I have 2 x 2 x 2 mixed design with two between subject factors (sex, organizer) and one within subjects factor (task). The group sizes of the ‘sex’-factor is unequal. When I perform a factorial repeated measures ANOVA, I get the following warning message:
Warning: Data is unbalanced (unequal N per group). Make sure you
specified a well-considered value for the type argument to ezANOVA().
I used the following model in R:
model <- ezANOVA(data=df, dv=.(top_start), wid=.(id), between=.(sex, org), within=.(task), type = 3, detailed = TRUE)
type = 3for the Anova because, as I understood, it is suited for unbalanced group sizes.
I have the following questions:
- Do I need to code contrasts when all my factors have only two levels?
- Did I use the right type of Anova?
- Are there other ways to do this analysis?
If you use type 3 for ANOVAs it is critical in R that you set the contrast to effect coding (i.e.,
The default contrast in R is dummy coding (or in R parlance, treatment coding) in which 0 represents the first factor level. This doesn’t make too much sense when having interactions as explaind on the page I linked to.
To set effect coding, run the following:
Alternatively, you can use the
afex package, which has similar goals as
ez, with the difference that it automatically sets the contrasts to effects coding and uses type 3 as default.