3 credits
Spring 2026 Lecture Distance Learning
This course provides an introduction to classic structural equation models with latent variables (SEM). The primary steps of implementing SEMs will be covered to include: model specification, model identification, parameter estimation, and model evaluation (model fit). Data preparation, hypothesis testing and estimation approaches for SEMs are introduced throughout the course. While mathematical basics of statistical methods are covered, emphasis is placed on model development, the conceptual understanding of models, and interpretation of model results. Prerequisites: STAT 50100 and STAT 50200 or HDFS 61300 and HDFS 61700, or other similar sequence with instructor approval.
Learning Outcomes1Develop a basic understanding of structural equation models including proper application, interpretation, and evaluation of the models.
2Develop an understanding of the underlying statistics including parameter identification and estimation as well as model fit measures.
3Learn the benefits of SEMs including when it is advantageous to use this modeling approach.
4Learn the limitations of the model and the most common mistakes in using SEMs.
5Be able to apply the method to a topic relevant to your own research.
6Be able to analyze SEM models using a SEM software package.
Restrictions
made by Purdue students.