This is an advanced applied statistics course designed for doctoral or advanced master's students and serving as a comprehensive introduction to multilevel modelling, also known as "hierarchical linear modelling (HLM)" or "mixed effects modelling." These powerful models have become very common in educational research, both for the analysis of data with a multilevel structure (e.g., students nested in schools, school boards, provinces, or countries) and for the study of educational change (e.g., student learning/growth, school improvement, or organizational change).

The course covers two-level and three-level cross-sectional and growth curve models, as well as model selection, assumptions, and diagnostics. Examples and assignments will draw on data from large-scale national and international datasets; the course will also serve as an introduction to the HLM software package. The objective of the course is to equip students with the skills to use, interpret, and write about multilevel models in their own research.