Is Omega Squared Less Biased? a Comparison of Three Major Effect Size Indices in One-Way Anova

By Kensuke Okada. 2013

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e834254c41b398b137c686078c6e12aa2cf4c7cf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Toby Mordkoff
2. Casper J. Albers
3. Milica Miočević
4. R. van de Schoot
5. Daniël Lakens
6. Daniel M. McNeish

In Computer Networks and Communications

1. Casper J. Albers
2. J. Toby Mordkoff
3. Milica Miočević
4. R. van de Schoot
5. Daniël Lakens
6. Farid Anvari

Citations in the corpus

• When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias (2018, methodology): “These values are also consistent with Okada's (2013) simulation design.”
• Small Sample Size Solutions (2020, methodology): “We have added the effect size ω2, because it is less biased than effect size η2 for small samples (Okada, 2013).”
• A Simple Method for Removing Bias From a Popular Measure of Standardized Effect Size: Adjusted Partial Eta Squared (2019, background): “Second, in several comparisons (e.g., Carroll & Nordholm, 1975; Keselman, 1975; Okada, 2013), ε p 2 has been shown to be very close to completely unbiased, whereas ω p 2 has been found to have a small but consistent (negative) bias.”