When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS(R)
This course teaches 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...
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This course teaches 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.
The self-study e-learning includes:
- Annotatable course notes in PDF format.
- Virtual lab time to practice.
Skills Gained
- Use basic multilevel models.
- Use three-level and cross-classified models.
- Use generalized multilevel models for discrete dependent variables.
Who Can Benefit
- Researchers in psychology, education, social science, medicine, and business, or others analyzing data with multilevel nesting structure
Prerequisites
- 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 generalized 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.
Course Details
Introduction to Multilevel Models
- Nested data structures.
- Ignoring dependence.
- Methods for modeling dependent data structures.
- The random-effects ANOVA model.
Basic Multilevel Models
- Random-effects regression.
- Centering predictors in multilevel models.
- Model building.
- A comment on notation (self-study).
- Intercepts as outcomes.
Slopes as Outcomes and Model Evaluation
- Slopes as outcomes.
- Model assumptions.
- Model assessment and diagnostics.
- Maximum likelihood estimation.
The Analysis of Repeated Measures
- The conceptualization of a growth curve.
- The multilevel growth model.
- Time-invariant predictors of growth (self-study).
- Multiple groups models.
Three-Level and Cross-Classified Models
- Three-level models.
- Three-level models with random slopes.
- Cross-classified models.
Multilevel Models for Discrete Dependent Variables
- Discrete dependent variables.
- Generalized linear models.
- Multilevel generalized linear models.
- Additional considerations.
Generalized Multilevel Linear Models for Longitudinal Data (Self-Study)
- Complexities of longitudinal data structures.
- The unconditional growth model for discrete dependent variables.
- Conditional growth models for discrete dependent variables.