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American Association for Public Opinion Research

Webinar Details

Standards and guidelines for designing human-centered mobile surveys


Thursday, September 15, 2022
1:00 - 2:00 PM Eastern Time

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Speaker: Lin Wang, Research Psychologist

About Lin:MURPHY_Photo.PNG
Dr. Lin Wang is a Research Psychologist specialized in human performance analysis with a multidisciplinary approach including both quantitative and qualitative methods. He has conducted a variety of behavioral studies in fields of survey instrument design, data dissemination, visual rehabilitation, driving, and air traffic control. His research has been published in Ergonomics, Psychophysiology, Investigative Ophthalmology & Visual Science, Social Science Computer Review, Journal of Usability Studies, Survey Practice, among others. Dr. Wang was the technical lead of the U.S. Census Bureau Innovation-and-Operational-Efficiency project “Development of Evidence-Based Framework of Mobile User Interface Design for Survey Operations” upon which this webinar is based. He is currently serving as the leader of the Human Factors Research Group at the U.S. Census Bureau. 

Description:
With the increasing ubiquity of smartphones, more and more surveys are now conducted on the internet with smartphones. Online surveys on smartphones have indisputable advantages, such as convenience to respond, greater coverage, and lower cost. On the other hand, it brought challenges to designing a mobile survey that facilitates accurate responses and provides a satisfactory user experience. To address this issue, we took a systematic and human-centered approach to developing evidence-based standards and guidelines for mobile survey design.

The systematic approach is rooted in two knowledge domains: The Total Survey Error framework of survey methodology and human-computer interaction of human factors. In the Total Survey Error framework, errors could be introduced in every stage during the survey lifecycle. Mobile survey design can play a significant role in reducing measurement errors during data collection. The interaction between a survey respondent and an online survey on a smartphone constitutes a closed-loop of operator-machine interaction. Measurement error is one kind of operator error. It is thus possible to reduce survey measurement error through improving mobile survey UI design.

In this webinar, we will start with an overview of our approach to developing evidence-based mobile survey standards and guidelines, followed by a description of standards and guidelines we have developed so far, for adoption by survey methodologists. The approach to generating standards and guidelines included constructing a model of a mobile survey respondent with critical cognitive capability and a survey information processing flow, conducting brainstorming sessions to formulate topics for standards and guidelines, systematic literature review, and behavioral experiments.

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