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ERIC Number: EJ1004549
Record Type: Journal
Publication Date: 2013-Jun
Pages: 28
Abstractor: As Provided
Reference Count: 49
ISBN: N/A
ISSN: ISSN-1076-9986
Latent Partially Ordered Classification Models and Normal Mixtures
Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith
Journal of Educational and Behavioral Statistics, v38 n3 p267-294 Jun 2013
Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for deeper insight into how functioning can be affected by neurological conditions or by interventions that impact cognition or learning. Responses to NP measures or test items are used as a basis for classification. A natural and useful extension for response models that can be employed in cognitively diagnostic modeling is the implementation of nonparametric density estimation methods. For instance, an issue with NP assessment data is that complex response distributions can arise, such as for populations that are in part comprised of cognitively impaired subjects. To model such complexity, a Dirichlet process prior approach to Bayesian nonparametric density estimation for latent poset models is described. These methods are demonstrated with an analysis of NP data from a study of schizophrenia. (Contains 3 tables and 4 figures.)
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A