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

<p>Higher levels of perceived burden by respondents can lead to ambiguous responses to a questionnaire, item nonresponse, or refusals to continue participation in the survey which can introduce bias and downgrade the quality of the data. Therefore, it is important to understand what might influence the perception of burden in respondents. In this article, we demonstrate, using U.S. Consumer Expenditure Survey data, how regression tree models can be used to analyze the associations between perceived burden and objective burden measures conditioning on household demographics and other explanatory variables. The structure of the tree models allows these associations to easily be explored.</p>
<p>Our analysis shows a relationship between perceived burden and some of the objective measures after conditioning on different demographic and household variables and that these relationships are quite affected by different respondent characteristics and the mode of the survey. Since the tree models were constructed using an algorithm that accounts for the sample design, inferences from the analysis can be made about the population. Therefore, any insights could be used to help guide future decisions about survey design and data collection to help reduce respondent burden.</p>
</abstract>

Higher levels of perceived burden by respondents can lead to ambiguous responses to a questionnaire, item nonresponse, or refusals to continue participation in the survey which can introduce bias and downgrade the quality of the data. Therefore, it is important to understand what might influence the perception of burden in respondents. In this article, we demonstrate, using U.S. Consumer Expenditure Survey data, how regression tree models can be used to analyze the associations between perceived burden and objective burden measures conditioning on household demographics and other explanatory variables. The structure of the tree models allows these associations to easily be explored.
Our analysis shows a relationship between perceived burden and some of the objective measures after conditioning on different demographic and household variables and that these relationships are quite affected by different respondent characteristics and the mode of the survey. Since the tree models were constructed using an algorithm that accounts for the sample design, inferences from the analysis can be made about the population. Therefore, any insights could be used to help guide future decisions about survey design and data collection to help reduce respondent burden.

Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Journal of Official Statistics

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

Higher levels of perceived burden by respondents can lead to ambiguous responses to a questionnaire, item nonresponse, or refusals to continue participation in...

Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Published Online: Dec 03, 2022

Page range: 1125 - 1144

Received: Feb 01, 2021

Accepted: May 01, 2022

DOI: https://doi.org/10.2478/jos-2022-0048

Keywords
nonparametric, nonresponse, respondent burden, sample design, survey data

© 2022 Daniel K. Yang et al., published by Sciendo

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

Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Published Online: Dec 03, 2022

Page range: 1125 - 1144

Received: Feb 01, 2021

Accepted: May 01, 2022

DOI: https://doi.org/10.2478/jos-2022-0048

Keywordsnonparametric, nonresponse, respondent burden, sample design, survey data

© 2022 Daniel K. Yang et al., published by Sciendo

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

Keywords
nonparametric, nonresponse, respondent burden, sample design, survey data