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Principles and practice of structural equation modeling

Author: Kline, Rex B. Series: Methodology in the social sciences Publisher: Guilford Press 1998.Language: EnglishDescription: 354 p. ; 23 cm.ISBN: 1572303379Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print H61.25 .K55 1998
(Browse shelf)
32419001041205
Available 32419001041205
Total holds: 0

Includes bibliographical references and index

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Principles and Practice of Structural Equation Modeling Contents I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1 Foreword 3 1.2 Plan of the Book 3 1.3 Notation 5 1.4 Computer Programs for SEM 5 1.5 Statistical Journeys 7 1.6 Family Values 7 1.7 Family History 13 1.8 Summary 14 3 2. Basic Statistical Concepts 2.1 Foreword 15 2.2 Variable Labels 16 2.3 Level of Measurement 16 2.4 Standardized and Unstandardized Variables 17 2.5 Bivariate Correlation and Regression 19 2.6 Factors That Affect the Magnitudes of Correlations 25 2.7 Partial Correlation 28 2.8 Multiple Correlation and Regression 30 2.9 Significance Testing 41 2.10 Summary 44 2.11 Recommended Readings 46 15 3. SEM Family Tree 3.1 Foreword 47 3.2 Symbols for Model Diagrams 47 47 3.3 Steps of SEM 48 3.4 Path Analysis: A Structural Model of Delinquency 50 3.5 Confirmatory Factor Analysis: A Measurement Model of Cognitive Processes 55 3.6 A Hybrid Model: Familial Risk Factors for Psychopathology and Child Adjustment 60 3.7 Overview of Techniques 63 3.8 Summary 65 3.9 Recommended Readings 65 4. Data Preparation and Screening 4.1 Foreword 67 4.2 Form of the Input Data 68 4.3 Accuracy of Data Entry 72 4.4 Missing Data 72 4.5 Multicollinearity 77 4.6 Outliers 78 4.7 Normality 81 4.8 Linearity and Homoscedasticity 84 4.9 Other Screening Issues 84 4.10 Example of Data Screening 85 4.11 Summary 89 4.12 Recommended Readings 91 67 II. CORE SEM TECHNIQUES 5. Structural Models with Observed Variables and Path Analysis: I. Fundamentals, Recursive Models 5.1 Foreword 95 5.2 Correlation and Causation 96 5.3 Specification of Path Models 99 5.4 Types of Path Models 105 5.5 Principles of Identification 108 5.6 The Role of Sample Size 111 5.7 Overview of Estimation Options 112 5.8 Estimation of Recursive Path Models with Multiple Regression 113 5.9 Maximum Likelihood Estimation 125 5.10 Testing Path Models 131 5.11 Other Issues 142 5.12 Summary 146 5.13 Recommended Readings 148 Appendix 5.A Effect Size Interpretation of Path Coefficients 149 Appendix 5.B Significance Tests of Indirect and Total Effects Using a Regression Program 150 95 Appendix 5.0 Recommendations for Starting Values for Recursive Path Models 152 Appendix 5.D Comparing Hierarchical Recursive Path Models with Multiple Regression 152 6. Structural Models with Observed Variables and Path Analysis: II. Nonrecursive Models, Multiple Group Analysis 6.1 Foreword 155 6.2 Overview of the Identification Status of Path Models 156 6.3 Nonrecursive Path Models with All Possible Disturbance Correlations 159 6.4 Nonrecursive Path Models with All Possible Disturbance Correlations within Recursively Related Blocks 166 6.5 Empirical Underidentification 169 6.6 "None of the Above" Nonrecursive Path Models and Empirical Checks for Identification 170 6.7 A Healthy Perspective on Identification 172 6.8 Issues in the Estimation of Nonrecursive Path Models 173 6.9 Example of the Analysis of a Nonrecursive Path Model 177 6.10 Multiple Group Path Analysis 180 6.11 Summary 184 6.12 Recommended Readings 188 155 7. Measurement Models and Confirmatory Factor Analysis 7.1 Foreword 189 7.2 Conceptual Nature of Latent Variables 190 7.3 Principles of Measurement 192 7.4 CFA Models and Their Specification 199 7.5 Requirements for the Identification of CFA Models 203 7.6 Estimation 207 7.7 Testing CFA Models 211 7.8 Multiple Group CFA 224 7.9 Specialized Types of CFA Models 228 7.10 Other Issues 236 7.11 Summary 238 7.12 Recommended Readings 240 Appendix 7.A Coefficient of Determination 241 Appendix 7.B Recommendations for Starting Values for CFA Models 242 189 8. Hybrid Models with Structural and Measurement Components 8.1 Foreword 244 8.2 Characteristics of Hybrid Models 245 8.3 Analysis of Hybrid Models 246 8.4 Detailed Example of the Analysis of a Hybrid Model 252 8.5 Modeling Two-Wave Longitudinal Data 258 8.6 Other Issues 264 8.7 Summary 267 8.8 Recommended Readings 268 244 III. AVOIDING MISTAKES; ADVANCED TECHNIQUES; SOFTWARE 9. How to Fool Yourself with SEM 9.1 Foreword 273 9.2 Tripping at the Starting Line: Specification 273 9.3 Improper Care and Feeding: Data 275 9.4 Checking Critical Judgment at the Door: Analysis and Respecification 276 9.5 The Garden Path: Interpretation 278 9.6 Summary 280 9.7 Recommended Readings 280 273 10. Other Horizons: Overview of Advanced Techniques 10.1 Foreword 282 10.2 Nonlinear (Curvilinear and Interactive) Effects 283 10.3 Latent Categorical Variables 291 10.4 Analysis of Means 293 10.5 Power Analysis 308 10.6 Bootstrapping 310 10.7 Internet Resources for SEM 312 10.8 Summary 312 282 11. Software for SEM: Amos, EQS, and LISREL 11.1 Foreword 314 11.2 Amos 315 11.3 EQS 320 11.4 LISREL 327 314 References Index 333 347

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