Context-informed scheduling and analysis : improving accuracy of mobile self-reports |
|
Author: | van Berkel, Niels1; Goncalves, Jorge1; Koval, Peter1; |
Organizations: |
1The University of Melbourne, Australia 2University of Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.9 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2019050614365 |
Language: | English |
Published: |
Association for Computing Machinery,
2019
|
Publish Date: | 2019-05-06 |
Description: |
AbstractMobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this paper, we investigate how participant context can affect response accuracy and help identify strategies to improve the accuracy of mobile self-report data. In a 3-week study we collect over 2,500 questionnaires containing both verifiable and non-verifiable questions. We find that response accuracy is higher for questionnaires that arrive when the phone is not in ongoing or very recent use. Furthermore, our results show that long completion times are an indicator of a lower accuracy. Using contextual mechanisms readily available on smartphones, we are able to explain up to 13% of the variance in participant accuracy. We offer actionable recommendations to assist researchers in their future deployments of mobile self-report studies. see all
|
ISBN Print: | 978-1-4503-5970-2 |
Pages: | 1 - 12 |
Article number: | 51 |
DOI: | 10.1145/3290605.3300281 |
OADOI: | https://oadoi.org/10.1145/3290605.3300281 |
Host publication: |
CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK |
Conference: |
ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Copyright information: |
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK, http://dx.doi.org/10.1145/3290605.3300281. |