This is how we did it for ages
# devtools::install_github("neuropsychology/psycho.R")
library(psycho)
df <- psycho::affective
aov_results <- aov(Adjusting ~ Sex * Salary, data=df)
Df Sum Sq Mean Sq F value Pr(>F)
Sex 1 35.9 35.94 18.162 2.25e-05 ***
Salary 2 9.4 4.70 2.376 0.0936 .
Sex:Salary 2 3.0 1.51 0.761 0.4674
Residuals 859 1699.9 1.98
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
386 observations deleted due to missingness
And this is what R-bloggers recommends
analyze(aov_results) The effect of Sex is significant (F(1, 859) = 18.16, p < .001) and can be considered as small (Partial Omega-squared = 0.019). The effect of Salary is not significant (F(2, 859) = 2.38, p = 0.09°) and can be considered as very small (Partial Omega-squared = 0.0032). The interaction between Sex and Salary is not significant (F(2, 859) = 0.76, p > .1) and can be considered as very small (Partial Omega-squared = 0).
Seriously!
