Data generation for composite-based structural equation modeling methods

Journal
Advances in data analysis and classification  
Volume
14
Issue
4
Start Page
747
End Page
757
Citation
Advances in Data Analysis and Classification 14: 747-757 (2020-05-26)
Publisher DOI
10.1007/s11634-020-00396-6
Scopus ID
2-s2.0-85085369084
Publisher
Springer
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.
Subjects
Composite models
Data generation
Generalized structural component analysis
GSCA
Partial least squares
PLS
Structural equation modeling
SEM
DDC Class
510: Mathematik
More Funding Information
Otto-von-Guericke-Universität Magdeburg (3121)
Lizenz
https://creativecommons.org/licenses/by/4.0/
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