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Principles and Practice of Structural Equation Modeling, Fourth Edition: Fourth Edition 4th edition [Kietas viršelis]

4.11/5 (192 ratings by Goodreads)
  • Formatas: Hardback, 534 pages, aukštis x plotis: 254x178 mm, weight: 1100 g
  • Serija: Methodology in the Social Sciences
  • Išleidimo metai: 19-Apr-2016
  • Leidėjas: Guilford Press
  • ISBN-10: 1462523358
  • ISBN-13: 9781462523351
Kitos knygos pagal šią temą:
Principles and Practice of Structural Equation Modeling, Fourth Edition: Fourth Edition 4th editionDidesnis paveikslėlis
  • Formatas: Hardback, 534 pages, aukštis x plotis: 254x178 mm, weight: 1100 g
  • Serija: Methodology in the Social Sciences
  • Išleidimo metai: 19-Apr-2016
  • Leidėjas: Guilford Press
  • ISBN-10: 1462523358
  • ISBN-13: 9781462523351
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781462523351.html
Raktažodžiai:
Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book's examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan).

New to This Edition
*Extensively revised to cover important new topics: Pearl's graphing theory and SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more.
*Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping.
*Expanded coverage of psychometrics.
*Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan).
*Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models.

Pedagogical Features
*Exercises with answers, plus end-of-chapter annotated lists of further reading.
*Real examples of troublesome data, demonstrating how to handle typical problems in analyses.
*Topic boxes on specialized issues, such as causes of nonpositive definite correlations.
*Boxed rules to remember.
*Website promoting a learn-by-doing approach, including syntax and data files for six widely used SEM computer tools.

Recenzijos

. -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, 04/06/2015The 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, 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, 04/06/2015This 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 SEMs...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, 04/06/2015

