Dedication

List of tables

List of figures

List of boxes

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