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# Principles and Practice of Structural Equation Modeling, Third Edition

**by**Kline, Rex B.

3rd

### 9781606238769

1606238760

Paperback

8/4/2010

The Guilford Press

## Questions About This Book?

What version or edition is this?

This is the 3rd edition with a publication date of 8/4/2010.

What is included with this book?

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**New**copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

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## Customer Reviews

Excellent guide for beginners! August 9, 2011

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It's more complete and includes several detailed examples. Very useful for people interesting in conducting structural equation models. The textbook I ordered was exactly the same as what they had listed. I am very pleased with them and will order other books, if possible, in the future.

Principles and Practice of Structural Equation Modeling, Third Edition:
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## Summary

Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) in the social sciences. This is an accessible volume which covers introductory techniques, including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods, such as the evaluation of nonlinear effects, the analysis of means in covariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the author offers clear instructions on the preparation and screening of data, common mistakes to avoid, and features of widely used software programs (Amos, EQS, and LISREL). Readers will acquire the skills necessary to begin to use SEM in their own research and to interpret and critique the use of the method by others.

## Author Biography

Rex B. Kline is Professor of Psychology at Concordia University in Montral, Qubec, Canada.

## Table of Contents

Concepts and Tools | |

Introduction | p. 3 |

The Book's Website | p. 3 |

Pedagogical Approach | p. 4 |

Getting Ready to Learn about SEM | p. 5 |

Characteristics of SEM | p. 7 |

Widespread Enthusiasm, but with a Cautionary Tale | p. 13 |

Family History and a Reminder about Context | p. 15 |

Extended Latent Variable Families | p. 16 |

Plan of the Book | p. 17 |

Summary | p. 18 |

Fundamental Concepts | p. 19 |

Multiple Regression | p. 19 |

Partial Correlation and Part Correlation | p. 28 |

Other Bivariate Correlations | p. 31 |

Logistic Regression | p. 32 |

Statistical Tests | p. 33 |

Bootstrapping | p. 42 |

Summary | p. 43 |

Recommended Readings | p. 44 |

Exercises | p. 45 |

Data Preparation | p. 46 |

Forms of Input Data | p. 46 |

Positive Definiteness | p. 49 |

Data Screening | p. 51 |

Selecting Good Measures and Reporting about Them | p. 68 |

Summary | p. 72 |

Recommended Readings | p. 72 |

Exercises | p. 73 |

Computer Tools | p. 75 |

Ease of Use, Not Suspension of Judgment | p. 75 |

Human-Computer Interaction | p. 77 |

Core SEM Programs and Book Website Resources | p. 77 |

Other Computer Tools | p. 86 |

Summary | p. 87 |

Recommended Readings | p. 87 |

Core Techniques | |

Specification | p. 91 |

Steps of SEM | p. 91 |

Model Diagram Symbols | p. 95 |

Specification Concepts | p. 96 |

Path Analysis Models | p. 103 |

CFA Models | p. 112 |

Structural Regression Models | p. 118 |

Exploratory SEM | p. 121 |

Summary | p. 121 |

Recommended Readings | p. 122 |

Exercises | p. 122 |

Identification | p. 124 |

General Requirements | p. 124 |

Unique Estimates | p. 130 |

Rule for Recursive Structural Models | p. 132 |

Rules for Standard CFA Models | p. 137 |

Rules for Nonstandard CFA Models | p. 138 |

Rules for SR Models | p. 144 |

A Healthy Perspective on Identification | p. 146 |

Empirical Underidentification | p. 146 |

Managing Identification Problems | p. 147 |

Summary | p. 148 |

Recommended Readings | p. 149 |

Exercises | p. 149 |

Appendix 6.A. Evaluation of the Rank Condition | p. 151 |

Estimation | p. 154 |

Maximum Likelihood Estimation | p. 154 |

Detailed Example | p. 160 |

Brief Example with a Start Value Problem | p. 172 |

Fitting Models to Correlation Matrices | p. 175 |

Alternative Estimators | p. 176 |

A Healthy Perspective on Estimation | p. 182 |

Summary | p. 182 |

Recommended Readings | p. 183 |

Exercises | p. 183 |

Appendix 7.A Start Value Suggestions for Structural Models | p. 185 |

Appendix 7.B Effect Decomposition in Nonrecursive Models and the Equilibrium Assumption | p. 186 |

