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01-25-2013 08:40 AM

Hello,

I have a question and I will be grateful if you can help me,

I am running a regression which I have one dependent variable (ranked from 1 to 6) and several independent variable, which some of them are dummy variables(like gender) and some of them are metric variables like age, and I have some other variables like education which I ranked them .I am using SAS for my research.I used proc logistic for my regression, but it just considers 3 levels of my dependent variable,(as I mentioned before it has 6 levels).

Would you please help me?

Thanks a lot.

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01-25-2013
10:52 AM

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01-25-2013 10:52 AM

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01-25-2013 08:58 AM

Run a quick cross-tab on your data using PROC FREQ. While your dependent variable may be allowed to take on values from 1 to 6, it seems that your sample only has three values.

Steve Denham

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01-25-2013 12:31 PM

Thank you for the answer.

actually I am sure that I have data in all 6 levels.still I have the same problem.

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01-25-2013 02:18 PM

Time to share a partial dataset, and the code you are using. I have no idea whatsoever how PROC LOGISTIC would only see three levels if the data actually had six, aside from 's observation that independent variables are missing values, resulting in the cases not being included. It is why I recommend a cross tab in PROC FREQ to see what might be happening.

If this turns out to be the case, then I would not trust imputation as a method. Missing values will be imputed based on similarity of cases, but not based on the response variable, since it takes on unique values for missing independent variables. As a result, bad fit of the imputed dataset to the dependent variable. I think you will have to choose from the following: A)reduce the number of independent variables so that cases are not excluded, B) consolidate scores, so that the dependent variable takes on only three levels, C)accept the analysis as is. I would prefer B, as I believe the results will be much easier to interpret.

Steve Denham

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01-25-2013
10:52 AM

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01-25-2013 10:52 AM

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01-25-2013 12:37 PM

Yes you are right,

thanks a lot

but do you know how can I solve this problem?

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01-25-2013 01:15 PM

Only solutions I can think of are to either find values for the missing, possibly by imputation, or remove the offending variables from the model.

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01-25-2013 06:43 PM

Thanks a lot for your help.