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Principles and Practice of Structural Equation Modeling, Fifth Edition 5th edition [Kietas viršelis]

4.11/5 (192 ratings by Goodreads)
(Concordia University, Montreal, QC, Canada)
  • Formatas: Hardback, 494 pages, weight: 1120 g
  • Serija: Methodology in the Social Sciences
  • Išleidimo metai: 14-Jun-2023
  • Leidėjas: Guilford Press
  • ISBN-10: 1462552005
  • ISBN-13: 9781462552009
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 494 pages, weight: 1120 g
  • Serija: Methodology in the Social Sciences
  • Išleidimo metai: 14-Jun-2023
  • Leidėjas: Guilford Press
  • ISBN-10: 1462552005
  • ISBN-13: 9781462552009
Kitos knygos pagal šią temą:
Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and a new self-test on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.
 
New to This Edition
*Chapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis.
*Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM.
*Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects.
 
Pedagogical Features
*New self-test of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers.
*End-of-chapter suggestions for further reading and exercises with answers.
*Troublesome examples from real data, with guidance for handling typical problems in analyses.
*Topic boxes on special issues and boxed rules to remember.
*Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.

Recenzijos

"In this ambitious work, Kline thoughtfully and patiently presents diverse perspectives, effectively enlarging the world of SEM while maintaining coherence. The fifth edition's breadth and timeliness make it an easy choice as the primary text in a graduate course on SEM, with readability that journal articles often lack. Researchers will appreciate the book as an entry point to a range of literatures within the SEM world. Klines embrace of open-source R software for SEM is very welcome, as it makes the books computer examples immediately accessible to readers everywhere."--Edward E. Rigdon, PhD, Marketing RoundTable Professor, Robinson College of Business, Georgia State University

"Klines fifth edition is thoroughly updated and greatly expanded. I love the emphasis on Open Science, and I am impressed by the variety of new methodological techniques in SEM that Kline has managed to effectively introduce in the fifth edition. I cant wait to use this text in my SEM class!"--D. Betsy McCoach, PhD, Neag School of Education, University of Connecticut

"A wonderful introductory book that can be used by individuals without extensive quantitative backgrounds. I use this book to teach an introductory SEM course, but I also use it as a personal reference for my research. It is very readable, which is the number-one reason why I assign this text to my students. I love the walk-through examples with references to real data and syntax. I have always liked Klines practical recommendations, and they continue to be really helpful for newbies to SEM--my students constantly reference these sections."--Naomi Ekas, PhD, Department of Psychology, Texas Christian University

"One of the primary strengths of Klines book is that it is written in plain English, but with sufficient sophistication that the reader is well prepared to read more technical books or articles on advanced topics. Another strength is the helpful remedies and hints, such as the topic box on the causes of nonpositive definite data matrices and solutions. The most practical advantage of Klines text is the exercises at the end of each chapter, and the corresponding answers and explanations."--Stephanie Castro, PhD, College of Business, Florida Atlantic University

