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Re: st: SV: Latent variable DVs in gllamm


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: SV: Latent variable DVs in gllamm
Date   Wed, 11 Feb 2009 12:21:07 -0600

You expand the data to create a single variable, let's call it
-response-. For each original observation identified by variable
called -id-, then you will need to create three dummy variables, say
-item1-, -item2- and -item3-, to keep track of where that response
came from. Then your model will essentially look like

eq response : item1 item2 item3
eq latent : all regressors for the latent variable

gllamm response, id( id ) eq( response ) geq( latent ) link() family()

The -eq- options says what is the pattern of the loadings of the
latent variable on the observed ones, and -geq- says what is the
structural model for that latent variable -- in your case, your
regression.

On 2/11/09, Rahsaan Maxwell <rahsaan@polsci.umass.edu> wrote:
> Thanks for the response.  I see that I need to expand the data but I'm not sure
>  how to then combine the three observed variables into one multivariate
>  response?
>
>  From what I can tell, section 8.4 in the gllamm manual is the closest to my
>  situation but it looks like they are regressing y on the different items that
>  comprise the latent variable, whereas I want to be regressing the items of the
>  latent variable on other variables.  Or is that a necessary first step to get
>  the factor loadings?  In which case how do I define y?  That doesn't seem to be
>  addressed in the manual.
>
>  Thanks,
>
>
>  -Rahsaan
>
>
>  Quoting Mads Meier Jæger <Mads@sfi.dk>:
>
>  > Rahsaan,
>  >
>  > This can be done in gllamm by expanding the data so that the three observed
>  > variables (trust in police, etc.) form a multivariate response for each
>  > individual, and then by using a random effect to model the latent DV. You
>  > need to expand the data to "trick" gllamm into treating the three observed
>  > variables as a multivariate response, see chapter 4 and 8 in the gllamm
>  > manual. It should be easy enough also to include more random effects to
>  > account for additional multilevel (neighbourhood, etc.) clustering. You could
>  > also use eqs() to model covariate effects on the latent DV.
>  >
>  > Mads
>  >
>  >
>  >
>  > -----Oprindelig meddelelse-----
>  > Fra: owner-statalist@hsphsun2.harvard.edu
>  > [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Rahsaan Maxwell
>  > Sendt: 11. februar 2009 06:49
>  > Til: statalist@hsphsun2.harvard.edu
>  > Emne: st: Latent variable DVs in gllamm
>  >
>  > Does anyone know if it is possible to build a multi-level mixed effects model
>  > with a latent DV using glamm?
>  >
>  > I have a unobserved response variable (political trust) that is comprised of
>  > three observed variables (trust in police, trust in government, trust in
>  > politicians).  I am trying to run a multi-level model with fixed effects IVs
>  > at
>  > the individual and neighborhood level and a random intercept for the
>  > neighborhood grouping level.
>  >
>  > However, I cannot figure out how to construct the DV as a latent variable.  I
>  > have been trying to use the eqs function but that does not seem to work.
>  >
>  > Thanks,
>  >
>  > -Rahsaan
>  >
>  >
>  > Rahsaan Maxwell, Ph.D.
>  > Assistant Professor
>  > Department of Political Science
>  > University of Massachusetts, Amherst
>  >
>  > Postdoctoral Fellow
>  > Transatlantic Academy
>  > German Marshall Fund of the United States
>  > http://rahsaanmaxwell.googlepages.com
>  > *
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>  >
>  >
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>  >
>
>
>  Rahsaan Maxwell, Ph.D.
>  Assistant Professor
>  Department of Political Science
>  University of Massachusetts, Amherst
>
>  Postdoctoral Fellow
>  Transatlantic Academy
>  German Marshall Fund of the United States
>  http://rahsaanmaxwell.googlepages.com
>  *
>  *   For searches and help try:
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>  *   http://www.stata.com/support/statalist/faq
>  *   http://www.ats.ucla.edu/stat/stata/
>


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

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