An App-Assisted Travel Survey in Official Statistics: Possibilities and Challenges

McCool, Danielle; Lugtig, Peter; Mussmann, Ole; Schouten, Barry

doi:10.2478/jos-2021-0007

Cited by 15 publications

(22 citation statements)

References 35 publications

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“…Data collection using smartphone sensors has been investigated by statistical agencies and large-scale panels in numerous studies (e.g., Ja ¨ckle et al 2019, Kreuter et al 2020, McCool et al 2021. If participants who share smartphone sensor data differ on key outcomes from those who refuse, research conclusions can be biased.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have often provided devices to study participants. Only a few recent feasibility studies using smartphone sensors have collected data from general populations (Scherpenzeel 2017;Ja ¨ckle et al 2019;Kreuter et al 2020;McCool, Schouten, Mussmann, and Lugtig 2021). More typically, these studies are conducted with volunteer panels rather than randomly sampled members of the population.…”

Section: Introductionmentioning

confidence: 99%

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Sharing Data Collected with Smartphone Sensors

Struminskaya¹,

Lugtig²,

Toepoel³

et al. 2021

Public Opinion Quarterly

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Smartphone sensors allow measurement of phenomena that are difficult or impossible to capture via self-report (e.g., geographical movement, physical activity). Sensors can reduce respondent burden by eliminating survey questions and improve measurement accuracy by replacing/augmenting self-reports. However, if respondents who are not willing to collect sensor data differ on critical attributes from those who are, the results can be biased. Research on the mechanisms of willingness to collect sensor data mostly comes from (nonprobability) online panels and is hypothetical (i.e., asks participants about the likelihood of participation in a sensor-based study). In a cross-sectional general population randomized experiment, we investigate how features of the request and respondent characteristics influence willingness to share (WTS) and actually sharing smartphone-sensor data. We manipulate the request to either mention or not mention (1) how participation will benefit the participant, (2) participants’ autonomy over data collection, and (3) that data will be kept confidential. We assess nonparticipation bias using the administrative records. WTS and actually sharing varies by sensor task, participants’ autonomy over data sharing, their smartphone skills, level of privacy concerns, and attitudes toward surveys. Fewer people agree to share photos and a video than geolocation, but all who agreed to share photos or a video actually did. Some nonresponse and nonparticipation biases are substantial and make each other worse, but others jointly reduce the overall bias. Our findings suggest that sensor-data-sharing decisions depend on sample members’ situation when asked to share and the nature of the sensor task rather than the sensor type.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sharing Data Collected with Smartphone Sensors

Struminskaya¹,

Lugtig²,

Toepoel³

et al. 2021

Public Opinion Quarterly

Self Cite

View full text Add to dashboard Cite

show abstract

“…App studies fielded on new cross-sectional samples of the general population are likely to yield even lower participation rates. McCool, Lugtig, Mussmann, and Schouten (2021) conducted a field test of a travel study in which participants were asked to download an app and provide seven days of time-location sensor data. Half the sample came from the Dutch population register, while the balance had previously participated in a travel diary survey.…”

Section: Introductionmentioning

confidence: 99%

Increasing Participation in a Mobile App Study: The Effects of a Sequential Mixed-Mode Design and In-Interview Invitation

Jäckle

Wenz

Burton

et al. 2022

Journal of Survey Statistics and Methodology

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Mobile apps are an attractive and versatile method of collecting data in the social and behavioral sciences. In samples of the general population, however, participation in app-based data collection is still rather low. In this article, we examine two potential ways of increasing participation and potentially reducing participation bias in app-based data collection: (1) inviting sample members to a mobile app study within an interview rather than by post and (2) offering a browser-based follow-up to the mobile app. We use experimental data from Spending Study 2, collected on the Understanding Society Innovation Panel and on the Lightspeed UK online access panel. Sample members were invited to download a spending diary app on their smartphone or use a browser-based online diary to report all their purchases for one month. The results suggest that inviting sample members to an app study within a face-to-face interview increases participation rates but does not bring in different types of participants. In contrast, the browser-based alternative can both increase participation rates and reduce biases in who participates if offered immediately once the app had been declined. We find that the success of using mobile apps for data collection hinges on the protocols used to implement the app.

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“…The IAB-SMART study, implemented on a subsample of the German PASS study (Panel Study Labour Market and Social Security) included a similar experiment, which did have an effect: Haas et al ( 2020) reported a higher participation rate if respondents were offered €20 for installing a survey app (16 per cent), than if they were offered €10 (13 per cent). McCool et al (2021) conducted an incentive experiment in a travel app study in the Netherlands. Sample individuals were randomised to three incentive groups, all of whom received an initial €5 incentive: (1) the group received an extra €5 when they installed the app and another €5 if the app was active for seven days;…”

Section: Respondent Incentivesmentioning

confidence: 99%

Understanding Society: minimising selection biases in data collection using mobile apps

Jäckle,

Burton,

Couper

2023

Fiscal Studies

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Understanding Society, the UK Household Longitudinal Study has a programme of research and development that underpins innovations in data collection methods. One of our current focuses is on using mobile applications to collect additional data that supplement data collected in annual interviews. To date, we have used mobile apps to collect data on consumer expenditure, well‐being, anthropometrics and cognition. In this paper, we review the potential barriers to data collection using mobile apps and experimental evidence collected with the Understanding Society Innovation Panel, on what can be done to reduce these barriers.

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An App-Assisted Travel Survey in Official Statistics: Possibilities and Challenges

Cited by 15 publications

References 35 publications

Sharing Data Collected with Smartphone Sensors

Sharing Data Collected with Smartphone Sensors

Increasing Participation in a Mobile App Study: The Effects of a Sequential Mixed-Mode Design and In-Interview Invitation

Understanding Society: minimising selection biases in data collection using mobile apps

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