"The substantially revised fifth edition lives up to the reputation of prior editions and will be a valuable resource to anyone learning SEM. The online primers are very thorough and give students great refreshers on background topics, including exercises with answers. This edition has appropriate balance between the three 'families' of SEM; I appreciate the detailed descriptions of Pearls structural causal model."--Jam Khojasteh, PhD, College of Education and Human Services, Oklahoma State University-Perfectly addresses the needs of social scientists like me without formal training in mathematical statistics....Can be read by any graduate in psychology or even by keen undergraduates interested in exploring new vistas. Yet it will also constitute a surprisingly good read for experienced researchers in search of some refreshing insights in their favorite techniques....A real tour de force....Succeeds in reconciling comprehensiveness and comprehensibility. (on the second edition)--The Psychologist, 1/1/2006The greatest strength of this book is Kline's ability to present materials in an engaging, accessible manner. In nearly all situations, Kline is able to describe even the more complex material in practical, (relatively) jargon-free terms....In this regard, this book is unparalleled, and I suspect that this strength alone will make this the book of choice for many who are eager to learn SEM but who do not possess extensive quantitative backgrounds....This book could be readily adapted to courses for students with a basic understanding of correlation and regression or as part of a course for more advanced students. (on the second edition)--PsycCRITIQUES, 6/29/2005This wonderfully written book is an impressive introduction to structural equation models (SEM) containing a sharp mix of expert analysis and observations....Contains important resources for both theoretical and applied researchers interested in SEM....Appropriate as a text for graduate students and a reference for researchers, providing both audiences with valuable insight into the subject matter. (on the second edition)--Journal of the American Statistical Association, 3/1/2006 "In this ambitious work, Kline thoughtfully and patiently presents diverse perspectives, effectively enlarging the world of SEM while maintaining coherence. The fifth edition's breadth and timeliness make it an easy choice as the primary text in a graduate course on SEM, with readability that journal articles often lack. Researchers will appreciate the book as an entry point to a range of literatures within the SEM world. Klines embrace of open-source R software for SEM is very welcome, as it makes the books computer examples immediately accessible to readers everywhere."--Edward E. Rigdon, PhD, Marketing RoundTable Professor, Robinson College of Business, Georgia State University

"Klines fifth edition is thoroughly updated and greatly expanded. I love the emphasis on Open Science, and I am impressed by the variety of new methodological techniques in SEM that Kline has managed to effectively introduce in the fifth edition. I cant wait to use this text in my SEM class!"--D. Betsy McCoach, PhD, Neag School of Education, University of Connecticut

"A wonderful introductory book that can be used by individuals without extensive quantitative backgrounds. I use this book to teach an introductory SEM course, but I also use it as a personal reference for my research. It is very readable, which is the number-one reason why I assign this text to my students. I love the walk-through examples with references to real data and syntax. I have always liked Klines practical recommendations, and they continue to be really helpful for newbies to SEM--my students constantly reference these sections."--Naomi Ekas, PhD, Department of Psychology, Texas Christian University

"One of the primary strengths of Klines book is that it is written in plain English, but with sufficient sophistication that the reader is well prepared to read more technical books or articles on advanced topics. Another strength is the helpful remedies and hints, such as the topic box on the causes of nonpositive definite data matrices and solutions. The most practical advantage of Klines text is the exercises at the end of each chapter, and the corresponding answers and explanations."--Stephanie Castro, PhD, College of Business, Florida Atlantic University

"The substantially revised fifth edition lives up to the reputation of prior editions and will be a valuable resource to anyone learning SEM. The online primers are very thorough and give students great refreshers on background topics, including exercises with answers. This edition has appropriate balance between the three 'families' of SEM; I appreciate the detailed descriptions of Pearls structural causal model."--Jam Khojasteh, PhD, College of Education and Human Services, Oklahoma State University-Perfectly addresses the needs of social scientists like me without formal training in mathematical statistics....Can be read by any graduate in psychology or even by keen undergraduates interested in exploring new vistas. Yet it will also constitute a surprisingly good read for experienced researchers in search of some refreshing insights in their favorite techniques....A real tour de force....Succeeds in reconciling comprehensiveness and comprehensibility. (on the second edition)--The Psychologist, 1/1/2006ĘĘThe greatest strength of this book is Kline's ability to present materials in an engaging, accessible manner. In nearly all situations, Kline is able to describe even the more complex material in practical, (relatively) jargon-free terms....In this regard, this book is unparalleled, and I suspect that this strength alone will make this the book of choice for many who are eager to learn SEM but who do not possess extensive quantitative backgrounds....This book could be readily adapted to courses for students with a basic understanding of correlation and regression or as part of a course for more advanced students. (on the second edition)--PsycCRITIQUES, 6/29/2005ĘĘThis wonderfully written book is an impressive introduction to structural equation models (SEM) containing a sharp mix of expert analysis and observations....Contains important resources for both theoretical and applied researchers interested in SEM....Appropriate as a text for graduate students and a reference for researchers, providing both audiences with valuable insight into the subject matter. (on the second edition)--Journal of the American Statistical Association, 3/1/2006

