Principles and Practice of Structural Equation Modeling

Principles and Practice of Structural Equation Modeling

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This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools. New to This Edition*Thoroughly revised and restructured to follow the phases of most SEM analyses.*Syntax, data, and output files for all detailed research examples are now provided online.*Chapter on computer tools.*Exercises with answers, which support self-study.*Topic boxes on specialized issues, such as dealing with problems in the analysis; the assessment of construct measurement reliability; and more.*Updated coverage of a more rigorous approach to hypothesis and model testing; the evaluation of measurement invariance; and more.*"Troublesome" examples have been added to provide a context for discussing how to handle various problems that can crop up in SEM more

Product details

  • Paperback | 422 pages
  • 180.34 x 254 x 25.4mm | 771.1g
  • Guilford Publications
  • New York, United States
  • English
  • Revised
  • 3rd Revised edition
  • black & white tables, figures
  • 1606238760
  • 9781606238769
  • 120,974

Review quote

"Kline provides a text that is accessible for graduate students, practitioners, and researchers who are not intimately familiar with SEM techniques. In addition, he effortlessly summarizes current information that researchers who already use SEM should have...A major strength of the book is the individual chapter examples with explanation of the values provided from a variety of statistical analysis packages." - James B. Schreiber, Center for Advancing the Study of Teaching and Learning, Duquesne University, USA "The coverage is excellent and the writing style is friendly and direct, with a subtle humor that I find refreshing. I especially like the new topic boxes in the third edition, most of which discuss issues that I have had to address separately in lectures." - Jacob Marszalek, Division of Counseling and Educational Psychology, University of Missouri-Kansas City, USA "This is now the #1 book I will recommend to students and substantive researchers (who are not quantitative specialists) who want to learn SEM! Compared to most SEM books that I have seen, this one strikes a better balance between accessibility and breadth. In the third edition, Kline not only has updated the material, but has substantially improved it." - Noel A. Card, Division of Family Studies and Human Development, University of Arizona, USA "In the third edition, Kline has improved the pedagogical value of his book relative to prior editions and to other SEM books. The Web page featuring complete computer syntax and data for the examples is very helpful. Other new material further supports a reader's understanding of SEM." - Craig Wells, School of Education, University of Massachusetts-Amherst, USA "Of all the introductory SEM texts, this one is the most interesting to read. Anyone who has taken a course in basic algebra or introductory statistics will be able to understand the ideas and work through the exercises, and those who work their way through the book will have a good foundation in SEM and will be able to use it effectively." - David F. Gillespie, George Warren Brown School of Social Work, Washington University in St. Louis, USAshow more

About Rex B. Kline

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montreal, Quebec, Canada. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, child clinical assessment, structural equation modeling, training of behavioral science researchers, and usability engineering in computer science. Dr. Kline has published five books, six chapters, and more than 40 articles in research more

