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IMG_20150105_172256

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. Some papers include R, Mx, Mplus, OpenBUGS, or LatentGOLD scripts.

Response and Response Time Modeling
Molenaar, D., & Bolsinova, M. (in press). A Heteroscedastic Generalized Linear Model with a Non-Normal Speed Factor for Responses and Response Times. British Journal of Mathematical and Statistical Psychology.

Bolsinova, M., Tijmstra, J., & Molenaar, D. (in press). Response moderation models for conditional dependence between response time and accuracy. British Journal of Mathematical and Statistical Psychology. 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., 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 Handbook of Modern Item Response Theory, W.J van der Linden (Eds.), Vol 1, Chapter 17, 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 diffIRTJournal 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

Non-Normality in Psychometric Measurement Models
De Korte, J., Dolan, C., Lubke, G., & Molenaar, D. (in press). Studying the strength of prediction using indirect mixture modeling: non-linear latent regression with heteroscedastic residuals. Structural Equation Modeling.

Molenaar, D., & Dolan, C.V. (forth coming). Non-Normality in Latent Trait Modeling . Wiley Blackwell Handbook of Psychometric Testing.
Molenaar, D. (2015). Heteroscedastic Latent Trait Models for Dichotomous Data. Psychometrika, 80, 625-644. PDF | R code (incl. dll)
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., 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

Modeling of Genotype by Environment Interactions
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 fenotype 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., 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.1, 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

Modeling of Intelligence Test Scores
Molenaar D. (2016). On the Distortion of Model fit in Comparing the Bifactor Model and the Higher-Order Factor Model. Intelligence, 57, 60–63.
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
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
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
Xenidou-Dervou, I., Molenaar, D., Ansari, D., Van der Schoot, M., & Van Lieshout, E.C.D.M. (in press). Nonsymbolic and Symbolic Magnitude Comparison Skills as Longitudinal Predictors of Mathematical Achievement. Learning and Instruction.

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
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
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.
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. (in press). Are rumination and worry two sides of the same coin? A structural equation modelling approach. Journal of Experimental Psychopathology.
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

 

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