Introduction 1(1)
What's New
1(1)
Book Website
2(1)
Pedagogical Approach
3(1)
Principles > Software
3(1)
Symbols and Notation
3(1)
Enjoy the Ride
3(1)
Plan of the Book
3(4)
PART I CONCEPTS, STANDARDS, AND TOOLS
7(1)
1 Promise and Problems
7(1)
Preparing to Learn SEM
7(2)
Definition of SEM
9(1)
Basic Data Analyzed in SEM
9(1)
Family Matters
10(4)
Pedagogy and SEM Families
14(1)
Sample Size Requirements
15(1)
Big Numbers, Low Quality
16(2)
Limits of This Book
18(1)
Summary
18(1)
Learn More
18(1)
2 Background Concepts and Self-Test
19(13)
Uneven Background Preparation
19(1)
Potential Obstacles to Learning about SEM
20(3)
Significance Testing
23(1)
Measurement and Psychometrics
24(1)
Regression Analysis
25(1)
Summary
26(1)
Self-Test
27(1)
Scoring Criteria
28(4)
3 Steps and Reporting
32(14)
Basic Steps
32(6)
Optional Steps
38(1)
Reporting Standards
39(2)
Reporting Example
41(3)
Summary
44(1)
Learn More
44(2)
4 Data Preparation
46(21)
Forms of the Input Data
46(2)
Positive Definiteness
48(1)
Missing Data
49(5)
Classical (Obsolete) Methods for Incomplete Data
54(1)
Modern Methods for Incomplete Data
55(1)
Other Data Screening Issues
56(6)
Summary
62(1)
Learn More
62(1)
Exercises
63(1)
Appendix 4.A Steps of Multiple Imputation
64(3)
5 Computer Tools
67(12)
Ease of Use, Not Suspension of Judgment
67(1)
Human-Computer Interaction
68(1)
Tips for SEM Programming
68(2)
Commercial versus Free Computer Tools
70(1)
R Packages for SEM
71(2)
Free SEM Software with Graphical User Interfaces
73(1)
Commercial SEM Computer Tools
73(3)
SEM Resources for Other Computing Environments
76(1)
Summary
76(3)
PART II SPECIFICATION, ESTIMATION, AND TESTING
6 Nonparametric Causal Models
79(21)
Graph Vocabulary and Symbolism
79(1)
Contracted Chains and Confounding
80(1)
Covariate Selection
81(1)
Instrumental Variables
82(2)
Conditional Independencies and Other Types of Bias
84(4)
Principles for Covariate Selection
88(1)
D-Separation and Basis Sets
89(3)
Graphical Identification Criteria
92(4)
Detailed Example
96(2)
Summary
98(1)
Learn More
99(1)
Exercises
99(1)
7 Parametric Causal Models
100(17)
Model Diagram Symbolism
100(3)
Diagrams for Contracted Chains and Assumptions
103(2)
Confounding in Parametric Models
105(1)
Models with Correlated Causes or Indirect Effects
106(3)
Recursive, Nonrecursive, and Partially Recursive Models
109(2)
Detailed Example
111(1)
Summary
111(1)
Learn More
112(1)
Exercises
112(1)
Appendix 7.A Advanced Topics in Parametric Models
113(4)
8 Local Estimation and Piecewise SEM
117(14)
Rationale of Local Estimation
117(1)
Piecewise SEM
118(2)
Detailed Example
120(10)
Summary
130(1)
Learn More
130(1)
Exercises
130(1)
9 Global Estimation and Mean Structures
131(25)
Simultaneous Methods and Error Propagation
131(1)
Maximum Likelihood Estimation
132(3)
Default ML
135(2)
Analyzing Nonnormal Data
137(1)
Robust ML
138(1)
FIML for Incomplete Data versus Multiple Imputation
138(2)
Alternative Estimators for Continuous Outcomes
140(1)
Fitting Models to Correlation Matrices
141(1)
Healthy Perspective on Estimators and Global Estimation
142(1)
Detailed Example
142(5)
Introduction to Mean Structures
147(3)
Precis of Global Estimation
150(1)
Summary
151(1)
Learn More
151(1)
Exercises
151(2)
Appendix 9.A Types of Information Matrices and Computer Options
153(2)
Appendix 9.B Casewise ML Methods for Data Missing Not at Random
