Discovering structural equation modeling using Stata /

Discovering structural equation modeling using Stata /

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Bibliographic Details
Author / Creator:Acock, Alan C., 1944- author.
Edition:Revised edition.
Imprint:College Station, Texas : Stata Press, [2013]
©2013
Description:xxv, 306 pages : illustrations ; 24 cm
Language:English
Subject:
Stata.
Stata.
Structural equation modeling.
Statistics -- Computer programs.
Statistics -- Computer programs.
Structural equation modeling.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12705821
Hidden Bibliographic Details
ISBN:9781597181334
1597181331
9781597181396
1597181390
Notes:"The Revised Edition includes output, syntax, and instructions for fitting models with the SEM Builder that have been updated for Stata 13."Page 4 of cover.
Includes bibliographical references (pages 297-298) and indexes.
Summary:"Discovering Structural Equation Modeling Using Stata is devoted to Stata's sem command and all it can do. Learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. Each model covered is presented along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. The datasets used are downloadable, and you are encouraged to run the programs in a hands-on approach to learning. A particularly exciting feature of Stata is the SEM Builder. This graphic interface for structural equation modeling allows you to draw publication-quality path diagrams and to fit the models without writing any programming code. When you fit a model with the SEM builder, Stata automatically generates the complete code that you can save for future use. Use of this unique tool is extensively covered in an appendix, and brief examples appear throughout the text. A minimal background in multiple regression is sufficient to benefit from this text. While it would be helpful to have some experience with Stata, it is not essential. Though the primary audience is those who are new to structural equation modeling, those who are already familiar with it will find this text useful for the Stata code it covers. Overall, the text is intended to be practical and will serve as a useful reference."--Page 4 of cover.
Table of Contents:
  • Dedication
  • List of tables
  • List of figures
  • Preface
  • 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 error-variances 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?
  • 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
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