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Identifying differential item functioning in multi-stage computer adaptive testing.

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Title: Identifying differential item functioning in multi-stage computer adaptive testing.
Authors: Gierl, Mark J.1 mark.gierl@ualberta.ca, Lai, Hollis1, Li, Johnson1
Source: Educational Research & Evaluation. Apr2013, Vol. 19 Issue 2/3, p188-203. 16p.
Subject Terms: *Adaptive testing, *Competency tests (Education), *Sequential approach (Teaching method), *Response styles (Examinations), *Adapted examinations, Statistical sampling
Abstract: The purpose of this study is to evaluate the performance of CATSIB (Computer Adaptive Testing-Simultaneous Item Bias Test) for detecting differential item functioning (DIF) when items in the matching and studied subtest are administered adaptively in the context of a realistic multi-stage adaptive test (MST). MST was simulated using a 4-item module in a 7-panel administration. Three independent variables, expected to affect DIF detection rates, were manipulated: item difficulty, sample size, and balanced/unbalanced design. CATSIB met the acceptable criteria, meaning that the Type I error and power rates met 5% and 80%, respectively, for the large reference/moderate focal sample and the large reference/large focal sample conditions. These results indicate that CATSIB can be used to consistently and accurately detect DIF on an MST, but only with moderate to large samples. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract:The purpose of this study is to evaluate the performance of CATSIB (Computer Adaptive Testing-Simultaneous Item Bias Test) for detecting differential item functioning (DIF) when items in the matching and studied subtest are administered adaptively in the context of a realistic multi-stage adaptive test (MST). MST was simulated using a 4-item module in a 7-panel administration. Three independent variables, expected to affect DIF detection rates, were manipulated: item difficulty, sample size, and balanced/unbalanced design. CATSIB met the acceptable criteria, meaning that the Type I error and power rates met 5% and 80%, respectively, for the large reference/moderate focal sample and the large reference/large focal sample conditions. These results indicate that CATSIB can be used to consistently and accurately detect DIF on an MST, but only with moderate to large samples. [ABSTRACT FROM AUTHOR]
ISSN:13803611
DOI:10.1080/13803611.2013.767622