Dissertation/Thesis Abstract

Cluster analysis for cognitive diagnosis: Theory and applications
by Chiu, Chia-Yi, Ph.D., University of Illinois at Urbana-Champaign, 2008, 109; 3337778
Abstract (Summary)

Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, K-means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice.

Indexing (document details)
Advisor: Douglas, Jeffrey
Commitee:
School: University of Illinois at Urbana-Champaign
School Location: United States -- Illinois
Source: DAI-A 69/11, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Educational psychology, Quantitative psychology
Keywords: Cluster analysis, Cognitive diagnosis, Latent class analysis
Publication Number: 3337778
ISBN: 9780549912057
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