Meta analytical structural equation modeling: comments on issues with current methods and viable alternatives

Yuan, Ke‐Hai

doi:10.1002/jrsm.1213

Cited by 8 publications

(9 citation statements)

References 56 publications

(137 reference statements)

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“…Besides the five articles in this special issue, two experts in meta‐analysis or SEM were invited to comment on issues, applications, and future directions in MASEM. Hedges () discussed issues from the perspective of meta‐analysis, while Yuan () addressed these issues from a SEM perspective. Their comments serve as valuable directions for future methodological development in MASEM.…”

Section: Resultsmentioning

confidence: 99%

Special issue on meta‐analytic structural equation modeling: introduction from the guest editors

Cheung

Hafdahl

2016

Research Synthesis Methods

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Section: Resultsmentioning

confidence: 99%

Special issue on meta‐analytic structural equation modeling: introduction from the guest editors

Cheung

Hafdahl

2016

Research Synthesis Methods

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“…Parallel to the results in Yuan and Chan (2005), GLS 2 and ML-GLS also yield estimators that have the same Acov matrix even when the normality assumption on raw data is not met. However, the covariance matrix of the combined correlations or the resulting estimates of the structural parameters is not consistently estimated by the inverse of the information matrix but a sandwich-type covariance matrix (see Yuan, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Considering that normally distributed data are rare in practice (Blanca, Arnau, López-Montiel, Bono, & Bendayan, 2013; Cain, Zhang, & Yuan, 2017; Micceri, 1989), we suspect that the reported heterogeneity of normalbold r t in the literature is partially due to nonnormally distributed data. While the effects of nonnormality and heterogeneity might be confounded in the observed normalbold r t , when the number of studies m is not too small, an improvement in testing the homogeneity of boldρ t is a rescaled statistic with the method GLS 1 (Yuan, 2016). Additional studies are needed for testing the homogeneity condition in practice when m is small.…”

Section: Violations Of the Homogeneity Condition And Methods Under A mentioning

confidence: 99%

Meta-Analytical SEM: Equivalence Between Maximum Likelihood and Generalized Least Squares

Yuan

Kano

2018

Journal of Educational and Behavioral Statistics

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Meta-analysis plays a key role in combining studies to obtain more reliable results. In social, behavioral, and health sciences, measurement units are typically not well defined. More meaningful results can be obtained by standardizing the variables and via the analysis of the correlation matrix. Structural equation modeling (SEM) with the combined correlations, called metaanalytical SEM (MASEM), is a powerful tool for examining the relationship among latent constructs as well as those between the latent constructs and the manifest variables. Three classes of methods have been proposed for MASEM:(1) generalized least squares (GLS) in combining correlations and in estimating the structural model, (2) normal-distribution-based maximum likelihood (ML) in combining the correlations and then GLS in estimating the structural model (ML-GLS), and (3) ML in combining correlations and in estimating the structural model (ML). The current article shows that these three methods are equivalent. In particular, (a) the GLS method for combining correlation matrices in meta-analysis is asymptotically equivalent to ML, (b) the three methods (GLS, ML-GLS, ML) for MASEM with correlation matrices are asymptotically equivalent, (c) they also perform equally well empirically, and (d) the GLS method for SEM with the sample correlation matrix in a single study is asymptotically equivalent to ML, which has being discussed extensively in the SEM literature regarding whether the analysis of a correlation matrix yields consistent standard errors and asymptotically valid test statistics. The results and analysis suggest that a sample-size weighted GLS method is preferred for combining correlations and for MASEM.

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“…For example, a special issue of Research Synthesis Methods discusses several recent methodological advances in MASEM. Hedges and Yuan, both well known in their areas of expertise (meta‐analysis and SEM), were invited to comment on the contributed articles. Another indicator is that MASEM is occasionally featured in applied fields , , , .…”

Section: Recent Developments In Masemmentioning

confidence: 99%

Some reflections on combining meta‐analysis and structural equation modeling

Cheung

2018

Research Synthesis Methods

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Meta-analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM.

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Meta analytical structural equation modeling: comments on issues with current methods and viable alternatives

Cited by 8 publications

References 56 publications

Special issue on meta‐analytic structural equation modeling: introduction from the guest editors

Special issue on meta‐analytic structural equation modeling: introduction from the guest editors

Meta-Analytical SEM: Equivalence Between Maximum Likelihood and Generalized Least Squares

Some reflections on combining meta‐analysis and structural equation modeling

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