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Averaging Dependent Effect Sizes in Meta-Analysis: a Cautionary Note about Procedures

Published online by Cambridge University Press:  10 April 2014

Fulgencio Marín-Martínez  and
Julio Sánchez-Meca
Fulgencio Marín-Martínez*
Affiliation:
University of Murcia
Julio Sánchez-Meca
Affiliation:
University of Murcia
*
Correspondence concerning this article should be addressed to Dr. Fulgencio Marín-Martínez, Departamento de Psicología Básica y Metodología. Facultad de Psicología.Universidad de Murcia. Campus de Espinardo, Apdo 4021. 30080 Murcia (Spain). E-mail: fulmarin@fcu.um.es
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Abstract

When a primary study includes several indicators of the same construct, the usual strategy to meta-analytically integrate the multiple effect sizes is to average them within the study. In this paper, the numerical and conceptual differences among three procedures for averaging dependent effect sizes are shown. The procedures are the simple arithmetic mean, the Hedges and Olkin (1985) procedure, and the Rosenthal and Rubin (1986) procedure. Whereas the simple arithmetic mean ignores the dependence among effect sizes, both the procedures by Hedges and Olkin and Rosenthal and Rubin take into account the correlational structure of the effect sizes, although in a different way. Rosenthal and Rubin's procedure provides the effect size for a single composite variable made up of the multiple effect sizes, whereas Hedges and Olkin's procedure presents an effect size estimate of the standard variable. The three procedures were applied to 54 conditions, where the magnitude and homogeneity of both effect sizes and correlation matrix among effect sizes were manipulated. Rosenthal and Rubin's procedure showed the highest estimates, followed by the simple mean, and the Hedges and Olkin procedure, this last having the lowest estimates. These differences are not trivial in a meta-analysis, where the aims must guide the selection of one of the procedures.

La estrategia usual para integrar meta-analíticamente los múltiples tamaños del efecto cuando un estudio primario incluye varios indicadores del mismo constructo, es la de promediarlos. En este trabajo se muestran las diferencias numéricas y conceptuales entre tres procedimientos para promediar tamaños del efecto dependientes. Los procedimientos son el de Hedges y Olkin (1985), el de Rosenthal y Rubin (1986) y el de la media aritmética. Mientras que el de la media aritmética ignora la dependencia entre los tamaños del efecto, tanto el de Hedges y Olkin como el de Rosenthal y Rubin tienen en cuenta, aunque de diferente forma, la estructura correlacional de los tamaños del efecto. El procedimiento de Rosenthal y Rubin proporciona el tamaño del efecto de una sola variable compuesta, obtenida a partir de los diversos tamaños del efecto, mientras que el de Hedges y Olkin aporta una estimación del efecto para la variable estándar. Los tres procedimientos se aplicaron a 54 condiciones, manipulándose la magnitud y homogeneidad del vector de los tamaños del efecto y de la matriz de correlaciones entre ellos. Con el procedimiento de Rosenthal y Rubin se obtuvieron las estimaciones más elevadas, seguido del de la media y del de Hedges y Olkin. Estas diferencias no son triviales en un meta-análisis, cuyos objetivos son los que deben guiar la elección de uno u otro de los procedimientos.

Keywords

meta-analysiseffect sizestandardized mean differencedependencemeta-análisistamaño del efectodiferencia media tipificadadependencia
Type
Articles
Information
The Spanish Journal of Psychology , Volume 2 , May 1999 , pp. 32 - 38
Copyright
Copyright © Cambridge University Press 1999

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