I have electrophysiological data in the form of skin resistance in fish. I wish to explore if there is any variation in resistance based on the location on the body where the skin is taken from. In addition, I wish to explore how a change in salinity affects the resistance of the skin.
I have used the general linear model (GLM) function in SPSS to create a model, with the resistance values as my response and four categorical predictors. The model has four main effects and three interactions. N=144. The R-squared value and a lack-of-fit test both seem to indicate that the model is a good fit for my data, explaining ~80% of the observed variation. However, the residuals show large heteroscedasticity and non-normality.
However, if I rank my data and run the same analysis, my residuals are both normal and homoscedastic. I have seen that one can do a one-way ANOVA on ranks, in what is essentially a one-way Kruskal-Wallis. However, I don't know if the same approach is valid with a GLM, or if it is even valid to rank this type of data?
I would really appreciate any help or suggestions with this. I hope that I have provided enough information but if not, please let me know. This is my first question on here :)