Multistage adaptive testing (MST) has become popular in the testing industry because the research has shown that it combines the advantages of both linear tests and item-level computer adaptive testing (CAT). The previous research efforts primarily focused on MST design issues such as panel design, module length, test length, distribution of test…

Setting cut scores on (MSTs) is difficult, particularly when the test spans several grade levels, and the selection of items from MST panels must reflect the operational test specifications. In this study, we describe, illustrate, and evaluate three methods for mapping panelists' Angoff ratings into cut scores on the scale underlying an MST. The…

The Nebraska Student-Centered Assessment System (NSCAS) is a statewide assessment system that embodies Nebraska's holistic view of students and helps them prepare for success in postsecondary education, career, and civic life. It uses multiple measures throughout the year to provide educators and decision-makers at all levels with the insights…

This article delves into a relatively unexplored area of measurement by focusing on adaptive testing with unidimensional pairwise preference items. The use of such tests is becoming more common in applied non-cognitive assessment because research suggests that this format may help to reduce certain types of rater error and response sets commonly…

Since researchers acknowledged the several advantages of computerized adaptive testing (CAT) over traditional linear test administration, the issue of item exposure control has received increased attention. Due to CAT's underlying philosophy, particular items in the item pool may be presented too often and become overexposed, while other items are…

Under a multidimensional item response theory (MIRT) computerized adaptive testing (CAT) testing scenario, a trait estimate (theta) in one dimension will provide clues for subsequently seeking a solution in other dimensions. This feature may enhance the efficiency of MIRT CAT's item selection and its scoring algorithms compared with its…

This study introduced a probabilistic model utilizing item response theory (IRT) for dealing with a variety of misconceptions. The model can be used for evaluating the transition behavior of error types, advancement of learning stages, or the stability and persistence of particular misconceptions. Moreover, it apparently can be used for relating…

Item parameter estimates vary for a variety of reasons, including estimation error, characteristics of the examinee samples, and context effects (e.g., item location effects, section location effects, etc.). Although we expect variation based on theory, there is reason to believe that observed variation in item parameter estimates exceeds what…