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Introduction to Multilevel Modeling

Many kinds of data have a multilevel, hierarchical, nested, or clustered structure. Animal and human studies of inheritance, for instance, deal with a natural multilevel structure where offspring are grouped within families. Offspring from the same parents tend to be more alike than individuals at large. Clinical trials and other data collections also create data hierarchies when groups rather than individual participants are randomly chosen (multistage sampling). Such clustering structures render many of the traditional statistical analysis techniques invalid. Often clustered-robust standard errors are used to correct for the increased similarly. This strategy, however, risks overlooking important relationships within and between those groups as they are left unmodelled.

This graduate-level course introduces students to multilevel modeling. The objective of this course is to get participants acquainted with multilevel models and to show how multilevel models can be used to

  1. account for uncertainty in estimation and prediction due to the clustering structure
  2. improve group-level inference and prediction
  3. learn about variability within and between groups
  4. learn about effect heterogeneity
  5. learn whether the within-group effect and the between-group effect of a predictor differ


Feel free to use any of these materials but please cite me using this reference:
Rosche, Benjamin (2022). Introduction to Multilevel Modeling.