Florian Keusch
Biographic Data
| ID | 214421 |
|---|---|
| NAME | Florian Keusch |
| GIVEN NAMES | Florian |
| FAMILY NAME | Keusch |
| SIGNATURE | KEUSCH F |
| AFFILIATIONS | University of Mannheim |
| ORCID | 0000-0003-1002-4092 |
| VERIFIED | Yes |
| TOTAL WORKS | 33 |
| TOTAL CITATIONS | 179 |
| WORKS AS AUTHOR | 33 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 7 |
Causal Inferences from Digital Behavioral Data
In recent years, digital behavioral data (DBD) have emerged as a powerful resource in social science research. Their ubiquity, granularity, complexity, and continuous collection provide new opportunities for examining social processes in great detail. However, because DBD are diverse in type and often constitute found data—not generated for research purposes—their potential for causal analysis is commonly underestimated. To address this issue, th…
To share or not to share – understanding individuals’ willingness to share biomarkers, sensor data, and medical records
Technological advances in the recent past made it possible for researchers to collect and analyze large amounts of health data at unprecedented scale and speed. For example, fitness trackers and smartwatches produce steady flows of information on individuals’ health. Biomarker data and medical records allow to study individuals at new levels of granularity. The COVID-19 pandemic has highlighted that access to such data for health research and evi…
Using hidden Markov models to assess and correct for measurement error in digital trace data
Digital trace data are increasingly used across the social and behavioral sciences. They allow researchers to access large volumes of highly detailed and continuous information. Such scale and speed cannot be achieved when using traditional sources, such as surveys. Digital traces are also believed to overcome some of the limitations that surveys are criticized for. However, while their use undoubtedly presents researchers with new possibilities,…
An Experimental Approach to Assessing Young Women's Childbearing Preferences
Although the mean U.S. ideal family size has remained relatively stable in recent years, reflecting a widespread preference for two-child families, we know very little about the strength of this preference among young adults. To examine the relative strength of preferences for family size relative to other attributes of family life, in this research note we conduct a forced-choice online conjoint survey experiment using a nationally representativ…
Linking survey and Facebook data
Measuring Smartphone Use
While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage …
Asking for Traces
Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example,…
Estimating Measurement Quality in Digital Trace Data and Surveys Using the MultiTrait MultiMethod Model
Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphon…
The (mis)use of Google Trends data in the social sciences - A systematic review, critique, and recommendations
Researchers increasingly use aggregated search data from Google Trends to study a wide range of phenomena. Although this new data source possesses some important practical and methodological benefits, it also carries substantial challenges with respect to internal validity, reliability, and generalizability. In this paper, we describe and assess the existing applied research with Google Trends data in the social sciences. We conduct a systematic …
Do You Have Two Minutes to Talk about Your Data? Willingness to Participate and Nonparticipation Bias in Facebook Data Donation
Data donation is a novel approach to collecting digital trace data, where users are asked to download their retrospective data from a platform and share them with the researchers. Little is known about the willingness to donate data and the potential bias that may arise from nonparticipation. We conducted a study among over 900 German Facebook users asking them to donate two data packages. While around 80% of participants were willing to donate t…
Consent to data linkage for different data domains – the role of question order, question wording, and incentives
As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimen…
Coverage Error in Data Collection Combining Mobile Surveys With Passive Measurement Using Apps
Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targetin…
Increasing the Acceptance of Smartphone-Based Data Collection
To study human behavior, social scientists are increasingly collecting data from mobile apps and sensors embedded in smartphones. A major challenge of studies implemented on general population samples, however, is that participation rates are rather low. While previous research has started to investigate the factors affecting individuals’ decision to participate in such studies, less is known about features of the study design which are under the…
Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors
Did the GDPR increase trust in data collectors? Evidence from observational and experimental data
In the wake of the digital revolution and connected technologies, societies store an ever-increasing amount of data on humans, their preferences, and behavior. These modern technologies create a trust challenge, insofar as individuals have to trust data collectors such as private organizations, government institutions, and researchers that their data is not misused. Privacy regulations should increase trust because they provide laws that increase…
The second-level smartphone divide
Smartphones have become one of the most frequently used devices for accessing the Internet. Although a growing body of research has examined the second-level digital divide with a focus on general Internet and digital media use, little is known about patterns of smartphone use and smartphone-related skills in the general population. This paper examines inequalities in the use of smartphone technology based on two nationally representative samples…
Reactivity in measuring sensitive online behavior
Purpose Digital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very lit…
