Verfügbarkeit: Principles and practice of structural equation modeling

Principles and practice of structural equation modeling:

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Bibliographische Detailangaben
1. Verfasser:	Kline, Rex B. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	New York ; London The Guilford Press [2016]
Ausgabe:	Fourth edition
Schriftenreihe:	Methodology in the social sciences
Schlagworte:	Nauki społeczne / metody statystyczne Modele równań strukturalnych Strukturgleichungsmodell
Online-Zugang:	Inhaltsverzeichnis Inhaltsverzeichnis Klappentext
Beschreibung:	XVII, 534 Seiten Diagramme 26 cm
ISBN:	9781462523344 9781462523351

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Datensatz im Suchindex

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adam_text	Contents Introduction Book Website 2 Pedagogical Approach 2 Principles over Computer Tools 3 Symbols and Notation 3 Life’s a Journey, Not a Destination 3 Plan of the Book 4 PART I. CONCEPTS AND TOOLS 1 • Coming of Age Preparing to Learn SEM 7 Definition of SEM 9 Importance of Theory 10 A Priori, but Not Exclusively Confirmatory 11 Probabilistic Causation 11 Observed Variables and Latent Variables 12 Data Analyzed in SEM 13 SEM Requires Large Samples 14 Less Emphasis on Significance Testing 17 SEM and Other Statistical Techniques 17 SEM and Other Causal Inference Frameworks 18 Myths about SEM 20 Widespread Enthusiasm, but with a Cautionary Tale Family History 23 Summary 24 Learn More 24 XII Contents 2 • Regression Fundamentals Bivariate Regression 25 Multiple Regression 30 Left-Out Variables Error 35 Suppression 36 Predictor Selection and Entry 37 Partial and Part Correlation 39 Observed versus Estimated Correlations 41 Logistic Regression and Probit Regression 44 Summary 47 Learn More 47 Exercises 48 3 • Significance Testing and Bootstrapping Standard Errors 49 Critical Ratios 51 Power and Types of Null Hypotheses 52 Significance Testing Controversy 54 Confidence Intervals and Noncentral Test Distributions 57 Bootstrapping 60 Summary 62 Learn More 62 Exercises 63 4 • Data Preparation and Psychometrics Review Forms of Input Data 64 Positive Definiteness 67 Extreme Collinearity 71 Outliers 72 Normality 74 Transformations 77 Relative Variances 81 Missing Data 82 Selecting Good Measures and Reporting about Them 88 Score Reliability 90 Score Validity 93 Item Response Theory and Item Characteristic Curves 94 Summary 95 Learn More 96 Exercises 96 5 • Computer Tools 25 49 64 97 Ease of Use, Not Suspension of Judgment 97 Human-Computer Interaction 98 Contents xiii Tips for SEM Programming 100 SEM Computer Tools 101 Other Computer Resources for SEM 111 Computer Tools for the SCM 112 Summary 113 Learn More 113 PART II. SPECIFICATION AND IDENTIFICATION 6 • Specification of Observed Variable (Path) Models 117 Steps of SEM 117 Model Diagram Symbols 121 Causal Inference 122 Specification Concepts 126 Path Analysis Models 129 Recursive and Nonrecursive Models 135 Path Models for Longitudinal Data 138 Summary 141 Learn More 142 Exercises 142 APPENDIX 6.A. LISREL Notation for Path Models 1 43 7 • Identification of Observed-Variable (Path) Models 145 General Requirements 145 Unique Estimates 148 Rule for Recursive Models 149 Identification of Nonrecursive Models 150 Models with Feedback Loops and All Possible Disturbance Correlations 150 Graphical Rules for Other Types of Nonrecursive Models 153 Respecification of Nonrecursive Models That Are Not Identified 155 A Healthy Perspective on Identification 157 Empirical Underidentification 157 Managing Identification Problems 158 Path Analysis Research Example 159 Summary 159 Learn More 160 Exercises 160 APPENDIX 7.A. Evaluation of the Rank Condition 8 • Graph Theory and the Structural Causal Model Introduction to Graph Theory 164 Elementary Directed Graphs and Conditional Independences 166 161 164 xiv Contents Implications for Regression Analysis 170 d-Separation 170 Basis Set 173 Causal Directed