Structural equation modeling (SEM) is a prevalent approach for studying constructs.Traditionally, these constructs are modeled as reflectively measured latent variables - common factors that account for the variance-covariance structure of their associated indicators. Over the past two decades, there has been growing interest in an alternative way of modeling constructs: the composite, i.e., a linear combination of indicators. However, existing approaches to estimating composite models either limit researchers from fully leveraging SEM's capabilities, such as handling missing data, evaluating overall model fit, and testing group differences, or significantly increase complexity of the model specification by introducing additional variables. Against this background, this paper presents a new way of integrating both common factors and composites in the traditional SEM framework. Our presented model specification, along with its model-implied variance-covariance matrix, enables researchers to: (i) utilize well-established SEM estimators, including maximum likelihood and generalized least squares estimators, and (ii) can leverage developments from the traditional SEM framework in terms of model specification, evaluation, and handling of missing data. This way of analyzing structural equation models involving common factors and composites is referred to as factor- and composite-based SEM (FC-SEM). This advancement aims to enhance the flexibility and applicability of SEM in analyzing constructs.

翻译：结构方程模型（SEM）是研究构念的主流方法。传统上，这些构念被建模为反射性测量的潜变量——即解释其观测指标方差-协方差结构的共同因子。过去二十年中，学界对另一种构念建模方式——组合（即指标的线性组合）的兴趣日益增长。然而，现有组合模型估计方法要么限制研究者充分利用SEM的完整功能（如处理缺失数据、评估整体模型拟合度和检验组间差异），要么通过引入额外变量显著增加模型设定的复杂度。在此背景下，本文提出一种在传统SEM框架中整合共同因子和组合的新方法。我们提出的模型设定及其模型隐含方差-协方差矩阵，使研究者能够：（i）使用成熟的SEM估计方法，包括最大似然估计和广义最小二乘估计；（ii）可借鉴传统SEM框架在模型设定、评估和缺失数据处理方面的发展。这种分析涉及共同因子和组合的结构方程模型的方法被称为基于因子和组合的SEM（FC-SEM）。这一进展旨在提升SEM在分析构念时的灵活性与适用性。