How interchangeable are different estimators of effect size?

Abstract

The computation of effect sizes is a key feature of meta-analysis. In treatment outcome meta-analyses, the standardized mean difference statistic on posttest scores (d) is usually the effect size statistic used. However, when primary studies do not report the statistics needed to compute d, many methods for estimating d from other data have been developed. Little is known about the accuracy of these estimates, yet meta-analysts frequently use them on the assumption that they are estimating the same population parameter as d. This study investigates that assumption empirically. On a sample of 140 psychosocial treatment or prevention studies from a variety of areas, the present study shows that these estimates yield results that are often not equivalent to d in either mean or variance. The frequent mixing of d and other estimates of d in past meta-analyses, therefore, may have led to biased effect size estimates and inaccurate significance tests.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 137 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.