Completion conditions and response behavior in smartphone surveys: a prediction approach using acceleration data

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• dc.contributor.author Kern, Christoph
• dc.contributor.author Höhne, Jan Karem
• dc.contributor.author Schlosser, Stephan
• dc.contributor.author Revilla, Melanie
• dc.date.accessioned 2022-11-29T06:46:11Z
• dc.date.available 2022-11-29T06:46:11Z
• dc.date.issued 2021
• dc.description Includes supplementary materials for the online appendix.
• dc.description.abstract This study utilizes acceleration data from smartphone sensors to predict motion conditions of smartphone respondents. Specifically, we predict whether respondents are moving or nonmoving on a survey page level to learn about distractions and the situational conditions under which respondents complete smartphone surveys. The predicted motion conditions allow us to (1) estimate the proportion of smartphone respondents who are moving during survey completion and (2) compare the response behavior of moving and nonmoving respondents. Our analytical strategy consists of two steps. First, we use data from a lab experiment that systematically varied motion conditions of smartphone respondents and train a prediction model that is able to accurately infer respondents’ motion conditions based on acceleration data. Second, we use the trained model to predict motion conditions of respondents in two cross-sectional surveys in order to compare response behavior of respondents with different motion conditions in a field setting. Our results indicate that active movement during survey completion is a relatively rare phenomenon, as only about 3%–4% of respondents were predicted as moving in both cross-sectional surveys. When comparing respondents based on their predicted motion conditions, we observe longer completion times of moving respondents. However, we observe little differences when comparing moving and nonmoving respondents with respect to indicators of superficial responding, indicating that moving during survey completion does not pose a severe threat to data quality.
• dc.format.mimetype application/pdf
• dc.identifier.citation Kern C, Höhne JK, Schlosser S, Revilla M. Completion conditions and response behavior in smartphone surveys: a prediction approach using acceleration data. Social Science Computer Review. 2021 Dec;39(6):1253–71. DOI: 10.1177/0894439320971233
• dc.identifier.doi http://dx.doi.org/10.1177/0894439320971233
• dc.identifier.issn 0894-4393
• dc.identifier.uri http://hdl.handle.net/10230/55022
• dc.language.iso eng
• dc.publisher SAGE Publications
• dc.relation.ispartof Social Science Computer Review. 2021 Dec;39(6):1253–71
• dc.relation.isreferencedby https://doi.org/10.25384/SAGE.13337153.v1
• dc.rights This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Acceleration data
• dc.subject.keyword Data quality
• dc.subject.keyword Machine learning
• dc.subject.keyword Multitasking
• dc.subject.keyword Smartphone surveys
• dc.subject.keyword Survey motion
• dc.title Completion conditions and response behavior in smartphone surveys: a prediction approach using acceleration data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion