Abstract #300382

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JSM 2003 Abstract #300382
Activity Number: 443
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #300382
Title: Weighting Adjustments for Unit Nonresponse with Multiple Outcome Variables
Author(s): Sonya L. Vartivarian*+ and Roderick Joseph Little
Companies: University of Michigan and University of Michigan
Address: 1875 Lindsay Lane, Ann Arbor, MI, 48104-4167,
Keywords: sampling weights ; survey inference ; unit nonresponse adjustment
Abstract:

Weighting is a common form of unit nonresponse adjustment in sample surveys. Weights are inversely proportional to the probability of selection and response. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. When the number of cells thus created is too large, a coarsening method such as response propensity stratification can be applied to reduce the number of adjustment cells. Simulations indicate improved efficiency and robustness of weighting adjustments based on the joint classification of the sample by two key potential stratifiers: the response propensity and the predictive mean. Predictive mean stratification has the disadvantage that it leads to a different set of weights for each key outcome. Here, we consider the efficiency and robustness of weights that jointly classify on the response propensity and predictive mean, but that base the predictive mean dimension on a single canonical outcome variable.

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