Longitudinal Data Analysis

Author: Jason Newsom
Publisher: Routledge
ISBN: 1136705465
Format: PDF, ePub
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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 the Behavioral Sciences Using R

Author: Jeffrey D. Long
Publisher: SAGE
ISBN: 1412982685
Format: PDF, Kindle
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This book is unique in its focus on showing students in the behavioral sciences how to analyze longitudinal data using R software. The book focuses on application, making it practical and accessible to students in psychology, education, and related fields, who have a basic foundation in statistics. It provides explicit instructions in R computer programming throughout the book, showing students exactly how a specific analysis is carried out and how output is interpreted.

Longitudinal and Panel Data

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 9780521535380
Format: PDF, Docs
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An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

The Wiley Blackwell Handbook of Adulthood and Aging

Author: Martin J, Sliwinski
Publisher: John Wiley & Sons
ISBN: 1119237882
Format: PDF, Kindle
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This exceptional collection draws on the most recent demographic data and combines classic research with cutting-edge approaches to provide an invaluable overview of the developmental psychology of the adult years. Covers a wide range of topics within adult development and aging, from theoretical perspectives to specific content areas Includes newly commissioned essays from the top researchers in the field Takes a biopsychosocial perspective, covering the biological, psychological and social changes that occur in adulthood

Longitudinal Structural Equation Modeling

Author: Jason T. Newsom
Publisher: Routledge
ISBN: 1317975340
Format: PDF, ePub, Docs
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This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling options are available for which hypotheses. The author demonstrates how SEM is related to other longitudinal data techniques throughout. By exploring connections between models, readers gain a better understanding of when to choose one analysis over another. The book explores basic models to sophisticated ones including the statistical and conceptual underpinnings that are the building blocks of the analyses. Accessibly written, research examples from the behavioral and social sciences and results interpretations are provided throughout. The emphasis is on concepts and practical guidance for applied research rather than on mathematical proofs. New terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues. Each chapter also includes examples of each model type, comment sections that provide practical guidance, model extensions, and recommended readings. Highlights include: Covers the major SEM approaches to longitudinal analysis in one resource. Explores connections between longitudinal SEM models to enhance integration. Numerous examples that help readers match research questions to appropriate analyses and interpret results. Reviews practical issues related to model specification and estimation to reinforce connections. Analyzes continuous and discrete (binary and ordinal) variables throughout for breadth not found in other sources. Reviews key SEM concepts for those who need a refresher (Ch. 1). Emphasizes how to apply and interpret each model through realistic data examples. Provides the book’s data sets at www.longitudinalsem.com along with the Mplus and R-lavaan syntax used to generate the results. Introduces the LISREL notation system used throughout (Appendix A). The chapters can be read out of order but it is best to read chapters 1 – 4 first because most of the later chapters refer back to them. The book opens with a review of latent variables and analysis of binary and ordinal variables. Chapter 2 applies this information to assessing longitudinal measurement invariance. SEM tests of dependent means and proportions over time points are explored in Chapter 3, and stability and change, difference scores, and lagged regression are covered in Chapter 4. The remaining chapters are each devoted to one major type of longitudinal SEM -- repeated measures analysis models, full cross-lagged panel models and simplex models, modeling stability with state-trait models, linear and nonlinear growth curve models, latent difference score models, latent transition analysis, time series analysis, survival analysis, and attrition. Missing data is discussed in the context of many of the preceding models in Chapter 13. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this text also appeals to researchers in these fields. Intended for those without an extensive math background, prerequisites include familiarity with basic SEM. Matrix algebra is avoided in all but a few places.

Modeling Longitudinal Data

Author: Robert E. Weiss
Publisher: Springer Science & Business Media
ISBN: 0387283145
Format: PDF
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The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.

Longitudinal Structural Equation Modeling

Author: Todd D. Little
Publisher: Guilford Press
ISBN: 1462510272
Format: PDF, Docs
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Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website (www.guilford.com/little-materials) provides datasets for all of the examples--which include studies of bullying, adolescent students' emotions, and healthy aging--with syntax and output from LISREL, Mplus, and R (lavaan).

Health Intervention Research

Author: Souraya Sidani
Publisher: SAGE
ISBN: 1473909244
Format: PDF, Kindle
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At a time when evidence-based practice is the standard bearer for understanding health behaviour, problems and interventions, ensuring that researchers know the appropriate designs and methods for their research is more paramount than ever. Health Intervention Research will equip those doing research in these communities with the knowledge and tools they need to inform their methodological decisions when planning and conducting studies. This book describes both commonly used (e.g., randomized clinical trials) and advanced (e.g. preference trials, pragmatic trials) designs and methods for health intervention research. It outlines the theoretical reasoning underlying these different approaches, and synthesizes the evidence which supports or disputes different designs and methods. To achieve its aims, the book is divided into three main sections. The first section points to the need to base methodological decisions on evidence and highlights the importance of carefully selecting research designs and methods to maintain validity. The second section focuses on designs to determine the effects of intervention on outcomes, outlining their features and discussing how these can be used to evaluate interventions. The last section covers methods used in conducting intervention evaluation research. For each design and method, the following is covered: what it is, what the logic underlying it is, what the evidence supporting its effectiveness is, and also includes its advantages, its limitations, and how can it be implemented. This will be key reading for postgraduates and novice researchers in health and clinical psychology, health sciences and nursing.

Applied Longitudinal Data Analysis

Author: Judith D. Singer
Publisher: Oxford University Press
ISBN: 0199882401
Format: PDF, Mobi
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Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: · Downloadable data sets · Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more · Additional material for data analysis