3
$\begingroup$

I am running one-way ANOVA to determine whether several groups have differing means. I also run post-hoc multiple comparison to determine which means are different. The problem is that I need to have it showed which sets of groups are not different (like REGWQ test does). The problem is that Levene's test shows that the data I use do not have equal variances, thus, I cannot run REGWQ test.

Is there any alternative to REGWQ for cases when equal variances are not assumed (except for non-parametric K-W test)?

I use SPSS but can also try R.

|cite|improve this question
$\endgroup$
1
$\begingroup$

If I understand your question correctly, then yes SPSS has Unequal Sample Sizes and Unequal Variances (Post Hoc Tests algorithms): Games-Howell, Tamhane's T2, Dunnett's T3, and Dunnett's C. You would have to figure out yourself which one to use given your data, design and research objectives.

As for R, you can run the Games-Howell test in the userfriendlyscience R package (see also here for an example) and Dunnett's T3. For the other tests there don't seem to be packages/functions available for R.

|cite|improve this answer
$\endgroup$
  • 1
    $\begingroup$ Dear @Stefan, thanks for your answer. I am more interested, though, in a special REGWQ post-hoc test and its alternative. As far as I know, REGWQ can only be applied to situations, when equal variances are assumed. Then it groups variables with similar means (i.e. those that are not different) (egret.psychol.cam.ac.uk/statistics/…). My question is if there is an alternative to REGWQ in cases when equal variances are not assumed (because there is no such option in SPSS). $\endgroup$ – Tomas Jan 5 '17 at 7:31
  • 1
    $\begingroup$ @Tomas Oh I see, so the mean differences are first ordered from smallest to largest and then tested in a step-wise fashion. Sorry I don't know of any test that would do that for samples where the assumption of equal variances is not met. However, instead of testing this assumption with a formal test (e.g. Levene's test), you should inspect you data visually. Depending on your sample size these tests may or may not be very useful (e.g. here). $\endgroup$ – Stefan Jan 5 '17 at 15:21

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.