Research
My research focuses on the development, implementation, and assessment of modern methods of collecting data for the behavioral and social sciences. In particular, I am interested in how to jointly collect self-reports (usually via (mobile) web surveys) and passive measurement from smartphone and wearable sensors, online and device log files, Internet search queries, and other digital traces to better study research questions on migration, employment, and inequality. Here are a number of research projects, I am currently working on:
Integration research 2.0 – Harnessing the power of new data sources to advance knowledge on behavior and attitudes of migrants and natives
For decades, social scientists have relied on self-reported data from surveys to study integration efforts of refugees and migrants as well as natives’ attitudes on immigrants and immigration policies. Together with administrative records, these data are an important resource for decision-makers on every federal level to manage integration tasks and design integration policies. However, the collection of these data can be slow and expensive, and they are susceptible to social desirable reporting. As a consequence, the resulting findings are often only available after an extended period of time and potentially biased. To overcome these issues, we study three new forms of data and novel approaches to data collection that promise faster, more frequent, and potentially also more accurate information for social science research in general and studies on immigration and integration in particular. Read more...
IAB-SMART: Evaluating digital trace data to examine social integration, social networks, and work-related stress in the labor market context
In the landmark “Marienthal Study,” Jahoda et al. (1933) observed and recorded activities in a small Austrian town after a massive lay-off. Field workers were deployed and measured the inhabitants’ walking speed in key parts of town. Because in-person observations did not scale, such data collections have not taken off and instead surveys were used to report on behavior. Using smartphone technology, these and other activities do not necessarily have to be observed by researchers but can be measured passively with sensors. Read more...
Concerns and willingness to use smartphones for data collection
Smartphone use is on the rise worldwide, and researchers are exploring novel ways to leverage the capabilities of smartphones for data collection, including mobile surveys and the use of smartphone features that allow researchers to automatically measure an even broader set of characteristics and behaviors of users (e.g., taking pictures of receipts to better measure expenditure, tracking of movements to create exact measures of mobility and transportation, automatically log app use, Internet searches, and phone calling and text messaging behavior to measure social interaction). These new forms of data collection provide rich data and have the potential to decrease respondent burden and measurement error. However, agreeing to engage in these forms of data collection from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing specific data with researchers due to security, privacy, and confidentiality concerns. Read more...
Supplementing and substituting survey data with digital trace data
For many years, surveys were the standard tool to measure attitudes and behavior for social science research. In recent years, however, researchers have shifted their focus to new sources of data, especially in the online world. For instance, researchers have analyzed the potentials of replacing or supplementing survey data with data from social media (e.g., Twitter, Reddit), smart devices (e.g., smartphones or fitness tracker), and data from other places where people leave digital traces. In this project, we explore the feasibility of using behavioral records of individuals’ online activities to study political attitudes and behavior. Read more...
Trust when sharing data online
Decisions about confidentiality protection measures to be applied to data dissemination must be informed by evidence about the utility associated with the quality of the data and the willingness to trade utility against the estimated risk. Doing so requires measurement of data utility, risk, and the willingness of individuals to trade risk for utility. From the theoretical literature on measuring privacy (Nissenbaum 2011) and trust (Bauer and Freitag 2018), perceptions of trust and privacy are context dependent. There are three dimensions that are particular important: (1) to whom the data is provided, (2) what is done with the data (i.e., whether there are benefits for the one receiving the data vs. benefits for the one providing the data), and (3) what kind of data is shared (i.e., the sensitivity of the data). Read more...
Older projects
Integration research 2.0 – Harnessing the power of new data sources to advance knowledge on behavior and attitudes of migrants and natives
For decades, social scientists have relied on self-reported data from surveys to study integration efforts of refugees and migrants as well as natives’ attitudes on immigrants and immigration policies. Together with administrative records, these data are an important resource for decision-makers on every federal level to manage integration tasks and design integration policies. However, the collection of these data can be slow and expensive, and they are susceptible to social desirable reporting. As a consequence, the resulting findings are often only available after an extended period of time and potentially biased. To overcome these issues, we study three new forms of data and novel approaches to data collection that promise faster, more frequent, and potentially also more accurate information for social science research in general and studies on immigration and integration in particular. Read more...
IAB-SMART: Evaluating digital trace data to examine social integration, social networks, and work-related stress in the labor market context
In the landmark “Marienthal Study,” Jahoda et al. (1933) observed and recorded activities in a small Austrian town after a massive lay-off. Field workers were deployed and measured the inhabitants’ walking speed in key parts of town. Because in-person observations did not scale, such data collections have not taken off and instead surveys were used to report on behavior. Using smartphone technology, these and other activities do not necessarily have to be observed by researchers but can be measured passively with sensors. Read more...
Concerns and willingness to use smartphones for data collection
Smartphone use is on the rise worldwide, and researchers are exploring novel ways to leverage the capabilities of smartphones for data collection, including mobile surveys and the use of smartphone features that allow researchers to automatically measure an even broader set of characteristics and behaviors of users (e.g., taking pictures of receipts to better measure expenditure, tracking of movements to create exact measures of mobility and transportation, automatically log app use, Internet searches, and phone calling and text messaging behavior to measure social interaction). These new forms of data collection provide rich data and have the potential to decrease respondent burden and measurement error. However, agreeing to engage in these forms of data collection from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing specific data with researchers due to security, privacy, and confidentiality concerns. Read more...
Supplementing and substituting survey data with digital trace data
For many years, surveys were the standard tool to measure attitudes and behavior for social science research. In recent years, however, researchers have shifted their focus to new sources of data, especially in the online world. For instance, researchers have analyzed the potentials of replacing or supplementing survey data with data from social media (e.g., Twitter, Reddit), smart devices (e.g., smartphones or fitness tracker), and data from other places where people leave digital traces. In this project, we explore the feasibility of using behavioral records of individuals’ online activities to study political attitudes and behavior. Read more...
Trust when sharing data online
Decisions about confidentiality protection measures to be applied to data dissemination must be informed by evidence about the utility associated with the quality of the data and the willingness to trade utility against the estimated risk. Doing so requires measurement of data utility, risk, and the willingness of individuals to trade risk for utility. From the theoretical literature on measuring privacy (Nissenbaum 2011) and trust (Bauer and Freitag 2018), perceptions of trust and privacy are context dependent. There are three dimensions that are particular important: (1) to whom the data is provided, (2) what is done with the data (i.e., whether there are benefits for the one receiving the data vs. benefits for the one providing the data), and (3) what kind of data is shared (i.e., the sensitivity of the data). Read more...
Older projects
