{"product_id":"multilevel-statistical-models-4e-9780470748657","title":"Multilevel Statistical Models 4e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThroughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models.  \u003cp\u003eKey Features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a clear introduction and a comprehensive account of multilevel models.\u003c\/li\u003e \u003cli\u003eNew methodological developments and applications are explored.\u003c\/li\u003e \u003cli\u003eWritten by a leading \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial. See the review of the third edition.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 December 2013)\u003c\/p\u003e \u003cp\u003e\"This book would also serve as an outstanding general reference on multilevel models, since it offers concise and easy to follow descriptions of the various multilevel models and their applications, in addition to the references on which this work is based. I really enjoyed reading this book, and am sure that others will have a similar pleasurable experience.\" (\u003ci\u003eJournal of Biopharmaceutical Statistics (JBS)\u003c\/i\u003e, 2012)\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContents\u003cbr\u003e Dedication\u003cbr\u003e Preface\u003cbr\u003e Acknowledgements\u003cbr\u003e Notation\u003cbr\u003e A general classification notation and diagram\u003cbr\u003e Glossary\u003cbr\u003e Chapter 1 An introduction to multilevel models\u003cbr\u003e 1.1 Hierarchically structured data\u003cbr\u003e 1.2 School effectiveness\u003cbr\u003e 1.3 Sample survey methods\u003cbr\u003e 1.4 Repeated measures data\u003cbr\u003e 1.5 Event history and survival models\u003cbr\u003e 1.6 Discrete response data\u003cbr\u003e 1.7 Multivariate models\u003cbr\u003e 1.8 Nonlinear models\u003cbr\u003e 1.9 Measurement errors\u003cbr\u003e 1.10 Cross classifications and multiple membership structures.\u003cbr\u003e 1.11 Factor analysis and structural equation models\u003cbr\u003e 1.12 Levels of aggregation and ecological fallacies\u003cbr\u003e 1.13 Causality\u003cbr\u003e 1.14 The latent normal transformation and missing data\u003cbr\u003e 1.15 Other texts\u003cbr\u003e 1.16 A caveat  \u003cp\u003eChapter 2 The 2-level model\u003cbr\u003e 2.1 Introduction\u003cbr\u003e 2.2 The 2-level model\u003cbr\u003e 2.3 Parameter estimation\u003cbr\u003e 2.4 Maximum likelihood estimation using Iterative Generalised Least Squares (IGLS)\u003cbr\u003e 2.5 Marginal models and Generalized Estimating Equations (GEE)\u003cbr\u003e 2.6 Residuals\u003cbr\u003e 2.7 The adequacy of Ordinary Least Squares estimates.\u003cbr\u003e 2.8 A 2-level example using longitudinal educational achievement data\u003cbr\u003e 2.9 General model diagnostics\u003cbr\u003e 2.10 Higher level explanatory variables and compositional effects\u003cbr\u003e 2.11 Transforming to normality\u003cbr\u003e 2.12 Hypothesis testing and confidence intervals\u003cbr\u003e 2.13 Bayesian estimation using Markov Chain Monte Carlo (MCMC)\u003cbr\u003e 2.14 Data augmentation\u003cbr\u003e Appendix 2.1 The general structure and maximum likelihood estimation for a multilevel model\u003cbr\u003e Appendix 2.2 Multilevel residuals estimation\u003cbr\u003e Appendix 2.3 Estimation using profile and extended likelihood\u003cbr\u003e Appendix 2.4 The EM algorithm\u003cbr\u003e Appendix 2.5 MCMC sampling\u003c\/p\u003e \u003cp\u003eChapter 3. Three level models and more complex hierarchical structures.\u003cbr\u003e 3.1 Complex variance structures\u003cbr\u003e 3.2 A 3-level complex variation model example.