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Growth Modeling: Structural Equation and - Hardcover, by Grimm Kevin J.; - New h
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Specifiche dell'oggetto
- Condizione
- Book Title
- Growth Modeling: Structural Equation and Multilevel Modeling Appr
- ISBN
- 9781462526062
Informazioni su questo prodotto
Product Identifiers
Publisher
Guilford Publications
ISBN-10
1462526063
ISBN-13
9781462526062
eBay Product ID (ePID)
219229454
Product Key Features
Number of Pages
537 Pages
Language
English
Publication Name
Growth Modeling : Structural Equation and Multilevel Modeling Approaches
Publication Year
2016
Subject
Methodology, Nursing / Research & Theory, Research, Statistics
Type
Textbook
Subject Area
Social Science, Psychology, Medical
Series
Methodology in the Social Sciences Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
40.1 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2016-021792
Dewey Edition
23
Reviews
"The implementation details are superb and the level of technical detail quite stunning. It will be so helpful for longitudinal researchers to have this compendium of growth models, complete with sample code from both SEM and multilevel modeling frameworks. It is wonderful to see the item response theory and SEM frameworks so nicely integrated. The authors have hit the trifecta--pulling together multilevel modeling, SEM, and item response theory. There is truly no other book on the market that covers latent growth modeling so completely and comprehensively."--D. Betsy McCoach, PhD, Measurement, Evaluation, and Assessment Program, Neag School of Education, University of Connecticut "I highly recommend this book. It is a tour de force in model building with latent growth curves. The authors' use of three programming languages (Mplus, SAS, and R) is great, and they work with computer programs in an unusually careful way. The book will be of value to anyone dealing with longitudinal data."--John J. McArdle, PhD, Department of Psychology, University of Southern California "This is the most thorough work on this subject that I know of; the coverage of nonlinear models is among the best I have seen. The book is written at a level suitable for an advanced graduate student learning this material or an applied researcher seeking a reference on the subject. It introduces the basics, discusses the relevant model theory/specification, and presents programming code for several packages. The authors do an exceptional job of explaining the computer code and providing insight into convergence issues and how to remedy them. It is good to have this all in one place (along with the respective output) for comparative purposes."--Daniel A. Powers, PhD, Department of Sociology, University of Texas at Austin, "The implementation details are superb and the level of technical detail quite stunning. It will be so helpful for longitudinal researchers to have this compendium of growth models, complete with sample code from both SEM and multilevel modeling frameworks. It is wonderful to see the item response theory and SEM frameworks so nicely integrated. The authors have hit the trifecta--pulling together multilevel modeling, SEM, and item response theory. There is truly no other book on the market that covers latent growth modeling so completely and comprehensively."--D. Betsy McCoach, PhD, Measurement, Evaluation, and Assessment Program, Neag School of Education, University of Connecticut "I highly recommend this book. It is a tour de force in model building with latent growth curves. The authors' use of three programming languages (Mplus, SAS, and R) is great, and they work with computer programs in an unusually careful way. The book will be of value to anyone dealing with longitudinal data."--John J. McArdle, PhD, Department of Psychology, University of Southern California "This is the most thorough work on this subject that I know of; the coverage of nonlinear models is among the best I have seen. The book is written at a level suitable for an advanced graduate student learning this material or an applied researcher seeking a reference on the subject. It introduces the basics, discusses the relevant model theory/specification, and presents programming code for several packages. The authors do an exceptional job of explaining the computer code and providing insight into convergence issues and how to remedy them. It is good to have this all in one place (along with the respective output) for comparative purposes."--Daniel A. Powers, PhD, Department of Sociology, University of Texas at Austin "This well-written book starts with clear statements about what research questions can be answered using growth models. Usefully, the authors include both multilevel modeling and SEM approaches, and analyze the example data within each framework using one proprietary program and one freely available R package. Viewing the detailed code and the results of each analysis gives the reader a chance to understand the strengths and weaknesses of each approach. Later chapters address such developments as nonlinear growth models and growth models for noncontinuous outcomes. Code for each variation is given, which expand the researcher's capacity to fit these complex models."--Yasuo Miyazaki, PhD, Associate Professor of Educational Research and Evaluation Program, Virginia Tech, An accessible resource that provides a thorough introduction to frequently used longitudinal models....An invaluable resource for students and scholars....This book would be excellent reading material for students in various disciplines, such as psychology and education, that provide either introductory or advanced longitudinal graduate courses., "The implementation details are superb and the level of technical detail quite stunning. It will be so helpful for longitudinal researchers to have this compendium of growth models, complete with sample code from both SEM and multilevel modeling frameworks. It is wonderful to see the item response theory and SEM frameworks so nicely integrated. The authors have hit the trifecta--pulling together multilevel modeling, SEM, and item response theory. There is truly no other book on the market that covers latent growth modeling so completely and comprehensively."--D. Betsy McCoach, PhD, Measurement, Evaluation, and Assessment Program, Neag School of Education, University of Connecticut "I highly recommend this book. It is a tour de force in model building with latent growth curves. The authors' use of three programming languages (Mplus, SAS, and R) is great, and they work with computer programs in an unusually careful way. The book will be of value to anyone dealing with longitudinal data."--John J. McArdle, PhD, Department of Psychology, University of Southern California "This is the most thorough work on this subject that I know of; the coverage of nonlinear models is among the best I have seen. The book is written at a level suitable for an advanced graduate student learning this material or an applied researcher seeking a reference on the subject. It introduces the basics, discusses the relevant model theory/specification, and presents programming code for several packages. The authors do an exceptional job of explaining the computer code and providing insight into convergence issues and how to remedy them. It is good to have this all in one place (along with the respective output) for comparative purposes."--Daniel A. Powers, PhD, Department of Sociology, University of Texas at Austin "This well-written book starts with clear statements about what research questions can be answered using growth models. Usefully, the authors include both multilevel modeling and SEM approaches, and analyze the example data within each framework using one proprietary program and one freely available R package. Viewing the detailed code and the results of each analysis gives the reader a chance to understand the strengths and weaknesses of each approach. Later chapters address such developments as nonlinear growth models and growth models for noncontinuous outcomes. Code for each variation is given, which expand the researcher's capacity to fit these complex models."--Yasuo Miyazaki, PhD, Associate Professor of Educational Research and Evaluation Program, Virginia Tech "This is the by far the most comprehensive, up-to-date, and ready-to-use book on growth modeling that I have ever seen. The authors have proven records in effectively teaching classes and workshops on longitudinal data analysis. This is a 'must have' for anyone who wants to develop or apply growth models. The SAS, Mplus, and OpenMx example scripts and instructions are long-needed complements to those programs' respective manuals. Coverage includes the most recent developments in growth modeling, and each chapter essentially can stand by itself, providing enough information for researchers to apply the respective models in their studies to answer more complex and interesting empirical questions. The book can be used in a range of classes either as a main text or a supplement. I will definitely recommend it to students in my Structural Equation Modeling class when I teach structural growth curve modeling."--Zhiyong Johnny Zhang, PhD, Department of Psychology, University of Notre Dame
Illustrated
Yes
Dewey Decimal
300.727
Synopsis
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 ......, 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 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
LC Classification Number
HA29.G7734 2016
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