000			03470nam a22002297a 4500
005			20241219185642.0
008			241219b |||||||| |||| 00| 0 eng d
020			_a9781032202860
082			_a519.5 _bNEW
100			_aNewsom, Jason T _918086
245			_aLongitudinal structural equation modeling: _ba comprehensive introduction
250			_a2nd
260			_bRoutledge _aNew York _c2024
300			_axx, 501 p.
365			_aGBP _b71.99
490			_aMultivariate Applications Book Series
500			_aTable of content: Contents List of Figures List of Tables Preface to the Second Editon Preface to the First Edition Acknowledgements Example Data Sets Chapter 1. Review of Some Key Latent Variable Principles Chapter 2. Longitudinal Measurement Invariance Chapter 3. Structural Models for Comparing Dependent Means and Proportions Chapter 4. Fundamental Concepts of Stability and Change Chapter 5. Cross-Lagged Panel Models Chapter 6. Latent State-Trait Models Chapter 7. Linear Latent Growth Curve Models Chapter 8. Nonlinear Latent Growth Curve Models Chapter 9. Nonlinear Latent Growth Curve Models Chapter 10. Latent Class and Latent Transition Chapter 11. Growth Mixture Models Chapter 12. Intensive Longitudinal Models: Time Series and Dynamic Structural Equation Models Chapter 13. Survival Analysis Models Chapter 14. Missing Data and Attrition Appendix A: Notation Appendix B: Why Does the Single Occasion Scaling Constraint Approach Work? Appendix C: A Primer on the Calculus of Change Glossary Index [https://www.routledge.com/Longitudinal-Structural-Equation-Modeling-A-Comprehensive-Introduction/Newsom/p/book/9781032202860]
520			_aLongitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses. This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, comment sections that provide practical guidance, and recommended readings. Expanded with new and updated material, this edition includes many recent developments, a new chapter on growth mixture modeling, and new examples. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this new edition will continue to appeal to researchers in these fields. (https://www.routledge.com/Longitudinal-Structural-Equation-Modeling-A-Comprehensive-Introduction/Newsom/p/book/9781032202860)
650			_aStructural equation modeling
650			_aLongitudinal method--Mathematical models _919845
942			_cBK _2ddc
999			_c7538 _d7538