Discovering Structural Equation Modeling Using Stata, Revised Edition

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Discovering Structural Equation Modeling Using Stata, Revised Edition, by Alan Acock, successfully introduces both the statistical principles involved in structural equation modeling (SEM) and the use of Stata to fit these models. The book uses an application-based approach to teaching SEM. Acock demonstrates how to fit a wide variety of models that fall within the SEM framework and provides datasets that enable the reader to follow along with each example. As each type of model is discussed, concepts such as identification, handling of missing data, model evaluation, and interpretation are covered in detail.
Author Alan C. Acock 978-1-59718-139-6 306 2013 Paperback

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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
1.7 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
4.5 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
5.3.3 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.2.2 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