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Handling Missing Data in Social Research
(Englisch)
Scott M. Lynch & J. Scott Brown & Sarah A. Mustillo (University of Notre Dame, Indiana, USA)

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Produktbeschreibung

Introduction. Simulation Design. Data-Based Solutions: Listwise Deletion. Data-Based Solutions: Mean/Mode Imputation. Data-Based Solutions: Regression-Based Imputation. Data-Based Solutions: Multiple Imputation. Method/Model-Based Solutions: EM Algorithms.



For social scientists, it is often confusing how to determine when missing data is a problem in analyses and how to handle it. This book presents a comprehensive overview of the available methods, focusing on which method should be used for specific problems. It features numerous real and simulated data examples to illustrate how the methods can be applied once the appropriate technique has been determined. It also includes exercises, computer-based problems, and suggestions for further reading. R code and the data sets are available on the book´s website.



Inhaltsverzeichnis



Introduction. Simulation Design. Data-Based Solutions: Listwise Deletion. Data-Based Solutions: Mean/Mode Imputation. Data-Based Solutions: Regression-Based Imputation. Data-Based Solutions: Multiple Imputation. Method/Model-Based Solutions: EM Algorithms.


Klappentext



For social scientists, it is often confusing how to determine when missing data is a problem in analyses and how to handle it. This book presents a comprehensive overview of the available methods, focusing on which method should be used for specific problems. It features numerous real and simulated data examples to illustrate how the methods can be applied once the appropriate technique has been determined. It also includes exercises, computer-based problems, and suggestions for further reading. R code and the data sets are available on the book¿s website.



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