• Summary
  • Contents
  • Subject index

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.

The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and ...

Longitudinal Data Modeling
Longitudinal data modeling
NanM.LairdHarvard School of Public Health
GarrettM.FitzmauriceHarvard School of Public Health & McLean Hospital

Data collected in a longitudinal study consist of repeated measures made on individuals over time. In this chapter we show how longitudinal data can be put into the multilevel framework, and we also highlight important distinctions between longitudinal and general multilevel data. Longitudinal data can be considered a special case of 2-level data where measurement occasions (the level 1 units) are clustered within individuals (the level 2 units). Unlike many multilevel designs, there is a natural ordering of the measurement occasions that has important implications for longitudinal analysis. In some instances longitudinal data have more than two levels, e.g., observations from a multicenter longitudinal clinical trial can be regarded ...

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