Statistical and measurement pitfalls in the use of meta-regression in meta-analysis

Statistical and measurement pitfalls in the use of meta-regression in meta-analysis PurposeMeta-regression is widely used and misused today in meta-analyses in psychology, organizational behavior, marketing, management, and other social sciences, as an approach to the identification and calibration of moderators, with most users being unaware of serious problems in its use. The purpose of this paper is to describe nine serious methodological problems that plague applications of meta-regression.Design/methodology/approachThis paper is methodological in nature and is based on well-established principles of measurement and statistics. These principles are used to illuminate the potential pitfalls in typical applications of meta-regression.FindingsThe analysis in this paper demonstrates that many of the nine statistical and measurement pitfalls in the use of meta-regression are nearly universal in applications in the literature, leading to the conclusion that few meta-regressions in the literature today are trustworthy. A second conclusion is that in almost all cases, hierarchical subgrouping of studies is superior to meta-regression as a method of identifying and calibrating moderators. Finally, a third conclusion is that, contrary to popular belief among researchers, the process of accurately identifying and calibrating moderators, even with the best available methods, is complex, difficult, and data demanding.Practical implicationsThis paper provides useful guidance to meta-analytic researchers that will improve the practice of moderator identification and calibration in social science research literatures.Social implicationsToday, many important decisions are made on the basis of the results of meta-analyses. These include decisions in medicine, pharmacology, applied psychology, management, marketing, social policy, and other social sciences. The guidance provided in this paper will improve the quality of such decisions by improving the accuracy and trustworthiness of meta-analytic results.Originality/valueThis paper is original and valuable in that there is no similar listing and discussion of the pitfalls in the use of meta-regression in the literature, and there is currently a widespread lack of knowledge of these problems among meta-analytic researchers in all disciplines. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Career Development International Emerald Publishing

Statistical and measurement pitfalls in the use of meta-regression in meta-analysis

Career Development International, Volume 22 (5): 8 – Sep 11, 2017

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1362-0436
D.O.I.
10.1108/CDI-08-2017-0136
Publisher site
See Article on Publisher Site

Abstract

PurposeMeta-regression is widely used and misused today in meta-analyses in psychology, organizational behavior, marketing, management, and other social sciences, as an approach to the identification and calibration of moderators, with most users being unaware of serious problems in its use. The purpose of this paper is to describe nine serious methodological problems that plague applications of meta-regression.Design/methodology/approachThis paper is methodological in nature and is based on well-established principles of measurement and statistics. These principles are used to illuminate the potential pitfalls in typical applications of meta-regression.FindingsThe analysis in this paper demonstrates that many of the nine statistical and measurement pitfalls in the use of meta-regression are nearly universal in applications in the literature, leading to the conclusion that few meta-regressions in the literature today are trustworthy. A second conclusion is that in almost all cases, hierarchical subgrouping of studies is superior to meta-regression as a method of identifying and calibrating moderators. Finally, a third conclusion is that, contrary to popular belief among researchers, the process of accurately identifying and calibrating moderators, even with the best available methods, is complex, difficult, and data demanding.Practical implicationsThis paper provides useful guidance to meta-analytic researchers that will improve the practice of moderator identification and calibration in social science research literatures.Social implicationsToday, many important decisions are made on the basis of the results of meta-analyses. These include decisions in medicine, pharmacology, applied psychology, management, marketing, social policy, and other social sciences. The guidance provided in this paper will improve the quality of such decisions by improving the accuracy and trustworthiness of meta-analytic results.Originality/valueThis paper is original and valuable in that there is no similar listing and discussion of the pitfalls in the use of meta-regression in the literature, and there is currently a widespread lack of knowledge of these problems among meta-analytic researchers in all disciplines.

Journal

Career Development InternationalEmerald Publishing

Published: Sep 11, 2017

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