Measurement error correlation within blocks of indicators in consistent partial least squares : Issues and remedies
Please always quote using this URN: urn:nbn:de:bvb:20-opus-224901
- 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 ofPurpose 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.…
Author: | Manuel E. Rademaker, Florian Schuberth, Theo K. Dijkstra |
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URN: | urn:nbn:de:bvb:20-opus-224901 |
Document Type: | Journal article |
Faculties: | Wirtschaftswissenschaftliche Fakultät / Betriebswirtschaftliches Institut |
Language: | English |
Parent Title (English): | Internet Research |
Year of Completion: | 2019 |
Volume: | 29 |
Issue: | 3 |
Pagenumber: | 448-463 |
Source: | Internet Research, Vol. 29 No. 3, pp. 448-463 |
DOI: | https://doi.org/10.1108/IntR-12-2017-0525 |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Tag: | Consistent partial least squares; Measurement error correlation; Model specification; Monte Carlo simulation; Structural equation modelling |
Release Date: | 2022/12/05 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |