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A Comparison of Aggregation Rules for Selecting Anchor Items in Multigroup DIF Analysis
Author(s) -
Huelmann Thorben,
Debelak Rudolf,
Strobl Carolin
Publication year - 2019
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12246
Subject(s) - anchoring , rasch model , differential item functioning , computer science , false discovery rate , selection (genetic algorithm) , statistics , false alarm , item response theory , econometrics , data mining , artificial intelligence , psychology , mathematics , social psychology , psychometrics , chemistry , biochemistry , gene
This study addresses the topic of how anchoring methods for differential item functioning (DIF) analysis can be used in multigroup scenarios. The direct approach would be to combine anchoring methods developed for two‐group scenarios with multigroup DIF‐detection methods. Alternatively, multiple tests could be carried out. The results of these tests need to be aggregated to determine the anchor for the final DIF analysis. In this study, the direct approach and three aggregation rules are investigated. All approaches are combined with a variety of anchoring methods, such as the “all‐other purified” and “mean p‐value threshold” methods, in two simulation studies based on the Rasch model. Our results indicate that the direct approach generally does not lead to more accurate or even to inferior results than the aggregation rules. The min rule overall shows the best trade‐off between low false alarm rate and medium to high hit rate. However, it might be too sensitive when the number of groups is large. In this case, the all rule may be a good compromise. We also take a closer look at the anchor selection method “next candidate,” which performed rather poorly, and suggest possible improvements.