Nonuniform DIF Detection using Discriminant Logistic Analysis and Multinomial Logistic Regression: A comparison for polytomous items

Nonuniform DIF Detection using Discriminant Logistic Analysis and Multinomial Logistic... This study focused on the effectiveness in nonuniform polytomous item DIF detection using Discriminant Logistic Analysis (DLA) and Multinomial Logistic Regression (MLR). A computer simulation study was conducted to compare the effect of using DLA and MLR, applying either an iterative test purification procedure or non-iterative to detect nonuniform DIF. The conditions under study were: DIF effect size (0.5, 1.0 and 1.5), sample size (500 and 1000), percentage of DIF items in the test (0, 10 and 20%) and DIF type (nonuniform). The results suggest that DLA is more accurate than MLR in detecting DIF. However, the purification process only improved the correct detection rate when MLR was applied. The false positive rates for both procedures were similar. Moreover, when the test purification procedure was used, the proportion of non-DIF items that were detected as DIF decreased for both procedures, although the false positive rates were smaller for DLA than for MLR. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Nonuniform DIF Detection using Discriminant Logistic Analysis and Multinomial Logistic Regression: A comparison for polytomous items

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2006 by Springer
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-005-3964-2
Publisher site
See Article on Publisher Site

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