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Correction of rater effects in longitudinal research with a cross-classified random effects model created by Shenyang Guo

By: Material type: TextTextSeries: ; Volume , number ,USA : Sage; 2013Content type:
  • text
Media type:
  • unmediated
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  • volume
Subject(s): Online resources: Summary: 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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Item type Current library Call number Vol info Copy number Status Notes Date due Barcode
Journal Article Journal Article 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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