This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples. Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research. This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

Latent growth curve modeling (LGM)—a special case of confirmatory factor analysis designed to model change over time—is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features · Provides easy-to-follow, didactic examples of several common growth modeling approaches · Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit · Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data · Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models

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.

An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs. This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include: Chapter introductions and summaries that provide a quick overview of highlights Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics Extensive footnoting that points the way to the primary literature for more information on particular topics With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.

By focusing primarily on the application of structural equation modeling (SEM) techniques in example cases and situations, this book provides an understanding and working knowledge of advanced SEM techniques with a minimum of mathematical derivations. The book was written for a broad audience crossing many disciplines, assumes an understanding of graduate level multivariate statistics, including an introduction to SEM.

Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face. Intended Audience While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.

This practical introduction to second-order and growth mixture models using Mplus introduces simple and complex techniques through incremental steps. The authors extend latent growth curves to second-order growth curve and mixture models and then combine the two. To maximize understanding, each model is presented with basic structural equations, figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results. Examples from a variety of disciplines demonstrate the use of the models and exercises allow readers to test their understanding of the techniques. A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels. The book’s datasets are available on the web. Highlights include: -Illustrative examples using Mplus 7.4 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data. -Exercises with an answer key allow readers to practice the skills they learn. -Applications to a variety of disciplines appeal to those in the behavioral, social, political, educational, occupational, business, and health sciences. -Data files for all the illustrative examples and exercises at www.routledge.com/9781138925151 allow readers to test their understanding of the concepts. -Point to Remember boxes aid in reader comprehension or provide in-depth discussions of key statistical or theoretical concepts. Part 1 introduces basic structural equation modeling (SEM) as well as first- and second-order growth curve modeling. The book opens with the basic concepts from SEM, possible extensions of conventional growth curve models, and the data and measures used throughout the book. The subsequent chapters in part 1 explain the extensions. Chapter 2 introduces conventional modeling of multidimensional panel data, including confirmatory factor analysis (CFA) and growth curve modeling, and its limitations. The logical and theoretical extension of a CFA to a second-order growth curve, known as curve-of-factors model (CFM), are explained in Chapter 3. Chapter 4 illustrates the estimation and interpretation of unconditional and conditional CFMs. Chapter 5 presents the logical and theoretical extension of a parallel process model to a second-order growth curve, known as factor-of-curves model (FCM). Chapter 6 illustrates the estimation and interpretation of unconditional and conditional FCMs. Part 2 reviews growth mixture modeling including unconditional growth mixture modeling (Ch. 7) and conditional growth mixture models (Ch. 8). How to extend second-order growth curves (curve-of-factors and factor-of-curves models) to growth mixture models is highlighted in Chapter 9. Ideal as a supplement for use in graduate courses on (advanced) structural equation, multilevel, longitudinal, or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) taught in psychology, human development and family studies, business, education, health, and social sciences, this book’s practical approach also appeals to researchers. Prerequisites include a basic knowledge of intermediate statistics and structural equation modeling.