Nowadays, the educational context is one of the most important and interesting applicative field of social science. The object of interest is often the relation between student ability and motivation. In the context of social science the multilevel structure and the latent variable models are often encountered. In this work, we present an extension of the multilevel mixture factor models (MMFM) (Riggi & Vermunt, in press, 2011 ; Varriale & Vermunt, in press, 2009), with an application to the Italian school system. These models are a combination of Factor Analysis and Latent Class. The purpose of the MMFA is multiple: the teacher classification according to class motivation structure, and the analysis of home and teacher influences on pupil reading motivation.
(2011). Mixture factor model for hierarchical data structure and applications to the italian educational school system. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2011).
Mixture factor model for hierarchical data structure and applications to the italian educational school system
RIGGI, DANIELE
2011
Abstract
Nowadays, the educational context is one of the most important and interesting applicative field of social science. The object of interest is often the relation between student ability and motivation. In the context of social science the multilevel structure and the latent variable models are often encountered. In this work, we present an extension of the multilevel mixture factor models (MMFM) (Riggi & Vermunt, in press, 2011 ; Varriale & Vermunt, in press, 2009), with an application to the Italian school system. These models are a combination of Factor Analysis and Latent Class. The purpose of the MMFA is multiple: the teacher classification according to class motivation structure, and the analysis of home and teacher influences on pupil reading motivation.File | Dimensione | Formato | |
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