Evaluation of MIMIC-Model Methods for DIF Testing With Comparison to Two-Group Analysis

2009/02/10 by Carol M. Woods · 197 citations
Computer Science · Decision Sciences · Mathematics · Psychology · #Advanced Statistical Modeling Techniques #Algorithm #Computer science #Construct (python library) #Differential item functioning #Group (periodic table) #Group testing #Item response theory #Mathematics #Parameterized complexity #Psychology #Psychometric Methodologies and Testing #Psychometrics #Statistics #Structural equation modeling

paper · doi:10.1080/00273170802620121

published in Multivariate Behavioral Research 44(1), 1-27 (Taylor & Francis)

openalex publication_date 2009/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Differential item functioning (DIF) occurs when an item on a test or questionnaire has different measurement properties for 1 group of people versus another, irrespective of mean differences on the construct. This study focuses on the use of multiple-indicator multiple-cause (MIMIC) structural equation models for DIF testing, parameterized as item response models. The accuracy of these methods, and the sample size requirements, are not well established. This study examines the accuracy of MIMIC methods for DIF testing when the focal group is small and compares results with those obtained using 2-group item response theory (IRT). Results support the utility of the MIMIC approach. With small focal-group samples, tests of uniform DIF with binary or 5-category ordinal responses were more accurate with MIMIC models than 2-group IRT. Recommendations are offered for the application of MIMIC methods for DIF testing.

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