<abstract xmlns="http://www.w3.org/1999/xhtml">Sample surveys are often affected by nonresponse. These surveys have in common that their outcomes depend at least partly on a human decision whether or not to participate. If it would be completely clear how this decision mechanism works, estimates could be corrected. An often used approach is to introduce the concept of the response probability. Of course, these probabilities are a theoretical concept and therefore unknown. The idea is to estimate them by using the available data. If it is possible to obtain good estimates of the response probabilities, they can be used to improve estimators of population characteristics.Estimating response probabilities relies heavily on the use of models. An often used model is the logit model. In the article, this model is compared with the simple linear model.Estimation of response probabilities models requires the individual values of the auxiliary variables to be available for both the respondents and the nonrespondents of the survey. Unfortunately, this is often not the case. This article explores some approaches for estimating response probabilities that have less heavy data requirements. The estimated response probabilities were also used to measure possible deviations from representativity of the survey response. The indicator used is the coefficient of variation (CV) of the response probabilities.</abstract>

Sample surveys are often affected by nonresponse. These surveys have in common that their outcomes depend at least partly on a human decision whether or not to participate. If it would be completely clear how this decision mechanism works, estimates could be corrected. An often used approach is to introduce the concept of the response probability. Of course, these probabilities are a theoretical concept and therefore unknown. The idea is to estimate them by using the available data. If it is possible to obtain good estimates of the response probabilities, they can be used to improve estimators of population characteristics.Estimating response probabilities relies heavily on the use of models. An often used model is the logit model. In the article, this model is compared with the simple linear model.Estimation of response probabilities models requires the individual values of the auxiliary variables to be available for both the respondents and the nonrespondents of the survey. Unfortunately, this is often not the case. This article explores some approaches for estimating response probabilities that have less heavy data requirements. The estimated response probabilities were also used to measure possible deviations from representativity of the survey response. The indicator used is the coefficient of variation (CV) of the response probabilities.

Working with Response Probabilities

Journal of Official Statistics

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

{"article-title":"Working with Response Probabilities"}

Sample surveys are often affected by nonresponse. These surveys have in common that their outcomes depend at least partly on a human decision whether or not to...

Working with Response Probabilities

Online veröffentlicht: 24. Juli 2020

Seitenbereich: 647 - 674

Eingereicht: 01. Juli 2018

Akzeptiert: 01. Sept. 2019

DOI: https://doi.org/10.2478/jos-2020-0033

Schlüsselwörter
Nonresponse, adjustment weighting, response propensity, representativity

© 2020 Jelke Bethlehem, published by Sciendo

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

Working with Response Probabilities

Online veröffentlicht: 24. Juli 2020

Seitenbereich: 647 - 674

Eingereicht: 01. Juli 2018

Akzeptiert: 01. Sept. 2019

DOI: https://doi.org/10.2478/jos-2020-0033

SchlüsselwörterNonresponse, adjustment weighting, response propensity, representativity

© 2020 Jelke Bethlehem, published by Sciendo

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

Schlüsselwörter
Nonresponse, adjustment weighting, response propensity, representativity