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

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2006 NATIONAL SURVEY ON DRUG USE AND HEALTH QUESTIONNAIRE DWELLING UNIT-LEVEL AND PERSON PAIR-LEVEL SAMPLING WEIGHT CALIBRATION Prepared for the 2006 Methodological Resource Book RTI Project No. 0209009.275.002 Contract No. 283-2004-00022 Deliverable No. 39 Authors: Matthew Westlake Kortnee Barnett-Walker Patrick Chen Harper Gordek Jeff Laufenberg Project Director: Thomas G. Virag Prepared for: Substance Abuse and Mental Health Services Administration Rockville, Maryland 20857 Prepared by: RTI International Research Triangle Park, North Carolina 27709 January 2008
Preface This report documents the method of weight calibration used for producing the final set of questionnaire dwelling unit (QDU) and pair weights for the National Survey of Drug Use and Health (NSDUH) data from 2006. The weighting team faced several challenges in this task and was able to address them by resorting to innovative modifications of certain basic statistical ideas. These are listed below. • Under Brewer's method, high weights may occur due to small pair selection probabilities. In any calibration exercise, some treatment of extreme value (ev) in weights is needed, but there is a danger of introducing too much bias by overtreatment. In the generalized exponential model (GEM), which is described in detail in Chen et al. (2008), extreme value control is built in, but one needs to define suitable ev domains so that not too many evs are defined. If too many design variables are used to define ev domains, then each domain will be very sparse and will not be of much use in defining thresholds for ev. As in past surveys, a hierarchy of domains was defined using pair age (each pair member being in one of the three categories: 12 to 25, 26 to 49, and 50+) and number of persons aged 12 to 25 in the household, State, and clusters of States (see Section 5.2 for details). • Control of extreme values in weights helps reduce instability of estimates to some extent, but there is a need for methods that do not introduce much bias. Following the famous suggestion of Hajek (1971) in his comments on Basu's fabled example of circus elephants, we performed ratio adjustment (a form of poststratification) to estimated totals obtained from the household data on the number of persons belonging to the pair domain of interest. This was implemented in a multivariate manner to get one set of final weights. • In the absence of a suitable source of poststratification controls for the person pairlevel weights and the household-level weights, the inherent two-phase nature of the survey design was capitalized upon to estimate these controls from the first phase of the large screener sample. The first-phase sample weight was poststratified to personlevel U.S. Bureau of the Census counts to get more efficient estimated counts for pair and household data. • The problem of multiplicities complicated the issue of providing one set of final weights. When dealing with person-level parameters involving drug-related behaviors among members of the same household, it is possible for an individual to manifest himself or herself in the pair sample through different pairs. To avoid overcounting, the pair weights have to be divided by multiplicity factors, which tend to be domain specific. For this reason, multiplicity factors for a key set of pair analysis domains also are produced along with a set of final calibrated pair weights. iii
Page 1: 2006 NATIONAL SURVEY ON DRUG USE AN
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 and 18: 1. Introduction Traditionally, most
Page 19 and 20: produced a priori. It was anticipat
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
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pair member's roster and another pa
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Table 6.6 Age Group Pair Number Age
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13. MSA (metropolitan statistical a
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different ways depending upon the a
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5. parent-child (child 12 to 17), p
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ecause, even though the parent-chil
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collapsed with the two-parent house
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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
Page 338:
• The number of roster members yo
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