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Department Faculty

Paul De Boeck

Paul De Boeck has a Ph.D. degree from the KU Leuven in Belgium (Flanders), where he also has spent most of his career. From 2009 to 2012, he was affiliated with the University of Amsterdam. He is a former president of the Psychometric Society (2008) and was the first editor of the Applied Research and Case Studies section of Psychometrika.

He is interested in individual differences in various domains, and in quantitative approaches in general. His early quantitative work concerns primarily classification models based on disjunctive and conjunctive rules (HICLAS), while his more recent work concerns model development and applications in the domain of item response theory (IRT) and logistic mixed models. Within IRT he focuses on explanatory models and explanatory measurement, and recently also on IRTree models for response scales (e.g., Likert scales), decision making and cognitive processes.

Recently he also started research on the low replication rate in psychological studies. Based on an analysis of the results of a large-scale replication study published in Science, the probability of null hypotheses seems very low. This may be due to a rather large variation of effect size that is difficult if not impossible to capture following existing methodologies while it generates statistically significant results and misleadingly hopeful confidence intervals and effect size estimates when using the present ways of designing studies and analyzing the data from those studies.

Why be interested in psychometrics? Psychometrics is fundamental to answering some of Psychology’s difficult problems
Examples for interested prospective students and researchers in psychology in general

Here are some hypotheses I have addressed from my own research.

  • Is depression simply a more intense variety of the sadness everyone feels now and then, or is depression something qualitatively different from sadness?
  • How are mood disorder symptoms related to core affect? Can the types of affect dimensions assist in understanding mood disorders?
  • What does it mean to have a psychological trait (e.g., being extraverted, being conscientious)? What does it mean to have less of a trait? Is it showing a lower level of the trait in one’s behaviors or does it instead mean that the trait has less influence on one’s behavior?
  • Does slow intelligence exist and is it a mode of thinking different from fast intelligence? Can people who are slow in solving problems still solve very difficult problems? Can slow and fast intelligence be differentiated? Or is slow intelligence the same as fast intelligence but just slower in response time?
  • Likert scales are often seen as interval scale measurement. Some believe they are only ordinal. In fact, however, there are clear indications that Likert scales are not even ordinal. If they are not, what does a Likert-scale response mean and how can one still measure constructs?

These are all among the topics of my research and things that I would be willing to be an advisor for.

Psychometrics can do more of course, such as:

    For Testing Data
  • Researching whether measurement instruments are fair and which items are possibly biased
  • Validating whether a test measures the same construct in different groups
  • Specifying the score-specific precision of a measure (not all scores are equally precise)
  • Formulating more efficient and shorter tests
  • Constructing the best global score if the measurement instrument is multidimensional
  • Avoiding floor and ceiling effects
  • Measuring people not just in comparison with other people but also in comparison with items from the test

    For Experimental Data
  • Creating a data analysis method for binary and other categorical data
  • Analyzing in detail individual differences in experimental data

Selected Publications

For a list of most publications since 2000, go to, from where you can also download the papers.

Recent journal articles

Bolsinova, M., De Boeck, P., & Tijmstra, J. (2016). Modelling conditional dependence between response time and accuracy. Psychometrika, doi:10.1007/s11336-016-9537-6.

Cho, S.-J., De Boeck, P., & Lee, W.-J. (accepted). Evaluating testing, profile likelihood confidence interval estimation, and model comparisons for item covariate effects in linear logistic test models. Applied Psychological Measurement.

De Boeck, P., Chen, H., & Davison, M. (accepted). Spontaneous and imposed speed of cognitive test responses. British Journal of Mathematical and Statistical Psychology.

DiTrapani, J., Jeon, M., De Boeck, P., & Partchev, I. (2016). Attempting to differentiate fast and slow intelligence: Using generalized item response trees to examine the role of speed on intelligence tests. Intelligence, 56, 82-92.

Jeon, M. & De Boeck, P. (2016). A generalized item response tree model for psychological assessments. Behavior Research Methods, 48, 1070–1085.

Jeon, M., & De Boeck, P. (accepted). Decision qualities of Bayes factor and p-value based hypothesis testing. Psychological Methods.

Jeon, M., De Boeck, P., & van der Linden, W. (accepted). Modeling item response and change behavior: An application of a generalized item response tree model. Journal of Educational and Behavioral Statistics.

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, 1-20.

Molenaar,D., Oberski, D., Vermunt, J., De Boeck, P. (in press). Hidden Markov IRT models for responses and response Times. Multivariate Behavioral Research.

Recent handbook chapters

De Boeck, P. (forthcoming October 1, 2017). Psychological testing. In W.J. van der Linden (Ed.). Handbook of item response theory. Vol. 3. Applications. Boca Raton, FL: Chapman & Hall/CRC.

De Boeck, P., Cho,S.-J., Wilson, M. (2016). Explanatory item response models: An approach to cognitive assessment. In A. Rupp, & Leighton, J. (Eds.), Handbook of cognition and assessment (pp. 249-266). Harvard, MA: Wiley Blackwell.

De Boeck, P., & Elosua, P. (2016). Reliability and validity: History, notions, methods, and discussion. In F.T.L. Leong, Bartram, D., Cheung, F.M., Geisinger, K.F., & Iliescu, D. (Eds.), The ITC international handbook of testing and assessment (pp. 408-421). New York, NY: Oxford University Press.

De Boeck, P., & Wilson, M. (2015). Multidimensional explanatory item response models. In S.P. Reise, & D.A. Revicki (Eds.), Item Response Theory modeling: Applications to typical performance assessment (pp. 250-271). New York, NY: Routledge/Taylor & Francis Group.

De Boeck, P., & Wilson M. (2016). Explanatory item response models. In W.J. van der Linden (Ed.), Handbook of item response theory. Vol. 1. Models (pp. 565-580). Boca Raton, FL: Chapman & Hall/CRC.

Other selected publications

De Boeck, P. (2008). Random item IRT models. Psychometrika, 73, 533-559

De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39, 1-28.

De Boeck, P., & Partchev, I. (2012). IRTrees: Tree-based item response models of the GLMM family. Journal of Statistical Software, 48, 1-28.

De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York: Springer.

De Boeck, P., Wilson, M., & Acton, G. S. (2005). A conceptual and psychometric framework for distinguishing categories and dimensions. Psychological Review, 112, 129-158.

Germeijs, V., & De Boeck, P. (2002). A measurement scale for indecisiveness and its relationship to career indecision and other types of indecision. European Journal of Psychological Assessment, 18, 113-122.

Partchev, I., & De Boeck, P. (2012). Can fast and slow intelligence be differentiated? Intelligence, 40, 23-32.

González, J., De Boeck, P., & Tuerlinckx, F. (2008). A double-structure structural equation model for three-mode data. Psychological Methods, 13, 337-353.

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.

Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8, 185-205.

Smits, D. J. M., & De Boeck, P. (2003). A componential IRT model for guilt. Multivariate Behavioral Research, 38, 161-188.

Smits, D. J. M., & De Boeck, P. (2007). From anger to verbal aggression: Inhibition at different levels. Personality and Individual Differences, 43, 47-57.

Storms, G., De Boeck, P., & Ruts, W. (2000). Prototype and exemplar-based information in natural language categories. Journal of Memory and Language, 42, 51-73.

Tuerlinckx, F., & De Boeck, P. (2005). Two interpretations of the discrimination parameter. Psychometrika, 70, 629-650.

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.

Van Mechelen, I., Bock, H.-H., & De Boeck, P. (2004). Two-mode clustering methods: A structured overview. Statistical Methods in Medical Research, 13, 363-394


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