Clustering Binary Variables in Subscales Using an Extended Rasch Model and Akaike Information Criterion

DOI10.1081/STA-120030149zbMath1114.62368MaRDI QIDQ3155324

No author found.

Publication date: 14 January 2005

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to psychology (62P15)

Related Items (3)

Isotonic regression with unknown permutations: statistics, computation and adaptation ⋮ Unmixing Rasch scales: how to score an educational test ⋮ A class of multidimensional IRT models for testing unidimensionality and clustering items

Cites Work

• Sufficient statistics and latent trait models
• Loglinear multidimensional IRT models for polytomously scored items
• The theoretical detect index of dimensionality and its application to approximate simple structure

This page was built for publication: Clustering Binary Variables in Subscales Using an Extended Rasch Model and Akaike Information Criterion