Estimation of Item Parameters

KB Christensen

Estimation of Item Parameters

Biostatistisk afdeling

Abstract

This chapter describes item parameter estimation in dichotomous and polytomous Rasch models. It discusses three different ways of estimating item parameters: (1) conditional maximum likelihood (CML) estimation, (2) pairwise CML estimation and (3) marginal maximum likelihood (MML) estimation. Much research interest has focused on the possibility of doing marginal inference without having to make distributional assumptions about the latent variable and the chapter touches upon the extended likelihood function. Reduced rank parameterization is discussed next. Finally, the chapter illustrates the methods using data from a study validating a French translation of the Diabetes Health Profile (DHP).

Originalsprog	Udefineret/Ukendt
Titel	In:Rasch Models in Health
Redaktører	Mesbah M Christensen KB Kreiner S
Antal sider	13
Forlag	Wiley
Publikationsdato	4 mar. 2013
Sider	49-61
Status	Udgivet - 4 mar. 2013

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Citationsformater

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AB - This chapter describes item parameter estimation in dichotomous and polytomous Rasch models. It discusses three different ways of estimating item parameters: (1) conditional maximum likelihood (CML) estimation, (2) pairwise CML estimation and (3) marginal maximum likelihood (MML) estimation. Much research interest has focused on the possibility of doing marginal inference without having to make distributional assumptions about the latent variable and the chapter touches upon the extended likelihood function. Reduced rank parameterization is discussed next. Finally, the chapter illustrates the methods using data from a study validating a French translation of the Diabetes Health Profile (DHP).

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