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st: RE: Fitting Latent Mixed Response Models in GLLAMM


From   "Alpheus Bongo C Adi" <bongo.adi@developmentsociety.org>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Fitting Latent Mixed Response Models in GLLAMM
Date   Tue, 11 Jul 2006 09:53:01 +0000

Relating to the issues below, I will appreciate hints on how to accommodate dichotomous and polytomous responses in a gllamm model.  Precisely, I am working on a latent variable model and my structural equation contains variables of the dichotomous response type as well as others of ranked responses.  Furthermore, the model is of two levels - households nested in regions.  I specified a random intercept, rather than a random coefficient, effect to allow intercepts to varry at regional level.    I have not succeeded in finding previous work combining both approaches as most have restricted their variable choice to the dichotomous response types.   Any suggestion on the best way to proceed is most welcome.  Thanks.

abc. Adi




----------------------------------------
> Date: Tue, 11 Jul 2006 08:33:33 +0100
> From: maartenbuis@yahoo.co.uk
> Subject: Re: st: multiply imputed values for outcome
> To: statalist@hsphsun2.harvard.edu
>
> --- "Leslie R Hinkson asked:
>> I have a data set that has multiply imputed values (5) for the
>> outcome variable. I have previously used HLM software to conduct my
>> analysis but I was told that with the new GLM features in Stata 9
>> that it should be possible to do the same in Stata. Unfortunately, I
>> haven't found that way yet. Any thoughts?
>
> --- Austin Nichols answered:
>> If you used HLM, you may want -xtmixed- or -gllamm- (-ssc install
>> gllamm-) but I don't know about the "five plausible values".
>
> The trick with multiple imputation is that you multiple plausible
> values for each missing value, thus creating multiple "complete"
> datasets. Next you estimate your model just as if you had a real
> complete dataset on each "complete" dataset. In your case you would
> than have five sets of estimates. The final point estimates are the
> means over these five sets of estimates and the final standard error is
> made with two components: the mean variance (squared standard error) of
> each estimate and the variance between sets. The formulas can be found
> on: http://www.stat.psu.edu/~jls/mifaq.html#howto . J. B. Carlin, N.
> Li, P. Greenwood, & C. Coffey have writen tools for analyzing multiple
> imputed datasets, that implement these formulas  (-findit mifit-), but
> I don't think they include -xtmixed- or -gllamm-. However, these
> formulas are pretty simple and can be implemented by hand if needs be.
>
> --- "Leslie R Hinkson also asked:
>> Also, is it possible to conduct standard linear regression analysis
>> with multiple plausible values for the dependent variable using
>> Stata 9?
>
> -mifit- can handle standard linear regression analysis.
>
> HTH,
> Maarten
>
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting adress:
> Buitenveldertselaan 3 (Metropolitan), room Z214
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>
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