Partial omega-squared as an effect size in analysis of variance

Partial $$\omega^{text{2}$$ has been suggested by Keppel (1991),pp 222-224) and Olejnik and Algina (2003) as an alternative to partial $$\eta}text{2}$$ when comparing the size of sources of variation across studies from analysis of variance.

$$\omega^text{2} = \frac{\mbox{SS(effect)-df(effect) MSE(effect)}}{\mbox{SS(effect)+(N-df(effect))MSE}}$$

In the case of a between subjects ANOVA with b groups and a total of N subjects

SS(effect)+(N-df(effect))MSE = SS(group) + (N-df(group))MSE

= SS(group) + (N-(b-1))MSE = SS(group) + (N-b+1)MSE = SS(group) + (N-b)MSE + MSE

= Total SS + MSE

since in a one-way ANOVA between subjects design the total SS = SS(group) + (N-b)MSE.

$$\omega^text{2}$$ may take values between $$\pm$$ 1 with a value of zero indicating no effect. A negative value will result if the observed F is less than one.

The numerator in $$\omega^text{2}$$ compares the mean square of the effect (=SS(effect)/df(effect)) to its mean square error which theoretically should be equal (giving a difference, as measured in the numerator, of zero) because the ratio of the mean squares may be approximated by a F distribution which takes a minimum value of one indicating no statistical evidence of the effect.

$$\omega^text{2}$$ is unaffected by small sample sizes.

References

Olejnik S and Algina J (2003). Generalized Eta and Omega Squared Statistics: Measures of effect size for some common research designs. Psychological Methods 8(4) 434-447.

Keppel G (1991). Design and analysis:A researcher's handbook. Prentice-Hall:Englewood Cliffs, NJ