omega_squared: Omega Squared for ANOVA

In rstatix: Pipe-Friendly Framework for Basic Statistical Tests

Omega Squared for ANOVA

Description

Compute (classic, full) omega-squared and partial omega-squared for all terms in a between-subjects ANOVA model. Omega squared is a less-biased alternative to eta squared, estimating the population effect size rather than the sample one.

See the Datanovia tutorial One-Way ANOVA in R for a worked walkthrough.

Usage

omega_squared(model)

partial_omega_squared(model)

Arguments

model

an object of class aov or anova (a between-subjects design). Repeated-measures and mixed models are not supported, as for eta_squared().

Value

a named numeric vector of effect sizes, one per model term. A negative point estimate (which can arise for a term with F < 1) is floored at 0, since omega squared estimates a non-negative proportion of variance.

Functions

• omega_squared(): compute the classic (full) omega squared.

• partial_omega_squared(): compute partial omega squared.

References

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8(4), 434-447.

See Also

eta_squared(), anova_test(). The Datanovia tutorial: One-Way ANOVA in R.

Examples

# Data preparation
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Fit the model
res.aov <- aov(len ~ supp * dose, data = df)

# Effect size
omega_squared(res.aov)
partial_omega_squared(res.aov)

rstatix documentation built on July 24, 2026, 1:06 a.m.