One common question about multivariate/multi-level meta-analysis is how such models assign weight to individual effect size estimates. When a version of the question came up recently on the R-sig-meta-analysis listserv, Dr. Wolfgang Viechtbauer offered a whole blog post in reply, demonstrating how weights work in simpler fixed effect and random effects meta-analysis and then how things get more complicated in multivariate models. In this post, I'll try to add some further intuition on how weights work in certain multivariate meta-analysis models. Most of the discussion will apply to models that include multiple level of random effects, but no predictors. I'll also comment briefly on meta-regression models with only study-level predictor variables, and finally give some pointers to work on more complicated models.
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Positive affect has demonstrated unique benefits in the context of health-related stress and is emerging as an important target for psychosocial interventions. The primary objective of this meta-analysis was to determine whether …
As I’ve discussed in previous posts, meta-analyses in psychology, education, and other areas often include studies that contribute multiple, statistically dependent effect size estimates. I’m interested in methods for meta-analyzing and meta-regressing effect sizes from data structures like this, and studying this sort of thing often entails conducting Monte Carlo simulations.
In meta-analyses of psychology, education, and other social science research, it is very common that some of the included studies report more than one relevant effect size. For example, in a meta-analysis of intervention effects on reading outcomes, some studies may have used multiple measures of reading outcomes (each of which meets inclusion criteria), or may have measured outcomes at multiple follow-up times; some studies might have also investigated more than one version of an intervention, and it might be of interest to include effect sizes comparing each version to the no-intervention control condition; and it’s even possible that some studies may have all of these features, potentially contributing lots of effect size estimates.
Objective: Self-efficacy expectations are associated with improvements in problematic outcomes widely considered clinically significant (i.e., emotional distress, fatigue, pain), related to positive health behaviors, and, as a type of personal …
Meaning and purpose in life are associated with the mental and physical health of patients with cancer and survivors and also constitute highly valued outcomes in themselves. Because meaning and purpose are often threatened by a cancer diagnosis and …
At AERA this past weekend, one of the recurring themes was how software availability (and its usability and default features) influences how people conduct meta-analyses. That got me thinking about the R packages that I’ve developed, how to understand the extent to which people are using them, how they’re being used, and so on.
This year, Dr. Laura Dunne and I are serving as program co-chairs for the AERA special interest group on Systematic Reviews and Meta-Analysis, which is a great group of scholars interested in the methodology and application of research synthesis to questions in education and the broader social sciences.
Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size …
Having surveyed the history and methods of meta‐regression in a previous paper,1 in this paper we review which and how meta‐regression methods are applied in recent research syntheses. To do so, we reviewed studies published in 2016 across four …