Multiple means comparision for non-parametric data?

Question

I am analyzing some data that is so strongly left-skewed that it has to be analyzed nonparametrically. Although my preferred question would be to see whether three wasp species differ in their preferences among three baits (a factorial design would work great if the data could be normalized), I think I’d settle for the Kruskal-Wallis analysis for each species separately IF I could then do the multiple pairwise comparisons, but I'm not sure I can even do that. One could do multiple Wilcoxon tests with Bonferroni corrections, for example. Is this possible with JMP? I don’t see an obvious way to set it up.

Thanks in advance for any insights.

Alan

unknown · Answer

I'm not entirely comfortable with how I've organized the data. I try to make it so that each row represents a single experimental subject, but in this case each row represents a single trap-species combination. That is, if trap #123 has 9 yellowjackets and 5 hornets, then this represents two rows, one for the 'jackets and another for the hornets. Thus, number of wasps is one variable, and species identity is another. To me it makes more sense conceptually to have one row per trap, with each species counts in its own column, but I don't think you can then compare the different species to each other (e.g., regarding bait types).

ms · Answer

Ok, I misunderstood how you had organized your data. And a lot of ties may be a problem for the Anova approach.

How are the wasp bait preferences represented? As proportions or frequencies? Or is each individual assigned just one dominating bait type? Although I still not know exactly what you are trying to do, I was thinking that you may use the logistic platform or a contingency table to look for differences instead of Kruskal-Wallis.

unknown · Answer

MS,

Thanks for the tips. I don't think I can include bait in the By field since it's already a dependent variable (I tried anyways, and was told a variable can't be both).

I'll have to try the global ranks approach next. My instincts are these may still not be normalizable data, but there's only one way to find out! I also would need the direct ranking method, since I have beaucoups of ties (nearly 1100 values that range from 0 to 31!).

Message was edited by: Alan H