Editing Effect size (section)

===== Omega-squared (''ω''<sup>2</sup>) =====
{{see also|Coefficient of determination#Adjusted R2{{!}}Adjusted ''R''<sup>2</sup>}}
A less biased estimator of the variance explained in the population is ''ω''<sup>2</sup><ref name="Tabachnick 2007, p. 55">Tabachnick, B.G. & Fidell, L.S. (2007). Chapter 4: "Cleaning up your act. Screening data prior to analysis", p. 55 In B.G. Tabachnick & L.S. Fidell (Eds.), ''Using Multivariate Statistics'', Fifth Edition. Boston: Pearson Education, Inc. / Allyn and Bacon.</ref>
<math display="block">\omega^2 = \frac{\text{SS}_\text{treatment}-df_\text{treatment} \cdot \text{MS}_\text{error}}{\text{SS}_\text{total} + \text{MS}_\text{error}} .</math>

This form of the formula is limited to between-subjects analysis with equal sample sizes in all cells.<ref name="Tabachnick 2007, p. 55"/> Since it is less biased (although not ''un''biased), ''ω''<sup>2</sup> is preferable to η<sup>2</sup>; however, it can be more inconvenient to calculate for complex analyses. A generalized form of the estimator has been published for between-subjects and within-subjects analysis, repeated measure, mixed design, and randomized block design experiments.<ref name=OlejnikAlgina>{{cite journal | last1 = Olejnik | first1 = S. | last2 = Algina | first2 = J. | year = 2003 | title = Generalized Eta and Omega Squared Statistics: Measures of Effect Size for Some Common Research Designs | url = http://cps.nova.edu/marker/olejnik2003.pdf | journal = Psychological Methods | volume = 8 | issue = 4 | pages = 434–447 | doi = 10.1037/1082-989x.8.4.434 | pmid = 14664681 | s2cid = 6931663 | access-date = 2011-10-24 | archive-date = 2010-06-10 | archive-url = https://web.archive.org/web/20100610101507/http://cps.nova.edu/marker/olejnik2003.pdf | url-status = dead }}</ref> In addition, methods to calculate partial ''ω''<sup>2</sup> for individual factors and combined factors in designs with up to three independent variables have been published.<ref name=OlejnikAlgina/>