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Longitudinal Analysis

Longitudinal Analysis Author Lesa Hoffman
ISBN-10 9781317591085
Release 2015-01-30
Pages 654
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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.



Intensive Longitudinal Methods

Intensive Longitudinal Methods Author Niall Bolger
ISBN-10 9781462506927
Release 2013-01-22
Pages 256
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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).



History and Systems of Psychology

History and Systems of Psychology Author James F. Brennan
ISBN-10 9781107178670
Release 2017-10-26
Pages 450
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History and Systems of Psychology provides an engaging introduction to the rich story of psychology's past. Retaining the clarity and accessibility praised by readers of earlier editions, this classic textbook provides a chronological history of psychology from the pre-Socratic Greeks to contemporary systems, research, and applications. The new edition also features expanded coverage of Eastern as well as Western traditions, influential women in psychology, professional psychology in clinical, educational, and social settings, and new directions in twenty-first century psychology as a cognitive and a positive science. Assuming little prerequisite knowledge, the authors discuss the people, places, and concepts that have shaped psychology's story, and show that we remain fascinated by the same enduring questions that confronted our ancestors - namely, our wonder at our subjectivity and consciousness of self. The seventh edition is fully supported by robust pedagogical features, instructor resources, and a companion website to aid student learning.



Longitudinal Structural Equation Modeling

Longitudinal Structural Equation Modeling Author Jason T. Newsom
ISBN-10 9781317975342
Release 2015-06-19
Pages 434
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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.



Methods and Applications of Longitudinal Data Analysis

Methods and Applications of Longitudinal Data Analysis Author Xian Liu
ISBN-10 9780128014820
Release 2015-09-11
Pages 530
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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.



Introduction to Statistical Mediation Analysis

Introduction to Statistical Mediation Analysis Author David MacKinnon
ISBN-10 9781136676130
Release 2012-10-02
Pages 488
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This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations. Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology. The book opens with a review of the types of research questions the mediation model addresses. Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions. Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate level research methods or statistics course is assumed. The overview of mediation analysis and the guidelines for conducting a mediation analysis will be appreciated by all readers.



Longitudinal Data Analysis for the Behavioral Sciences Using R

Longitudinal Data Analysis for the Behavioral Sciences Using R Author Jeffrey D. Long
ISBN-10 9781412982689
Release 2011-10-31
Pages 542
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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.



Manual of Clinical Psychopharmacology

Manual of Clinical Psychopharmacology Author Alan F. Schatzberg
ISBN-10 9781585625215
Release 2015-04-28
Pages 795
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Standard of care in psychiatry requires that practitioners stay current on new agents, interactions, side effects, and dosing guidelines -- a daunting task for the modern clinician grappling with today's challenging medical environment. The Manual of Clinical Psychopharmacology has been the psychiatrist's trusted companion for nearly three decades, and this new, eighth edition delivers the cutting-edge information clinicians need in a down-to-earth style, facilitating the integration of biological and psychopharmacological information into practice. The book's primary purpose is to provide the reader-practitioner with a practical, usable clinical guide to the selection and prescription of appropriate drug therapies for individual patients, drawing on the authors' clinical experience as well as on the scientific literature. Students of psychiatry and psychopharmacology also will find the book useful as both text and reference. The eighth edition retains many of its most popular features, while adding others to enhance coverage and promote comprehension: Some sections dealing with less commonly used drugs (e.g., barbiturates) have been shortened or eliminated, making space for the huge number of new agents that have been approved (e.g., vortioxetine) or are likely to receive approval from the U.S. Food and Drug Administration (FDA). This makes the volume easier to use. The book has been thoroughly updated to reflect the release of DSM-5, which introduced dimensional measures of key dimensions (e.g., anxiety and depression) across diagnostic categories to better describe patients' disorders. In the chapter on diagnosis and classification, the authors review these major changes and the implications for prescribing. Features of particular utility for students include the introductory chapter on the general principles of psychopharmacological treatment and the summary medication tables, which serve as quick-reference guides on classes of psychotropics. Although largely evidence-based, the authors also draw on their extensive clinical experience in discussing various drugs' efficacy, dosing and side effects and other important factors. Two appendixes offer suggested readings for clinicians and for patients and families, as well as information on strengths and costs of antidepressant and antipsychotic drugs. There has never been a more meticulously researched and referenced guide to prescribing psychotropic medications, nor one more down-to-earth and practical. The Manual of Clinical Psychopharmacology richly deserves its reputation as a classic in the field and has earned a place on every psychiatrist's desk.



