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Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM

Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM

Ned Kock, Shaun Sexton
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 23
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781522512905|DOI: 10.4018/IJSDS.2017100102
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MLA

Kock, Ned, and Shaun Sexton. "Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM." IJSDS vol.8, no.4 2017: pp.46-68. http://doi.org/10.4018/IJSDS.2017100102

APA

Kock, N. & Sexton, S. (2017). Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM. International Journal of Strategic Decision Sciences (IJSDS), 8(4), 46-68. http://doi.org/10.4018/IJSDS.2017100102

Chicago

Kock, Ned, and Shaun Sexton. "Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM," International Journal of Strategic Decision Sciences (IJSDS) 8, no.4: 46-68. http://doi.org/10.4018/IJSDS.2017100102

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Abstract

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data analytics; and is in fact a characteristic of composite-based models in general, where latent variables are modeled as exact linear combinations of their indicators. The underestimation is often of around 10% per path in models that meet generally accepted measurement quality assessment criteria. The authors propose a numeric solution to this problem, which they call the factor-based partial least squares regression (FPLSR) algorithm, whereby variation lost in composites is restored in proportion to measurement error and amount of attenuation. Six variations of the solution are developed based on different reliability measures, and contrasted in Monte Carlo simulations. The authors' solution is nonparametric and seems to perform generally well with small samples and severely non-normal data.

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