Structural Equation Modelling (SEM) in Research: Narrative Literature Review

Abstract

The structural equation modelling (SEM) method has stronger predicting power than path analysis and multiple regression because SEM is able to analyze at the deepest level the variables or constructs studied. This literature review aimed to describe the use of structural equation modelling in research. In general, SEM can be used to analyze research models that have several independent (exogenous) and dependent (endogenous) variables, as well as moderating or intervening variables. SEM provides several benefits and advantages for researchers, including building research models with many variables, examining variables or constructs that cannot be observed or cannot be measured directly (unobserved), testing measurement errors (measurement errors) for observed variables or constructs ( observed) and confirmatory factor analysis. Broadly speaking, SEM methods can be classified into two types, namely covariance-based structural equation modelling (CB-SEM) and variance or component-based SEM (VB-SEM), which includes partial least squares (PLS) and generalized structured component analysis (GSCA). This literature review aimed to describe the use of structural equation modelling in research.