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Data Analytic Methods for Latent Partially Ordered Classification Models

Data Analytic Methods for Latent Partially Ordered Classification Models SummaryA general framework is presented for data analysis of latent finite partially ordered classification models. When the latent models are complex, data analytic validation of model fits and of the analysis of the statistical properties of the experiments is essential for obtaining reliable and accurate results. Empirical results are analysed from an application to cognitive modelling in educational testing. It is demonstrated that sequential analytic methods can dramatically reduce the amount of testing that is needed to make accurate classifications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society Series C (Applied Statistics) Oxford University Press

Data Analytic Methods for Latent Partially Ordered Classification Models

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References (24)

Copyright
© 2002 Royal Statistical Society
ISSN
0035-9254
eISSN
1467-9876
DOI
10.1111/1467-9876.00272
Publisher site
See Article on Publisher Site

Abstract

SummaryA general framework is presented for data analysis of latent finite partially ordered classification models. When the latent models are complex, data analytic validation of model fits and of the analysis of the statistical properties of the experiments is essential for obtaining reliable and accurate results. Empirical results are analysed from an application to cognitive modelling in educational testing. It is demonstrated that sequential analytic methods can dramatically reduce the amount of testing that is needed to make accurate classifications.

Journal

Journal of the Royal Statistical Society Series C (Applied Statistics)Oxford University Press

Published: Jul 30, 2002

Keywords: Analysis of experiments; Cognitive modelling; Model fitting; Partially ordered set; Sequential classification

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