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ERIC Number: EJ1174579
Record Type: Journal
Publication Date: 2018
Pages: 12
Abstractor: As Provided
ISSN: ISSN-1536-6367
A Generalized Approach to Defining Item Discrimination for DCMs
Henson, Robert; DiBello, Lou; Stout, Bill
Measurement: Interdisciplinary Research and Perspectives, v16 n1 p18-29 2018
Diagnostic classification models (DCMs, also known as cognitive diagnosis models) hold the promise of providing detailed classroom information about the skills a student has or has not mastered. Specifically, DCMs are special cases of constrained latent class models where classes are defined based on mastery/nonmastery of a set of attributes (or "facets"). In addition to identifying an examinee's mastery profile, DCMs provide student information that can be used to describe the quality of an item, or the item discrimination. This paper discusses a unified item and test discrimination approach for identifying good and bad DCM items for polytomous models that is on an interpretable scale. Furthermore, this index is defined in a way such that for dichotomous DCMs, such as the DINA it reduces to traditional measures of item discrimination. Finally, using a simulation study, this index is shown to be related to both Kullback-Liebler-based indices and correct classification rates.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: 0918552; 0920242; 0815065; R305A100475; R305D140023