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ROBUMETA: Stata module to perform robust variance estimation in meta-regression with dependent effect size estimates

Author

Listed:
  • Eric C. Hedberg

    (National Opinion Research Center at the University of Chicago)

Programming Language

Stata

Abstract

robumeta provides a robust method for estimating standard errors in meta-regression, particularly when there are dependent effects. Dependent effects occur in two basic models: (1) correlated effects and (2) hierarchical meta-regression. In (1), the dependency arises as a result of correlated estimation errors; for example, a study collects two outcome measures on each participant and then summarizes these as two effect size measures. In (2), the dependency arises as a result of correlated parameters; for example, the same research group may publish several studies and there may be elements of these studies that are similar to one another. Importantly, the robust standard error procedure used here does not require the underlying correlation structure to be known; additionally, it works for any weights and can be used to estimate the mean effect size as well as meta-regression models. Finally, note that the procedure also can be used with independent effects, particularly when distributional assumptions might be violated. For more information on the underlying theory, see the references at the end of this help file.

Suggested Citation

  • Eric C. Hedberg, 2011. "ROBUMETA: Stata module to perform robust variance estimation in meta-regression with dependent effect size estimates," Statistical Software Components S457219, Boston College Department of Economics, revised 23 Apr 2014.
    • Handle: RePEc:boc:bocode:s457219
    Note: This module should be installed from within Stata by typing "ssc install robumeta". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/r/robumeta.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/r/robumeta.sthlp
    File Function: help file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/h/hedgesexample.dta
    File Function: sample data file
    Download Restriction: no
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    Citations

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    Cited by:

    1. Erica K. Chuang & Barbara S. Mensch & Stephanie R. Psaki & Nicole A. Haberland & Meredith L. Kozak, 2019. "PROTOCOL: Policies and interventions to remove gender‐related barriers to girls’ school participation and learning in low‐ and middle‐income countries: A systematic review of the evidence," Campbell Systematic Reviews, John Wiley & Sons, vol. 15(3), September.
    2. Pustejovsky, James E & Tipton, Elizabeth, 2020. "Meta-Analysis with Robust Variance Estimation: Expanding the Range of Working Models," MetaArXiv vyfcj, Center for Open Science.
    3. Elizabeth Tipton & James E. Pustejovsky, 2015. "Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 604-634, December.
    4. Steven Windisch & Susann Wiedlitzka & Ajima Olaghere, 2021. "PROTOCOL: Online interventions for reducing hate speech and cyberhate: A systematic review," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(1), March.

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