Thanks for your help. I'm a little confused, because you say on the one hand that a polychoric transformation would confuse the interpretation, but on the other hand that treating a Likert scale as a continuous variable in EFA/CFA/SEM is useful. There is a SAS procedure which does polychoric transformations of the answers simply, in addition to providing the polychoric correlation matrix: proc prinqual data=op out=op_prinqual3 plot=all maxiter = 100 standard scores n=3 replace; transform monotone (Dis1-Dis2 Know1-Know4 Yrs4gp OpFreq Stigma1-Stigma8); * Maxiter: maximum iternations (default=30); * standard: Standardize output to Variance = 1 N=3 means make 3 axes; * replace: Replace original values; * scores: outputs principal component scores; * Transform monotone for ordinal data; * Transform opscore for nominal data; run; So this transforms the Likert answers from the flat 1, 2, 3, 4, 5 to 1.12, 2.28, 2.78, 4.51, 4.94 for example, with different numbers for each question. It's not the correlation matrix, which is also produced. Then, I would input the transformed values into the EFA - something along the lines of this: proc factor data=op_p3 method=ml rotate=promax corr msa scree residuals preplot plot; var DisT1-DisT2 KnowT1-KnowT4 Yrs4gpT OpFreqT StigmaT1-StigmaT8; * DisT etc. are the new variables and answers from the polychoric transformation; run; From here, assuming they're interretable and grouping as expected, the resultant factors would be used in a General Linear Model analysis. This still allows factor scores to be produced, and I don't think it messes up interpretation at all. Obviously, interpretation is everything for us. Do you think this resolve the interpretation (and other) issues?
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