I am recording animal behaviour in four different conditions but in the same environment. So my data are several values (the number of which vary) representing the frequency of a single behaviour for each of the four conditions.
Previously people have used a one-way ANOVA to analyse this data, however, I have realised that the data are not actually normally distributed. So I thought of using a Kruskall Wallis test, but the data are also of repeated measures. Then I thought of using a Friedman test but the data groups (conditions) have unequal numbers of values…
If anyone knows of a test I can use for this data I would be very grateful for some help.
Best Answer
Some possibilities (though I still am not sure of the actual details):
If the response is a frequency then you generally want to use a binomial or poisson outcome for counts, but your sample data is not integers, is it counts divided by time or something similar? If so you could still use a poisson regression model to analyze the data (generalized linear models, glm). If there is concern about the dependence in the data then generalized estimating equations (gee) or generalized mixed effects models (gmm) extend the poisson regression this way.
A flexible non-parametric method is permutation testing (many of the other non-parametric methods are special cases of permutation testing).
I don't remember enough about SPSS to give more specific suggestions.