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Hierarchical models are used when there is nesting of observational units in the data and variables are observed on multiple levels of the hierarchy. Failure to account for the hierarchy in the data may result in invalid conclusions. However, hierarchical models are not always needed for nested data as the intraclass correlation coefficient determines the requirement. This app focuses on illustrating the concept of hierarchical models by comparing the method to the two others at the extremes: the pooled and unpooled methods. Users are shown mathematically and visually how the hierarchical estimates are weighted averages and how they serve as a balance between the pooled and unpooled estimates; the two related ideas of shrinkage and borrowing strength are illustrated in this process.

Users have the capability to either use sample data sets or upload their own data to learn about hierarchical models through case studies. The three different scenarios for learning are varying-intercept, varying-intercept and varying-slope, and varying-intercept and varying-slope with level 2 predictor. In each scenario, users are first presented outputs and graphs of the pooled and unpooled method. Then they proceed to the hierarchical model and different concepts of this method are explained in compartments. Interpretations are included throughout the outputs for users to comprehend the ideas. Additionally, each scenario contains a comparison of the three modelling methods with visualizations. For those who are familiar with Bayesian methods, a tab is available to run a Bayesian hierarchical model. After grasping the concept of hierarchical models, users can analyze their own data with their own specified model.

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