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The Children's Health Act mandated the National Children's Study (NCS) in 2000 with one of its purposes being to authorize the National Institute of Child Health and Human Development (NICHD) to study the environmental influences (including physical, chemical, biological, and psychosocial) on children's health and development. The NCS examines all aspects of the environment including air, water, diet, noise, family dynamics, and genetics, on the growth, development, and health of children across the United States, for a period of 21 years. The purpose of NCS is to improve the health and well-being of children and to contribute to understanding the role of these factors on health and disease. The research plan for the NCS was developed from 2005 to 2007 in collaboration among the Interagency Coordinating Committee, the NCS Advisory Committee, the NCS Program Office, Westat, the Vanguard Center principal investigators, and federal scientists. The current design of the study, however, uses a separate pilot to assess quality of scientific output, logistics, and operations and a'Main Study'to examine exposure-outcome relationships. The NCS proposed the use of a multilayered cohort approach for the Main Study, which was one of the topics for discussion at the workshop that is the subject of this publication. In the fall of 2012, NICHD requested that the Committee on National Statistics (CNSTAT) of the NRC and the IOM convene a joint workshop, to be led by CNSTAT. The workshop was to focus on issues related to the overall design (including the framework for implementation) of the NCS. The committee was provided a background paper which it used to select the challenges that were discussed at the workshop. Design of the National Children's Study: A Workshop Summary presents an overview of the workshop held on January 11, 2013. The publication includes summaries of the four sessions of the workshop, a list of participants, and the agenda.

This volume is the product of a course on longitudinal prospective re­ search arranged by the three editors in Arhus, Denmark, in 1978. The course was supported by the Nordisk Kulturfond for young researchers from the Nordic countries, who had planned or had simply involved themselves in longitudinal prospective research projects of various kinds. The twenty-six participants represented a wide range of professions: statisticians, psychologists, psychiatrists, nutritionists, and public health researchers. The teachers came from many countries and represented many disciplines. The course was very successful, especially from the point of view of the quality and investment of the teachers. We felt also that the course met a strong need in this relatively new field of research. Therefore, we asked the teachers to prepare written versions of their lectures so that they could have wider dissemination; they agreed to do so. The present book is composed of these contributions. The first chap­ ter, after outlining some of the problems with traditional strategies in mental health research, goes on to suggest some of the possible preven­ tive applications of longitudinal research methods. Included in Parts II and III are papers on design problems and on the tools of long-term research, such as genetics and classification, biological measurements, epidemiological guidelines, statistical models, disease registers, and de­ velopmental psychology.

Human immunosenescence contributes to morbidity and mortality in later life. The age-associated increasing incidence of cancer and cardiovascular disease plateaus at around 80 years of age in industrialised countries, but death due to infectious disease continues to increase up to 100 years of age and beyond. Understanding the reasons for age-associated alterations to protective immunity in the elderly would facilitate the development of interventions to reconstitute appropriate immune function, increase responsiveness to vaccination and extend healthspan. The majority of the papers collected in this volume therefore address not only the mechanisms responsible for immune ageing in humans but consider what might be accomplished to redress the erosion of immune competence with age.

The twentieth century saw significant increases in both life expectancy and retirement rates-changes that have had dramatic impacts on nearly every aspect of society and the economy. Forecasting future trends in health and retirement rates, as we must do now, requires investigation of such long-term trends and their causes. To that end, this book draws on new data-an extensive longitudinal survey of Union Army veterans born between 1820 and 1850-to examine the factors that affected health and labor force participation in nineteenth-century America. Contributors consider the impacts of a variety of conditions-including social class, wealth, occupation, family, and community-on the morbidity and mortality of the group. The papers investigate and address a number of special topics, including the influence of previous exposure to infectious disease, migration, and community factors such as lead in water mains. They also analyze the roles of income, health, and social class in retirement decisions, paying particular attention to the social context of disability. Economists and historians who specialize in demography or labor, as well as those who study public health, will welcome the unique contributions offered by this book, which offers a clearer view than ever before of the workings and complexities of life, death, and labor during the nineteenth century.

Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change. Such studies and the data arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates vary with time, and bias can arise if patients drop out of the study.Provides the authority and expertise of leading contributors from an international board of authorsPresents the latest release in the Handbook of Statistics seriesUpdated release includes the latest information on Modeling and Analysis of Longitudinal Data

This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents'emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book's figures. New to This Edition: •Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux. •Chapter on longitudinal mixture modeling, with Whitney Moore. •Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne. •Chapter on Bayesian SEM, with Mauricio Garnier. •Revised throughout with new developments and discussions, such as how to test models of experimental effects.

Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book.

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

This Proceedings of a Workshop summarizes the presentations and discussions at the Workshop on Improving Consent and Response in Longitudinal Studies of Aging, which was held virtually and live-streamed on September 27-28, 2021. The workshop was convened by the Committee on National Statistics of the National Academies of Science, Engineering, and Medicine to assist the National Institute on Aging (NIA) with its methodological research agenda and inform the different longitudinal survey programs sponsored by NIA about practices and research to improve response and consent in other survey programs. The workshop was structured to bring together scientists and researchers from multiple disciplines and countries to share their research and insights on how to improve response and consent in large, representative longitudinal studies on aging.

Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas.Key Features:•Includes the most up-to-date methods•Use simple examples to demonstrate complex methods•Uses real data from a number of areas•Examples utilize R code

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.

Advances in Longitudinal Survey Methodology Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodologyAdvances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, “Methodology of Longitudinal Surveys”, published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting.New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents'consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of:A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequencyAn exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studiesAn examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement.An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.

First published Open Access under a Creative Commons license as What is Quantitative Longitudinal Data Analysis?, this title is now also available as part of the Bloomsbury Research Methods series.Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution.

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state–trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

The number of longitudinal data archives is growing almost daily, yet no resource exists to help understand the relationship between research questions and archival data--until now. Drawing on a single project, the Lewis Terman Study at Stanford University, the authors illustrate how to use the model-fitting process to select and fit the right data set to a particular research problem. Employing a step-by-step approach, this handy volume covers the measurement of historical influences, the adaptation of existing coding schemes to temporal patterns that are characteristic of life records, and the recasting of archival materials to illuminate contemporary questions that the data were not designed to answer.

Obwohl in den letzten Jahren immer mehr qualitative Studien auch in einem Längsschnittdesign durchgeführt werden, stellen sie in der empirischen Sozial- und Bildungsforschung nach wie vor eine Ausnahme dar. Das Buch bietet eine erste systematische und differenzierte Zusammenführung der unterschiedlichen theoretischen und methodischen Perspektiven sowie forschungspraktischen Erfahrungen bei der Umsetzung von qualitativen Längsschnittstudien. Erkenntnispotentiale und offene Fragen zentraler Ansätze und Ergebnisse aus ausgewählten Forschungsfeldern werden diskutiert und reflektiert.

This is the first book to systematically track postwar changes in family formation in Western Europe and the United States. Cohabitation and motherhood outside of marriage have become more widespread at the same time that women's social roles are evolving. Women are attaining higher levels of education, marrying at an older age, more frequently working outside the home, and have more reproductive freedom due to new advances in contraception. In this original collection of essays, sociologists and demographers from eight Western European countries and the United States use longitudinal data to compare national variations and explain the connection between the new role of women and family formation in postwar society. The contributors provide a thorough review of the social demographic literature to advance a variety of hypotheses about the relationships between changing women's education and family formation outcomes, which are empirically examined and compared across countries.

Mary Lynne Derrington's Qualitative Longitudinal Methods: Researching Implementation and Change addresses the use of the qualitative longitudinal methods, their unique methodological features, and the challenges and benefits to this approach. This short supplemental text uses examples of published studies, and the author's own stories and examples, to show application of the concepts. A chapter on how to prepare a manuscript for publication concentrates on the distinctive aspects of publishing longitudinal studies. The book will be useful to those researching change and its impact on organizations and individuals resulting from the implementation of programs and policies.

This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. This volume offers a new introduction to an evolving research method in the social sciences. Qualitative Longitudinal (QL) research is conducted through time. In its qualitative dimensions it opens up the potential to'think dynamically'in creative, flexible and innovative ways. QL enquiry is rooted in a long-established tradition of qualitative temporal research, spanning the fields of social anthropology, sociological re-studies and biographical research. But over the past two decades, a growing body of scholarship has begun to document this approach and explore its theoretical underpinnings. This in turn has fuelled a growing interest in and rapid uptake of QL methodology across the disciplines and in international context. This practical volume will be a first port of call for students and researchers wishing to use QL research in their own projects. The chapters follow a logical development, from conceptual and methodological foundations, to research practice and ethics, to the generation and analysis of data. Each chapter offers practical examples drawn from the research field to illustrate key themes and the rich possibilities for new applications.

