د/ايمان زغلول قاسم

استاذ تكنولوجيا التعليم المساعد بكلية التربية بالزلفي

book B53

The adjusted effect sizes have been checked for potential outliers by evaluating
whether certain effect sizes differed more than three SDs from several group mean effect
sizes, as they might have too large an impact on the summary statistics or even create a bias.
In meta-analysis, one might assume that all included studies estimate the same
population mean. Models under this assumption are called fixed effects models. In this case,
differences between effect size estimates and the true population mean can only be attributed
to sampling error (Shadish & Haddock, 1994). The homogeneity test assesses if the observed
variability in the results are more heterogeneous than expected from sampling variance alone.
This test is based on the Q statistic (Lipsey & Wilson, 2001), which is distributed as a chisquare
distribution with the number of effects sizes minus one degree of freedom (Hedges &
Olkin, 1985). If the homogeneity of study results is rejected, random and mixed effects models
are alternatives for the fixed effects model. In the former, studies are assumed to estimate a
distribution of effect sizes, whereas in the latter, variance in effect sizes can be seen as both
having a random and a systematic component. Study characteristics can be used to account for
the systematic part of the differences between effects sizes estimates.
Because the included studies have different sample sizes, the estimates from these
studies also differ in precision. A common strategy in meta-analysis is therefore to attach
different weights to studies depending on the level of precision of the studies. Optimal
weights are constructed using the standard error of the effect size (Hedges, 1982; Hedges &
Olkin, 1985)

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