Item-fit evaluation in biased tests: a study under Rasch model

Item-fit evaluation in biased tests: a study under Rasch model In this paper, the power rates and distributional properties of the Outfit, Infit, Lz, ECI2z and ECI4z statistics when they are used in tests with biased or differential item functioning (DIF) were explored. In this study, different conditions of sample size, sample size ratio focal and reference group, impact between groups, DIF effect size, and percentage of DIF items were manipulated. In addition, examinee responses were generated to simulate uniform DIF. Results suggest that item fit statistics generally detected medium percents of DIF in large samples (1000/500 or 1000/1000) only when DIF effect size was relatively high and when the mean of focal and reference group was different. Moreover, when groups had equal mean, low correct identification rates were found in the five item-fit indices. In general, the results showed adequate control of false positive rates. These findings lead to the conclusion that all indices used in this study are partially adequate fit measures for detecting biased items, mainly when impact between groups is present and sample size is large. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Item-fit evaluation in biased tests: a study under Rasch model

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Publisher
Springer Netherlands
Copyright
Copyright © 2010 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-010-9318-8
Publisher site
See Article on Publisher Site

Abstract

In this paper, the power rates and distributional properties of the Outfit, Infit, Lz, ECI2z and ECI4z statistics when they are used in tests with biased or differential item functioning (DIF) were explored. In this study, different conditions of sample size, sample size ratio focal and reference group, impact between groups, DIF effect size, and percentage of DIF items were manipulated. In addition, examinee responses were generated to simulate uniform DIF. Results suggest that item fit statistics generally detected medium percents of DIF in large samples (1000/500 or 1000/1000) only when DIF effect size was relatively high and when the mean of focal and reference group was different. Moreover, when groups had equal mean, low correct identification rates were found in the five item-fit indices. In general, the results showed adequate control of false positive rates. These findings lead to the conclusion that all indices used in this study are partially adequate fit measures for detecting biased items, mainly when impact between groups is present and sample size is large.

Journal

Quality & QuantitySpringer Journals

Published: Mar 14, 2010

References

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