Journal of Educational Measurement最新文献

Culturally responsive assessments have been proposed as potential tools to ensure equity and fairness for examinees from all backgrounds including those from traditionally underserved or minoritized groups. However, these assessments are relatively new and, with few exceptions, are yet to be implemented in large scale. Consequently, there is a lack of guidance on how one can compute comparable scores on various versions of these assessments. In this paper, the multigroup multidimensional Rasch model is repurposed for modeling data originating from various versions of a culturally responsive assessment and for analyzing such data to compute comparable scores. Two simulation studies are performed to evaluate the performance of the model for data simulated from hypothetical culturally responsive assessments and to find the conditions under which the computed scores are accurate. Recommendations are made for measurement practitioners interested in culturally responsive assessments.

This paper presents the item and test information functions of the Rank two-parameter logistic models (Rank-2PLM) for items with two (pair) and three (triplet) statements in forced-choice questionnaires. The Rank-2PLM model for pairs is the MUPP-2PLM (Multi-Unidimensional Pairwise Preference) and, for triplets, is the Triplet-2PLM. Fisher's information and directional information are described, and the test information for Maximum Likelihood (ML), Maximum A Posterior (MAP), and Expected A Posterior (EAP) trait score estimates is distinguished. Expected item/test information indexes at various levels are proposed and plotted to provide diagnostic information on items and tests. The expected test information indexes for EAP scores may be difficult to compute due to a typical test's vast number of item response patterns. The relationships of item/test information with discrimination parameters of statements, standard error, and reliability estimates of trait score estimates are discussed and demonstrated using real data. Practical suggestions for checking the various expected item/test information indexes and plots are provided.

The purpose of this study was to investigate multidimensional DIF with a simple and nonsimple structure in the context of multidimensional Graded Response Model (MGRM). This study examined and compared the performance of the IRT-LR and Wald test using MML-EM and MHRM estimation approaches with different test factors and test structures in simulation studies and applying real data sets. When the test structure included two dimensions, the IRT-LR (MML-EM) generally performed better than the Wald test and provided higher power rates. If the test included three dimensions, the methods provided similar performance in DIF detection. In contrast to these results, when the number of dimensions in the test was four, MML-EM estimation completely lost precision in estimating the nonuniform DIF, even with large sample sizes. The Wald with MHRM estimation approaches outperformed the Wald test (MML-EM) and IRT-LR (MML-EM). The Wald test had higher power rate and acceptable type I error rates for nonuniform DIF with the MHRM estimation approach.The small and/or unbalanced sample sizes, small DIF magnitudes, unequal ability distributions between groups, number of dimensions, estimation methods and test structure were evaluated as important test factors for detecting multidimensional DIF.

In this paper, we develop a mixed stochastic approximation expectation-maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four-parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of the stochastic approximation expectation-maximization (SAEM) algorithm for multidimensional data, but it also alleviates the potential instability caused by label-switching, and then improved the estimation accuracy. Simulation studies are conducted to illustrate the good performance of the proposed MSAEM method, where MSAEM consistently performs better than SAEM and some other existing methods in multidimensional item response theory. Moreover, the proposed method is applied to a real data set from the 2018 Programme for International Student Assessment (PISA) to demonstrate the usefulness of the 4PNO model as well as MSAEM in practice.

The usual interpretation of the person and task variables in between-persons measurement models such as item response theory (IRT) is as attributes of persons and tasks, respectively. They can be viewed instead as ensemble descriptors of patterns of interactions among persons and situations that arise from sociocognitive complex adaptive system (CASs). This view offers insights for interpreting and using between-persons measurement models and connecting with sociocognitive research. In this article, we use data generated from an agent-based model to illustrate relations between “social” and “cognitive” features of a simple underlying CAS and the variables of an IRT model fit to resulting data. We note how the ideas connect to explanatory item response modeling and briefly comment on implications for score interpretations and uses in practice.

Many language proficiency tests include group oral assessments involving peer interaction. In such an assessment, examinees discuss a common topic with others. Human raters score each examinee's spoken performance on specially designed criteria. However, measurement models for analyzing group assessment data usually assume local person independence and thus fail to consider the impact of peer interaction on the assessment outcomes. This research advances an extended many-facet Rasch model for group assessments (MFRM-GA), accounting for local person dependence. In a series of simulations, we examined the MFRM-GA's parameter recovery and the consequences of ignoring peer interactions under the traditional modeling approach. We also used a real dataset from the English-speaking test of the Language Proficiency Assessment for Teachers (LPAT) routinely administered in Hong Kong to illustrate the efficiency of the new model. The discussion focuses on the model's usefulness for measuring oral language proficiency, practical implications, and future research perspectives.

In multidimensional computerized adaptive testing (MCAT), item selection strategies are generally constructed based on responses, and they do not consider the response times required by items. This study constructed two new criteria (referred to as DT-inc and DT) for MCAT item selection by utilizing information from response times. The new designs maximize the amount of information per unit time. Furthermore, these two new designs were extended to the DT_S-inc and DT_S designs to efficiently estimate intentional abilities. Moreover, the EAP method for ability estimation was also equipped with response time. The performances of the response-time-based EAP (RT-based EAP) and the new designs were evaluated in simulation and empirical studies. The results showed that the RT-based EAP significantly improved the ability estimation precision compared with the EAP without using response time, and the new designs dramatically saved testing times for examinees with a small sacrifice of ability estimation precision and item pool usage.