Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
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DOI：
10.1007/s11747-017-0517-x
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发表时间：
2017-09-01
影响因子：
18.2
通讯作者：
Thiele, Kai Oliver
中科院分区：
文献类型：
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作者：
Hair, Joseph F.;Hult, G. Tomas M.;Thiele, Kai Oliver
基于复合的结构方程模型 (SEM)，尤其是偏最小二乘路径模型 (PLS)，在营销领域得到了越来越广泛的传播。为了充分发挥这些方法的潜力，研究人员必须了解它们的相对性能以及有利于每种方法使用的设置。虽然许多模拟研究旨在评估基于复合材料的 SEM 方法的性能，但实际上所有这些研究都使用公因子模型定义群体，从而基于错误的理由评估这些方法。这项研究首次在复合模型数据的基础上，考虑了广泛的模型群，对基于复合的 SEM 技术进行了全面评估。大规模模拟研究的结果证实，当基础总体基于复合模型时，PLS 和广义结构化成分分析是一致的估计量。虽然这两种方法在参数恢复方面均优于总分回归，但 PLS 的统计功效略高。
Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method's use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.