[ I've also sent this message to other lists. Sorry for multiple messaging ]
Dear colleagues,
I want to perform a repeated measures two-way ANOVA (two fixed crossed
factors). I've found the way to do it (in SPSS, and also in R), but anyway
I think my data don't meet the requirements for that analysis (that is,
normality, sphericity and so on).
Anyway, I was told (here in this list and also in person) to consider the
subjects IDs as a third factor, and to perform a classical ANOVA (because
if I do that I will have one single observation for each combination of the
three factors). That way, I wouldn't need to check for "sphericity" but for
the usual ANOVA assumptions.
My first question is related to that:
(1) Just for checking if I understood this right: are both (parametric)
approaches equivalent? I mean: is "pure" repeated measures ANOVA (the one
available in SPSS, for instance) equivalent to by-passing it by making
subjects work as a factor and then applying "classical" ANOVA?
Another different approach is using non-parametric alternatives. I've found
ONE-way non-parametric tests both for repeated measures comparisons and for
independent sets of observations. And also I've been told about the
existence of multiple-factor non-parametric ANOVA (based on ranks, and also
based on permutation exact tests).
My research is not about ANOVA; I just want to use it as a way to compare
results in a more sophisticated and scientific way than just saying which
combination is better. So I am looking for the easiest approach.
And because of that, I am thinking of performing an exact
(permutation-based, non-parametric) ANOVA, using subjects as a factor.
(2) I know that I didn't tell you anything about my data or my context, but
do you think that approach can be appropriate?
Looking forward to your answers,
--
vicent
@vginer_upv
about.me/vginer_upv
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