University of Amsterdam
Department of Psychology
PO box 15906
1001 NK Amsterdam
The Netherlands

Phone: +31205256584
E-Mail: D [dot] Molenaar [at] uva [dot] nl

 Please find my papers below. For any questions, comments, or request, please email me.

Last updated 29 Apr 2025

Statistical Learning/Machine Learning/AI and Psychometric Inference

Molenaar, D., Grasman, R.P.P.P, & Cúri, M. (in press). Autoencoders for Amortized Joint Maximum Likelihood Estimation of Confirmatory Item Factor Models. Multivariate Behavioral Research. Online Appendix | Scripts

Tabak, G., Molenaar, D., & Cúri, M. (in press). An Evolutionary Neural Architecture Search For Item Response Theory Autoencoders. Behaviormetrika.

Veldkamp, K., Grasman, R.P.P.P., Molenaar, D. (in press). Handling Missing Data In Variational Autoencoder based Item Response Theory. British Journal of Mathematical and Statistical Psychology. Scripts (GitHub)

Veldkamp, K., Grasman, R.P.P.P., Molenaar, D. (in press). Recommendation with Item Response Theory. Behaviormetrika.

Response and Response Time Modeling
Molenaar, D., & Feskens, R. (in press). Relating Violations of Measurement Invariance to Group Differences in Response Times. Psychological Methods. PDF  | Mplus-script
De Jong, P., & Molenaar, D. (2025). Shifting Reading Processes and the Development of Word Reading Fluency. Scientific Studies of Reading, 29(3), 328–349.
Kang, I., Molenaar, D., & Ratcliff, R. (2023). A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data. Psychometrika, 88, 940-974. PDF
Becker, B., van Rijn, P., Molenaar, D., & Debeer, D. (2022). Item Order and Speededness: Implications for Test Fairness in Higher Educational High-Stakes Testing. Assessment and Evaluation in Higher Education, 47, 1030-1042. PDF
Molenaar, D., & Schnipke, D. (2022). Item response time modeling. In: J. Weiner & S. Sireci (Eds.), Guidelines for technical based assessment. International Testing Commission and Association of Test Publishers.  Full guidelines | Our blog
Molenaar, D., Rózsa, S., & Kõ, N. (2021). Modeling Asymmetry in the Time-Distance Relation of Ordinal Personality Items. Applied Psychological Measurement, 45, 178-194. OpenBUGS-code
Kuijpers, R.E., Visser, I., & Molenaar, D. (2011). Testing the Within-State Distribution in Mixture Models for Responses and Response Times. Journal of Educational and Behavioral Statistics, 46, 348-373. PDF
Tamimy, Z., Rózsa, S., Kõ, N., & Molenaar, D. (2020). A Practical Cross-Sectional Framework to Contextual Reactivity in Personality: Response Times as Indicators of Reactivity to Contextual Cues. Psych, 2(4), 253-268. PDF| LatentGOLD-scripts
Molenaar, D., & Van Rijn, P. (forthcoming). Modeling Item Responses with Response Times as Collateral Information in Large-Scale Educational Assessments. In: L. Khorramdel, M. von Davier, & K. Yamamoto (Eds.), Innovative Computer Based International Large-Scale Assessments. Springer.
Molenaar, D., Rósza, S., & Bolsinova, M. (2019). A Heteroscedastic Hidden Markov Mixture Model for Responses and Categorized Response Times. Behavior Research Methods, 51, 676-696. PDF
