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

(i.e., total of averages over pairs (p) associated with the individual i, over all i in domain d and in
the household h) (Chromy & Singh, 2001). Here M hi (d) denotes the multiplicity (i.e., the number
of pairs associated with the person i in domain d), and N h (d) can be thought of as the multiplicity
count for the household h (i.e., the number of persons in the household that are in domain d).
This latter multiplicity count is equivalent to the household-level person count described in the
next paragraph. For the sake of simplicity, the weights are not shown in the above estimator.
The predictor variables used for all previous generalized exponential model (GEM) 9
adjustment steps also were used in the respondent pair poststratification (ps) step. In addition to
these variables, 10 covariates derived from 10 pair domains were included in the weight
adjustment process. This was done to obtain more stable pair-level analysis weights. (A total of
14 pair domains were identified for the purposes of creating multiplicities, as discussed in
Section 6.3. Ten of those were used to poststratify the pair-level analysis weights, as discussed in
Section 6.4.) Each covariate was defined by the appropriate pair relationship divided by its
associated multiplicity. In this ps step, for these 10 pair domains, the nonresponse (nr)-adjusted
weights were poststratified to the final questionnaire dwelling unit (QDU) weights. The
household-level person counts, which are counts of the number of pairs in the household
belonging to a given pair domain, were used to form the control totals in the ps step for these
domains. For other domains, the control totals were formed by the screener dwelling unit (SDU)
weights from all the possible screener pairs associated with the number of possible pairs in the
dwelling unit.
In the process of setting up variables for analyses at the pair level, three types of
variables, which are not weights, required editing and imputation. The procedures associated
with these three types of variables are referred to as stages. Stage one refers to the creation and
imputation of the variables that identify the pair relationships. The multiplicity and householdlevel
person counts that are described in the preceding paragraph were created and imputed in
stages two and three, respectively. Missing values in all three stages were imputed using the
semiparametric predictive mean neighborhood (PMN) imputation procedure, which uses
predicted means from models to find donors in a nearest neighbor hot deck. The hot deck is
described in Appendix M, and the PMN procedure is described in detail in Appendix N.
6.2 Stage One: Creation and Imputation of Pair Relationships
6.2.1 Editing the Household Roster of Each Pair Member
Prior to the identification of the relationships between selected pair members, a key step
was to edit the questionnaire household rosters for each pair member. This involved identifying
situations where the relationship listed in the roster for a particular roster member was not
possible given the roster member's age and relationship to the respondent. In many cases, this
resulted in setting the relationship code to bad data, and sometimes the roster member's age also
was set to bad data. In general, no effort was made to try to match values of roster-derived
household composition variables between pair members, since interviews of the different
members of the same household could have taken place at different times. However, information
9 The GEM macro, which was written in SAS/IML ® software, was developed at RTI for weighting
procedures.
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