  \Multiple imputation (MI) is a popular and well-established method for
handling missing data in multivariate data sets, but its practicality for use
in massive and complex data sets has been questioned. One such data set is the
Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of
household income and wealth in the U.S. Missing data for this survey are
currently handled using traditional hot deck methods because of the simple
implementation; however, the univariate hot deck results in large random wealth
fluctuations. MI is effective but faced with operational challenges. We use a
sequential regression/ chained-equation approach, using the software IVEware,
to multiply impute cross-sectional wealth data in the 2013 PSID, and compare
analyses of the resulting imputed data with those from the current hot deck
approach. Practical difficulties, such as non-normally distributed variables,
skip patterns, categorical variables with many levels, and multicollinearity,
are described together with our approaches to overcoming them. We evaluate the
imputation quality and validity with internal diagnostics and external
benchmarking data. MI produces improvements over the existing hot deck approach
by helping preserve correlation structures, such as the associations between
PSID wealth components and the relationships between the household net worth
and socio-demographic factors, and facilitates completed data analyses with
general purposes. MI incorporates highly predictive covariates into imputation
models and increases efficiency. We recommend the practical implementation of
MI and expect greater gains when the fraction of missing information is large.
