References
- Allison, P. D. (2001). Missing data (Vol. 136). Thousand Oaks, CA: Sage publications.
- Becker, B. J. (1992). Using results from replicated studies to estimate linear models. Journal of Educational Statistics, 17, 341–362. doi:10.2307/1165128
- Becker, B. J. (1995). Corrections to “using results from replicated studies to estimate linear models.” Journal of Educational and Behavioral Statistics, 20, 100–102. doi:10.2307/1165390
- Cheung, M. W.-L. (2014). Fixed-and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46, 29–40. doi:10.3758/s13428-013-0361-y
- Cheung, M. W.-L. (2015a). Meta-analysis: A structural equation modeling approach. Chichester, UK: John Wiley & Sons, Inc.
- Cheung, M. W.-L. (2015b). metaSEM: An R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5, 1521. doi:10.3389/fpsyg.2014.01521
- Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40–64. doi:10.1037/1082-989X.10.1.40
- Cheung, M. W.-L., & Cheung, S. F. (2016). Random-effects models for meta-analytic structural equation modeling: Review, issues, and illustrations. Research Synthesis Methods, 7, 140–155. doi:10.1002/jrsm.1166
- Cheung, S. F. (2000). Examining solutions to two practical issues in meta-analysis: Dependent correlations and missing data in correlation matrices (Unpublished doctoral dissertation). Hong Kong, China: The Chinese University of Hong Kong.
- Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430–457. doi:10.1207/S15328007SEM0803_5
- Furlow, C. F., & Beretvas, S. N. (2005). Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data. Psychological Methods, 10, 227–254. doi:10.1037/1082-989X.10.2.227
- Jak, S., & Cheung, M. W.-L. (2018). Accounting for missing correlation coefficients in fixed-effects MASEM. Multivariate Behavioral Research, 53, 1–14. doi:10.1080/00273171.2017.1375886
- Lv, J., & Maeda, Y. (2019). Evaluation of the efficacy of meta-analytic structural equation modeling with missing correlations. Structural Equation Modeling, 1–24. Advance online publication. doi:10.1080/10705511.2019.1646651.
- Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36. Retrieved from http://www.jstatsoft.org/v48/i02/
- Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: John Wiley and Sons.
- van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67.
- Viswesvaran, C., & Ones, D. S. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865–885. doi:10.1111/j.1744-6570.1995.
- Zhang, Y. (2011). Meta-analytic structural equation modeling: Comparison of the multivariate methods (Doctoral dissertation). Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-053.