The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art
Abstract
Diagnostic classification models (DCM) are frequently promoted by psychometricians as important modelling alternatives for analyzing response data in situations where multivariate classifications of respondents are made on the basis of multiple postulated latent skills. In this review paper, a definitional boundary of the space of DCM is developed, core DCM within this space are reviewed, and their defining features are compared and contrasted with those of other latent variable models. The models to which DCM are compared include unrestricted latent class models, multidimensional factor analysis models, and multidimensional item response theory models. Attention is paid to both statistical considerations of model structure, as well as substantive considerations of model use.
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Cited by
Applying Bayesian Network to Assess the Levels of Skills Mastering in Adaptive Dynamic OER-Systems
Nekhaev, I., Zhuykov, I., Manukyants, S., & Maslennikov, A. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-63322-6_94 · Cited by 3
Metadata
- Authors
- Rupp, A. A., & Templin, J. L.
- Venue
- Measurement: Interdisciplinary Research & Perspective
- Publisher
- Informa UK Limited
- Published
- 2008-11-20
- Volume
- 6
- Issue
- 4
- Pages
- 219-262
- ISSN
- 1536-6359 (ISSN-L)
- 1536-6367
- OpenAlex
- W2005093659
- Cited by
- 291
- References
- 79
- FWCI
- 20.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rupp, A. A., & Templin, J. L. (2008). Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art. Measurement: Interdisciplinary Research & Perspective. https://doi.org/10.1080/15366360802490866
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
