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Originally published in 1976, the authors of six of the most widely quoted works in behavioural science related to education, at the time, here describe in detail their research work, including its origins, planning and implementation. The accounts are unusual, not only for their technical detail but for their candour. The brief was to put the heart and brains back into accounts of research so the authors comment not only on the research design, but on the personal and professional problems they had to overcome. They also reflect on the reception of their work, and the way in which it has been adapted, misunderstood or deliberately distorted to support arguments of widely differing ideological pressure groups. The book shows how ingenuity and persistence as well as technical competence lie at the heart of the research process. The authors do not give the normal depersonalised, streamlined account which gives a false, mechanical picture of research as an occupation, but show it to be a profound personal and professional experience as they comment on the thought that lay behind their work and the way it was finally produced for publication. Dr Shipman has written a short introduction to each chapter, and contributed a concluding chapter relating the six research experiences to conventional views on the research process and to the part played by research evidence in policy making.

This volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available. Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications such as heterogeneity on the patterns of a firm's profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. This new volume is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.

Sixty years ago, a group of prominent psychoanalysts, developmentalists, pediatricians, and educators at the Yale Child Study Center joined together with the purpose of formulating a general psychoanalytic theory of children's early development. The group's members composed detailed narratives about their work with the study's children, interviewed families regularly and visited them in their homes, and over the course of a decade met monthly for discussion. The contributors to this volume consider the significance of the Child Study Center's landmark study from various perspectives, focusing particularly on one child's unfolding sense of herself, her gender, and her relationships.

When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer. In this book, McArdle and Nesselroade identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores. The book covers a wealth of models in a straightforward, understandable manner. Rather than overwhelm the reader with an extensive amount of algebra, the authors use path diagrams and emphasize methods that are appropriate for many uses.

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.

This innovative volume demonstrates the use of a range of statistical approaches that examine'turning points'(a change in direction, magnitude, or meaning) in real data. Analytic techniques are illustrated with real longitudinal data from a variety of fields. As such the book will appeal to a variety of researchers including: Developmental researchers interested in identifying factors precipitating turning points at various life stages. Medical or substance abuse researchers looking for turning points in disease or recovery. Social researchers interested in estimating the effects of life experiences on subsequent behavioral changes. Interpersonal behavior researchers looking to identify turning points in relationships. Brain researchers needing to discriminate the onset of an experimentally produced process in a participant. The book opens with the goals and theoretical considerations in defining turning points. An overview of the methods presented in subsequent chapters is then provided. Chapter goals include discriminating'local'from long-term effects, identifying variables altering the connection between trajectories at different life stages, locating non-normative turning points, coping with practical distributional problems in trajectory analyses, and changes in the meaning and connections between variables in the transition to adulthood. From an applied perspective, the book explores such topics as antisocial/aggressive trajectories at different life stages, the impact of imprisonment on criminal behavior, family contact trajectories in the transition to adulthood, sustained effects of substance abuse, alternative models of bereavement, and identifying brain changes associated with the onset of a new brain process. Ideal for advanced students and researchers interested in identifying significant change in data in a variety of fields including psychology, medicine, education, political science, criminology, and sociology.

We have seen these children—the shy and the sociable, the cautious and the daring—and wondered what makes one avoid new experience and another avidly pursue it. At the crux of the issue surrounding the contribution of nature to development is the study that Jerome Kagan and his colleagues have been conducting for more than two decades. In The Long Shadow of Temperament, Kagan and Nancy Snidman summarize the results of this unique inquiry into human temperaments, one of the best-known longitudinal studies in developmental psychology. These results reveal how deeply certain fundamental temperamental biases can be preserved over development.Identifying two extreme temperamental types—inhibited and uninhibited in childhood, and high-reactive and low-reactive in very young babies—Kagan and his colleagues returned to these children as adolescents. Surprisingly, one of the temperaments revealed in infancy predicted a cautious, fearful personality in early childhood and a dour mood in adolescence. The other bias predicted a bold childhood personality and an exuberant, sanguine mood in adolescence. These personalities were matched by different biological properties. In a masterly summary of their wide-ranging exploration, Kagan and Snidman conclude that these two temperaments are the result of inherited biologies probably rooted in the differential excitability of particular brain structures. Though the authors appreciate that temperamental tendencies can be modified by experience, this compelling work—an empirical and conceptual tour-de-force—shows how long the shadow of temperament is cast over psychological development.

This edited volume introduces the latest advances in quantitative methods and illustrates ways to apply these methods to important questions in substance use research. The goal is to provide a forum for dialogue between methodologists developing innovative multivariate statistical methods and substance use researchers who have produced rich data sets.Reflecting current research trends, the book examines the use of longitudinal techniques to measure processes of change over time. Researchers faced with the task of studying the causes, course, treatment, and prevention of substance use and abuse will find this volume helpful for applying these techniques to make optimal use of their data.This innovative volume: introduces the use of latent curve methods for describing individual trajectories of adolescent substance use over time; explores methods for analyzing longitudinal data for individuals nested within groups, such as families, classrooms, and treatment groups; demonstrates how different patterns of missing data influence the interpretation of results; reports on some recent advances in longitudinal growth modeling; illustrates methods to assess mediation when there are multiple mediating pathways underlying an intervention effect; describes methods to identify moderating relations in structural equation models; demonstrates the use of structural equation models to evaluate a preventive intervention; applies epidemic modeling techniques to understand the spread of substance use in society; illustrates the use of latent transition analysis to model substance use as a series of stages; and applies logistic regression to prospectively predict smoking cessation.