Bootstrapping partial least squares structural equation modelling with massive data
Lamberti, Giuseppe (Universitat Autònoma de Barcelona. Departament d'Economia i d'Història Econòmica)
La Rocca, Michele (Università degli Studi di Salerno)

Imprint:	Springer Netherlands, 2025
Description:	37 pàg.
Abstract:	New scenarios in data analysis call for new methods capable of fully exploiting the potential of the available data. In the specific context of massive data, this means tackling two principal issues: the computational cost of applying a method, since convergence must be fast, and a level of accuracy that ensures precise estimation. We explore the applicability of the partial least squares structural equation modelling (PLS-SEM) algorithm to a massive data context. Considering the classical bootstrap procedure used to validate model coefficients, we show that bootstrapping becomes very expensive computationally once a sample size becomes massive. We consequently adapted the subsampled double bootstrap (SDB) algorithm to the PLS-SEM context to reduce the computational cost without sacrificing accuracy in confidence interval estimates, using a straightforward procedure that is easy to implement. The accuracy and the speed of convergence of the SDB PLS-SEM are demonstrated in simulation studies, and the new method is successfully tested with a model of European internet use.
Grants:	European Commission 870691-INVENT Generalitat de Catalunya SGR / 1056
Note:	Altres ajuts: acords transformatius de la UAB
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	PLS-SEM ; Big Data ; Bootstrap procedure ; Inference accuracy ; Computational cost ; Massive secondary data ; Internet use
Published in:	Quality and Quantity, 24 November 2025, ISSN 1573-7845

DOI: 10.1007/s11135-025-02483-2

37 p, 3.3 MB

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Articles > Research articles
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