Part of the Structural Equation Modeling (SEM) series.  This workshop will help participants develop skills in understanding and conducting latent variable models, particularly from the perspective of structural equation modeling. After a conceptual overview, a broad view of matrix factorization techniques will be provided along with specific examples (e.g. PCA, ‘factor analysis’).  In addition, measurement error issues, reliability, and scale development will be discussed (e.g. ‘confirmatory’ factor analysis).

Prerequisites: One should have a firm understanding of basic regression. R will be the program of choice, but nothing beyond very basic skill is assumed (e.g. import data, run a regression).  Demonstration will be conducted with R, and the psych and lavaan packages in particular.

Link: https://m-clark.github.io/sem/latent-variables.html/