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2014-04-15

I have run two psychological experiments. The dependent variable is a rating provided by the participant, that is, an integer number from 0 to 100.

The first experiment is a mixed 2x2 design, with one between-subject factor (treatment) and one within-subject factor (question). Each of these factors has two levels. That is, participants are asked two questions in the experiment (Q1 and Q2), while a factor varies systematically between the two groups (Treatment1 and Treatment2).

The second experiment is identical, except for that the design becomes completely within-subjects. That is, it is a repeated measures 2x2 design; participants are asked four questions that encode the two manipulations in a factorial form: Q1Treatment1, Q1Treatment2, Q2Treatment1, and Q2Treatment2.

I am interested in the potential interactions between the two factors (question and treatment).

Normally, I would conduct repeated measures ANOVAs for these experiments. However, the problem is that my data deviate strikingly from normality (in fact, so do the residuals). Therefore, I am looking for nonparametric equivalents to ANOVA for these two designs.


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2014-4-15 00:50:36
       
These aren't exactly nonparametric, but there are nonlinear mixed effect models that may suit your problem.
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2014-4-15 00:51:34
You may be able to come up with permutation procedures that encode your null hypotheses, in which case you can use a permutation test. In general, however, testing interactions via nonparametrics is difficult, because it intrinsically involves a deviation from additivity, which seems (??) impossible to reconcile with the kind of arbitrary rescalability that goes with nonparametric procedures. In your position I would be inclined to see if there's some parametric model that works. What are your responses? Likert? Binary?

Ben Bolker
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