Applications of meta-analytic structural equation modelling in health psychology: examples, issues, and recommendations

ABSTRACT

Statistical methods play an important role in behavioural, medical, and social sciences. Two recent statistical advances are structural equation modelling (SEM) and meta-analysis. SEM is used to test hypothesised models based on substantive theories, which can be path, confirmatory factor analytic, or full structural equation models. Meta-analysis is used to synthesise research findings in a particular topic. This article demonstrates another recent statistical advance – meta-analytic structural equation modelling (MASEM) – that combines meta-analysis and SEM to synthesise research findings for the purpose of testing hypothesised models. Using the theory of planned behaviour as an example, we show how MASEM can be used to address important research questions that cannot be answered by univariate meta-analyses on Pearson correlations. Specifically, MASEM allows researchers to: (1) test whether the proposed models are consistent with the data; (2) estimate partial effects after controlling for other variables; (3) estimate functions of parameter estimates such as indirect effects; and (4) include latent variables in the models. We illustrate the procedures with an example on the theory of planned behaviour. Practical issues in MASEM and suggested solutions are discussed.

KEYWORDS:

• Structural equation modelling
• meta-analysis
• meta-analytic structural equation modelling
• theory of planned behaviour
• health psychology

Acknowledgements

We thank Cleo Tay for extracting data for the illustrations and Pamela Rackow for providing comments on an early version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Mike W.-L. Cheung was supported by the Academic Research Fund Tier 1 (FY2013-FRC5-002) from the Ministry of Education, Singapore.