Data Analytic Methods for Latent Partially Ordered Classification Models
Curtis M. Tatsuoka · Journal of the Royal Statistical Society Series C (Applied Statistics) · 2002
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.