155(1)
10 Model Testing and Indexing
156(26)
Model Testing
156(1)
Model Chi-Square
156(5)
Scaled Chi-Squares and Robust Standard Errors for Nonnormal Distributions
161(2)
Model Fit Indexing
163(3)
RMSEA
166(2)
CFI
168(1)
SRMR
169(1)
Thresholds for Approximate Fit Indexes
170(2)
Recommended Approach to Fit Evaluation
172(1)
Global Fit Statistics for the Detailed Example
173(1)
Power and Precision
174(3)
Summary
177(2)
Learn More
179(1)
Exercises
179(1)
Appendix 10.A Significance Testing Based on the RMSEA
180(2)
11 Comparing Models
182(21)
Nested Models
182(1)
Building and Trimming
183(1)
Empirical versus Theoretical Respecification
184(1)
Chi-Square Difference Test
184(3)
Modification Indexes and Related Statistics
187(1)
Intelligent Automated Search Strategies
188(1)
Model Building for the Detailed Example
188(2)
Comparing Nonnested Models
190(4)
Equivalent Models
194(2)
Coping with Equivalent or Nearly Equivalent Models
196(2)
Summary
198(1)
Learn More
199(1)
Exercises
199(1)
Appendix 11.A Other Types of Model Relations and Tests
200(3)
12 Comparing Groups
203(14)
Issues in Multiple-Group SEM
204(1)
Detailed Example for a Path Model of Achievement and Delinquency
205(6)
Tests for Conditional Indirect Effects Over Groups
211(1)
Summary
212(1)
Learn More
213(1)
Exercises
213(4)
PART III MULTIPLE-INDICATOR APPROXIMATION OF CONCEPTS
13 Multiple-Indicator Measurement
217(12)
Concepts, Indicators, and Proxies
218(1)
Reflective Measurement and Effect Indicators
219(1)
Causal-Formative Measurement and Causal Indicators
220(1)
Composite Measurement and Composite Indicators
221(1)
Mixed-Model Measurement
222(1)
Considerations in Selecting a Measurement Model
223(1)
Cautions on Formative Measurement
224(1)
Alternative Measurement Models and Approaches
225(2)
Summary
227(1)
Learn More
228(1)
14 Confirmatory Factor Analysis
229(34)
EFA versus CFA
229(2)
Suggestions for Selecting Indicators
231(1)
Basic CFA Models
232(2)
Other Methods for Scaling Factors
234(2)
Detailed Example for a Basic CFA Model of Cognitive Abilities
236(7)
Respecification of CFA Models
243(3)
Estimation Problems
246(3)
Equivalent CFA Models
249(2)
Special Tests with Equality Constraints
251(1)
Models for Multitrait-Multimethod Data
252(3)
Second-Order and Bifactor Models with General Factors
255(3)
Summary
258(1)
Learn More
259(1)
Exercises
259(1)
Appendix 14.A Identification Rules for Correlated Errors or Multiple Loadings
260(3)
15 Structural Regression Models
263(21)
Full SR Models
263(2)
Two-Step Modeling
265(3)
Other Modeling Strategies
268(1)
Detailed Example of Two-Step Modeling in a High-Risk Sample
269(5)
Partial SR Models with Single Indicators
274(4)
Example for a Partial SR Model
278(3)
Summary
281(2)
Learn More
283(1)
Exercises
283(1)
16 Composite Models
284(25)
Modern Composite Analysis in SEM
285(1)
Disambiguation of Terms
285(3)
Special Computer Tools
288(1)
Motivating Example
289(5)
Alternative Composite Model
294(3)
Partial Least Squares Path Modeling Algorithm
297(3)
PLS-PM Analysis of the Composite Model
300(1)
Henseler-Ogasawara Specification and ML Analysis
301(3)
Summary
304(1)
Learn More
305(1)
Exercises
305(4)
PART IV ADVANCED TECHNIQUES
17 Analyses in Small Samples
309(10)
Suggestions for Analyzing Common Factor Models
309(2)
Analysis of a Common Factor Model in a Small Sample
311(4)
