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I'm planning to write a meta-analysis on a topic in which there are both clinical trials and structural equation modeling papers.

For clinical trials i can compute easily the effect size and analyze them with a fixed or random effects model, is there a way to calculate an effect size in a systematic way from a structural equation modeling result? Can i compute the effect size using the r of correlation in the models? Are there any reference regarding this approach?

I know that there are meta-analytic structural equation modeling approach but i want to conduct a "regular" meta-analysis with data from equation modeling studies

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    $\begingroup$ It will be useful to explain what a "regular" meta-analysis is. Do you want to summarize the findings in a structural equation model with one single effect size? $\endgroup$ – Mike Cheung Apr 20 '16 at 12:39
  • $\begingroup$ a regular meta-analysis is a meta analysis conducted with the canonic fixed or random effects model, not using a structural equation modeling approach. I'd like to extract an effect size regarding a specific relationship in the structural equation between a single variable in the equation and the output variable. $\endgroup$ – GGA Apr 20 '16 at 14:33
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    $\begingroup$ I'd have thought that for a meta-analysis you need an effect and a standard error. You can get them from a clinical trial, and you should be able to get them from an SEM. $\endgroup$ – Jeremy Miles Apr 21 '16 at 15:45
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It depends on what metric of effect size you are using. If it's merely correlation from regression models versus SEM, remember that covariance-based SEM is similarly based in assumptions of linearity and normality, so standardized coefficients to get a correlation coefficient should compare favorably to standardized regression coefficients from linear regressions.

You may, however, want to use model-source as a covariate, just to check to see if you get different results from studies using SEMs versus linear regressions.

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