TY  - JOUR
A1  - Rademaker, Manuel E.
A1  - Schuberth, Florian
A1  - Dijkstra, Theo K.
T1  - Measurement error correlation within blocks of indicators in consistent partial least squares : Issues and remedies
JF  - Internet Research
N2  - Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. Design/methodology/approach Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. Findings In the presence of population measurement error correlation, estimated parameter bias is generally small for original and modified PLSc, with the latter outperforming the former for large sample sizes. In terms of the root mean squared error, the results are virtually identical for both original and modified PLSc. Only for relatively large sample sizes, high population measurement error correlation, and low population composite reliability are the increased standard errors associated with the modification outweighed by a smaller bias. These findings are regarded as initial evidence that original PLSc is comparatively robust with respect to misspecification of the structure of measurement error correlations within blocks of indicators. Originality/value Introducing and investigating a new approach to address measurement error correlation within blocks of indicators in PLSc, this paper contributes to the ongoing development and assessment of recent advancements in partial least squares path modeling.
KW  - Monte Carlo simulation
KW  - Structural equation modelling
KW  - Consistent partial least squares
KW  - Measurement error correlation
KW  - Model specification
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-224901
VL  - 29
IS  - 3
ER  - 
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  -