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ERIC Number: ED597219
Record Type: Non-Journal
Publication Date: 2016-Apr-12
Pages: 6
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-
EISSN: N/A
The Probabilistic-Inputs, Noisy Conjunctive Model for Cognitive Diagnosis
Zhan, Peida; Wang, Wen-Chung; Li, Xiaomin; Bian, Yufang
AERA Online Paper Repository, Paper presented at the Annual Meeting of the American Educational Research Association (Washington, DC, Apr 8-12, 2016)
To measure individual difference in latent attributes more precisely, this study proposed a new cognitive diagnosis model (CDM), which is referred as the probabilistic-inputs, noisy conjunctive (PINC) model, by treating the deterministic binary latent attributes as probabilistic, and directly estimating the probability in the model. Simulation studies were conducted to evaluate parameter recovery of the new model, and the results revealed that the parameters can be recovered well with WinBUGS. An empirical example of the Examination for the Certificate of Proficiency in English was provided to demonstrate applications of the new model.
AERA Online Paper Repository. Available from: American Educational Research Association. 1430 K Street NW Suite 1200, Washington, DC 20005. Tel: 202-238-3200; Fax: 202-238-3250; e-mail: subscriptions@aera.net; Web site: http://www.aera.net
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A