Description
Book SynopsisAn 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 au
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"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)
"…an authoritative account of the subject…" (Journal of the American Statistical Association, December 2007)
Table of ContentsPreface. 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 Statistical Power.
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 Latent Curve 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 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.
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.
