2
$\begingroup$

I have a dataset with a total of about 6 groups set up, and there is a minimum of n=150-200 samples per group. Now when I look at the data, its not normally distributed, and the variances are not equal. (eg, smallest standard deviation is 20 and the largest is 60 or simlar). The data is a function of counts, eg. how many times a player jumped during a game.

Now, if there were two groups I'd do a two sample KS test. However, since there are 6 groups, I am wondering if the lack of homogeneous variances will affect my test result if I perform a Kruskal Wallis ANOVA?

In other words, is homogeneity of variances a strict requirement that must be fulfilled for a Kruskal Wallis one way ANOVA?

Or am I wrong in using this test, and there is something else which I have not thought of?

EDIT: I am working with SPSS

EDIT2: Saw this and tried doing it with GLM, but in the end when looking at the model, it ends up being an ANOVA

$\endgroup$
  • $\begingroup$ When you did a GLM ... how exactly did you do it? If it came out the same as an ANOVA (same estimates and p-values etc) you're not fitting the right GLM model. You need a GLM for counts (such as a Poisson model or a negative binomial for example). $\endgroup$ – Glen_b -Reinstate Monica Aug 17 '15 at 19:18
  • $\begingroup$ @Glen_b I did it in SPSS, wherein I put in all variables I had the data for in the model, searched for any interactions, and removed those without any. Would you like to chat about this? chat.stackexchange.com/rooms/27067/… $\endgroup$ – Rover Eye Aug 17 '15 at 19:33
  • $\begingroup$ @Glen_b when looking more closely at the ANOVA vs GLM, though the significance values are the same, the F statistic is halved in the GLM (about 10) when comparing the ANOVA (about 20). Is this what you meant> $\endgroup$ – Rover Eye Aug 17 '15 at 19:44
  • $\begingroup$ sorry can't chat now have to sleep $\endgroup$ – Glen_b -Reinstate Monica Aug 17 '15 at 20:02
2
$\begingroup$

To be optimal, the proportional odds assumption must be satisfied for K-W. This is often a weaker assumption than constant variance. To check the assumption, compute the empirical distribution function for each of the 6 groups, take the $\log\frac{p}{1-p}$ transformation of it, and plot this on the $y$-axis vs. the original values on the $x$ axis; check for parallelism.

K-W can be valid (though without optimal power) if the prop. odds assumption is violated, if you are careful in how $P$-values are computed.

| cite | improve this answer | |
$\endgroup$
  • $\begingroup$ Thanks. Any idea if this can be done in SPSS? $\endgroup$ – Rover Eye Aug 17 '15 at 17:30
  • $\begingroup$ I've never used SPSS. It is easy to do in R. $\endgroup$ – Frank Harrell Aug 17 '15 at 17:43
  • $\begingroup$ @FrankHarrell I was pointed to this answer that I thought could be useful for my problem as well. Unfortunatelly I am not sure and I think that you could help me. Do you mind to check my question? stats.stackexchange.com/questions/167463/… $\endgroup$ – gabboshow Aug 18 '15 at 13:29

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.