Introduction 1(6)
Book Website
2(1)
Pedagogical Approach
2(1)
Principles over Computer Tools
3(1)
Symbols and Notation
3(1)
Life's a Journey, Not a Destination
3(1)
Plan of the Book
4(3)
Part I. Concepts And Tools
1 Coming of Age
7(18)
Preparing to Learn SEM
7(2)
Definition of SEM
9(1)
Importance of Theory
10(1)
A Priori, but Not Exclusively Confirmatory
11(1)
Probabilistic Causation
11(1)
Observed Variables and Latent Variables
12(1)
Data Analyzed in SEM
13(1)
SEM Requires Large Samples
14(3)
Less Emphasis on Significance Testing
17(1)
SEM and Other Statistical Techniques
17(1)
SEM and Other Causal Inference Frameworks
18(2)
Myths about SEM
20(1)
Widespread Enthusiasm, but with a Cautionary Tale
21(2)
Family History
23(1)
Summary
24(1)
Learn More
24(1)
2 Regression Fundamentals
25(24)
Bivariate Regression
25(5)
Multiple Regression
30(5)
Left-Out Variables Error
35(1)
Suppression
36(1)
Predictor Selection and Entry
37(2)
Partial and Part Correlation
39(2)
Observed versus Estimated Correlations
41(3)
Logistic Regression and Probit Regression
44(3)
Summary
47(1)
Learn More
47(1)
Exercises
48(1)
3 Significance Testing and Bootstrapping
49(15)
Standard Errors
49(2)
Critical Ratios
51(1)
Power and Types of Null Hypotheses
52(2)
Significance Testing Controversy
54(3)
Confidence Intervals and Noncentral Test Distributions
57(3)
Bootstrapping
60(2)
Summary
62(1)
Learn More
62(1)
Exercises
63(1)
4 Data Preparation and Psychometrics Review
64(33)
Forms of Input Data
64(3)
Positive Definiteness
67(4)
Extreme Collinearity
71(1)
Outliers
72(2)
Normality
74(3)
Transformations
77(4)
Relative Variances
81(1)
Missing Data
82(6)
Selecting Good Measures and Reporting about Them
88(2)
Score Reliability
90(3)
Score Validity
93(1)
Item Response Theory and Item Characteristic Curves
94(1)
Summary
95(1)
Learn More
96(1)
Exercises
96(1)
5 Computer Tools
97(20)
Ease of Use, Not Suspension of Judgment
97(1)
Human—Computer Interaction
98(2)
Tips for SEM Programming
100(1)
SEM Computer Tools
101(10)
Other Computer Resources for SEM
111(1)
Computer Tools for the SCM
112(1)
Summary
113(1)
Learn More
113(4)
Part II. Specification And Identification
6 Specification of Observed Variable (Path) Models
117(28)
Steps of SEM
117(4)
Model Diagram Symbols
121(1)
Causal Inference
122(4)
Specification Concepts
126(3)
Path Analysis Models
129(6)
Recursive and Nonrecursive Models
135(3)
Path Models for Longitudinal Data
138(3)
Summary
141(1)
Learn More
142(1)
Exercises
142(1)
Appendix 6.A. Lisrel Notation for Path Models
143(2)
7 Identification of Observed-Variable (Path) Models
145(19)
General Requirements
145(3)
Unique Estimates
148(1)
Rule for Recursive Models
149(1)
Identification of Nonrecursive Models
150(1)
Models with Feedback Loops and All Possible Disturbance Correlations
150(3)
Graphical Rules for Other Types of Nonrecursive Models
153(2)
Respecification of Nonrecursive Models That Are Not Identified
155(2)
A Healthy Perspective on Identification
157(1)
Empirical Underidentification
157(1)
Managing Identification Problems
158(1)
Path Analysis Research Example
159(1)
Summary
159(1)
Learn More
160(1)
Exercises
160(1)
Appendix 7.A. Evaluation of the Rank Condition
161(3)
8 Graph Theory and the Structural Causal Model
164(24)
Introduction to Graph Theory
164(2)
Elementary Directed Graphs and Conditional Independences
166(4)
Implications for Regression Analysis
170(1)
d-Separation
170(3)
Basis Set
173(1)
Causal Directed Graphs
174(2)
Testable Implications
176(1)
Graphical Identification Criteria
177(3)
Instrumental Variables
180(1)
Causal Mediation
181(3)
Summary
184(1)
Learn More
185(1)
Exercises
185(1)
Appendix 8.A. Locating Conditional Independences in Directed Cyclic Graphs
186(1)
Appendix 8.B. Counterfactual Definitions of Direct and Indirect Effects
187(1)
9 Specification and Identification of Confirmatory Factor Analysis Models
188(24)
Latent Variables in CFA
188(1)
Factor Analysis
189(2)
Characteristics of EFA Models
191(2)
Characteristics of CFA Models
193(2)
Other CFA Specification Issues
195(3)
Identification of CFA Models
198(3)
Rules for Standard CFA Models
201(1)
Rules for Nonstandard CFA Models
202(4)
Empirical Underidentification in CFA
206(1)
CFA Research Example
206(1)
Summary
207(1)
Learn More
207(2)
Exercises
209(1)
Appendix 9.A. Lisrel Notation for CFA Models
210(2)
10 Specification and Identification of Structural Regression Models
212(19)
Causal Inference with Latent Variables
