• 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 ...

Point-Referenced Spatial Modeling
Point-referenced spatial modeling
AndrewO.FinleyMichigan State University, USA
SudiptoBanerjeeUniversity of Minnesota, USA
31.1 Introduction

This chapter is inherently different from most of the other chapters in this Handbook. While multilevel modeling (MLM) customarily refers to settings where observations are clustered by subjects within groups, or, perhaps, subjects within time, in the current chapter we envision observing multiple outcomes at each spatial location. While this is not entirely dissimilar to longitudinal/within-subject analysis, an important distinction is that the multiple observations in spatial data typically come from different variables that are themselves posited to be associated with each other. In this regard, our approach resembles multivariate modeling rather than repeated measures.

Inferential interest requires accounting for two types of association. The first is the association between observations of the same ...

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