Hello,
Very new to the SAS world and have been teaching myself over the past month. I am interested in using PROC MI to replace missing values for several variables from a single measure of self-efficacy (7-itme scale). See the code below, but my primary question pertain to PROC REG - what is the nature of one's model if I do not really have IV and DV's. I am simply trying to replace missing values for items on a scale - that is, no prediction of a DV from IV. What am I missing here?
PROC MI Data=EWCA.final seed=40080 out=miout;
mcmc;
var se1 se2 se3 se4 se5 se6 se7;
run;
PROC REG Data=collem Outest=a NOPRINT;
MODEL ??????; ***I am not sure what the model would be as I am not running a regression
BY REPLICATE;
PROC mianalyze data=a;
Modeleffects intercept se1 se2 se3 se4 se5 se6 se7;
Run;
Typically you would only use multiple imputation when you have a specific analysis in mind. Once you impute the data, what do you plan to do with it?
Yes it makes sense. To impute ordinal data like you have you would need to use either the DISCRIM or LOGISTIC method, with the fomer being preferred if you have 7 levels. You can use either the FCS or MONOTONE statement, depending on the missing data pattern.
That will take care of the imputation step, but you cannot use multiple imputation and cluster analysis because the theory does not lend itself to calculating combined estimates after running the cluster step due to the absence of point estimates and standard errors.
So I would not use multiple imputation in this case. You could try some single imputation method or use FASTCLUS rather than CLUSTER which is more forgiving with missing values.
If you only wanted to impute then, yes, using the FCS DISCRIM or LOGISTIC method should work.
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