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st: Some questions on IRT using GLLAMM


From   Marcello Pagano <pagano@hsph.harvard.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Some questions on IRT using GLLAMM
Date   Fri, 11 Aug 2006 14:01:59 -0400

Prathiba Natesan wrote:

Dear Statalist:

I have been working on GLLAMM for some research projects . I find the program very handy and useful particularly when it comes to Item Response Theory.

However, I have some specific questions about applying GLLAMM to IRT. I would appreciate it if someone could get back to me with answers to these questions. I am trying to estimate the difficulty, discrimination and threshold values for a polytomous dataset. I have run the gllamm with item specific thresholds and now have the parameters. I need some assistance in identifying the parameters. I have _cut11 for d2-d6 and cons,..., _cut14 for d2-d6 and cons and the level 2 coefficients for random effect 1. Here are my specific questions:

1. There should be only 3 thresholds (for 4 categories). So how do I tell which of the parameters are the threshold, difficulty and discrimination parameters?
2. I constrained the standard deviation of the latent variable using the constraint "def 1 [id1_1]d1=1" syntax and then estimated the oprobit model again with the constraint and the frload option. Are the level 2 coefficients I have now, the discrimination parameters?

On a different note:

1. For a polytomous dataset, what causes the estimates to not converge? Can something be done about it? Does it say something about the data?

2. When the program says, flat or discontinuous region encountered, I understand that there is trouble in evaluating the integral due to this. But why does this happen? Or rather, what do we do in such situations?

3. This is a very basic question. When I have certain factors (from the factor analysis), should I perform GLLAMM for each factor (GLLAMM on items belonging to this factor) individually? I am doing this because I think that it would be
easier to tackle unidimensional models than multidimensional models. Will there be any problems in doing this (I am thinking analogous to performing many ANOVAs instead of one MANOVA which inflates the type-I error rate)?


Any advice would be of great help. Thanks

Prathiba
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