Discovering Structural Equation Modeling Using Stata Revised Edition

Discovering Structural Equation Modeling Using Stata, Revised Edition 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 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, offering a hands-on approach to learning. A particularly exciting feature of Stata is the SEM Builder. This graphical 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.

Introduction to confirmatory factor analysis IntroductionThe "do not even think about it" approachThe principal component factor analysis approachAlpha reliability for our nine-item scaleGenerating a factor score rather than a mean or summative scoreWhat can CFA add?Fitting a CFA modelInterpreting and presenting CFA resultsAssessing goodness of fit A two-factor model ParcelingExtensions and what is nextExercisesUsing the SEM Builder to run a CFA Using structural equation modeling for path models IntroductionPath model terminology A substantive example of a path modelEstimating a model with correlated residuals Auxiliary variablesTesting equality of coefficientsA cross-lagged panel designModerationNonrecursive models ExercisesUsing the SEM Builder to run path models Structural equation modeling IntroductionThe classic example of a structural equation model Equality constraintsProgramming constraintsStructural model with formative indicators Exercises Latent growth curves Discovering growth curvesA simple growth curve modelIdentifying 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)Exercises Group comparisons Interaction as a traditional approach to multiple-group comparisonsThe 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 modelsExercises Epilogue—what now?What is next? The graphical user interface IntroductionMenus for Windows, Unix, and Mac Designing a structural equation modelDrawing an SEM modelFitting a structural equation modelPostestimation commandsClearing preferences and restoring the defaultsB Entering data from summary statistics

Alan C. Acock is a sociologist and a University Distinguished Professor in the School of Social and Behavioral Health Sciences at Oregon State University. He was also recognized as the Alumni Distinguished Professor based on his work with students. He has published more than 130 articles in leading journals across the social and behavioral sciences, including Structural Equation Modeling, Psychological Bulletin, Multivariate Behavioral Research, Journal of Gerontology, Journal of Adolescence, American Journal of Public Health, American Sociological Review, Journal of Marriage and Family, Social Forces, Educational and Psychological Measurement, Journal of Politics, Prevention Science, and American Journal of Preventive Medicine. He also authored the text A Gentle Introduction to Stata.