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
PublisherSpringer New York LLC
Number of pages9
ISBN (Print)9781461493471
StatePublished - 2013
Externally publishedYes
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


Conference77th Annual Meeting of the Psychometric Society, 2012
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2013.


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