Abstract
PurposeStructural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.Design/methodology/approachAs a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.FindingsThe author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.Originality/valueFor each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.
Subject
Industrial and Manufacturing Engineering,Strategy and Management,Computer Science Applications,Industrial relations,Management Information Systems
Reference69 articles.
1. Between-group equivalence in comparisons using PLS: results from three simulation studies;Communications of the Association for Information Systems,2015
2. Consistent and asymptotically normal PLS estimators for linear structural equations;Computational Statistics and Data Analysis,2015
3. Consistent partial least squares path modeling;MIS Quarterly,2015
4. Consistent partial least squares for nonlinear structural equation models;Psychometrika,2014
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