Bolsinova, M., & Molenaar, D. (2018). Modeling nonlinear conditional dependence between response time and accuracy. Frontiers in Psychology: Quantitative Psychology and Measurement, 9, 1525. PDF
Molenaar, D., & De Boeck, P. (2018). Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times. Psychometrika, 83, 279-297. PDF| OpenBUGS-script
Molenaar, D., Bolsinova, M., & Vermunt, J.K. (2018). A Semi-Parametric Within-Subject Mixture Approach to the Analyses of Responses and Response Times. British Journal of Mathematical and Statistical Psychology, 71, 205-228. PDF | LatentGOLD-script
Molenaar, D., & Visser, I. (Ed.) (2017). Cognitive and psychometric modelling of responses and response times [special issue]. British Journal of Mathematical and Statistical Psychology, 70(2), 185-186. Editorial (pdf) | Full special issue
Molenaar, D., & Bolsinova, M. (2017). A heteroscedastic generalized linear model with a non‐normal speed factor for responses and response times. British Journal of Mathematical and Statistical Psychology, 70(2), 297-316. PDF | Mplus code + documentation
Bolsinova, M., Tijmstra, J., & Molenaar, D. (2017). Response moderation models for conditional dependence between response time and response accuracy. British Journal of Mathematical and Statistical Psychology, 70(2), 257-279. PDF
Bolsinova, M., Tijmstra, J., Molenaar, D., & De Boeck, P. (2017). Conditional dependence between response time and accuracy: An overview of its possible sources and directions for distinguishing between them. Frontiers in psychology, 8. PDF
Molenaar, D., Bolsinova, M., Rozsa, S., & De Boeck, P. (2016). Response Mixture Modeling of Intraindividual Differences in Responses and Response Times to the Hungarian WISC-IV Block Design Test. Journal of Intelligence, 4, 10. PDF | OpenBUGS-scripts
Molenaar, D., Oberski, D., Vermunt, J.K., De Boeck, P. (2016). Hidden Markov IRT Models for Responses and Response Times. Multivariate Behavioral Research, 51, 606-626. PDF | LatentGOLD-scripts
Tuerlinckx, F., Molenaar, D., & van der Maas, H.L.J. (2016). Diffusion-based item response modeling. In W.J van der Linden (Eds.), Handbook of Modern Item Response Theory Volume 1 (pp. 283-300), Chapman and Hall/CRC Press.
Molenaar, D. (2015). The value of response times in item response modeling. Measurement: Interdisciplinary Research and Perspectives, 13, 177-181. PDF
Molenaar, D., Tuerlinckx, F., & van der Maas, H.L.J. (2015). Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R-Package diffIRT. Journal of Statistical Software, 66, 1-34. PDF | R-package
Molenaar, D., Tuerlinckx, F., & van der Maas, H.L.J. (2015). A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times. Multivariate Behavioral Research, 50, 56-74. PDF | Scripts  
Molenaar, D., Tuerlinckx, F., & van der Maas, H.L.J. (2015). A Generalized Linear Factor Model Approach to the Hierarchical Framework for Responses and Response Times. British Journal of Mathematical and Statistical Psychology, 68, 197-219. PDF
van der Maas, H.L.J., Molenaar, D., Maris, G., Kievit, R.A., & Borsboom, D. (2011). Cognitive Psychology Meets Psychometric Theory: On the Relation Between Process Models for Decision Making and Latent Variable Models for Individual Differences. Psychological Review, 118, 339-356. PDF | Scripts

