mulder (2023). How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models. Network Science, 11(2), 267–294. org/10.1017/nws.2023.5
  2. Gravel, Valasik, Mulder, Leenders, Butts, Brantingham, & Tita (2023). Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models. Network Science, 11(2), 324-350. doi.org/10.1017/nws.2023.8
  1. Kavelaars, Mulder, Kaptein (2023). Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity. BMC Medical Research Methodology. org/10.1186/s12874-023-02034-z

                                    2022

  1. Mulder, Wagenmakers, & Marsman (2022). A generalization of the Savage-Dickey density ratio for equality and order constrained testing. The American Statistician, 76, 102-109. doi.org/10.1080/00031305.2020.1799861
  1. Mulder and Gelissen (2022). Bayes factor testing of equality and order constraints on measures of association in social research. Journal of Applied Statistics, 50 (2), 315–351. doi.org/10.1080/02664763.2021.1992360
  1. Mulder and Raftery (2022). BIC extensions for order-constrained model selection. Sociological Methods and Research, 51, 471-498. org/10.1177/00491241198824
  2. Mulder & Gu (2022). Bayesian Testing of Scientific Expectations under Multivariate Normal Linear Models. Multivariate Behavioral Research, 57 (5), 767–783. doi.org/10.1080/00273171.2021.1904809
  1. Gu, Hoijtink, & Mulder. (2022). Bayesian one-sided variable selection. Multivariate Behavioral Research, 57, 264-278.org/10.1080/00273171.2020.1813067
  2. Briganti, William, Mulder, & Linkowski. (2022). Bayesian network structure and predictability of autistic traits. Psychological Reports, 125, 344-357. doi.org/10.1177/00332941209781
  1. van Aert & Mulder (2022). Bayesian hypothesis testing and estimation under the marginalized random-effects meta-analysis model. Psychonomic Bulletin & Review, 29, 55–69. org/10.3758/s13423-021-01918-9
  2. Meijerink, Leenders, & Mulder (2022). Dynamic relational event modeling: Testing, exploring, and applying. PLOS ONE.org/10.1371/journal.pone.0272309
  3. Arena, Mulder, & Leenders (2022). A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks. Sociological Methods & Research. doi.org/10.1177/00491241221113875
  1. Heck et al. (2022). A review of applications of the Bayes factor in psychological research. Psychological Methods, 28(3), 558-579. org/10.1037/met0000454

                                    2021

  1. Mulder, Williams, Gu, …, van Lissa (2021). BFpack: Flexible Bayes factor testing of scientific theories in R. Journal of Statistical Software, 100, 18, 1-63. org/10.18637/jss.v100.i18
  2. Williams, Mulder, Rouder, & Rast. (2021). Beneath the Surface: Unearthing Within-Person Variability and Mean Relations with Bayesian Mixed Models. Psychological Methods, 26, 1, 74-89. org/10.1037/met0000270
  3. van Lissa, Gu, Mulder, Rosseel, van Zundert, & Hoijtink (2021). Teacher’s Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models. Structural Equation Modeling, 28, 2, 292-301.org/10.1080/10705511.2020.1745644
  4. Böing-Messing, F. & Mulder, J. Bayes factors for testing order constrained hypotheses on variances of dependent observations. The American Statistician, 75, 152-161. doi.org/10.1080/00031305.2020.1715257

                                    2020

  1. Mulder, J., Berger, J.O., Peña, V., & Bayarri, M.J. (2020). On the prevalence of information inconsistency in normal linear models. TEST, 30, 103-132. org/10.1007/s11749-020-00704-4
  2. Williams, D. W. and Mulder, J. (2020). Bayesian Hypothesis Testing for Gaussian Graphical Models: Conditional Independence and Order Constraints. Journal of Mathematical Psychology, 22. org/10.1016/j.jmp.2020.102441
  3. Williams, D. W., Rast, P., Pericchi, L. R, and Mulder, J. (2020). Comparing Gaussian Graphical Models with the Posterior Predictive Distribution and Bayesian Model Selection. Psychological Methods, 25(5), 653–672.org/10.1037/met0000254
  4. Kavelaars, Mulder, & Kaptein (2020). Bayesian analysis of clinical trial designs with multiple binary endpoints. Statistical Methods in Medical Research, 29(11), 3265-3277. doi.org/10.1177/0962280220922256
  1. Williams & Mulder (2020). BGGM: A R Package for Bayesian Gaussian Graphical Models. Journal of Open Source Software. org/10.21105/joss.02111
  2. Dittrich, D., Leenders, R.Th.A.J., & Mulder, J. (2020). Network autocorrelation modeling: Bayesian techniques for estimating and testing multiple network autocorrelations. Sociological Methodology, 50(1), 168-214. org/10.1177/0081175020913899

