|Date: ||Sun, 8 Mar 2009 16:24:08 -0400|
|Reply-To: ||Wensui Liu <liuwensui@GMAIL.COM>|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||Wensui Liu <liuwensui@GMAIL.COM>|
|Subject: ||Re: ordinal data|
|Content-Type: ||text/plain; charset=windows-1252|
while you've got many good advices, i have to say that i will agree
with the reviewers. if i were you, i probably will fit a ordinal logit
model instead of a gaussian model with continous outcome.
just my $0.02USD subject to inflation.
On Thu, Mar 5, 2009 at 4:19 PM, nuria <firstname.lastname@example.org> wrote:
> I am analysing a variable (lameness visual score for cows) that has 10
> levels, from 1 to 5, with half points ( 1, 1.5, 2, 2.5.... and so on).
> I have used GLM and MIXED and I have plotted the residuals and the
> assumptions of normalitiy and homogeneity of variance are met.
> The reviewer of a journal is telling me that I shouldn't use GLM or
> MIXED. I want to argue that, but I first want to make sure I am right.
> Is there any reference that say that parametrics stats are robust
> enough for ordinal data?
> I do not want to use my variable as a categorical variable, because it
> has 10 levels....
> Thanks so much
Acquisition Risk, Chase
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