Misspecifications in structural equation modeling: The choice of latent variables, causal-formative constructs or composites
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结构方程建模中构念类型选择的误设问题
- 研究问题
- 把潜变量、因果形成性构念或复合构念错误地相互替代，会如何影响路径系数，模型拟合指标能否识别这种误设？
- 方法与思路
- 作者模拟真实与假定构念类型的全部组合，统一使用最大似然估计以考察误设，再以另一估计方法重复模拟。
- 材料陈述的贡献
- 摘要称构念误设会造成路径系数偏差，所考察的拟合准则均不能有效区分正确构念模型与误设模型。
- 阅读关注点
- 应核对三类构念的生成机制、模型参数的可比性、另一估计方法的设定，以及拟合指标失效结论覆盖的模拟范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实；摘要未给出模拟设定和具体拟合准则，不能推广为所有指标均无法检测误设。
原文摘要
Empirical research in many social disciplines involves constructs that are not directly observable, such as behaviors. To model them, constructs must be operationalized using their relations with indicators. Structural equation modeling (SEM) is the primary approach for this purpose. In SEM, three types of constructs are distinguished: latent variables, causal-formative constructs, and composites. To estimate the parameters of the different models, various estimators have been developed. Many Monte Carlo studies have examined the estimation performances of different estimators for the construct types. One aspect evaluated is the consequences of construct misspecification - when the true construct type differs from the modeling choice - on parameter estimates and model fit. For example, parameter bias in models that misspecify latent variables as composites is often attributed to the chosen estimator, although model parameters depend on different estimators, making it impossible to examine the factors individually. This article aims to disentangle the issues of construct misspecification and parameter estimation by a comprehensive Monte Carlo study of all combinations between true and assumed construct types. To focus on misspecification, we used the same estimator for all models, namely the maximum likelihood (ML) estimator. To generalize beyond ML, we replicated the simulation using another estimator. We aim to examine the role of construct misspecification, not estimator choice, on the estimation performance and show that misspecification leads indeed to biased path coefficient estimates. Further, we evaluate whether fit measures can distinguish models with correct from those with misspecified constructs. We find that none of the criteria considered is suited for this. These findings stress the importance of thoughtful construct specification and the need for further research.