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Using the Attribute Hierarchy Method to Identify and Interpret Cognitive Skills that Produce Group Differences
Author(s) -
Gierl Mark J.,
Zheng Yinggan,
Cui Ying
Publication year - 2008
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.1745-3984.2007.00052.x
Subject(s) - cognition , set (abstract data type) , task (project management) , hierarchy , test (biology) , psychology , group (periodic table) , sample (material) , artificial intelligence , natural language processing , computer science , cognitive psychology , machine learning , market economy , paleontology , chemistry , management , organic chemistry , chromatography , neuroscience , economics , biology , programming language
The purpose of this study is to describe how the attribute hierarchy method (AHM) can be used to evaluate differential group performance at the cognitive attribute level. The AHM is a psychometric method for classifying examinees' test item responses into a set of attribute‐mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated on each attribute for each examinee thereby providing specific information about the examinee's attribute‐mastery level. These probabilities can also be compared across groups. We describe a four‐step procedure for estimating and interpreting group differences using the AHM. We also provide an example using student response data from a sample of algebra items on the SAT to illustrate our pattern recognition approach for studying group differences .