Data generation for composite-based structural equation modeling methods

Rainer Schlittgen (), Marko Sarstedt () and Christian M. Ringle ()
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Rainer Schlittgen: University of Hamburg
Marko Sarstedt: Otto-von-Guericke-University Magdeburg
Christian M. Ringle: Hamburg University of Technology

Advances in Data Analysis and Classification, 2020, vol. 14, issue 4, No 1, 747-757

Abstract: 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.

Keywords: Composite models; Data generation; Generalized structural component analysis; GSCA; Partial least squares; PLS; Structural equation modeling; SEM; 62H12; 62J99; 91C05; 91E45 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-020-00396-6

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