Atjaunināt sīkdatņu piekrišanu

E-grāmata: Handbook of Structural Equation Modeling

Edited by
  • Formāts: 785 pages
  • Izdošanas datums: 10-Nov-2022
  • Izdevniecība: Guilford Press
  • Valoda: eng
  • ISBN-13: 9781462550715
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 136,50 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: 785 pages
  • Izdošanas datums: 10-Nov-2022
  • Izdevniecība: Guilford Press
  • Valoda: eng
  • ISBN-13: 9781462550715
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologistswho have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--

"The definitive one-stop resource on structural equation modeling (SEM) from leading methodologists is now in a significantly revised second edition. Twenty-three new chapters cover model selection, bifactor models, item parceling, multitrait-multimethodmodels, exploratory SEM, mixture models, SEM with small samples, and more. The book moves from fundamental SEM topics (causality, visualization, assumptions, estimation, model fit, and managing missing data); to major model types focused on unobserved causes of covariance between observed variables; to more complex, specialized applications. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with the reader's data. The expanded companion website presents full datasets, code, and output for many of the chapters, as well as bonus selected chapters from the prior edition. New to This Edition *Chapters on additional topics not mentioned above: SEM-based meta-analysis, dynamic SEM, machine-learning approaches, and more. *Chapters include computer code associated with example analyses (in Mplus and/or the R package lavaan), along with written descriptions of results. *60% new material reflects a decade's worth ofdevelopments in the mechanics and application of SEM. *Many new contributors and fully rewritten chapters. "--

Psychologists update the reference on structural equation modeling, an analysis and modeling approach widely used in social and behavioral sciences. Beginning with foundations, then moving from basic to specialized and advanced models and applications, they consider such aspects as latent variables in structural equation modeling, fitting structural equation models with missing data, mediation/indirect effects in structural equation modeling, latent curve modeling of longitudinal growth data, and nonlinear structural equation models: advanced methods and applications. The material is appropriate for advanced graduate students and post-graduate researchers with graduate-level training in applied statistical methods that includes multiple regression analysis and at least some basic factor analysis.. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)

The definitive one-stop resource on structural equation modeling (SEM) from leading methodologists is now in a significantly revised second edition. Twenty-three new chapters cover model selection, bifactor models, item parceling, multitrait–multimethod models, exploratory SEM, mixture models, SEM with small samples, and more. The book moves from fundamental SEM topics (causality, visualization, assumptions, estimation, model fit, and managing missing data); to major model types focused on unobserved causes of covariance between observed variables; to more complex, specialized applications. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with the reader's data. The expanded companion website presents full data sets, code, and output for many of the chapters, as well as bonus selected chapters from the prior edition.
 
New to This Edition
*Chapters on additional topics not mentioned above: SEM-based meta-analysis, dynamic SEM, machine-learning approaches, and more.
*Chapters include computer code associated with example analyses (in Mplus and/or the R package lavaan), along with written descriptions of results.
*60% new material reflects a decade's worth of developments in the mechanics and application of SEM.
*Many new contributors and fully rewritten chapters.

Recenzijas

"A tremendous resource that will be of great value to researchers and students who study SEM and apply it in their research. SEM statistical models and methods have continued to be developed over the past decade; the second edition of this volume incorporates these developments along with coverage of new estimation algorithms, computer programs, and empirical applications. The Handbookcould be used in graduate courses--students could be introduced to its basic chapters for contemporary articulations of SEM models and methods, and to subsequent chapters for specialized models and applications."--Kenneth C. Land, PhD, John Franklin Crowell Professor Emeritus of Sociology and Demography and Research Professor, Social Science Research Institute, Duke University

"The world of SEM--in terms of both applications and methodological advances--has expanded dramatically in recent years. Just learning SEM in the first place can be hard enough, let alone keeping up with all of the new developments. The second edition of this handbook is an invaluable resource for anyone seeking a comprehensive summary of current possibilities and best practices using SEM. The authors are all leading experts who have tackled an exhaustive list of topics, with an eye toward accessibility for new learners, but without shying away from the materials inherent complexity."--Lesa Hoffman, PhD, Department of Psychological and Quantitative Foundations, College of Education, University of Iowa

"SEM provides unity to disparate analyses and facilitates model evaluation and modification in the everyday practice of uncountable researchers. This is an impressive, 'must-read' book for practitioners of SEM and those who want to apprehend the strength and usefulness of the SEM idea."--Albert Satorra, PhD, Emeritus Professor of Statistics, Universitat Pompeu Fabra, Barcelona, Spain

