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plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares
Author(s) -
Sergio Venturini,
Mehmet Mehmetoglu
Publication year - 2019
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v088.i08
Subject(s) - structural equation modeling , partial least squares regression , latent variable , covariance , computer science , set (abstract data type) , least squares function approximation , variable (mathematics) , mathematics , econometrics , algorithm , statistics , programming language , mathematical analysis , estimator
We provide a package called plssem that fits partial least squares structural equation models, which is often considered an alternative to the commonly known covariance-based structural equation modeling. plssem is developed in line with the algorithm provided by Wold (1975) and Lohmoller (1989). To demonstrate its features, we present an empirical application on the relationship between perception of self-attractiveness and two specific types of motivations for working out using a real-life data set. In the paper we also show that, in line with other software performing structural equation modeling, plssem can be used for putting in relation single-item observed variables too and not only for latent variable modeling.

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