A Nonparametric Approach to Cognitive Diagnosis by Proximity to Ideal Response Patterns

Chia Yi Chiu, Jeff Douglas

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

A trend in educational testing is to go beyond unidimensional scoring and provide a more complete profile of skills that have been mastered and those that have not. To achieve this, cognitive diagnosis models have been developed that can be viewed as restricted latent class models. Diagnosis of class membership is the statistical objective of these models. As an alternative to latent class modeling, a nonparametric procedure is introduced that only requires specification of an item-by-attribute association matrix, and classifies according to minimizing a distance measure between observed responses, and the ideal response for a given attribute profile that would be implied by the item-by-attribute association matrix. This procedure requires no statistical parameter estimation, and can be used on a sample size as small as 1. Heuristic arguments are given for why the nonparametric procedure should be effective under various possible cognitive diagnosis models for data generation. Simulation studies compare classification rates with parametric models, and consider a variety of distance measures, data generation models, and the effects of model misspecification. A real data example is provided with an analysis of agreement between the nonparametric method and parametric approaches.

Original languageEnglish (US)
Pages (from-to)225-250
Number of pages26
JournalJournal of Classification
Volume30
Issue number2
DOIs
StatePublished - Jul 2013

All Science Journal Classification (ASJC) codes

  • Library and Information Sciences
  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty

Keywords

  • Cognitive diagnosis
  • Nonparametric classification
  • Residual sum

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