Statistical Methods Course Description
Overview: This is an intensive short course on the principles of structural equation modeling. Topics include confirmatory factor analysis, multiple-group comparisons, factorial invariance as well as extended applications such as hierarchical models & multi-level SEM.
This is an intensive short course on the principles of structural equation modeling. Topics include confirmatory factor analysis, multiple-group comparisons, factorial invariance as well as extended applications such as hierarchical models and multi-level SEM.
Instructor: Todd D. Little
Professor of Educational Psychology and Leadership at Texas Tech University. Little is also the director of the Institute for Measurement, Methodology, Analysis, and Policy (IMMAP) at Texas Tech University. He holds a Ph.D. in developmental psychology from the University of California-Riverside. Little in 2013 received the APA Division 5 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring.
Instructor: Elizabeth Grandfield
Doctoral student in Quantitative Psychology at the University of Kansas. Her research focuses on evaluating measurement invariance with an emphasis in longitudinal designs. In areas of applied research, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center, both in Kansas City.
Below are links to course files for those who enrolled in the course. Please download these files onto your computer before the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.
Click here to Access The SEM Foundations and Extended Applications Course Files
If you need to analyze the covariance structure of multivariate data and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You do not need to know matrix algebra, calculus, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication, will benefit from the course.
The course will support LISREL, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL, Mplus or Laavan is assumed. Furthermore, nearly all the techniques taught in the course can be translated fairly easily to most other packages.