A preregistered vignette experiment on determinants of health data sharing behavior
The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data …
New Data in Social and Behavioral Research
A decade ago, in this journal's 75th anniversary special issue, Groves (2011) called for the survey research community to collectively assess the role of what he called "organic" data in scientifically measuring social phenomena.What distinguishes these new potential sources of data-also called "found" (Taylor 2013) or "ready-made" (Salganik 2018)-is that they are not elicited by researchers, but rather are continuously generated by ongoing, ever…
Using Smartphone Technology for Research on Refugees
Researchers attempting to survey refugees over time face methodological issues because of the transient nature of the target population. In this article, we examine whether applying smartphone technology could alleviate these issues. We interviewed 529 refugees and afterward invited them to four follow-up mobile web surveys and to install a research app for passive mobile data collection. Our main findings are as follows: First, participation in …
Do surveys change behaviour? Insights from digital trace data
Surveys continue to be a popular way of collecting data in the social sciences. But, despite their popularity they have a number of limitations including the possibility of changing the behaviour of respondents. Such mere-measure or question-behaviour effects can compromise the external validity of social data. In this article, we use digital trace data collected from PCs and mobile devices to investigate the effects of surveys on news and politi…
Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data
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 beh…
Augmenting Surveys With Data From Sensors and Apps
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…
New Data Sources in Social Science Research
Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are…
Predicting Voting Behavior Using Digital Trace Data
A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we invest…
Willingness to Participate in Passive Mobile Data Collection
The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonco…
Collecting Survey and Smartphone Sensor Data With an App
The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability app study in which participants were asked through a GDPR compliant consent process to share a series of digital trace data, including geolocation, accelerometer data, phone and text messaging logs, app usage, and access to their address books. With abo…
New Data Sources in Social Science Research
Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are…
Do You Have Two Minutes to Talk about Your Data? Willingness to Participate and Nonparticipation Bias in Facebook Data Donation
Data donation is a novel approach to collecting digital trace data, where users are asked to download their retrospective data from a platform and share them with the researchers. Little is known about the willingness to donate data and the potential bias that may arise from nonparticipation. We conducted a study among over 900 German Facebook users asking them to donate two data packages. While around 80% of participants were willing to donate t…
The Effects of the Direction of Rating Scales on Survey Responses in a Telephone Survey
Rating scales are used extensively in surveys. One design feature of a rating scale that remains understudied is its direction. A rating scale can run from zero to 10, or from “strongly agree” to “strongly disagree.” It can also run from 10 to zero, or “strongly disagree” to “strongly agree.” For rating scales with the same number of scale points, the same numerical and verbal labels, the key research question is whether varying the direction of …
Augmenting Surveys With Data From Sensors and Apps
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…
Predicting Voting Behavior Using Digital Trace Data
A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we invest…
A Typology of Web Survey Paradata for Assessing Total Survey Error
This article reviews the existing literature on the collection of paradata in web surveys and extends the research in this area beyond the commonly studied measurement error problem to paradata that can be collected for managing and mitigating other important sources of error. To do so, and in keeping with the nature of paradata as process-oriented, we develop a typology of web survey paradata that incorporates information from all steps in the w…
Web Versus Mobile Web
Due to a rising mobile device penetration, Web surveys are increasingly accessed and completed on smartphones or tablets instead of desktop computers or laptops. Mobile Web surveys are also gaining popularity as an alternative self-administered data collection mode among survey researchers. We conducted a methodological experiment among iPhone owners and compared the participation and response behavior of three groups of respondents: iPhone owner…
The Impact of Weather on Mobility and Participation in Older US Adults
Objectives. We examined the impact of weather on the daily lives of US adults to understand which populations are most vulnerable to various weather conditions. Methods. Data came from a 2013 supplement to the University of Michigan–Thomson Reuters Surveys of Consumers, a nationally representative telephone survey of 502 adults in the contiguous United States. We used logistic regressions to assess the odds of mobility difficulty and participatio…
New Data in Social and Behavioral Research
A decade ago, in this journal's 75th anniversary special issue, Groves (2011) called for the survey research community to collectively assess the role of what he called "organic" data in scientifically measuring social phenomena.What distinguishes these new potential sources of data-also called "found" (Taylor 2013) or "ready-made" (Salganik 2018)-is that they are not elicited by researchers, but rather are continuously generated by ongoing, ever…
Consent to data linkage for different data domains – the role of question order, question wording, and incentives
