Application of Structural Equation Modeling in Quantitative Research

Abstract

The key aim of this study is to explore Structural Equation Modeling [SEM] is a more powerful statistical method for quantitative research. We applied document analysis method by using both latent and observable variables in comprehensive models where SEM makes it clear to examine intricate correlations between variables. SEM makes the collected data possible for academics to assess theoretic models. It also covers the benefits of SEM over the conventional statistical techniques such as making capacity to manage measurement error, take the latent variables into account, and examine intricate chains of causality. Nonetheless these difficulties, SEM delivers scholars with a flexible tool for developing new models and theories and grasping intricate phenomena in quantitative research. SEM offers a big framework for modeling of complex interactions that which advances in research of various domains in mathematics and statistics as well as public health, economics, psychology and sociology.