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首页> 外文期刊>Journal of Business Research >Estimation issues with PLS and CBSEM: Where the bias lies!
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Estimation issues with PLS and CBSEM: Where the bias lies!

机译：PLS和CBSEM的估计问题：偏差在哪里！

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摘要

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. (C) 2016 The Authors. Published by Elsevier Inc.
机译：有关不同结构方程建模方法的讨论使用了越来越多的概念和相关术语。结果，出现了对术语含义的误解，例如反射测量和公共因子模型以及形成性测量和复合模型。通过从估计的角度区分概念变量及其度量模型的可操作性，我们消除了术语之间的混淆，并开发了一个统一的框架来自模拟研究的结果证实了我们的概念上的考虑，强调了使用（1）基于复合的局部变量时出现的偏差最小二乘路径建模以估计公共因子模型，以及（2）基于公共因子的基于协方差的结构方程模型，以估计复合模型。结果表明，PLS的使用是可取的，特别是在不确定数据的性质是基于公因子还是基于复合的情况下。 （C）2016作者。由Elsevier Inc.发布

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