Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS
This course teaches students how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings.
Presented by Catherine Truxillo
Dr. Catherine Truxillo has been a Statistical Training Specialist at SAS since 2000 and has written or co-written SAS training courses for advanced statistical methods including: multivariate statistics, linear and generalised linear mixed models, multilevel models, structural equation models, multiple imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. Although she primarily works with advanced statistics topics, she also teaches SAS courses using SAS/IML (the interactive matrix language), Enterprise Guide, and JMP software.
Catherine's previous experiences with teaching, statistical consulting, and software design led her to a job teaching statistics for SAS.
Before moving to SAS, Catherine completed her Ph.D. in Social Psychology with an emphasis in Statistics at The University of Texas at Austin. While at UT Austin, she completed an internship with the Math and Computer Science department's statistical consulting help desk and taught a number of undergraduate courses. While teaching and performing her own graduate research, she worked for a software usability design company conducting experiments to assess the ease-of-use of various software interfaces and website designs.
She moved to Cary, North Carolina to join SAS in 2000, where she lives in the forest with her husband and two children. Her personal interests include bicycling, hiking, dancing, and singing.
Learn how to:
- use basic multilevel models
- use three-level and cross-classified models
- use generalised multilevel models for discrete dependent variables
Who should attend
Researchers in psychology, education, social science, medicine, and business, or others analysing data with multilevel nesting structure.
Before attending this course, you should:
- preferably, be familiar with the basic structure and concepts of SAS (for example, the DATA step and procedures)
- be familiar with concepts of linear models such as regression and ANOVA and with generalised linear models such as logistic regression
- be familiar with linear mixed models to enhance understanding, although this is not necessary to benefit from the course.
It is recommended that you complete SAS Programming 1: Essentials and Statistics 2: ANOVA and Regression, or have equivalent knowledge before taking this course.
This course addresses SAS/STAT software.