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Analysis of Functional Magnetic Reso...

Lipton, Abraham K.

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Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA)./
Author:	Lipton, Abraham K.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	178 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
Subject:	Quantitative psychology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29170023
ISBN:	9798834057505

Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA).
Lipton, Abraham K.

Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA). - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 178 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--Fordham University, 2022.

This item must not be sold to any third party vendors.

Structural Equation Modeling (SEM) is a broad family of models which can be utilized to assess the relationship between latent variables. In the context of fMRI analysis, extended unified SEM (euSEM) and Generalized structured components analysis (GSCA) represent two different SEM approaches. This dissertation examined how these models perform under non-normality, low correlations between indicators, and model misspecification via simulations. The four criteria used to assess the performance were bias, Type I error, power, and convergence percentage. The results from the simulations were applied to an empirical dataset. In summary, GSCA was found to be more powerful but also more biased than euSEM. This was true for all levels of normality, correlation, and model specification.

ISBN: 9798834057505Subjects--Topical Terms:

2144748
Quantitative psychology.
Subjects--Index Terms:

GSCA

Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA).
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245 1 0 $a Analysis of Functional Magnetic Resonance Imaging Data: A Comparison of Extended Unified Structural Equation Modeling (euSEM) and Generalized Structured Components Analysis (GSCA).
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 178 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
500 $a Advisor: Kim, Se-Kang;Cham, Hei.
502 $a Thesis (Ph.D.)--Fordham University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Structural Equation Modeling (SEM) is a broad family of models which can be utilized to assess the relationship between latent variables. In the context of fMRI analysis, extended unified SEM (euSEM) and Generalized structured components analysis (GSCA) represent two different SEM approaches. This dissertation examined how these models perform under non-normality, low correlations between indicators, and model misspecification via simulations. The four criteria used to assess the performance were bias, Type I error, power, and convergence percentage. The results from the simulations were applied to an empirical dataset. In summary, GSCA was found to be more powerful but also more biased than euSEM. This was true for all levels of normality, correlation, and model specification.
590 $a School code: 0072.
650 4 $a Quantitative psychology. $3 2144748
650 4 $a Medical imaging. $3 3172799
653 $a GSCA
653 $a Structural equation modeling
653 $a Statistical power
653 $a Magnetic resonance imaging
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690 $a 0574
710 2 $a Fordham University. $b Psychology. $3 3183238
773 0 $t Dissertations Abstracts International $g 84-01B.
790 $a 0072
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29170023