Methods for estimating the sampling variance of the standardized mean difference

One of the most widely used effect size indices for meta-analysis in psychology is the standardized mean difference (SMD). The most common way to synthesize a set of estimates of the SMD is to weight them by the inverse of their variances. For this, it is necessary to estimate the corresponding samp...

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Detalles Bibliográficos
Autores: Suero Suñe, Manuel, Botella Ausina, Juan, Durán Pacheco, Juan Ignacio
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/711311
Acceso en línea:http://hdl.handle.net/10486/711311
https://dx.doi.org/10.1037/met0000446
Access Level:acceso abierto
Palabra clave:Standardized Mean Difference
Effect Size Variance
Sampling Variance of d
Sampling Variance of g
Meta-analysis
Psicología
Descripción
Sumario:One of the most widely used effect size indices for meta-analysis in psychology is the standardized mean difference (SMD). The most common way to synthesize a set of estimates of the SMD is to weight them by the inverse of their variances. For this, it is necessary to estimate the corresponding sampling variances. Meta-analysts have a formula for obtaining unbiased estimates of sampling variances, but they often use a variety of alternative, simpler methods. The bias and efficiency of five different methods that have been proposed and that are implemented in different computerized calculation tools are compared and assessed. The data from a set of published meta-analyses are also reanalyzed, calculating the combined estimates and their confidence intervals, as well as estimates of the specific, between-studies variance, using the five estimation methods. This test of sensitivity shows that the results of a meta-analysis can change noticeably depending on the method used to estimate the sampling variance of SMD values, especially under a random-effects model. Some practical recommendations are made about how to choose and implement the methods in calculation resources