<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.</p></abstract>

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

Dynamic Question Ordering in Online Surveys

Journal of Official Statistics

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

{"article-title":"Dynamic Question Ordering in Online Surveys"}

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach,...

Dynamic Question Ordering in Online Surveys

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 625 - 657

Eingereicht: 01. März 2016

Akzeptiert: 01. Mai 2017

DOI: https://doi.org/10.1515/jos-2017-0030

Schlüsselwörter
Adaptive survey design, cognitive aspects of survey methodology, cost-based dynamic question ordering, questionnaire design

© 2017 Kirstin Early et al., published by De Gruyter Open

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

Dynamic Question Ordering in Online Surveys

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 625 - 657

Eingereicht: 01. März 2016

Akzeptiert: 01. Mai 2017

DOI: https://doi.org/10.1515/jos-2017-0030

SchlüsselwörterAdaptive survey design, cognitive aspects of survey methodology, cost-based dynamic question ordering, questionnaire design

© 2017 Kirstin Early et al., published by De Gruyter Open

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

Schlüsselwörter
Adaptive survey design, cognitive aspects of survey methodology, cost-based dynamic question ordering, questionnaire design