I ran a confirmatory factor analysis in Stata using the
sem command. The idea was to extract two factors: TRUST, extracted from four variables measuring the trust towards different groups of people (friends, strangers etc.) on a scale from 1 to 7; SELF-RELIANCE, extracted from four variables measuring the consent of the test-subject regarding statements declaring socio-economic self-reliance as a virtue in our society on a scale from 1 to 4. I intend to use SELF-RELIANCE as a dependent variable in a multiple linear regression. TRUST will be one of the predictors in said regression.
I get pretty decent factor scores, have a decent model fit, everything seems fine. But then I try to generate the factor variables TRUST and SELF-RELIANCE from the PCA using
predict <varname>, latent(factorname)
I expected the factors to have the same scale as their endogenous variables (1-7; 1-4), but they have a range from -2.944997 to 1.157046 (in the case of TRUST). What does that even mean? Did I use the wrong predict option?
My professor said the variables with the constrained factor scores in
sem serve as the scale template for the factors, so I don't understand why my generated variables would have such a weird scale. Does anybody know what this means?
Here's my syntax for the CFA (names are in German, as am I, so I'm sorry if I formulated something wrong):
sem (vertrverw vertrfreund vertrbek vertrfremd <- VERTRAUEN) (einkuntmot ranguntakz sozuntger <- EIGENV) estat gof, stats(all) estat mindices predict VERTRAUEN, latent(VERTRAUEN) predict EIGENV, latent(EIGENV)