Structural Equation Modeling (SEM) stands as a cornerstone in modern sociological research, offering a robust framework to examine complex relationships among variables. Rooted in both mathematics and sociology, SEM integrates elements of path analysis, factor analysis, and regression into a unified analytical approach. Its versatility makes it a favored tool for exploring intricate causal networks, mediating factors, and latent constructs in social science research. At its core, SEM allows researchers to test and refine theoretical models by estimating the strength and direction of relationships between observed and latent variables. Observed variables are directly measured, while latent variables are unobserved constructs inferred from multiple observed indicators. This distinction is crucial in sociology, where many phenomena are abstract and multifaceted, such as social capital, social status, or cultural attitudes. One of the key strengths of SEM is its ability to account for measurement error. By incorporating measurement models, researchers can separate true variance from error variance, leading to more accurate estimation of relationships. This is particularly advantageous in sociology, where variables like attitudes or behaviors are often measured with imperfect instruments prone to bias and imprecision. The book aims to shed light on some of the unexplored aspects of Structural Equation Modeling and the recent researches in this field. The objective of this book is to give a general view of the different areas of Structural Equation Modeling and its applications. The topics covered herein offer the readers new insights in this field.