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 <[email protected]> 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 <[email protected]>:
>
> > 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: [email protected]
> > [mailto:[email protected]] På vegne af Rahsaan Maxwell
> > Sendt: 11. februar 2009 06:49
> > Til: [email protected]
> > 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
> > *
> > * For searches and help try:
> > * http://www.stata.com/help.cgi?search
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/help.cgi?search
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
>
> 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:
> * http://www.stata.com/help.cgi?search
> * 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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