期刊名称：Practical Assessment, Research and Evaluation
印刷版ISSN：1531-7714
电子版ISSN：1531-7714
出版年度：2016
卷号：21
期号：11
出版社：ERIC: Clearinghouse On Assessment and Evaluation
摘要：Structural equation modeling (SEM) has become widespread in educational and psychologicalresearch. Its flexibility in addressing complex theoretical models and the proper treatment ofmeasurement 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 andrelatively large sample sizes) that could discourage practitioners from applying the model. Partialleast squares SEM (PLS-SEM) is a nonparametric technique which makes no distributionalassumptions and can be estimated with small sample sizes. In this paper a general introduction toPLS-SEM is given and is compared with conventional SEM. Next, step by step procedures, alongwith R functions, are presented to estimate the model. A data set is analyzed and the outputs areinterpreted.
