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  - Schuberth, Florian
A1  - Henseler, Jörg
A1  - Dijkstra, Theo K.
T1  - Partial least squares path modeling using ordinal categorical indicators
JF  - Quality & Quantity
N2  - This article introduces a new consistent variance-based estimator called ordinal consistent partial least squares (OrdPLSc). OrdPLSc completes the family of variance-based estimators consisting of PLS, PLSc, and OrdPLS and permits to estimate structural equation models of composites and common factors if some or all indicators are measured on an ordinal categorical scale. A Monte Carlo simulation (N =500) with different population models shows that OrdPLSc provides almost unbiased estimates. If all constructs are modeled as common factors, OrdPLSc yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient. If some constructs are modeled as composites, OrdPLSc is virtually without competition.
KW  - polychoric correlation
KW  - composites
KW  - common factors
KW  - ordinal categorical indicators
KW  - consistent partial least squares
KW  - structural equation models
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-144016
ER  -