Lancaster, Gillian and Green, Michael (2002) Latent variable models for categorical data. Statistics and Computing, 12 (2). pp. 153-161. ISSN 0960-3174
Full text not available from this repository.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: | 20 Feb 2013 10:26 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/11533 |
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