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Activity Number: 332 - Power of Adaptive Design in Controlling Survey Errors and Costs
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Government Statistics Section
Abstract #328643 Presentation
Title: Dynamic Question Ordering in Online Surveys
Author(s): Kirstin Early* and Jennifer Mankoff and Stephen E. Fienberg
Companies: Oath and University of Washington and Carnegie Mellon University
Keywords: adaptive survey design; cost-effective data collection; questionnaire design
Abstract:

Online surveys have the potential to support adaptive questions, where later questions depend on prior 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 on data from three government surveys.


Authors who are presenting talks have a * after their name.

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