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Other titles in the Wiley Series in Probability and Statistics series:
Latent Curve Models: A Structural Equation Approachby Patrick J. Curran
Synopses & Reviews
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:
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.
Book News Annotation:
The social sciences have become increasingly dependent upon quantitative studies and statistics, but many texts fail to cover the latent curve model (LCM) and its applications. The LCM features random intercepts and slopes that allow each case in a sample to have a different trajectory over time, thereby aiding the prediction of parameters. These specialized text includes a brief history of LCMs and their uses, the unconditional LCM, missing data and alternate metrics of time, nonlinear trajectories and the coding of time, conditional LCMs, the analysis of groups, multivariate latent curve models, and extensions of LCMs. Bollen (sociology, U. of North Carolina) and Curran (psychology, U. of North Carolina) also provide a very helpful author index.
Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)
Book News Annotation:
The social sciences have become increasingly dependent upon quantitative studies and statistics, but many texts fail to cover the latent curve model (LCM) and its applications. The LCM features random intercepts and slopes that allow each case in a sample to have a different trajectory over time, thereby aiding the prediction of parameters. These specialized text includes a brief history of LCMs and their uses, the unconditional LCM, missing data and alternate metrics of time, nonlinear trajectories and the coding of time, conditional LCMs, the analysis of groups, multivariate latent curve models, and extensions of LCMs. Bollen (sociology, U. of North Carolina) and Curran (psychology, U. of North Carolina) also provide a very helpful author index. Annotation Â©2006 Book News, Inc., Portland, OR (booknews.com)
This volume represents a comprehensive treatment of a model sometimes referred to as latent curve or growth curve models. Latent Curve Modelsanalyzes LTMs from the perspective of structural equation modeling (SEM) with latent variables. Although the authors discuss simple regression-based procedures that are helpful in the early stages of LTM, most of the presentation will use SEMs as a driving tool throughout the text.
A review of the state of the art in smart antennas and document in an easy-to-understand format, coverage includes the analysis and design issues crucial for a successful design. It provides the reader with tools to enhance the development cycle by tying the theory and practice together, producing superior results.
This thorough resource explains the principles and techniques of smart antenna applications in wireless and mobile communications systems. It presents topics and issues in the design of advanced antenna systems in an easy-to-follow methodology.
Practicing engineers and designers in wireless communications, engineering managers, and graduate students will value this up-to-date treatment of important current topics in wireless communications.
About the Author
KENNETH A. BOLLEN, PhD, is Henry Rudolph Immerwahr Distinguished Professor of Sociology, Director of the Odum Institute for Research in Social Science, and an Adjunct Professor of Statistics at The University of North Carolina at Chapel Hill. He is the author of two books, including Structural Equations with Latent Variables (Wiley), and more than 100 scholarly papers.
PATRICK J. CURRAN, PhD, is Associate Professor of Psychology in the L. L. Thurstone Psychometric Laboratory at The University of North Carolina at Chapel Hill. He has made contributions to the development and application of new quantitative methodologies in the social sciences through his integrated program of research, writing, and teaching.
Table of Contents
1.1 Conceptualization and Analysis of Trajectories.
1.2 Three Initial Questions About Trajectories.
1.3 Brief History of Latent Curve Models.
1.4 Organization of the Remainder of the Book.
2 Unconditional Latent Curve Model.
2.1 Repeated Measures.
2.2 General Model and Assumptions.
2.4 Case-By-Case Approach.
2.5 Structural Equation Model Approach.
2.6 Alternative Approaches to the SEM.
Appendix 2A: Test Statistics, Nonnormality, and Statistical Power.
3 Missing Data and Alternative Metrics of Time.
3.1 Missing Data.
3.2 Missing Data and Alternative Metrics of Time.
4 Nonlinear Trajectories and the Coding of Time.
4.1 Modeling Nonlinear Functions of Time.
4.2 Nonlinear Curve Fitting: Estimated Factor Loadings.
4.3 Piecewise Linear Trajectory Models.
4.4 Alternative Parametric Functions.
4.5 Linear Transformations of the Metric of Time.
Appendix 4A: Identification of Quadratic and Piecewise Latent Curve Models.
4A.1 Quadratic LCM.
4A.2 Piecewise LCM.
5 Conditional Latent Curve Models.
5.1 Conditional Model and Assumptions.
5.3 Structural Equation Modeling Approach.
5.4 Interpretation of Conditional Model Estimates.
5.5 Empirical Example.
6 The Analysis of Groups.
6.1 Dummy Variable Approach.
6.2 Multiple-Group Analysis.
6.3 Unknown Group Membership.
Appendix 6A: Case-by-Case Approach to Analysis of Various Groups.
6A.1 Dummy Variable Method.
6A.2 Multiple-Group Analysis.
6A.3 Unknown Group Membership.
6A.4 Appendix Summary.
7 Multivariate Latent Curve Models.
7.1 Time-Invariant Covariates.
7.2 Time-Varying Covariates.
7.3 Simultaneous Inclusion of Time-Invariant and Time-Varying Covariates.
7.4 Multivariate Latent Curve Models.
7.5 Autoregressive Latent Trajectory Model.
7.6 General Equation for All Models.
7.7 Implied Moment Matrices.
8 Extensions of Latent Curve Models.
8.1 Dichotomous and Ordinal Repeated Measures.
8.2 Repeated Latent Variables with Multiple Indicators.
8.3 Latent Covariates.
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