{"product_id":"latent-curve-models-9780471455929","title":"Latent Curve Models","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn effective technique for data analysis in the social sciences  \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eThe 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 au\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This useful new text on growth curve modeling fills a critical gap in the applied methodological literature in longitudinal modelling. ... We see it as an important text for those working in longitudinal modeling to own and be able to refer to in the context of model development and instruction.\" (Psychometrika, 2011)  \u003c\/p\u003e\u003cp\u003e\"…an authoritative account of the subject…\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, December 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Conceptualization and Analysis of Trajectories.\u003c\/p\u003e \u003cp\u003e1.2 Three Initial Questions About Trajectories.\u003c\/p\u003e \u003cp\u003e1.3 Brief History of Latent Curve Models.\u003c\/p\u003e \u003cp\u003e1.4 Organization of the Remainder of the Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Unconditional Latent Curve Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Repeated Measures.\u003c\/p\u003e \u003cp\u003e2.2 General Model and Assumptions.\u003c\/p\u003e \u003cp\u003e2.3 Identification.\u003c\/p\u003e \u003cp\u003e2.4 Case-By-Case Approach.\u003c\/p\u003e \u003cp\u003e2.5 Structural Equation Model Approach.\u003c\/p\u003e \u003cp\u003e2.6 Alternative Approaches to the SEM.\u003c\/p\u003e \u003cp\u003e2.7 Conclusions.\u003c\/p\u003e \u003cp\u003eAppendix 2A: Test Statistics, Nonnormality, and Statistical Power.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Missing Data and Alternative Metrics of Time.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Missing Data.\u003c\/p\u003e \u003cp\u003e3.2 Missing Data and Alternative Metrics of Time.\u003c\/p\u003e \u003cp\u003e3.3 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Nonlinear Trajectories and the Coding of Time.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Modeling Nonlinear Functions of Time.\u003c\/p\u003e \u003cp\u003e4.2 Nonlinear Curve Fitting: Estimated Factor Loadings.\u003c\/p\u003e \u003cp\u003e4.3 Piecewise Linear Trajectory Models.\u003c\/p\u003e \u003cp\u003e4.4 Alternative Parametric Functions.\u003c\/p\u003e \u003cp\u003e4.5 Linear Transformations of the Metric of Time.\u003c\/p\u003e \u003cp\u003e4.6 Conclusions.\u003c\/p\u003e \u003cp\u003eAppendix 4A: Identification of Quadratic and Piecewise Latent Curve Models.\u003c\/p\u003e \u003cp\u003e4A.1 Quadratic LCM.\u003c\/p\u003e \u003cp\u003e4A.2 Piecewise LCM.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Conditional Latent Curve Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Conditional Model and Assumptions.\u003c\/p\u003e \u003cp\u003e5.2 Identification.\u003c\/p\u003e \u003cp\u003e5.3 Structural Equation Modeling Approach.\u003c\/p\u003e \u003cp\u003e5.4 Interpretation of Conditional Model Estimates.\u003c\/p\u003e \u003cp\u003e5.5 Empirical Example.\u003c\/p\u003e \u003cp\u003e5.6 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 The Analysis of Groups.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Dummy Variable Approach.\u003c\/p\u003e \u003cp\u003e6.2 Multiple-Group Analysis.\u003c\/p\u003e \u003cp\u003e6.3 Unknown Group Membership.\u003c\/p\u003e \u003cp\u003e6.4 Conclusions.\u003c\/p\u003e \u003cp\u003eAppendix 6A: Case-by-Case Approach to Analysis of Various Groups.\u003c\/p\u003e \u003cp\u003e6A.1 Dummy Variable Method.\u003c\/p\u003e \u003cp\u003e6A.2 Multiple-Group Analysis.\u003c\/p\u003e \u003cp\u003e6A.3 Unknown Group Membership.\u003c\/p\u003e \u003cp\u003e6A.4 Appendix Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multivariate Latent Curve Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Time-Invariant Covariates.\u003c\/p\u003e \u003cp\u003e7.2 Time-Varying Covariates.\u003c\/p\u003e \u003cp\u003e7.3 Simultaneous Inclusion of Time-Invariant and Time-Varying Covariates.\u003c\/p\u003e \u003cp\u003e7.4 Multivariate Latent Curve Models.\u003c\/p\u003e \u003cp\u003e7.5 Autoregressive Latent Trajectory Model.\u003c\/p\u003e \u003cp\u003e7.6 General Equation for All Models.\u003c\/p\u003e \u003cp\u003e7.7 Implied Moment Matrices.\u003c\/p\u003e \u003cp\u003e7.8 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Extensions of Latent Curve Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Dichotomous and Ordinal Repeated Measures.\u003c\/p\u003e \u003cp\u003e8.2 Repeated Latent Variables with Multiple Indicators.\u003c\/p\u003e \u003cp\u003e8.3 Latent Covariates.\u003c\/p\u003e \u003cp\u003e8.4 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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