Maintaining Precision in Survey Estimates while Adjusting for Conditional Bias at the Subnational Level through Calibration Weighting

  • Bonnie E Shook-Sa DrPH Candidate, Department of Biostatistics University of North Carolina at Chapel Hill
  • Phillip Kott Division for Statistical and Data Sciences, RTI International
  • Marcus Berzofsky Division for Statistical and Data Sciences, RTI International
  • G. Lance Couzens Division for Statistical and Data Sciences, RTI International
  • Andrew Moore Division for Statistical and Data Sciences, RTI International
  • Philip Lee Division for Statistical and Data Sciences, RTI International
  • Lynn Langton Bureau of Justice Statistics
  • Michael Planty Bureau of Justice Statistics
Keywords: variance estimation, linearization

Abstract

Calibration weighting improves inference by adjusting for observed differences between the realized sample and the population. Unfortunately, a commonly-used linearization-based variance estimator often does not account for the increased efficiency provided by the calibration process. As a result, precision estimates based on calibrated weights can be artificially high. Using a relatively new alternative linearization-based variance estimator allows analysts to utilize calibration-weighting techniques while producing more accurate precision estimates. We use calibration weighting to produce more reliable subnational estimates and assess the differences in point estimates resulting from these weight adjustments in the National Crime Victimization Survey, a nationally representative survey designed to calculate victimization rates solely at the national level. We then assess the estimated precision of these point estimates using a conventional linearization-based variance estimator and the alternative estimator. We find that the calibration adjustments mostly reduced the standard errors in subnational estimates but to successfully measure the reduction required using the alternative variance estimator.

Author Biographies

Bonnie E Shook-Sa, DrPH Candidate, Department of Biostatistics University of North Carolina at Chapel Hill
Bonnie Shook-Sa is a DrPH candidate in the Department of Biostatistics at the University of North Carolina at Chapel Hill. Previously, she was a Senior Research Statistician at RTI International in Research Triangle Park, NC.
Phillip Kott, Division for Statistical and Data Sciences, RTI International
Senior Research Statistician
Marcus Berzofsky, Division for Statistical and Data Sciences, RTI International
Senior Research Statistician
G. Lance Couzens, Division for Statistical and Data Sciences, RTI International
Research Statistician
Andrew Moore, Division for Statistical and Data Sciences, RTI International
Research Statistician
Philip Lee, Division for Statistical and Data Sciences, RTI International
Statistician
Published
2017-12-13
How to Cite
Shook-Sa, B. E., Kott, P., Berzofsky, M., Couzens, G. L., Moore, A., Lee, P., Langton, L., & Planty, M. (2017). Maintaining Precision in Survey Estimates while Adjusting for Conditional Bias at the Subnational Level through Calibration Weighting. Survey Research Methods, 11(4), 405-414. https://doi.org/10.18148/srm/2017.v11i4.6789
Section
Articles