Graphs 174 Testable Implications 176 Graphical Identification Criteria 177 Instrumental Variables 180 Causal Mediation 181 Summary 184 Learn More 185 Exercises 185 APPENDIX 8.A. Locating Conditional Independences in Directed Cyclic Graphs APPENDIX 8.B. Counterfactual Definitions of Direct and Indirect Effects 9 • Specification and Identification of Confirmatory Factor Analysis Models Latent Variables in CFA 188 Factor Analysis 189 Characteristics of EFA Models 191 Characteristics of CFA Models 193 Other CFA Specification Issues 195 Identification of CFA Models 198 Rules for Standard CFA Models 201 Rules for Nonstandard CFA Models 202 Empirical Underidentification in CFA 206 CFA Research Example 206 Summary 207 Learn More 207 Exercises 209 APPENDIX 9.A. LISREL Notation for CFA Models 10 • Specification and Identification of Structural Regression Models Causal Inference with Latent Variables 212 Types of SR Models 213 Single Indicators 214 Identification of SR Models 217 Exploratory SEM 219 SR Model Research Examples 220 Summary 223 Learn More 225 Exercises 225 186 187 188 210 212 APPENDIX 10.A. LISREL Notation for SR Models 226 Contents PART III. ANALYSIS 11« Estimation and Local Fit Testing Types of Estimators 231 Causal Effects in Path Analysis 232 Single-Equation Methods 233 Simultaneous Methods 235 Maximum Likelihood Estimation 235 Detailed Example 239 Fitting Models to Correlation Matrices 253 Alternative Estimators 255 A Healthy Perspective on Estimation 258 Summary 259 Learn More 259 Exercises 260 APPENDIX 1 l.A. Start Value Suggestions for Structural Models 12« Global Fit Testing State of Practice, State of Mind 262 A Healthy Perspective on Global Fit Statistics 263 Model Test Statistics 265 Approximate Fit Indexes 266 Recommended Approach to Fit Evaluation 268 Model Chi-Square 270 RMSEA 273 SRMR 277 Tips for Inspecting Residuals 278 Global Fit Statistics for the Detailed Example 278 Testing Hierarchical Models 280 Comparing Nonhierarchical Models 286 Power Analysis 290 Equivalent and Near-Equivalent Models 292 Summary 297 Learn More 298 Exercises 298 APPENDIX 12.A. Model Chi-Squares Printed by LISREL 13 • Analysis of Confirmatory Factor Analysis Models Fallacies about Factor or Indicator Labels 300 Estimation of CFA Models 301 Detailed Example 304 Respecification of CFA Models 309 Special Topics and Tests 312 Equivalent CFA Models 315 Special CFA Models 319 xv 231 261 262 299 300 xvi Contents Analyzing Likert-Scale Items as Indicators 323 Item Response Theory as an Alternative to CFA 332 Summary 333 Learn More 333 Exercises 334 APPENDIX 13.A. Start Value Suggestions for Measurement Models APPENDIX 13.B. Constraint Interaction in CFA Models 335 336 14» Analysis of Structural Regression Models 338 Two-Step Modeling 338 Four-Step Modeling 339 Interpretation of Parameter Estimates and Problems 340 Detailed Example 341 Equivalent SR Models 348 Single Indicators in a Nonrecursive Model 349 Analyzing Formative Measurement Models in SEM 352 Summary 361 Learn More 362 Exercises 362 APPENDIX 14.A. Constraint Interaction in SR Models 363 APPENDIX 14.B. Effect Decomposition in Nonrecursive Models 364 and the Equilibrium Assumption APPENDIX 14.C. Corrected Proportions of Explained Variance 365 for Nonrecursive Models PART IV. ADVANCED TECHNIQUES AND BEST PRACTICES 15 • Mean Structures and Latent Growth Models 369 Logic of Mean Structures 369 Identification of Mean Structures 373 Estimation of Mean Structures 374 Latent Growth Models 374 Detailed Example 375 Comparison with a Polynomial Growth Model 387 Extensions of Latent Growth Models 390 Summary 392 Learn More 392 Exercises 393 # Multiple-Samples Analysis and Measurement Invariance 394 Rationale of Multiple-Samples SEM 394 Measurement Invariance 396 Testing Strategy and Related Issues 399 Example with Continuous Indicators 403 Contents xvii Example with Ordinal Indicators 411 Structural Invariance 420 Alternative Statistical Techniques 420 Summary 421 Learn More 421 