Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling
本文介绍了基于方差的SEM技术，能处理复合、共同因子和因果形成测量模型，并提出了评估不同测量模型效度的方法，对广告和消费者行为研究者有用。
Advertising research is a scientific discipline that studies artifacts (e.g., various forms of marketing communication) as well as natural phenomena (e.g., consumer behavior). Empirical advertising research therefore requires methods that can model design constructs as well as behavioral constructs, which typically require different measurement models. This article presents variance-based structural equation modeling (SEM) as a family of techniques that can handle different types of measurement models: composites, common factors, and causal–formative measurement. It explains the differences between these types of measurement models and clears up possible ambiguity regarding formative endogenous constructs. The article proposes confirmatory composite analysis to assess the nomological validity of composites, confirmatory factor analysis (CFA) and the heterotrait-monotrait ratio of correlations (HTMT) to assess the construct validity of common factors, and the multiple indicator, multiple causes (MIMIC) model to assess the external validity of causal–formative measurement.