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Yixi Wang – ProQuest LLC, 2020
Binary item response theory (IRT) models are widely used in educational testing data. These models are not perfect because they simplify the individual item responding process, ignore the differences among different response patterns, cannot handle multidimensionality that lay behind options within a single item, and cannot manage missing response…
Descriptors: Item Response Theory, Educational Testing, Data, Models
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Suthathip Thirakunkovit – Language Testing in Asia, 2025
Establishing a cut score is a crucial aspect of the test development process since the selected cut score has the potential to impact students' performance outcomes and shape instructional strategies within the classroom. Therefore, it is vital for those involved in test development to set a cut score that is both fair and justifiable. This cut…
Descriptors: Cutting Scores, Culture Fair Tests, Language Tests, Test Construction
Berman, Amy I.; Haertel, Edward H.; Pellegrino, James W. – National Academy of Education, 2020
This National Academy of Education (NAEd) volume provides guidance to key stakeholders on how to accurately report and interpret comparability assertions concerning large-scale educational assessments as well as how to ensure greater comparability by paying close attention to key aspects of assessment design, content, and procedures. The goal of…
Descriptors: Educational Assessment, Educational Testing, Scores, Comparative Analysis
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Wright, Daniel B. – Educational Measurement: Issues and Practice, 2019
There is much discussion about and many policies to address achievement gaps in education among groups of students. The focus here is on a different gap and it is argued that it also should be of concern. Speed gaps are differences in how quickly different groups of students answer the questions on academic assessments. To investigate some speed…
Descriptors: Academic Achievement, Achievement Gap, Reaction Time, Educational Testing