Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.
The field of public opinion is one of the most diverse in political science. Over the last 60 years, scholars have drawn upon the disciplines of psychology, economics, sociology, and even biology to learn how ordinary people come to understand the complicated business of politics. But much of the path-breaking research in the field of public…
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