Study on Structural Equation Modeling for Analyzing Data - Khairi, M. Ihsan; Susanti, Dwi; Sukono, Sukono (Research Collaboration Community (Rescollacom), 2021)
Structural Equation Model (SEM) is a combination of two separate statistical methods, namely factor analysis developed in psychology and psychometry and simultaneous equation model developed in econometrics. Factor analysis was first introduced by Galton in 1869 and Pearson (Pearson and Lee, 1904). Spearman's (1904) research is the development of a general factor analysis model in his research relating to the structure of mental abilities, Spearman stated that the intercorrelation test between mental abilities can determine general ability factors and special ability factors. SEM is a combination of factor analysis and path analysis into one comprehensive statistical method. Path analysis itself is the forerunner of the structural equation of Sewwl Wright's research in the field of biometrics. Wright's contribution is to be able to show that the correlation between variables is related to the parameters of a model described by a path (path diagram). In SEM there are 2 variables, namely latent variables (exogenous and endogenous) and indicator variables. SEM has 2 equation models, namely the measurement equation model and the structural equation model. SEM also has 2 errors, namely the error for the measurement equation model and the error for the structural equation model. In general, SEM is formed from the relationship between latent variables and their respective indicator variables. To test whether the existing indicator variables are valid indicators for measuring the latent construct, Confirmatory Factor Analysis (CFA) is used. Data analysis with SEM must meet the existing SEM assumptions. The model feasibility test is carried out based on the goodness of fit criteria. The stages in SEM analysis are theoretical model development, flow chart drawing, flow chart conversion into equation form, input matrix and model parameter estimation techniques, model problem identification, evacuating model parameter estimates, model interpretation and model modification.