{"product_id":"longitudinal-structural-equation-modeling-9781032202860","title":"Longitudinal Structural Equation Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eLongitudinal Structural Equation Modeling\u003c\/em\u003e 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. \u003c\/p\u003e\u003cp\u003eThis 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, comme\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\"This is a \"must have\" volume on examining change from a SEM perspective. It is thoughtfully put together beginning with a number of basic principles\/concepts in the latent variable approach to change (e.g., longitudinal measurement invariance, linear and nonlinear growth). It then moves into a number of intermediate approaches (cross-lagged panel models, latent class, latent transition, and latent growth mixture models). The final chapters provide more advanced topics (time series and dynamic structural equation models, survival analysis, and missing data). The various topics covered are extensive, clearly presented, and well supported with examples and references that readers can use to work through the analyses.\"\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRonald H. Heck\u003c\/strong\u003e, University of Hawaii\u003c\/p\u003e\u003cp\u003e\"This book offers a schematic, comprehensive, and well-structured resource for understanding, applying, and teaching most of the techniques related to Longitudinal SEM. The book follows a specific flow based on the difficulties of the topics. It starts with a clear introduction to latent variable modeling, then moves on widely used longitudinal applications (e.g., measurement invariance, cross-lagged panel models), and finally offers chapters on more advanced and recent topics (e.g., LST, Mixture Modeling, and DSEM). The structure of the book also allows the reader to directly access the topics of interest. Both from an applied and teaching perspective, it is difficult to think of a more complete and better structured book on longitudinal SEM.\"\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEnrico Perinelli\u003c\/strong\u003e\u003cem\u003e, \u003c\/em\u003eUniversity of Trento (Italy)\u003c\/p\u003e\u003cp\u003e\"I've cited Jason Newsom's first edition of \u003ci\u003eLongitudinal Structural Equation Modeling\u003c\/i\u003e many times, and his second edition continues the tradition of clear, accessible presentations that cover both the basics of analysis and modeling strategies for longitudinal data and extra details that experts would appreciate. An impressive, authoritative work.\"\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRex Kline, \u003c\/strong\u003eConcordia University\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e\u003cp\u003eList of Figures\u003c\/p\u003e\u003cp\u003eList of Tables\u003c\/p\u003e\u003cp\u003ePreface to the Second Editon\u003c\/p\u003e\u003cp\u003ePreface to the First Edition\u003c\/p\u003e\u003cp\u003eAcknowledgements\u003c\/p\u003e\u003cp\u003eExample Data Sets\u003c\/p\u003e\u003cp\u003eChapter 1. Review of Some Key Latent Variable Principles\u003c\/p\u003e\u003cp\u003eChapter 2. Longitudinal Measurement Invariance\u003c\/p\u003e\u003cp\u003eChapter 3. Structural Models for Comparing Dependent Means and Proportions \u003c\/p\u003e\u003cp\u003eChapter 4. Fundamental Concepts of Stability and Change \u003c\/p\u003e\u003cp\u003eChapter 5. Cross-Lagged Panel Models\u003c\/p\u003e\u003cp\u003eChapter 6. Latent State-Trait Models\u003c\/p\u003e\u003cp\u003eChapter 7. Linear Latent Growth Curve Models \u003c\/p\u003e\u003cp\u003eChapter 8. Nonlinear Latent Growth Curve Models\u003c\/p\u003e\u003cp\u003eChapter 9. Nonlinear Latent Growth Curve Models\u003c\/p\u003e\u003cp\u003eChapter 10. Latent Class and Latent Transition\u003c\/p\u003e\u003cp\u003eChapter 11. Growth Mixture Models \u003c\/p\u003e\u003cp\u003eChapter 12. Intensive Longitudinal Models: Time Series and Dynamic Structural Equation Models \u003c\/p\u003e\u003cp\u003eChapter 13. Survival Analysis Models \u003c\/p\u003e\u003cp\u003eChapter 14. Missing Data and Attrition \u003c\/p\u003e\u003cp\u003eAppendix A: Notation \u003c\/p\u003e\u003cp\u003eAppendix B: Why Does the Single Occasion Scaling Constraint Approach Work? \u003c\/p\u003e\u003cp\u003eAppendix C: A Primer on the Calculus of Change\u003c\/p\u003e\u003cp\u003eGlossary\u003c\/p\u003e\u003cp\u003eIndex\u003c\/p\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Default Title","offer_id":51018897457495,"sku":"9781032202860","price":68.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781032202860.jpg?v=1750778571","url":"https:\/\/bookcurl.com\/products\/longitudinal-structural-equation-modeling-9781032202860","provider":"Book Curl","version":"1.0","type":"link"}