\u003cbr\u003e 3.3 Parameter Constraints\u003cbr\u003e 3.4 Weighting units\u003cbr\u003e 3.5 Robust (Sandwich) Estimators and Jacknifing\u003cbr\u003e 3.6 The bootstrap\u003cbr\u003e 3.7 Aggregate level analyses\u003cbr\u003e 3.8 Meta analysis\u003cbr\u003e 3.9 Design issues\u003c\/p\u003e \u003cp\u003eChapter 4. Multilevel Models for discrete response data\u003cbr\u003e 4.1 Generalised linear models\u003cbr\u003e 4.2 Proportions as responses\u003cbr\u003e 4.3 Examples\u003cbr\u003e 4.4 Models for multiple response categories\u003cbr\u003e 4.5 Models for counts\u003cbr\u003e 4.6 Mixed discrete - continuous response models\u003cbr\u003e 4.7 A latent normal model for binary responses\u003cbr\u003e 4.8 Partitioning variation in discrete response models\u003cbr\u003e Appendix 4.1. Generalised linear model estimation\u003c\/p\u003e \u003cp\u003eAppendix 4.2 Maximum likelihood estimation for generalised linear models\u003c\/p\u003e \u003cp\u003eAppendix 4.3 MCMC estimation for generalised linear models\u003c\/p\u003e \u003cp\u003eAppendix 4.4. Bootstrap estimation for generalised linear models\u003c\/p\u003e \u003cp\u003eChapter 5. Models for repeated measures data\u003cbr\u003e 5.1 Repeated measures data\u003cbr\u003e 5.2 A 2-level repeated measures model\u003cbr\u003e 5.3 A polynomial model example for adolescent growth and the prediction of adult height\u003cbr\u003e 5.4 Modelling an autocorrelation structure at level 1.\u003cbr\u003e 5.5 A growth model with autocorrelated residuals\u003cbr\u003e 5.6 Multivariate repeated measures models\u003cbr\u003e 5.7 Scaling across time\u003cbr\u003e 5.8 Cross-over designs\u003cbr\u003e 5.9 Missing data\u003cbr\u003e 5.10 Longitudinal discrete response data\u003c\/p\u003e \u003cp\u003eChapter 6. Multivariate multilevel data\u003cbr\u003e 6.1 Introduction\u003cbr\u003e 6.2 The basic 2-level multivariate model\u003cbr\u003e 6.3 Rotation Designs\u003cbr\u003e 6.4 A rotation design example using Science test scores\u003cbr\u003e 6.5 Informative response selection: subject choice in examinations\u003cbr\u003e 6.6 Multivariate structures at higher levels and future predictions\u003cbr\u003e 6.7 Multivariate responses at several levels\u003cbr\u003e 6.8 Principal Components analysis\u003c\/p\u003e \u003cp\u003eAppendix 6.1 MCMC algorithm for a multivariate normal response model with constraints\u003c\/p\u003e \u003cp\u003eChapter 7. Latent normal models for multivariate data\u003cbr\u003e 7.1 The normal multilevel multivariate model\u003cbr\u003e 7.2 Sampling binary responses\u003cbr\u003e 7.3 Sampling ordered categorical responses\u003cbr\u003e 7.4 Sampling unordered categorical responses\u003cbr\u003e 7.5 Sampling count data\u003cbr\u003e 7.6 Sampling continuous non-normal data\u003cbr\u003e 7.7 Sampling the level 1 and level 2 covariance matrices\u003cbr\u003e 7.8 Model fit\u003cbr\u003e 7.9 Partially ordered data\u003cbr\u003e 7.10 Hybrid normal\/ordered variables\u003cbr\u003e 7.11 Discussion\u003c\/p\u003e \u003cp\u003eChapter 8. Multilevel factor analysis, structural equation and mixture models\u003c\/p\u003e \u003cp\u003e8.1 A 2-stage 2-level factor model\u003c\/p\u003e \u003cp\u003e8.2 A general multilevel factor model\u003c\/p\u003e \u003cp\u003e8.3 MCMC estimation for the factor model\u003c\/p\u003e \u003cp\u003e8.4 Structural equation models\u003c\/p\u003e \u003cp\u003e8.5 Discrete response multilevel structural equation models\u003c\/p\u003e \u003cp\u003e8.6 More complex hierarchical latent variable models\u003c\/p\u003e \u003cp\u003e8.7 Multilevel mixture models \u003c\/p\u003e \u003cp\u003eChapter 9. Nonlinear multilevel models\u003cbr\u003e 9.1 Introduction\u003cbr\u003e 9.2 Nonlinear functions of linear components\u003cbr\u003e 9.3 Estimating population means\u003cbr\u003e 9.4 Nonlinear functions for variances and covariances\u003cbr\u003e 9.5 Examples of nonlinear growth and nonlinear level 1 variance\u003cbr\u003e Appendix 9.1 Nonlinear model estimation\u003c\/p\u003e \u003cp\u003eChapter 10. Multilevel modelling in sample surveys\u003cbr\u003e 10.1 Sample survey structures\u003cbr\u003e 10.2 Population structures\u003cbr\u003e 10.3 Small area estimation\u003cbr\u003e \u003cbr\u003e Chapter 11 Multilevel event history and survival models\u003cbr\u003e 11.1 Introduction\u003cbr\u003e 11.2 Censoring\u003cbr\u003e 11.3 Hazard and survival funtions\u003cbr\u003e 11.4 Parametric proportional hazard models\u003cbr\u003e 11.5 The semiparametric Cox model\u003cbr\u003e 11.6 Tied observations\u003cbr\u003e 11.7 Repeated events proportional hazard models\u003cbr\u003e 11.8 Example using birth interval data\u003cbr\u003e 11.9 Log duration models\u003cbr\u003e 11.10 Examples with birth interval data and children’s activity episodes\u003cbr\u003e 11.11 The