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E-raamat: Structural Equation with lavaan [Wiley Online]

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  • Formaat: 304 pages
  • Ilmumisaeg: 05-Apr-2019
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119579031
  • ISBN-13: 9781119579038
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Structural Equation with lavaanSuurem pilt
  • Formaat: 304 pages
  • Ilmumisaeg: 05-Apr-2019
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119579031
  • ISBN-13: 9781119579038
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119579038

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).

Preface ix
Introduction xi
Chapter 1 Structural Equation Modeling
1(52)
1.1 Basic concepts
2(19)
1.1.1 Covariance and bivariate correlation
2(3)
1.1.2 Partial correlation
5(2)
1.1.3 Linear regression analysis
7(3)
1.1.4 Standard error of the estimate
10(1)
1.1.5 Factor analysis
11(7)
1.1.6 Data distribution normality
18(3)
1.2 Basic principles of SEM
21(15)
1.2.1 Estimation methods (estimators)
27(9)
1.3 Model evaluation of the solution of the estimated model
36(9)
1.3.1 Overall goodness-of-fit indices
36(7)
1.3.2 Local fit indices (parameter estimates)
43(1)
1.3.3 Modification indices
44(1)
1.4 Confirmatory approach in SEM
45(2)
1.5 Basic conventions of SEM
47(2)
1.6 Place and status of variables in a hypothetical model
49(1)
1.7 Conclusion
49(1)
1.8 Further reading
50(3)
Chapter 2 Structural Equation Modeling Software
53(16)
2.1 R environment
54(4)
2.1.1 Installing R software
55(1)
2.1.2 R console
55(3)
2.2 lavaan
58(2)
2.2.1 Installing the lavaan package
58(1)
2.2.2 Launching lavaan
58(2)
2.3 Preparing and importing a dataset
60(5)
2.3.1 Entry and import of raw data
60(3)
2.3.2 What to do in the absence of raw data?
63(2)
2.4 Major operators of lavaan syntax
65(1)
2.5 Main steps in using lavaan
66(2)
2.6 Lavaan fitting functions
68(1)
Chapter 3 Steps in Structural Equation Modeling
69(88)
3.1 The theoretical model and its conceptual specification
70(1)
3.2 Model parameters and model identification
71(2)
3.3 Models with observed variables (path models)
73(17)
3.3.1 Identification of a path model
74(2)
3.3.2 Model specification using lavaan (step 2)
76(2)
3.3.3 Direct and indirect effects
78(2)
3.3.4 The statistical significance of indirect effects
80(1)
3.3.5 Model estimation with lavaan (step 3)
81(1)
3.3.6 Model evaluation (step 4)
82(1)
3.3.7 Recursive and non-recursive models
83(2)
3.3.8 Illustration of a path analysis model
85(5)
3.4 Actor-partner interdependence model
90(5)
3.4.1 Specifying and estimating an APIM with lavaan
92(1)
3.4.2 Evaluation of the solution
93(1)
3.4.3 Evaluating the APIM re-specified with equality constraints
94(1)
3.5 Models with latent variables (measurement models and structural models)
95(53)
3.5.1 The measurement model or Confirmatory Factor Analysis
97(51)
3.6 Hybrid models
148(1)
3.7 Measure with a single-item indicator
149(2)
3.8 General structural model including single-item latent variables with a single indicator
151(1)
3.9 Conclusion
152(3)
3.10 Further reading
155(2)
Chapter 4 Advanced Topics: Principles and Applications
157(94)
4.1 Multigroup analysis
157(15)
4.1.1 The steps of MG-CFA
162(4)
4.1.2 Model solutions and model comparison tests
166(5)
4.1.3 Total invariance versus partial invariance
171(1)
4.1.4 Specification of a partial invariance in lavaan syntax
172(1)
4.2 Latent trait-state models
172(41)
4.2.1 The STARTS model
173(24)
4.2.2 The Trait-State-Occasion Model
197(14)
4.2.3 Concluding remarks
211(2)
4.3 Latent growth models
213(36)
4.3.1 General overview
213(10)
4.3.2 Illustration of an univariate linear growth model
223(5)
4.3.3 Illustration of an univariate non-linear (quadratic) latent growth model
228(4)
4.3.4 Conditional latent growth model
232(8)
4.3.5 Second-order latent growth model
240(9)
4.4 Further reading
249(2)
References 251(18)
Index 269
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.
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