I want to create factors from various binary items. Using the polycor package (Fox, 2006) and R-Essentials I created a tetrachoric correlation matrix in SPSS. The items are all exploratory, so I followed recommendations from Russell (2002) and performed a PAF with a Promax rotation on the data (instead of a PCA with Varimax, which already has been discussed here).
I now have a pattern matrix and a structure matrix. As the factors correlate with each other, some suggest using the structure matrix is best for computing factor scores (e.g. Gorsuch, 1983). But, as far as I could figure it out, there is no clear guideline yet (e.g. Grice, 2001). I know about the methods that exist to create scores in general (as discussed in the forum here) and I know some methods use the pattern, others the structure matrix.
Still, I am not sure which method is the best for creating factor scores from a PAF with Promax rotation that is based on tetrachoric correlations. Unfortunately, I could not find any recommendations for that in the internet... in the forum I found that for a PCA based on tetrachoric correlations classic methods won't do properly and one would have to use special estimations such as expected or maximum a posteriori (EAP, MAP). Is that the same for a PAF? What would be the correct way to compute factor scores? Is there a possibility to do that in SPSS?
Thanks in advance!
Fox, J. (2006). Teacher’s corner: Structural Equation Modeling With the sem Package in R. Structural Equation Modeling: A Multidisciplinary Journal, 13(3), 465–486. https://doi.org/10.1207/s15328007sem1303_7
Gorsuch, R. L. (1983). Factor Analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, ISBN: 978-0898592023
Grice, J. W. (2001). Computing and evaluating factor scores. Psychological methods, 6(4), 430.
Russell, D. W. (2002). In Search of Underlying Dimensions: The Use (and Abuse) of Factor Analysis in Personality and Social Psychology Bulletin. Personality and Social Psychology Bulletin, 28(12), 1629–1646. https://doi.org/10.1177/014616702237645