In selected-response assessments such as attitude surveys with Likert-type rating scales, examinees often select from rating scale categories to reflect their locations on a construct. Researchers have observed that some examinees exhibit "response styles," which are systematic patterns of responses in which examinees are more likely to…

Person-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the…

Survey score interpretations are often plagued by sources of construct-irrelevant variation, such as response styles. In this study, we propose the use of an IRTree Model to account for response styles by making use of self-report items and anchoring vignettes. Specifically, we investigate how the IRTree approach with anchoring vignettes compares…

Cognitive diagnosis models (CDMs) have gained increasing popularity recently because of their potential to provide diagnostic inferences that can inform learning and teaching. However, the development of cognitive diagnostic assessments (CDAs) is lagging behind the development of the associated psychometric models. At present, it is not clear…

The "extended Rasch modeling" (eRm) package in R provides users with a comprehensive set of tools for Rasch modeling for scale evaluation and general modeling. We provide a brief introduction to Rasch modeling followed by a review of literature that utilizes the eRm package. Then, the key features of the eRm package for scale evaluation…

Advanced modeling of item response data through the item response theory (IRT) or item factor analysis frameworks is becoming increasingly popular. In the social and behavioral sciences, the underlying structure of tests/assessments is often multidimensional (i.e., more than 1 latent variable/construct is represented in the items). This review…

Mixture item response theory (MixIRT) models combine IRT models with latent class model and assume that there exist latent subpopulations in the data. Identification of latent subpopulations via MixIRT models produces more detailed information. Detailed information about the response processing of examinees provides a better understanding of the…

Bayesian item response theory (IRT) modeling stages include (a) specifying the IRT likelihood model, (b) specifying the parameter prior distributions, (c) obtaining the posterior distribution, and (d) making appropriate inferences. The latter stage, and the focus of this research, includes model criticism. Choice of priors with the posterior…

Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure,…

The Vanishing Tetrad Test (VTT) (Bollen, Lennox, & Dahly, 2009; Bollen & Ting, 2000; Hipp, Bauer, & Bollen, 2005) is an extension of the Confirmatory Tetrad Analysis (CTA) proposed by Bollen and Ting (Bollen & Ting, 1993). VTT is a powerful tool for detecting model misspecification and can be particularly useful in cases in which…

Diagnostic classification models (DCMs) classify examinees based on the skills they have mastered given their test performance. This classification enables targeted feedback that can inform remedial instruction. Unfortunately, applications of DCMs have been criticized (e.g., no validity support). Generally, these evaluations have been brief and…

In this commentary, the authors congratulate Professor Alberto Maydeu-Olivares on his article [EJ1023617: "Goodness-of-Fit Assessment of Item Response Theory Models, Measurement: Interdisciplinary Research and Perspectives," this issue] as it provides a much needed overview on the mathematical underpinnings of the theory behind the…

In this commentary, Englehard and Perkins remark that Maydeu-Olivares has presented a framework for evaluating the goodness of model-data fit for item response theory (IRT) models and correctly points out that overall goodness-of-fit evaluations of IRT models and data are not generally explored within most applications in educational and…

These authors congratulate Albert Maydeu-Olivares on his lucid and timely overview of goodness-of-fit assessment in IRT models, a field to which he himself has contributed considerably in the form of limited information statistics. In this commentary, Oberski and Vermunt focus on two aspects of model fit: (1) what causes there may be of misfit;…

Von Davier, Naemi, and Roberts (this issue) present a nice summary of the statistical ambiguity often encountered in making distinctions between qualitative and quantitative constructs. In this commentary, the author begins with two broad points. The first is that the mixture/factor arguments are most intriguing when firmly embedded in a…