                                    2019

  1. Mulder, J. and Leenders, R.Th.A.J. (2019). Modeling the evolution of interaction behavior in social networks: a dynamic relational event approach for real-time analysis. Chaos, Solitons & Fractals, 119, 73-85.org/10.1016/j.chaos.2018.11.027
  2. Mulder, J. and Olsson-Collentine, A. (2019). Simple Bayesian testing of scientific expectations in linear regression models. Behavior Research Methods, 51, 1117-1130. doi.org/10.3758/s13428-018-01196-9
  1. Van Erp, S., Oberski, D., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31-50. doi.org/10.1016/j.jmp.2018.12.004
  1. Gu, X., Rosseel, Y., Mulder, J., & Hoijtink, H. (2019). Bain: A program for the evaluation of inequality constrained hypotheses using Bayes factors in structural equation models. Journal of Statistical Computation and Simulation, 89(8), 1526-1553. doi.org/10.1080/00949655.2019.1590574
  1. Meens, E.E.M., Bakx, A., Mulder, J., Denissen, J.J.A. (2019). The development and validation of an Interest and Skill inventory on Educational Choices. European Journal of Psychological Assessment. org/10.1027/1015-5759/a000546
  2. Dittrich, D., Leenders, R., & Mulder, J. (2019). Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses. Sociological Methods & Research, 48, 642-676.org/10.1177/0049124117729712
  3. Hoijtink, H., Gu, X., & Mulder, J. (2019). Bayesian Evaluation of Informative Hypotheses for Multiple Populations. British Journal of Mathematical and Statistical Psychology, 72(2), 219-243. org/10.1111/bmsp.12145
  4. Hoijtink, H., Gu, X., Mulder, J., and Rosseel, Y. (2019). Computing Bayes factors from data with missing values. Psychological Methods, 24(2), 253-doi.org/10.1037/met0000187
  5. Hoijtink, H., Mulder, J., van Lissa, C.J., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods, 24(5), 539- doi.org/10.1037/met0000201

                                    2018

  1. Mulder, J. and Pericchi, L.R. (2018). The matrix-F prior for estimating and testing covariance matrices. Bayesian Analysis, 13, 1189-1210. org/10.1214/17-BA1092
  2. Mulder, J. and Fox, J.-P. (2019). Bayes factor testing of multiple intraclass correlation coefficients. Bayesian Analysis, 14, 521-552. org/10.1214/18-BA1115
  3. Van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23, 363-388. doi.org/10.1037/met0000162
  1. Böing-Messing, F. & Mulder, J. (2018). Automatic Bayes factors for testing equality and inequality constrained hypotheses on variances. Psychometrika, 83, 586-617. doi.org/10.1007/s11336-018-9615-z
  1. Gu, X., Mulder, J. & Hoijtink, H. (2018) Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses. British Journal of Mathematical and Statistical Psychology, 71(2), 229-261. org/10.1111/bmsp.12110
  2. Flore, P. C., Mulder, J., and Wicherts, J. (2018). The influence of gender stereotype threat on mathematics test scores of Dutch high school students: A registered report. Comprehensive Results in Social Psychology, 3(2), 140–174. doi.org/10.1080/23743603.2018.1559647

                                    2017

  1. J.-P., Mulder, J., & Sinharay, S. (2017). Bayes Factor Covariance Testing in Item Response Models.Psychometrika, 82, 976-1006. doi.org/10.1007/s11336-017-9577-6
  2. Dittrich, D., Leenders, R., & Mulder, J. (2017). Bayesian estimation of the network autocorrelation model. Social Networks, 48, 213-246. org/10.1016/j.socnet.2016.09.002
  3. Böing-Messing, F., Van Assen, M., Hoijtink, H., Hoffman, A., & Mulder, J. (2017). Bayesian evaluation of equality and inequality constrained hypotheses on variances. Psychological Methods, 22, 262-287. org/10.1037/met0000116
  4. De Jong, J., Rigotti, T., & Mulder, J. (2017). One after the other: Effects of sequence patterns of breaches and overfulfilled obligations. European Journal of Work and Organizational Psychology, 26, 337-355.org/10.1080/1359432X.2017.1287074
  5. Kollenburg, G., Mulder, J., & Vermunt, J.K. (2017). Posterior calibration of posterior predictive p-values. Psychological Methods, 22, 382-396. org/10.1037/met0000142

