2020/01/01 by Rainer Schlittgen, Marko Sarstedt, Schlittgen, Rainer +3
Computer Science · Medicine · Social Sciences · #Advanced Statistical Modeling Techniques #Composite models #Data generation #Diverse Approaches in Healthcare and Education Studies #Education, Safety, and Science Studies #GSCA #Generalized structural component analysis #Mathematik #PLS #Partial least squares #SEM #Structural equation modeling
paper · doi:10.15480/882.5127
openalex publication_date 2020/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.