This handbook is an outstanding desk reference on a wide variety of advanced and foundational topics. I recommend it either for personal use or for a class with students with strong quantitative backgrounds. The second edition has excellent coverage of exciting developments regarding analysis of longitudinal data. The chapter authors are a 'who's who' of the field.--Jeffrey S. Simons, PhD, Department of Psychology, University of South Dakota

I have always recommended this book for anyone interested in conducting or teaching SEM. The second edition continues to be my favorite comprehensive SEM reference book, with new chapters covering advanced topics.--Dana Joseph, PhD, College of Business Administration, University of Central Florida-The breadth of topics covered in this reference text leaves little doubt that a firm grasp of data analysis with latent variables is essential to the advancement of scholarship in the social and behavioral sciences....An excellent resource for the many conceptual and analytical problems frequently encountered by researchers making use of SEM.Readers will find themselves returning to the Handbook again and again as a starting point for their work on virtually any topic currently within the broad reach of SEM. (on the first edition)--Structural Equation Modeling, 4/1/2013 "A tremendous resource that will be of great value to researchers and students who study SEM and apply it in their research. SEM statistical models and methods have continued to be developed over the past decade; the second edition of this volume incorporates these developments along with coverage of new estimation algorithms, computer programs, and empirical applications. The Handbookcould be used in graduate courses--students could be introduced to its basic chapters for contemporary articulations of SEM models and methods, and to subsequent chapters for specialized models and applications."--Kenneth C. Land, PhD, John Franklin Crowell Professor Emeritus of Sociology and Demography and Research Professor, Social Science Research Institute, Duke University

"The world of SEM--in terms of both applications and methodological advances--has expanded dramatically in recent years. Just learning SEM in the first place can be hard enough, let alone keeping up with all of the new developments. The second edition of this handbook is an invaluable resource for anyone seeking a comprehensive summary of current possibilities and best practices using SEM. The authors are all leading experts who have tackled an exhaustive list of topics, with an eye toward accessibility for new learners, but without shying away from the materials inherent complexity."--Lesa Hoffman, PhD, Department of Psychological and Quantitative Foundations, College of Education, University of Iowa

"SEM provides unity to disparate analyses and facilitates model evaluation and modification in the everyday practice of uncountable researchers. This is an impressive, 'must-read' book for practitioners of SEM and those who want to apprehend the strength and usefulness of the SEM idea."--Albert Satorra, PhD, Emeritus Professor of Statistics, Universitat Pompeu Fabra, Barcelona, Spain

This handbook is an outstanding desk reference on a wide variety of advanced and foundational topics. I recommend it either for personal use or for a class with students with strong quantitative backgrounds. The second edition has excellent coverage of exciting developments regarding analysis of longitudinal data. The chapter authors are a 'who's who' of the field.--Jeffrey S. Simons, PhD, Department of Psychology, University of South Dakota

I have always recommended this book for anyone interested in conducting or teaching SEM. The second edition continues to be my favorite comprehensive SEM reference book, with new chapters covering advanced topics.--Dana Joseph, PhD, College of Business Administration, University of Central Florida-The breadth of topics covered in this reference text leaves little doubt that a firm grasp of data analysis with latent variables is essential to the advancement of scholarship in the social and behavioral sciences....An excellent resource for the many conceptual and analytical problems frequently encountered by researchers making use of SEMā¦.Readers will find themselves returning to the Handbook again and again as a starting point for their work on virtually any topic currently within the broad reach of SEM.Ā (on the first edition)--Structural Equation Modeling, 4/1/2013

