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Re: st: Factor Analysis with ordinal and binary variables


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Factor Analysis with ordinal and binary variables
Date   Wed, 3 Jun 2009 13:32:25 -0500

I wrote -polychoric- package some while ago... never perfectly
polished it though, and it breaks down when overloaded with large
amounts of poor data. Joreskog's ideas can be found on LISREL website
-- google Joreskog+ordinal.

On Wed, Jun 3, 2009 at 5:12 AM, stefan.duke@gmail.com
<stefan.duke@gmail.com> wrote:
> 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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>
> *
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>



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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