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A Beginner's Guide to Structural Equation Modeling: Third Edition,9781841698915
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A Beginner's Guide to Structural Equation Modeling: Third Edition



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Taylor and Fran
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Customer Reviews

Excellent Book on SEM  April 24, 2011

The authors bring us the world of SEM into 1 textbook. However, there is no such thing as only 1 big having all of the information that you will need about SEM. Yet, this textbook does a fine job describing many of the important aspects of analyzing data using latent modeling, such as estimators, parameter estimating, and certain assumptions, the book arrived in a very good condition and in expected time for arrival, I recommend ecampus for textbook purchases

A Beginner's Guide to Structural Equation Modeling: Third Edition: 5 out of 5 stars based on 1 user reviews.


This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples.

Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding.

The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models.

Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters.

Table of Contents

About the Authorsp. xv
Prefacep. xvii
Introductionp. 1
What Is Structural Equation Modeling?p. 2
History of Structural Equation Modelingp. 4
Why Conduct Structural Equation Modeling?p. 6
Structural Equation Modeling Software Programsp. 8
Summaryp. 10
Referencesp. 11
Data Entry and Data Editing Issuesp. 13
Data Entryp. 14
Data Editing Issuesp. 18
Measurement Scalep. 18
Restriction of Rangep. 19
Missing Datap. 20
LISREL-PRELIS Missing Data Examplep. 21
Outliersp. 27
Linearityp. 27
Nonnormalityp. 28
Summaryp. 29
Referencesp. 31
Correlationp. 33
Types of Correlation Coefficientsp. 33
Factors Affecting Correlation Coefficientsp. 35
Level of Measurement and Range of Valuesp. 35
Nonlinearityp. 36
Missing Datap. 38
Outliersp. 39
Correction for Attenuationp. 39
Nonpositive Definite Matricesp. 40
Sample Sizep. 41
Bivariate, Part, and Partial Correlationsp. 42
Correlation versus Covariancep. 46
Variable Metrics (Standardized versus Unstandardized)p. 47
Causation Assumptions and Limitationsp. 48
Summaryp. 49
Referencesp. 51
SEM Basicsp. 55
Model Specificationp. 55
Mode] Identificationp. 56
Model Estimationp. 59
Model Testingp. 63
Model Modificationp. 64
Summaryp. 67
Referencesp. 69
Model Fitp. 73
Types of Model-Fit Criteriap. 74
Datap. 77
Programp. 80
Outputp. 81
Model Fitp. 85
Chi-Square (X2)p. 85
Goodness-of-Fit Index (GFI) and Adjusted Goodness-of-Fit Index (AGFI)p. 86
Root-Mean-Square Residual Index (RMR)p. 87
Model Comparisonp. 88
Tucker-Lewis Index (TLI)p. 88
Normed Fit Index (NFI) and Comparative Fit Index (CFI)p. 88
Model Parsimonyp. 89
Parsimony Normed Fit Index (PNFI)p. 90
Akaike Information Criterion (AIC)p. 90
Summaryp. 91
Parameter Fitp. 92
Power and Sample Sizep. 93
Model Fitp. 94
Powerp. 94
Sample Sizep. 99
Model Comparisonp. 108
Parameter Significancep. 111
Summaryp. 113
Two-Step Versus Four-Step Approach to Modelingp. 114
Summaryp. 116
Chapter Footnotep. 118
Standard Errorsp. 118
Chi-Squaresp. 118
Referencesp. 120
Regression Modelsp. 125
Overviewp. 126
An Examplep. 130
Model Specificationp. 130
Model Identificationp. 131
Model Estimationp. 131
Model Testingp. 133
Model Modificationp. 134
Summaryp. 135
Measurement Errorp. 136
Additive Equationp. 137
Chapter Footnotep. 138
Regression Model with Intercept Termp. 138
LISREL-SIMPLIS Program (Intercept Term)p. 138
Referencesp. 139
Path Modelsp. 143
An Examplep. 144
Model Specificationp. 147
Model Identificationp. 150
Model Estimationp. 151
Model Testingp. 154
Model Modificationp. 155
Summaryp. 156
Appendix: LISREL-SIMPLIS Path Model Programp. 156
Chapter Footnotep. 158
Another Traditional Non-SEM Path Model-Fit Indexp. 158
LISREL-SIMPLIS programp. 158
Referencesp. 161
Confirmatory Factor Modelsp. 163
An Examplep. 164
Model Specificationp. 166
Model Identificationp. 167
Model Estimationp. 169
Model Testingp. 170
Model Modificationp. 173
Summaryp. 174
Appendix: LISREL-SIMPLIS Confirmatory Factor Model Programp. 174
Referencesp. 177
Developing Structural Equation Models: Part Ip. 179
Observed Variables and Latent Variablesp. 180
Measurement Modelp. 184
Structural Modelp. 186
Variances and Covariance Termsp. 189
