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A Short Course on Structural Equation Modeling

Workshop Overview

Structural equation modeling (SEM) is a statistical methodology that is gaining popularity with researchers from a broad range of fields including sociology, psychology, clinical sciences, ecology, and biology. SEM provides a general framework for examining complex relationships between variables that may be either observed (manifest) or unobserved (latent). SEM can be used to investigate direct and indirect effects, handle correlated independent variables, and analyze longitudinal data. Path analysis and linear models are both special cases of SEMs.

This course will cover the fundamental theory behind SEM and provide attendees with the knowledge and skills necessary to confidently apply these methods to their own research. No previous background in SEM is necessary. Researchers from all fields are welcome.

Specific topics to be covered include:

  • Overview of SEM methods
  • Review of linear models
  • Path analysis
  • Direct and indirect effects
  • Exploratory factor analysis
  • Confirmatory factor analysis
  • Reliability and validity
  • Regression with latent variables
  • Goodness of fit measures
  • Handling missing data via SEM
  • Generalized structural equation modeling
  • Latent growth curve models

Statistical Software

This course will have a substantial hands-on component using R via RStudio. Participants should have a working knowledge of both R and RStudio (e.g., be comfortable importing and manipulating data, performing simple statistical analyses, and fitting linear regression models). Prior to the SEM short course, participants will be given an opportunity to register for a free introduction to R webinar that will cover the material that participants are expected to be familiar with. All sessions will be held in a computer lab with PCs that have the most recent versions of R and RStudio installed. Participants are also welcome to bring your own laptop to use during the course, but please make sure to have the most recent version of both R and RStudio already installed. To download and install R, visit https://www.r-project.org and to download and install RStudio, visit https://www.rstudio.com.