Generalized linear mixed models are a type of statistical model that can handle different types of data, like binary responses or counts, and account for clustering in the data. They are useful for situations like students in schools or repeated observations of the same individuals over time. The researchers applied a logistic random intercept model to study reading proficiency in students nested within schools. They also explored random coefficient models, different response types, and how to estimate these models. The findings suggest that these models can provide valuable insights into complex data structures and relationships.