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

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pairs, and persons for each of the 5 years (2002–2006). The distribution of pair data for different pairs of age groups may vary considerably (see Chapter 2 for details). It is seen that for certain age group domains, the realized sample size may not be sufficient to yield reliable estimates. Also, there may be problems of extreme weights due to small pair selection probabilities under Brewer's method that may cause instability of estimates. These and some other estimation issues related to pair data are discussed below, along with some solutions that were adopted. Table 1.1 2002–2006 NSDUH Sample Sizes Sample Unit 2002 2003 2004 2005 2006 SDU Selected 150,162 143,485 142,612 146,912 151,288 Completed 136,349 130,602 130,130 134,055 137,057 QDU Selected 55,686 56,184 56,251 57,243 58,257 Completed 48,088 47,753 47,651 47,893 47,909 Pair Selected 24,895 25,447 25,722 26,562 26,777 Completed 20,038 20,031 20,109 20,415 19,893 Person Selected 80,581 81,631 81,973 83,805 85,034 Completed 68,126 67,784 67,760 68,308 67,802 First, we note that for studying drug-related behavioral relationships among members of the same household, pair data is required because the outcome variable generally is defined with respect to the specific other member selected from the household. However, the parameter of interest is generally at the person level and is not at the pair level. For example, in the parentchild pairs, one may be interested in the proportion of children that have used drugs in the past year who have parents that report talking to their child about drugs. Here the target population consists only of children, and not all possible pairs. Note that the pair-level (two persons per QDU) sample forms a subsample of the larger person-level (one or two persons per QDU) sample, with the QDUs themselves selected from the larger sample of SDUs. NSDUH has features of a two-phase design, which turns out to be useful for estimating calibration controls for poststratification of household-level weights and person pair-level weights. No other outside source is available for obtaining these controls. For this purpose, the screener-level household weights are poststratified to person-level census counts to obtain more efficient estimated controls for pair and household data. In estimation for pair domains, two major problems arise: one is that of multiplicities because, for a given domain defined by the pair relationship, when the parameter of interest is at the person level, several pairs in the household could be associated with the same person. The other problem is that of extreme weights that may arise due to small selection probabilities for certain pair age groups, which may lead to unstable estimates. Each of these issues is discussed in turn. If several pairs in the household are associated with the same person, it is necessary to use the average measure of behavior relationships for each member, which gives rise to multiplicities. Thus, the pair weights need to be divided by the person-level multiplicity factors for each domain of interest, and, therefore, multiplicity factors need to be produced along with the final set of calibrated weights. Because it is not straightforward to create these multiplicities, analyses would have to be necessarily limited to pair relationships where the multiplicities were 2
produced a priori. It was anticipated that analyses of interest would be limited to 14 pair domains, listed in Table 1.2. Since no multiplicity was necessary for the spouse-spouse/partnerpartner pair relationships (by definition, each pair member could have only one partner or one spouse), multiplicity factors were produced for only 12 of these domains. Note that a single pair relationship might have two domains associated with it, since the parameter of interest might be associated with only one member of the pair (the "focus" member), and the multiplicity would differ depending upon which pair member was the focus member. Table 1.2 Pair Domains Pair Relationship Parent-child, child aged 12-14 Parent-child, child aged 12-14 Parent-child, child aged 12-17 Parent-child, child aged 12-17 Parent-child, child aged 12-20 Parent-child, child aged 12-20 Parent-child, child aged 15-17 Parent-child, child aged 15-17 Sibling-sibling, older sibling 15-17, younger sibling 12-14 Sibling-sibling, older sibling 15-17, younger sibling 12-14 Sibling-sibling, older sibling 18-25, younger sibling 12-17 Sibling-sibling, older sibling 18-25, younger sibling 12-17 Spouse-spouse and partner-partner Spouse-spouse and partner-partner, with children aged 0-17 Focus Parent Child Parent Child Parent Child Parent Child Older sibling Younger sibling Older sibling Younger sibling No multiplicity necessary No multiplicity necessary Some of the multiplicities, including counts of all possible pairs in a household for a given domain, were used for poststratification. Details are provided in Chapter 4. A resolution to the extreme weight problem is to use a Hajek-type modification (Hajek, 1971). This modification essentially entails calibration (like poststratification) to controls for the number of persons in households belonging to each domain of interest. These controls can be obtained from the larger sample of singles and pairs (i.e., one or two persons selected from DUs). Note, however, that the multiplicity factor, being domain specific, renders the calibration adjustment factor domain specific. This raises the question of finding one set of calibration weights for use with all domains or outcome variables. To get around this problem, we performed a multivariate calibration with respect to a key set of pair domains. This type of poststratification then was followed by a repeat poststratification to further control the extreme weights by imposing separate bound restrictions on the initially identified extreme weights. 3
Page 1 and 2: 2006 NATIONAL SURVEY ON DRUG USE AN
Page 3 and 4: Preface This report documents the m
Page 5 and 6: Table of Contents Chapter Page Pref
Page 7 and 8: Table of Contents (continued) Appen
Page 9 and 10: List of Tables Table Page Table 1.1
Page 11 and 12: List of Tables (continued) Table Pa
Page 13 and 14: List of Exhibits Exhibit Page Exhib
Page 15 and 16: CBSA DU ev GEM Half step Household-
Page 17: 1. Introduction Traditionally, most
Page 21: to perform some treatment (such as
Page 24 and 25: P hij ( ) ⎡P P ⎤⎡ 1 1 ⎤ hi
Page 26 and 27: The QDU selection probability was d
Page 29 and 30: 3. Brief Description of the General
Page 31 and 32: 4. Predictor Variables for the Ques
Page 33 and 34: Exhibit 4.1 Definitions of Levels f
Page 35: Exhibit 4.2 Definitions of Levels f
Page 38 and 39: e. Youth Only; f. Young Adult Only;
Page 41 and 42: 6. Editing and Imputation of Pair R
Page 43 and 44: from other pair members was sometim
Page 45 and 46: Table 6.1 Levels of the Variable PA
Page 47 and 48: Table 6.3 Measures of the Quality o
Page 49 and 50: identified pair member "A" as "othe
Page 51 and 52: performed at the household level ra
Page 53 and 54: pair member's roster and another pa
Page 55 and 56: Table 6.6 Age Group Pair Number Age
Page 57 and 58: 13. MSA (metropolitan statistical a
Page 59 and 60: different ways depending upon the a
Page 61 and 62: 5. parent-child (child 12 to 17), p
Page 63 and 64: ecause, even though the parent-chil
Page 65 and 66: collapsed with the two-parent house
Page 67 and 68: Sibling-sibling pairs. The likeness
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with children) lived within the hou
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6.4.1.2 Sibling-Sibling Domains Whe
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4. The respondent had a child and a
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parent-focus and child-focus counts
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the random imputation described ear
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6.4.3.3.3 Spouse-Spouse Counts For
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7. Weight Calibration at Questionna
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Exhibit 7.2 Summary of 2006 NSDUH P
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Table 7.1 Model Group QDU Sample Si
