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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.
The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
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