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Re: st: Factor Analysis with ordinal and binary variables


From   "stefan.duke@gmail.com" <stefan.duke@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Factor Analysis with ordinal and binary variables
Date   Wed, 3 Jun 2009 12:12:14 +0200

Hi ya'll,
thanks for you advice, so I know now where to look.
Currently, I don't have a particular problem. I was just curious where
to look and how to proceed when encountering such a mix of variables.
Best,
Stefan


On Tue, Jun 2, 2009 at 10:04 PM, Robert A Yaffee <bob.yaffee@nyu.edu> wrote:
> On this issue,  the polyserial and polychoric correlations
> can be used for binary and ordinal variables, respectively,
> as input to a factor analysis, according to Joreskog and
> Sorbom, who did the research back in the 1980s.
>    Bengt Muthen has also studied this matter.
> Both teams have incorporated their findings into their
> structural equation modeling packages.
>      - Bob
>
>
> 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: Robert A Yaffee <bob.yaffee@nyu.edu>
> Date: Tuesday, June 2, 2009 3:34 pm
> Subject: Re: st: Factor Analysis with ordinal and binary variables
> To: statalist@hsphsun2.harvard.edu
>
>
>> 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: "stefan.duke@gmail.com" <stefan.duke@gmail.com>
>> Date: Tuesday, June 2, 2009 7:51 am
>> Subject: st: Factor Analysis with ordinal and binary variables
>> To: statalist@hsphsun2.harvard.edu
>>
>>
>> > 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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