A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias

Type of content
Discussion / Working Papers
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
University of Canterbury. Department of Economics and Finance
Journal Title
Journal ISSN
Volume Title
Language
Date
2014
Authors
Reed, W. R.
Florax, R. J. G. M.
Poot, J.
Abstract

This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision Effect Estimate with Standard Errors (PEESE) estimator. We consider two types of publication bias: publication bias directed against statistically insignificant estimates, and publication bias directed against wrong-signed estimates. Finally, we consider three cases concerning the distribution of the true effect: the Fixed Effects case, where there is only estimate per study, and all studies have the same true effect; the Random Effects case, where there is only one estimate per study, and there is heterogeneity in true effects across studies; and the Panel Random Effects case, where studies have multiple estimates, and true effects are random both across and within studies. Our simulations produce a number of findings that challenge results from previous research.

Description
Citation
Reed, W. R., Florax, R. J. G. M., Poot, J. (2014) A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias. University of Canterbury. 85pp..
Keywords
Meta-analysis, Random effects, Fixed effects, publication bias, Monte Carlo, Simulations
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Field of Research::08 - Information and Computing Sciences::0899 - Other Information and Computing Sciences::089999 - Information and Computing Sciences not elsewhere classified
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