05466nam 22006734a 450 991014357710332120170815113201.01-280-40940-197866104094020-470-32167-90-471-74609-60-471-74608-8(CKB)1000000000355717(EBL)257069(OCoLC)475972754(SSID)ssj0000301350(PQKBManifestationID)11273062(PQKBTitleCode)TC0000301350(PQKBWorkID)10263495(PQKB)11257895(MiAaPQ)EBC257069(EXLCZ)99100000000035571720050330d2006 uy 0engur|n|---|||||txtccrLatent curve models[electronic resource] a structural equation perspective /Kenneth A. Bollen, Patrick J. CurranHoboken, N.J. Wiley-Intersciencec20061 online resource (307 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-471-45592-X Includes bibliographical references (p. 263-273) and indexes.Latent Curve Models; Contents; Preface; 1 Introduction; 1.1 Conceptualization and Analysis of Trajectories; 1.1.1 Trajectories of Crime Rates; 1.1.2 Data Requirements; 1.1.3 Summary; 1.2 Three Initial Questions About Trajectories; 1.2.1 Question 1: What Is the Trajectory for the Entire Group?; 1.2.2 Question 2: Do We Need Distinct Trajectories for Each Case?; 1.2.3 Question 3: If Distinct Trajectories Are Needed, Can We Identify Variables to Predict These Individual Trajectories?; 1.2.4 Summary; 1.3 Brief History of Latent Curve Models; 1.3.1 Early Developments: The Nineteenth Century1.3.2 Fitting Group Trajectories: 1900-19371.3.3 Fitting Individual and Group Trajectories: 1938-1950s; 1.3.4 Trajectory Modeling with Latent Variables: 1950s-1984; 1.3.5 Current Latent Curve Modeling: 1984-present; 1.3.6 Summary; 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; 2.4.1 Assessing Model Fit; 2.4.2 Limitations of Case-by-Case Approach; 2.5 Structural Equation Model Approach; 2.5.1 Matrix Expression of the Latent Curve Model2.5.2 Maximum Likelihood Estimation2.5.3 Empirical Example; 2.5.4 Assessing Model Fit; 2.5.5 Components of Fit; 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; 3.1.1 Types of Missing Data; 3.1.2 Treatment of Missing Data; 3.1.3 Empirical Example; 3.1.4 Summary; 3.2 Missing Data and Alternative Metrics of Time; 3.2.1 Numerical Measure of Time; 3.2.2 When Wave of Assessment and Alternative Metrics of Time Are Equivalent3.2.3 When Wave of Assessment and Alternative Metrics of Time Are Different3.2.4 Reorganizing Data as a Function of Alternative Metrics of Time; 3.2.5 Individually Varying Values of Time; 3.2.6 Summary; 3.2.7 Empirical Example: Reading Achievement; 3.3 Conclusions; 4 Nonlinear Trajectories and the Coding of Time; 4.1 Modeling Nonlinear Functions of Time; 4.1.1 Polynomial Trajectories: Quadratic Trajectory Model; 4.1.2 Polynomial Trajectories: Cubic Trajectory Models; 4.1.3 Summary; 4.2 Nonlinear Curve Fitting: Estimated Factor Loadings; 4.2.1 Selecting the Metric of Change4.3 Piecewise Linear Trajectory Models4.3.1 Identification; 4.3.2 Interpretation; 4.4 Alternative Parametric Functions; 4.4.1 Exponential Trajectory; 4.4.2 Parametric Functions with Cycles; 4.4.3 Nonlinear Transformations of the Metric of Time; 4.4.4 Nonlinear Transformations of the Repeated Measures; 4.5 Linear Transformations of the Metric of Time; 4.5.1 Logic of Recoding the Metric of Time; 4.5.2 General Framework for Transforming Time; 4.5.3 Summary; 4.6 Conclusions; Appendix 4A: Identification of Quadratic and Piecewise Latent Curve Models; 4A.1 Quadratic LCM; 4A.2 Piecewise LCM5 Conditional Latent Curve ModelsAn 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 findWiley series in probability and statistics.Latent structure analysisLatent variablesElectronic books.Latent structure analysis.Latent variables.519.5/35621.384135015118Bollen Kenneth A144978Curran Patrick J.1965-502145MiAaPQMiAaPQMiAaPQBOOK9910143577103321Latent curve models731606UNINA