Course Level: Beginner
Format Requirements: Participants will experience a mixture of lecture and discussion. We will introduce basic concepts and vocabulary of SEM, give real-world examples and conduct sample analyses using SEM software. No prior knowledge of SEM is required. Participants with a basic understanding of statistics will benefit most from this course.
Background: This short course will make Structural Equation Modeling (SEM) accessible to a wide audience of researchers across many disciplines. SEM is a very general and powerful technique to link conceptual models, path diagrams, factor analysis and other mathematical models. SEM has many advantages including (1) correcting for measurement error, (2) modeling of causal relationships including multiple direct and indirect effects in a single analysis and (3) cutting-edge techniques for model selection and comparison. These advantages are particularly applicable to research questions and theory development in medical decision making.
Description and Objectives: We present a basic overview of SEM principles, some common nomenclature, diagrams, a little algebra (with only a handful of Greek letters!), a few real world examples, and then a glimpse into more advanced SEM techniques such as measurement invariance testing and latent growth curve modeling. Whether you just want to know how to read or critique an article that uses SEM, or want to engage a few SEM researchers in some feisty methods discussions, signup for this course...we'd love to visit with you.
- Gain knowledge of important SEM resources
- Be introduced to SEM Software
- Be able to interpret SEM analysis (by knowing SEM symbols, notation and modeling guidelines)
- Understand advantages of SEM techniques for medical decision making researchers