Hierarchical Models
- Models data with nested or multi-level structure and account for dependencies within groups.
- Can represent varying effects at different levels (e.g., group-level vs. individual-level).
- Includes forms such as multi-level models and mixed effects models (which combine fixed and random effects).
Definition
Section titled “Definition”Hierarchical models, also known as hierarchical linear models, are a type of statistical modeling technique used to analyze data that is structured in a hierarchical or nested format.
Explanation
Section titled “Explanation”Hierarchical models are designed for data with multiple levels of nesting (for example, individuals nested within groups, and groups nested within larger units). They explicitly model the structure induced by nesting so that effects at each level can be estimated separately. A common form is a multi-level model, which handles multiple nested levels; another form is a mixed effects model, which models both fixed effects and random effects to capture population-level and group-level variation.
Examples
Section titled “Examples”Multi-level model
Section titled “Multi-level model”A study on student achievement where data are structured with students nested within classrooms, classrooms nested within schools, and schools nested within districts. A multi-level model can be used to determine the effects of factors such as teacher quality or school resources on student achievement.
Mixed effects model
Section titled “Mixed effects model”A study on employee productivity where data are structured with employees nested within departments, departments nested within divisions, and divisions nested within the overall company. A mixed effects model can be used to determine the effects of factors such as departmental policies or divisional strategies on employee productivity.
Use cases
Section titled “Use cases”- Analyzing complex data structures with multiple nested levels.
- Accounting for the effects of nested factors on an outcome of interest.
- Helping researchers understand relationships between different levels of data and supporting informed decisions and predictions.
Related terms
Section titled “Related terms”- Hierarchical linear models
- Multi-level model
- Mixed effects model
- Fixed effects
- Random effects