Confirmatory Factor Analysis and Structural Equation Modeling

Abstract

In this chapter, we discuss structural equation models in R. We show how R can be used for both covariance-based and partial least squares modeling, and present basic guidelines for model assessment. We also demonstrate the power of R to simulate data and use such simulation to inform our expectations. Structural models are helpful when your modeling needs meet any of these conditions: you need to evaluate interconnections of multiple data points that do not map neatly to the division between predictors and an outcome variable (as would be the case in linear modeling); you wish to include unobserved latent variables such as attitudes and estimate their relationships to one another or to observed data; or you wish to estimate the overall fit between observed data and a proposed model with latent variables or complex connections. From this point of view, structural models are closely related to both linear modeling because they estimate associations and model fit, and to factor analysis because they use latent variables.