Exercises 422 APPENDIX 16.A. Welch-James Test 423 17 • Interaction Effects and Multilevel 424 Structural Equation Modeling Interactive Effects of Observed Variables 424 Interactive Effects in Path Analysis 431 Conditional Process Modeling 432 Causal Mediation Analysis 435 Interactive Effects of Latent Variables 437 Multilevel Modeling and SEM 444 Summary 450 Learn More 450 Exercises 451 18* Best Practices in Structural Equation Modeling 452 Resources 452 Specification 454 Identification 457 Measures 458 Sample and Data 458 Estimation 461 Respecification 463 Tabulation 464 Interpretation 465 Avoid Confirmation Bias 466 Bottom Lines and Statistical Beauty 466 Summary 467 Learn More 467 Suggested Answers to Exercises 469 References 489 Author Index Subject Index About the Author 534 516 510 The companion website www.guilford.com/kline-materials provides downloadable data, syntax, and output for all the book’s examples in six widely used SEM computer tools and links to related web pages. Contents Introduction Book Website 2 Pedagogical Approach 2 Principles over Computer Tools 3 Symbols and Notation 3 Life’s a Journey, Not a Destination 3 Plan of the Book 4 PART I. CONCEPTS AND TOOLS 1 • Coming of Age Preparing to Learn SEM 7 Definition of SEM 9 Importance of Theory 10 A Priori, but Not Exclusively Confirmatory 11 Probabilistic Causation 11 Observed Variables and Latent Variables 12 Data Analyzed in SEM 13 SEM Requires Large Samples 14 Less Emphasis on Significance Testing 17 SEM and Other Statistical Techniques 17 SEM and Other Causal Inference Frameworks 18 Myths about SEM 20 Widespread Enthusiasm, but with a Cautionary Tale Family History 23 Summary 24 Learn More 24 XII Contents 2 • Regression Fundamentals 25 Bivariate Regression 25 Multiple Regression 30 Left-Out Variables Error 35 Suppression 36 Predictor Selection and Entry 37 Partial and Part Correlation 39 Observed versus Estimated Correlations 41 Logistic Regression and Probit Regression 44 Summary 47 Learn More 47 Exercises 48 3 • Significance Testing and Bootstrapping 49 Standard Errors 49 Critical Ratios 51 Power and Types of Null Hypotheses 52 Significance Testing Controversy 54 Confidence Intervals and Noncentral Test Distributions 57 Bootstrapping 60 Summary 62 Learn More 62 Exercises 63 4 • Data Preparation and Psychometrics Review 64 Forms of Input Data 64 Positive Definiteness 67 Extreme Collinearity 71 Outliers 72 Normality 74 Transformations 77 Relative Variances 81 Missing Data 82 Selecting Good Measures and Reporting about Them 88 Score Reliability 90 Score Validity 93 Item Response Theory and Item Characteristic Curves 94 Summary 95 Learn More 96 Exercises 96 5 • Computer Tools Ease of Use, Not Suspension of Judgment 97 Human-Computer Interaction 98 97 Contents x¡¡¡ Tips for SEM Programming 100 SEM Computer Tools 101 Other Computer Resources for SEM 111 Computer Tools for the SCM 112 Summary 113 Learn More 113 PART II. SPECIFICATION AND IDENTIFICATION 6 • Specification of Observed Variable (Path) Models 117 Steps of SEM 117 Model Diagram Symbols 121 Causal Inference 122 Specification Concepts 126 Path Analysis Models 129 Recursive and Nonrecursive Models 135 Path Models for Longitudinal Data 138 Summary 141 Learn More 142 Exercises 142 APPENDIX 6.A. LISREL Notation for Path Models 143 7 • Identification of Observed-Variable (Path) Models 145 General Requirements 145 Unique Estimates 148 Rule for Recursive Models 149 Identification of Nonrecursive Models 150 Models with Feedback Loops and All Possible Disturbance Correlations 150 Graphical Rules for Other Types of Nonrecursive Models 153 Respecification of Nonrecursive Models That Are Not Identified 155 A Healthy Perspective on Identification 157 Empirical Underidentification 157 Managing Identification Problems 158 Path Analysis Research Example 159 Summary 159 Learn More 160 Exercises 160 APPENDIX 7.A. Evaluation of the Rank Condition 1 61 8 • Graph Theory and the Structural Causal Model 164 