I'm working on patient satisfaction with first level health care in Mexico. The Data base contains a variable called 'factores de expansion final' to allow the sample to be representative of the whole population. According to what I know, this variable should be included in the weight option of any procedure used in SAS.
The data base contains a lot of potential independant variables ( variables of partial satisfaction) which are all correlated to each others. Thus I decided to run a factor analisis. As the variables are defined on lickert scale, I choose to transform them with proc prinqual and then run the factor analisis with the transformed variables.
This solution was really meaningfull and logical as long as I didn't use the weight option. With the weight option, factors and transformations had no sense, even with rotations.
I dont understand why the inclusion of this option is changing so much the correlations between variables.
Then I used the factors from the solution without weight in a binary logistic regression to modelize global satisfaction and this time, the solution with the weight option in proc logit is very very good (R2=0.9, too good?) and very very bad without it ( R2 =0.1).
I stilll dont understand why does the solution changes so much with or without the option.
Which solutin should I use? Should I never use the option weight, always use it or only use it when the results are coherent?
Please help me, this analisis is suppose to help improve the mexican health car system,
Thanks for your attention and help,
Message was edited by: Chompu
If your need is urgent, your best resource is SAS Technical Support. These forums should not be used in place of seeking help with Tech Support. Posting more than once is not going to net you more results.
I'm not a statistician, but I'd think it would depend upon whether your measure is is still meaningful using weight. Realize that the weight option is adding records with precisely the same values, thus all of the actual within unit variance is lost.
In the case shown above I would think that only the non-aggregated, non-weighted correlation actually reflects reality. Yes, there are instances where the use of weighted, aggregate values makes sense, but that would all depend on your data.
p.s. FWIW, I disagree with the previous comment that cross posting doesn't sometimes help. I never would have seen your post if it had only been posted in the statistics' section.