Assessing statistical differences between parameters estimates in Partial Least Squares path modeling
Please always quote using this URN: urn:nbn:de:bvb:20-opus-226403
- 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 whetherStructural 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.…
Author: | Macario Rodríguez-Entrena, Florian Schuberth, Carsten Gelhard |
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URN: | urn:nbn:de:bvb:20-opus-226403 |
Document Type: | Journal article |
Faculties: | Wirtschaftswissenschaftliche Fakultät / Betriebswirtschaftliches Institut |
Language: | English |
Parent Title (English): | Quality & Quantity |
Year of Completion: | 2018 |
Volume: | 52 |
Issue: | 1 |
Pagenumber: | 57-69 |
Source: | Qual Quant (2018) 52:57–69 |
DOI: | https://doi.org/10.1007/s11135-016-0400-8 |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Tag: | Bootstrap; Confidence interval; Consistent partial least squares; Practitioner's guide; Statistical misconception; Testing parameter difference |
Release Date: | 2023/02/07 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |