A Generalized Logistic Regression Procedure to Detect Differential Item Functioning Among Multiple Groups

2011 ◽  
Vol 11 (4) ◽  
pp. 365-386 ◽  
Author(s):  
David Magis ◽  
Gilles Raîche ◽  
Sébastien Béland ◽  
Paul Gérard
2016 ◽  
Vol 41 (6) ◽  
pp. 559-592 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz

Detection of differential item functioning (DIF) by use of the logistic modeling approach has a long tradition. One big advantage of the approach is that it can be used to investigate nonuniform (NUDIF) as well as uniform DIF (UDIF). The classical approach allows one to detect DIF by distinguishing between multiple groups. We propose an alternative method that is a combination of recursive partitioning methods (or trees) and logistic regression methodology to detect UDIF and NUDIF in a nonparametric way. The output of the method are trees that visualize in a simple way the structure of DIF in an item showing which variables are interacting in which way when generating DIF. In addition, we consider a logistic regression method, in which DIF can be induced by a vector of covariates, which may include categorical but also continuous covariates. The methods are investigated in simulation studies and illustrated by two applications.


Psych ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 44-51
Author(s):  
Vladimir Shibaev ◽  
Andrei Grigoriev ◽  
Ekaterina Valueva ◽  
Anatoly Karlin

National IQ estimates are based on psychometric measurements carried out in a variety of cultural contexts and are often obtained from Raven’s Progressive Matrices tests. In a series of studies, J. Philippe Rushton et al. have argued that these tests are not biased with respect to ethnicity or race. Critics claimed their methods were inappropriate and suggested differential item functioning (DIF) analysis as a more suitable alternative. In the present study, we conduct a DIF analysis on Raven’s Standard Progressive Matrices Plus (SPM+) tests administered to convenience samples of Yakuts and ethnic Russians. The Yakuts scored lower than the Russians by 4.8 IQ points, a difference that can be attributed to the selectiveness of the Russian sample. Data from the Yakut (n = 518) and Russian (n = 956) samples were analyzed for DIF using logistic regression. Although items B9, B10, B11, B12, and C11 were identified as having uniform DIF, all of these DIF effects can be regarded as negligible (R2 <0.13). This is consistent with Rushton et al.’s arguments that the Raven’s Progressive Matrices tests are ethnically unbiased.


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