{"product_id":"longitudinal-data-analysis-9780471420279","title":"Longitudinal Data Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis text presents and describes methods for analysis of longitudinal data, with a strong emphasis on application of these methods to problems in the biomedical and behavioral sciences. Applied Longitudinal Data Analysis is geared more toward users, and not developers, of statistics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"…a useful resource for students and mathematically inclined practitioners…I would not hesitate to recommend this book…\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, September 2007)  \u003cp\u003e\"Comparing this book with…other books on this subject…this is certainly one of the foremost.\" (\u003ci\u003eJournal of Biopharmaceutical Statistics\u003c\/i\u003e, Vol. 17, Issue 3, 2007)\u003c\/p\u003e \u003cp\u003e\"...this book represents a unique and important contribution to the field of psychology.\" (\u003ci\u003ePsycCRITIQUES\u003c\/i\u003e, March 14, 2007)\u003c\/p\u003e \u003cp\u003e\"This innovative classroom-tested book is…highly recommended.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, October 2006)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003cbr\u003e \u003c\/b\u003e\u003cb\u003e\u003cbr\u003e \u003c\/b\u003e\u003cb\u003eAcknowledgments.\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003e\u003cb\u003eAcronyms.\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003e\u003cb\u003e1.\u003c\/b\u003e \u003cb\u003eIntroduction.\u003c\/b\u003e  \u003cp\u003e1.1 Advantages of Longitudinal Studies.\u003c\/p\u003e \u003cp\u003e1.2 Challenges of Longitudinal Data Analysis.\u003c\/p\u003e \u003cp\u003e1.3 Some General Notation.\u003c\/p\u003e \u003cp\u003e1.4 Data Layout.\u003c\/p\u003e \u003cp\u003e1.5 Analysis Considerations.\u003c\/p\u003e \u003cp\u003e1.6 General Approaches.\u003c\/p\u003e \u003cp\u003e1.7 The Simplest Longitudinal Analysis.\u003c\/p\u003e \u003cp\u003e1.8 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. ANOVA Approaches to Longitudinal Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1Single-Sample Repeated Measures ANOVA.\u003c\/p\u003e \u003cp\u003e2.2 Multiple-Sample Repeated Measures ANOVA.\u003c\/p\u003e \u003cp\u003e2.3 Illustration.\u003c\/p\u003e \u003cp\u003e2.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3.\u003c\/b\u003e \u003cb\u003eMANOVA Approaches to Longitudinal Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Data Layout for ANOVA versus MANOVA.\u003c\/p\u003e \u003cp\u003e3.2 MANOVA for Repeated Measurements.\u003c\/p\u003e \u003cp\u003e3.3 MANOVA of Repeated Measures-s Sample Case.\u003c\/p\u003e \u003cp\u003e3.4 Illustration.\u003c\/p\u003e \u003cp\u003e3.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4.\u003c\/b\u003e \u003cb\u003eMixed-Effects Regression Models for Continuous Outcomes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 A Simple Linear Regression Model.\u003c\/p\u003e \u003cp\u003e4.3 Random Intercept MRM.\u003c\/p\u003e \u003cp\u003e4.4 Random Intercept and Trend MRM.  \u003c\/p\u003e \u003cp\u003e4.5 Matrix Formulation.\u003c\/p\u003e \u003cp\u003e4.6 Estimation .\u003c\/p\u003e \u003cp\u003e4.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5.\u003c\/b\u003e \u003cb\u003eMixed-Effects Polynomial Regression Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Curvilinear Trend Model.\u003c\/p\u003e \u003cp\u003e5.3 Orthogonal Polynomials.\u003c\/p\u003e \u003cp\u003e5.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.\u003c\/b\u003e \u003cb\u003eCovariance Pattern Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Covariance Pattern Models.\u003c\/p\u003e \u003cp\u003e6.3 Model Selection.\u003c\/p\u003e \u003cp\u003e6.4 Example.\u003c\/p\u003e \u003cp\u003e6.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7.\u003c\/b\u003e \u003cb\u003eMixed Regression Models with Autocorrelated Errors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 MRMs with AC Errors.\u003c\/p\u003e \u003cp\u003e7.3 Model Selection.\u003c\/p\u003e \u003cp\u003e7.4 Example.\u003c\/p\u003e \u003cp\u003e7.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8.\u003c\/b\u003e \u003cb\u003eGeneralized Estimating Equations (GEE) Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Generalized Linear Models (GLMs).\u003c\/p\u003e \u003cp\u003e8.3 Generalized Estimating Equations (GEE) Models.\u003c\/p\u003e \u003cp\u003e8.4 GEE Estimation.\u003c\/p\u003e \u003cp\u003e8.5 Example.\u003c\/p\u003e \u003cp\u003e8.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9.\u003c\/b\u003e \u003cb\u003eMixed-Effects Regression Models for Binary Outcomes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Logistic Regression Model.\u003c\/p\u003e \u003cp\u003e9.3 Probit Regression Models.\u003c\/p\u003e \u003cp\u003e9.4 Threshold Concept.\u003c\/p\u003e \u003cp\u003e9.5 Mixed-Effects Logistic Regression Model.\u003c\/p\u003e \u003cp\u003e9.6 Estimation.\u003c\/p\u003e \u003cp\u003e9.7 Illustration.\u003c\/p\u003e \u003cp\u003e9.8 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Mixed-Effects Regression Models for Ordinal Outcomes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Mixed-Effects Proportional Odds Model.\u003c\/p\u003e \u003cp\u003e10.3 Psychiatric Example.\u003c\/p\u003e \u003cp\u003e10.4 Health Services Research Example.\u003c\/p\u003e \u003cp\u003e10.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11.\u003c\/b\u003e \u003cb\u003eMixed-Effects Regression Models for Nominal Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Mixed-Effects Multinomial Regression Model.\u003c\/p\u003e \u003cp\u003e11.2 Health Services Research Example.\u003c\/p\u003e \u003cp\u003e1 1.3 Competing Risk Survival Models.\u003c\/p\u003e \u003cp\u003e11.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12.\u003c\/b\u003e \u003cb\u003eMixed-effects Regression Models for Counts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Poisson Regression Model.\u003c\/p\u003e \u003cp\u003e12.2 Modified Poisson Models.\u003c\/p\u003e \u003cp\u003e12.3 The ZIP Model.\u003c\/p\u003e \u003cp\u003e12.4 Mixed-Effects Models for Counts.\u003c\/p\u003e \u003cp\u003e12.5 Illustration.\u003c\/p\u003e \u003cp\u003e12.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Mixed-Effects Regression Models for Three-Level Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Three-Level Mixed-Effects Linear Regression Model.\u003c\/p\u003e \u003cp\u003e13.1.1 Illustration.\u003c\/p\u003e \u003cp\u003e13.2 Three-Level Mixed-Effects Nonlinear Regression Models.\u003c\/p\u003e \u003cp\u003e13.3 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Missing Data in Longitudinal Studies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Missing Data Mechanisms.\u003c\/p\u003e \u003cp\u003e14.3 Models and Missing Data Mechanisms.\u003c\/p\u003e \u003cp\u003e14.4 Testing MCAR.\u003c\/p\u003e \u003cp\u003e14.5 Models for Nonignorable Missingness.\u003c\/p\u003e \u003cp\u003e14.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTopic Index.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402591117655,"sku":"9780471420279","price":125.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471420279.jpg?v=1730480882","url":"https:\/\/bookcurl.com\/products\/longitudinal-data-analysis-9780471420279","provider":"Book Curl","version":"1.0","type":"link"}