95 % CI for standardized beta coefficients in proc glm

[Rick_SAS]

Here is an example, based on my previous guidance. I use KSharp's example data so you can compare the methods:

data DrugTest; input Drug $ Gender $ X Y @@; 
datalines; 
A F 9 25 A F 3 19 A F 4 18 A F 11 28 A F 7 23 A M 11 27 
A M 9 24 A M 9 25 A M 10 28 A M 10 26 D F 4 37 D F 12 54 
D F 3 33 D F 6 41 D F 9 47 D M 5 36 D M 4 36 D M 7 40 
D M 10 46 D M 8 42 G F 10 70 G F 11 75 G F 7 60 G F 9 69 
G F 10 71 G M 3 47 G M 8 60 G M 11 70 G M 4 49 G M 4 50 
; 

/* If you don't need CL, you can use the STB option in 
   PROC GLMSELECT to get the standardized regression coefficients */
proc glmselect data=DrugTest;
   class Drug Gender; 
   model Y = X Drug Gender / selection=none STB;
   ods select ParameterEstimates;
quit;

/* If you require CLB, you have to use a longer manual calculation */
/* Step 1: Use the OUTDESIGN= option in PROC GLMSELECT to output the design matrix
           for the model. */
proc glmselect data=DrugTest noprint
               outdesign(addinputvars fullmodel)=GLSDesign;
class Drug Gender; 
model Y = X Drug Gender; 
quit;

/* Step 2: Use PROC STDIZE to standardize the explanatory variables, 
           including the dummy variables. */
proc stdize data=GLSDesign out=StdData method=std OPREFIX SPREFIX=Std;
   var  Y X Drug_A Drug_D Drug_G Gender_F Gender_M;
run;

/* Step 3: Use PROC REG to compute the parameter estimates for the (standardized) variables. */
options nolabel;   /* suppress labels: blogs.sas.com/content/iml/2012/08/13/suppress-variable-labels-in-sas-procedures.html */
proc reg data=StdData plots=none;
   Stdize: model StdY = StdX 
                        StdDrug_A StdDrug_D StdDrug_G 
                        StdGender_F StdGender_M / CLB;
   ods select ParameterEstimates;
quit;

[Ksharp]

Rick,
You missed CLB option of MODEL in PROC REG ？

[Rick_SAS]

I wrote a summary of my thoughts in a blog post:
Standardize regression coefficients for models that include categorical variables - The DO Loop (sas...