• 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 ...

LarryV.HedgesNorthwestern University, USA
KimberlyS.MaierMichigan State University, USA
27.1 Introduction

In many fields, the growth of the empirical research enterprise has led to an accumulation of empirical research findings that must be organized and synthesized to clearly identify what is known, and to identify critical areas where research has been insufficient, inconclusive, or even contradictory. Examples of areas in which quantitative syntheses of research have become routine include physics (e.g., Birge, 1932 or Rosenfeld, 1975), agriculture (e.g., Cochran, 1937), and chemistry (e.g., Zwolinski and Chao, 1972). Other sciences such as medicine (where an early example, Pearson, 1904, predates routine use in that science by more than 60 years), ecology (e.g., Gurevitch and Hedges, 1993), and software engineering (e.g., Kampenes, Dybå, Hannay, and Sjøberg, 2007) have embraced quantitative synthesis ...

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