Correction of rater effects in longitudinal research with a cross-classified random effects model created by Shenyang Guo
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Item type | Current library | Call number | Vol info | Copy number | Status | Notes | Date due | Barcode | |
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Main Library - Special Collections | BF39 APP (Browse shelf(Opens below)) | Vol. 38, No. 1 pages 37-60 | SP18166 | Not for loan | For in-house use only |
This study examines adverse consequences of using hierarchical linear modeling (HLM) that ignores rater effects to analyze ratings collected by multiple raters in longitudinal research. The most severe consequence of using HLM ignoring rater effects is the biased estimation of Levels 1 and 2 fixed effects and potentially incorrect significance tests about them. A cross-classified random effects model (CCREM) is proposed as an alternative to HLM. A Monte Carlo study and an empirical evaluation confirm that CCREM performs better than does HLM in dealing with rater effects. Strengths, limitations, and implications of the study are discussed.
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