- Thread starter jcoleman51
- Start date

John:

Thanks. I pretty much figured ANOVA would be OK with it. I guess my main concern had to do with the logic behind it. I mean, just because a data set is shown to be normal and homogeneous, it is a valid leap (statistically speaking, not intuitively) to make the conclusion that subsets of it will also be normally distributed and homogeneous? It seems to me that once you start removing data points or groups of data points, you've created a whole new set of values with its own characteristics and qualities. Or am I being too picky?

John:

Excuse my dunce-ness. So...when we're testing for normality (e.g. Shapiro-Wilk's), we're really checking that the individual groups are normally distributed, not the whole set of data points from ALL the groups??? And if the critical value is exceeded, it means that (at least) one of the GROUPS is not normally distributed?

Does the same hold true for homogeneity of variance?

Just to tag onto this thread.. My data is ordinal, so I plan to use a non-parametric test (Wilcoxon-signed ranks) but am aware that the homogeneity of variance assumption should not be violated. What is the best test for checking for homogeneity of variance across two groups (i.e. pre and post test)? I'm not sure, but I think that the Levene test can be computed as part of a two sample t-test in SPSS, however is there a better method?

Thanks in advance.