Introduction to Graph Theory 164 Elementary Directed Graphs and Conditional Independences 166 XIV Contents Implications for Regression Analysis 170 d-Separation 170 Basis Set 173 Causal Directed Graphs 174 Testable Implications 176 Graphical identification Criteria 177 Instrumental Variables 180 Causal Mediation 181 Summary 184 Learn More 183 Exercises 185 APPENDIX 8.A. Locating Conditional Independences in Directed Cyclic Graphs APPENDIX 8.B. Counterfactual Definitions of Direct and Indirect Effects 9 • Specification and Identification of Confirmatory Factor Analysis Models Latent Variables in CFA 188 Factor Analysis 189 Characteristics of EFA Models 191 Characteristics of CFA Models 193 Other CFA Specification Issues 195 Identification of CFA Models 198 Rules for Standard CFA Models 201 Rules for Nonstandard CFA Models 202 Empirical Underidentification in CFA 206 CFA Research Example 206 Summary 207 Learn More 207 Exercises 209 APPENDIX 9.A. LISREL Notation for CFA Models 10 • Specification and Identification of Structural Regression Models Causal Inference with Latent Variables 212 Types of SR Models 213 Single Indicators 214 Identification of SR Models 217 Exploratory SEM 219 SR Model Research Examples 220 Summary 223 Learn More 225 Exercises 225 APPENDIX 10.A. LISREL Notation for SR Models 186 187 188 210 212 226 Contents xv PART III. ANALYSIS 11* Estimation and Local Fit Testing 231 Types of Estimators 231 Causal Effects in Path Analysis 232 Single-Equation Methods 233 Simultaneous Methods 235 Maximum Likelihood Estimation 235 Detailed Example 239 Fitting Models to Correlation Matrices 253 Alternative Estimators 255 A Healthy Perspective on Estimation 258 Summary 259 Learn More 259 Exercises 260 APPENDIX 11.A. Start Value Suggestions for Structural Models 261 12 • Global Fit Testing 262 State of Practice, State of Mind 262 A Healthy Perspective on Global Fit Statistics 263 Model Test Statistics 265 Approximate Fit Indexes 266 Recommended Approach to Fit Evaluation 268 Model Chi-Square 270 RMSEA 273 SRMR 277 Tips for Inspecting Residuals 278 Global Fit Statistics for the Detailed Example 278 Testing Hierarchical Models 280 Comparing Nonhierarchical Models 286 Power Analysis 290 Equivalent and Near-Equivalent Models 292 Summary 297 Learn More 298 Exercises 298 APPENDIX 12.A. Model Chi-Squares Printed by L1SREL 299 13 • Analysis of Confirmatory Factor Analysis Models 300 Fallacies about Factor or Indicator Labels 300 Estimation of CFA Models 301 Detailed Example 304 Respecification of CFA Models 309 Special Topics and Tests 312 Equivalent CFA Models 315 Special CFA Models 319 xvi Contents Analyzing Likert-Scale Items as Indicators 323 Item Response Theory as an Alternative to CFA 332 Summary 333 Learn More 333 Exercises 334 APPENDIX 13.A. Start Value Suggestions for Measurement Models APPENDIX 13.B. Constraint Interaction in CFA Models 335 336 14 • Analysis of Structural Regression Models 338 Two-Step Modeling 338 Four-Step Modeling 339 Interpretation of Parameter Estimates and Problems 340 Detailed Example 341 Equivalent SR Models 348 Single Indicators in a Nonrecursive Model 349 Analyzing Formative Measurement Models in SEM 352 Summary 361 Learn More 362 Exercises 362 APPENDIX 14.A. Constraint Interaction in SR Models 363 APPENDIX 14.B. Effect Decomposition in Nonrecursive Models 364 and the Equilibrium Assumption APPENDIX 14.C. Corrected Proportions of Explained Variance 365 for Nonrecursive Models PART IV. ADVANCED TECHNIQUES AND BEST PRACTICES 15« Mean Structures and Latent Growth Models 369 Logic of Mean Structures 369 Identification of Mean Structures 373 Estimation of Mean Structures 374 Latent Growth Models 374 Detailed Example 375 Comparison with a Polynomial Growth Model 387 Extensions of Latent Growth Models 390 Summary 392 Learn More 392 Exercises 393 • Multiple-Samples Analysis and Measurement Invariance 394 Rationale of Multiple-Samples SEM 394 Measurement Invariance 396 Testing Strategy and Related