etaSquared( x, type = 2, anova = FALSE )
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.
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
, 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