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Longitudinal Data Analysis, Newsom


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Цена: 22202.00р.
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Автор: Newsom
Название:  Longitudinal Data Analysis
ISBN: 9780415874144
Издательство: Taylor&Francis
Классификация:

ISBN-10: 0415874149
Обложка/Формат: Hardback
Страницы: 405
Вес: 0.68 кг.
Дата издания: 15.07.2011
Серия: Multivariate applications series
Язык: English
Размер: 233 x 153 x 28
Читательская аудитория: Postgraduate, research & scholarly
Подзаголовок: A practical guide for researchers in aging, health, and social sciences
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание:

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.




Analysis of longitudinal data

Название: Analysis of longitudinal data
ISBN: 0199676755 ISBN-13(EAN): 9780199676750
Издательство: Oxford Academ
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Цена: 8395.00 р.
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Описание: 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.

Applied Longitudinal Analysis

Автор: Fitzmaurice, Fitzmaurice Garrett M., Laird Nan M.
Название: Applied Longitudinal Analysis
ISBN: 0470380276 ISBN-13(EAN): 9780470380277
Издательство: Wiley
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Цена: 18366.00 р.
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Описание: Since the publication of the first edition, the authors have solicited feedback from both the instructors who use the book as a text for their courses as well as the researchers who use the book as a resource for their research.

Longitudinal Analysis: Modeling Within-Person Fluctuation and Change

Автор: Hoffman Lesa
Название: Longitudinal Analysis: Modeling Within-Person Fluctuation and Change
ISBN: 0415876028 ISBN-13(EAN): 9780415876025
Издательство: Taylor&Francis
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Цена: 13779.00 р.
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Описание:

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.

Joint Models of Longitudinal and Time-to-Event Data

Автор: Rizopoulos Dimitris
Название: Joint Models of Longitudinal and Time-to-Event Data
ISBN: 1439872864 ISBN-13(EAN): 9781439872864
Издательство: Taylor&Francis
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Цена: 13779.00 р.
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Описание:

In follow-up studies it is often of interest to investigate how a longitudinal outcome that is repeatedly measured in time is associated with a time to an event of interest. Typical examples in this setting come from biomarker research, such as HIV studies where longitudinal CD4 cell counts and viral load are collected in conjunction to the time-to-death, and prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence.

This book is the first providing a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.


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