e-journal

Correction of Rater Effects in Longitudinal Research With a Cross-Classified Random Effects Model

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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 crossclassified 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.

Keywords
cross-classified, HLM, longitudinal research, rater effects

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Informasi Detail

Judul Seri

Applied Psychological Measurement

No. Panggil

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Penerbit

: The Author., 2013

Deskripsi Fisik

Applied Psychological Measurement 38(1) 37–60

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

EKONOMI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

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