Exploratory Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2023-12-12 DOI:10.1007/s11336-023-09942-5
Naoto Yamashita
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Abstract

Generalized structured component analysis (GSCA) is a structural equation modeling (SEM) procedure that constructs components by weighted sums of observed variables and confirmatorily examines their regressional relationship. The research proposes an exploratory version of GSCA, called exploratory GSCA (EGSCA). EGSCA is analogous to exploratory SEM (ESEM) developed as an exploratory factor-based SEM procedure, which seeks the relationships between the observed variables and the components by orthogonal rotation of the parameter matrices. The indeterminacy of orthogonal rotation in GSCA is first shown as a theoretical support of the proposed method. The whole EGSCA procedure is then presented, together with a new rotational algorithm specialized to EGSCA, which aims at simultaneous simplification of all parameter matrices. Two numerical simulation studies revealed that EGSCA with the following rotation successfully recovered the true values of the parameter matrices and was superior to the existing GSCA procedure. EGSCA was applied to two real datasets, and the model suggested by the EGSCA’s result was shown to be better than the model proposed by previous research, which demonstrates the effectiveness of EGSCA in model exploration.

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通过同步旋转对简单结构进行基于成分的结构方程建模的探索程序
广义结构化成分分析(GSCA)是一种结构方程建模(SEM)程序,它通过观察变量的加权和来构建成分,并确认它们之间的回归关系。本研究提出了一种探索性的 GSCA 版本,称为探索性 GSCA(EGSCA)。EGSCA 类似于探索性 SEM(ESEM),是一种基于探索性因子的 SEM 程序,它通过参数矩阵的正交旋转来寻求观察变量与成分之间的关系。首先说明了正交旋转在 GSCA 中的不确定性,为所提出的方法提供了理论支持。然后介绍了整个 EGSCA 程序,以及专门用于 EGSCA 的新旋转算法,该算法旨在同时简化所有参数矩阵。两项数值模拟研究表明,带有以下旋转算法的 EGSCA 成功地恢复了参数矩阵的真实值,优于现有的 GSCA 程序。将 EGSCA 应用于两个真实数据集,结果表明 EGSCA 提出的模型优于之前研究提出的模型,这证明了 EGSCA 在模型探索方面的有效性。
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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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