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An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models
Author(s) -
Cho SunJoo,
Suh Youngsuk,
Lee Wooyeol
Publication year - 2015
Publication title -
educational measurement: issues and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 52
eISSN - 1745-3992
pISSN - 0731-1745
DOI - 10.1111/emip.12093
Subject(s) - differential item functioning , item response theory , latent class model , local independence , latent variable model , latent variable , probabilistic latent semantic analysis , mixture model , item analysis , latent inhibition , computer science , statistics , econometrics , psychometrics , machine learning , artificial intelligence , mathematics , classical conditioning , conditioning
The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called latent DIF analysis, is provided and its applications in the literature are surveyed. Then, the methodological issues pertaining to latent DIF analysis are described, including mixture item response models, parameter estimation, and latent DIF detection methods. Finally, recommended steps for latent DIF analysis are illustrated using empirical data.