Differences

This shows you the differences between two versions of the page.

--- tips [2020/05/27 21:05] – Wolfgang Viechtbauer
+++ tips [2021/11/08 16:04] – Wolfgang Viechtbauer
@@ Line 13: / Line 13: @@
   * [[tips:two_stage_analysis|Two-Stage Analysis versus Linear Mixed-Effects Models for Longitudinal Data]]: An illustration of two different approaches to analyzing longitudinal data: A two-stage analysis (which the ''rma.mv()'' function can be used for) and linear mixed-effects models (e.g., using the ''lme()'' function).
-  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting fixed- and random-effects models.
+  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting equal- and random-effects models.
   * [[tips:rma_vs_lm_lme_lmer|A Comparison of the rma() and the lm(), lme(), and lmer() Functions]]: An illustration of the difference between the models fitted by the ''rma()'' function and the models fitted by the ''lm()'', ''lme()'', and ''lmer()'' functions (or: why the ''lm()'', ''lme()'', and ''lmer()'' functions cannot be used to fit meta-analytic models).
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   * [[tips:clogit_paired_binary_data|Conditional Logistic Regression for Paired Binary Data]]: An illustration of how to fit the conditional logistic regression model for paired binary data.
-  * [[tips:i2_multilevel_multivariate|I^2 for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
+  * [[tips:i2_multilevel_multivariate|$I^2$ for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
   * [[tips:hunter_schmidt_method|Hunter and Schmidt Method]]: A discussion of how one can conduct meta-analyses according to the Hunter & Schmidt method.
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   * [[tips:computing_adjusted_effects|Computing Adjusted Effects Based on Meta-Regression Models]]: A discussion of how to compute 'adjusted effects' based on meta-regression models.
+  * [[tips:weights_in_rma.mv_models|Weights in Models Fitted with the rma.mv() Function]]: A discussion of how weighting works in more complex models, such as those that can be fitted with the ''rma.mv()'' function.