Why are you interested in forming a weighted linear composite of the DVs? Why not perform separate univariate analyses?
> On Nov 29, 2013, at 12:09 AM, "Matthia's" <
[hidden email]> wrote:
>
> I want study the anxiety and depression scores in three types of pets with
> equal size ( 10 dogs, 10 cats and 10 hamsters). So we have here :
> Independent variable : type of pet
> Dependent variables : Anxiety scores - Depression scores.
>
> I checked the following assumptions :
> - The correlation between the dependant variables because r = 0,30
> - We have homogeneity of between-group for depression scores
> (siginficance>0,05), but not for anxiety scores in (siginficance<0,05) so I
> employ Brown-Forsythe and Welch's F test concerning anxiety.
> - We have homogeneity of variance-covariance matrices because Sig.>0,01, so
> the correlation between the dependant variables is equal across the groups.
>
> My questions :
> 1)- I know we can't choose Hotelling's Trace concerning F test but I read
> that Wilk's Lambda is the best choice even if Pillai's Trace is more
> powerful. But I don't know why we shouldn't choose Pillai's Trace or Roy's
> Largest Root F test?
> 2)- Concerning Post hoc test, I read that the best test for "Equal Variances
> Assumed" is Tukey, and for ""Equal Variances not Assumed" is Games-Howell.
> Why?
> 3)- I did the Brown-Forsythe and Welch's F test concerning anxiety :
>
> I read that "there is highly significant difference is anxiety scores across
> pet type " and I don't understand why?
> and that "The violation of homogeneity of variances poses no threat to the
> validity of our results" I don't undestand why?
>
> Thanks in advance :)
>
>
>
> --
> View this message in context:
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>
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