PART I FOUNDATIONS
1 Structural Equation Modeling: An Overview
3(14)
Rick H. Hoyle
2 A Brief History of Structural Equation Modeling
17(32)
Ross L. Matsueda
3 The Causal Foundations of Structural Equation Modeling
49(27)
Judea Pearl
4 Visualizations for Structural Equation Modeling
76(21)
Jolynn Pek
Erin K. Davisson
Rick H. Hoyle
5 Latent Variables in Structural Equation Modeling
97(13)
Kenneth A. Bollen
Rick H. Hoyle
6 Simulation Methods in Structural Equation Modeling
110(18)
Walter L. Leite
Deborah L. Bandalos
Zuchao Shen
7 Assumptions in Structural Equation Modeling
128(17)
Rex B. Kline
8 On the Estimation of Structural Equation Models with Latent Variables
145(18)
Yunxiao Chen
Irini Moustaki
Siliang Zhang
9 Power Analysis within a Structural Equation Modeling Framework
163(21)
Yi Feng
Gregory R. Hancock
10 Model Fit in Structural Equation Modeling
184(22)
Stephen G. West
Wei Wu
Daniel McNeish
Andrea Savord
11 Model Selection in Structural Equation Modeling
206(17)
Kristopher J. Preacher
Haley E. Yaremych
12 Fitting Structural Equation Models with Missing Data
223(18)
Craig K. Enders
13 Structural Equation Modeling with the Mplus and lavaan Programs
241(20)
Christian Geiser
PART II BASIC MODELS AND APPLICATIONS
14 Confirmatory Factor Analysis
261(16)
Timothy A. Brown
15 Confirmatory Measurement Models for Dichotomous and Ordered Polytomous Indicators
277(19)
Natalie A. Koziol
16 Item Parceling in SEM: A Researcher Degree-of-Freedom Ripe for Opportunistic Use
296(20)
Sonya K. Sterba
Jason D. Rights
17 Using Factor Scores in Structural Equation Modeling
316(13)
Ines Devlieger
Yves Rosseel
18 Bifactor Measurement Models
329(20)
Steven P. Reise
Maxwell Mansolf
Mark G. Haviland
19 Multitrait-Multimethod Models
349(18)
Michael Eid
Tobias Koch
Christian Geiser
20 Investigating Measurement Invariance Using Confirmatory Factor Analysis
367(18)
Keith F. Widaman
Margarita Olivera-Aguilar
21 Flexible Structural Equation Modeling Approaches for Analyzing Means
385(24)
Marilyn S. Thompson
Yixing Liu
Samuel B. Green
22 Mediation/Indirect Effects in Structural Equation Modeling
409(18)
Oscar Gonzalez
Matthew J. Valente
JeeWon Cheong
David P. MacKinnon
23 Latent Interaction Effects
427(20)
Augustin Kelava
Holger Brandt
24 Dynamic Moderation with Latent Interactions: General Cross-Lagged Panel Models with Interaction Effects over Time
447(15)
Michael J. Zyphur
Ozlem Ozkok
25 Psychometric Scale Evaluation Using Structural Equation Modeling and Latent Variable Modeling
462(19)
Tenko Raykov
26 Multilevel Structural Equation Modeling
481(22)
Ronald H. Heck
Tingting Reid
PART III SPECIALIZED AND ADVANCED MODELS AND APPLICATIONS
27 Exploratory Structural Equation Modeling
503(22)
Alexandre J. S. Morin
28 Structural Equation Modeling with Small Samples and Many Variables
525(18)
Katerina M. Marcoulides
Ke-Hai Yuan
Lifang Deng
29 Mixture Models
543(13)
Douglas Steinley
30 Latent Curve Modeling of Longitudinal Growth Data
556(20)
Kevin J. Grimm
John J. McArdle
31 Dynamic Structural Equation Modeling as a Combination of Time Series Modeling, Multilevel Modeling, and Structural Equation Modeling
576(21)
Ellen L. Hamaker
Tihomir Asparouhov
Bengt Muthen
32 Continuous-Time Dynamic Models: Connections to Structural Equation Models and Other Discrete-Time Models
597(18)
Sy-Miin Chow
Diane Losardo
Jonathan Park
Peter C. M. Molenaar
33 Latent Trait-State Models
615(19)
David A. Cole
Qimin Liu
34 Longitudinal Models for Assessing Dynamics in Dyadic Data
634(12)
Meng Chen
Hairong Song
Emilio Ferrer
35 Structural Equation Modeling in Genetics
646(18)
Susanne Bruins
Sanja Franic
Conor V. Dolan
Denny Borsboom
Dorret I. Boomsma
36 Structural Equation Modeling-Based Meta-Analysis
664(17)
Mike W.-L. Cheung
37 Nonlinear Structural Equation Models: Advanced Methods and Applications
681(20)
Jeffrey R. Harring
Jinwang Zou
38 Foundations and Extensions of Bayesian Structural Equation Modeling
701(21)
Sarah Depaoli
David Kaplan
Sonja D. Winter
39 Machine Learning Approaches to Structural Equation Modeling
722(19)
Andreas M. Brandmaier
Ross C. Jacobucci
Author Index 741(16)
Subject Index 757(22)
About the Editor 779(2)
Contributors 781
Rick H. Hoyle, PhD, is Professor of Psychology and Neuroscience and Director of the Center for the Study of Adolescent Risk and Resilience at Duke University. He is a Fellow of the Association for Psychological Science, the American Psychological Association (Divisions 1, 5, 8, and 9), and the Society for Experimental Social Psychology. Dr. Hoyle has written extensively on structural equation modeling and other statistical and methodological strategies for the study of complex social and behavioral processes.