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Detection of differential item functioning in Rasch models by boosting techniques
Author(s) -
Schauberger Gunther,
Tutz Gerhard
Publication year - 2016
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12060
Subject(s) - rasch model , boosting (machine learning) , covariate , categorical variable , differential item functioning , polytomous rasch model , gradient boosting , machine learning , artificial intelligence , parametric statistics , computer science , item response theory , mathematics , statistics , econometrics , psychometrics , random forest
Methods for the identification of differential item functioning ( DIF ) in Rasch models are typically restricted to the case of two subgroups. A boosting algorithm is proposed that is able to handle the more general setting where DIF can be induced by several covariates at the same time. The covariates can be both continuous and (multi‐)categorical, and interactions between covariates can also be considered. The method works for a general parametric model for DIF in Rasch models. Since the boosting algorithm selects variables automatically, it is able to detect the items which induce DIF . It is demonstrated that boosting competes well with traditional methods in the case of subgroups. The method is illustrated by an extensive simulation study and an application to real data.