Controlling Measurement Error in Manifest-Variable Path Models
315(1)
Adjusted Test Statistics for Small Samples
316(1)
Bayesian Methods and Regularized SEM
317(1)
Summary
318(1)
Learn More
318(1)
Exercises
318(1)
18 Categorical Confirmatory Factor Analysis
319(12)
Basic Estimation Options for Categorical Data
319(1)
Overview of Continuous/Categorical Variable Methodology
320(1)
Latent Response Variables and Thresholds
321(1)
Polychoric Correlations
321(2)
Measurement Model and Diagram
323(1)
Methods to Scale Latent Response Variables
323(1)
Estimators, Adjusted Test Statistics, and Robust Standard Errors
324(1)
Models with Continuous and Ordinal Indicators
325(1)
Detailed Example for Items about Self-Rated Depression
325(2)
Other Estimation Options for Categorical CFA
327(2)
Item Response Theory and CFA
329(1)
Summary
329(1)
Learn More
330(1)
Exercises
330(1)
19 Nonrecursive Models with Causal Loops
331(18)
Causal Loops
331(2)
Assumptions of Causal Loops
333(1)
Identification Requirements
333(3)
Respecification of Nonrecursive Models That Are Not Identified
336(1)
Order Condition and Rank Condition
337(1)
Detailed Example for a Nonrecursive Partial SR Model
338(6)
Blocked-Error R2 for Nonrecursive Models
344(1)
Summary
345(1)
Learn More
345(1)
Exercises
346(1)
Appendix 19.A Evaluation of the Rank Condition
347(2)
20 Enhanced Mediation Analysis
349(23)
Mediation Analysis in Cross-Sectional Designs
350(3)
Effect Sizes for Indirect Effects
353(3)
Cross-Lag Panel Designs for Mediation
356(2)
Conditional Process Analysis
358(2)
Causal Mediation Analysis Based on Nonparametric Models and Counterfactuals
360(8)
Reporting Standards for Mediation Studies
368(3)
Summary
371(1)
Learn More
371(1)
Exercises
371(1)
21 Latent Growth Curve Models
372(21)
Basic Latent Growth Models
372(2)
Data Set for Analyzing Basic Growth Models with No Covariates
374(5)
Example Analyses of Basic Growth Models
379(3)
Example for a Growth Predictor Model with Time-Invariant Covariates
382(3)
Practical Suggestions for Latent Growth Modeling
385(1)
Extensions of Latent Growth Models
385(4)
Summary
389(1)
Learn More
390(1)
Exercises
390(1)
Appendix 21.A Unequal Measurement Intervals and Options for Defining the Intercept
391(2)
22 Measurement Invariance
393(21)
Levels of Invariance
395(3)
Analysis Decisions
398(3)
Partial Measurement Invariance
401(1)
Detailed Example for a Two-Factor Model of Divergent Thinking
402(6)
Practical Suggestions for Measurement Invariance Testing
408(1)
Measurement Invariance Testing in Categorical CFA
409(1)
Other Statistical Approaches to Estimating Measurement Invariance
410(2)
Summary
412(1)
Learn More
412(1)
Exercises
413(1)
23 Best Practices in SEM
414(11)
Resources
414(1)
Bottom Lines and Statistical Beauty
414(1)
Mightily Distinguish Your Work (Be a Hero)
415(1)
Family Relations
416(1)
Specification
417(1)
Identification
418(1)
Measures
419(1)
Sample and Data
419(1)
Estimation
420(2)
Respecification
422(1)
Tabulation
422(1)
Interpretation
423(1)
Summary
424(1)
Learn More
424(1)
Suggested Answers to Exercises 425(16)
References 441(30)
Author Index 471(8)
Subject Index 479(15)
About the Author 494
Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montréal, Québec, Canada. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. Dr. Kline has published a number of chapters, journal articles, and books in these areas.