Skip to content
Journal article

A basic introduction to fixed-effect and random-effects models for meta-analysis

Borenstein M, Hedges L, Higgins J, Rothstein H ...see all

Research Synthesis Methods, vol. 1, issue 2 (2010) pp. 97-111

  • 467

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.
  • N/A

    Views

    ScienceDirect users who have downloaded this article.
Sign in to save reference

Abstract

There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics.In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Michael Borenstein

  • Larry V. Hedges

  • Julian P.T. Higgins

  • Hannah R. Rothstein

Cite this document

Choose a citation style from the tabs below