Aplikasi Structural Equation Modeling-Partial Least Squares dalam Menentukan Faktor yang Mempengaruhi Kinerja Karyawan - Amanda Permata Dewi; I Made Sumertajaya; Aji Hamim Wigena (Xplore: Journal of Statistics, 2018)
Structural Equation Modeling (SEM combines factor and path analysis, so researchers can see the relationship between latent variables and their indicators and the relationship between latent variables. Partial Least Square is a soft modeling approach on SEM that has no assumption of data distribution and minimum number of observations which is often called SEM-PLS. The data used in this study is the performance of 70 constructions company employees. The number of observations is too small and couldn’t fulfill the data normality assumption so the analysis method used is SEM-PLS. This study applies SEM-PLS to identify the factors that influence the performance based on competence data from each of the existing employees. The results of this study indicate that both variables have a significant influence on the performance variables. The model tested in the research is good enough to explain the diversity of the performance variables with the evaluation value of Q2 of 75.24%.