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1998 National Household Survey on Drug Abuse:  Population Estimates

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PROCEDURES FOR DERIVING POPULATION ESTIMATES

Development of Weights

by age group, gender, race/ethnicity, and Hispanic origin. These poststratification totals were appropriately adjusted using State-level population projections that also were obtained from the Census Bureau. With the State-level demographic control totals so obtained, a three-level "State" variable (i.e., California, Arizona, and remainder of the United States) was used in the poststratification to produce stable State-level estimates for California and Arizona. In 1998, regional control totals also were obtained and used during poststratification. In general, the interview samples from each quarter were poststratified to one-fourth of the projected population totals. These totals represent the population at the midpoint of each quarter's data collection period (the 15th day of February, May, August, and November 1998). The resulting quarterly analysis weights sum to the average of the four quarter-specific projections. The final analysis weight can be viewed as the number of population members that each respondent represents.

Adjusting for Nonresponse Through Imputation

Sampling Error and Confidence Intervals

where the quantity in parentheses that is multiplied by 1.96 estimates the standard error (SE) of L. Applying the inverse logistic transformation to the confidence interval endpoints, A and B, yields a 95% confidence interval for the proportion, P, as

where "exp" denotes the inverse log transformation. The lower and upper confidence interval endpoints for percentage estimates are obtained by multiplying the lower and upper endpoints for proportions by 100. The confidence interval for the corresponding population estimate is obtained by multiplying the confidence interval endpoints by the estimated number of individuals in the population subgroup constituting the base or denominator of the associated proportion.

where RSE[-ln(p)] is the RSE(p)/-ln(p). For computational purposes, this is equivalent to where SE(p) is the standard error estimate of p. The log transformation of p is used to provide a more balanced treatment of measuring the quality of small, large, and intermediate p values. The switch to (1-p) for p greater than 0.5 yields a symmetric suppression rule across the range of possible p values. Because the sample sizes for subgroup populations are relatively large, low precision generally occurs only for prevalence rates that are near either 0% or 100%.
4 These 1998 population projections were based on the 1990 U.S. Census counts.
5 Shah, B.V., Barnwell, B.G., & Bieler, G.S. (1997). SUDAAN user's manual: Version 7.5. Research
Triangle Park, NC: Research Triangle Institute.
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This page was last updated on June 03, 2008.