In this article, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement errors incorporated (GSCAM) in a unified manner and can estimate both factor- and component-model parameters, including component and factor loadings, component and factor path coefficients, and path coefficients connecting factors and components. We conduct 2 simulation studies to investigate the performance of IGSCA under models with both factors and components. The first simulation study assesses how existing approaches for structural equation modeling and IGSCA recover parameters. This study shows that only consistent partial least squares (PLSc) and IGSCA yield unbiased estimates of all parameters, whereas the other approaches always provided biased estimates of several parameters. As such, we conduct a second, extensive simulation study to evaluate the relative performance of the 2 competitors (PLSc and IGSCA), considering a variety of experimental factors (model specification, sample size, the number of indicators per factor/component, and exogenous factor/component correlation). IGSCA exhibits better performance than PLSc under most conditions. We also present a real data application of IGSCA to the study of genes and their influence on depression. Finally, we discuss the implications and limitations of this approach, and recommendations for future research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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具有因子和分量的结构方程建模方法：综合广义结构化分量分析。

在本文中，我们提出了综合广义结构化成分分析 (IGSCA)，这是一种通用统计方法，用于同时分析同一模型中的成分和因素的数据。这种方法以统一的方式将广义结构化成分分析 (GSCA) 和广义结构化成分分析与测量误差结合 (GSCAM) 结合起来，可以估计因子和成分模型参数，包括成分和因子载荷、成分和因子路径系数、和路径系数连接因素和组件。我们进行了 2 项模拟研究，以研究 IGSCA 在具有因子和组件的模型下的性能。第一个模拟研究评估了结构方程建模和 IGSCA 的现有方法如何恢复参数。这项研究表明，只有一致的偏最小二乘法 (PLSc) 和 IGSCA 才能对所有参数产生无偏估计，而其他方法总是提供对几个参数的有偏估计。因此，我们进行了第二次广泛的模拟研究，以评估两个竞争对手（PLSc 和 IGSCA）的相对表现，考虑到各种实验因素（模型规范、样本大小、每个因素/组件的指标数量以及外生因素）因子/成分相关性）。在大多数情况下，IGSCA 表现出比 PLSc 更好的性能。我们还展示了 IGSCA 在基因研究中的真实数据应用及其对抑郁症的影响。最后，我们讨论了这种方法的影响和局限性，以及对未来研究的建议。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。