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Theorems and Methods of a Complete Q Matrix With Attribute Hierarchies Under Restricted Q-Matrix Design

Overview of attention for article published in Frontiers in Psychology, August 2018
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Title
Theorems and Methods of a Complete Q Matrix With Attribute Hierarchies Under Restricted Q-Matrix Design
Published in
Frontiers in Psychology, August 2018
DOI 10.3389/fpsyg.2018.01413
Pubmed ID
Authors

Yan Cai, Dongbo Tu, Shuliang Ding

Abstract

The design of test Q matrix can directly influence the classification accuracy of a cognitive diagnostic assessment. In this paper, we focus on Q matrix design when attribute hierarchies are known prior to test development. A complete Q matrix design is proposed and theorems are presented to demonstrate that it is a necessary and sufficient condition to guarantee the identifiability of ideal response patterns. A simulation study is also conducted to detect the effects of the proposed design on a family of conjunctive diagnostic models. The results revealed that the proposed Q matrix design is the key condition for guaranteeing classification accuracy. When only one type of item pattern in R matrix is missing from the associated test Q matrix, the related attribute-wise agreement rate will decrease dramatically. When the entire R matrix is missing, both the pattern-wise and attribute-wise agreement rates will decrease sharply. This indicates that the proposed procedures for complete Q matrix design with attribute hierarchies can serve as guidelines for test blueprint development prior to item writing in a cognitive diagnostic assessment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 17%
Student > Doctoral Student 4 13%
Professor > Associate Professor 4 13%
Student > Master 4 13%
Researcher 3 10%
Other 6 20%
Unknown 4 13%
Readers by discipline Count As %
Social Sciences 7 23%
Psychology 6 20%
Mathematics 4 13%
Computer Science 3 10%
Decision Sciences 2 7%
Other 3 10%
Unknown 5 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2018.
All research outputs
#18,643,992
of 23,096,849 outputs
Outputs from Frontiers in Psychology
#22,625
of 30,483 outputs
Outputs of similar age
#254,628
of 331,152 outputs
Outputs of similar age from Frontiers in Psychology
#652
of 725 outputs
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