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E-grāmata: Beginner''s Guide to Structural Equation Modeling

, (University of Alabama, USA)
  • Formāts: 418 pages
  • Izdošanas datums: 27-Apr-2022
  • Izdevniecība: Routledge
  • Valoda: eng
  • ISBN-13: 9781000569742
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  • Formāts: 418 pages
  • Izdošanas datums: 27-Apr-2022
  • Izdevniecība: Routledge
  • Valoda: eng
  • ISBN-13: 9781000569742

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A Beginners Guide to Structural Equation Modeling, fifth edition, has been redesigned with consideration of a true beginner in structural equation modeling (SEM) in mind. The book covers introductory through intermediate topics in SEM in more detail than in any previous edition.

All of the chapters that introduce models in SEM have been expanded to include easy-to-follow, step-by-step guidelines that readers can use when conducting their own SEM analyses. These chapters also include examples of tables to include in results sections that readers may use as templates when writing up the findings from their SEM analyses. The models that are illustrated in the text will allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models, up to testing higher order models as well as multiple group modeling techniques. Updated information about methodological research in relevant areas will help students and researchers be more informed readers of SEM research. The checklist of SEM considerations when conducting and reporting SEM analyses is a collective set of requirements that will help improve the rigor of SEM analyses.

This book is intended for true beginners in SEM and is designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and healthcare sciences. This book also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.
Preface xi
Acknowledgments xvi
About the Authors xvii
Chapter 1 Introduction
1(20)
What is Structural Equation Modeling?
1(1)
Notation and Terminology
2(4)
History of Structural Equation Modeling
6(2)
Why Conduct Structural Equation Modeling?
8(2)
Brief Consideration of Causality in SEM
10(1)
Structural Equation Modeling Software
10(4)
Software Considerations
14(1)
Book Website
15(1)
Summary
16(1)
Exercises
16(2)
References
18(3)
Chapter 2 Data Entry and Editing Issues
21(26)
Data Set Formats
21(5)
Data Editing Issues
26(16)
Summary
42(1)
Exercises
43(2)
References
45(2)
Chapter 3 Correlation and Regression Methods
47(39)
Types of Correlation Coefficients
47(2)
Factors Affecting Correlation Coefficients
49(5)
Outliers
54(1)
Simple Linear Regression
54(6)
Multiple Linear Regression
60(2)
Bivariate, Part, and Partial Correlations in Multiple Regression
62(5)
Multicollinearity and Suppressor Variables
67(2)
Correlation Versus Covariance
69(2)
Variable Metrics (Standardized Versus Unstandardized)
71(1)
Correction for Attenuation
72(1)
Multiple Regression Example
73(7)
Summary
80(1)
Chapter Footnote
81(1)
Exercises
82(2)
References
84(2)
Chapter 4 Path Models
86(42)
Path Model Diagrams
86(2)
Decomposition of the Correlation Matrix
88(5)
Decomposition for Unstandardized Path Models
93(3)
Path Model Example
96(12)
Indirect Effects
108(13)
Causation Assumptions and Limitations
121(1)
Summary
122(1)
Exercises
123(2)
References
125(3)
Chapter 5 SEM Basics
128(42)
SEM Modeling Steps
128(2)
Model Estimation
130(4)
Model Testing
134(15)
Model Modification and Re-specification
149(9)
Summary
158(1)
Exercises
159(4)
Chapter Footnote
163(1)
References
164(6)
Chapter 6 Factor Analysis
170(58)
Exploratory Factor Analysis
170(4)
EFA Example
174(11)
Pattern and Structure Matrices
185(1)
Confirmatory Factor Analysis
186(1)
CFA Example
186(17)
CFA with Missing Continuous Data
203(7)
CFA Caveats
210(3)
CFA with Missing Ordinal Indicators
213(7)
CFA Model Comparisons
220(2)
Summary
222(2)
Exercises
224(1)
References
225(3)
Chapter 7 Full SEM
228(34)
Full Structural Equation Models
228(1)
Model Specification in Full SEM
228(3)
Model Identification
231(1)
Two-step Versus Four-step SEM Model Testing
232(3)
Model Estimation
235(4)
Model Testing and Model Modification
239(16)
Indirect Effects in Full SEM Models
255(2)
Summary
257(1)
Exercises
257(4)
References
261(1)
Chapter 8 Extensions of CFA Models
262(27)
Second-order Factor Model
262(13)
Bifactor Model
275(9)
Model Comparisons Between the Second-order and Bifactor Models
284(2)
Summary
286(1)
Exercises
287(1)
References
288(1)
Chapter 9 Multiple Group (Sample) Models
289(61)
Brief Summary of Multiple Group Modeling
289(1)
Multiple Group Path Analysis Model
290(23)
Multiple Group CFA/Measurement Model
313(29)
Strict Invariance Testing
342(1)
Structural Model Group Differences
343(1)
Multiple Group Models with Ordinal Indicators
343(1)
Cautions about Invariance Testing
344(1)
Summary
345(1)
Exercises
346(1)
References
347(3)
Chapter 10 SEM Considerations
350(15)
Best Practices in SEM
350(2)
Checklist for SEM
352(1)
Model Specification
353(1)
Model Identification
354(1)
Data Preparation
354(2)
Model Estimation
356(1)
Model Testing
357(1)
Model Modification/Re-specification
358(1)
Non-recursive Models
358(2)
Equivalent Models
360(2)
Summary
362(1)
References
363(2)
Appendix 1 Introduction to Matrix Operations 365(13)
Appendix 2 Statistical Tables 378(10)
Name Index 388(4)
Subject Index 392
Tiffany A. Whittaker is an Associate Professor in the Department of Educational Psychology at The University of Texas at Austin, USA, where she teaches courses in structural equation modeling, statistical analysis for experimental data, and advanced statistical modeling.

Randall E. Schumacker is a Professor of Educational Research at The University of Alabama, USA, where he teaches courses in multiple regression, multivariate statistics, and structural equation modeling.