Applications of Structural Equation Modeling In Social Sciences Research
Jackson de Carvalho, Felix O. Chima

Abstract
Structural equation modeling (SEM) is a comprehensive statistical modeling tool for analyzing multivariate data involving complex relationships between and among variables (Hoyle, 1995). SEM surpasses traditional regression models by including multiple independent and dependent variables to test associated hypothesizes about relationships among observed and latent variables. SEM explain why results occur while reducing misleading results by submitting all variables in the model to measurement error or uncontrolled variation of the measured variables. The purpose of this article is to provide basic knowledge of structural equation modeling methodology for testing relationships between indicator variables and latent constructs where SEM is the analysis technique of the research statistical design. It is noteworthy, SEM provides a way to test the specified set of relationships among observed and latent variables as a whole, and allow theory testing even when experiments are not possible. Consequently, these methodological approaches have become ubiquitous in the scientific research process of all disciplines.

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