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Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models./
Author:	Xiang, Rui.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9781303046605

Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models.
Xiang, Rui.

Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2013.

Includes bibliographical references

A key issue of cognitive diagnostic models (CDMs) is the correct identification of Q-matrix which indicates the relationship between attributes and test items. Previous CDMs typically assumed a known Q-matrix provided by domain experts such as those who developed the questions. However, misspecifications of Q-matrix had been discovered in the past studies. The primary purpose of this research is to set up a mathematical framework to estimate the true Q-matrix based on item response data. The model considers all Q-matrix elements as parameters and estimates them through EM algorithm. Two simulation designs are conducted to evaluate the feasibility and performance of the model. An empirical study is addressed to compare the estimated Q-matrix with the one designed by experts. The results show that the model performs well and is able to identify 60% to 90% of correct elements of Q-matrix. The model also indicates possible misspecifications of the designed Q-matrix in the fraction subtraction test.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303046605Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models.
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500 $a Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.
500 $a Adviser: Matthew S. Johnson.
502 $a Thesis (Ph.D.)--Columbia University, 2013.
504 $a Includes bibliographical references
520 $a A key issue of cognitive diagnostic models (CDMs) is the correct identification of Q-matrix which indicates the relationship between attributes and test items. Previous CDMs typically assumed a known Q-matrix provided by domain experts such as those who developed the questions. However, misspecifications of Q-matrix had been discovered in the past studies. The primary purpose of this research is to set up a mathematical framework to estimate the true Q-matrix based on item response data. The model considers all Q-matrix elements as parameters and estimates them through EM algorithm. Two simulation designs are conducted to evaluate the feasibility and performance of the model. An empirical study is addressed to compare the estimated Q-matrix with the one designed by experts. The results show that the model performs well and is able to identify 60% to 90% of correct elements of Q-matrix. The model also indicates possible misspecifications of the designed Q-matrix in the fraction subtraction test.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Cognitive psychology. $3 556029
650 4 $a Educational tests & measurements. $3 1180442
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