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features:• Provides an overview of parametric and semiparametric methods • Shows smoothing methods for unstructured nonparametric models • Covers structured nonparametric models with time-varying coefficients • Discusses nonparametric shared-parameter and mixed-effects models • Presents nonparametric models for conditional distributions and functionals • Illustrates implementations using R software packages • Includes datasets and code in the authors'website • Contains asymptotic results and theoretical derivations

Die vorliegende Festschrift würdigt die wissenschaftliche Karriere von Hans-Jürgen Andreß, während der er die Methodenentwicklung im Bereich längsschnittlicher Analysemethoden geprägt und einflussreiche empirische Studien zu Armut, Ungleichheit und den Folgen kritischer Lebensereignisse vorgelegt hat. Der Band vereint nun diese Aspekte und versammelt empirische Studien, welche auf Basis längsschnittlicher analytischer Designs verschiedene Formen sozialer Ungleichheit in Deutschland untersuchen. In unterschiedlichen thematischen Feldern erarbeiten die Beiträge Erklärungen für Ungleichheitspositionen und untersuchen insbesondere deren Einbettung in den Lebensverlauf. Neben ihrem konkreten empirischen Forschungsbeitrag demonstrieren die Studien damit gleichzeitig die analytischen Potentiale längsschnittlicher Designs und der vorhandenen längsschnittlichen Dateninfrastruktur in Deutschland. Die HerausgeberProf. Dr. Marco Giesselmann ist Juniorprofessor an der Universität Bielefeld und Mitarbeiter des DIW Berlin.Prof. Dr. Katrin Golsch ist Professorin für Quantitative Methoden der empirischen Sozialforschung und Sozialstrukturanalyse am Institut für Sozialwissenschaften der Universität Osnabrück.Prof. Dr. Henning Lohmann ist Professur für Soziologie am Fachbereich Sozialökonomie der Universität Hamburg.Dr. Alexander Schmidt-Catran ist akademischer Rat am Institut für Soziologie und Sozialpsychologie (ISS) an der Universität zu Köln.

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com.Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution. The book is designed for postgraduates and researchers across the social sciences considering the use of quantitative longitudinal data resources in their research. It will also be an excellent text for undergraduate and postgraduate courses on advanced quantitative methods.

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results. User-Friendly Features •Real, worked-through longitudinal data examples serving as illustrations in each chapter. •Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data. •'Important Considerations'sections offering caveats, warnings, and recommendations for the use of specific models. •Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling. Winner--Barbara Byrne Book Award from the Society of Multivariate Experimental Psychology

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.

Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues.Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website.This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.

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.

The book provides a comprehensive summary of the best known and most highly respected well-controlled long-term prospective follow-up studies in ADHD. These studies followed children with ADHD and matched controls into young adulthood (mean age 20-25 years) and middle-age (mean age 41 years). They explore a wide variety of outcome areas, e.g. education, occupation, emotional and psychiatric functioning, substance use and abuse, sexual behavior, as well as legal problems. One chapter focuses particularly on the outcome of girls with ADHD. Outcome areas explored are thus comprehensive and clinically very relevant. The book also explores the possible predictors of adult outcome. A whole chapter is devoted to treatment (medication and psychosocial) as a predictor of outcome. In addition to treatment, predictors explored include characteristics of the child (e.g., IQ, severity of initial ADHD symptoms, initial comorbidity characteristics of the family, e.g., socioeconomic status, single parenthood, parental pathology, and family functioning. In a summary chapter, the impact and importance of these various predictors in different outcome areas, e.g. education, occupation, emotional/social functioning, antisocial behavior, substance use and abuse and risky sexual and driving behaviors are explored. In summary, the book provides a comprehensive view of the prognosis, e.g., long-term outcome of ADHD and key factors which can influence this outcome. Professionals and the general public will thus get a clear view of what can happen to children with ADHD as they proceed through adolescence and adulthood and address important prognostic and predictive factors in their treatment approaches to ensure better long-term outcome in patients with ADHD.

The outcomes of psychoanalysis, as with other psychotherapies, vary considerably. Psychoanalytic Treatment in Adults examines the results of a longitudinal study of change during psychoanalysis, illuminating the characteristics of patients, analysts and analyses which can help to predict outcomes of treatment. Written by experienced psychologists and psychoanalysts, chapters in the book draw upon sixty case studies to consider how patients with very different analytic outcomes respond at both the beginning and end of their analysis. Psychoanalysts used a clinician report measure, the Shedler-Westen Assessment Procedure, to describe a patient at the beginning of psychoanalysis and every six months until the analysis ended. This allowed the authors to learn about changes over analysis and, in turn, improved treatment planning and practice for the well-being of other patients. Chapters explore five outcomes: a negative therapeutic reaction; attrition when the patient drops out; attrition due to external events; mutual agreement between patient and analyst without maximum benefits; and mutual agreement between patient and analyst with maximum benefits. The findings from these chapters will be of interest to researchers and academics in the fields of psychoanalysis, psychotherapy, psychodynamic therapy, psychoanalytic education, psychiatry and psychology. The results should also help clinicians recognize potential problems early in analytic treatments so that they can work more effectively with patients.

