The comparison of two input statistics for heuristic cognitive diagnosis

Hans Friedrich Köhn, Chia Yi Chiu, Michael J. Brusco

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cognitive diagnosis models of educational test performance decompose ability in a domain into a set of specific binary skills called attributes. (Non-)mastery of attributes documents an examinee’s strengths and weaknesses in the domain as a profile of mental aptitude. Distinct attribute profiles define classes of intellectual proficiency to which examinees can be assigned. Nonparametric, model-free classification methods have been proposed as heuristic or approximate alternatives to maximum likelihood estimation procedures for assigning examinees to proficiency classes. These classification techniques use as input a statistic obtained by aggregating each examinee’s test item scores into a profile of attribute sum-scores. This study demonstrates that clustering examinees into proficiency classes based on their item scores rather than on their attribute sum-score profiles results in a more accurate classification of examinees.

Original languageEnglish (US)
Title of host publicationNew Developments in Quantitative Psychology - Presentations from the 77th Annual Psychometric Society Meeting
EditorsL. Andries van der Ark, Roger E. Millsap, Daniel M. Bolt, Carol M. Woods
Number of pages9
ISBN (Print)9781461493471
StatePublished - 2013
Event77th Annual Meeting of the Psychometric Society, 2012 - Lincoln, United States
Duration: Jul 9 2012Jul 12 2012

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Other77th Annual Meeting of the Psychometric Society, 2012
Country/TerritoryUnited States

ASJC Scopus subject areas

  • General Mathematics


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