If your going to use discriminant analysis you will need a lot of data and
it does assume the predictors are multivariate normal. Generalized linear
models would seem best, particularly in the event that you don't know if
they are ordinal. You can do a multinomial followed by a cummulative logit
model to see if the data are approximately ordinal.
Paul R. Swank, Ph.D.
Professor
Developmental Pediatrics
UT Houston Health Science Center
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]]On Behalf Of Silvert, Henry
Sent: Wednesday, August 15, 2001 10:36 AM
To: 'Melady Preece'; [EMAIL PROTECTED]
Subject: RE: Categorical data Take 2
Why not a discriminant analysis? You might want to develop profiles of
people who go into the 5 different success categories -- although they might
all be equally sucess except for the last one.
Henry M. Silvert Ph.D.
Research Statistician
The Conference Board
845 3rd. Avenue
New York, NY 10022
Tel. No.: (212) 339-0438
Fax No.: (212) 836-3825
> -----Original Message-----
> From: Melady Preece [SMTP:[EMAIL PROTECTED]]
> Sent: Wednesday, August 15, 2001 10:57 AM
> To: [EMAIL PROTECTED]
> Subject: Categorical data Take 2
>
> The discussion of categorical data has got me thinking about a project I
> am about begin. The goal is to use a variety of individual predictors
> (IQ, previous work experience, education, personality) to develop a model
> to predict "success" after a vocational rehabilitation program for
> psychiatric patients.
>
> The problem is how to define success. The current data provides 5
> possible outcomes: Full-time employment, part-time employment, ongoing
> education, volunteer work, or no change. Clearly there is no argument to
> be made that these are linear, but even ordinal is questionable.
>
> I had thought of using a number of logit regression analyses for the
> various outcomes. Or, to use linear regression, rescaling as number of
> hours per week employed, and combining the two employment outcomes;
> scaling education training in terms of program length; combining volunteer
> and no change into number of hours involved in productive activity. That
> would give be three outcomes.
>
> Any suggestions would be much appreciated!
>
> Melady Preece, Ph.D.
>
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