Correlation-Based Meta-Analytic Structural Equation Modeling: Effects of Parameter Covariance on Point and Interval Estimates
Publication in refereed journal
已正式接受出版

香港中文大學研究人員
替代計量分析
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其它資訊
摘要More and more researchers use meta-analysis to conduct multivariate analysis to summarize previous findings. In the correlation-based meta-analytic structural equation modeling (cMASEM), the average sample correlation matrix is used to estimate the average population model. Using a simple mediation model, we illustrated that random effects covariation in population parameters can theoretically bias the path coefficient estimates and lead to nonnormal random effects distribution of the correlations. We developed an R function for researchers to examine by simulation the impact of random effects in other models. We then reanalyzed two real data sets and conducted a simulation study to examine the magnitude of the impact on realistic situations. Simulation results suggest parameter bias is typically negligible (less than .02), parameter bias and root mean square error do not differ across methods, 95% confident intervals are sometimes more accurate for the two-stage structural equation modeling approach with a diagonal random effects model, and power is sometimes higher for the traditional Viswesvaran-Ones approach. Given the increasing popularity of cMASEM in organizational research, these simulation results form the basis for us to make several recommendations on its application.
出版社接受日期15.03.2018
著者Shu Fai CHEUNG, Rong Wei SUN, Darius K.-S. CHAN
期刊名稱Organizational Research Methods
出版年份2018
國際標準期刊號1094-4281
電子國際標準期刊號1552-7425
語言美式英語
關鍵詞meta-analysis, structural equation modeling, meta-analytic structural equation modeling

上次更新時間 2020-05-08 於 04:30