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.

广义结构化成分分析 (GSCA) 是一种结构方程建模 (SEM) 程序，它通过观测变量的加权和构建成分并验证性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 类似于作为基于探索性因素的 SEM 程序而开发的探索性 SEM (ESEM)，它通过参数矩阵的正交旋转来寻找观测变量和分量之间的关系。首先展示了 GSCA 中正交旋转的不确定性，作为该方法的理论支持。然后提出了整个 EGSCA 程序，以及专门用于 EGSCA 的新旋转算法，其目的是同时简化所有参数矩阵。两项数值模拟研究表明，具有以下旋转的 EGSCA 成功地恢复了参数矩阵的真实值，并且优于现有的 GSCA 程序。将EGSCA应用于两个真实数据集，EGSCA结果提出的模型优于先前研究提出的模型，这证明了EGSCA在模型探索方面的有效性。