Assessing statistical differences between parameters estimates in Partial Least Squares path modeling

Macario Rodríguez-Entrena, Florian Schuberth () and Carsten Gelhard ()
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Florian Schuberth: University of Würzburg, Sanderring 2
Carsten Gelhard: University of Twente

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 1, No 4, 57-69

Abstract: Abstract Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

Keywords: Testing parameter difference; Bootstrap; Confidence interval; Practitioner’s guide; Statistical misconception; Consistent partial least squares (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s11135-016-0400-8

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