Principles and Practice of Structural Equation Modeling, Second Edition / Edition 2

Principles and Practice of Structural Equation Modeling, Second Edition / Edition 2

3.5 2
by Rex B. Kline
     
 


This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of… See more details below

Overview


This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.

Product Details

ISBN-13:
9781593850753
Publisher:
Guilford Publications, Inc.
Publication date:
11/28/2004
Series:
Methodology In The Social Sciences Series
Edition description:
Second Edition
Pages:
366
Product dimensions:
6.00(w) x 9.00(h) x 1.20(d)

Meet the Author


Rex B. Kline, PhD, is an associate professor of Psychology at Concordia University in Montréal. Since earning a PhD in psychology, his areas of research and writing have included the psychometric evaluation of cognitive abilities, child clinical assessment, structural equation modeling, and usability engineering in computer science. Dr. Kline has published three books and more than 40 articles in research journals and is the coauthor of a teacher-informant rating scale for referred children.

Table of Contents


Contents
I. Fundamental Concepts
1. Introduction
1.1. Plan of the Book
1.2. Notation
1.3. Computer Programs for SEM
1.4. Statistical Journeys
1.5. Family Values
1.6. Extend Latent Variable Families
1.7. Family History
1.8. Internet Resources
1.9. Summary
2. Basic Statistical Concepts: I. Correlation and Regression
2.1. Standardized and Unstandardized Variables
2.2. Bivariate Correlation and Regression
2.3. Partial Correlation
2.4. Multiple Correlation and Regression
2.5. Statistical Tests
2.6. Bootstrapping
2.7. Summary
2.8. Recommended Readings
3. Basic Statistical Concepts: II. Data Preparation and Screening
3.1. Data Preparation
3.2. Data Screening
3.3. Score Reliability and Validity
3.4. Summary
3.5. Recommended Readings
4. Core SEM Techniques and Software
4.1. Steps of SEM
4.2. Path Analysis: A Structural Model of Illness Factors
4.3. Confirmatory Factor Analysis: A Measurement Model of Arousal
4.4. A Structural Regression Model of Family Risk and Child Adjustment
4.5. Extensions
4.6. SEM Computer Programs
4.7. Summary
4.8. Recommended Readings
II. Core SEM Techniques
5. Introduction to Path Analysis
5.1. Correlation and Causation
5.2. Specification of Path Models
5.3. Types of Path Models
5.4. Principles of Identification
5.5. Sample Size
5.6. Overview of Estimation Options
5.7. Maximum Likelihood Estimation
5.8. Other Issues
5.9. Summary
5.10. Recommended Readings
Appendix 5.a. Recommendations for Start Values
Appendix 5.b. Effect Size Interpretation of Standardized Path Coefficients
6. Details of Path Analysis
6.1. Detailed Analysis of a Recursive Model of Illness Factors
6.2. Assessing Model Fit
6.3. Testing Hierarchical Models
6.4. Comparing Nonhierarchical Models
6.5. Equivalent Models
6.6. Power Analysis
6.7. Other Estimation Options
6.8. Summary
6.9. Recommended Readings
Appendix 6.a. Statistical Tests for Indirect Effects in Recursive Path Models
Appendix 6.b. Amos Basic Syntax
Appendix 6.c. Estimation of Recursive Path Models with Multiple Regression
7. Measurement Models and Confirmatory Factor Analysis
7.1. Specification of CFA Models
7.2. Identification of CFA Models
7.3. Naming and Reification Fallacies
7.4. Estimation of CFA Models
7.5. Testing CFA Models
7.6. Equivalent CFA Models
7.7. Analyzing Indicators with Non-Normal Distributions
7.8. Special Types of CFA Models
7.9. Other Issues
7.10. Summary
7.11. Recommended Readings
Appendix 7.a. Recommendations for Start Values
Appendix 7.b. CALIS Syntax
8. Models with Structural and Measurement Components
8.1. Characteristics of SR Models
8.2. Analysis of SR Models
8.3. Estimation of SR Models
8.4. A Detailed Example
8.5. Other Issues
8.6. Summary
8.7. Recommended Readings
Appendix 8.a. SEPATH Syntax
III. Advanced Techniques, Avoiding Mistakes
9. Nonrecursive Structural Models
9.1. Specification of Nonrecursive Models
9.2. Identification of Nonrecursive Models
9.3. Estimation of Nonrecursive Models
9.4. Examples
9.5. Summary
9.6. Recommended Readings
Appendix 9.a. EQS Syntax
10. Mean Structures and Latent Growth Models
10.1. Introduction to Mean Structures
10.2. Identification of Mean Structures
10.3. Estimation of Mean Structures
10.4. Structured Means in Measurement Models
10.5. Latent Growth Models
10.6. Extensions
10.7. Summary
10.8. Recommended Readings
Appendix 10.a. Mplus Syntax
11. Multiple-Sample SEM
11.1. Rationale of Multiple-Sample SEM
11.2. Multiple-Sample Path Analysis
11.3. Multiple-Sample CFA
11.4. Extensions
11.5. MIMIC Models as an Alternative to Multiple-Sample Analysis
11.6. Summary
11.7. Recommended Readings
Appendix 11.a. LISREL SIMPLIS Syntax
12. How to Fool Yourself with SEM
12.1. Tripping at the Starting Line: Specification
12.2. Improper Care and Feeding: Data
12.3. Checking Critical Judgment at the Door: Analysis and Respecification
12.4. The Garden Path: Interpretation
12.5. Summary
12.6. Recommended Readings
13. Other Horizons
13.1. Interaction and Curvilinear Effects
13.2. Multilevel Structural Equation Models
13.3. Summary
13.4. Recommended Readings

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