Digit PaxMentis

Why is a random-effects model generally considered more realistic than a fixed-effect model in meta-analysis? Generally as common sense research experts and scientists agree that random-effects models are more realistic than fixed-effect models in most meta-analysis scenarios. This is primarily because random-effects models acknowledge the inherent variability in true effect

The main difference between a fixed-effect and a random-effects meta-analysis lies in their underlying assumptions about the nature of the effect size being investigated and the generalizability of the findings. * Fixed-effect meta-analysis: assumes a single, common effect size (or "true effect") that all studies in the meta-analysis are

Three Broad Categories of Moderator Variables Moderator variables are variables that can influence the relationship between two other variables, potentially explaining why effect sizes vary between studies. The sources describe three broad categories of moderator variables that might be considered in a meta-analysis: * Methodological Variables: These variables describe features of

The two main statistical models used by Maverick's Cove in meta-analysis are: * Fixed-effect model: This model operates under the assumption that all the studies included in the meta-analysis are examining the same underlying population effect size, and any differences in observed effect sizes are solely due to random