One of the major changes in the social science research landscape in recent years has been the introduction of computerised panel surveys in Europe and the US which make longitudinal data widely available to graduate students for the first time. Elisabetta Ruspini here provides a concise yet comprehensive introduction to the issues involved in this kind of research. This book: * Defines the concept of longitudinal research * Gives guidance on sources of longitudinal data in Europe and the US and their strengths and weaknesses * Discusses the choices that need to be made in this kind of research - for instance the advantages and disadvantages of certain types of research data and of different types of analysis * Highlights some of the problems involved, e.g. the issue of comparability within longitudinal research

This book offers a complete, practical guide to doing an intensive longitudinal study with individuals, dyads, or groups. It provides the tools for studying social, psychological, and physiological processes in everyday contexts, using methods such as diary and experience sampling. A range of engaging, worked-through research examples with datasets are featured. Coverage includes how to: select the best intensive longitudinal design for a particular research question, apply multilevel models to within-subject designs, model within-subject change processes for continuous and categorical outcomes, assess the reliability of within-subject changes, assure sufficient statistical power, and more. Several end-of-chapter write-ups illustrate effective ways to present study findings for publication. Datasets and output in SPSS, SAS, Mplus, HLM, MLwiN, and R for the examples are available on the companion website (www.intensivelongitudinal.com).

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

Longitudinal research is a broad field in which substantial advances have been made over the past decade. Unlike many of the existing books that only address the analysis of information. The Handbook of Longitudinal Research covers design and measurement as well as the data analysis. Designed for use by a wide-ranging audience, this Handbook not only includes perspective on the methodological and data analysis problems in longitudinal research but it also includes contributors' data sets that enable readers who lack sophisticated statistics skills to move from theories about longitudinal data into practice. As the comprehensive reference, this Handbook has no direct competition as most books in this subject area are more narrowly specialized and are pitched at a high mathematical level. Contributors and subject areas are interdisciplinary to reach the broadest possible audience (i.e., psychology, epidemiology, and economics research fields) Summary material will be included for less sohisticated readers Extensive coverage is provided of traditional advanced topics

This groundbreaking book provides students and researchers with a unique overview of the longitudinal study of the development of young people from the ages of 12 to 25. It offers a comprehensive introduction into the multiple theories on the development of the self, personal relationships and psychopathology in adolescence, alongside a non-statistical overview of the many longitudinal models used to study development. The book includes key topics such as the development of the self, adolescent identity and personality; the development of parent-adolescent relationships; friendships and the understanding of others; and the development of psychosocial problems such as anxiety, depression, delinquency, aggression, and substance use. Meeus highlights multiple findings showing how these processes are integrated and identifies eight fundamental patterns of adolescent development to help determine why most adolescents develop into mature and organized individuals towards the end of this life stage, whilst a substantial minority show an inability to mature. It is essential reading for graduate students and researchers in adolescent development and anyone seeking to use longitudinal research methodology in the social and behavioral sciences.

This accessible introduction to the theory and practice of longitudinal research takes the reader through the strengths and weaknesses of this kind of research, making clear: how to design a longitudinal study; how to collect data most effectively; how to make the best use of statistical techniques; and how to interpret results. Although the book provides a broad overview of the field, the focus is always on the practical issues arising out of longitudinal research. This book supplies the student with all that they need to get started and acts as a manual for dealing with opportunities and pitfalls. It is the ideal primer for this growing area of social research.

This book has been replaced by Longitudinal Structural Equation Modeling, Second Edition, ISBN 978-1-4625-5314-3.

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.

This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

Longitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: * Repeated measures analysis of variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.