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A Meta-Analysis Package for R

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tips:weights_in_rma.mv_models

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tips:weights_in_rma.mv_models [2020/06/07 20:59] – created Wolfgang Viechtbauertips:weights_in_rma.mv_models [2021/04/16 07:46] – Wolfgang Viechtbauer
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 The example above shows that the weighting scheme underlying more complex models (that can be fitted with the ''rma.mv()'' function) is not as simple as in the 'standard' fixed- and random-effects models (that can be fitted with the ''rma()'' function). Depending on the random effects included in the model (and the var-cov matrix of the sampling errors), the model may imply a certain degree of covariance between the estimates, which needs to be taken into consideration when estimating the fixed effects (e.g., the pooled/summary estimate) and their corresponding standard errors. The example above shows that the weighting scheme underlying more complex models (that can be fitted with the ''rma.mv()'' function) is not as simple as in the 'standard' fixed- and random-effects models (that can be fitted with the ''rma()'' function). Depending on the random effects included in the model (and the var-cov matrix of the sampling errors), the model may imply a certain degree of covariance between the estimates, which needs to be taken into consideration when estimating the fixed effects (e.g., the pooled/summary estimate) and their corresponding standard errors.
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 +**Note:** James Pustejovsky has written up a very nice [[https://www.jepusto.com/weighting-in-multivariate-meta-analysis/|blog post]] which goes even deeper into this topic.
  
 ==== References ==== ==== References ====
tips/weights_in_rma.mv_models.txt · Last modified: 2023/08/03 13:37 by Wolfgang Viechtbauer