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

Macario Rodriguez-Entrena, Florian Schuberth, Carsten Gelhard (Corresponding Author)

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
42 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)57-69
Number of pages13
JournalQuality and quantity
Volume52
Issue number1
DOIs
Publication statusPublished - 2018

Fingerprint

Partial Least Squares
Technology Acceptance
Path
Structural Equation Modeling
Modeling
Estimate
Guidance
Two Parameters
acceptance
lack
Group
Model

Keywords

  • UT-Hybrid-D
  • Testing parameter difference
  • Bootstrap
  • Confidence interval
  • Practitioner’s guide
  • Statistical misconception
  • Consistent partial least squares

Cite this

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Assessing statistical differences between parameters estimates in Partial Least Squares path modeling. / Rodriguez-Entrena, Macario; Schuberth, Florian ; Gelhard, Carsten (Corresponding Author).

In: Quality and quantity, Vol. 52, No. 1, 2018, p. 57-69.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Schuberth, Florian

AU - Gelhard, Carsten

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N2 - 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.

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KW - UT-Hybrid-D

KW - Testing parameter difference

KW - Bootstrap

KW - Confidence interval

KW - Practitioner’s guide

KW - Statistical misconception

KW - Consistent partial least squares

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