Details
| Original language | English |
|---|---|
| Pages (from-to) | 1253-1271 |
| Number of pages | 19 |
| Journal | Social science computer review |
| Volume | 39 |
| Issue number | 6 |
| Early online date | 4 Dec 2021 |
| Publication status | Published - Dec 2021 |
| Externally published | Yes |
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.
Keywords
- acceleration data, data quality, machine learning, multitasking, smartphone surveys, survey motion
ASJC Scopus subject areas
- Social Sciences(all)
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Library and Information Sciences
- Social Sciences(all)
- Law
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In: Social science computer review, Vol. 39, No. 6, 12.2021, p. 1253-1271.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Completion Conditions and Response Behavior in Smartphone Surveys
T2 - A Prediction Approach Using Acceleration Data
AU - Kern, Christoph
AU - Höhne, Jan Karem
AU - Schlosser, Stephan
AU - Revilla, Melanie
N1 - Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We acknowledge financial support by the German Science Foundation through the Collaborative Research Center 884 “Political Economy of Reforms” at the University of Mannheim in Germany for conducting Cross-Sectional Survey 2 (Data Source 3).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - acceleration data
KW - data quality
KW - machine learning
KW - multitasking
KW - smartphone surveys
KW - survey motion
UR - http://www.scopus.com/inward/record.url?scp=85097166624&partnerID=8YFLogxK
U2 - 10.1177/0894439320971233
DO - 10.1177/0894439320971233
M3 - Article
AN - SCOPUS:85097166624
VL - 39
SP - 1253
EP - 1271
JO - Social science computer review
JF - Social science computer review
SN - 0894-4393
IS - 6
ER -