Using Beta Coefficients to Impute Missing Correlations in Meta-Analysis Research: Reasons for Caution

• 成果类型: Article
• 署名作者: Roth, Philip L.; Le, Huy; Oh, In-Sue; Van Iddekinge, Chad H.; Bobko, Philip
• 署名单位: Clemson University; University of Texas System; University of Texas at San Antonio; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; State University System of Florida; Florida State University; Virginia Polytechnic Institute & State University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0000293
• 发表日期: 2018
• 页码: 644-658
• 关键词: beta estimation procedures; Meta-analysis; Missing Data

摘要

Meta-analysis has become a well-accepted method for synthesizing empirical research about a given phenomenon. Many meta-analyses focus on synthesizing correlations across primary studies, but some primary studies do not report correlations. Peterson and Brown (2005) suggested that researchers could use standardized regression weights (i.e., beta coefficients) to impute missing correlations. Indeed, their beta estimation procedures (BEPs) have been used in meta-analyses in a wide variety of fields. In this study, the authors evaluated the accuracy of BEPs in meta-analysis. We first examined how use of BEPs might affect results from a published meta-analysis. We then developed a series of Monte Carlo simulations that systematically compared the use of existing correlations (that were not missing) to data sets that incorporated BEPs (that impute missing correlations from corresponding beta coefficients). These simulations estimated (rho)over-bar (mean population correlation) and SD rho (true standard deviation) across a variety of meta-analytic conditions. Results from both the existing meta-analysis and the Monte Carlo simulations revealed that BEPs were associated with potentially large biases when estimating (rho)over-bar and even larger biases when estimating SD rho. Using only existing correlations often substantially outperformed use of BEPs and virtually never performed worse than BEPs. Overall, the authors urge a return to the standard practice of using only existing correlations in meta-analysis.