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.
- From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis
- Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection
- Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
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. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
Leseprobe
Chapter 1 Introduction
Abstracts
Serving as introduction to the book, Chapter 1 is focused on the description of the definition, historical background, data features and structures, and some other general specifications applied in longitudinal data analysis. The purpose of the chapter is to lead the reader into the realm of longitudinal data analysis by addressing its significance, underlying hypotheses, basic expressions of longitudinal modeling, and existing issues. The presence of missing data and intra-individual correlation are the two primary features in longitudinal data, and therefore, their impacts on longitudinal data analysis are presented and discussed. The chapter also summarizes the organization of the book with a chapter-by-chapter description. Given the emphasis on applications and practices for this book, two longitudinal datasets are used for empirical illustrations throughout the text, with one from a randomized controlled clinical trial and one from a large-scale longitudinal survey. In Chapter 1, these two datasets are described in details.
KeywordsIntra-individual correlation longitudinal data missing data patterns multivariate and univariate data structures pattern of change over time repeated measurements
Chapter outline
1.1 What is Longitudinal Data Analysis? 1
1.2 History of Longitudinal Analysis and its Progress 3
1.3 Longitudinal Data Structures 4
1.3.1 Multivariate Data Structure 5
1.3.2 Univariate Data Structure 6
1.3.3 Balanced and Unbalanced Longitudinal Data 7
1.4 Missing Data Patterns and Mechanisms 9
1.5 Sources of Correlation in Longitudinal Processes 10
1.6 Time Scale and the Number of Time Points 12
1.7 Basic Expressions of Longitudinal Modeling 13
1.8 Organization of the Book and Data Used for Illustrations 16
1.8.1 Randomized Controlled Clinical Trial on the Effectiveness of Acupuncture Treatment on PTSD 17
1.8.2 Asset and Health Dynamics among the Oldest Old (AHEAD) 18
1.1. What is longitudinal data analysis?We live in a dynamic world full of change. A person grows, ages, and dies. During that process, we may contract disease, develop functional disability, and lose mental ability. Accompanying this biological life course, social change also occurs. We attend school, develop a career and retire. In the meantime, many of us experience family disruption, become involved in social activities, cultivate personal habits and hobbies, and make adjustments to our daily activities according to our physical and mental conditions. Indeed, change characterizes almost all aspects of our social lives, ranging from the aforementioned social facets to unemployment, drug use recidivism, occupational careers, and other social events. In these biological and social processes, the gradual changes and developments over a life course reflect a pattern of change over time. More formally, such changes and developments may be referred to as an individual's trajectory. In a wider scope, trajectories are also seen in the pattern of change referring to such phenomena as the decaying quality over time of a commercial product or the collapse of a political system in a country. In the field of business management, change in consumer purchasing behavior is generally linked both with individual characteristics and with competing products. In population studies, demographers are concerned with such