结构方程建模
破折号
现存分类群
集合(抽象数据类型)
主题
蒙特卡罗方法
可能性
主题(文档)
计算机科学
计量经济学
社会学
实证经济学
认识论
管理科学
心理学
运筹学
经济
数学
统计
哲学
机器学习
图书馆学
操作系统
教育学
逻辑回归
进化生物学
课程
生物
程序设计语言
作者
Florian Schuberth,Geoffrey S. Hubona,Ellen Roemer,Sam Zaza,Tamara Schamberger,Francis Chuah,Gabriel Cepeda‐Carrión,Jörg Henseler
标识
DOI:10.1016/j.techfore.2023.122665
摘要
Ganesh Dash and Justin Paul authored an article titled "CB-SEM vs. PLS-SEM methods for research in social science and technological forecasting" in a special issue of Technological Forecasting and Social Change, co-edited by Justin Paul. Unfortunately, the article's central conclusion – "CB or PLS or PLSc do not matter" – is misleading and at odds with practically all extant conceptual and empirical research on this subject. This commentary identifies an unsuitable research design to be the major cause of the erroneous conclusion and aims to set the record straight. A Monte Carlo simulation demonstrates that the choice of the approach to structural equation modeling can have a substantial impact on the results and their validity. In general, analysts should choose a structural equation modeling approach that fits their conceptual model.
科研通智能强力驱动
Strongly Powered by AbleSci AI