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The Stata module "Geekel2d"
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.
Type "findit geekel2d" or "ssc install geekel2d" directly from your Stata browser.
Syntax (version 4.3)
geekel2d varlist [, coef(matrix) nbit(#) critconv(#) ll quad(#) novar ]
This program requires an access to the following program(s):
- coef(matrix): matrix containing the coeficients B wich rely the items and the latent traits. Each row represents an item and there is one or two columns, in function of the supposed number of latent traits. The coefficient are choosen, in general, among the first integers, but geekel2d allows using real coefficients. By default, the Rasch model is supposed (the matrix defined by coef is a vector of 1).
- nbit(#): defines the maximal number of iterations in the estimation algorithm. By default, this number is fixed to 30
- critconv(#): 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
- ll: estimates the marginal log-likelihood and the Akaike Information Criterion (AIC) by Gauss-Hermite quadratures
- quad(#): defines the number of nodes of quadratures (12 by default)
- novar: avoids to compute the standards errors of the estimators
geekel2d item1 item2 item3 item4
. geekel2d itemA2 itemA5 itemB3 itemB6 , coef(B) nbit(50) critconv(1e-30)