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.
摘要：
广义结构化成分分析（GSCA）是一种结构方程建模（SEM）程序，该程序通过观测变量的加权和来构造成分，并确定地检查它们的回归关系。该研究提出了GSCA的探索性版本，称为探索性GSCA(EGSCA)。EGSCA类似于探索性SEM（ESEM），是基于探索性因素的SEM程序，它通过参数矩阵的正交旋转来寻找观察到的变量和分量之间的关系。GSCA中正交旋转的不确定性首先被证明是所提出方法的理论支持。然后介绍整个EGSCA过程，加上专门针对EGSCA的新旋转算法，旨在同时简化所有参数矩阵。两项数值模拟研究表明，经过以下旋转的EGSCA成功恢复了参数矩阵的真实值，并且优于现有的GSCA程序。EGSCA被应用于两个真实的数据集，EGSCA结果提出的模型比以往研究提出的模型更好，这证明了EGSCA在模型探索中的有效性。
