Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.
Chapter 1.: The Need for Multilevel Modeling
The Need for Multilevel Modeling
I should venture to assert that the most pervasive fallacy of philosophic thinking goes back to neglect of context.
—John Dewey, 1931
Background and Rationale
When one considers almost any phenomenon of interest to social and health scientists, it is hard to overestimate the importance of context. For example, we know that the likelihood of developing depression is influenced by social and environmental stressors. The psychoactive effects of drugs can vary based on the social frame of the user. Early childhood development is strongly influenced by a whole host of environmental conditions: diet, amount of stimulation in the environment, presence of environmental pollutants, quality of relationship with mother, and so on. Physical activity is shaped by neighborhood environment; people who live in ...