Multilevel models (also referred to as mixed models or hierarchical models) are used when observations are not independent. Data clustering occurs with many experimental designs (e.g., split plot designs), with social science data collected simultaneously on different units of analysis (e.g., households and their individual members), with measurements taken on the same units over several time periods (longitudinal studies) and with spatial data.
The purpose of this workshop is to introduce the concepts that form the basis of these models, the underlying statistical model and the estimation techniques that are used. Many examples will be presented during the workshop to enable participants to recognize such models when encountered in their own research, to analyze them and interpret the results.
The workshop is intended for participants who have the equivalent of two semesters of statistics and some previous experience with ANOVA and linear regression. The workshop will be taught through a combination of lectures and hands-on computer exercises. The workshop will be appropriate for faculty, research staff, and graduate students.
| Fee: |
None to members of the Cornell community |
| Registration: | Registration is required. Since space is limited, early registration is encouraged. |
| Instructor: | Francoise Vermeylen |
