Keywords

Partially clustered data; multilevel models; intraclass correlation; intervention studies

Abstract

Partially clustered designs, where clustering occurs in some conditions and not others, are common in psychology, particularly in prevention and intervention trials. This paper reports results from a simulation comparing five approaches for analyzing partially clustered data, including Type I errors, parameter bias, efficiency, and power. Results indicate that multilevel models adapted for partially clustered data are relatively unbiased and efficient and consistently maintain the nominal Type I error rate when using appropriate degrees of freedom. To attain sufficient power in partially clustered designs, researchers should attend primarily to the number of clusters in the study. An illustration is provided using data from a partially clustered eating disorder prevention trial.

Document Type

Peer-Reviewed Article

Publication Date

2011

Language

English

College

Family, Home, and Social Sciences

Department

Psychology

Included in

Psychology Commons

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