Moderated, Non-linear, Non-Normal, and/or Heteroscedastic Latent Variable Modeling
Kolbe, L., Molenaar, D., Jak, S., & Jorgensen, T. D. (2024). Assessing Measurement Invariance with Moderated Nonlinear Factor Analysis Using the R Package OpenMx. Psychological Methods, 29, 388-406.  PDF
Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. Journal of Educational and Behavioral Statistics, 47, 693-735. PDF | R-code
Molenaar, D. (2021). A Flexible Moderated Factor Analysis Approach to Test for Measurement Invariance across a Continuous Variable. Psychological Methods, 26, 660–679. R-code
Kolbe, L., Jorgensen, T. D., & Molenaar, D. (2021). The impact of unmodeled heteroscedasticity on assessing measurement invariance in single-group models. Structural Equation Modeling: A Multidisciplinary Journal, 28, 82-98. PDF
Bolsinova, M., & Molenaar, D. (2019). Nonlinear indicator-level moderation in latent variable models. Multivariate Behavioral Research, 54, 62-84. PDF
Molenaar, D., & Dolan, C.V. (2018). Non-Normality in Latent Trait Modeling. In P. Irwing, T. Booth, D.J. Hughes (Eds). Wiley Blackwell Handbook of Psychometric Testing. UK: John Wiley & Sons Ltd.
De Korte, J., Dolan, C., Lubke, G., & Molenaar, D. (2017). Studying the strength of prediction using indirect mixture modeling: non-linear latent regression with heteroscedastic residuals. Structural Equation Modeling, 24, 301-313. PDF | OpenMx scripts
Molenaar, D., Kő, N., Rózsa, S., & Mészáros, A. (2017). Differentiation of cognitive abilities in the WAIS-IV at the item level. Intelligence, 65, 48-59. PDF | Mplus-script
Murray, A.L., Molenaar, D., Johnson, W., & Krueger, B. (2016). Dependence of gene-by-environment interactions (GxE) on scaling: Comparing the use of sum scores and IRT scores of the phenotype in tests of GxE. Behavior Genetics, 4, 552–572. PDF
Molenaar, D., Middeldorp, C.M., Willemsen, G., Ligthart, L., Nivard, M.G., & Boomsma, D.I. (2016).  Evidence for Gender-dependent Genotype by Environment Interaction in Adult Depression. Behavior Genetics, 46, 59-71. PDF 
Molenaar, D. (2015). Heteroscedastic Latent Trait Models for Dichotomous Data. Psychometrika, 80, 625-644. PDF | R code (incl. dll)
Molenaar, D., Middeldorp, C., Beijsterveldt, T., & Boomsma, D. I. (2015). Analysis of Behavioral and Emotional Problems in Children Highlights the Role of Genotype× Environment Interaction. Child development, 86, 1999-2016. PDF
Molenaar, D., & Dolan, C. V. (2014). Testing systematic genotype by environment interactions using item level data. Behavior genetics, 44, 212-231. PDF
Molenaar, D., van der Sluis, S., Boomsma, D.I., Haworth, C.M.A, Hewitt, J.K., Plomin, R., Wright, M.J., & Dolan, C.V. (2013). Genotype by Environment Interactions in Cognitive Ability Tested in 14 Different Studies. Behavior Genetics, 43, 208-219. PDF
Molenaar, D., Dolan, C.V., & de Boeck, P. (2012). The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses related to Skewed Item Category Functions. Psychometrika 77, 455-478. PDF | Mx code
Molenaar, D., & Dolan, C.V., (2012). Substantively Motivated Extensions of the Traditional Latent Trait Model. Netherlands Journal of Psychology, 67, 48-57. PDF
Molenaar, D., van der Sluis, S., Boomsma, D.I., & Dolan, C.V. (2012). Detecting Specific Genotype by Environment Interaction using Marginal Maximum Likelihood Estimation in the Classical Twin Design. Behavior Genetics, 42, 483-499. PDF | Mx code | Univariate application
Molenaar, D., Dolan, C.V., & van der Maas, H.L.J. (2011). Modeling ability differentiation in the second-order factor model. Structural Equation Modeling, 18, 578-594. PDF
Molenaar, D., Dolan, C.V., Wicherts, J.M., & van der Maas, H.L.J. (2010). Modeling Differentiation of Cognitive Abilities within the Higher-Order Factor Model using Moderated Factor Analysis. Intelligence, 38, 611-624. PDF | Mx code
Molenaar, D., Dolan, C.V., & Verhelst, N.D. (2010). Testing and modeling non-normality within the one factor model. British Journal of Mathematical and Statistical Psychology, 63, 293-317. PDF | Mx code
Molenaar, D. & Verhelst, N.D. (2007). Accounting for non-normality in latent regression models using a cumulative normal selection function. Measurement and Research Department Reports, 3. Arnhem: Cito. PDF

Misc. Psychometrics
Molenaar, D., (forthcoming). Split-half reliability. In: Sinharay, S. (Eds.), Encyclopedia of Social Measurement, Second Edition.

