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Data analytic methods for latent partially ordered classification models
Author(s) -
Tatsuoka Curtis
Publication year - 2002
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00272
Subject(s) - computer science , latent variable , latent class model , machine learning , data mining , latent variable model , artificial intelligence
Summary. A 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.