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

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

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

This Handbook is a very timely contribution to organization and business studies. Most calls for longitudinal research are made in sections of published work that deal with limitations of the study or suggestions for further research. This book places longitudinal research methods at center stage. With its practical, hands-on approach it guides us how to design a longitudinal study in and around organizations whether qualitative or quantitative and how to implement it. I warmly recommend this Handbook to ambitious senior and junior researchers. It makes the commonly presented excuses for not undertaking longitudinal research completely redundant. Rebecca Piekkari, Aalto University, School of Business in Helsinki, Finland This is a very timely book that fills an important gap in the field of research methods. So far very little attention has been paid to longitudinal research methods, while the usefulness of this type of research has often been discussed in many papers and conferences. Insights provided by scholars who have been doing this type of research provide useful guidelines for anyone interested in research methods from senior scholars to young researchers and PhD candidates. This volume will serve as an excellent complement to the existing range of books on research methods. Pervez Ghauri, Kings College London, UK This innovative Handbook demonstrates that there is no single best approach to conducting longitudinal studies. At their best, longitudinal research designs yield rich, contextualised, multilevel and deep understanding of the studied phenomenon. The lack of resources in terms of time, funding and people can pose a serious challenge to conducting longitudinal research. This book tackles many of these challenges and discusses the role of longitudinal research programmes in overcoming such obstacles. This book shows how longitudinal research methods enable the understanding of dynamics, mechanisms, causalities and interrelationships of organizational and business concepts in context and in relation to time. It discusses the richness and versatility of longitudinal research and offers, to students and experienced scholars alike, numerous viewpoints, reflections and personal accounts about conducting longitudinal research, from planning and fieldwork to reporting and managing of research projects.

A time series is a set of repeated measurements of the same phenomenon taken sequentially over time. Capturing the data creates a time series "memory" to document correlations or lack, and to help them make decisions based on this data.

Johnny Saldana outlines the basic elements of longitudinal qualitative data, focusing on micro-levels of change observed within individual cases and groups of participants. He draws upon his primary experience in theater education to examine time and change in longitudinal qualitative studies; contending that "playwrights and qualitative researchers write for the same purpose: to create a unique, insightful, and engaging text about the human condition." Offering sixteen specific questions through which researchers may approach the analysis of longitudinal qualitative data, Professor Saldana presents a text intended as a primer for fellow newcomers to long term inquiry, based on traditional social science methods from traditional qualitative and quantitative paradigms, but enriched by an artist-educator's unconventional perspective.

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).