Research data in the field of social research often have a so-called hierarchical structure. Here, a finer structure than applied within the simple linear regression framework is used. More precisely, the data are structured into different hierarchies and a linear model is set up for each of these hierarchies. An example of a hierarchical structure is the assignment of students to classes, classes to schools, schools to districts etc.

During the HLM-training we demonstrate how to use hierarchical models to pursue empirical research questions quantitatively. You will learn that the intuitive operation and clarity of the software HLM proves to be particularly helpful when setting up hierarchical models.

At the training, we will start with simple regression models, turn over to analyse fixed and random effects models and finally end up with the investigation of mixed models. A special emphasis is given to the HLM-estimation of the corresponding model parameters. Here, you will learn to interpret the HLM-output properly.

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HLM Training - Hierarchical Models

Apply models to hierarchical data structures, that are frequently found in practical applications. The training shows how to define and run up to ...

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HLM Trainings

Behavioral and social data commonly have a nested, hierarchical structure, which is usally modeled using linear models...

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