Molenaar, D. (2023). A factor analysis approach to item-level change score reliability. In: A. van der Ark, W. Emons, & R. Meijer (Eds.), Practical measurement: Essays on Contemporary Psychometrics. Springer. LINK

Pronk, T., Molenaar, D., Wiers, R.D., & Murre, J. (2022). Methods to Split Cognitive Task Data for Estimating Split-Half Reliability: A Comprehensive Review and a Systematic Assessment. Psychonomic Bulletin and Review, 29, 44-54
Molenaar, D., Uluman, M., Tavşancıl, E., & De Boeck, P. (2021). The Hierarchical Rater Thresholds Model for Multiple Raters and Multiple Items. Open Education Studies, 3, 33-48. PDF | R-code
Molenaar, D., (2020). Review of Handbook of Item Response Theory, Volume II: Statistical Tools. Journal of Educational and Behavioral Statistics, 45, 507-511.
Booth, T., Murray, A.L., & Molenaar, D. (2016). Personality differentiation by cognitive ability: An application of the moderated factor model. Personality and Individual Differences, 100, 73-78. PDF
Murray, A.L., Booth, T. & Molenaar, D. (2016). When middle really means "top" or "bottom": An analysis the 16PF5 using Bock’s nominal response model. Journal of Personality Assesment, 98, 319-331. PDF
Molenaar, D. (2016). On the Distortion of Model fit in Comparing the Bifactor Model and the Higher-Order Factor Model. Intelligence, 57, 60–63. PDF
Borsboom, D. & Molenaar, D. (2015). Psychometrics. In J.D. Wright (Ed.), International encyclopedia of the social & behavioral sciences. - 2nd ed (pp. 418-422). Amsterdam: Elsevier.
Molenaar, D. (2015). Intelligence tests. The Blackwell Encyclopedia of Race, Ethnicity and Nationalism. DOI: 10.1002/9781118663202.wberen544
Molenaar, D., & Borsboom, D. (2013). The Formalization of Fairness: Issues in Testing for Measurement Invariance Using Subtest Scores. Educational research and evaluation, 2, 223-244. PDF
Matzke, D., Dolan, C.V., & Molenaar, D. (2010). The issue of power in the identification of g with lower-order factors. Intelligence, 38, 336-344. PDF
Molenaar, D., Dolan, C.V., & Wicherts, J.M. (2009). The power to detect sex differences in IQ test scores using multi-group covariance and mean structure analysis. Intelligence, 37, 396-404. PDF

Misc. Applications

Van der Reep, T.H.A., Molenaar, D., Loeffler, W. (2025). Direct measurement of darkness using a standard single-photon avalanche photodiode. Journal of the Optical Society of America A, 24, 506-511.

Su, S., Cousijn, J., Molenaar, D., Freichel. R., Larsen, H., Wiers, R.W. (in press). From Everyday Life to Measurable Problematic Smartphone Use: The Development and Validation of The Smartphone Use Problems Identification Questionnaire (SUPIQ). Journal of Behavioral Addictions.
Hoogeveen, S., Borsboom, S., Kucharský, Š., Marsman, M., Molenaar, D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E-J. (in press). Prevalence, patterns, and predictors of paranormal beliefs in the Netherlands: A several-analysts approach. Royal Society Open Science.

Junker, T.L., Bakker, A.B., Derks, D., & Molenaar, D. (2023). Agile Work Practices: Measurement and Mechanisms. European Journal of Work and Organizational Psychology, 32, 1-22.

