Re: factor analysis on dichotomous data

Posted by Kirill Orlov on Dec 21, 2015; 11:59am
URL: http://spssx-discussion.165.s1.nabble.com/factor-analysis-on-dichotomous-data-tp1081162p5731159.html

Mirjam
Allow me to meddle.
For binary variables CATPCA is equivalent to standard PCA. Because a dichotomous numeric variable can be quantified monotonically but in a linear fashion. So, you don't have to use CATPCA unless some of your variables are tritomous or more.

The slight difference between PCA and CATPCA you are observing with all variables binary are merely due to the fact that CATPCA is iterative and uses just slightly different standardization. You may ignore the difference.

For finding of varimax error - I can't say anything.

Are you doing Factor analysis or PCA? You may do PCA on binary variables. However, it is not quite proper to do linear factor analysis on binary data. See my response http://stats.stackexchange.com/a/16335/3277 and http://stats.stackexchange.com/a/186026/3277

21.12.2015 13:36, Mirjam пишет:

Hi Hector

The answer you gave Kaat is also very helpful for me. I have 2 additional
questions:

1. Do you have any literature at hand I could cite concerning the fact that
for authentic binary variables classical factor analysis (not CATPCA) should
be used?

2. I performed a factor analysis and a CATPCA with the same data (13 binary
variables, 3 components) using SPSS 23. The resulting component loadings are
practically the same with classical factor analysis as with CATPCA, but with
CATPCA (not with classical factor analysis) the varimax-rotation fails
(error message "Rotation failed to converge in 5 iterations. (Convergence =
.000)."). Do you have an explanation for this difference?

Thank you!
Mirjam



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