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# MIxed procedure with repeated measures on ordinal data

A reviewer has questioned my use of Proc Mixed for repeated measures on ordinal data, saying that I should be using non-parametric methods with ordinal data. What is your advice?

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## Re: MIxed procedure with repeated measures on ordinal data

Proc mixed assumes Gaussian random effects and errors.

PG
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## Re: MIxed procedure with repeated measures on ordinal data

That is too concise a response for me to understand whether you agree with the reviewer or not. Is there a different procedure that I should use?

I have a continuous dependent variable (body temperature) and ordinal independent variable (comfort rating) which I measure at regular intervals over a period of time. If proc mixed is not appropriate, then please advise what would be?

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## Re: MIxed procedure with repeated measures on ordinal data

This is the script - Sens is ordinal (a rating scale) where bodytemp is continuous.

proc mixed data=mydata method=ml covtest asycov ;
class subject_ID ;
model sens = bodytemp /DDFM=KR CL s noint;
repeated / type=UN subject= subject_ID R;
run;

SAS Employee
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## Re: MIxed procedure with repeated measures on ordinal data

For an ordinal response, you should use either PROC GLIMMIX or PROC GENMOD instead.

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## Re: MIxed procedure with repeated measures on ordinal data

I agree, if in fact SENS is intended to be the response variable. That is not yet clear to me, given the contradictory posts.

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## Re: MIxed procedure with repeated measures on ordinal data

[ Edited ]

The "dependent" variable is the response variable. In one post you identify it as body temperature. It is not unreasonable to assume that body temperature follows a normal distribution, as a first pass.

The "independent" variable is the explanatory or predictor variable. You identify it as comfort rating, and you say that comfort rating is measured on an ordinal scale. You might choose to treat comfort rating as if it is nominal and use an ANOVA-like model. Or you might choose to treat comfort rating as if it is continuous and use a regression-like model. In my opinion, it depends on the shape of the relationship between the response (dependent) variable and the explanatory (dependent) variable as well as the number of levels of the explanatory variable.

BUT!!! in a subsequent post, you reverse the identities of response and explanatory: in your MIXED code, you identify SENS as the response (dependent) and BODYTEMP as the explanatory (independent) variable. So, I have no idea what role your variables play in your study, or how to express those roles in a statistical model.

Plus in your model, you use the REPEATED statement, but you have not identified a repeated measures factor in either the MODEL or the REPEATED statement.