Structural Equation Modeling

This video will teach you a basic understanding of the theory and applications of structural equation modeling (SEM). SEM is a tool for understanding the behaviors of latent variables. That is, variables that are not directly measured, but inferred from other variables. In this series, manifest variables are discussed, and then latent variables are introduced. This gives way to factor analysis and then full SEM and fit statistics. This video is recommended for those with some experience with basic linear algebra and regression.

Topics to be covered include:

How can variables be related? This clip discusses the ways that a number of data vectors, or variables, can be related to one another. Linear and nonlinear relationships are shown, as well as causal pathways between variables. The third variable problem is introduced along with how SEM leverages this problem to simplify complex interactions.
General Path Diagrams. This clip discusses the structure of general path diagrams in the framework of path modeling, or path analysis. SEM is an extended form of path analysis that includes latent variables. In this section, the components of a path diagram are introduced. These components are then related to the equivalent linear models. This section also discusses the advantages of path modeling to standard linear modeling.
Latent Variables. This clip discusses the concept of a latent variable, a core quality of using SEM. In this section, multiple definitions of a latent variable are introduced along with a new component to path diagrams (the circle). Examples are given of latent variables in multiple fields and the beginnings of factor analysis are shown near the end of this section.
Factor Analysis. This clip introduces both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Exploratory factor analysis is used to discover latent variables within a data set. Confirmatory factor analysis is used to confirm the existence of hypothesized latent variables in a given data set. Real world examples are given for both, as well ad data requirements and assumptions.
Structural Equation Modeling. This clip expands on factor analysis to full structural equation models (SEM). SEM allows for the estimation of regression paths between latent variables. SEM is introduced as an expansion of confirmatory factor analysis and expanded upon mathematically. Data requirements and assumptions of SEM are discussed, as well as times when SEM would be applicable to a data set.
Fit Statistics. This clip discusses the concept of fit in the structural equation modeling framework. In this section, multiple fit indices are described on a mathematical level such as CFI, TLI, and RMSEA. Suggestions are then given for when to use specific fit indices, as well as their strengths and weaknesses.