212(1)
Types of SR Models
213(1)
Single Indicators
214(3)
Identification of SR Models
217(2)
Exploratory SEM
219(1)
SR Model Research Examples
220(3)
Summary
223(2)
Learn More
225(1)
Exercises
225(1)
Appendix 10.A. Lisrel Notation for SR Models
226(5)
Part III. Analysis
11 Estimation and Local Fit Testing
231(31)
Types of Estimators
231(1)
Causal Effects in Path Analysis
232(1)
Single-Equation Methods
233(2)
Simultaneous Methods
235(1)
Maximum Likelihood Estimation
235(4)
Detailed Example
239(14)
Fitting Models to Correlation Matrices
253(2)
Alternative Estimators
255(3)
A Healthy Perspective on Estimation
258(1)
Summary
259(1)
Learn More
259(1)
Exercises
260(1)
Appendix 11.A. Start Value Suggestions for Structural Models
261(1)
12 Global Fit Testing
262(38)
State of Practice, State of Mind
262(1)
A Healthy Perspective on Global Fit Statistics
263(2)
Model Test Statistics
265(1)
Approximate Fit Indexes
266(2)
Recommended Approach to Fit Evaluation
268(2)
Model Chi-Square
270(3)
RMSEA
273(4)
SRMR
277(1)
Tips for Inspecting Residuals
278(1)
Global Fit Statistics for the Detailed Example
278(2)
Testing Hierarchical Models
280(6)
Comparing Nonhierarchical Models
286(4)
Power Analysis
290(2)
Equivalent and Near-Equivalent Models
292(5)
Summary
297(1)
Learn More
298(1)
Exercises
298(1)
Appendix 12.A. Model Chi-Squares Printed by LISREL
299(1)
13 Analysis of Confirmatory Factor Analysis Models
300(38)
Fallacies about Factor or Indicator Labels
300(1)
Estimation of CFA Models
301(3)
Detailed Example
304(5)
Respecification of CFA Models
309(3)
Special Topics and Tests
312(3)
Equivalent CFA Models
315(4)
Special CFA Models
319(4)
Analyzing Likert-Scale Items as Indicators
323(9)
Item Response Theory as an Alternative to CFA
332(1)
Summary
333(1)
Learn More
333(1)
Exercises
334(1)
Appendix 13.A. Start Value Suggestions for Measurement Models
335(1)
Appendix 13.B. Constraint Interaction in CFA Models
336(2)
14 Analysis of Structural Regression Models
338(31)
Two-Step Modeling
338(1)
Four-Step Modeling
339(1)
Interpretation of Parameter Estimates and Problems
340(1)
Detailed Example
341(7)
Equivalent SR Models
348(1)
Single Indicators in a Nonrecursive Model
349(3)
Analyzing Formative Measurement Models in SEM
352(9)
Summary
361(1)
Learn More
362(1)
Exercises
362(1)
Appendix 14.A. Constraint Interaction in SR Models
363(1)
Appendix 14.B. Effect Decomposition in Nonrecursive Models and the Equilibrium Assumption
364(1)
Appendix 14.C. Corrected Proportions of Explained Variance for Nonrecursive Models
365(4)
Part IV. Advanced Techniques And Best Practices
15 Mean Structures and Latent Growth Models
369(25)
Logic of Mean Structures
369(4)
Identification of Mean Structures
373(1)
Estimation of Mean Structures
374(1)
Latent Growth Models
374(1)
Detailed Example
375(12)
Comparison with a Polynomial Growth Model
387(3)
Extensions of Latent Growth Models
390(2)
Summary
392(1)
Learn More
392(1)
Exercises
393(1)
16 Multiple-Samples Analysis and Measurement Invariance
394(30)
Rationale of Multiple-Samples SEM
394(2)
Measurement Invariance
396(3)
Testing Strategy and Related Issues
399(4)
Example with Continuous Indicators
403(8)
Example with Ordinal Indicators
411(9)
Structural Invariance
420(1)
Alternative Statistical Techniques
420(1)
Summary
421(1)
Learn More
421(1)
Exercises
422(1)
Appendix 16.A. Welch—James Test
423(1)
17 Interaction Effects and Multilevel Structural Equation Modeling
424(28)
Interactive Effects of Observed Variables
424(7)
Interactive Effects in Path Analysis
431(1)
Conditional Process Modeling
432(3)
Causal Mediation Analysis
435(2)
Interactive Effects of Latent Variables
437(7)
Multilevel Modeling and SEM
444(6)
Summary
450(1)
Learn More
450(1)
Exercises
451(1)
18 Best Practices in Structural Equation Modeling
452(17)
Resources
452(2)
Specification
454(3)
Identification
457(1)
Measures
458(1)
Sample and Data
458(3)
Estimation
461(2)
Respecification
463(1)
Tabulation
464(1)
Interpretation
465(1)
Avoid Confirmation Bias
466(1)
Bottom Lines and Statistical Beauty
466(1)
Summary
467(1)
Learn More
467(2)
Suggested Answers to Exercises 469(20)
References 489(21)
Author Index 510(6)
Subject Index 516(18)
About the Author 534
Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montréal. 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 books, chapters, and journal articles in these areas. His website is http://tinyurl.com/rexkline.