I'm learning a bit about exploratory factor analysis working with some datasets, but some doubts came in mind.
I'm using this data to practice turtle data. I have two groups of turtles: male and female and measures of 3 variables length, width and height.
The output of factor model using R is
Uniquenesses:
length width height
0.017 0.026 0.053
Loadings:
Factor1
length 0.991
width 0.987
height 0.973
Factor1
SS loadings 2.904
Proportion Var 0.968
The degrees of freedom for the model is 0 and the fit was 0
The above result, shows that a single factor is good enough to describe the covariance structure of the data. Then I calculated the factor scores using the regression option and did a plot of it
(1) Clearly there are differences between males and females, but what conclusions could I draw from the scores?
(2) In this case I have a single factor and can't do a biplot, how I could check what variable is the one that most differentiates males and females?
(3) I used some rotation (varimax and promax) and results were the same that I get without rotation. Is it because I have a single factor?
(4) Is there any difference between use Regression Scores and Barlett scores?