Content

1. Introduction to hierarchical data structures

2. The linear two-level model with random intercepts

3. The linear two-level model with random intercepts and random slopes

4. Cross-classified, multiple membership and generalized mixed models

5.  Analyzing panel data with Random and Fixed Effects Models

6. Growth curve models for panel data

7. Contextual effects in multilevel models

8. Multilevel models with more than two levels, the within-between specification for pooled cross-sectional data

9. Comparing and testing in multilevel modeling, estimation techniques and some advanced topics

Course material

All material will be provided in Toledo. These include the slides from the lecture, a reading list, data sets, R scripts and exercises for the hands-on sessions.

Format: more information

We will use the statistical software R for this course. During the course we alternate between lecture-style presentations and hands-on session with R. The course consists of 12 sessions, which are provided in four weeks of block teaching (three sessions each week). In the first nine session we alternate between lectures and hands-on exercises, in which students apply the statistical models in R. The last three sessions are for additional discussions (Q&A), individual consultations on students’ assignments or projects and (non-mandatory) student presentations.

Videos are made available, in which the material is explained in an uniterrupted presentation. These are not livestreamed or recorded in class, but separately recorded.