# Testing normality assumptions for linear mixed models and mixed (repeated) GLM ANOVA in SPSS

I have a mixed design that includes both repeated (condition) and between (sex and genotype) subjects factors. I would like to assess whether my data meets the normality assumptions for 1) General linear models (repeated) and 2) linear mixed models using SPSS. What is the best method for doing so? Assuming that I find violations to the normality assumptions how do I select the optimal method(s) for transformation?

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I wouldn't do a test of normality (on the residuals). Make a quantile quantile plot of the residuals (qqnorm() in R). Now, put that aside and make some more but this time of just random normal data with the same N as your residuals. How does your plot look compared to the simulations? If it's in the range of what you might expect to see it's normal. If not then you may have some concerns.
+1, I would note that in SPSS (which the OP mentions they are using) one could get a QQ plot of an observed distribution vs a theoretical one in the PPLOT command. Also some quick googling it appears there exist techniques for 95% confidence intervals for QQplots. It may be easier to code up 19 simulations though and plot the min-max at each quantile as you suggest. –  Andy W Jul 25 '12 at 2:00