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Applied Missing Data Analysis (Methodology in the
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Description
The most user-friendly and authoritative resource
on missing data has been completely revised to
make room for the latest developments that make
handling missing data more effective. The second
edition includes new methods based on factored
regressions, newer model-based imputation
strategies, and innovations in Bayesian analysis.
State-of-the-art technical literature on missing
data is translated into accessible guidelines for
applied researchers and graduate students. The
second edition takes an even, three-pronged approa
ch to maximum likelihood estimation (MLE),
Bayesian estimation as an alternative to MLE, and
multiple imputation. Consistently organized
chapters explain the rationale and procedural
details for each technique and illustrate the
analyses with engaging worked- through examples
on such topics as young adult smoking, employee
turnover, and chronic pain. The companion website
(www.appliedmissingdata.com) includes data sets
and analysis examples from the book, up-to-date
software information, and other
resources. nbspNew to This Edition Expanded
coverage of Bayesian estimation, including a
new chapter on incomplete categorical variables.
New chapters on factored regressions,
model- based imputation strategies, multilevel
missing data-handling methods, missing not
at random analyses, and other timely topics.
Presents cutting-edge methods developed
since the 2010 first edition includes dozens of
new data analysis examples. Most of the book
is entirely new.