Effect size statistics for anova

Description

Returns the (partial) eta-squared, (partial) omega-squared statistic or Cohen's F for all terms in an anovas. anova_stats() returns a tidy summary, including all these statistics and power for each term.

Usage

eta_sq(model, partial = FALSE, ci.lvl = NULL, n = 1000)

omega_sq(model, partial = FALSE, ci.lvl = NULL, n = 1000)

cohens_f(model)

anova_stats(model, digits = 3)

Arguments

Details

For eta_sq() (with partial = FALSE), due to non-symmetry, confidence intervals are based on bootstrap-methods. In this case, n indicates the number of bootstrap samples to be drawn to compute the confidence intervals.

For partial eta-squared (eta_sq() with partial = TRUE), confidence intervals are based on get.ci.partial.eta.squared and for omega-squared, confidence intervals are based on conf.limits.ncf. Confidence intervals for partial omega-squared is also based on bootstrapping.

Value

A data frame with the term name(s) and effect size statistics; if ci.lvl is not NULL, a data frame including lower and upper confidence intervals is returned. For anova_stats(), a tidy data frame with all statistics is returned (excluding confidence intervals).

References

Levine TR, Hullett CR (2002): Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research (pdf)

Tippey K, Longnecker MT (2016): An Ad Hoc Method for Computing Pseudo-Effect Size for Mixed Model. (pdf)

Examples

# load sample data
data(efc)

# fit linear model
fit <- aov(
  c12hour ~ as.factor(e42dep) + as.factor(c172code) + c160age,
  data = efc
)

eta_sq(fit)
omega_sq(fit)
eta_sq(fit, partial = TRUE)

# CI for eta-squared requires apaTables packages
## Not run: 
if (requireNamespace("apaTables", quietly = TRUE)) {
  eta_sq(fit, partial = TRUE, ci.lvl = .8)
}
## End(Not run)

anova_stats(car::Anova(fit, type = 2))