A Cautionary Note on Post-stratification Adjustment with Estimated Control Totals
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Weight adjustment for non-response and, to a lesser degree, coverage error are applied to the initial weights to produce the final analytic weights. Frequently the adjustment is applied using post-stratification. As Cochran suggested, the effectiveness of the post-stratification adjustment completely depends on available true population parameters, or the control totals. In practice, however, the population parameters are frequently estimated directly or indirectly through modeling. This paper demonstrates potential adverse effects of using incorrect population parameters in post-stratification adjustments, by applying log-linear models to a cross-tabulation of sex, race/ethnicity, and age from 2010 U.S. Census data. In particular, a caution is advised when we apply raking ratio adjustments to the initial weights with estimated control totals - in general, the last step for well-designed surveys but frequently the only step for not-so-well-designed surveys.
Abstract
Weight adjustment for non-response and, to a lesser degree, coverage error are applied to the initial weights to produce the final analytic weights. Frequently the adjustment is applied using post-stratification. As Cochran suggested, the effectiveness of the post-stratification adjustment completely depends on available true population parameters, or the control totals. In practice, however, the population parameters are frequently estimated directly or indirectly through modeling. This paper demonstrates potential adverse effects of using incorrect population parameters in post-stratification adjustments, by applying log-linear models to a cross-tabulation of sex, race/ethnicity, and age from 2010 U.S. Census data. In particular, a caution is advised when we apply raking ratio adjustments to the initial weights with estimated control totals - in general, the last step for well-designed surveys but frequently the only step for not-so-well-designed surveys.
참고문헌 (19)
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