grouped discrete time hazards model\u003cbr\u003e 11.12 Discrete time latent normal event history models\u003c\/p\u003e \u003cp\u003eChapter 12. Cross classified data structures\u003cbr\u003e 12.1 Random cross classifications\u003cbr\u003e 12.2 A basic cross classified model\u003cbr\u003e 12.3 Examination results for a cross classification of schools\u003cbr\u003e 12.4 Interactions in cross classifications\u003cbr\u003e 12.5 Cross classifications with one unit per cell\u003cbr\u003e 12.6 Multivariate cross classified models\u003cbr\u003e 12.7 A general notation for cross classifications\u003cbr\u003e 12.8 MCMC estimation in cross classified models\u003cbr\u003e Appendix 12.1 IGLS Estimation for cross classified data.\u003c\/p\u003e \u003cp\u003eChapter 13 Multiple membership models\u003cbr\u003e 13.1 Multiple membership structures\u003cbr\u003e 13.2 Notation and classifications for multiple membership structures\u003cbr\u003e 13.3 An example of salmonella infection\u003cbr\u003e 13.4 A repeated measures multiple membership model\u003cbr\u003e 13.5 Individuals as higher level units\u003cbr\u003e 13.5.1 Example of research grant awards\u003cbr\u003e 13.6 Spatial models\u003cbr\u003e 13.7 Missing identification models\u003c\/p\u003e \u003cp\u003eAppendix 13.1 MCMC estimation for multiple membership models.\u003c\/p\u003e \u003cp\u003eChapter 14 Measurement errors in multilevel models\u003cbr\u003e 14.1 A basic measurement error model\u003cbr\u003e 14.2 Moment based estimators\u003cbr\u003e 14.3 A 2-level example with measurement error at both levels.\u003cbr\u003e 14.4 Multivariate responses\u003cbr\u003e 14.5 Nonlinear models\u003cbr\u003e 14.6 Measurement errors for discrete explanatory variables\u003cbr\u003e 14.7 MCMC estimation for measurement error models\u003cbr\u003e Appendix 14.1 Measurement error estimation\u003cbr\u003e 14.2 MCMC estimation for measurement error models\u003c\/p\u003e \u003cp\u003eChapter 15. Smoothing models for multilevel data.\u003cbr\u003e 15.1 Introduction\u003cbr\u003e 15.2. Smoothing estimators\u003cbr\u003e 15.3 Smoothing splines\u003cbr\u003e 15.4 Semi parametric smoothing models\u003cbr\u003e 15.5 Multilevel smoothing models\u003cbr\u003e 15.6 General multilevel semi-parametric smoothing models\u003cbr\u003e 15.7 Generalised linear models\u003cbr\u003e 15.8 An example\u003cbr\u003e Fixed\u003cbr\u003e Random\u003cbr\u003e 15.9 Conclusions\u003c\/p\u003e \u003cp\u003eChapter 16. Missing data, partially observed data and multiple imputation\u003cbr\u003e 16.1 Creating a completed data set\u003cbr\u003e 16.2 Joint modelling for missing data\u003cbr\u003e 16.3 A two level model with responses of different types at both levels.\u003cbr\u003e 16.4 Multiple imputation\u003cbr\u003e 16.5 A simulation example of multiple imputation for missing data\u003cbr\u003e 16.6 Longitudinal data with attrition\u003cbr\u003e 16.7 Partially known data values\u003cbr\u003e 16.8 Conclusions\u003c\/p\u003e \u003cp\u003eChapter 17 Multilevel models with correlated random effects\u003cbr\u003e 17.1 Non-independence of level 2 residuals\u003cbr\u003e 17.2 MCMC estimation for non-independent level 2 residuals\u003cbr\u003e 17.3 Adaptive proposal distributions in MCMC estimation\u003cbr\u003e 17.4 MCMC estimation for non-independent level 1 residuals\u003cbr\u003e 17.5 Modelling the level 1 variance as a function of explanatory variables with random effects\u003cbr\u003e 17.6 Discrete responses with correlated random effects\u003cbr\u003e 17.7 Calculating the DIC statistic\u003cbr\u003e 17.8 A growth data set\u003cbr\u003e 17.9 Conclusions\u003c\/p\u003e \u003cp\u003eChapter 18. Software for multilevel modelling\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAuthor index\u003c\/p\u003e \u003cp\u003eSubject index\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e  \u003cbr\u003e \u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402425770327,"sku":"9780470748657","price":63.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470748657.jpg?v=1730480362","url":"https:\/\/bookcurl.com\/products\/multilevel-statistical-models-4e-9780470748657","provider":"Book Curl","version":"1.0","type":"link"}