Hi C?tia, I'm not sure what you mean by checking the strength of the correlation between effect sizes. If it's possible to get data about this correlation or make an assumption that is supported by empirical evidence, then yes definitely do so. But unfortunately, we are often in a situation where there's not much information available about the correlations, and so we need to make a somewhat arbitrary assumption and then support it with sensitivity analysis. James On Fri, May 28, 2021 at 11:11 AM C?tia Ferreira De Oliveira <
cmfo500 at york.ac.uk> wrote:
Thank you for your prompt reply! If you do not have an idea of the strength of the correlation between effect sizes within each study, would you suggest it being checked before using *impute_covariance_matrix() *function? Or would it be preferable to run the function for some of the possible correlations as is done in sensitivity analyses? Best wishes, Catia On Wed, 26 May 2021 at 16:58, James Pustejovsky <jepusto at gmail.com> wrote:
Hi C?tia, This is a good question. Robust variance estimation protects against mis-specified assumptions regarding the sampling variances and covariances of effect size estimates, as well as mis-specification of the random effects structure of the model. Therefore, whether you to use it or not depends on how much you trust the modeling assumptions that you're making. In the Konstantopoulos example, each sample provides one independent effect size estimate, so there is not much reason to be concerned about mis-specifiation of sampling variances or covariances of effect size estimates. The random effects structure also seems pretty reasonable (insofar as it captures the hierarchical structure of the data), so there is not a particular reason to be concerned about mis-specification of that aspect of the model. Therefore, RVE does not seem necessary here. You might still prefer to use it if you are especially cautious or skeptical of multi-level modeling assumptions in general, but I think there is a very reasonable argument that it isn't necessary. James On Wed, May 26, 2021 at 10:19 AM C?tia Ferreira De Oliveira < cmfo500 at york.ac.uk> wrote:
Hello, I have been going through some of the metafor resources and I was wondering if it would still be recommended to run the robust() from clubSandwich when having a dataset similar to this the one by Konstantopoulos or whether the approaches presented here https://www.rdocumentation.org/packages/metafor/versions/2.4-0/topics/dat.konstantopoulos2011 are enough. Best wishes, Catia [[alternative HTML version deleted]]
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