Van der Reep, T.H.A., Molenaar, D., Loeffler, W., Pinto, Y. (2023). Quantum Detector Tomography applied to the Human Visual System: A Feasibility Study. Journal of the Optical Society of  America A, 40, 285-293. 
Breit, M., Brunner, M., Molenaar, D., & Preckel, F. (2022). Differentiation Hypotheses of Intelligence: A Systematic Review of the Empirical Evidence and an Agenda for Future Research. Psychological Bulletin, 148, 518-554.
Francken, J., Beerendonk, L., Molenaar, D., Fahrenfort, J., Kiverstein, J., Seth, A., & van Gaal, S. (2022). An academic survey on theoretical foundations, common assumptions and the current state of consciousness science. Neuroscience of Consciousness, 1, 1-13.
Klein Haneveld, E., Molenaar, D., de Vogel, V., Smid, W., & Kamphuis, J.H. (2022). Do we Hold Males and Females to the Same Standard? A Measurement Invariance Study on the Psychopathy Checklist-Revised. Journal of Personality Assessment, 104, 368-379.
Jovanović, V., Molenaar, D., Gavrilov Jerković, V., & Lazić, M. (2021). Positive expectancies and subjective well-being: A prospective study among undergraduates in Serbia. Journal of Happiness Studies, 22, 1239-1258. PDF
Jovanović, V., Lazić, M., Gavrilov-Jerković, V., & Molenaar, D. (2020). The Scale of Positive and Negative Experience (SPANE): Evaluation of measurement invariance and convergent and discriminant validity. European Journal of Psychological Assessment, 36, 694–704.
Kovacs, K., Molenaar, D., & Conway, A.R.A. (2019). The domain specificity of working memory is a matter of ability. Journal of Memory and Language, 109, 104048. PDF
Xenidou-Dervou, I., Molenaar, D., Ansari, D., van der Schoot, M., & van Lieshout, E. C. (2017). Nonsymbolic and symbolic magnitude comparison skills as longitudinal predictors of mathematical achievement. Learning and Instruction, 50, 1-13. PDF
Wicherts, J.M., Bakker, M., Molenaar, D. (2011). Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results. PLoSONE, 6, e26828. PDF
Visch, V., Tan, E.S.H., & Molenaar, D. (2010). The emotional and cognitive effect of immersion in film viewing. Cognition & Emotion, 24, 1439-1445. PDF
Topper, M., Molenaar, D., Emmelkamp, P.M.G., & Ehring, T. (2014). Are rumination and worry two sides of the same coin? A structural equation modelling approach. Journal of Experimental Psychopathology, 5, 363-381. PDF
Wicherts, J.M., Borsboom, D., Kats, J., & Molenaar, D. (2006). The poor availability of psychological research data for reanalysis. American Psychologist, 61, 726-728. PDF

Proceedings of the Psychometric Society
Wiberg, M., Molenaar, D., González, J., Kim, K.S., & Hwang, H. (Eds.). (2023). Quantitative Psychology: The 87nd Annual Meeting of the Psychometric Society, Bologna, Italy, 2022. Springer.  LINK
Wiberg, M., Molenaar, D., González, J., Kim, K.S., & Hwang, H. (Eds.). (2022). Quantitative Psychology: The 86nd Annual Meeting of the Psychometric Society Virtual, 2021. Springer. LINK
Wiberg, M., Molenaar, D., González, J., Bockenholt, U., & Kim, K.S. (Eds.). (2021). Quantitative Psychology: The 85nd Annual Meeting of the Psychometric Society Virtual, 2020. Springer. LINK
Wiberg, M., Molenaar, D., González, J., Bockenholt, U., & Kim, K.S. (Eds.). (2020). Quantitative Psychology: The 84nd Annual Meeting of the Psychometric Society, Santiago de Chile, Chile, 2019. Springer. LINK
Wiberg, M., Culpepper, S., Janssen, R., González, J., & Molenaar, D. (Eds.). (2019). Quantitative Psychology: The 83rd Annual Meeting of the Psychometric Society, New York, NY, 2018. Springer. LINK
Wiberg, M., Culpepper, S., Janssen, R., González, J., & Molenaar, D. (Eds.). (2018). Quantitative Psychology: The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017. Springer. LINK

Logo by Allan Sonnenberg
Photo by Ivailo Partchev