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ERIC Number: EJ1115196
Record Type: Journal
Publication Date: 2016-Sep
Pages: 16
Abstractor: As Provided
ISSN: EISSN-1531-7714
Partial Least Squares Structural Equation Modeling with R
Ravand, Hamdollah; Baghaei, Purya
Practical Assessment, Research & Evaluation, v21 n11 Sep 2016
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A