Higher-Order Latent Trait Models for Cognitive Diagnosis

Autor/inn/en	de la Torre, Jimmy; Douglas, Jeffrey A.
Titel	Higher-Order Latent Trait Models for Cognitive Diagnosis
Quelle	In: Psychometrika, 69 (2004) 3, S.333-353 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/BF02295640
Schlagwörter	Cognitive Tests; Diagnostic Tests; Markov Processes; Monte Carlo Methods; Item Response Theory; Models; Cognitive Ability + Suchen Sie Ihr Suchwort? Kognitiver Fähigkeitstest; Diagnostic test; Diagnostischer Test; Markowscher Prozess; Monte-Carlo-Methode; Item-Response-Theorie; Analogiemodell; Denkfähigkeit
Abstract	Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly defined latent trait resembling the [theta] of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. (Author).
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Update	2017/4/10