Partial Least Squares Structural Equation Modeling with R
DOI
https://doi.org/10.7275/d2fa-qv48
Abstract
Structural equation modeling (SEM) has become widespread in educational and psychological research. Its flexibility in addressing complex theoretical models and the proper treatment of measurement error has made it the model of choice for many researchers in the social sciences. Nevertheless, the model imposes some daunting assumptions and restrictions (e.g. normality and relatively large sample sizes) that could discourage practitioners from applying the model. Partial least squares SEM (PLS-SEM) is a nonparametric technique which makes no distributional assumptions and can be estimated with small sample sizes. In this paper a general introduction to PLS-SEM is given and is compared with conventional SEM. Next, step by step procedures, along with R functions, are presented to estimate the model. A data set is analyzed and the outputs are interpreted Accessed 5,694 times on https://pareonline.net from September 16, 2016 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ravand, Hamdollah and Baghaei, Purya
(2019)
"Partial Least Squares Structural Equation Modeling with R,"
Practical Assessment, Research, and Evaluation: Vol. 21, Article 11.
DOI: https://doi.org/10.7275/d2fa-qv48
Available at:
https://scholarworks.umass.edu/pare/vol21/iss1/11