Is it also time to revisit situational specificity?

Relevant meta-analytic considerations Fixed versus random effects models Early meta-analyses relied on a fixed-effect model, in which all studies share a common effect size and any observed differences between primary study effect sizes are due to sampling error (Borenstein et al., 2009). [...]soon after the introduction of meta-analysis, Larry Hedges (1983, p. 388) developed an alternative random-effects model in which “characteristics of a study may influence the magnitude of its effect size.” [...]Hedges (1983) noted that interpreting the average effect size in a random-effects model is not meaningful in the absence of an estimate of variation. By accounting for measurement error (a function of measurement) and range restriction (a function of the sample), VG is intentionally accounting for artifactual sources of effect size variation attributable to study characteristics and is generally considered a random-effects model. Because the VG perspective seeks to explain effect size variation as a function of artifactual variance between primary studies, an evaluation of the VG perspective needs to focus on whether meaningful (i.e., non artifactual) sources of effect size variation also exist.