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001-es BibID:	BIBFORM033095
Első szerző:	Rijmen, Frank
Cím:	Mixed model estimation methods for the Rasch model / Frank Rijmen, Francis Tuerlinckx, Michael Meulders, Dirk J. M. Smits, Katalin Balazs
Dátum:	2005
Megjegyzések:	Mixed models take the dependency between observations based on the same person into account by introducing one or more random effects. After introducing the mixed model framework, it is explained, by taking the Rasch model as a generic example, how item response models can be conceptualized as generalized linear and nonlinear mixed models. Common estimation methods for generalized linear and nonlinear models are discussed. In a simulation study, the performance of four estimation methods is assessed for the Rasch model under different conditions regarding the number of items and persons, and the degree of interindividual differences. The estimation methods included in the study are: an approximation of the integral over the random effect by means of Gaussian quadrature; direct maximization with a sixth-order Laplace approximation to the integrand; a linearized approximation of the nonlinear model employing PQL2; and finally a Bayesian MCMC method. It is concluded that the estimation methods perform almost equally well, except for a slightly worse recovery of the variance parameter for PQL2 and MCMC
Tárgyszavak:	Bölcsészettudományok Pszichológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban heterogeneity binary data covariates PCA marginal modeling Dimtest Detect
Megjelenés:	Journal of Applied Measurement. - 6 : 3 (2005), p. 273-288. -
További szerzők:	Tuerlinckx, Francis Meulders, Michael Smits, Dirk J. M. Balázs Katalin (1979-) (pszichológus, pszichológia tanár)
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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