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Latent Partially Ordered Classification Models and Normal Mixtures

Author

Listed:
  • Curtis Tatsuoka

    (Department of Neurology, Case Western Reserve University)

  • Ferenc Varadi

    (Tanar Software)

  • Judith Jaeger

    (Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine)

Abstract

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.

Suggested Citation

  • Curtis Tatsuoka & Ferenc Varadi & Judith Jaeger, 2013. "Latent Partially Ordered Classification Models and Normal Mixtures," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 267-294, June.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:3:p:267-294
    DOI: 10.3102/1076998612458318
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    References listed on IDEAS

    as
    1. Curtis Tatsuoka & Thomas Ferguson, 2003. "Sequential classification on partially ordered sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 143-157, February.
    2. Edward Ip, 2000. "Adjusting for information inflation due to local dependency in moderately large item clusters," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 73-91, March.
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    Cited by:

    1. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2015. "On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 995-1019, December.

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