My data set consists of students (CourseID variable), their test
questions (Question) and 0/1 indicator of whether they've answered the
question correctly. The data are in the long form appropriate for
GLLAMM. I am modeling the questions as fixed/parameters of the model,
and students as random factors. Here's what I have:
* generate dummy variables for questions
xi i.Question, noomit
* specify the equation for the random factor: the dummy variables from
the previous command
eq diff : _IQ*
* variance of the random factor identification
constr 55 [Cou1_1]_IQuestion_4 == 1
* call to gllamm: 2-parameter IRT
gllamm Correct _IQ* , fam( bin ) link( logit ) i( CourseID ) eq( diff
) nocons constr(55)
I am specifying -nocons- so that each question has its own intercept
(sensitivity times difficulty, in IRT terms), and the factor loadings
from -eq()- option should give me the sensitivity.
-gllamm-, however, has trouble converging. Does it have to do with
empirical underidentification? Do I need to search for a better
identifying variable? Question 4 above was the first on the list that
had any variability; everybody answered the first three questions. It
is probably not a terrific question to give identification, too: only
a couple people missed it. My sample sizes are not terrific, either: I
have about 40 students and about 30 questions. And there are lots of
easy questions that were missed by one or two or three students only.
If I have only one student who missed a question, then I probably
won't be able to identify two parameters for that question, right?
Finally, since we are talking about random effects logit in Stata, is
there any way to run this with -xtmelogit-? It should be faster, at
least.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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