It shouldn't be necessary to make the conversion and use Proc MEANS.  You could just use Proc SURVEYFREQ instead which gives standard errors for both the percentages and the frequencies.  You could do something similar to the example below.   /* 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'; proc sort data=SIS_Survey; by _imputation_; /*Run SURVEYFREQ by _IMPUTATION_ assuming the MI step is already done*/ proc surveyfreq data=SIS_Survey; by _imputation_; tables state*Response/wtfreq; ods output crosstabs=ctab; ods trace on; run; proc print; run; /*Sort the data by the TABLES variable which is called RESPONSE here*/ proc sort; by state response _imputation_; run; /*Run MIANALYZE with STDERR option for percentages*/ proc mianalyze data=ctab; by state response;*this would be the TABLES variable; modeleffects percent; stderr stdErr; title 'Results of for Proportions'; run; /*Run MIANALYZE with STDERR option for Frequencies*/ proc mianalyze data=ctab; by state response;*this would be the TABLES variable; modeleffects WgtFreq; stderr StdDev; title 'Results of for Frequencies'; run; ... 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To anyone who is interested, I found this:    https://github.com/bcjaeger/R2FixedEffectsGLMM/blob/master/Glimmix_R2_V3.sas         ... View more

This is an interesting question. It is outside my areas of expertise, but let me share a few thoughts and maybe those with more experience in this area can correct or add to my comments.   I think you should consider whether it is more appropriate to do ONE analysis with a multinomial response, rather than THREE analyses with a count model for each analysis.    I think you should read the papers by Robin High @rhigh on modeling count data by using NLMIXED, NLIN, COUNTREG, FMM, and more. I don't know the best approach for your data, but I would start by looking at the following:  High and ElRayes, 2017, "Fitting Statistical Models with PROCs NLMIXED and MCMC" High, 2017, "Unconventional Statistical Models with the NLMIXED Procedure" In addition to Robin High's papers, you might be interested in the work of Jorge Morel. He gave a wonderful SAS Global Forum 2014 talk on overdispersion in count models and has written a book Overdispersion Models in SAS. Chapter 7 deals with multinomial responses.   ... View more

A little quick digging through the reference for the RANDOM statement:   1. ods output solutionr=solutionr; will put the values into a dataset 2. The results are EBLUPS for the random effects. A good way to think of them is as residuals of the fixed effects (y minus X * beta hat) times (G hat *Z prime *V hat-inverse), so they are scaled by a quadratic form.   For a quick review, look at Mixed Models Theory in the Details section of the MIXED documentation.   Steve Denham ... View more

Eric, Check out the documentation for the TEST statement and in particular where it leads to Figure 58.6.  In that example, test3: a1=a2=a3 is a global test with two degrees of freedom.  So for your example, try test: beta1=beta2=...=betan; where the beta's should be replaced with the terms as presented in the MODELEFFECTS statement. Steve Denham ... View more