title: Modeling local item dependence with the hierarchical generalized linear model.
abstract: Local item dependence (LID) can emerge when the test items are nested within common stimuli or item groups. This study proposes a three-level hierarchical generalized linear model (HGLM) to model LID when LID is due to such contextual effects. The proposed three-level HGLM was examined by analyzing simulated data sets and was compared with the Rasch-equivalent two-level HGLM that ignores such a nested structure of test items. The results demonstrated that the proposed model could capture LID and estimate its magnitude. Also, the two-level HGLM resulted in larger mean absolute differences between the true and the estimated item difficulties than those from the proposed three-level HGLM. Furthermore, it was demonstrated that the proposed three-level HGLM estimated the ability distribution variance unaffected by the LID magnitude, while the two-level HGLM with no LID consideration increasingly underestimated the ability variance as the LID magnitude increased.
authors.affiliation: Psychometrics and Research Services, Harcourt Assessment, Inc., 19500 Bulverde Road, San Antonio, TX 78259, USA. hong_jiao@harcourt.com
authors.forename: Hong
authors.initials: H
authors.lastname: Jiao
authors.forename: Shudong
authors.initials: S
authors.lastname: Wang
authors.forename: Akihito
authors.initials: A
authors.lastname: Kamata
mesh_terms: D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000706:statistics & numerical data*; D016014:Linear Models