Table of contents

Part 1. Concepts and Tools. 1. Introduction. The Book's Website. Pedagogical Approach. Getting Ready to Learn about SEM. Characteristics of SEM. Widespread Enthusiasm, but with a Cautionary Tale. Family History and a Reminder about Context. Extended Latent Variable Families. Plan of the Book. Summary. 2. Fundamental Concepts. Multiple Regression. Partial Correlation and Part Correlation. Other Bivariate Correlations. Logistic Regression. Statistical Tests. TOPIC BOX 2.1. The "Big Five" Misinterpretations of Statistical Significance. Bootstrapping. Summary. Recommended Readings. Exercises. 3. Data Preparation. Forms of Input Data. Positive Definiteness. TOPIC BOX 3.1. Causes of Nonpositive Definiteness and Solutions. Data Screening. Selecting Good Measures and Reporting about Them. Summary. Recommended Readings. Exercises. 4. Computer Tools. Ease of Use, Not Suspension of Judgment. Human-Computer Interaction. TOPIC BOX 4.1. Graphical Isn't Always Better. Core SEM Programs and Book Website Resources. Other Computer Tools. Summary. Recommended Readings. Part 2. Core Techniques. 5. Specification Steps of SEM. Model Diagram Symbols. Specification Concepts. Path Analysis Models. CFA Models. Structural Regression Models. Exploratory SEM. Summary. Recommended Readings. Exercises. 6. Identification. General Requirements. Unique Estimates. Rule for Recursive Structural Models. Rules for Nonrecursive Structural Models. Rules for Standard CFA Models. Rules for Nonstandard CFA Models. Rules for SR Models. A Healthy Perspective on Identification. Empirical Underidentification. Managing Identification Problems. Summary. Recommended Readings. Exercises. APPENDIX 6.A. Evaluation of the Rank Condition. 7. Estimation. Maximum Likelihood Estimation. TOPIC BOX 7.1. Two-Stage Least Squares Estimation. Detailed Example. Brief Example with a Start Value Problem. Fitting Models to Correlation Matrices. Alternative Estimators. A Healthy Perspective on Estimation. Summary. Recommended Readings. Exercises. APPENDIX 7.A. Start Value Suggestions for Structural Models. APPENDIX 7.B. Effect Decomposition in Nonrecursive Models and the Equilibrium Assumption. 8. Hypothesis Testing. Eyes on the Prize. State of Practice, State of Mind. A Healthy Perspective on Fit Statistics. Types of Fit Statistics and "Golden Rules." Model Chi-Square. Approximate Fit Indexes. Visual Summaries of Fit. Recommended Approach to Model Fit Evaluation. Detailed Example. Testing Hierarchical Models. Comparing Nonhierarchical Models. Power Analysis. Equivalent and Near-Equivalent Models. Summary. Recommended Readings. Exercises. 9. Measurement Models and Confirmatory Factor Analysis. Naming and Reification Fallacies. Estimation of CFA Models. Detailed Example. Respecification of Measurement Models. Special Topics and Tests. TOPIC BOX 9.1. Reliability of Construct Measurement. Items as Indicators and Other Methods for Analyzing Items. Estimated Factor Scores. Equivalent CFA Models. Hierarchical CFA Models. Models for Multitrait-Multimethod Data. Measurement Invariance and Multiple-Sample CFA. Summary. Recommended Readings. Exercises. APPENDIX 9.A. Start Value Suggestions for Measurement Models. APPENDIX 9.B. Constraint Interaction in Measurement Models. 10. Structural Regression Models. Analyzing SR Models. Estimation of SR Models. Detailed Example. Equivalent SR Models. Single Indicators in Partially Latent SR Models. Cause Indicators and Formative Measurement. TOPIC BOX 10.1. Partial Least Squares Path Modeling. Invariance Testing of SR Models. Reporting Results of SEM Analyses. Summary. Recommended Readings. Exercises. APPENDIX 10.A. Constraint Interaction in SR Models. Part 3. Advanced Techniques, Avoiding Mistakes. 11. Mean Structures and Latent Growth Models. Logic of Mean Structures. Identification of Mean Structures. Estimation of Mean Structures. Latent Growth Models. Structured Means in Measurement Models. MIMIC Models as an Alternative to Multiple-Sample Analysis. Summary. Recommended Readings. 12. Interaction Effects and Multilevel SEM. Interaction Effects of Observed Variables. Interaction Effects in Path Models. Mediation and Moderation Together. Interactive Effects of Latent Variables. Estimation with the Kenny-Judd Method. Alternative Estimation Methods. Rationale of Multilevel Analysis Basic Multilevel Techniques. Convergence of SEM and MLM. Multilevel SEM. Summary. Recommended Readings. 13. How to Fool Yourself with SEM. Tripping at the Starting Line: Specification. Improper Care and Feeding: Data. Checking Critical Judgment at the Door: Analysis and Respecification. The Garden Path: Interpretation. Summary. Recommended Readings. Suggested Answers to more

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