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.
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.
"A great resource for both students and experienced researchers. Newsom not only covers the details of contemporary structural equation modeling with longitudinal data, he also clearly explains its rationale and offers many step-by-step examples." – Rex B. Kline, Concordia University, Canada
"Newsom’s book provides clear coverage of the many important new developments in longitudinal modeling. The book combines the background for methods in addition to practical examples. It will be a valuable resource for anyone trying to understand change over time." – David Mackinnon, Arizona State University, USA
"This book is truly unique. It not only offers a comprehensive overview of SEM based approaches for longitudinal data analysis, but is also written in a clear and comprehensible way that will appeal to novices as well as more advanced readers alike. An excellent resource for teaching and research." – Manuel Voelkle, Humboldt University of Berlin, Germany
"This book will have a valued place among current longitudinal offerings, with its didactic clarity, hands-on accessibility, and thoughtful incorporation of traditional analytical approaches into the more versatile modern longitudinal modeling framework." – Gregory R. Hancock, University of Maryland, USA
"The description of the models and analytic steps is done … in a way that can be understood by anyone. … It [is] appealing to have a book that covers so much ground … [and one that makes] … a true connection between theoretical notions and analytic techniques. … I would strongly recommend this book and would use it in my course." – Emilio Ferer, University of California, Davis, USA
"The breadth of topics covered is perfect for my course. … Connections are drawn between types of models and their similarities/differences … [and] … students often ask questions related to "when to use what model." … I am also excited about inclusion of continuous and discrete variables. It is difficult to find texts that include both. … I would absolutely … recommend it to colleagues and students. I also would consider using this text as a primary textbook for my courses." – Natalie D. Eggum, Arizona State University, USA
"Newsom is a particularly gifted writer. … He explains complex material clearly, without over-simplifying it …The book will be very popular among applied scientists. … [and it is] … appropriate for graduate courses in either SEM or longitudinal modeling. … I would strongly consider adopting this book … as the primary textbook." – David L. Roth, Johns Hopkins University, USA
"I could see this text being … required for … SEM … [in] … all of the social sciences. … While there are other … texts on longitudinal analysis, they do not emphasize the use of SEM. … Strengths: … addresses confusion that [readers] have [when] deciding which analytic technique to choose … [and includes] analysis on both continuous and discrete variables. …The author is a good writer, who can readily explain these advanced statistical topics" – Brian A. Lawton, George Mason University, USA
"Many of the current texts on longitudinal data analysis only tangentially mention SEM or do not cover the full spectrum of approaches. …This book is really needed in the social science field. … I like the organization … Newsom starts with the basic issues in longitudinal SEM and moves to more advanced topics through the volume. I also like the way the chapter was formatted with respect to defining terms/concepts and then providing an example with data for each concept." – Kristin D. Mickelson, Kent State University, USA
1. Review of Some Key Latent Variable Principles 2. Longitudinal Measurement Invariance 3. Structural Models for Comparing Dependent Means and Proportions 4. Fundamental Concepts of Stability and Change 5. Cross-Lagged Panel Models 6. Latent State-Trait Models 7. Linear Latent Growth Curve Models 8. Nonlinear Latent Growth Curve Models 9. Latent Difference Score Models 10. Latent Transition and Growth Mixture Models 11. Time Series Analysis 12. Survival Analysis Models 13. Missing Data and Attrition Appendix A: Notation Appendix B: A Primer on the Calculus of Change
This series of books offers highly accessible and widely applicable methodological topics that have broad appeal and are written in easy-to understand language. Sponsored by the Society of Multivariate Experimental Psychology http://www.smep.org/, it welcomes methodological applications from a variety of disciplines, such as psychology, public health, sociology, education, and business. Authored or edited volumes should feature one of several approaches:
Interested persons should e-mail: Lisa L. Harlow at LHarlow@uri.edu.