结构方程建模
偏最小二乘回归
计算机科学
统计软件
统计分析
软件
管理科学
数据科学
数学
统计
工程类
机器学习
程序设计语言
作者
Joseph F. Hair,G. Tomas M. Hult,Christian M. Ringle,Marko Sarstedt
出处
期刊:
[Kennesaw State University]
日期:2014-01-01
被引量:12633
摘要
The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity.
科研通智能强力驱动
Strongly Powered by AbleSci AI