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Statistical Inference and Experiment...

Zhang, Stephanie S.

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Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models./
Author:	Zhang, Stephanie S.
Description:	105 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
Contained By:	Dissertation Abstracts International75-08B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621026
ISBN:	9781303917875

Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models.
Zhang, Stephanie S.

Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models. - 105 p.

Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2014.

There has been growing interest in recent years in using cognitive diagnosis models for diagnostic measurement, i.e., classification according to multiple discrete latent traits. The Q-matrix, an incidence matrix specifying the presence or absence of a relationship between each item in the assessment and each latent attribute, is central to many of these models. Important applications include educational and psychological testing; demand in education, for example, has been driven by recent focus on skills-based evaluation. However, compared to more traditional models coming from classical test theory and item response theory, cognitive diagnosis models are relatively undeveloped and suffer from several issues limiting their applicability. This thesis exams several issues related to statistical inference and experimental design for Q-matrix based cognitive diagnosis models.

ISBN: 9781303917875Subjects--Topical Terms:

517247
Statistics.

Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models.
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245 1 0 $a Statistical Inference and Experimental Design for Q-matrix Based Cognitive Diagnosis Models.
300 $a 105 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
500 $a Advisers: Zhiliang Ying; Jingchen Liu.
502 $a Thesis (Ph.D.)--Columbia University, 2014.
520 $a There has been growing interest in recent years in using cognitive diagnosis models for diagnostic measurement, i.e., classification according to multiple discrete latent traits. The Q-matrix, an incidence matrix specifying the presence or absence of a relationship between each item in the assessment and each latent attribute, is central to many of these models. Important applications include educational and psychological testing; demand in education, for example, has been driven by recent focus on skills-based evaluation. However, compared to more traditional models coming from classical test theory and item response theory, cognitive diagnosis models are relatively undeveloped and suffer from several issues limiting their applicability. This thesis exams several issues related to statistical inference and experimental design for Q-matrix based cognitive diagnosis models.
520 $a We begin by considering one of the main statistical issues affecting the practical use of Q-matrix based cognitive diagnosis models, the identifiability issue. In statistical models, identifiability is prerequisite for most common statistical inferences, including parameter estimation and hypothesis testing. With Q-matrix based cognitive diagnosis models, identifiability also affects the classification of respondents according to their latent traits. We begin by examining the identifiability of model parameters, presenting necessary and sufficient conditions for identifiability in several settings.
520 $a Depending on the area of application and the researchers degree of control over the experiment design, fulfilling these identifiability conditions may be difficult. The second part of this thesis proposes new methods for parameter estimation and respondent classification for use with non-identifiable models. In addition, our framework allows consistent estimation of the severity of the non-identifiability problem, in terms of the proportion of the population affected by it. The implications of this measure for the design of diagnostic assessments are also discussed.
590 $a School code: 0054.
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650 4 $a Educational tests & measurements. $3 3168483
650 4 $a Quantitative psychology. $3 2144748
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710 2 $a Columbia University. $b Statistics. $3 1682152
773 0 $t Dissertation Abstracts International $g 75-08B(E).
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