
Methods and Applications of Longitudinal Data Analysis
Xian Liu(Author)
Academic Press
Published on 8. September 2015
Book
Hardback
530 pages
978-0-12-801342-7 (ISBN)
Description
Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include:
descriptive methods for delineating trends over time
linear mixed regression models with both fixed and random effects
covariance pattern models on correlated errors
generalized estimating equations
nonlinear regression models for categorical repeated measurements
techniques for analyzing longitudinal data with non-ignorable missing observations
Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.
Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists.
descriptive methods for delineating trends over time
linear mixed regression models with both fixed and random effects
covariance pattern models on correlated errors
generalized estimating equations
nonlinear regression models for categorical repeated measurements
techniques for analyzing longitudinal data with non-ignorable missing observations
Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.
Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Statisticians, demographers, policymakers and insurance companies involved in longitudinal data analysis, as well as professionals, academics and graduate students of various disciplines including sociology, population studies, economics, psychology, geography and political science, biology, medicine and public health. Applied statisticians and other quantitative methodologists can also use the book as a convenient reference.
Product notice
Laminated cover
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 29 mm
Weight
1122 gr
ISBN-13
978-0-12-801342-7 (9780128013427)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
09/2015
Academic Press
€108.00
Available for download
Content
1 Introduction2 Traditional Methods of Longitudinal Data Analysis3 Linear Mixed-effects Models4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models5 Patterns of Residual Covariance Structure6 Residual and Influence Diagnostics7 Special Topics on Linear Mixed Models 8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data9 Generalized Estimating Equations Models (GEEs)10 Mixed-effects Regression Model for Binary Longitudinal Data11 Mixed-effects Multinomial Logit Model for Nominal Outcomes12 Longitudinal Transition Models for Categorical Response Data13 Latent Growth, Latent Growth Mixture, and Group-based Models14 Methods for Handling Missing DataAppendix A: Orthogonal PolynomialsAppendix B: The Delta Method Appendix C: Quasi-likelihood Functions and PropertiesAppendix D: Model Specification and SAS Program for Random Coefficient Multinomial Logit Model on Health States among Older AmericansReferencesSubject Index