I thought that factor analysis would give me an orthogonal set of data on which I could do the multivariate analysis. Yes, it will give orthogonal data (new predictors) on which you could do the analysis. Our point is that there are better techniques. Why? Because both Factor Analysis and PCA determine the orthogonal vectors without regard to their ability to predict Y; you can see examples in the literature, or conjure up your own, where the first (and second and third and ...) vectors are nearly uncorrelated with the Y variables and thus are not predictive of Y. PLS avoids this possibility, if there are vectors of the predictor variables that are "predictive" of Y. PLS also gives you orthogonal linear combinations of your X variables, but does so in a way that they need to be "predictive" of Y. And while I realize you can't explain your project in detail, your example with salary reverted to using the individual predictors by themselves, throwing us off the trail. This is not what PLS or Factor Analysis or PCA do. They create orthogonal linear combinations of your predictors, and so your new orthogonal predictors are a weighted sum of ALL of your predictors. It is not clear from your writing that you understand this aspect of what these procedures do. ... View more