For decades, researchers and practitioners have made a great deal of effort to study a variety of methods to increase parameter accuracy, but only recently can researchers start focusing on improving parameter estimations by using a joint model that could incorporate RT and students information as CI.

The purpose of this study is to investigate the effect of missing data in computerized adaptive tests (CAT) on test construct validity.

These studies are conducted based on assumptions under regular conditions for fixed test forms, such as no missing responses and normal distribution of unidimensional ability for a population.

This study, using real data, provides empirical evidence of construct and invariance construct of MAP scales across grades at different academic calendars for 10 different states.

In this article, the authors explain how CAT provides a more precise, accurate picture of the achievement levels of both low-achieving and high-achieving students by adjusting questions as the testing goes along. The immediate, informative test results enable teachers to differentiate instruction to meet individual students’ current academic needs.

This integrative review examines the motivational benefits of computerized adaptive tests (CATs), and demonstrates that they can have important advantages over conventional tests in both identifying instances when examinees are exhibiting low effort, and effectively addressing the validity threat posed by unmotivated examinees.

Whenever the purpose of measurement is to inform an inference about a student’s achievement level, it is important that we be able to trust that the student’s test score accurately reflects what that student knows and can do. Such trust requires the assumption that a student’s test event is not unduly influenced by construct-irrelevant factors that could distort his score. This article examines one such factor—test-taking motivation—that tends to induce a person-specific, systematic negative bias on test scores.