                                    2016

  1. Mulder J. (2016). Bayes Factors for Testing Order-Constrained Hypotheses on Correlations. Journal of Mathematical Psychology, 72, 104-115. org/10.1016/j.jmp.2014.09.004
  2. 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. doi.org/10.1016/j.jmp.2016.01.002
  1. Fox, J.-P., Marsman, M., Mulder, J., & Verhagen, J. (2016). Complex latent variable modeling in educational assessment. Communications in Statistics, 45, 1499-1510. doi.org/10.1080/03610918.2014.939518
  1. Böing-Messing, F. & Mulder J. (2016). Automatic Bayes Factors for Testing Variances of Two Independent Normal Distributions. Journal of Mathematical Psychology, 72, 158-170. doi.org/10.1016/j.jmp.2015.08.001
  2. Gu, X., Hoijtink, H., & Mulder, J. (2016). Error probabilities in default Bayesian hypothesis testing. Journal of Mathematical Psychology, 72, 130-143. org/10.1016/j.jmp.2015.09.001

                                    2015

  1. Braeken, J., Mulder, J., & Wood, S. (2015). Relative effects at work: Bayes factors for order hypotheses. Journal of Management, 41, 544-573. org/10.1177/0149206314525206
  2. Van Kollenburg, G., Mulder, J., & Vermunt, J. K. (2015). Assessing model fit when asymptotics do not hold. Methodology, 11, 65-79. org/10.1027/1614-2241/a00009

                                    2014

  1. Mulder, J. (2014). Bayes factors for testing inequality constrained hypotheses: Issues with prior specification. British Journal of Mathematical and Statistical Psychology, 67, 153-171. doi.org/10.1111/bmsp.12013
  1. Mulder, J. (2014). Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses. Computational Statistics and Data Analysis, 71, 448-463. doi.org/10.1016/j.csda.2013.07.017
  2. Gu, X., Mulder, J., Dekovic, M., & Hoijtink, H. (2014). Bayesian evaluation of inequality constrained hypotheses. Psychological Methods, 19, 511-527. doi.org/10.1037/met0000017

                                    2013

  1. Mulder, J. & Fox, J.-P. (2013). Bayesian tests for variance components in a compound symmetry covariance structure. Statistics and Computing, 23, 109-122. doi.org/ 10.1007/s11222-011-9295-3

                                    2012

  1. 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). org/10.18637/jss.v046.i02
  2. 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). doi.org/10.3389/fpsyg.2012.00002

                                    2011

  1. 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. org/10.1080/17405629.2011.621799
  2. 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. doi.org/10.1037/a0020957

                                    2010

  1. Mulder, J., Hoijtink, H., & Klugkist, (2010). Equality and inequality constrained multivariate linear models: Objective model selection using constrained posterior priors. Journal of Statistical Planning and Inference, 140, 887-906. doi.org/10.1016/j.jspi.2009.09.022

                                    2009

  1. Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with optimal design criterion for item selection. Psychometrika, 74, 273-296. doi.org/10.1007/S11336-008-9097-5
  1. 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.org/10.1016/j.jmp.2009.09.003
  2. 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. doi.org/10.1007/s00221-009-2009-9
  1. 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.org/10.3102/1076998609332751

 

BOOKS, OR CONTRIBUTIONS TO BOOKS

  1. Mulder, J. (2016). Bayesian Testing of Constrained Hypotheses. In J. Robertson & M.C. Kaptein (Eds.), Modern Statistical Methods for HCI. Springer-Verlag. link
  2. Mulder, J. (2010). Bayesian Model Selection for Constrained Multivariate Normal Linear Models. PhD thesis, Utrecht University. link
  3. 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.link
  4. Klugkist, & 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. link
  5. Schouten, G., Arena, G., van Leeuwen, F.C.A., Heck, P., Mulder, J., Aalbers, R., Leenders, R.Th.A.J., and Böing-Messing, F (2022). Data science in action. In Data Science for Entrepreneurship: Principles and Methods for Data Engineering, Analytics, Entrepreneurship, and the Society. (Liebregts, van den Heuvel, van de Born, Eds.). link