Effect size calculations for ANOVAs
Calculates eta-squared and partial eta-squared
etaSquared( x, type = 2, anova = FALSE )
- An analysis of variance (aov) object.
- What type of sum of squares to calculate?
- Should the full ANOVA table be printed out in addition to the effect sizes
Calculates the eta-squared and partial eta-squared measures of effect size that are commonly used in analysis of variance. The input
x should be the analysis of variance object itself.
For unbalanced designs, the default in
etaSquared is to compute Type II sums of squares (
type=2), in keeping with the
Anova function in the
car package. It is possible to revert to the Type I SS values (
type=1) to be consistent with
anova, but this rarely tests hypotheses of interest. Type III SS values (
type=3) can also be computed.
anova=FALSE, the output is an M x 2 matrix. Each of the M rows corresponds to one of the terms in the ANOVA (e.g., main effect 1, main effect 2, interaction, etc), and each of the columns corresponds to a different measure of effect size. Column 1 contains the eta-squared values, and column 2 contains partial eta-squared values. If
anova=TRUE, the output contains additional columns containing the sums of squares, mean squares, degrees of freedom, F-statistics and p-values.
This package is under development, and has been released only due to teaching constraints. Until this notice disappears from the help files, you should assume that everything in the package is subject to change. Backwards compatibility is NOT guaranteed. Functions may be deleted in future versions and new syntax may be inconsistent with earlier versions. For the moment at least, this package should be treated with extreme caution.
Anova (in the car package)
#### Example 1: one-way ANOVA #### outcome <- c( 1.4,2.1,3.0,2.1,3.2,4.7,3.5,4.5,5.4 ) # data treatment1 <- factor( c( 1,1,1,2,2,2,3,3,3 )) # grouping variable anova1 <- aov( outcome ~ treatment1 ) # run the ANOVA summary( anova1 ) # print the ANOVA table etaSquared( anova1 ) # effect size #### Example 2: two-way ANOVA #### treatment2 <- factor( c( 1,2,3,1,2,3,1,2,3 )) # second grouping variable anova2 <- aov( outcome ~ treatment1 + treatment2 ) # run the ANOVA summary( anova2 ) # print the ANOVA table etaSquared( anova2 ) # effect size