验证性因素分析
偏最小二乘回归
结构方程建模
背景(考古学)
形成性评价
人工智能
统计
计算机科学
机器学习
数学
生物
古生物学
作者
Joe F. Hair,Matt C. Howard,Christian Nitzl
标识
DOI:10.1016/j.jbusres.2019.11.069
摘要
Confirmatory factor analysis (CFA) has historically been used to develop and improve reflectively measured constructs based on the domain sampling model. Compared to CFA, confirmatory composite analysis (CCA) is a recently proposed alternative approach applied to confirm measurement models when using partial least squares structural equation modeling (PLS-SEM). CCA is a series of steps executed with PLS-SEM to confirm both reflective and formative measurement models of established measures that are being updated or adapted to a different context. CCA is also useful for developing new measures. Finally, CCA offers several advantages over other approaches for confirming measurement models consisting of linear composites.
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