Structural Equation Modeling with lavaan

 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 3. Januar 2019
  • |
  • 304 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-57900-7 (ISBN)
 
This book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading).
1. Auflage
  • Englisch
  • Newark
  • |
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 12,52 MB
978-1-119-57900-7 (9781119579007)
weitere Ausgaben werden ermittelt
Kamel Gana is Professor of Health Psychology at the University of Bordeaux, France. He has been teaching introductory workshops on SEM in various universities for 15 years.

Guillaume Broc, a Doctor in Psychology, is currently a Postdoctoral Fellow at Claude Bernard University Lyon 1, France. He is a specialist in health psychology and is interested in quantitative and qualitative data analysis.
  • Cover
  • Half-Title Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Introduction
  • 1. Structural Equation Modeling
  • 1.1. Basic concepts
  • 1.1.1. Covariance and bivariate correlation
  • 1.1.2. Partial correlation
  • 1.1.3. Linear regression analysis
  • 1.1.4. Standard error of the estimate
  • 1.1.5. Factor analysis
  • 1.1.6. Data distribution normality
  • 1.2. Basic principles of SEM
  • 1.2.1. Estimation methods (estimators)
  • 1.3. Model evaluation of the solution of the estimated model
  • 1.3.1. Overall goodness-of-fit indices
  • 1.3.2. Local fit indices (parameter estimates)
  • 1.3.3. Modification indices
  • 1.4. Confirmatory approach in SEM
  • 1.5. Basic conventions of SEM
  • 1.6. Place and status of variables in a hypothetical model
  • 1.7. Conclusion
  • 1.8. Further reading
  • 2. Structural Equation Modeling Software
  • 2.1. R environment
  • 2.1.1. Installing R software
  • 2.1.2. R console
  • 2.2. lavaan
  • 2.2.1. Installing the lavaan package
  • 2.2.2. Launching lavaan
  • 2.3. Preparing and importing a dataset
  • 2.3.1. Entry and import of raw data
  • 2.3.2. What to do in the absence of raw data?
  • 2.4. Major operators of lavaan syntax
  • 2.5. Main steps in using lavaan
  • 2.6. lavaan fitting functions
  • 3. Steps in Structural Equation Modeling
  • 3.1. The theoretical model and its conceptual specification
  • 3.2. Model parameters and model identification
  • 3.3. Models with observed variables (path models)
  • 3.3.1. Identification of a path model
  • 3.3.2. Model specification using lavaan (step 2)
  • 3.3.3. Direct and indirect effects
  • 3.3.4. The statistical significance of indirect effects
  • 3.3.5. Model estimation with lavaan (step 3)
  • 3.3.6. Model evaluation (step 4)
  • 3.3.7. Recursive and non-recursive models
  • 3.3.8. Illustration of a path analysis model
  • 3.4. Actor-partner interdependence model
  • 3.4.1. Specifying and estimating an APIM with lavaan
  • 3.4.2. Evaluation of the solution
  • 3.4.3. Evaluating the APIM re-specified with equality constraints
  • 3.5. Models with latent variables (measurement models and structural models)
  • 3.5.1. The measurement model or Confirmatory Factor Analysis
  • 3.6. Hybrid models
  • 3.7. Measure with a single-item indicator
  • 3.8. General structural model including single-item latent variables with a single indicator
  • 3.9. Conclusion
  • 3.10. Further reading
  • 4. Advanced Topics: Principles and Applications
  • 4.1. Multigroup analysis
  • 4.1.1. The steps of MG-CFA
  • 4.1.2. Model solutions and model comparison tests
  • 4.1.3. Total invariance versus partial invariance
  • 4.1.4. Specification of a partial invariance in lavaan syntax
  • 4.2. Latent trait-state models
  • 4.2.1. The STARTS model
  • 4.2.2. The Trait-State-Occasion Model
  • 4.2.3. Concluding remarks
  • 4.3. Latent growth models
  • 4.3.1. General overview
  • 4.3.2. Illustration of an univariate linear growth model
  • 4.3.3. Illustration of an univariate non-linear (quadratic) latent growth model
  • 4.3.4. Conditional latent growth model
  • 4.3.5. Second-order latent growth model
  • 4.4. Further reading
  • References
  • Index
  • Other titles from iSTE in Mathematics and Statistics
  • EULA

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