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Neither fixed nor random: weighted least squares meta-analysis

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posted on 2013-01-01, 00:00 authored by T D Stanley, Chris DoucouliagosChris Doucouliagos
This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects.

History

Series

School Working Paper - Economics Series ; SWP 2013/1

Pagination

1 - 17

Publisher

Deakin University, School of Accounting, Economics and Finance

Place of publication

Geelong, Vic.

Language

eng

Publication classification

CN.1 Other journal article

Copyright notice

2013, The Authors

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