Stefan,
Karl Joreskog and Dag Sorbom
analyzed the problem back in the 1980s and found
that you could use polyserial and polychoric correlations
for a factor analysis of dichotomous or ordinal variables.
If the ordinal variables have at least 15 levels they can
be treated as continuous.
They have incorporated this finding in their program for
structural equation modeling.
Regards,
Bob Yaffee
Bengt Muthen may have also written
on this subject in the 1980s or early 1990s.
Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University
NSF grant:
http://www.colorado.edu/ibs/es/nuclear_disaster_risk/principal_investigators.html
Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
CV: http://homepages.nyu.edu/~ray1/vita.pdf
----- Original Message -----
From: "[email protected]" <[email protected]>
Date: Tuesday, June 2, 2009 7:51 am
Subject: st: Factor Analysis with ordinal and binary variables
To: [email protected]
> Hello,
>
> I have question concerning factor analysis on variables with different
> measurement levels.
>
> The questionnaire consists of binary and ordinal variables. If I would
> have just binary variables, I would use the tetrachoric correlation
> coefficients. For the ordinal I assume approx. normality and then use
> the ordinary factor analysis capability.
>
> But what do I do when I have both variables? Is it an option to
> construct the variance-covariance matrix by hand? And what do I take
> for the correlation between binary and ordinal?
>
> Maybe is there a model class which takes care of that, that yields
> similar outcomes as factor analysis but can deal with such kind of
> data (e.g. correspondence analysis).
>
> I am grateful for every hint.
>
> Best,
> Stefan
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