Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire. From a technical perspective, SEMwas developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) lowdimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.

中文翻译：

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结构方程模型：从路径到网络（Westland 2019）

结构方程建模 (SEM) 是一种统计分析框架，允许研究人员使用观察到的和潜在（或不可观察到的）变量及其通常的线性关系来指定和测试模型。在过去的几十年中，SEM 已成为行为、教育、心理学和社会科学研究人员的标准统计分析技术。从技术角度来看，SEM 是作为两个统计领域（路径分析和数据简化）的混合体而发展起来的。路径分析用于指定和检查观察变量之间的方向关系，而数据简化用于揭示（未观察到的）观察变量的低维表示，这些表示被称为潜在变量。由于两种不同的数据缩减技术（即 因子分析和主成分分析）可用于统计界，SEM 也演变成两个领域——基于因子和基于成分（例如，Jöreskog 和 Wold 1982）。在基于因子的 SEM 中，心理测量或心理测量传统受到强烈影响，假设每个潜在变量作为独立于观察变量的实体存在，但也作为唯一来源，一个（共同）因子代表一个潜在变量观察变量之间的关联。相反，在基于分量的 SEM 中，更符合传统的多元统计，加权复合或观测变量的分量代表潜在变量，假设后者是观测变量的聚合（或直接结果）。