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Neither fixed nor random: weighted least squares meta-regression

Stanley, T.D. and Doucouliagos, Hristos 2016, Neither fixed nor random: weighted least squares meta-regression, Research synthesis methods, In Press, pp. 1-24, doi: 10.1002/jrsm.1211.

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Title Neither fixed nor random: weighted least squares meta-regression
Author(s) Stanley, T.D.
Doucouliagos, Hristos
Journal name Research synthesis methods
Season In Press
Start page 1
End page 24
Total pages 24
Publisher Wiley
Place of publication Chichester, Eng.
Publication date 2016-06-20
ISSN 1759-2879
1759-2887
Keyword(s) meta-regression
weighted least squares
random effects
fixed effect
meta-regression analysis
Summary Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of‘mixed-effects’ or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the ‘true’ regression coefficient.
Language eng
DOI 10.1002/jrsm.1211
Field of Research 140302 Econometric and Statistical Methods
Socio Economic Objective 919999 Economic Framework not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30088181

Document type: Journal Article
Collection: Department of Economics
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