Dear Sophia:
Thank you for your response. I have read chapter 4 as you suggested,
but I am a little unclear as to the capability of this GLLAMM program to
create scoring coefficients after running the factor analysis.
Chihmao.
-----------------------------------------------------
Chihmao Hsieh
John M. Olin School of Business
Washington University
Box 1133, One Brookings Drive
St. Louis, MO 63130
Email: [email protected]
http://students.olin.wustl.edu/~hsieh
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Sophia
Rabe-Hesketh
Sent: Monday, September 08, 2003 1:47 PM
To: [email protected]
Subject: st: Re: Factor analysis of count data
Dear Chihmao Hsieh,
You can estimate confirmatory factor models for count-data
(and many other response types) by maximum likelihood
using gllamm. gllamm uses adaptive quadrature to evaluate
the likelihood. If your model involves only one or two factors, it
shouldn't take that long to estimate the model.
For more information on gllamm, see
http://www.iop.kcl.ac.uk/IoP/Departments/BioComp/programs/gllamm.html
You may find Chapter 4 of the gllamm manual useful.
Best regards,
Sophia
----- Original Message -----
From: "Chih-Mao Hsieh" <[email protected]>
To: <[email protected]>
Sent: Monday, September 08, 2003 6:13 PM
Subject: st: Factor analysis of count data
> Dear Statalisters,
>
>
> While work has been done on developing factor analytical methods for
non-normal data like categorical variables, less has been done until
recently on factor analysis of count data. Is there any technical
information on how STATA might handle factor analysis of count data
(especially code)? I've looked at the manuals but have not found good
stuff. I expect my observations numbering in the tens of thousands, and
significant levels of overdispersion.
>
> Many thanks for any advice.
>
>
> Chihmao Hsieh
> PhD candidate, Strategy
> Olin School of Business
> Washington University in St. Louis
>
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