This book addresses a broad array of pressing challenges of longitudinal surveys and provides innovative solutions to methodological problems based on the example of the NEPS. It covers longitudinal issues such as sampling, weighting, recruiting and fieldwork management, the design of longitudinal surveys and the implementation of constructs, conducting competence tests over the life course, effective methods to improve and to maintain the highest level of data quality, data management tools for large-scale longitudinal surveys, the dissemination of research data to heterogeneous scientific communities, as well as establishing a long-term public relations and communications unit integrating a study's stakeholder community over time.

Revised and updated with the latest epidemiologic research, the Second Edition of Life Cycle Nutrition: An Evidence-Based Approach explores nutritional foundations and the growth, development and normal functioning of individuals through each stage of life. With subjects as diverse as media influences on eating, skipping breakfast, fruit juice consumption, and clinical nutrition, this text gives students current knowledge, helps them evaluate emerging knowledge, and prepares them to uncover new knowledge for the public, their clients, and themselves.Key Features: Evidence-Based Practice sections contains research articles that provide students with an opportunity to create peer-reviewed studies and clinical trials. Cultural Diversity Sidebars, integrated throughout the text, present issues that challenge students to think critically about the nutritional and health differences among ethnic groups. Interesting Vignettes, based on nutrition and health, enhance student understanding of the material presented. Issues to Debate sections provides timely topics for debate and further consideration such as withholding and withdrawing nutrition, the ethical implications of nutrition care, and right to die case law. Students will learn the physiological, moral, ethical, and legal issues surrounding these emotionally charged issues. Case Studies and Nutritional Management help students develop knowledge and skills within a wide variety of subjects and improve critical thinking, public speaking, and group interactions.

This open access book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time.The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population.

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.

The National Children's Study (NCS) was authorized by the Children's Health Act of 2000 and is being implemented by a dedicated Program Office in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The NCS is planned to be a longitudinal observational birth cohort study to evaluate the effects of chronic and intermittent exposures on child health and development in the U.S.. The NCS would be the first study to collect a broad range of environmental exposure measures for a national probability sample of about 100,000 children, followed from birth or before birth to age 21. Detailed plans for the NCS were developed by 2007 and reviewed by a National Research Council / Institute of Medicine panel. At that time, sample recruitment for the NCS Main Study was scheduled to begin in 2009 and to be completed within about 5 years. However, results from the initial seven pilot locations, which recruited sample cases in 2009-2010, indicated that the proposed household-based recruitment approach would be more costly and time consuming than planned. In response, the Program Office implemented a number of pilot tests in 2011 to evaluate alternative recruitment methods and pilot testing continues to date. At the request of Congress, The National Children's Study 2014 reviews the revised study design and proposed methodologies for the NCS Main Study. This report assesses the study's plan to determine whether it is likely to produce scientifically sound results that are generalizable to the United States population and appropriate subpopulations. The report makes recommendations about the overall study framework, sample design, timing, content and need for scientific expertise and oversight. The National Children's Study has the potential to add immeasurably to scientific knowledge about the impact of environmental exposures, broadly defined, on children\'s health and development in the United States. The recommendations of this report will help the NCS will achieve its intended objective to examine the effects of environmental influences on the health and development of American children.

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.

For a wide spectrum of scientists from biomedical and dental researchers to primatologists and physical anthropologists, Emet Schneiderman offers the most accurate and up-to-date presentation of the normal growth of the lower facial skeleton in a primate species. His study is based on a sample of thirty-five captive rhesus monkeys, whose facial growth was traced over a ten-year period spanning from infancy to adulthood. The author identifies the relative contribution of various sites of growth, quantifies the relative roles of different types of development--such as appositional and condylar--and sheds light on several long-standing controversies as to how the primate face grows. Unlike many of the traditional cephalometric measurements, the ones included in this work were chosen to reflect the positional, dimensional, and localized remodeling changes that occur during ontogeny. Using a new statistical approach designed for longitudinal data, Schneiderman avoids the misleading information that has often resulted from older, cross-sectional statistical methods. This book serves as a foundation for future experimental and normal studies in the rhesus monkey and, from a methodological standpoint, as a general model for future longitudinal growth studies.Originally published in 1992.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

