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<b>Multiple Imputation and its Application</b><p><b>The most up-to-date edition of a bestselling guide to analyzing partially observed data</b><p>In this comprehensively revised Second Edition of<i>Multiple Imputation and its Application,</i> a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.<p>With an accessible and carefully structured presentation aimed at quantitative researchers,<i>Multiple Imputation and its Application</i> is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find:<ul><li>A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets</li><li>Careful discussion of key concepts</li><li>A range of examples illustrating the key ideas</li><li>Practical advice on using multiple imputation</li><li>Exercises and examples designed for use in the classroom and/or private study</li></ul><p>Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic,<i>Multiple Imputation and its Application</i> will also earn a place in the libraries of graduate students undertaking quantitative analyses.