Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/80397
Title: Data generation for composite-based structural equation modeling methods
Author(s): Schlittgen, Rainer
Sarstedt, MarkoLook up in the Integrated Authority File of the German National Library
Ringle, Christian M.
Issue Date: 2020
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-823514
Subjects: Composite models
Data generation
Generalized structural component analysis
GSCA
Partial least squares
PLS
Structural equation modeling
SEM
Abstract: Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze their performance on erroneous grounds (i.e., factor model data instead of composite model data). A potential reason for this malpractice lies in the lack of available composite model-based data generation procedures for prespecified model parameters in the structural model and the measurements models. Addressing this gap in research, we derive model formulations and present a composite model-based data generation approach. The findings will assist researchers in their composite-based SEM simulation studies.
URI: https://opendata.uni-halle.de//handle/1981185920/82351
http://dx.doi.org/10.25673/80397
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: Projekt DEAL 2020
Journal Title: Advances in data analysis and classification
Publisher: Springer
Publisher Place: Berlin
Volume: 14
Issue: 4
Original Publication: 10.1007/s11634-020-00396-6
Page Start: 747
Page End: 757
Appears in Collections:Fakultät für Wirtschaftswissenschaft (OA)

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