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{* 2july2005}{...}
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help for {hi:geekel2d}{right:Jean-Benoit Hardouin}
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{title:Estimation of the parameters of undimensional and bidimensional IRT models}

{p 8 14 12}{cmd:geekel2d} {it:varlist} [{cmd:,} {cmdab:coef}({it:matrixname}) {cmdab:nbit}({it:#}) {cmdab:critconv}({it:#}) {cmdab:ll} {cmdab:quad}({it:#}) {cmdab:novar}]

{p 8 14 12}{it:varlist} is a list of two existing dichotomous variables or more.

{title:Description}

{p 4 8 2}{cmd:geekel2d} estimates, by Generalized Estimating
Equations (GEE), the parameters of the model defined by Kelderman (1994) with
one or two dimensions and dichotomic items. This model includes the Rasch model
and the One Parameter Logistic Model (OPLM) for the unidimensional models, the
Multidimensional Generalized Rasch Model (MGRM) and the Multidimensional
Completely Sufficient Rasch Model (MMSRM) for the two-dimensional models.

{title:Options}

{p 4 8 2}{cmd:coef} is the name of a matrix which contains the coeficients B. This
matrix relies the items and the latent traits. Each row represents an item and
there is  as many colmuns than the supposed number of latent traits (one or two). 
The coefficients are choosen, in general, among the first intergers, but 
{cmd:geekel2d} allows using real coefficients. By default, the Rasch model is
supposed (the matrix {cmd: coef} is a vector of 1).

{p 4 8 2}{cmd:nbit} defines the maximal number of iterations in the estimation
algorithm. By default, this number is fixed to 30.

{p 4 8 2}{cmd:critconv} is the value of the convergence criterion, calculated
as the square of the cross-product of the vector containing the difference
between two successive iterations of the parameters estimations. By default,
this criterion is fixed to 1e-15.

{p 4 8 2}{cmd:ll} estimates the marginal log-likelihood and the Akaike
Information Criterion (AIC) by Gauss-Hermite quadratures.

{p 4 8 2}{cmd:quad} defines the number of nodes of quadratures.

{p 4 8 2}{cmd:novar} avoids to compute the standards errors of the estimators (faster).

{title:Remarks}

{p 4 8 2}For detailed informations on the Kelderman model, see Kelderman and
Rijkes (1994) or Adams and al. (1997).

{p 4 8 2}{cmd:geekel2d} don't allows using of polytomous items.

{p 4 8 2}The {cmd:ghquadm} Stata module is needed (use {cmd:findit ghquadm} to obtain it).


{title:Example}

{p 4 8 2}{cmd:. geekel2d item1 item2 item3 item4} /*Rasch model*/

{p 4 8 2}{cmd:. matrix B=(1,0\1,0\0,1\0,1)}

{p 4 8 2}{cmd:. geekel2d item1 item2 item3 item4 , coef(B) nbit(50) critconv(1e-30)}


{title:References}

{p 4 8 2}Kelderman H. and Rijkes C. P. M., Loglinear multidimensional IRT models for polytomously scored items. {it:Psychometrika}, 1994, {it:59}, 149-176.

{p 4 8 2}Adams R. J., Wilson M. R. and Wang W., The multidimensional random coefficient multinomial logit model. {it:Applied Psychological Measurement}, 1997, {it:21}, 1-23.

{title:Author}

{p 4 8 2}Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte
Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France. You can contact the
author at 
{browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org}
and visit the websites {browse "http://anaqol.free.fr":AnaQol} 
and {browse "http://freeirt.free.fr":FreeIRT}
