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Smart surveys: Data collection and logistics 2
| Dr Anne Elevelt (Statistics Netherlands)
Dr Bella Struminskaya (Utrecht University)
Dr Vera Toepoel (Statistics Netherlands)
|Wednesday 19 July, 14:00 - 15:00
Smartphones and sensors can be used to extend traditional data collection. Smart surveys combine primary and secondary data collection and are a hybrid from between traditional types of data (e.g. survey data) and new forms of data (e.g. sensor data and other forms of big data). This includes smartphone apps, external sensors (e.g. wearables or smart meters) and data donation. Smart surveys aim at easing the response task, decreasing the respondent burden and/or improving data measurement accuracy.
Smart surveys are thus very promising, but there are still many questions about how we can successfully apply them on a large scale. Do people really want to participate? How do we convince them? And will they continue to participate if a survey takes several days? In this session we focus on how on collect data in large-scale smart survey projects. We would like to share experiences from ongoing pilots and large-scale projects, and open up a discussion so that we can better exploit smart surveys potential.
In this session, we invite papers that focus on the logistics and data collection strategies of smart survey projects. This includes topics such as:
- Examples of logistics and data collection strategies
- Current practices, implementations or survey protocols
- Design choices in smart survey projects
- Sampling and contacting respondents
- Recruitment strategies, such as interviewers, letters and incentives
- Strategies to reduce drop-out and increase respondent motivation over time
In addition, all other topics relevant to the session are welcome. For questions please contact the session organisers.
Keywords: Smart surveys; Data collection; Logistics; Recruitment; Motivation.
Ms Evelien Rodenburg (Ministry of Social Affairs and Employment)
Professor Barry Schouten (Statistics Netherlands)
Dr Bella Struminskaya (Utrecht University) - Presenting Author
Mr Tom Oerlemans (Statistics Netherlands)
The wide-spread and increased use of smartphones for daily activities enables innovations in data collection in the social sciences and official statistics. For diary studies, smartphone-based data collection can potentially reduce recall errors and response burden. Using smartphone’s cameras, participants can take pictures of receipts for budget surveys and food intake questionnaires; passive geolocation measurement can replace questions in travel surveys. However, to ensure representation, participants have to be willing and able to use their smartphones to perform such tasks. Moreover, participants’ motivation to provide information is key to ensuring high measurement quality. A randomized experiment was implemented in the general population app-based Household Budget Survey (part of the Eurostat’s ESSnet Smart Surveys I) in three countries (Netherlands, Luxembourg, Spain; N=4,000). Three factors were manipulated: (1) whether interviewers recruited participants vs. mail-based recruitment, (2) whether participants were promised personalized feedback on their spending vs. no feedback mentioned, and (3) whether participants were shown insights on the automated text extraction from the uploaded photos of receipts vs. no insights. Interviewer assistance can lower the initial hurdle of app installation and increase participants’ motivation to provide data. Personalized feedback can also increase motivation: people are used to receiving insights about their behavior from commercial apps (e.g., on physical activity, screen time). We will report on the recruitment success and the data quality by these conditions across countries. For the Netherlands, will assess nonparticipation bias using the administrative data linked to the app data. As providing insights can improve data quality (e.g., participants correct information about purchases) or have negative effects (e.g., participants change spending behavior), we compare the participation outcomes and data quality indicators among the two insights groups.
Mr Patrick Lusyne (Statistics Belgium)
Ms Hannelore Van Der Beken (Statistics Belgium) - Presenting Author
Mr Joeri Minnen (Hbits)
Mr Theun van Tienoven (Free University Brussels)
Over the last few years, Statistics Belgium has steadily built up know-how and experience in implementing surveys via smartphone. We use the MO:TUS platform for this purpose, a data platform and mobile phone application developed by the Free University Brussels and Hbits. We tested MO:TUS in several innovative projects, which resulted in various experimental use cases.
Last year, in spring, the step to real implementation was taken by launching the 2022 wave of the Belgian Time Use Survey (BETUS 22). However, early in the fieldwork, it became apparent that results were lagging. Therefore, the fieldwork was put on hold in autumn.
In this paper, we bring an open-hearted account of this experience. We describe the research in terms of organization and logistics and speculate about the 'why's' for the failure. While the main culprit is easy to identify, the dramatically low participation rate, hiding behind respondents' reluctance to participate, would be too easy. Instead, we dig deeper and try to identify causes related to the interplay of design choices, the communication- and data-collection strategy, our overall approach to this research, and mobile surveys in general.
We strongly advocate the idea behind Edward Albee's quote in the title. While going into the field involved a certain amount of risk, we nevertheless learned a lot from this experience, even to the extent of providing an incentive to participate in future projects. In this paper, we also look ahead to these plans.
Mr Ricardo Gonzalez (LEAS at Universidad Adolfo Ibañez) - Presenting Author
Mr Adolfo Fuentes (The University of Edinburgh)
In Chile, research on mobility patterns and daily travel routines has usually relied on self-reports from probability, face-to-face surveys or self-completed, non-probability surveys. Such studies are severely limited by sampling and measurement errors. Particularly, research comparing self-reports with GPS data shows that self-reporting respondents underestimate the number and distance of trips and overestimate their duration. We develop a smartphone-based travel app to collect data on respondents’ trips collected passively through GPS sensors to overcome these problems. However, this requires respondents to use the app over the study’s time span. Recent quantitative studies show the demographic profile of participants deviates from the general population across different contexts. Nevertheless, there is scant evidence on respondents’ motivations and barriers to participate in a study involving a smartphone-based travel app.
This research delves deeper into identifying both motivations and barriers to survey participation in a smartphone-based travel app study by conducting in-depth interviews with members of the public who agreed to use the app for a few days. Participants were recruited from Chile’s Metropolitan region with a mix of gender, age, socioeconomic background and labor status. We classify participants into frequent respondents, infrequent respondents, and nonrespondents. We show how these three groups’ motivations and barriers are associated with traditional theories of survey participation (e.g. leverage-salience theory, benefit-cost theory, theory of reasoned action) and with six attributes of the app itself: user interface attractiveness, privacy and security, portability, compatibility, ease of use, and relative advantages.
We believe that a deep understanding of respondents’ motivations and barriers to participation is needed to inform survey practitioners on what can be done to increase usually low participation rates in a study involving a smartphone-based travel app.