A Speeded Item Response Model with Gradual Process Change

An item response theory model for dealing with test speededness is proposed. The model consists of two random processes, a problem solving process and a random guessing process, with the random guessing gradually taking over from the problem solving process. The involved change point and change rate are considered random parameters in order to model examinee differences in both respects. The proposed model is evaluated on simulated data and in a case study. The research reported in this paper was supported by IAP P5/24 and GOA/2005/04, both awarded to Paul De Boeck and Iven Van Mechelen, and by IAP P6/03, awarded to Iven Van Mechelen. Yuri Goegebeur’s research was supported by a grant of the Danish Natural Science Research Council.     

Graph Theory Approach to Detect Examinees Involved in Test Collusion

Test collusion (TC) is sharing of test materials or answers to test questions before or during the test (important special case of TC is item preknowledge). Because of potentially large advantages for examinees involved, TC poses a serious threat to the validity of score interpretations. The proposed approach applies graph theory methodology to response similarity analyses for identifying groups of examinees involved in TC without using any knowledge about parts of test that were affected by TC. The approach supports different response similarity indices (specific to a particular type of TC) and different types of groups (connected components, cliques, or near-cliques). A comparison with an up-to-date method using real and simulated data is presented. Possible extensions and practical recommendations are given.     

Noncompensatory MIRT For Passage-Based Tests

Psychometrika - We consider a multidimensional noncompensatory approach for binary items in passage-based tests. The passage-based noncompensatory model (PB-NM) emphasizes two underlying components...     

Application of a Multidimensional Nested Logit Model to Multiple-Choice Test Items

Nested logit models have been presented as an alternative to multinomial logistic models for multiple-choice test items (Suh and Bolt in Psychometrika 75:454–473, 2010) and possess a mathematical structure that naturally lends itself to evaluating the incremental information provided by attending to distractor selection in scoring. One potential concern in attending to distractors is the possibility that distractor selection reflects a different trait/ability than that underlying the correct response. This paper illustrates a multidimensional extension of a nested logit item response model that can be used to evaluate such distinctions and also defines a new framework for incorporating collateral information from distractor selection when differences exist. The approach is demonstrated in application to questions faced by a university testing center over whether to incorporate distractor selection into the scoring of its multiple-choice tests. Several empirical examples are presented.