Are achievement gap estimates biased by differential student test effort?
By: James Soland
New research shows that test effort differs substantially across student gender and racial subgroups. What does this mean for achievement gap estimates?
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In the visualizations in this exhibit, you can compare the performance and growth of various groups of high achievers to that of their peers over multiple years.
Most previous research involving the study of response times has been conducted using locally developed instruments. The purpose of the current study was to examine the amount of rapid-guessing behavior within a commercially available, low-stakes instrument.
By: Steven Wise, J. Carl Setzer, Jill R. van den Heuvel, Guangming Ling
Some of our assumptions about the growth and performance of students from high-poverty schools relative to their peers from wealthier schools may be challenged in this data gallery, where you can explore how school poverty level interacts with student growth, college readiness, and college access.
This study examines the academic growth of 35,000 elementary and middle school students in 31 states—all of them high achievers within their own schools—over a three-year period.
In this study from the Thomas B. Fordham Institute, achievement trends from NWEA’s longitudinal growth database were used to track students who scored at or above the 90th percentile on this assessment in order to see if they maintained their high achievement.
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.
By: Steven Wise
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.
By: Steven Wise