Leyland, Alastair H.
Multilevel Modelling for Public Health and Health Services Research : Health in Context. - 1st ed. - 1 online resource (293 pages)
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.
9783030348014
Electronic books.
RA1-1270
Multilevel Modelling for Public Health and Health Services Research : Health in Context. - 1st ed. - 1 online resource (293 pages)
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.
9783030348014
Electronic books.
RA1-1270