How to meta-analyze coefficient-of-stability estimates: some recommendations based on Monte Carlo studies.

Reliability generalization studies have provided estimates of the mean reliability coefficients and examined factors that explain the variability in the reliability estimates across studies for many different tests and measures. Different authors have used different data analyses to do such meta-ana...

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Detalles Bibliográficos
Publicado en:Educational & Psychological Measurement Vol. 67; no. 5; pp. 765 - 784
Autores principales: Mason C, Allam R, Brannick MT
Formato: equations & formulas research tables/charts Journal Article
Publicado: Sage Publications Inc. Oct2007
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Reliability generalization studies have provided estimates of the mean reliability coefficients and examined factors that explain the variability in the reliability estimates across studies for many different tests and measures. Different authors have used different data analyses to do such meta-analyses, and little research has addressed whether some methods are more accurate than others. Three methods of meta-analysis for reliability data were compared using Monte Carlo techniques. The meta-analytic methods were those described by Hedges and Vevea, Hunter and Schmidt, and Vacha-Haase. The results suggested that a combination of methods worked best and that Hunter and Schmidt's method should be used to estimate the mean and random-effect variance component, but weighted regression should be used to model continuous moderators.