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ERIC Number: EJ1449069
Record Type: Journal
Publication Date: 2024-Sep
Pages: 24
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0022-0655
EISSN: EISSN-1745-3984
Available Date: N/A
A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties
Matthew J. Madison; Stefanie A. Wind; Lientje Maas; Kazuhiro Yamaguchi; Sergio Haab
Journal of Educational Measurement, v61 n3 p408-431 2024
Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or nonproficiency of specified latent characteristics. These models are well suited for providing diagnostic and actionable feedback to support intermediate and formative assessment efforts. Several DCMs have been developed and applied in different settings. This study examines a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate and prove that the proposed DCM has measurement properties akin to the Rasch and one-parameter logistic item response theory models, including sum score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We introduce some potential applications for this model, and discuss the implications and limitations of these developments, as well as directions for future research.
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www-wiley-com.bibliotheek.ehb.be/en-us
Related Records: ED661518
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF), Division of Social and Economic Sciences (SES); Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: 1921373; R305D220020
Author Affiliations: N/A