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A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations
British Journal of Mathematical and Statistical Psychology ( IF 2.6 ) Pub Date : 2021-10-18 , DOI: 10.1111/bmsp.12255
Gyeongcheol Cho 1 , Marko Sarstedt 2, 3 , Heungsun Hwang 1
Affiliation  

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.

中文翻译:

在(不)正确的构造表示下对基于因子和基于组件的结构方程建模方法的比较评估

结构方程建模 (SEM) 已经演变成两个领域,基于因子和基于分量,这取决于构造是在统计上表示为公共因子还是分量。这两个 SEM 域在概念上是不同的,每个域都假设他们自己的人口模型具有任何一个统计构造代理,并且统计 SEM 方法应该用于估计其构造表示对应于他们假设的模型。然而,SEM 方法通常仅在人口因素模型下进行评估和比较,从而提供关于其相对性能的误导性结论。这部分是因为人口组成模型及其关系尚未明确制定。还,研究 SEM 方法对潜在的结构错误陈述的稳健程度至关重要,因为研究人员可能经常缺乏明确的理论来确定一个因素或组件是否更能代表给定的结构。针对这些问题,本研究首先阐明了几个人口组成模型及其关系,然后对四种 SEM 方法进行了全面评估——基于因子的 SEM 的最大似然方法和因子得分回归以及广义结构化成分分析 (GSCA)和基于组件的 SEM 的偏最小二乘路径建模 (PLSPM) - 在各种实验条件下。我们确认基于因子的 SEM 方法应该是估计因子模型的首选,而组件模型应选择基于组件的 SEM 方法。重要的是,基于组件的方法通常比基于因素的方法更能构建虚假陈述。在基于组件的方法中,应该选择 GSCA 而不是 PLSPM,无论结构是否被误传。
更新日期:2021-10-18
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