While most books on missing data focus on applying sophisticated
statistical techniques to deal with the problem after it has
occurred, this volume provides a methodology for the control and
prevention of missing data. In clear, nontechnical language, the
authors help the reader understand the different types of missing
data and their implications for the reliability, validity, and
generalizability of a study's conclusions. They provide practical
recommendations for designing studies that decrease the likelihood
of missing data, and for addressing this important issue when
reporting study results. When statistical remedies are needed--such
as deletion procedures, augmentation methods, and single imputation
and multiple imputation procedures--the book also explains how to
make sound decisions about their use. Patrick E. McKnight's website
offers a periodically updated annotated bibliography on missing
data and links to other Web resources that address missing data.
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