Latent Curve Models
A Structural Equation Perspective
Published on 16. March 2006
256 pages
978-0-471-74608-9 (ISBN)
Description
An effective technique for data analysis in the social sciences
The recent explosion in longitudinal data in the social scienceshighlights the need for this timely publication. Latent CurveModels: A Structural Equation Perspective provides an effectivetechnique to analyze latent curve models (LCMs). This type of datafeatures random intercepts and slopes that permit each case in asample to have a different trajectory over time. Furthermore,researchers can include variables to predict the parametersgoverning these trajectories.
The authors synthesize a vast amount of research and findingsand, at the same time, provide original results. The book analyzesLCMs from the perspective of structural equation models (SEMs) withlatent variables. While the authors discuss simple regression-basedprocedures that are useful in the early stages of LCMs, most of thepresentation uses SEMs as a driving tool. This cutting-edge workincludes some of the authors' recent work on the autoregressivelatent trajectory model, suggests new models for method factors inmultiple indicators, discusses repeated latent variable models, andestablishes the identification of a variety of LCMs.
This text has been thoroughly class-tested and makes extensiveuse of pedagogical tools to aid readers in mastering and applyingLCMs quickly and easily to their own data sets. Key featuresinclude:
* Chapter introductions and summaries that provide a quickoverview of highlights
* Empirical examples provided throughout that allow readers totest their newly found knowledge and discover practicalapplications
* Conclusions at the end of each chapter that stress theessential points that readers need to understand for advancement tomore sophisticated topics
* Extensive footnoting that points the way to the primaryliterature 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 trajectorymodels as well as a supplemental text for courses in structuralmodeling. This book is an excellent aid and reference forresearchers in quantitative social and behavioral sciences who needto analyze longitudinal data.
Reviews / Votes
"This useful new text on growth curve modeling fills a critical gapin the applied methodological literature in longitudinal modelling.... We see it as an important text for those working inlongitudinal modeling to own and be able to refer to in the contextof model development and instruction." (Psychometrika, 2011) "...an authoritative account of the subject..."(Journal of the American Statistical Association, December2007)More details
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Kenneth A. Bollen | Patrick J. Curran
Latent Curve Models - A Structural Equation Perspective
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Persons
KENNETH A. BOLLEN, PhD, is Henry Rudolph ImmerwahrDistinguished Professor of Sociology, Director of the OdumInstitute for Research in Social Science, and an Adjunct Professorof Statistics at The University of North Carolina at Chapel Hill.He is the author of two books, including Structural Equations withLatent Variables (Wiley), and more than 100 scholarly papers.
PATRICK J. CURRAN, PhD, is Associate Professor ofPsychology in the L. L. Thurstone Psychometric Laboratory at TheUniversity of North Carolina at Chapel Hill. He has madecontributions to the development and application of newquantitative methodologies in the social sciences through hisintegrated program of research, writing, and teaching.
Content
Preface.
1 Introduction.
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.
2.3 Identification.
2.4 Case-By-Case Approach.
2.5 Structural Equation Model Approach.
2.6 Alternative Approaches to the SEM.
2.7 Conclusions.
Appendix 2A: Test Statistics, Nonnormality, and StatisticalPower.
3 Missing Data and Alternative Metrics of Time.
3.1 Missing Data.
3.2 Missing Data and Alternative Metrics of Time.
3.3 Conclusions.
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.
4.6 Conclusions.
Appendix 4A: Identification of Quadratic and Piecewise LatentCurve Models.
4A.1 Quadratic LCM.
4A.2 Piecewise LCM.
5 Conditional Latent Curve Models.
5.1 Conditional Model and Assumptions.
5.2 Identification.
5.3 Structural Equation Modeling Approach.
5.4 Interpretation of Conditional Model Estimates.
5.5 Empirical Example.
5.6 Conclusions.
6 The Analysis of Groups.
6.1 Dummy Variable Approach.
6.2 Multiple-Group Analysis.
6.3 Unknown Group Membership.
6.4 Conclusions.
Appendix 6A: Case-by-Case Approach to Analysis of VariousGroups.
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-VaryingCovariates.
7.4 Multivariate Latent Curve Models.
7.5 Autoregressive Latent Trajectory Model.
7.6 General Equation for All Models.
7.7 Implied Moment Matrices.
7.8 Conclusions.
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.
8.4 Conclusions.
References.
Author Index.
Subject Index.
