I am currently analyzing some DV which represents aproportion and a fractional logit model seems most appropriate. In addition to this, my data has a multilevel structure, implying that I include a random intercept (hhic, legal and age are measures at the group-level agency). This is the syntax: proc glimmix data=consultations empirical; class agency consultationtype(ref='1') complexitytext(ref="1") com_dummy(ref="0") format targetgroup; model p_nonregulated=legal age hhic hhic*legal legal*age consultationtype complexitytext format duration l_mob_total /dist=binomial link=logit solution; random _residual_/subject=agency type=vc; covtest/wald; output out=predp pred(ilink)=p; run; My question concerns the interpretation of the coefficients, which I find difficult to interpret (in terms of change in the independent variable corresponds with variation in the dependent variable). More specifically, I have some coefficients which are pretty large (<3.00), although standard errors seems normal. My hunch is that this related to the scaling of the dependent and independent variable. Namely, the estimates where I have large coefficients concern covariates that range between 0 and 1 (HHIC is a proportion), while the esitmate (fixed effect) concerns how one unit change in the dependent variable shapes the outcome variable. See the output attached. Jan
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