Hello Everyone, I am new to modelling and have the following bit of code. Predictor: V_ASIAN (Race) Outcome: SC_ETHRACE_AM (discrimination) Controls: Age5 (Age groups), Sex, Edu_Level (5 groups), Region (5 groups), LFS (labour force status 3 groups) Parametrization: I'm using dummy coding with specific reference groups listed Purpose: To see what the effect of reporting a given racialized group (3 groups) will have on the reported perceived discrimination (YES, NO - dichotomous) while controlling for age, sex, education level, region and labour force status (See above). Output: At the moment the basic output that PROC LOGISTIC is spitting out are the odds ratio for each pair combination. In Stata there is a statement ('margin') that will allow for an estimated proportion given the model. So that instead of saying a given Racialized group has 10x the odds of being discriminated against compared to the reference category, I can say what the adjusted proportion of discrimination for a given racialized group would be while having controlled for several characteristics. Prep: I've removed non-response for predictors, outcome and control variables and I need to use normalized survey weights. Question: Is there a transformation technique/statement that would provide me the estimated proportions (see above) + the standard error + Confidence intervals + Pvalues? In STATA this is easily done with the "MARGIN" statement. Thank you all so much! proc logistic data=work.V2 ;
CLASS V_ASIAN (ref='3') AGE5(ref='3') SEX(ref='1')
EDU_LEVEL(ref='4') REGION (ref='3') LFS (REF='1') /param=ref ;
model SC_ETHRACE_AM = V_ASIAN age5 sex edu_level region lfs; weight wght_per / norm; run;
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