I applied PROC LOGISTIC to predict a binary outcome variable, coded as 0 and 1. I have eleven predictors. Seven predictors are binary (VAR1 - VAR7), three predictors are continuous variable (VAR9 - VAR 11), and one predictor is categorical with four levels (VAR8: EDU4). Regarding the four-level categorical variable, (1) I found the "type 3 analysis of effects" was not significant (df = 3, chi-sq = 6.1259, p =0.1055). However, one of the three estimates in "Analysis of Maximum Likelihood Estimates" was significant (df = 1, estimate = 0.9442, chi-sq = 5.2407, p = 0.0221) when"one level compared to the reference level" (Bachelor's degree or above vs. high school and some post-secondary). In this case, should I interpret this variable has a significant effect on my binary outcome? (3) In the model, I applied WEIGHT statement in PROC LOGISTIC. Should I use PROC SURVEYLOGISTIC to get a better estimate? Here is my SAS code using PROC LOGISTIC: PROC LOGISTIC data=SDE descending; CLASS VAR1 VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR8 / desc order = formatted param = ref; model DV = VAR1 VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR8 VAR9 VAR10 VAR11 / lackfit rsquare; weight RESPWT; run; Thank you for your insights.
... View more