1. Final Lecture Integrating factor analysis & logit models

2. Producing & Saving Factor Scores Both R and Stata permit you to save factor scores In R (starting with “complete_data” and renamed CT variables): – CT_set<-data.frame(Eg1, Eg2, Eg3, Hi1, Hi2, Hi3, In1, In2, In3) – CT<-factanal(CT_set, factors=3, scores=c("regression"), rotation="varimax") – print(CT, digits=2, cutoff=.3, sort=TRUE) # we can see the way the CT variables – loaded on each factor – summary(CT$scores) # This shows the 3 factor scores – CT_factors<-CT$scores # Now save the scores for use in the next step – new_CT<-data.frame(complete_data, CT_factors) # now we dump the FS into the dataset

3. The Stata version In Stata, use the drop-down menus and options – But if you want the commands… factor e92_egal_1 e93_indiv1 e94_hier1 e95_egal2 e96_indiv2 e97_hier2 e98_egal3 e99_indiv3 e100_hier3, ml factors(3) rotate predict EgalF IndivF HierF, regression logit e28_gcc EgalF IndivF HierF e1b_edu_rec e2_age e3_gend e124_ideol

4. Logits with Factor Scores R version: – logit1<-glm(gcc ~ Factor1+Factor2+Factor3+gender+age+educ, family=binomial) Stata version: – logit e28_gcc EgalF IndivF HierF e1b_edu_rec e2_age e3_gend e124_ideol

5. Beyond… Reversing the logits – Use Excel Calculate mean values of IVs Multiply by estimated coefficients Calculate L and reverse it =1/(1+exp(-L)) Vary each IV, holding all others at their mean – Calculating “types” Conservative, Male, Hierarch… etc

6. Final Quiz Posted Wednesday, May 6 th Will require a simple factor analysis – Using NS08 dataset Will use factor analysis in logit model Will require interpretation of the logit – Extra credit for reversing the logit Will be due Tuesday, May 12 th, at noon Will be the shortest exam for this class. – Really.