The importance that practitioners are placing on longitudinal designs and analyses signals a critical shift toward methods that enable a better understanding of developmental processes thought to underlie many human attributes and behaviors. A simple scan of one's own applied literature reveals evidence of this trend through the increasing number of articles adopting longitudinal methods as their primary analytic tools. Advances in Longitudinal Methods in the Social and Behavioral Sciences is a resource intended for advanced graduate students, faculty, and applied researchers interested in longitudinal data analysis, especially in the social and behavioral sciences. The chapters are written by established methodological researchers from diverse research domains such as psychology, biostatistics, educational statistics, psychometrics, and family sciences. Each chapter exposes the reader to some of the latest methodological developments and perspectives in the analysis of longitudinal data, and is written in a didactic tone that makes the content accessible to the broader research community. This volume will be particularly appealing to researchers in domains including, but not limited to: human development, clinical psychology, educational psychology, school psychology, special education, epidemiology, family science, kinesiology, communication disorders, and education policy and administration. The book will also be attractive to members of several professional organizations such as the American Educational Research Association (AERA), the American Psychological Association (APA), the American Psychological Society (APS), the Society for Research on Adolescence (SRA), the Society of Research in Child Development (SRCD), Society for Research in Adult Development (SRAD), British Psychological Society (BPS), Canadian Psychological Association (CPA), and other related organizations.

Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers'understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data.Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book's accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.

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 volume brings together a sample of the best of the studies that illustrate two recent trends in research on deviant behavior. The first of these trends is the investigation of deviant behavior in longitudinal perspective. Panels of subjects are followed over long periods of time to establish temporal relationships be­ tween deviant behavior and the antecedents and consequences of deviant behav­ ior. The second trend in contemporary research on deviance is the recognition of the association among forms of deviant behavior such as violence, drug abuse, and theft. The recognition of the covariation among forms of deviance stimulated questions regarding the nature of the relationships among multiple forms of de­ viance. Is one form of deviant behavior a cause or a consequence of other forms of deviant behavior? What variables mediate and moderate such causal relation­ ships? Do different forms of deviant behavior have common antecedents and consequences? Independent of the foregoing relationships, do particular forms of deviant behavior have unique antecedents and consequences? The eight original research studies that, along with the introduction and overview, constitute this volume are based on data drawn from among the most influential longitudinal studies in the general area of deviant behavior. These studies variously consider common and pattern-specific antecedents and conse­ quences, reciprocal influences, and intervening and moderating variables in causal relationships among drug use, crime, and other forms of deviance.

This book discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation and comparison of the results of the different techniques. The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten. The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Finally, an extensive overview and comparison of different software packages is provided. This practical guide is essential for non-statisticians and researchers working with longitudinal data from epidemiological and clinical studies.

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

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.

International Review of Research in Developmental Disabilities is an ongoing scholarly look at research into the causes, effects, classification systems, syndromes, etc. of developmental disabilities. Contributors come from wide-ranging perspectives, including genetics, psychology, education, and other health and behavioral sciences. Provides the most recent scholarly research in the study of developmental disabilities A vast range of perspectives is offered, and many topics are covered An excellent resource for academic researchers

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

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.

In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time. The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization. Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.

The dissemination of the MIXED procedure in SAS has provided a whole class of statistical models for routine use. We believe that both the ideas be­ hind the techniques and their implementation in SAS are not at all straight­ forward and users from various applied backgrounds, including the phar­ maceutical industry, have experienced difficulties in using the procedure effectively. Courses and consultancy on PROC MIXED have been in great demand in recent years, illustrating the clear need for resource material to aid the user. This book is intended as a contribution to bridging this gap. We hope the book will be of value to a wide audience, including applied statisticians and many biomedical researchers, particularly in the pharma­ ceutical industry, medical and public health research organizations, con­ tract research organizations, and academic departments. This implies that our book is explanatory rather than research oriented and that it empha­ sizes practice rather than mathematical rigor. In this respect, clear guidance and advice on practical issues are the main focus of the text. Nevertheless, this does not imply that more advanced topics have been avoided. Sections containing material of a deeper level have been sign posted by means of an asterisk.

Explaining Long-Term Trends in Health and Longevity is a collection of essays by Nobel laureate Robert W. Fogel on the theory and measurement of ageing and health-related variables. Dr Fogel analyzes historic data on height, health, nutrition and life expectation to provide a clearer understanding of the past, illustrate the costs and benefits of using such measures and note the difficulties of drawing conclusions from data intended for different purposes. Dr Fogel explains how the basic findings of the anthropometric approach to historical analysis have helped reinterpret the nature of economic growth. Rising life expectancies and lower disease rates in countries experiencing economic growth highlight the importance of improving nutrition and agricultural productivity.