Sample Balancing for
Extremely Small Population Response Rates
Bruce Ratner, Ph.D.
Data analysts typically build a response model with a random sample drawn from a population with an extremely small response rate. The sample is characterized by an abundance of nonresponders that far exceeds the number of responders, e.g., 10,000 responders and 1,490,000 nonresponders yielding a minikin 0.67% response rate. Processing and analyzing all nonresponders is costly in terms of computer time and resources, and the efficiency of the resultant response model. The standard approach to modeling a minikin response rate of large sample is to: 1) retain the paucity of responders, 2) sample down the nonresponders to a multiple of the number of responders, and 3) balance the reduced sample to reflect the population response rate. The three-step approach is easy to understand; however, it is arithmetically cumbersome to perform. The purpose of this article is to provide a simple SAS-code program for sample balancing for extremely small population response rates, which should be a welcomed entry in the tool kit of data analysts who frequently work on building response models.
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