Be sure you are using the PARAM=GLM or PARAM=REF option in the CLASS statement. Note that the computation of the odds ratio depends on the model and the parameterization used for categorical (CLASS) predictors. For detailed explanation, see this note. Also see this note.  ... View more

Thanks! ... View more

Sure.  I actually started with a polychoric transformation prior to the EFA, to help linearize the responses.  This helped the questions group properly, although one of the questions ended up grouping with another factor, which is understandable in context.   * Polychoric Transformation, Step 1; proc prinqual data=phys out=phys_poly4 plots=transformation maxiter = 100 standard scores n=4 replace; where outmiss < 4; transform monotone (knowi1-knowi5 hes1 hesi2 hesi3 stigi1-stigi3 outi1-outi4); id record_ID; run;   * Exploratory Factor Analysis, Step 2; proc factor data = phys nfactors = 4 rotate = promax method = ml priors = smc  scree reorder msa score Heywood residuals out=phys_EFA1 outstat=phys_stat plots=all; where outmiss < 4; var Tknowi2-Tknowi4 Thes1 Thesi2 Thesi3 Tstigi1-Tstigi3 Touti1-Touti4; pathdiagram notitle arrange=grip label=[Factor1="Pt Pain Care" Factor2="Knowledge/ Experience" Factor3="Stigma" Factor4="Hesitancy"]; run;   ... View more

Indeed, I could see that working.    Keep in mind you need the priors=smc option in proc factor to make it a factor analysis; otherwise the default is principal component.  ... View more