2019 Early Career Award
Using traditional psychometric models like the linear factor model and the two-parameter logistic item response theory model, the simple main effects of items and subjects can be inferred from the first two moments of the matrix of observed scores. The resulting parameters are statistically useful for various practical issues including item calibration, test equating, and adaptive testing, and for various substantive issues like establishing group differences in IQ and personality. However, some substantive hypotheses predict statistical effects that go beyond the first two moments of the data. Examples of these higher-order effects include non-linear effects, non-normal effects, heteroskedastic effects, and mixtures of different effects. Studying phenomena like these is substantively interesting but statistically challenging as the distributional assumptions underlying common psychometric models may distort the modeling results once violated. In this presentation, the challenges of studying higher-order statistical effects are illustrated using cases from the fields of intelligence, personality, and response time research.
ABOUT THE SPEAKER
Dylan Molenaar is an assistant professor at the University of Amsterdam, The Netherlands. He obtained his PhD in 2012 from the same university. His dissertation, entitled “Testing distributional assumptions in psychometric measurement models” and supervised by Conor Dolan, received the Dissertation Prize by the Psychometric Society in 2013. In 2014, he was a visiting scholar at the Ohio State University. His current research is funded by a personal grant from the Netherlands Organization for Scientific Research and focusses on psychometric models for responses and response times. Other research interests include factor analysis and item response theory in general, and modeling of intelligence and personality test data.