Online calibration is a key technology for item calibration in computerized adaptive testing (CAT) and has been widely used in various forms of CAT, including unidimensional CAT, multidimensional CAT (MCAT), CAT with polytomously scored items, and cognitive diagnostic CAT. However, as multidimensional and polytomous assessment data become more…

Differential item functioning (DIF) of test items should be evaluated using practical methods that can produce accurate and useful results. Among a plethora of DIF detection techniques, we introduce the new "Residual DIF" (RDIF) framework, which stands out for its accessibility without sacrificing efficacy. This framework consists of…

Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…

By tailoring test forms to the test-taker's proficiency, Computerized Adaptive Testing (CAT) enables substantial increases in testing efficiency over fixed forms testing. When used for formative assessment, the alignment of task difficulty with proficiency increases the chance that teachers can derive useful feedback from assessment data. The…

New items are often evaluated prior to their operational use to obtain item response theory (IRT) item parameter estimates for quality control purposes. Fixed parameter calibration is one linking method that is widely used to estimate parameters for new items and place them on the desired scale. This article provides detailed descriptions of two…

A key consideration when giving any computerized adaptive test (CAT) is how much adaptation is present when the test is used in practice. This study introduces a new framework to measure the amount of adaptation of Rasch-based CATs based on looking at the differences between the selected item locations (Rasch item difficulty parameters) of the…

The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across…

A conceptualization of multiple-choice exams in terms of signal detection theory (SDT) leads to simple measures of item difficulty and item discrimination that are closely related to, but also distinct from, those used in classical item analysis (CIA). The theory defines a "true split," depending on whether or not examinees know an item,…

One common phenomenon in Angoff standard setting is that panelists regress their ratings in toward the middle of the probability scale. This study describes two indices based on taking ratios of standard deviations that can be utilized with a scatterplot of item ratings versus expected probabilities of success to identify whether ratings are…

Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we investigated whether the efficiency of calibration under the Rasch model could be enhanced by improving the match between item difficulty and student ability. We introduced targeted multistage calibration designs, a design type that…

Test items must often be broad in scope to be ecologically valid. It is therefore almost inevitable that secondary dimensions are introduced into a test during test development. A cognitive test may require one or more abilities besides the primary ability to correctly respond to an item, in which case a unidimensional test score overestimates the…

Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to…

This study investigates the effect of several design and administration choices on item exposure and person/item parameter recovery under a multistage test (MST) design. In a simulation study, we examine whether number-correct (NC) or item response theory (IRT) methods are differentially effective at routing students to the correct next stage(s)…

In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

Although the root-mean squared deviation (RMSD) is a popular statistical measure for evaluating country-specific item-level misfit (i.e., differential item functioning [DIF]) in international large-scale assessment, this paper shows that its sensitivity to detect misfit may depend strongly on the proficiency distribution of the considered…