Psychometrics in Psychological Research: Role Model or Partner in Science?

This is a reaction to Borsboom's (2006) discussion paper on the issue that psychology takes so little notice of the modern developments in psychometrics, in particular, latent variable methods. Contrary to Borsboom, it is argued that latent variables are summaries of interesting data properties, that construct validation should involve studying nomological networks, that psychological research slowly but definitely will incorporate latent variable methods, and that the role of psychometrics in psychology is that of partner, not role model. Requests for reprints should be sent to Klaas Sijtsma, Department of Methodology and Statistics, FSW, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.     

Global, local, and graphical person-fit analysis using person-response functions

Person-fit statistics test whether the likelihood of a respondent's complete vector of item scores on a test is low given the hypothesized item response theory model. This binary information may be insufficient for diagnosing the cause of a misfitting item-score vector. The authors propose a comprehensive methodology for person-fit analysis in the context of nonparametric item response theory. The methodology (a) includes H. Van der Flier's (1982) global person-fit statistic U3 to make the binary decision about fit or misfit of a person's item-score vector, (b) uses kernel smoothing (J. O. Ramsay, 1991) to estimate the person-response function for the misfitting item-score vectors, and (c) evaluates unexpected trends in the person-response function using a new local person-fit statistic (W. H. M. Emons, 2003). An empirical data example shows how to use the methodology for practical person-fit analysis.     

Review of Issues About Classical Change Scores: A Multilevel Modeling Perspective on Some Enduring Beliefs

Change scores obtained in pretest–posttest designs are important for evaluating treatment effectiveness and for assessing change of individual test scores in psychological research. However, over the years the use of change scores has raised much controversy. In this article, from a multilevel perspective, we provide a structured treatise on several persistent negative beliefs about change scores and show that these beliefs originated from the confounding of the effects of within-person change on change-score reliability and between-person change differences. We argue that psychometric properties of change scores, such as reliability and measurement precision, should be treated at suitable levels within a multilevel framework. We show that, if examined at the suitable levels with such a framework, the negative beliefs about change scores can be renounced convincingly. Finally, we summarize the conclusions about change scores to dispel the myths and to promote the potential and practical usefulness of change scores.