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Multilevel Modeling of Social Problems

A Causal Perspective

  • Robert B. Smith

Table of contents

  1. Front Matter
    Pages i-xxxix
  2. Introductory Essays

    1. Front Matter
      Pages 1-1
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      Pages 3-21
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      Pages 23-34
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      Pages 35-58
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      Pages 59-101
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      Pages 103-124
  3. Contextual Studies

    1. Front Matter
      Pages 125-125
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      Pages 127-137
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      Pages 139-179
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      Pages 181-224
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      Pages 225-256
  4. Evaluative Research

    1. Front Matter
      Pages 257-257
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      Pages 259-281
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      Pages 283-329
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      Pages 331-380
  5. Research Summaries

    1. Front Matter
      Pages 381-381
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      Pages 383-397
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      Pages 399-429
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      Pages 431-450
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      Pages 451-483
  6. Back Matter
    Pages 485-535

About this book

Introduction

Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality, the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models. This book innovatively: • Develops procedures for studying social, economic, and human development. • Uses typologies to group (i.e., classify or nest) the level of random macro-level factors. • Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure. • Selects appropriate covariance structures for generalized linear mixed models. • Applies difference-in-differences study designs in the multilevel modeling of intervention studies. • Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data. • Uses the Kenward-Rogers correction in mixed models of repeated measures. • Explicates differences between associational and causal analysis of multilevel models. • Consolidates research findings via meta-analysis and methodological critique. • Develops criteria for assessing a study's validity and zone of causality. Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures, policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.

Keywords

Assessing Causal Effects in Multilevel Models Contextual Analysis and Multilevel Models Evaluative Research and Multilevel Models HLM Illustrative Uses for Multilevel Models MLM Multilevel Modeling of Social Problems Notions of Causality

Authors and affiliations

  • Robert B. Smith
    • 1
  1. 1.CambridgeUSA

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