As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimen…
Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data
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 beh…
A preregistered vignette experiment on determinants of health data sharing behavior
The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data …
Causal Inferences from Digital Behavioral Data
In recent years, digital behavioral data (DBD) have emerged as a powerful resource in social science research. Their ubiquity, granularity, complexity, and continuous collection provide new opportunities for examining social processes in great detail. However, because DBD are diverse in type and often constitute found data—not generated for research purposes—their potential for causal analysis is commonly underestimated. To address this issue, th…
Estimating Measurement Quality in Digital Trace Data and Surveys Using the MultiTrait MultiMethod Model
Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphon…
The (mis)use of Google Trends data in the social sciences - A systematic review, critique, and recommendations
Researchers increasingly use aggregated search data from Google Trends to study a wide range of phenomena. Although this new data source possesses some important practical and methodological benefits, it also carries substantial challenges with respect to internal validity, reliability, and generalizability. In this paper, we describe and assess the existing applied research with Google Trends data in the social sciences. We conduct a systematic …
Increasing the Acceptance of Smartphone-Based Data Collection
To study human behavior, social scientists are increasingly collecting data from mobile apps and sensors embedded in smartphones. A major challenge of studies implemented on general population samples, however, is that participation rates are rather low. While previous research has started to investigate the factors affecting individuals’ decision to participate in such studies, less is known about features of the study design which are under the…
Using Smartphone Technology for Research on Refugees
Researchers attempting to survey refugees over time face methodological issues because of the transient nature of the target population. In this article, we examine whether applying smartphone technology could alleviate these issues. We interviewed 529 refugees and afterward invited them to four follow-up mobile web surveys and to install a research app for passive mobile data collection. Our main findings are as follows: First, participation in …
How to Increase Response Rates in List-Based Web Survey Samples
The rising popularity of online surveys in marketing research precipitates a flood of e-mail invitations requesting participation from potential respondents. As a result, response rates are diminishing, reflecting a decline in the willingness to participate in web surveys. To compare the effectiveness of different response-enhancing techniques in a list-based web survey, an experiment with a full factorial between-subjects design varying the fact…
Measuring Smartphone Use
While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage …
Coverage Error in Data Collection Combining Mobile Surveys With Passive Measurement Using Apps
Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targetin…
Asking for Traces
Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example,…
The second-level smartphone divide
Smartphones have become one of the most frequently used devices for accessing the Internet. Although a growing body of research has examined the second-level digital divide with a focus on general Internet and digital media use, little is known about patterns of smartphone use and smartphone-related skills in the general population. This paper examines inequalities in the use of smartphone technology based on two nationally representative samples…
Reactivity in measuring sensitive online behavior
Purpose Digital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very lit…
How to Increase Response Rates in List-Based Web Survey Samples
The rising popularity of online surveys in marketing research precipitates a flood of e-mail invitations requesting participation from potential respondents. As a result, response rates are diminishing, reflecting a decline in the willingness to participate in web surveys. To compare the effectiveness of different response-enhancing techniques in a list-based web survey, an experiment with a full factorial between-subjects design varying the fact…
The Impact of Weather on Mobility and Participation in Older US Adults
Objectives. We examined the impact of weather on the daily lives of US adults to understand which populations are most vulnerable to various weather conditions. Methods. Data came from a 2013 supplement to the University of Michigan–Thomson Reuters Surveys of Consumers, a nationally representative telephone survey of 502 adults in the contiguous United States. We used logistic regressions to assess the odds of mobility difficulty and participatio…
The Effects of the Direction of Rating Scales on Survey Responses in a Telephone Survey
Rating scales are used extensively in surveys. One design feature of a rating scale that remains understudied is its direction. A rating scale can run from zero to 10, or from “strongly agree” to “strongly disagree.” It can also run from 10 to zero, or “strongly disagree” to “strongly agree.” For rating scales with the same number of scale points, the same numerical and verbal labels, the key research question is whether varying the direction of …
Web Versus Mobile Web
Due to a rising mobile device penetration, Web surveys are increasingly accessed and completed on smartphones or tablets instead of desktop computers or laptops. Mobile Web surveys are also gaining popularity as an alternative self-administered data collection mode among survey researchers. We conducted a methodological experiment among iPhone owners and compared the participation and response behavior of three groups of respondents: iPhone owner…
