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Uses of classification error probabilities in the three-step approach to estimating cognitive diagnosis models

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Title
Uses of classification error probabilities in the three-step approach to estimating cognitive diagnosis models
Name (type = personal)
NamePart (type = family)
Iaconangelo
NamePart (type = given)
Charles Joseph
NamePart (type = date)
1985-
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Charles Joseph Iaconangelo
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RoleTerm (authority = RULIB)
author
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Gitomer
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Drew
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Drew Gitomer
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Advisory Committee
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chair
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NamePart (type = family)
de la Torre
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Jimmy
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Jimmy de la Torre
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Chiu
NamePart (type = given)
Chia-Yi
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Chia-Yi Chiu
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Douglas
NamePart (type = given)
Jeff
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Jeff Douglas
Affiliation
Advisory Committee
Role
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outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
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2017
Place
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xx
Language
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eng
Abstract (type = abstract)
Classification error probabilities (CEPs) are estimates of the amount of misclassification in the measurement model conditional on the true latent class memberships. CEPs can be used in several ways to improve the inferences drawn from cognitive diagnosis models (CDMs). To develop methodologies that facilitate the use of CDMs in practical research, this dissertation uses CEPs to accomplish three objectives: (1) to examine the conditional classification accuracy and generalizability of a cognitively diagnostic assessment; (2) to introduce correction weights that can improve a three-step approach for latent-class regression, which relate latent class memberships to predictor variables, and (3) to apply the same correction weights to select the best subset of predictor variables in the context of latent-class regression. In the first study, an application of CEPs fills a gap in literature on CDM validity by serving as an index of classification accuracy conditional on the latent class memberships. This index can also be extended to predict the classification accuracy of the assessment for a different population. Results show that the proposed index not only recovers the empirical values, but outperforms existing procedures based on the Monte Carlo approach. In the second study, CEPs are used to improve the inferences in latent-class regression. Compared to the one-step procedure, which estimates the measurement model and regression parameters simultaneously, the three-step procedure is desirable from an applied researchers’ perspective because it simplifies latent-class regression by implementing the estimations involved in separate steps. However, it also leads to parameter estimation bias. This study uses CEP-derived weights to improve parameter estimation in various types of latent-class regression. Finally, the third study extends the latent-class regression in the second study by incorporating a regularization procedure that permits variable selection. Results show that incorporating measurement error (as measured by CEP) in the variable selection process leads to a subset of nonredundant variables that more clearly shows the relationship between predictors and examinee classification. In addition, compared to the standard approach, using the CEP-based weights leads to fewer instances of estimation noncovergence. With a general aim to address needs in conditional classification accuracy, correcting bias in parameter estimation, and high-dimension variable selection in the context of CDMs, this dissertation uses CEPs to accomplish three objectives: (1) to examine the conditional classification accuracy and generalizability of the assessment, (2) introduce correction weights for the three-step approach that result in improved parameter estimation, and (3) apply these correction weights to regularized latent-class regression to select variables.
Subject (authority = RUETD)
Topic
Education
Subject (authority = ETD-LCSH)
Topic
Latent variables
Subject (authority = ETD-LCSH)
Topic
Latent structure analysis
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_8220
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (x, 108 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Charles Joseph Iaconangelo
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3W95D95
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Iaconangelo
GivenName
Charles
MiddleName
Joseph
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-05-30 14:19:28
AssociatedEntity
Name
Charles Iaconangelo
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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