Discovering Structural Equation Modeling Using Stata

Discovering Structural Equation Modeling Using Stata is devoted to Stata’s sem command and all it can do. You’ll learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. The book describes each model along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. Downloadable data sets enable you to run the programs and learn in a hands-on way. 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 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. Requiring minimal background in multiple regression, this practical reference is designed primarily for those new to structural equation modeling. Some experience with Stata would be helpful but is not essential. Readers already familiar with structural equation modeling will also find the book’s State code useful.

Table of Contents:
Introduction to Confirmatory Factor Analysis Introduction The "do not even think about it" approach The principal component factor analysis approach Alpha reliability for our nine-item scale Generating a factor score rather than a mean or summative score What can CFA add? Fitting a CFA model Interpreting and presenting CFA results Assessing goodness of fit A two-factor model Parceling Extensions and what is next Using Structural Equation Modeling for Path Models Introduction Path model terminology A substantive example of a path model Estimating a model with correlated residuals Auxiliary variables Testing equality of coefficients A cross-lagged panel design Moderation Nonrecursive models Structural Equation Modeling Introduction The classic example of a structural equation model Equality constraints Programming constraints Structural model with formative indicators Latent Growth Curves Discovering growth curves A simple growth curve model Identifying a growth curve model An example of a linear latent growth curve How can we add time-invariant covariates to our model? Explaining the random effects—time-varying covariates Constraining variances of error terms to be equal (optional) Group Comparisons Interaction as a traditional approach to multiple-group comparisons The range of applications of Stata’s multiple-group comparisons with sem A measurement model application Multiple-group path analysis Multiple-group comparisons of structural equation models Epilogue–What Now? Appendix A: The graphical user interface Appendix B: Entering data from summary statistics References Author Index Subject Index Exercises appear at the end of each chapter.

About the Author :
Alan Acock is a sociologist and a University Distinguished Professor in the School of Social and Behavioral Health Sciences at Oregon State University. He has published more than 130 articles in leading social and behavioral science journals. He is also the author of A Gentle Introduction to Stata.