자료유형  

 학위논문

Control Number  

0017165037

International Standard Book Number  

9798384462774

Dewey Decimal Classification Number  

379.1

Main Entry-Personal Name  

Yang, Junyeong.

Publication, Distribution, etc. (Imprint  

[S.l.] : The Ohio State University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

130 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Kim, Minjung.

Dissertation Note  

Thesis (Ph.D.)--The Ohio State University, 2024.

Summary, Etc.  

요약The latent growth model has been pivotal in understanding developmental processes for several decades. While most researchers have focused on growth factors' mean structure and variability, the within-subject residual variance-covariance structure has not received as much attention. The present study proposes a novel procedure for specifying a linear latent growth model's optimal within-subject residual variance-covariance structure. The method is based on the following ideas: approximating the true variance-covariance structure of the data, generating a series of replications with parameter values of the most probable within-subject residual variance-covariance structures within the data, and employing classification machine learning algorithms for prediction. A simulation examined the feasibility of the procedure predicting the true within-subject residual variance-covariance structure. Additionally, the performance of the proposed procedure was compared to the traditional approach of selecting models based on information criteria using the same replications. The results showed that the proposed method effectively detected true first-ordered autoregressive and banded main diagonal structures. In contrast, the information criteria, specifically the Bayesian Information Criterion and Sample-Size Adjusted BIC, effectively detected true identity and banded main diagonal structures, respectively. Based on these findings, suggestions were provided for researchers to consider when specifying the optimal within-subject structure of their data.

Subject Added Entry-Topical Term  

Educational evaluation.

Subject Added Entry-Topical Term  

Educational tests & measurements.

Subject Added Entry-Topical Term  

Educational psychology.

Subject Added Entry-Topical Term  

Quantitative psychology.

Subject Added Entry-Topical Term  

Computer science.

Index Term-Uncontrolled  

Latent growth model

Index Term-Uncontrolled  

Machine learning algorithms

Index Term-Uncontrolled  

Feasibility

Index Term-Uncontrolled  

Variance-covariance structure

Index Term-Uncontrolled  

Variability

Added Entry-Corporate Name  

The Ohio State University Educational Studies

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:655539