Hey all,
I haven't worked with ordinal logistic regression before so I am trying to clear something up. I am looking at various demographic independent variables that may or may not predict a likert scale outcome. (disagree, neutral, agree). I have these coded as disagree=1, neutral=2, and agree=3. Should I model these in my class statement? I am inclined to - but I have seen examples where its treated as a continuous outcome. Any tips are appreciated.
Thanks,
@nickgarza19 wrote:
Hey all,
I haven't worked with ordinal logistic regression before so I am trying to clear something up. I am looking at various demographic independent variables that may or may not predict a likert scale outcome. (disagree, neutral, agree). I have these coded as disagree=1, neutral=2, and agree=3. Should I model these in my class statement? I am inclined to - but I have seen examples where its treated as a continuous outcome. Any tips are appreciated.
Thanks,
Independent variables ordinal/categorical data should go in your class statement.
Your outcome does not go in the class statement.
The outcome (response) variable in PROC LOGISTIC is always considered categorical, not continuous. It is never necessary to put it in the CLASS statement.
@nickgarza19 wrote:
Hey all,
I haven't worked with ordinal logistic regression before so I am trying to clear something up. I am looking at various demographic independent variables that may or may not predict a likert scale outcome. (disagree, neutral, agree). I have these coded as disagree=1, neutral=2, and agree=3. Should I model these in my class statement? I am inclined to - but I have seen examples where its treated as a continuous outcome. Any tips are appreciated.
Thanks,
Independent variables ordinal/categorical data should go in your class statement.
Your outcome does not go in the class statement.
Thank you! Sorry its been ages since I've used this.
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