<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This article describes a two-step calibration-weighting scheme for a stratified simple random sample of hospital emergency departments. The first step adjusts for unit nonresponse. The second increases the statistical efficiency of most estimators of interest. Both use a measure of emergency-department size and other useful auxiliary variables contained in the sampling frame. Although many survey variables are roughly a linear function of the measure of size, response is better modeled as a function of the log of that measure. Consequently the log of size is a calibration variable in the nonresponse-adjustment step, while the measure of size itself is a calibration variable in the second calibration step. Nonlinear calibration procedures are employed in both steps. We show with 2010 DAWN data that estimating variances as if a one-step calibration weighting routine had been used when there were in fact two steps can, after appropriately adjusting the finite-population correct in some sense, produce standard-error estimates that tend to be slightly conservative.</p></abstract>

This article describes a two-step calibration-weighting scheme for a stratified simple random sample of hospital emergency departments. The first step adjusts for unit nonresponse. The second increases the statistical efficiency of most estimators of interest. Both use a measure of emergency-department size and other useful auxiliary variables contained in the sampling frame. Although many survey variables are roughly a linear function of the measure of size, response is better modeled as a function of the log of that measure. Consequently the log of size is a calibration variable in the nonresponse-adjustment step, while the measure of size itself is a calibration variable in the second calibration step. Nonlinear calibration procedures are employed in both steps. We show with 2010 DAWN data that estimating variances as if a one-step calibration weighting routine had been used when there were in fact two steps can, after appropriately adjusting the finite-population correct in some sense, produce standard-error estimates that tend to be slightly conservative.

Developing Calibration Weights and Standard-Error Estimates for a Survey of Drug-Related Emergency-Department Visits

RTI International, 6110 Executive Blvd., Rockville, MD 20852, U.S.A

Substance Abuse and Mental Health Services Administration, 1 Choke Cherry Road, Rockville MD 20857, U.S.A

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This article describes a two-step calibration-weighting scheme for a stratified simple random sample of hospital emergency departments. The first step adjusts...

Developing Calibration Weights and Standard-Error Estimates for a Survey of Drug-Related Emergency-Department Visits

Published Online: Sep 02, 2014

Page range: 521 - 532

Received: Nov 01, 2012

Accepted: May 01, 2014

DOI: https://doi.org/10.2478/jos-2014-0032

Keywords
Frame variable, response model, prediction model, general exponential model, finite population correction

© by Phillip S. Kott

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Developing Calibration Weights and Standard-Error Estimates for a Survey of Drug-Related Emergency-Department Visits

Published Online: Sep 02, 2014

Page range: 521 - 532

Received: Nov 01, 2012

Accepted: May 01, 2014

DOI: https://doi.org/10.2478/jos-2014-0032

KeywordsFrame variable, response model, prediction model, general exponential model, finite population correction

© by Phillip S. Kott

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Frame variable, response model, prediction model, general exponential model, finite population correction