You can use Proc SURVEYREG.   ... View more

You can use Proc SURVEYREG to fit an ANOVA to complex survey data.   There are no procedures to fit non-parametric models, although I suppose you could apply SURVEYREG to the ranks.  You would have to do a little research to see how this would be different from the SRS-world but it might be a possibility.  I believe this book even discusses it although I don't recall where exactly in the book it is.           ... View more

Perhaps you had an extra space or something in there, but the text from the note is below. Usage Note 24475: What causes the error, "An imputed variable value is not in the specified range after 100 tries," and how can I fix it?   Multiple imputation deals with random draws from the posterior distribution of the variables. When you impose the MIN= and MAX= options, you restrict the possible values that can be drawn. If PROC MI draws a value outside the range, it will attempt to draw another. If this continues 100 times, it aborts the process and issues this error message. Beginning in SAS 9, you can specify the MINMAXITER= option to increase the number of attempts, although in practice this rarely works. There are a number of other options to try as well when this occurs. You could change the imputation method, try specifying your own prior distribution, or remove the MIN= and MAX= options. If you remove the MIN= and MAX= options, you could then impose those restrictions in a DATA step after performing multiple imputations. This process might require some trial and error and is highly data dependent. ... View more

Try the suggestions in this note... http://support.sas.com/techsup/notes/v8/24/475.html      ... View more

Strange, the link works for me.  Here is the paper in pdf format... ... View more

The paper I referenced below shows how to fit a poisson if you choose to go that route. ... View more

Standardizing simply removes the units associted with the variables...not sure though how that would change the interpretation for your model. Either way the scale of the variables is causing the issue, so you may need to find a way to rescale the variable in a way that makes sense. ... View more

This is usually indicative of a scaling problem with your data.  When this happens the standard errors for some of your parameter estimates become very small and the chi-square test produced by the TEST statement is unreliable.  I would try standardizing the data first.   proc stdize data=mincer out=new;  run;        proc reg data=work.new plots=none; model CEp=CEIp; test CEIp=1; by EU28 sex; run;   ... View more

You could do something similar to the example below (it assumes the Proc MI step was already run).   /* Getting Started Example Generate Data */ proc format; value ResponseCode 1 = 'Very_Unsatisfied' 2 = 'Unsatisfied' 3 = 'Neutral' 4 = 'Satisfied' 5 = 'Very_Satisfied'; run; proc format; value UserCode 1 = 'New Customer' 0 = 'Renewal Customer'; run; proc format; value SchoolCode 1 = 'Middle School' 2 = 'High School'; run; proc format; value DeptCode 0 = 'Faculty' 1 = 'Admin/Guidance'; run; data SIS_Survey; format Response ResponseCode.; format NewUser UserCode.; format SchoolType SchoolCode.; format Department DeptCode.; do _imputation_=1 to 2; drop j; retain seed1 111; retain seed2 222; retain seed3 333; State = 'GA'; NewUser = 1; do School=1 to 71; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; end; end; NewUser = 0; do School=72 to 134; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; end; end; State = 'NC'; NewUser = 1; do School = 135 to 218; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; output; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; NewUser = 0; do School = 219 to 274; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; State = 'SC'; NewUser = 1; do School = 275 to 328; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; NewUser = 0; do School = 329 to 370; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; end; run; title 'School Information System Survey'; /*Run SURVEYLOGISTIC by _IMPUTATION_*/ proc surveylogistic data=SIS_Survey; by _imputation_; class department; model Response=department/covb; strata State NewUser; cluster School; weight SamplingWeight; store work.test; run; proc plm restore=work.test; estimate 'dept1' department 1/category=separate; estimate 'dept2' department -1/category=separate; estimate 'dept1 v dept2' department 2/category=separate; ods output Estimates=est_ds; run; proc sort data=est_ds; by response label _imputation_; run; proc mianalyze data=est_ds; by response label; modeleffects estimate; stderr stderr; run;   ... View more