Life span Developmental Psychology

Life span Developmental Psychology Author Paul B. Baltes
ISBN-10 9781317760337
Release 2014-01-02
Pages 286
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What are the changes we see over the life-span? How can we explain them? And how do we account for individual differences? This volume continues to examine these questions and to report advances in empirical research within life-span development increasing its interdisciplinary nature. The relationships between individual development, social context, and historical change are salient issues discussed in this volume, as are nonnormative and atypical events contributing to life-span change.



Handbook of Research Methods for Studying Daily Life

Handbook of Research Methods for Studying Daily Life Author Matthias R. Mehl
ISBN-10 9781609187477
Release 2012-01-01
Pages 676
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Bringing together leading authorities, this unique handbook reviews the breadth of current approaches for studying how people think, feel, and behave in everyday environments, rather than in the laboratory. The volume thoroughly describes experience sampling methods, diary methods, physiological measures, and other self-report and non-self-report tools that allow for repeated, real-time measurement in natural settings. Practical guidance is provided to help the reader design a high-quality study, select and implement appropriate methods, and analyze the resulting data using cutting-edge statistical techniques. Applications across a wide range of psychological subfields and research areas are discussed in detail.



Applied Longitudinal Analysis

Applied Longitudinal Analysis Author Garrett M. Fitzmaurice
ISBN-10 9781118551790
Release 2012-10-23
Pages 740
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Praise for the First Edition ". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis." —Journal of the American Statistical Association Features newly developed topics and applications of the analysis of longitudinal data Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences. The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including: Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material. With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.



Growth Modeling

Growth Modeling Author Kevin J. Grimm
ISBN-10 9781462526062
Release 2016-10-17
Pages 537
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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.



Growth Curve Analysis and Visualization Using R

Growth Curve Analysis and Visualization Using R Author Daniel Mirman
ISBN-10 9781466584334
Release 2016-04-19
Pages 188
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Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author’s website.



Couples Coping with Stress

Couples Coping with Stress Author Mariana K. Falconier
ISBN-10 9781317288879
Release 2016-05-12
Pages 350
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This is the first book that reviews both empirical and clinical applications of how couples jointly cope with stress - dyadic coping - around the globe. The Systemic-Transactional Stress Model (STM), developed by co-editor Guy Bodenmann, is used as a consistent framework so readers can better appreciate the contrasts and similarities across the fourteen cultures represented in the book. Written by scholars from the particular culture, each chapter provides a conceptual review of the dyadic coping research conducted in their specific cultures, and also provides empirical and clinical recommendations. Additional contributions include how to measure dyadic coping, so others can apply the STM model in other contexts. The latest treatment approaches for therapy and prevention are also highlighted, making this book ideal for professionals interested in expanding their cultural competence when working with couples from various backgrounds. Highlights include: -How couples in different cultures deal with stress and how values and traditions affect dyadic stress and coping. -Global applications, especially to couples in the regions highlighted in the book -- the U.S (including one chapter on Latino couples in the U.S.)., Australia, China, Greece, Hungary, Italy, Japan, Kenya, Nigeria, Pakistan, Portugal, Romania, and Switzerland. -Factors encountered in examining dyadic coping using the STM Model including measurement and assessment issues. -Suggestions for making treatment, prevention, and intervention programs for couples more effective. Ideal for relationship researchers, psychologists, mental health counselors, social workers, and advanced students who work with couples dealing with stress. This book is also appropriate for advanced courses on interpersonal processes, close relationships, stress and coping, multicultural issues in marriage and family therapy or counseling, or family systems, taught in a variety of social science disciplines.



Longitudinal Data Analysis Using Structural Equation Models

Longitudinal Data Analysis Using Structural Equation Models Author John J. McArdle
ISBN-10 1433817152
Release 2014
Pages 426
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When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer. 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. This volume 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. Book jacket.



Applied Longitudinal Data Analysis

Applied Longitudinal Data Analysis Author Judith D. Singer
ISBN-10 9780199882403
Release 2003-03-27
Pages 644
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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



Analysis of Longitudinal Data

Analysis of Longitudinal Data Author Peter Diggle
ISBN-10 9780199676750
Release 2013-03-14
Pages 400
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This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.