偏最小二乘结构方程建模。

Q2 Social Sciences
Hamdollah Ravand, Purya Baghaei
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引用次数: 98

摘要

结构方程模型(SEM)在教育和心理学研究中得到了广泛应用。它在处理复杂理论模型方面的灵活性和对测量误差的适当处理使其成为许多社会科学研究人员的首选模型。然而,该模型施加了一些令人生畏的假设和限制(例如,正态性和相对较大的样本量),这可能会阻碍从业者应用该模型。偏最小二乘扫描电镜(PLS-SEM)是一种不做分布假设的非参数技术,可以在小样本量下进行估计。本文对PLS-SEM进行了概述,并与传统SEM进行了比较。接下来,一步一步的程序,连同R函数,提出了估计模型。分析数据集并解释输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partial Least Squares Structural Equation Modeling with R.
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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来源期刊
CiteScore
2.60
自引率
0.00%
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