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Applied multilevel analysis: a practical guide

Author: Twisk, Jos W. R. Series: Practical guides to biostatistics and epidemiology Publisher: Cambridge University Press (CUP) 2006.Language: EnglishDescription: 182 p. : Ill. ; 17 cm.ISBN: 9780521614986Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
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Print QA279 .T95 2006
(Browse shelf)
001251760
Available 001251760
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Includes bibliographical references and index

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Applied multilevel analysis A practical guide Contents Preface Acknowledgements 1 Introduction 1.1 Introduction 1.2 Background of multilevel analysis 1.3 General approach 1.4 Prior knowledge 1.5 Example datasets 1.6 Software 2 Basic principles of multilevel analysis 2.1 Introduction 2.2 Example 2.3 Intraclass correlation coefficient 2.4 Random slopes 2.5 Example 2.6 Multilevel analysis with more than two levels 2.6.1 Example 2.7 Assumptions in multilevel analysis 2.8 Comments 2.8.1 Which regression coefficients can be assumed to be random? 2.8.2 Random regression coefficients versus fixed regression coefficients 2.8.3 Maximum likelihood versus restricted maximum likelihood page xi xii 1 1 2 3 4 4 5 6 6 10 14 16 18 22 22 26 27 27 28 29 3 What do we gain by applying multilevel analysis? 3.1 Introduction 3.2 Example with a balanced dataset 30 30 30 3.3 Example with an unbalanced dataset 3.4 Cluster randomisation 3.5 Conclusion 4 Multilevel analysis with different outcome variables 4.1 Introduction 4.2 Logistic multilevel analysis 4.2.1 Intraclass correlation coefficient in logistic multilevel analysis 4.3 Multinomial logistic multilevel analysis 4.4 Poisson multilevel analysis 4.5 Multilevel survival analysis 34 35 37 38 38 38 46 47 52 57 62 62 62 67 67 68 80 85 86 86 87 91 95 101 104 106 106 106 106 5 Multilevel modelling 5.1 Introduction 5.2 Multivariable multilevel analysis 5.3 Prediction models and association models 5.3.1 Introduction 5.3.2 Association models 5.3.3 Prediction or prognostic models 5.4 Comments Multilevel analysis in longitudinal studies 6.1 Introduction 6.2 Longitudinal studies 6.3 Example 6.4 Growth curves 6.4.1 An additional example 6.5 Other techniques to analyse longitudinal data 6.6 Comments 6.6.1 Extension of multilevel analysis for longitudinal data 6.6.2 Clustering of longitudinal data on a higher level 6.6.3 Missing data in longitudinal studies Multivariate multilevel analysis 7.1 Introduction 7.2 Multivariate multilevel analysis: the MLwiN approach 108 108 110 7.3 Multivariate multilevel analysis: the general approach 7.4 Comments 8 Sample-size calculations in multilevel studies 8.1 Introduction 8.2 Standard sample-size calculations 8.3 Sample-size calculations for multilevel studies 8.4 Example 8.5 Which sample-size calculation should be used? 8.6 Comments 9 Software for multilevel analysis 9.1 Introduction 9.2 Linear multilevel analysis 9.2.1 SPSS 9.2.2 STATA 9.2.3 SAS 9.2.4 R 9.2.5 Overview 9.3 Logistic multilevel analysis 9.3.1 Introduction 9.3.2 STATA 9.3.3 SAS 9.3.4 R 9.3.5 Overview 9.4 Poisson multilevel analysis 9.4.1 Introduction 9.4.2 STATA 9.4.3 SAS 9.4.4 R 9.4.5 Overview 9.5 Multinomial logistic multilevel analysis 9.5.1 Introduction 9.5.2 STATA 9.5.3 Overview References Index 117 121 123 123 124 125 125 126 129 130 130 131 131 136 141 146 151 152 152 153 154 156 158 158 158 159 160 161 162 163 163 163 165 167 177

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