LEADER 05466nam 22006734a 450 001 9910143577103321 005 20170815113201.0 010 $a1-280-40940-1 010 $a9786610409402 010 $a0-470-32167-9 010 $a0-471-74609-6 010 $a0-471-74608-8 035 $a(CKB)1000000000355717 035 $a(EBL)257069 035 $a(OCoLC)475972754 035 $a(SSID)ssj0000301350 035 $a(PQKBManifestationID)11273062 035 $a(PQKBTitleCode)TC0000301350 035 $a(PQKBWorkID)10263495 035 $a(PQKB)11257895 035 $a(MiAaPQ)EBC257069 035 $a(EXLCZ)991000000000355717 100 $a20050330d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLatent curve models$b[electronic resource] $ea structural equation perspective /$fKenneth A. Bollen, Patrick J. Curran 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (307 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-45592-X 320 $aIncludes bibliographical references (p. 263-273) and indexes. 327 $aLatent 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 Century 327 $a1.3.2 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 Model 327 $a2.5.2 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 Equivalent 327 $a3.2.3 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 Change 327 $a4.3 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 LCM 327 $a5 Conditional Latent Curve Models 330 $aAn effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and find 410 0$aWiley series in probability and statistics. 606 $aLatent structure analysis 606 $aLatent variables 608 $aElectronic books. 615 0$aLatent structure analysis. 615 0$aLatent variables. 676 $a519.5/35 676 $a621.384135015118 700 $aBollen$b Kenneth A$0144978 701 $aCurran$b Patrick J.$f1965-$0502145 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143577103321 996 $aLatent curve models$9731606 997 $aUNINA