**Learning Outcomes**

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

**Chapter Preview**

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).

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