Structural Equation Modeling: Path Analysis and Structural Regression

Structural equation modeling (SEM) is a general-purpose modeling framework that is useful for testing theories about complex interrelationships between variables, both observed and latent. For example, SEM could be applied to employee survey data to test competing theories about how motivation, stress, leadership, compensation, and job satisfaction influence one another. While traditionally applied in the social sciences, SEM has potential applications in the natural sciences and engineering, too.

This webinar, intended for academic researchers, will teach you SEM concepts and techniques for performing path analysis and structural regression, including how to implement these techniques in the JMP Student Edition, a no-cost, full-featured version of JMP exclusively for academic use. Topics include:

• Interactive path diagrams for visualizing and specifying model structures.

• Path analysis for testing theories of relationships between observed variables.

• Incorporating latent variables into path analysis (i.e., structural regression).

• Fitting conditional process models (i.e., mediation and moderation).

• One-click nested model comparison.

Get JMP software free for academic use at jmp.com/student