A Review of Issues in Gamified Surveys
The term “gamification” is used for a wide variety of techniques aimed to increase respondent engagement while filling out web surveys. Suggested approaches range from rephrasing questions to sound more game-like to embedding the entire survey into a game where respondent avatars adventure through a fantasy land while answering survey questions. So far, only a few experimental studies regarding the influence of gamification on survey responding h…
A Typology of Web Survey Paradata for Assessing Total Survey Error
This article reviews the existing literature on the collection of paradata in web surveys and extends the research in this area beyond the commonly studied measurement error problem to paradata that can be collected for managing and mitigating other important sources of error. To do so, and in keeping with the nature of paradata as process-oriented, we develop a typology of web survey paradata that incorporates information from all steps in the w…
Collecting Survey and Smartphone Sensor Data With an App
The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability app study in which participants were asked through a GDPR compliant consent process to share a series of digital trace data, including geolocation, accelerometer data, phone and text messaging logs, app usage, and access to their address books. With abo…
New Data Sources in Social Science Research
Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are…
Predicting Voting Behavior Using Digital Trace Data
A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we invest…
Willingness to Participate in Passive Mobile Data Collection
The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonco…
Do surveys change behaviour? Insights from digital trace data
Surveys continue to be a popular way of collecting data in the social sciences. But, despite their popularity they have a number of limitations including the possibility of changing the behaviour of respondents. Such mere-measure or question-behaviour effects can compromise the external validity of social data. In this article, we use digital trace data collected from PCs and mobile devices to investigate the effects of surveys on news and politi…
Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data
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 beh…
Augmenting Surveys With Data From Sensors and Apps
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…
New Data in Social and Behavioral Research
A decade ago, in this journal's 75th anniversary special issue, Groves (2011) called for the survey research community to collectively assess the role of what he called "organic" data in scientifically measuring social phenomena.What distinguishes these new potential sources of data-also called "found" (Taylor 2013) or "ready-made" (Salganik 2018)-is that they are not elicited by researchers, but rather are continuously generated by ongoing, ever…
Using Smartphone Technology for Research on Refugees
Researchers attempting to survey refugees over time face methodological issues because of the transient nature of the target population. In this article, we examine whether applying smartphone technology could alleviate these issues. We interviewed 529 refugees and afterward invited them to four follow-up mobile web surveys and to install a research app for passive mobile data collection. Our main findings are as follows: First, participation in …
Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors
Did the GDPR increase trust in data collectors? Evidence from observational and experimental data
In the wake of the digital revolution and connected technologies, societies store an ever-increasing amount of data on humans, their preferences, and behavior. These modern technologies create a trust challenge, insofar as individuals have to trust data collectors such as private organizations, government institutions, and researchers that their data is not misused. Privacy regulations should increase trust because they provide laws that increase…
The second-level smartphone divide
Smartphones have become one of the most frequently used devices for accessing the Internet. Although a growing body of research has examined the second-level digital divide with a focus on general Internet and digital media use, little is known about patterns of smartphone use and smartphone-related skills in the general population. This paper examines inequalities in the use of smartphone technology based on two nationally representative samples…
Reactivity in measuring sensitive online behavior
Purpose Digital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very lit…
A preregistered vignette experiment on determinants of health data sharing behavior
The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data …
Consent to data linkage for different data domains – the role of question order, question wording, and incentives
As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimen…
Coverage Error in Data Collection Combining Mobile Surveys With Passive Measurement Using Apps
Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targetin…
Increasing the Acceptance of Smartphone-Based Data Collection
To study human behavior, social scientists are increasingly collecting data from mobile apps and sensors embedded in smartphones. A major challenge of studies implemented on general population samples, however, is that participation rates are rather low. While previous research has started to investigate the factors affecting individuals’ decision to participate in such studies, less is known about features of the study design which are under the…
Linking survey and Facebook data
Measuring Smartphone Use
While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage …
Computer Science (28 works) · Psychology (22 works) · Internet privacy (19 works) · Survey Methodology and Nonresponse (17 works) · Data collection (15 works) · World Wide Web (14 works) · Statistics (13 works) · Social Psychology (12 works) · Business (11 works) · Data science (11 works)