Appendix 7.C Corrected Proportions of Explained Variance for Nonrecursive Models | p. 187 |

Hypothesis Testing | p. 189 |

Eyes on the Prize | p. 189 |

State of Practice, State of Mind | p. 190 |

A Healthy Perspective on Fit Statistics | p. 191 |

Types of Fit Statistics and "Golden Rules" | p. 193 |

Model Chi-Square | p. 199 |

Approximate Fit Indexes | p. 204 |

Visual Summaries of Fit | p. 209 |

Recommended Approach to Model Fit Evaluation | p. 209 |

Detailed Example | p. 210 |

Testing Hierarchical Models | p. 214 |

Comparing Nonhierarchical Models | p. 219 |

Power Analysis | p. 222 |

Equivalent and Near-Equivalent Models | p. 225 |

Summary | p. 228 |

Recommended Readings | p. 228 |

Exercises | p. 229 |

Measurement Models and Confirmatory Factor Analysis | p. 230 |

Naming and Reification Fallacies | p. 230 |

Estimation of CFA Models | p. 231 |

Detailed Example | p. 233 |

Respecification of Measurement Models | p. 240 |

Special Topics and Tests | p. 241 |

Items as Indicators and Other Methods for Analyzing Items | p. 244 |

Estimated Factor Scores | p. 245 |

Equivalent CFA Models | p. 245 |

Hierarchical CFA Models | p. 248 |

Models for Multitrait-Multimethod Data | p. 250 |

Measurement Invariance and Multiple-Sample CFA | p. 251 |

Summary | p. 261 |

Recommended Readings | p. 262 |

Exercises | p. 262 |

Appendix 9.A Start Value Suggestions for Measurement Models | p. 263 |

Appendix 9.B Constraint Interaction in Measurement Models | p. 264 |

Structural Regression Models | p. 265 |

Analyzing SR Models | p. 265 |

Estimation of SR Models | p. 269 |

Detailed Example | p. 270 |

Equivalent SR Models | p. 276 |

Single Indicators in Partially Latent SR Models | p. 276 |

Cause Indicators and Formative Measurement | p. 280 |

Invariance Testing of SR Models | p. 288 |

Reporting Results of SEM Analyses | p. 289 |

Summary | p. 293 |

Recommended Readings | p. 293 |

Exercises | p. 294 |

Appendix 10.A Constraint Interaction in SR Models | p. 295 |

Advanced Techniques, Avoiding Mistakes | |

Mean Structures and Latent Growth Models | p. 299 |

Logic of Mean Structures | p. 299 |

Identification of Mean Structures | p. 303 |

Estimation of Mean Structures | p. 304 |

Latent Growth Models | p. 304 |

Structured Means in Measurement Models | p. 316 |

MIMIC Models as an Alternative to Multiple-Sample Analysis | p. 322 |

Summary | p. 325 |

Recommended Readings | p. 326 |

Interaction Effects and Multilevel SEM | p. 327 |

Interaction Effects of Observed Variables | p. 327 |

Interaction Effects in Path Models | p. 331 |

Mediation and Moderation Together | p. 333 |

Interactive Effects of Latent Variables | p. 336 |

Estimation with the Kenny-Judd Method | p. 337 |

Alternative Estimation Methods | p. 340 |

Rationale of Multilevel Analysis | p. 343 |

Basic Multilevel Techniques | p. 345 |

Convergence of SEM and MLM | p. 348 |

Multilevel SEM | p. 350 |

Summary | p. 354 |

Recommended Readings | p. 354 |

How to Fool Yourself with SEM | p. 356 |

Tripping at the Starting Line: Specification | p. 356 |

Improper Care and Feeding: Data | p. 359 |

Checking Critical Judgment at the Door: Analysis and Respecification | p. 361 |

The Garden Path: Interpretation | p. 363 |

Summary | p. 366 |

Recommended Readings | p. 366 |

Suggested Answers to Exercises | p. 367 |

References | p. 387 |

Author Index | p. 405 |

Subject Index | p. 411 |

About the Author | p. 427 |

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