Journal of Official Statistics, Vol.27, No.3, 2011. pp. 433450

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Using Statistical Models for Sample Design of a Reinterview Program

The U.S. Census Bureau relies on reinterview programs as the primary method to evaluate field work and monitor the work of the interviewers. One purpose of the reinterviews is to identify falsification. Since falsification is a rare occurrence, reinterview programs generally identify very few falsified cases even when the reinterview sample is reasonably large. This study examines methods for designing a reinterview sample with the goal of identifying more falsified cases. With the Current Population Survey (CPS) as an example, we explore data that could be used for reinterview sampling beyond that currently used in the CPS program. We fit a logistic regression model to predict the likelihood of falsification with the data from original interviews, and use the predicted probabilities to construct alternative reinterview sampling designs. The alternative designs are compared to the current sampling method through cross validation and simulation methods.

Quality control, falsification, curb stone, rare event

Copyright Statistics Sweden 1996-2018.  Open Access
ISSN 0282-423X
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