Discovering structural equation modeling using Stata / Alan C. Acock, Oregon State University
By: Acock, Alan C [author].
Publisher: College Station, Texas : Stata Press, ©2013Copyright date: ©2013Edition: Revised Edition.Description: 306 p: illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781597181334 (pbk.); 1597181331 (pbk.); 9781597181396; 1597181390.Subject(s): Stata | Structural equation modeling | Statistics -- Computer programsGenre/Form: Print books.Current location | Call number | Status | Date due | Barcode | Item holds |
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On Shelf | QA278.3 .A26 2013 (Browse shelf) | Available | AU00000000017362 |
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QA278 .M3735 2017 Exploratory data analysis with MATLAB / | QA278.2 .B46 2023 Spatial statistics illustrated / | QA278.2 .M57 2021 Interpreting and visualizing regression models using Stata / | QA278.3 .A26 2013 Discovering structural equation modeling using Stata / | QA279 .D45775 2019 SPSS data analysis for univariate, bivariate, and multivariate statistics / | QA279.5 .K67 2011 Bayesian artificial intelligence / | QA279.5 .M597 2019 Bayesian cost-effectiveness analysis of medical treatments / |
Includes bibliographical references (pages 297-298) and indexes
Introduction to confirmatory factor analysis -- Using structural equation modeling for path models -- Structural equation modeling -- Latent growth curves -- Group comparisons -- Epilogue : what now? -- Graphical user interface -- Entering data from summary statistics
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 SIM 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 miminal 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