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Open AccessJournal ArticleDOI

lavaan: An R Package for Structural Equation Modeling

Yves Rosseel
- 24 May 2012 - 
- Vol. 48, Iss: 2, pp 1-36
TLDR
The aims behind the development of the lavaan package are explained, an overview of its most important features are given, and some examples to illustrate how lavaan works in practice are provided.
Abstract
Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. Over the years, many software packages for structural equation modeling have been developed, both free and commercial. However, perhaps the best state-of-the-art software packages in this field are still closed-source and/or commercial. The R package lavaan has been developed to provide applied researchers, teachers, and statisticians, a free, fully open-source, but commercial-quality package for latent variable modeling. This paper explains the aims behind the development of the package, gives an overview of its most important features, and provides some examples to illustrate how lavaan works in practice.

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Structural Equation Modeling

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TL;DR: In this article, the authors address Ronkko and Evermann's criticisms of the Partial Least Squares (PLS) approach to structural equation modeling and conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.
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Estimating Psychological Networks and their Accuracy : A tutorial paper

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References
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Book

Structural Equations with Latent Variables

TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Journal ArticleDOI

Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models

TL;DR: For comments on an earlier draft of this chapter and for detailed advice I am indebted to Robert M. Hauser, Halliman H. Winsborough, Toni Richards, several anonymous reviewers, and the editor of this volume as discussed by the authors.
Book

Testing Structural Equation Models

TL;DR: In this paper, Bollen et al. proposed a model fitting metric for Structural Equation Models, which is based on the Monte Carlo evaluation of Goodness-of-Fit measures.
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