I think in -gllamm- slang you would need to look at -geq- option that
describes equations explaining the random effects/latent variables.
On 8/28/07, Swati Agiwal <[email protected]> wrote:
> Hello All,
>
> I am using the GLLAMM program in Stata and wanted to know if anyone else is
> using the same for fitting ordinal item response models.
>
> I am fitting a simple one parameter as well as a two parameter item
> response model. I am interpreting the random effect as the latent variable.
> However, I am facing difficulties in introducing other explanatory
> variables into the existing item response models. I have looked through the
> GLLAMM manual and there arent any similar examples.
>
> The current model I am fitting is :
>
> g(P(y{i}{j}<s)) = k{s} - b{i} - n{j}...Model(1)
>
> where g() is the ordinal probit function. y{i}{j} is the ordinal response
> for person j on question/item i. k{s} are category thresholds, b{i} are
> item biases and n{j} are the person specific random intercepts, which I
> interpret as the latent variables. Model(1) is easy to fit in GLLAMM and is
> well documented in Chapter 8 of the GLLAMM manual.
>
> To this i want to incorporate:
>
> n{j}= a*w1{j}+ c*w2{j} + q{j}
>
> That is, the latent variable n{j} is itself a function of person j specific
> characteristics, say education w1{j} and income w2{j} and a new latent
> variable q{j}. a and c are coefficients of the explanatory variables w1 and
> w2.
>
> Thus the final model that i want to fit is :
>
> g(P(y{i}{j}<s)) = k{s} - b{i} - a*w1{j} - c*w2{j} - q{j}...Model(2)
>
> I am using all the items (87 of them) and all the persons (203 of them)
> together is estimating this model.
> If any does have an idea or suggestion as to how to fit Model(2) in GLLAMM
> please do let me know.
>
> Thanks in advance,
> Swati
>
>
>
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>
--
Stas Kolenikov, also found at http://stas.kolenikov.name
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*
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