Longitudinal Data Analysis

Author: Jason Newsom
Publisher: Routledge
ISBN: 1136705465
Format: PDF, ePub, Mobi
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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 Multivariate Psychology

Author: Emilio Ferrer
Publisher: Routledge
ISBN: 1351662724
Format: PDF
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This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.

Ver nderungsmessung des fachlichen Wissens von Studierenden

Author: Susanne Schmidt
Publisher: Springer-Verlag
ISBN: 365821919X
Format: PDF, Mobi
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Im Fokus dieses Buches steht die Analyse längsschnittlicher Daten zur Veränderungsmessung betriebswirtschaftlichen Wissens bei Studierenden im Studienverlauf. Neben der theoretischen Konzeptualisierung zeigt Susanne Schmidt eine komplexe statistische Modellierung des Fachwissenserwerbs. Um den besonderen methodischen Herausforderungen bei den längsschnittlichen Daten – wie Antwortausfälle bei wiederholten Messungen – zu begegnen, entwickelt die Autorin eine Variante eines Mehrebenenstrukturgleichungsmodells. Mittels dieser latenten Modellspezifikation kann unter anderem vorhergesagt werden, dass das Fachwissen im Studienverlauf einen positiven, jedoch abnehmenden Zuwachs aufweist. Auf Basis der Ergebnisse zur Wissensveränderung werden Implikationen für die Hochschullehre entwickelt.

Religion and Mental Health

Author: Harold G. Koenig
Publisher: Academic Press
ISBN: 0128112832
Format: PDF, ePub
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Religion and Mental Health: Research and Clinical Applications summarizes research on how religion may help people better cope or exacerbate their stress, covering its relationship to depression, anxiety, suicide, substance abuse, well-being, happiness, life satisfaction, optimism, generosity, gratitude and meaning and purpose in life. The book looks across religions and specific faiths, as well as to spirituality for those who don’t ascribe to a specific religion. It integrates research findings with best practices for treating mental health disorders for religious clients, also covering religious beliefs and practices as part of therapy to treat depression and posttraumatic stress disorder. Summarizes research findings on the relationship of religion to mental health Investigates religion’s positive and negative influence on coping Presents common findings across religions and specific faiths Identifies how these findings inform clinical practice interventions Describes how to use religious practices and beliefs as part of therapy

Longitudinal Structural Equation Modeling

Author: Jason T. Newsom
Publisher: Routledge
ISBN: 1317975359
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.

The Design and Analysis of Longitudinal Studies

Author: David S Goldstein
Format: PDF, ePub, Mobi
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The theory and practice of longitudinal studies; Sampling and design; The choice of measurement scales over time; The analysis of models for time related measurement; The analysis of models for relating measurements at differene occasions; Population standards or norms; Data processing.