Nonparametric Calibration of Item-by-Attribute Matrix in Cognitive Diagnosis

Youn Seon Lim, Fritz Drasgow

Research output: Contribution to journalArticlepeer-review

Abstract

A nonparametric technique based on the Hamming distance is proposed in this research by recognizing that once the attribute vector is known, or correctly estimated with high probability, one can determine the item-by-attribute vectors for new items undergoing calibration. We consider the setting where Q is known for a large item bank, and the q-vectors of additional items are estimated. The method is studied in simulation under a wide variety of conditions, and is illustrated with the Tatsuoka fraction subtraction data. A consistency theorem is developed giving conditions under which nonparametric Q calibration can be expected to work.

Original languageEnglish (US)
Pages (from-to)562-575
Number of pages14
JournalMultivariate Behavioral Research
Volume52
Issue number5
DOIs
StatePublished - Sep 3 2017

Keywords

  • Cognitive diagnosis
  • nonparametric classification
  • online calibration

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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