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Introduction to Multilevel Modeling

Multilevel models (also referred to as mixed models or hierarchical models) are used when observations are not independent. Data clustering occurs e.g. with many experimental designs, with social science data collected simultaneously on different units of analysis (e.g., households and their individual members) and with measurements taken on the same units over several time periods (longitudinal studies).

The purpose of this workshop is to introduce the basic concepts, the underlying statistical models, and the estimation techniques commonly used. Many examples will be presented during the workshop to enable participants to recognize when such models can be applied in their own research, along with an introduction to their implementation and interpretation.

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, but registration is required. Since space is limited, early registration is encouraged.

Files: Software Specific Files

For times and locations of upcoming workshops, please see the Workshop Schedule.