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.

Longitudinal Models

They always say time changes things, but you actually have to change them yourself.

—Andy Warhol, 1975

Longitudinal Data as Hierarchical: Time Nested Within Person

When we consider multilevel models, it is not unusual to think first of individual objects nested within a physical or social context, such as persons in neighborhoods or clinics in hospitals (see Table 1.2). However, as we saw earlier when we used generalized linear mixed-effects model (GLMM) to model votes on individual bills, mixed-effects models can be applied to multiple observations nested within a single object. This opens up multilevel modeling to a wide variety of useful applications. In particular, mixed-effects models can be applied to longitudinal data where the primary interest is in modeling the structure and predictors of ...

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