# Thread: Alternatives for Non-normality and Inequality of Variance?

1. ## Re: Alternatives for Non-normality and Inequality of Variance?

Originally Posted by nebulus
Interesting. I had tried to do a Box-Cox transformation in SAS prior to posting my question here and it suggested Lambda = -1 (i.e. the reciprocal transformation), which is essentially using developmental rate (1/d) rather than time. I tried that transformation and it did not help heteroskedacity. Since that is not a standard power transformation like you suggested I will use Minitab and see if I get the same results you reached. Thank you very much, this will definitely help me in the future.

Edit* What p-value did you get for the variance test you used? After transforming the data and using Levene's Test I got an F=2.75 and p=0.02 This is much better than any transformation I ever did but still not non-significant. I tried other values proposed in that range and X^0.33 seemed to be best with p-value of 0.0327.
I would be careful not to interpret the p-value as an indication of the effect size, how unequal the variances are in this case.

2. ## Re: Alternatives for Non-normality and Inequality of Variance?

Originally Posted by nebulus
Interesting. I had tried to do a Box-Cox transformation in SAS prior to posting my question here and it suggested Lambda = -1 (i.e. the reciprocal transformation), which is essentially using developmental rate (1/d) rather than time. I tried that transformation and it did not help heteroskedacity. Since that is not a standard power transformation like you suggested I will use Minitab and see if I get the same results you reached. Thank you very much, this will definitely help me in the future.

Edit* What p-value did you get for the variance test you used? After transforming the data and using Levene's Test I got an F=2.75 and p=0.02 This is much better than any transformation I ever did but still not non-significant. I tried other values proposed in that range and X^0.33 seemed to be best with p-value of 0.0327.
Don't focus too much on the equal variances. ANOVA is similar to regression in that equal variances in the residuals is what is really important. As you can see from the residual plots that I attached, the residuals vs. fitted values is fine. Don't worry about the normality plot. The low p-value there is due to the chunky nature of the data collected. With the number of samples, the normality test is extremely sensitive. Visually, the plot is acceptable.