Principles and practice of structural equation modeling /

Principles and practice of structural equation modeling /

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Bibliographic Details
Author / Creator:Kline, Rex B.
Imprint:New York : Guilford Press, ©1998.
Description:xiv, 354 pages : illustrations ; 24 cm.
Language:English
Series:Methodology in the social sciences
Methodology in the social sciences.
Subject:
Structural equation modeling.
Social sciences -- Statistical methods.
Social sciences -- Statistical methods.
Structural equation modeling.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11062521
Hidden Bibliographic Details
Varying Form of Title:Structural equation modeling
ISBN:1572303360
9781572303362
1572303379
9781572303379
Notes:Includes bibliographical references (pages 333-346) and index.
Standard no.:9781572303379
Table of 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
  • 6.9Recommended 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 t
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