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)…

Longitudinal data analysis assumes that scales meet the assumption of longitudinal measurement invariance (i.e., that scales function equivalently across measurement occasions). This simulation study examines the impact of violations to the assumption of longitudinal measurement invariance on growth models and whether modeling the invariance…

Multidimensional computerized adaptive testing (MCAT) is able to provide a vector of ability estimates for each examinee, which could be used to provide a more informative profile of an examinee's performance. The current literature on MCAT focuses on the fixed-length tests, which can generate less accurate results for those examinees whose…

Personnel selection shows an enduring need for short stand-alone tests consisting of, say, 5 to 15 items. Despite their efficiency, short tests are more vulnerable to measurement error than longer test versions. Consequently, the question arises to what extent reducing test length deteriorates decision quality due to increased impact of…

Content balancing is one of the most important components in the computerized adaptive testing (CAT) especially in the K to 12 large scale tests that complex constraint structure is required to cover a broad spectrum of content. The purpose of this study is to compare the weighted penalty model (WPM) and the weighted deviation method (WDM) under…

Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment.…

In this article, we introduce a person-fit statistic called the hierarchy consistency index (HCI) to help detect misfitting item response vectors for tests developed and analyzed based on a cognitive model. The HCI ranges from -1.0 to 1.0, with values close to -1.0 indicating that students respond unexpectedly or differently from the responses…