Development Structural Equation Modelling with Second Order Measurement Model and Mixed Scale Data (Journal of Applied Statistics and Data Science, 2024)
This research will develop structural equation modeling involving latent variables with a second order measurement model, as well as mixed measurement scales using simulation data. Applied to simple structural modeling which has one exogenous variable, one mediating endogenous variable, and one endogenous dependent variable. Based on the results of the analysis, if the coefficient of the measurement model and the structural model is greater, the error variance is 0.1, causing the correlation between indicators in each variable to be stronger. Another effect caused by the greater closeness of the coefficient of the measurement model and the structural model is that the greater the percentage of diversity generated in the measurement model. The results of the inner model analysis show that there is an insignificant relationship, namely the relationship between the Y1 variable and the Y2 variable but for other path relationships it shows significant. The total coefficient of determination for the second order SEM shows that the greater the closeness of the coefficient of the measurement model and the structural model in the error variance condition of 0.1, the greater the R2 value.