Open AccessHIERARCHICAL GENERAL DIAGNOSTIC MODELS
Author(s) -
Davier Matthias
Publication year - 2007
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.2007.tb02061.x
Subject(s) - multilevel model , latent class model , hierarchical database model , structural equation modeling , item response theory , computer science , scale (ratio) , class (philosophy) , trait , econometrics , statistics , data mining , machine learning , mathematics , artificial intelligence , psychometrics , geography , cartography , programming language
This paper introduces multilevel extensions for the general diagnostic model (GDM) following recent developments on extensions of latent class analysis (LCA) to hierarchical models. The GDM is based on LCA as well as discrete latent trait models and may be viewed as a general modeling framework for confirmatory multidimensional item response models. The multilevel extensions presented in this paper enable one to check the impact of clustered data, such as data for students within schools in large scale educational surveys, on the structural parameter estimates of the GDM. Moreover, the multilevel version of the GDM allows study of differences in skill distributions across these clusters.