Two-Step/Four-Step Approachp. 191
Summaryp. 192
Referencesp. 193
Developing Structural Equation Models: Part IIp. 195
An Examplep. 195
Model Specificationp. 197
Model Identificationp. 200
Model Estimationp. 202
Model Testingp. 203
Model Modificationp. 205
Summaryp. 207
Appendix: LISREL-SIMPLIS Structural Equation Model Programp. 207
Referencesp. 208
Reporting SEM Research: Guidelines and Recommendationsp. 209
Data Preparationp. 212
Model Specificationp. 213
Model Identificationp. 215
Model Estimationp. 216
Model Testingp. 217
Model Modificationp. 218
Summaryp. 219
Referencesp. 220
Model Validationp. 223
Key Conceptsp. 223
Multiple Samplesp. 223
Model A Computer Outputp. 226
Model B Computer Outputp. 227
Model C Computer Outputp. 228
Model D Computer Outputp. 229
Summaryp. 229
Cross Validationp. 229
ECVIp. 230
CVIp. 231
Bootstrapp. 234
PRELIS Graphical User Interfacep. 234
LISREL and PRELIS Program Syntaxp. 237
Summaryp. 241
Referencesp. 243
Multiple Sample, Multiple Group, and Structured Means Modelsp. 245
Multiple Sample Modelsp. 245
p. 247
p. 247
Multiple Group Modelsp. 250
Separate Group Modelsp. 251
Similar Group Modelp. 255
Chi-Square Difference Testp. 258
Structured Means Modelsp. 259
Model Specification and Identificationp. 259
Model Fitp. 261
Model Estimation and Testingp. 261
Summaryp. 263
Suggested Readingsp. 267
Multiple Samplesp. 267
Multiple Group Modelsp. 267
Structured Means Modelsp. 267
Chapter Footnotep. 268
SPSSp. 268
Referencesp. 269
Second-Order, Dynamic, and Multitrait Multimethod Modelsp. 271
Second-Order Factor Modelp. 271
Model Specification and Identificationp. 271
Model Estimation and Testingp. 272
Dynamic Factor Modelp. 274
Multitrait Multimethod Model (MTMM)p. 277
Model Specification and Identificationp. 279
Model Estimation and Testingp. 280
Correlated Uniqueness Modelp. 281
Summaryp. 286
Suggested Readingsp. 290
Second-Order Factor Modelsp. 290
Dynamic Factor Modelsp. 290
Multitrait Multimethod Modelsp. 290
Correlated Uniqueness Modelp. 291
Referencesp. 291
Multiple Indicator-Multiple Indicator Cause, Mixture, and Multilevel Modelsp. 293
Multiple Indicator-Multiple Cause (MIMIC) Modelsp. 293
Model Specification and Identificationp. 294
Model Estimation and Model Testingp. 294
Model Modificationp. 297
Goodness-of-Fit Statisticsp. 297
Measurement Equationsp. 297
Structural Equationsp. 298
Mixture Modelsp. 298
Model Specification and Identificationp. 299
Model Estimation and Testingp. 301
Model Modificationp. 302
Robust Statisticp. 305
Multilevel Modelsp. 307
Constant Effectsp. 313
Time Effectsp. 313
Gender Effectsp. 315
Multilevel Model Interpretationp. 318
Intraclass Correlationp. 319
Deviance Statisticp. 320
Summaryp. 320
Suggested Readingsp. 324
Multiple Indicator-Multiple Cause Modelsp. 324
Mixture Modelsp. 325
Multilevel Modelsp. 325
Referencesp. 325
Interaction, Latent Growth, and Monte Carlo Methodsp. 327
Interaction Modelsp. 327
Categorical Variable Approachp. 328
Latent Variable Interaction Modelp. 331
Computing Latent Variable Scoresp. 331
Computing Latent Interaction Variablep. 333
Interaction Model Outputp. 335
Model Modificationp. 336
Structural Equations-No Latent Interaction Variablep. 336
Two-Stage Least Squares (TSLS) Approachp. 337
Latent Growth Curve Modelsp. 341
Latent Growth Curve Programp. 343
Model Modificationp. 344
Monte Carlo Methodsp. 345
PRELIS Simulation of Population Datap. 346
Population Data from Specified Covariance Matrixp. 352
SPSS Approachp. 352
SAS Approachp. 354
LISREL Approachp. 355
Covariance Matrix from Specified Modelp. 359
Summaryp. 365
Suggested Readingsp. 368
Interaction Modelsp. 368
Latent Growth-Curve Modelsp. 368
Monte Carlo Methodsp. 368
Referencesp. 369
Matrix Approach to Structural Equation Modelingp. 373
General Overview of Matrix Notationp. 373
Free, Fixed, and Constrained Parametersp. 379
LISREL Model Example in Matrix Notationp. 382
LISREL8 Matrix Program Output (Edited and Condensed)p. 385
Other Models in Matrix Notationp. 400
Path Modelp. 400
Multiple-Sample Modelp. 404
Structured Means Modelp. 405
Interaction Modelsp. 410
PRELIS Computer Outputp. 412
LISREL Interaction Computer Outputp. 416
Summaryp. 421
Referencesp. 423
Introduction to Matrix Operationsp. 425
Statistical Tablesp. 439
Answers to Selected Exercisesp. 449
Author Indexp. 489
Subject Indexp. 495
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