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Purchase Benefits
What is included with this book?
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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