Development of Accessible Methodologies and Software in Hierarchical Models with Missing Data

This project integrated the appropriate modeling of multilevel data with rigorous methods for modeling with missing data. The project developed new methods that integrate these, making currently available methods broadly accessible for the first time through user-friendly software, and trained educational researchers to use these methods and software.

In experimental research, the dominant design involves randomly assigned classrooms or schools to treatments. Therefore, the key explanatory variables are at the classroom or school levels while the outcome is measured at the individual level. In most cases, classrooms or schools are matched or blocked prior to randomization, so that the design will often have two or more levels of variation. The longitudinal follow-up of students generates an additional level. Hierarchical models, also known as multilevel models, are appropriate for the analysis of such data. Similarly, educational surveys involve multi-stage samples. Because of student mobility across classrooms, schools or school districts, the analysis may require a cross-classified hierarchical model.