Paper Info
Title
Measurement Error Correlation Within Blocks Of Indicators In Consistent Partial Least Squares Issues And Remedies
Abstract
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.
Year
DOI
Venue
2019
10.1108/IntR-12-2017-0525
INTERNET RESEARCH
Keywords
DocType
Volume
Monte Carlo simulation, Structural equation modelling, Consistent partial least squares, Measurement error correlation, Model specification
Journal
29
Issue
ISSN
Citations 
3
1066-2243
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Manuel E. Rademaker100.34
Florian Schuberth231.78
Theo K. Dijkstra31016.46