<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Common practice to address nonresponse in probability surveys in National Statistical Offices is to follow up every non respondent with a view to lifting response rates. As response rate is an insufficient indicator of data quality, it is argued that one should follow up non respondents with a view to reducing the mean squared error (MSE) of the estimator of the variable of interest. In this article, we propose a method to allocate the nonresponse follow-up resources in such a way as to minimise the MSE under a quasi-randomisation framework. An example to illustrate the method using the 2018/19 Rural Environment and Agricultural Commodities Survey from the Australian Bureau of Statistics is provided.</p>
</abstract>

Common practice to address nonresponse in probability surveys in National Statistical Offices is to follow up every non respondent with a view to lifting response rates. As response rate is an insufficient indicator of data quality, it is argued that one should follow up non respondents with a view to reducing the mean squared error (MSE) of the estimator of the variable of interest. In this article, we propose a method to allocate the nonresponse follow-up resources in such a way as to minimise the MSE under a quasi-randomisation framework. An example to illustrate the method using the 2018/19 Rural Environment and Agricultural Commodities Survey from the Australian Bureau of Statistics is provided.

A Note on the Optimum Allocation of Resources to Follow up Unit Nonrespondents in Probability Surveys

Journal of Official Statistics

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

Common practice to address nonresponse in probability surveys in National Statistical Offices is to follow up every non respondent with a view to lifting...

A Note on the Optimum Allocation of Resources to Follow up Unit Nonrespondents in Probability Surveys

Published Online: Sep 07, 2023

Page range: 421 - 433

Received: Aug 01, 2022

Accepted: Feb 01, 2023

DOI: https://doi.org/10.2478/jos-2023-0020

Keywords
Data quality, nonresponse follow-up, propensity score, weighting adjustment

© 2023 Siu-Ming Tam et al., published by Sciendo

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

A Note on the Optimum Allocation of Resources to Follow up Unit Nonrespondents in Probability Surveys

Published Online: Sep 07, 2023

Page range: 421 - 433

Received: Aug 01, 2022

Accepted: Feb 01, 2023

DOI: https://doi.org/10.2478/jos-2023-0020

KeywordsData quality, nonresponse follow-up, propensity score, weighting adjustment

© 2023 Siu-Ming Tam et al., published by Sciendo

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

Keywords
Data quality, nonresponse follow-up, propensity score, weighting adjustment