Multilevel mixture factor models for the evaluation of educational programs’ effectiveness
Roberta Varriale, Caterina Giusti · Contributions to statistics · 2009
Factor models aim at explaining the associations among observed random variables in terms of fewer unobserved random variables, called common factors. When data have a hierarchical structure, multilevel mixture factor models are a powerful and flexible tool useful to correctly take into account the correlation between first-level units due to the data structure, and to evaluate the presence of latent sub-populations of units with some typical profile at different levels of the analysis. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.