A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices.
| Package: | metaSEM |
| Type: | Package |
| Version: | 1.2.4 |
| Date: | 2020-06-14 |
| License: | GPL (>=2) |
| LazyLoad: | yes |
Cheung, M. W.-L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13 (3), 182-202. https://doi.org/10.1037/a0013163
Cheung, M. W.-L. (2009). Constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16 (2), 267-294. https://doi.org/10.1080/10705510902751291
Cheung, M. W.-L. (2010). Fixed-effects meta-analyses as multiple-group structural equation models. Structural Equation Modeling, 17 (3), 481-509. https://doi.org/10.1080/10705511.2010.489367
Cheung, M. W.-L. (2013). Implementing restricted maximum likelihood estimation in structural equation models. Structural Equation Modeling, 20 (1), 157-167. https://doi.org/10.1080/10705511.2013.742404
Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20 (3), 429-454. https://doi.org/10.1080/10705511.2013.797827
Cheung, M. W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19 (2), 211-229. https://doi.org/10.1037/a0032968
Cheung, M. W.-L. (2014). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46 (1), 29-40. https://doi.org/10.3758/s13428-013-0361-y
Cheung, M. W.-L. (2015). metaSEM: An R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5 (1521). https://doi.org/10.3389/fpsyg.2014.01521
Cheung, M. W.-L. (2015). Meta-Analysis: A Structural Equation Modeling Approach. Chichester, West Sussex: John Wiley & Sons, Inc.
Cheung, M. W.-L. (2018). Issues in solving the problem of effect size heterogeneity in meta-analytic structural equation modeling: A commentary and simulation study on Yu, Downes, Carter, and O'Boyle (2016). Journal of Applied Psychology, 103 (7), 787-803. https://doi.org/10.1037/apl0000284
Cheung, M. W.-L. (2018). Computing multivariate effect sizes and their sampling covariance matrices with structural equation modeling: Theory, examples, and computer simulations. Frontiers in Psychology, 9 (1387). https://doi.org/10.3389/fpsyg.2018.01387
Cheung, M. W.-L. (2019). Some reflections on combining meta-analysis and structural equation modeling. Research Synthesis Methods, 10 (1), 15-22. https://doi.org/10.1002/jrsm.1321
Cheung, M. W.-L., & Chan, W. (2004). Testing dependent correlation coefficients via structural equation modeling. Organizational Research Methods, 7 (2), 206-223. https://doi.org/10.1177/1094428104264024
Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10 (1), 40-64. https://doi.org/10.1037/1082-989X.10.1.40
Cheung, M. W.-L., & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 16 (1), 28-53. https://doi.org/10.1080/10705510802561295
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 (2), 140-155. https://doi.org/10.1002/jrsm.1166
Jak, S., & Cheung, M. W.-L. (2018). Testing moderator hypotheses in meta-analytic structural equation modeling using subgroup analysis. Behavior Research Methods, 50 (4), 1359-1373. https://doi.org/10.3758/s13428-018-1046-3
Jak, S., & Cheung, M. W.-L. (2019). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological Methods. https://doi.org/10.1037/met0000245