NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1132358
Record Type: Journal
Publication Date: 2017
Pages: 20
Abstractor: As Provided
Reference Count: 26
ISBN: N/A
ISSN: ISSN-0895-7347
In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data
Yi, Yeon-Sook
Applied Measurement in Education, v30 n2 p82-101 2017
This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model emerged as the dominant model. It then fit the dominant model and the other models to confirm that the model provides the best fit to the data. The model found to represent the most number of items in the test was the Compensatory Reparameterized Unified Model (C-RUM) and other models compared were the Deterministic-Input, Noisy-And (DINA), Deterministic Input, Noisy-Or-gate (DINO), and Noisy Input, Deterministic-Or-gate (NIDO). The absolute (item-association root mean square error values) and relative (information criteria) model fit indices also indicated that the LCDM and the C-RUM were the best fit to the data. More detailed analyses on the functioning of the C-RUM were conducted and the interpretation of the results was included in the discussion section. The article ends with some suggestions for future research based on the limitations of the study.
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A