A Study on Factor Analytical Methods and Procedures for PLS-SEM (Partial Least Squares Structural Equation Modeling)

Myung-Seong Yim
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引用次数: 2

Abstract

Purpose - This study provides appropriate procedures for EFA to help researchers conduct empirical studies by using PLS-SEM. Research design, data, and methodology - This study addresses the absolute and relative sample size criteria, sampling adequacy, factor extraction models, factor rotation methods, the criterion for the number of factors to retain, interpretation of results, and reporting information. Results - The factor analysis procedure for PLS-SEM consists of the following five stages. First, it is important to look at whether both the Bartlett test of sphericity and the KMO MSA meet the qualitative criteria. Second, PAF is a better choice of methodology. Third, an oblique technique is a suitable method for PLS-SEM. Fourth, a combined approach is strongly recommended to factor retention. PA should be used at the onset. Next, it is recommended using the K1 criterion. In addition, it is necessary to extract factors that increase the total variance explanatory power through the PVA-FS. Finally, it is appropriate to select an item with a factor loading into 0.5 or higher and a communality of 0.5. Conclusions - It is expected that the accurate factor analysis processed for PLS-SEM as previously presented will help us extract more precise factors of the structural model.
PLS-SEM(偏最小二乘结构方程建模)因子分析方法与步骤研究
目的:本研究为研究人员利用PLS-SEM进行实证研究提供了合适的EFA程序。研究设计、数据和方法-本研究涉及绝对和相对样本量标准、抽样充分性、因素提取模型、因素轮换方法、保留因素数量的标准、结果的解释和报告信息。结果- PLS-SEM的因子分析程序包括以下五个阶段。首先,重要的是要看看Bartlett球度检验和KMO MSA是否符合定性标准。其次,PAF是一种更好的方法论选择。斜向技术是一种适用于PLS-SEM的方法。第四,强烈建议采用综合方法来衡量留存率。应在发病时使用PA。接下来,建议使用K1标准。此外,有必要通过PVA-FS提取增加总方差解释能力的因素。最后,选择因子加载值为0.5或更高,通用性为0.5的项目是合适的。结论-预计先前提出的针对PLS-SEM进行的准确因子分析将有助于我们提取更精确的结构模型因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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