Acknowledgments
1 Introduction to confirmatory factor analysis
1.1 Introduction
1.2 The "do not even think about it" approach
1.3 The principal component factor analysis approach
1.4 Alpha reliability for our nine-item scale
1.5 Generating a factor score rather than a mean or summative score
1.6 What can CFA add?
1.7 Fitting a CFA model
1.8 Interpreting and presenting CFA results
1.9 Assessing goodness of fit
1.9.1 Modification indices
1.9.2 Final model and estimating scale reliability
1.10 A two-factor model
1.10.1 Evaluating the depression dimension
1.10.2 Estimating a two-factor model
1.11 Parceling
1.12 Extensions and what is next
1.13 Exercises
1.A Using the SEM Builder to run a CFA
1.A.1 Drawing the model
1.A.2 Estimating the model
2 Using structural equation modeling for path models
2.1 Introduction
2.2 Path model terminology
2.2.1 Exogenous predictor, endogenous outcome, and endogenous mediator variables
2.2.2 A hypothetical path model
2.3 A substantive example of a path model
2.4 Estimating a model with correlated residuals
2.4.1 Estimating direct, indirect, and total effects
2.4.2 Strengthening our path model and adding covariates
2.5 Auxiliary variables
2.6 Testing equality of coefficients
2.7 A cross-lagged panel design
2.8 Moderation
2.9 Nonrecursive models
2.9.1 Worked example of a nonrecursive model
2.9.2 Stability of a nonrecursive model
2.9.3 Model constraints
2.9.4 Equality constraints
2.10 Exercises
2.B Using the SEM Builder to run path models
3 Structural equation modeling
3.1 Introduction
3.2 The classic example of a structural equation model
3.2.1 Identification of a full structural equation model
3.2.2 Fitting a full structural equation model
3.2.3 Modifying our model
3.2.4 Indirect effects
3.3 Equality constraints
3.4 Programming constraints
3.5 Structural model with formative indicators
3.5.1 Identification and estimation of a composite latent variable
3.5.2 Multiple indicators, multiple causes model
3.6 Exercises
4 Latent growth curves
4.1 Discovering growth curves
4.2 A simple growth curve model
4.3 Identifying a growth curve model
4.3.1 An intuitive idea of identification
4.3.2 Identifying a quadratic growth curve
4.4 An example of a linear latent growth curve
4.4.1 A latent growth curve model for BMI
4.4.2 Graphic representation of individual trajectories (optional)
4.4.3 Intraclass correlation (ICC) (optional)
4.4.4 Fitting a latent growth curve
4.4.5 Adding correlated adjacent error terms
4.4.6 Adding a quadratic latent slope growth factor
4.4.7 Adding a quadratic latent slope and correlating adjacent error terms
4.5 How can we add time-invariant covariates to our model?
4.5.1 Interpreting a model with time-invariant covariates
4.6 Explaining the random effects—time-varying covariates
4.6.1 Fitting a model with time-invariant and time-varying covariates
4.6.2 Interpreting a model with time-invariant and time-varying covariates
4.7 Constraining variances of error terms to be equal (optional)
4.8 Exercises
5 Group comparisons
5.1 Interaction as a traditional approach to multiple-group comparisons
5.2 The range of applications of Stata’s multiple-group comparisons with sem
5.2.1 A multiple indicators, multiple causes model
5.2.2 A measurement model
5.2.3 A full structural equation model
5.3 A measurement model application
5.3.1 Step 1: Testing for invariance comparing women and men
5.3.2 Step 2: Testing for invariant loadings
5.3.3 Step 3: Testing for an equal loadings and equal errorvariances model
5.3.4 Testing for equal intercepts
5.3.5 Comparison of models
5.3.6 Step 4: Comparison of means
5.3.7 Step 5: Comparison of variances and covariance of latent variables
5.4 Multiple-group path analysis
5.4.1 What parameters are different?
5.4.2 Fitting the model with the SEM Builder
5.4.3 A standardized solution
5.4.4 Constructing tables for publications
5.5 Multiple-group comparisons of structural equation models
5.6 Exercises
6 Epilogue—what now?
6.1 What is next?
A The graphical user interface
A.1 Introduction
A.2 Menus for Windows, Unix, and Mac
A.2.1 The menus, explained
A.2.2 The vertical drawing toolbar
A.3 Designing a structural equation model
A.4 Drawing an SEM model
A.5 Fitting a structural equation model
A.6 Postestimation commands
A.7 Clearing preferences and restoring the defaults
B Entering data from summary statistics
References
Author index
Subject index