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

<p>Along with the rapid emergence of web surveys to address time-sensitive priority topics, various propensity score (PS)-based adjustment methods have been developed to improve population representativeness for nonprobability- or probability-sampled web surveys subject to selection bias. Conventional PS-based methods construct pseudo-weights for web samples using a higher-quality reference probability sample. The bias reduction, however, depends on the outcome and variables collected in both web and reference samples. A central issue is identifying variables for inclusion in PS-adjustment. In this article, directed acyclic graph (DAG), a common graphical tool for causal studies but largely under-utilized in survey research, is used to examine and elucidate how different types of variables in the causal pathways impact the performance of PS-adjustment. While past literature generally recommends including all variables, our research demonstrates that only certain types of variables are needed in PS-adjustment. Our research is illustrated by NCHS’ Research and Development Survey, a probability-sampled web survey with potential selection bias, PS-adjusted to the National Health Interview Survey, to estimate U.S. asthma prevalence. Findings in this article can be used by National Statistics Offices to design questionnaires with variables that improve web-samples’ population representativeness and to release more timely and accurate estimates for priority topics.</p>
</abstract>

Along with the rapid emergence of web surveys to address time-sensitive priority topics, various propensity score (PS)-based adjustment methods have been developed to improve population representativeness for nonprobability- or probability-sampled web surveys subject to selection bias. Conventional PS-based methods construct pseudo-weights for web samples using a higher-quality reference probability sample. The bias reduction, however, depends on the outcome and variables collected in both web and reference samples. A central issue is identifying variables for inclusion in PS-adjustment. In this article, directed acyclic graph (DAG), a common graphical tool for causal studies but largely under-utilized in survey research, is used to examine and elucidate how different types of variables in the causal pathways impact the performance of PS-adjustment. While past literature generally recommends including all variables, our research demonstrates that only certain types of variables are needed in PS-adjustment. Our research is illustrated by NCHS’ Research and Development Survey, a probability-sampled web survey with potential selection bias, PS-adjusted to the National Health Interview Survey, to estimate U.S. asthma prevalence. Findings in this article can be used by National Statistics Offices to design questionnaires with variables that improve web-samples’ population representativeness and to release more timely and accurate estimates for priority topics.

Variable Inclusion Strategies through Directed Acyclic Graphs to adjust Health Surveys subject to Selection Bias for Producing National Estimates

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Journal of Official Statistics

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

Along with the rapid emergence of web surveys to address time-sensitive priority topics, various propensity score (PS)-based adjustment methods have been...

Variable Inclusion Strategies through Directed Acyclic Graphs to adjust Health Surveys subject to Selection Bias for Producing National Estimates

Published Online: Sep 12, 2022

Page range: 875 - 900

Received: Jul 01, 2021

Accepted: Apr 01, 2022

DOI: https://doi.org/10.2478/jos-2022-0038

Keywords
Kernel weighting, logistic regression, propensity score model, survey inference

© 2022 Yan Li et al., published by Sciendo

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

Variable Inclusion Strategies through Directed Acyclic Graphs to adjust Health Surveys subject to Selection Bias for Producing National Estimates

Published Online: Sep 12, 2022

Page range: 875 - 900

Received: Jul 01, 2021

Accepted: Apr 01, 2022

DOI: https://doi.org/10.2478/jos-2022-0038

KeywordsKernel weighting, logistic regression, propensity score model, survey inference

© 2022 Yan Li et al., published by Sciendo

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

Keywords
Kernel weighting, logistic regression, propensity score model, survey inference