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7.2.5 QDU Weight Component #15: Res
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8. Evaluation of Calibration Weight
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8.5 Sensitivity Analysis of Drug Us
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Table 8.2a Percentages of Youths (1
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Table 8.4 Percentages of Youths (12
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References Ault, K., Aldworth, J.,
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Singh, A., Grau, E., & Folsom, R.,
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A-2
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A.2 GEM Adjustments for Extreme-Val
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A-6
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B-2
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B.1.1 Person Level B.1.1.1 Receivin
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B-6
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C-2
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To help understand what effects wer
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Exhibit C.1 Definitions of Levels f
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C.3 How to Interpret Collapsing and
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The number of respondents in that c
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Exhibit C.2 Covariates for 2006 NSD
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C-14
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Table C.1b 2006 Distribution of Wei
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Exhibit C.1.1 Covariates for 2006 N
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C-22
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C-30
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C-38
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D-2
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D-4
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E-2
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E-4 Table E.1 2006 NSDUH Selected Q
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E-6 Table E.2 2006 NSDUH Respondent
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F-2
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Table F.1 2006 NSDUH QDU-Level Slip
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G-2
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G-4 Table G.1 2006 NSDUH Selected Q
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G-6 Table G.2 2006 NSDUH Respondent
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H-2
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Exhibit H.1 Definitions of Levels f
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Exhibit H.2 Covariates for 2006 NSD
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H-8
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Table H.1b 2006 Distribution of Wei
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Exhibit H.1.1 Covariates for 2006 N
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H-16
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H-18
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Table H.2b 2006 Pair Weight GEM Mod
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H-26
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I-2
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Table I.1 2006 NSDUH Person Pair-Le
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J-2
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J-4 Table J.1 Domain 2006 NSDUH Sel
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J-6 Table J.2 2006 NSDUH Respondent
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J-8 Table J.3 Domain 2006 NSDUH Res
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J-10
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K-2
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Table K.1 2006 NSDUH Respondent Pai
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L-2
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L-4 Table L.1 2006 NSDUH Selected P
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L-6 Table L.2 2006 NSDUH Respondent
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L-8 Table L.3 2006 NSDUH Respondent
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L-10 Table L.3 2006 NSDUH Responden
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M-2
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procedures were implemented indepen
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first sorting variables). In genera
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predicted mean(s), the neighborhood
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N-2
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The PMM method is only applicable t
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type of constraint, called a "logic
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N-8
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O-2
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Table O.1 Rules for Determining Mat
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Table O.1 Rules for Determining Mat
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O-8 Table O.2 Priority Condition Ru
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O-10 Table O.2 Priority Condition 2
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Table O.2 Priority Condition Rules
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P-2
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P-4 Table P.1 Priority Condition, F
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Table P.1 Priority Conditions Used
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P-8 Table P.1 Priority Condition, F
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P-10 Table P.2 Priority Condition,
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P-18
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Q-2
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data, which was adjusted to more re
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Table Q.1 Model Group 3 (15-17, 15-
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Table Q.1 Model Group 9 (18-20, 26+
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Table Q.2 Pair Domain Sibling (12-1
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Table Q.3 Model Group Parent- Child
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Table Q.3 Pair Domain Spouse- Spous
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Table Q.4 Pair Domain Sibling (12-1
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Q-18
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R-2
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was not imputed, the indirect count
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1. The counts disagreed if a househ
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S-2
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with parent(s) in the household) fo
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• Both had counts of children wit
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− The other conditions had not al
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• The number of children in the s
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5. Two family units might be in the
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− − No bad relationship codes i
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elationship codes, and the sum of t
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• The pair relationship was not a
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• The screener count of roster me
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15. The count of the number of spou
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member had a spouse-spouse-with-chi
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• If the spouse-spouse counts exc
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• The number of roster members yo
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