Coverage Error in Data Collection Combining Mobile Surveys With Passive Measurement Using Apps: Data From a German National Survey

Self Cite

As smartphones become increasingly prevalent, social scientists are recognizing the ubiquitous data generated by the sensors built into these devices as an innovative data source. Passively collected data from sensors that measure geolocation or movement provide an unobtrusive way to observe participants in everyday situations and are free from reactivity biases. Information on day-to-day geolocation could provide valuable insights into human behavior that cannot be collected via surveys. However, little is known about the quality of the resulting data. Using data from a 2018 German population-based probability app study, this article focuses on the measurement quality of geolocation sensor data, with a strong focus on missing measurements. Geolocation sensor data are an example of an available data type that is of interest to social science research. Our findings can be applied to the wider subject of sensor data. In our article, we demonstrate (1) that sensor data are far from error-free. Instead, device-related error sources, such as the manufacturer and operating system settings, design decisions of the research app, third-party apps, and the participant, can interfere with the measurement. To disentangle the different influences, we (2) apply a multistage error model to analyze and control the error sources in the specific missingness process of geolocation data. We (3) raise awareness of error sources in geolocation measurement, such as the use of GPS falsifier apps, or device sharing among participants. By identifying the different error sources and analyzing their determinants, we recommend (4) identification strategies for future research.

Section: The Iab-smart Studymentioning

confidence: 99%

Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data

Bähr

Haas

Keusch

et al. 2020

Self Cite

“…Moreover, mobile phone applications are able to provide respondents with information and questionnaires. Although previous research has shown that consent to using mobile phone apps is selective, there is little evidence on the reasoning behind this selectivity (an exception is the recent publication by Keusch & Bähr et al, 2020). Previous research on face-to-face interviews and interviewer effects gives rise to the hypothesis that interviewers play a crucial role.…”

Section: Interviewer Effects In Survey Datamentioning

confidence: 99%

“…Despite its great potential, evidence regarding the utility of smartphones and apps in social science research is mixed and many challenges remain. To begin with, presumably not all members of a target population own a smartphone (Keusch, Bähr et al, 2020). Therefore, field institutes need to find solutions that avoid underrepresenting substantial parts of the target population such as the elderly or poor (Fuchs & Busse, 2009; Hoogeveen et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Using a Mobile App When Surveying Highly Mobile Populations: Panel Attrition, Consent, and Interviewer Effects in a Survey of Refugees

Jacobsen

Kühne

2021

Panel attrition poses major threats to the survey quality of panel studies. Many features have been introduced to keep panel attrition as low as possible. Based on a random sample of refugees, a highly mobile population, we investigate whether using a mobile phone application improves address quality and response behavior. Various features, including geo-tracking, collecting email addresses and adress changes, are tested. Additionally, we investigate respondent and interviewer effects on the consent to download the app and sharing GPS geo-positions. Our findings show that neither geo-tracking nor the provision of email addresses nor the collection of address changes through the app improves address quality substantially. We further show that interviewers play an important role in convincing the respondents to install and use the app, whereas respondent characteristics are largely insignificant. Our findings provide new insights into the usability of mobile phone applications and help determine whether they are a worthwhile tool to decrease panel attrition.

“…The growing rate of smartphone adoption does not solve the coverage problem if those who use smartphones are different from those who do not in the characteristics of interest and if individuals do not have the smartphone skills required to install apps or share smartphone sensor data in browser. For example, in Germany, smartphone ownership correlates with age, educational attainment, immigrant status, nationality, region, and community size (Keusch, Bähr, et al, 2020). These variables are related to many concepts of interest in the social sciences.…”

Section: Challenges Of Sensor and App-based Data Collectionmentioning

confidence: 99%

“…These variables are related to many concepts of interest in the social sciences. Moreover, among those who use smartphones, there is an operating system divide: iOS users (i.e., iPhone owners) differ significantly from smartphone owners whose phones run on the Android OS in attitudinal and behavioral characteristics; and these differences cannot be corrected by weighting based on sociodemographic information (Keusch, Bähr, et al, 2020). Researchers implementing their studies with sensors on smartphones should be cognizant of possible existence of such biases.…”

Section: Challenges Of Sensor and App-based Data Collectionmentioning

confidence: 99%

Augmenting Surveys With Data From Sensors and Apps: Opportunities and Challenges

Struminskaya

Lugtig

Keusch

et al. 2020

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The increasing volume of “Big Data” produced by sensors and smart devices can transform the social and behavioral sciences. Several successful studies used digital data to provide new insights into social reality. This special issue argues that the true power of these data for the social sciences lies in connecting new data sources with surveys. While new digital data are rich in volume, they seldomly cover the full population nor do they provide insights into individuals’ feelings, motivations, and attitudes. Conversely, survey data, while well suited for measuring people’s internal states, are relatively poor at measuring behaviors and facts. Developing a methodology for integrating the two data sources can mitigate their respective weaknesses. Sensors and apps on smartphones are useful for collecting both survey data and digital data. For example, smartphones can track people’s travel behavior and ask questions about its motives. A general methodology on the augmentation of surveys with data from sensors and apps is currently missing. Issues of representativeness, processing, storage, data linkage, and how to combine survey data with sensor and app data to produce one statistic of interest pertain. This editorial to the special issue on “Using Mobile Apps and Sensors in Surveys” provides an introduction to this new field, presents an overview of challenges, opportunities, and sets a research agenda. We introduce the four papers in this special issue that focus on these opportunities and challenges and provide practical applications and solutions for integrating sensor- and app-based data collection into surveys.