Issues 399 Example with Continuous Indicators 403 Contents xvii Example with Ordinal Indicators 411 Structural Invariance 420 Alternative Statistical Techniques 420 Summary 421 Learn More 421 Exercises 422 APPENDIX 16.A. Welch-James Test 423 17* Interaction Effects and Multilevel 424 Structural Equation Modeling Interactive Effects of Observed Variables 424 Interactive Effects in Path Analysis 431 Conditional Process Modeling 432 Causal Mediation Analysis 435 Interactive Effects of Latent Variables 437 Multilevel Modeling and SEM 444 Summary 450 Learn More 450 Exercises 451 18* Best Practices in Structural Equation Modeling 452 Resources 452 Specification 454 Identification 457 Measures 458 Sample and Data 458 Estimation 461 Respecification 463 Tabulation 464 Interpretation 465 Avoid Confirmation Bias 466 Bottom Lines and Statistical Beauty 466 Summary 467 Learn More 467 Suggested Answers to Exercises 469 References Author Index Subject Index About the Author 489 510 516 534 The companion website www.guilford.com/kline-materials provides downloadable data, syntax, and output for all the book’s examples in six widely used SEM computer tools and links to related web pages. Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearls graph- ing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehen- sive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website {www.j uilford com/kline-materials) supplies data, syntax, and output for the books examples—now includ- ing files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan). New to This Edition • • Extensively revised to cover important new topics: Pearl’s graphing theory and the SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more. • Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping. • Expanded coverage of psychometrics. • Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan). • Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models.
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series2	Methodology in the social sciences
spelling	Kline, Rex B. Verfasser (DE-588)1026732069 aut Principles and practice of structural equation modeling Rex B. Kline Fourth edition New York ; London The Guilford Press [2016] XVII, 534 Seiten Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier Methodology in the social sciences Bibliogr. S. 489-509 Nauki społeczne / metody statystyczne jhpk Modele równań strukturalnych jhpk Strukturgleichungsmodell (DE-588)4252999-2 gnd rswk-swf Strukturgleichungsmodell (DE-588)4252999-2 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4625-2336-8 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000006&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA Klappentext
spellingShingle	Kline, Rex B. Principles and practice of structural equation modeling Bibliogr. S. 489-509 Nauki społeczne / metody statystyczne jhpk Modele równań strukturalnych jhpk Strukturgleichungsmodell (DE-588)4252999-2 gnd
subject_GND	(DE-588)4252999-2
title	Principles and practice of structural equation modeling
title_auth	Principles and practice of structural equation modeling
title_exact_search	Principles and practice of structural equation modeling
title_full	Principles and practice of structural equation modeling Rex B. Kline
title_fullStr	Principles and practice of structural equation modeling Rex B. Kline
title_full_unstemmed	Principles and practice of structural equation modeling Rex B. Kline
title_short	Principles and practice of structural equation modeling
title_sort	principles and practice of structural equation modeling
topic	Nauki społeczne / metody statystyczne jhpk Modele równań strukturalnych jhpk Strukturgleichungsmodell (DE-588)4252999-2 gnd
topic_facet	Nauki społeczne / metody statystyczne Modele równań strukturalnych Strukturgleichungsmodell
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028854533&sequence=000006&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA
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