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

11 Citations (Scopus)
52 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

Keywords

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

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