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Organizational Research Methods
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Article

Testing Mediation and Suppression Effects of Latent Variables: Bootstrapping With Structural Equation Models

Gordon W. Cheung1* and Rebecca S. Lau2

1 The Chinese University of Hong Kong
2 Virginia Polytechnic Institute and State University

* To whom correspondence should be addressed. E-mail: gordonc{at}cuhk.edu.hk.


  Abstract
Because of the importance of mediation studies, researchers have been continuously searching for the best statistical test for mediation effect. The approaches that have been most commonly employed include those that use zero-order and partial correlation, hierarchical regression models, and structural equation modeling (SEM). This study extends MacKinnon and colleagues (MacKinnon, Lockwood, Hoffmann, West, & Sheets, 2002; MacKinnon, Lockwood, & Williams, 2004, MacKinnon, Warsi, & Dwyer, 1995) works by conducting a simulation that examines the distribution of mediation and suppression effects of latent variables with SEM, and the properties of confidence intervals developed from eight different methods. Results show that SEM provides unbiased estimates of mediation and suppression effects, and that the bias-corrected bootstrap confidence intervals perform best in testing for mediation and suppression effects. Steps to implement the recommended procedures with Amos are presented.

First published on July 23, 2007, doi:10.1177/1094428107300343

Organizational Research Methods 2008;11:296.

A more recent version of this article appeared on April 1, 2008

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