Multilevel Modelling for Public Health and Health Services Research : : Health in Context.

Multilevel Modelling for Public Health and Health Services Research : : Health in Context.

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Bibliographic Details
:
Leyland, Alastair H.
TeilnehmendeR:
Groenewegen, Peter P.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2020.
©2020.
Year of Publication:2020
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (293 pages)
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Table of Contents:
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • About the Authors
  • Part I: Theoretical, Conceptual and Methodological Background
  • Chapter 1: Introduction
  • Importance of MLA for Research in Health and Care
  • The Scope of Public Health and Health Services Research
  • Research and Policy
  • Conclusion
  • References
  • Chapter 2: Health in Context
  • Relationships Between the Macro and Micro Levels
  • Micro Level: Behaviour of Patients and Providers
  • The Behaviour of Healthcare Providers
  • The Behaviour of Patients
  • Patient-Provider Interaction
  • From Macro to Micro Level
  • What Contexts Are Relevant?
  • From Micro to Macro Level
  • The Use of ``League Tables ́́-- Conclusion
  • References
  • Chapter 3: What Is Multilevel Modelling?
  • Methodological Background
  • Why Use Multilevel Modelling?
  • Aggregate Analysis
  • Individual Analysis
  • Separate Individual Analyses Within Each Higher Level Unit
  • Individual-Level Analysis with Dummy Variables
  • What Is a Multilevel Model?
  • What Is a Level?
  • How Many Units Do We Need at Each Level?
  • Hypotheses That Can Be Tested with Multilevel Analysis
  • Hypotheses About Variation
  • Individual-Level Hypotheses
  • Context Hypotheses
  • Aggregated Individual-Level Characteristics
  • Higher Level Characteristics
  • Cross-Level Interactions
  • Conclusion
  • References
  • Chapter 4: Multilevel Data Structures
  • Strict Hierarchies: The Basic Model
  • Multistage Sampling Designs
  • Evaluating Community Interventions and Cluster Randomised Trials
  • Designs Including Time
  • Multiple Responses
  • Non-hierarchical Structures
  • Cross-Classified Models
  • Multiple Membership Model
  • Correlated Cross-Classified Model
  • Other Multilevel Models
  • Pseudo-levels
  • Incomplete Hierarchies
  • Conclusion
  • References
  • Part II: Statistical Background
  • Chapter 5: Graphs and Equations.
  • Ordinary Least Squares (Single-Level) Regression
  • Random Intercept Model
  • Random Slope Model
  • Three-Level Model
  • Heteroscedasticity
  • Fixed Effects Model
  • Rankings and Institutional Performance
  • Conclusion
  • References
  • Chapter 6: Apportioning Variation in Multilevel Models
  • Variance Partitioning for Continuous Responses
  • Variance Partitioning for Multilevel Logistic Regression
  • Variance Partitioning for Models with Three or More Levels
  • Interpretation of Variances
  • Zero Variance
  • Multilevel Power Calculations
  • Software for Multilevel Power Calculations
  • Population Average and Cluster-Specific Estimates
  • Omitting a Level
  • Conclusion
  • References
  • Part III: The Modelling Process and Presentation of Research
  • Chapter 7: Context, Composition and How Their Influences Vary
  • Context or Composition?
  • Using Multilevel Modelling to Investigate Compositional and Contextual Effects
  • Model M0: Null Model
  • Model M1: Individual Social Capital
  • Model M2: Neighbourhood Social Capital
  • Model M3: Individual and Neighbourhood Social Capital
  • Model M4: Individual and Neighbourhood Social Capital and Their Interaction
  • Random Slopes and Cross-Level Interactions
  • Impact of Compositional and Contextual Variables on the Variances
  • Model Specification and Model Interpretation
  • Sources of Error Affecting the Estimation of Contextual Effects
  • Lack of Variation in the Contextual Variable
  • Precision of Estimates and Study Design
  • Selection Bias
  • Confounding
  • Information Bias
  • Model Specification
  • Conclusions
  • References
  • Chapter 8: Ecometrics: Using MLA to Construct Contextual Variables from Individual Data
  • Problems with Simple Aggregation
  • Single Variables
  • Composite Variables: The Traditional Method
  • Composite Variables: A Simple Multilevel Model
  • Ecometric Approach.
  • Application of the Ecometric Approach
  • Comparison of the Traditional and Ecometric Approach
  • Further Ecometric Properties of the Scale
  • Conclusions
  • References
  • Chapter 9: Modelling Strategies
  • Define the Data Structure
  • Measurement Level and Distribution of the Dependent Variable
  • The Baseline Model
  • Exploratory Research and Hypothesis Testing
  • Context and Composition
  • Modelling the Effects of Higher Level Characteristics
  • Random Effects at Higher Levels
  • Interpreting the Results in the Light of Common Assumptions
  • Conclusions
  • References
  • Chapter 10: Reading and Writing
  • Critical Reading
  • What Is the Research Question?
  • Which Levels Can Be Distinguished Theoretically?
  • What Is the Structure of the Actual Data Used?
  • What Statistical Model Was Used?
  • What Was the Modelling Strategy?
  • Does the Paper Report the Intercept Variation at Different Levels?
  • Cross-Level Interactions
  • What Are the Shortcomings and Strong Points of the Article?
  • Writing Up Your Own Research
  • The Introduction or Background Section
  • The Methods Section
  • The Results Section
  • The Conclusion and Discussion Section
  • Conclusions
  • References
  • Part IV: Tutorials with Example Datasets
  • Chapter 11: Multilevel Linear Regression Using MLwiN: Mortality in England and Wales, 1979-1992
  • Introduction to the Dataset
  • Research Questions
  • Introduction to MLwiN
  • Opening a Worksheet
  • Names Window
  • Data Window
  • Graph Window
  • Model Specification
  • Creating New Variables
  • Equations Window
  • Fitting the Model
  • Variance Components
  • A 2-Level Variance Components Model
  • Sorting the Data
  • The Hierarchy Viewer
  • Adding a Further Level
  • Interpreting the Model
  • Residuals
  • Predictions Window
  • Model Building
  • Adding More Fixed Effects
  • Intervals and Tests Window
  • Random Coefficients
  • Random Slopes.
  • Variance Function Window
  • Higher-Level Residuals
  • Complex Level 1 Variation
  • A Poisson Model: Introduction
  • Setting Up a Generalised Linear Model in MLwiN
  • The Offset
  • Non-linear Estimation
  • Model Interpretation
  • Predictions and Confidence Envelopes
  • References
  • Chapter 12: Multilevel Logistic Regression Using MLwiN: Referrals to Physiotherapy
  • Multilevel Logistic Regression Model
  • Example: Variation in the GP Referral Rate to Physiotherapy
  • The Data
  • Model Set-Up
  • Non-linear Settings
  • Model Interpretation and Model Building
  • A Note on Estimation
  • Further Exercises
  • References
  • Chapter 13: Untangling Context and Composition
  • The Data
  • Structure of the Analysis
  • Estimating the Null Model
  • Fixed Effects
  • Additional Models
  • References
  • Index.

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