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Latent variable models for categorical data

Lancaster, Gillian and Green, Michael (2002) Latent variable models for categorical data. Statistics and Computing, 12 (2). pp. 153-161. ISSN 0960-3174

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Abstract

Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared to determine their effectiveness.

Item Type: Article
Journal or Publication Title: Statistics and Computing
Uncontrolled Keywords: latent variable ; item-response analysis ; linear score model ; empirical Bayes estimate ; linear score ; log-bilinear model
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 11533
Deposited By: Mrs Yaling Zhang
Deposited On: 02 Sep 2008 09:57
Refereed?: Yes
Published?: Published
Last Modified: 09 Oct 2013 15:14
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/11533

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