Hi,

My data was only a toy example that matched the real situation, with real data, but i could not have posted the entire data.set and so i gave a self contained example of what i thought was my problem. Of course you can see with the naked eye that the data is unbalanced, (this was done intentionally) but like i said this was only a toy example, mimicking a problem from a real data set. 

Thank you and have a great ahead!


David Hewitt <dhewitt37@gmail.com> wrote: 


> I have the following problem: how appropriate is my aov model under the
> violation of anova assumptions?
> 
> Example:
> a<-c(1,1,1,1,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3)
> b<-c(101,1010,200,300,400, 202, 121, 234, 55,555,66,76,88,34,239, 30, 40,
> 50,50,60)
> z<-data.frame(a, b)
> fligner.test(z$b, factor(z$a))
> aov(z$b~factor(z$a))->ll
> TukeyHSD(ll)
> 
> Now from the aov i found that my  model is unbalanced, and from the
> flinger test  i found out that the assumption of homogeneity  of variances
> is rejected. Could my Tukey comparison be a valid one under these
> violations? From what i read the Tukey test is valid only when the model
> is balanced and when the assumption of homogeneity of variances is not
> rejected, am i wrong? Can anyone tell me what would be the correct test in
> this case?  
> 
> Doing a non-parametric Kruskal - wallis test would give me a different
> result. But what would be the correct multiple comparison test in this
> case?
> 

You shouldn't have needed aov to tell you that the data (not the model) are
unbalanced. I could see that without running the code! Seriously, you might
need to think more about the type of model you're using, and what you want
to know, and then consider how to estimate the effect sizes of interest.


-----
David Hewitt
Virginia Institute of Marine Science
http://www.vims.edu/fish/students/dhewitt/
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