A Simple Method for Removing Bias From a Popular Measure of Standardized Effect Size: Adjusted Partial Eta Squared

Abstract

Accurate estimates of population effect size are critical to empirical science, for both reporting experimental results and conducting a priori power analyses. Unfortunately, the current most-popular measure of standardized effect size, partial eta squared ([Formula: see text]), is known to have positive bias. Two less-biased alternatives, partial epsilon squared ([Formula: see text]) and partial omega squared ([Formula: see text]), have both existed for decades, but neither is often employed. Given that researchers appear reluctant to abandon [Formula: see text], this article provides a simple method for removing bias from this measure, to produce a value referred to as adjusted partial eta squared (adj [Formula: see text]). Some of the many benefits of adopting this measure are briefly discussed.

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 — 122 citations exist upstream.

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