Premium
Item‐Bundle DIF Hypothesis Testing: Identifying Suspect Bundles and Assessing Their Differential Functioning
Author(s) -
Douglas Jeffrey A.,
Roussos Louis A.,
Stout William
Publication year - 1996
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.1996.tb00502.x
Subject(s) - differential item functioning , suspect , bundle , construct (python library) , psychology , identification (biology) , differential (mechanical device) , embodied cognition , item response theory , psychometrics , cognitive psychology , artificial intelligence , statistics , computer science , mathematics , developmental psychology , engineering , biology , materials science , botany , criminology , composite material , programming language , aerospace engineering
This article proposes two multidimensional IRT model‐based methods of selecting item bundles (clusters of not necessarily adjacent items chosen according to some organizational principle) suspected of displaying DIF amplification. The approach embodied in these two methods is inspired by Shealy and Stout's (1993a, 1993b) multidimensional model for DIF. Each bundle selected by these methods constitutes a DIF amplification hypothesis. When SIBTEST (Shealy & Stout, 1993b) confirms DIF amplification in selected bundles, differential bundle functioning (DBF) is said to occur. Three real data examples illustrate the two methods for suspect bundle selection. The effectiveness of the methods is argued on statistical grounds. A distinction between benign and adverse DIF is made. The decision whether flagged DIF items or DBF bundles display benign or adverse DIF/DBF must depend in part on nonstatistical construct validity arguments. Conducting DBF analyses using these methods should help in the identification of the causes of DIF/DBF.