TY  - JOUR
A1  - Rodríguez-Entrena, Macario
A1  - Schuberth, Florian
A1  - Gelhard, Carsten
T1  - Assessing statistical differences between parameters estimates in Partial Least Squares path modeling
JF  - Quality & Quantity
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.
KW  - Testing parameter difference
KW  - Bootstrap
KW  - Confidence interval
KW  - Practitioner's guide
KW  - Statistical misconception
KW  - Consistent partial least squares
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-226403
VL  - 52
IS  - 1
ER  -