Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

作     者：Hair, Joseph F. Hult, G. Tomas M. Ringle, Christian M. Sarstedt, Marko Thiele, Kai Oliver 

作者机构：Univ S Alabama Mobile AL USA Michigan State Univ E Lansing MI 48824 USA Hamburg Univ Technol TUHH Hamburg Germany Univ Newcastle Callaghan NSW Australia Otto von Guericke Univ Magdeburg Germany 

出 版 物：《JOURNAL OF THE ACADEMY OF MARKETING SCIENCE》 (J. Acad. Mark. Sci.)

年 卷 期：2017年第45卷第5期

页      面：616-632页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 020205[经济学-产业经济学] 

主　　题：Composite Generalized structured component analysis GSCA Partial least squares PLS SEM Simulation Structural equation modeling Sum scores regression 

摘      要：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.