Principles and Practice of Structural Equation Modeling, Third Edition,9781606238769

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Principles and Practice of Structural Equation Modeling, Third Edition

by Rex B. Kline, PhD, Department of Psychology, Concordia University, Montreal

Edition:

3rd

ISBN13:

9781606238769

ISBN10:

1606238760

Format:

Trade Paper

Pub. Date:

8/4/2010

Publisher(s):

The Guilford Press

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Customer Reviews

Excellent guide for beginners! August 9, 2011
by David Tilley

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.

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Principles and Practice of Structural Equation Modeling, Third Edition: 4 out of 5 stars based on 1 user reviews.

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.

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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