Modeling local item dependence with the hierarchical generalized linear model.

Hong Jiao; Shudong Wang; Akihito Kamata

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PubMed · 15942073

Modeling local item dependence with the hierarchical generalized linear model.

Hong Jiao·Shudong Wang·Akihito Kamata

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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.

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Hong Jiao, Shudong Wang, Akihito Kamata. 2005. Modeling local item dependence with the hierarchical generalized linear model.. https://pubmed.ncbi.nlm.nih.gov/15942073/

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