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Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss

Authors

  • Emily E. Tanner-Smith,

    Corresponding author
    1. Peabody Research Institute, Department of Human and Organizational Development, Vanderbilt University, Nashville, TN, USA
    • Correspondence to: Emily E. Tanner-Smith, Peabody Research Institute Department of Human and Organizational Development, Vanderbilt University, Box 0181 GPC, 230 Appleton Place, Nashville, TN 37203, USA.

      E-mail: e.tanner-smith@vanderbilt.edu

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  • Elizabeth Tipton

    1. Department of Human Development, Teachers College, Columbia University, New York, NY, USA
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Abstract

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial on the implementation of the Stata and spss macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates. Copyright © 2013 John Wiley & Sons, Ltd.

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