Data generation for composite-based structural equation modeling methods

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

An alternative definition of the influence function

We define an asymptotic mean version of Tukey's sensitivity curve. For this we show that some of the more important features of Hampel's influence function hold for L- and M-estimators also. Therefore, we have an alternative approach to the influence function using only expectations and limits.Influence function Sensitivity curve L-estimator M-estimator