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Abstract Details
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Activity Number:
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630
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #303266 |
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Title:
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Statistical Analysis for Diagnostic Classification Models
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Author(s):
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Gongjun Xu*+ and Jingchen Liu and Zhiliang Ying
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Companies:
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Columbia University and Columbia University and Columbia University
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Address:
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, New York, NY, 10027, US
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Keywords:
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Diagnostic classification model ;
Q-matrix ;
self-learning ;
consistency
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Abstract:
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Diagnostic assessment has recently gained particular interest in psychometrics, education study and many other disciplines for cognitive assessment purposes. Most models for cognitive diagnosis start with specification of the Q-matrix, which plays an quintessential role in diagnosis assessment by representing the item-attribute structure. In literature, mostly the Q-matrix is based on prior research and provided by expert panels. This paper will provide an approach to estimate the underlying Q-matrix. In addition, the corresponding dimension reduction problem will also be considered.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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