Estimation issues with PLS and CBSEM: Where the bias lies!
澄清了结构方程模型中反射性测量与形成性测量、共同因子模型与复合模型等术语的混淆，通过模拟研究揭示了PLS和CBSEM在不同模型设定下的偏差，指出在数据性质未知时PLS更优。
Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.