Hardcovere-bookprint + e-book

Hardcover~~$76.00~~ **$57.00**

orderOctober 17, 2016

ISBN 9781462526062

Price: 537 Pages

Size: 7" x 10"

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

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.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.

“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.”

“This is 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.”

“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.”

“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.”

“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.”

“The importance that researchers and practitioners are placing on longitudinal designs and analyses signals a prominent shift toward methods that enable a better understanding of the developmental processes thought to underlie many human traits and behaviors. This book provides the essential background on latent growth models and covers several interesting methodological extensions, including models for nonlinear change, growth mixture models, and longitudinal models for assessing change in latent variables. Practical examples are woven throughout the text, accompanied by extensive annotated code in SAS, Mplus, and R, which makes both basic and more complex models accessible. This is a wonderful resource for anyone serious about longitudinal data analysis.”

“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.”

1. Overview, Goals of Longitudinal Research, and Historical Developments

Overview

Five Rationales for Longitudinal Research

Historical Development of Growth Models

Modeling Frameworks and Programs

2. Practical Preliminaries: Things to Do before Fitting Growth Models

Data Structures

Longitudinal Plots

Data Screening

Longitudinal Measurement

Time Metrics

Change Hypotheses

Incomplete Data

Moving Forward

**II. The Linear Growth Model and Its Extensions**

3. Linear Growth Models

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

4. Continuous Time Metrics

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

5. Linear Growth Models with Time-Invariant Covariates

Multilevel Model Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

6. Multiple-Group Growth Modeling

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

7. Growth Mixture Modeling

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Model Fit, Model Comparison, and Class Enumeration

Important Considerations

Moving Forward

8. Multivariate Growth Models and Dynamic Predictors

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

**III. Nonlinearity in Growth Modeling**

9. Introduction to Nonlinearity

Organization for Nonlinear Change Models

Moving Forward

10. Growth Models with Nonlinearity in Time

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

11. Growth Models with Nonlinearity in Parameters

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

12. Growth Models with Nonlinearity in Random Coefficients

Multilevel Modeling Framework

Multilevel Modeling Implementation

Structural Equation Modeling Framework

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

**IV. Modeling Change with Latent Entities**

13. Modeling Change with Ordinal Outcomes

Dichotomous Outcomes

Polytomous Outcomes

Illustration

Multilevel Modeling Implementation

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

14. Modeling Change with Latent Variables Measured by Continuous Indicators

Common-Factor Model

Factorial Invariance over Time

Second-Order Growth Model

Illustration

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

15. Modeling Change with Latent Variables Measured by Ordinal Indicators

Item Response Modeling

Second-Order Growth Model

Illustration

Important Considerations

Moving Forward

**V. Latent Change Scores as a Framework for Studying Change**

16. Introduction to Latent Change Score Modeling

General Model Specification

Models of Change

Illustration

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

17. Multivariate Latent Change Score Models

Autoregressive Cross-Lag Model

Multivariate Growth Model

Multivariate Latent Change Score Model

Illustration

Structural Equation Modeling Implementation

Important Considerations

Moving Forward

18. Rate-of-Change Estimates in Nonlinear Growth Models

Growth Rate Models

Latent Change Score Models

Illustration

Multilevel Modeling Implementation

Structural Equation Modeling Implementation

Important Considerations

Appendix A. A Brief Introduction to Multilevel Modeling

Illustrative Example

Multilevel Modeling and Longitudinal Data

Appendix B. A Brief Introduction to Structural Equation Modeling

Illustrative Example

Structural Equation Modeling and Longitudinal Data

References

Author Index

Subject Index

About the Authors

Nilam Ram, PhD, is Professor in the Departments of Communication and Psychology at Stanford University. He specializes in longitudinal research methodology and lifespan development, with a focus on how multivariate time-series and growth curve modeling approaches can contribute to our understanding of behavioral change. He uses a wide variety of longitudinal models to examine changes in human behavior at multiple levels and across multiple time scales. Coupling the theory and method with data collected using mobile technologies, Dr. Ram is integrating process-oriented analytical paradigms with data visualization, gaming, experience sampling, and the delivery of individualized interventions/treatment.

Ryne Estabrook, PhD, is Assistant Professor in the Department of Medical Social Sciences at Northwestern University. His research combines multivariate longitudinal methodology, open-source statistical software, and lifespan development. His methodological work pertains to developing new methods for the study of change and incorporating longitudinal and dynamic information into measurement. Dr. Estabrook is a developer of OpenMx, an open-source statistical software package for structural equation modeling and general linear algebra. He applies his methodological and statistical research to the study of lifespan development, including work on early childhood behavior and personality in late life.