- Thread starter matteusbeus
- Start date

ANOVA doesn't require a normally distributed dependent variable.

Prefereably, the dependent variable

But even this is not necessary if n (total) is large enough. How large is your total sample?

With kind regards

Karabiner

I suppose that with n=94 and equal groups, outliers do not affect assumptions (maybe someone wants to correct me here), but perhaps results. You could perform your split plot ANOVA with the untransformed data, and for checking robustness you could then repreat the analysis without extreme outliers. Or, if you have a substantial knowledge about the processes which produce the outliers, you could incorporate them into your model.

With kind regards

Karabiner

- Problem-related Distress - one outlier, studentized residual value of -3.01. Normally distributed, as assessed by Normal Q-Q Plot.
- Depression - three outliers, studentized residual values of 3.34, 3.90, 3.06. Not normally distributed, as assessed by Normal Q-Q Plot.
- Anxiety - six outliers, studentized residual values of 3.11, 3.18, 3.86, 3.86, 3.86, 4.21. Not normally distributed, as assessed by Normal Q-Q Plot.
- Stress - two outliers, studentized residual values of 3.36, 3.51. Not normally distributed, as assessed by Normal Q-Q Plot.
- DASS-21 - three outliers, studentized residual values of 3.14, 3.28, 3.62. Not normally distributed, as assessed by Normal Q-Q Plot.
- Helpfulness - no outliers. Not normally distributed, as assessed by Normal Q-Q Plot.
- Problem resolution - three outliers, studentized residual values of 3.20, 3.20. Not normally distributed, as assessed by Normal Q-Q Plot.
- Use again - no outliers. Not normally distributed, as assessed by Normal Q-Q Plot.