Chapter 1: An Introduction to Structural Equation Modeling

Learning Outcomes

  1. Understand the meaning of structural equation modeling (SEM) and its relationship to multivariate data analysis.
  2. Describe the basic considerations in applying multivariate data analysis.
  3. Comprehend the basic concepts of partial least squares structural equation modeling (PLS-SEM).
  4. Explain the differences between covariance-based structural equation modeling (CB-SEM) and PLS-SEM and when to use each.

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Social science researchers have been using statistical analysis tools for many years to extend their ability to develop, explore, and confirm research findings. Application of first-generation statistical methods such as factor analysis and regression analysis dominated the research landscape through the 1980s. But since the early 1990s, second-generation methods have expanded rapidly and, in some disciplines, represent almost 50% of the statistical tools applied in empirical research. In this chapter, we explain the fundamentals of second-generation statistical methods and establish a foundation that will enable you to understand and apply one of the emerging second-generation tools, referred to as partial least squares structural equation modeling (PLS-SEM).