Questionnaire Dwelling Unit-Level and Person Pair-Level Sampling ...

Appendix N: Univariate and Multivariate Predictive Mean
Neighborhood Imputation Methods
N.1 Introduction
Since the introduction of the computer-assisted interviewing (CAI) method in 1999 for
the National Survey on Drug Use and Health (NSDUH), 29 one imputation method has been used
for most variables requiring imputation: predictive mean neighborhood (PMN). It was developed
to cater to the specific needs of NSDUH. This approach has been used since the 1999 survey 30
and can be applied to one variable at a time or to several variables simultaneously. As described
in this appendix, PMN incorporates predicted means from models and the assignment of imputed
values using neighborhoods determined by those predicted means.
N.2 Overview
N.2.1 Predictive Mean Neighborhood Method: Derived from Combining Nearest
Neighbor Hot Deck and Predictive Mean Matching
The PMN method is a combination of two commonly used imputation methods: a
nonmodel-based hot deck (nearest neighbor) and a modification of the model-assisted predictive
mean matching (PMM) method of Rubin (1986). The PMN method enhances the PMM method.
Specifically, the PMN method can be applied to both discrete and continuous variables, either
individually or jointly. The PMN method also enhances the nearest neighbor hot-deck (NNHD)
method so that the distance function used to find neighbors is no longer ad hoc.
A commonly used imputation method is a random NNHD (Little & Rubin, 1987, p. 65).
With this method, donors and recipients are distinguished by the completeness of their records
with regard to the variable(s) of interest. (The donor has complete data, but the recipient does
not.) A donor set deemed close to the recipient with respect to a number of covariates is used to
select a donor at random. For NSDUH, the set of covariates typically included demographic
variables, as well as some other nonmissing pair-level variables. In the case of NSDUH, to
further ensure that a donor matched the recipient as closely as possible, discrete variables (or
discrete categories of continuous variables) strongly correlated with the response variables of
interest were often used to restrict the set of donors. Furthermore, other restrictions involving
outcome variables were imposed on the neighborhood.
Note that in NNHD, unlike sequential hot deck, a distance function is used to define
closeness between the recipient and a donor. So, there is less of a problem of sparseness of the
donor class, but the distance function involving categorical or nominal variables is typically ad
hoc and often hard to justify.
29 This report presents information from the 2006 National Survey on Drug Use and Health (NSDUH), an
annual survey of the civilian, noninstitutionalized population of the United States aged 12 years or older. Prior to
2002, the survey was called the National Household Survey on Drug Abuse (NHSDA).
30 After the 1999 survey, only a CAI sample was selected.
N-3