東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Nonlinear Penalized Estimation of Tr...

Xiang, Rui.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models./
Author:	Xiang, Rui.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2013,
Description:	111 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.
Contained By:	Dissertation Abstracts International74-08B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3559534
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. - Ann Arbor : ProQuest Dissertations & Theses, 2013 - 111 p.

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

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

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.

ISBN: 9781303046605Subjects--Topical Terms:

517247
Statistics.

Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models.
LDR:01979nmm a2200313 4500 001 2159183
005 20180622095236.5
008 190424s2013 ||||||||||||||||| ||eng d
020 $a 9781303046605
035 $a (MiAaPQ)AAI3559534
035 $a (MiAaPQ)columbia:11293
035 $a AAI3559534
040 $a MiAaPQ $c MiAaPQ
100 1 $a Xiang, Rui. $3 3347048
245 1 0 $a Nonlinear Penalized Estimation of True Q-Matrix in Cognitive Diagnostic Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2013
300 $a 111 p.
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.
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.
590 $a School code: 0054.
650 4 $a Statistics. $3 517247
650 4 $a Cognitive psychology. $3 523881
650 4 $a Educational tests & measurements. $3 3168483
690 $a 0463
690 $a 0633
690 $a 0288
710 2 $a Columbia University. $b TC: Measurement and Evaluation. $3 3194606
773 0 $t Dissertation Abstracts International $g 74-08B(E).
790 $a 0054
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3559534