Premium
Impact of Diagnosticity on the Adequacy of Models for Cognitive Diagnosis under a Linear Attribute Structure: A Simulation Study
Author(s) -
De La Torre Jimmy,
Karelitz Tzur M.
Publication year - 2009
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2009.00092.x
Subject(s) - computer science , item response theory , retrofitting , machine learning , estimation , artificial intelligence , cognition , test (biology) , data mining , linear model , statistics , econometrics , mathematics , psychometrics , psychology , engineering , paleontology , structural engineering , systems engineering , neuroscience , biology
Compared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM‐based data with a linear attribute structure. The study utilizes a procedure to make the IRM and CDM frameworks comparable and investigates how estimation accuracy is affected by test diagnosticity and the match between the true and fitted models. The study shows that comparable results can be obtained when highly diagnostic IRM data are retrofitted with CDM, and vice versa, retrofitting CDMs to IRM‐based data in some conditions can result in considerable examinee misclassification, and model fit indices provide limited indication of the accuracy of item parameter estimation and attribute classification.