About AmberDetty

AmberDetty · ‎03-01-2012

Thanks, Steve. Yes, basically we have about a 100 county/income stratifications (not school districts) and 377 schools neslted within those. Thanks for the reply, I'll attempt that code and see how the output looks. We had been advised to use proc glimmix by our statstical contractor due to the multilevel aspect of these models. However, they were not as helpful in being able to determine the best way to handle the sampling design variables in the proc glimmix statements, as SAS is not their primary statsitical software. They also advised us to include the ilink options, but you are correct that we probably do not need them because our analysis has determined that our distribution is fairly normal. I'm definately open to a discussion of whether proc surveyreg or proc glimmix are our better option here, especially because I am so much more familiar with proc surveyreg. I will look into proc hpmixed as well. We have fairly extensively looked at the interactions between the variables and done extensive covariance testing to come to this model. It also led to our decision to run the stratified models after seeing a signficant interaction between our student health variables and the indicator of the school health programs. While this model is not set in stone, it is the best we've come up with so far which is why I labled it final (at least for now). Thanks again, Amber

AmberDetty · ‎02-29-2012

This is the first time I've used proc glimmix procedure in SAS. Here's the basics: we are looking to use both indivdiual student health/demographic data and school-level geographical/size data to predict school performance. The data sources are two-fold, the first a stratified, clustered random sample of third-graders were taken to provide the individual-level data. That data was then merged with data from the state department of education on school performance indicators. My question is, how do you handle the strata and cluster variables in proc glimmix most effectively. Some sites have suggested just running both of those variables in a random statement, some suggest using the strata variable in the random statement and the cluster variable in the subject option of the random statement. I get this error when doing the latter: "ERROR: Integer overflow on computing amount of memory required." I think this is occuring because of the large number of options, then, in the random statement (there are 377 levels of the cluster variable, over a 100 options in the strata variable). I'm not sure how to fix this. To make matters even more complicated, we're interested in running stratified regression models based upon whether the school had a school-based program of interest. Here's the current code I have, where strata09_n is the strata varaible and buildingirn is the cluster variable and sealprog is the school-based program of interest: Option 1: Proc glimmix data= a; *final model; class ctytype untx ow2 strata09_n buildingirn; model perfindex = lowincome racialmin untx ow2 ctytype enrollment3 / solution; weight finalweight; random strata09_n buildingirn / solution; lsmeans ow2/ilink cl; lsmeans untx/ilink cl; where sealprog = 0; *where sealprog = 1; run; Option 2: Proc glimmix data= a; *final model; class ctytype untx ow2 strata09_n buildingirn; model perfindex = lowincome racialmin untx ow2 ctytype enrollment3 / solution; weight finalweight; random strata09_n / subject=buildingirn solution; lsmeans ow2/ilink cl; lsmeans untx/ilink cl; where sealprog = 0; *where sealprog = 1; run; Any advice/thoughts?

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Re: Handling complex survey designs in Proc Glimmix

Handling complex survey designs in Proc Glimmix

Re: Handling complex survey designs in Proc Glimmix

Handling complex survey designs in Proc Glimmix