Uses for Multilevel Models

Abstract

Drawing on the contextual, evaluative, and summarizing studies of this book, this chapter explicates 11 uses for multilevel models and defines relevant vocabulary, concepts, and notational conventions. Because multilevel models are composed of both fixed and random components, statisticians refer to them as mixed models (Littell et al. 2006). Because multilevel models focus on data at different hierarchical levels, educational researchers refer to them as hierarchical models (Raudenbush and Bryk 2002). In the contextual analyses of data at one point in time, the level-1 response variable and its covariates are conceptualized as being grouped (or contained) within the level-2 units. In the analyses of data at several points in time, the level-1 response variable is an observation at a time point on an entity, and the repeated observations on that entity are said to be grouped or contained by that entity. The entity (e.g., a person, an organization, a country) is the level-2 unit. Ideally, multilevel models assess change on disaggregated data at several points in time (e.g., the scores on the repeated assessments of individual students who are grouped into classrooms). When the chapters of this book model aggregated data, it is because the disaggregated data are not available.1 By applying special cases of generalized linear mixed models—the Poisson and logit—some chapters model response variables that are not normally distributed. By applying multilevel models, all of the core chapters address the clustering of level-1 units when they are contained within level-2 units.