Anchor methods for DIF detection: A comparison of the iterative forward, backward, constant and all-other anchor class

Kopf, Julia; Zeileis, Achim und Strobl, Carolin (13. März 2013): Anchor methods for DIF detection: A comparison of the iterative forward, backward, constant and all-other anchor class. Department of Statistics: Technical Reports, Nr. 141

Abstract

In the analysis of differential item functioning (DIF) using item response theory (IRT), a common metric is necessary to compare item parameters between groups of test-takers. In the Rasch model, the same restriction is placed on the item parameters in each group in order to define a common metric. However, the question how the items in the restriction - termed anchor items - are selected appropriately is still a major challenge. This article proposes a conceptual framework for categorizing anchor methods: The anchor class to describe characteristics of the anchor methods and the anchor selection strategy to guide how the anchor items are determined. Furthermore, a new anchor class termed the iterative forward anchor class is proposed. Several anchor classes are implemented with two different anchor selection strategies (the all-other and the single-anchor selection strategy) and are compared in an extensive simulation study. The results show that the newly proposed anchor class combined with the single-anchor selection strategy is superior in situations where no prior knowledge about the direction of DIF is available. Moreover, it is shown that the proportion of DIF items in the anchor - rather than the fact whether the anchor includes DIF items at all (termed contamination in previous studies) - is crucial for suitable DIF analysis.

• Dokumententyp: Paper
• Keywords: Item response theory (IRT), Rasch model, anchoring, anchor selection, contamination, differential item functioning (DIF), item bias
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Technische Reports
• Themengebiete: 100 Philosophie und Psychologie > 150 Psychologie ; 300 Sozialwissenschaften > 310 Statistiken
• Sprache: Englisch