Differential Item Functioning Analyses in Large-Scale Educational Surveys: Key Concepts and Modeling Approaches for Secondary Analysts Author: Xiao-Shu Zhu(Department of Measurement, Statistics & Evaluation, University of Maryland),Andre A. Rupp(Department of Measurement, Statistics & Evaluation, University of Maryland),Jing Gao(Office of Assessment & Evaluation, University College University of Maryland)

Vol.&No.：Vol. 56, No. 1
Date：March 2011
Pages：91-127
DOI：10.3966/2073753X2011035601004

Abstract：

Many educational surveys employ a multi-stage sampling design for students, which makes use of stratification and/or clustering of population units, as well as a complex booklet design for items from an item pool. In these surveys, the reliable detection of item bias or differential item functioning (DIF) across student groups is a key component for ensuring fair representations of different student groups. In this paper, we describe several modeling approaches that can be useful for detecting DIF in educational surveys. We illustrate the key ideas by investigating the performance of six hierarchical generalized linear models (HGLMs) using a small simulation study and by applying them to real data from the Trends in Mathematics and Science Study (TIMSS) study where we use them to investigate potential uniform gender DIF.

Keywords：complex booklet design, DIF, HGLMs, multi-stage sampling design

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