• 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 ...

Multilevel Models and Causal Inference
Multilevel models and causal inference
JenniferHillNew York University, USA
12.1 Introduction

This chapter discusses the use of multilevel models for causal inference. Multilevel models were not designed specifically to address the concerns most salient for causal inference. However, when data that are being used to identify causal effects have hierarchical structures (students nested within schools) or other types of group dependencies (families classified by religious affilliation, workers classified by industry, patients classified by illness) it may make most sense to incorporate a multilevel model in the analysis strategy. Moreover, causal considerations may dictate a different choice of multilevel model than the researcher would use in a non-causal setting. Choices made will have implications not only for the plausibility of the assumptions and the ...

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