Bella Struminskaya
Biographic Data
| ID | 197102 |
|---|---|
| NAME | Bella Struminskaya |
| GIVEN NAMES | Bella |
| FAMILY NAME | Struminskaya |
| SIGNATURE | STRUMINSKAYA B |
| AFFILIATIONS | Utrecht University |
| ORCID | 0000-0002-5944-8163 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 138 |
| AUTHOR COUNT | 17 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 6 |
Meaningful informed consent? How participants experience and understand data donation
Data donation makes it possible to invite participants to request and share their data from digital platforms for research purposes. While it is a user-centric approach to digital trace data collection, little is known about how participants experience and understand the data donation process. This study therefore asked twenty participants to verbalize their thoughts and actions as they went through data donation (from Google, YouTube, Facebook, …
How Much Data Should I Request? Balancing Richness and Compliance in Digital Trace Data Donations
Digital trace “data donation” studies offer researchers a unique opportunity to collect high-quality behavioral data, but decisions about the scope of requested data can impact both dataset richness and participant compliance. This paper examines the tradeoffs between requesting larger data packages, which include more extensive historical records, and participants’ willingness to donate. In a randomized experiment with Facebook and Instagram dat…
Best practices for studies using digital data donation
Digital trace data form a rich, growing source of data for social sciences and humanities. Data donation offers an innovative and ethical approach to collect these digital trace data. In data donation studies, participants request a copy of the digital trace data a data controller (e.g., large digital social media or video platforms) collected about them. The European Union’s General Data Protection Regulation obliges platforms to provide such a …
Can tailored recruitment messaging increase digital trace data donation compliance
Monitoring Attitudes Over Time: Real Change or the Result of Repeated Interviewing
Panel data are often used to study change and stability in social patterns. However, repeated interviewing may affect respondents' attitudes in a panel study by triggering reflection processes on the surveyed topics (cognitive stimulus hypothesis) . Using data from a survey experiment within a probability-based and a nonprobability panel in Germany, we investigate change-and the mechanisms underlying change-in respondents' abortion attitudes over…
Fulfilling data access obligations: How could (and should) platforms facilitate data donation studies
Research into digital platforms has become increasingly difficult. One way to overcome these difficulties is to build on data access rights in EU data protection law, which requires platforms to offer users a copy of their data. In data donation studies, researchers ask study participants to exercise this right and donate their data to science. However, there is increasing evidence that platforms do not comply with designated laws. We first discu…
Early and Late Participation during the Field Period: Response Timing in a Mixed-Mode Probability-Based Panel Survey
Reluctance of respondents to participate in surveys has long drawn the attention of survey researchers. Yet, little is known about what drives a respondent’s decision to answer the survey invitation early or late during the field period. Moreover, we still lack evidence on response timing in longitudinal surveys. That is, the questions on whether response timing is a rather stable respondent characteristic and what—if anything—affects change in r…
Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources
Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical eviden…
Augmenting Surveys with Paradata, Administrative Data, and Contextual Data
Understanding Willingness to Share Smartphone-Sensor Data
The growing smartphone penetration and the integration of smartphones into people’s everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipa…
Sharing Data Collected with Smartphone Sensors
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. Resear…
A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research
There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about proba…
Augmenting Surveys With Data From Sensors and Apps: Opportunities and Challenges
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…
Does panel conditioning affect data quality in ego-centered social network questions
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…
Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany: The Gesis Panel
Various open probability-based panel infrastructures have been established in recent years, allowing researchers to collect high-quality survey data. In this report, we describe the processes and deliverables of setting up the GESIS Panel, the first probability-based mixed-mode panel infrastructure in Germany open for data collection to the academic research community. The reference population for the GESIS Panel is the German-speaking population…
Respondent Conditioning in Online Panel Surveys: Results of Two Field Experiments
In this article, we investigate changes in survey reporting due to prior interviewing. Two field experiments were implemented in a probability-based online panel in which the order of the questionnaires was switched. Although experimental methods for studying panel conditioning are favorable, experiments in longitudinal studies are rare. Studies on conditioning demand additional resources and might influence respondents’ answers. Panel conditioni…
Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany: The Gesis Panel
Various open probability-based panel infrastructures have been established in recent years, allowing researchers to collect high-quality survey data. In this report, we describe the processes and deliverables of setting up the GESIS Panel, the first probability-based mixed-mode panel infrastructure in Germany open for data collection to the academic research community. The reference population for the GESIS Panel is the German-speaking population…
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…
Respondent Conditioning in Online Panel Surveys: Results of Two Field Experiments
In this article, we investigate changes in survey reporting due to prior interviewing. Two field experiments were implemented in a probability-based online panel in which the order of the questionnaires was switched. Although experimental methods for studying panel conditioning are favorable, experiments in longitudinal studies are rare. Studies on conditioning demand additional resources and might influence respondents’ answers. Panel conditioni…
Sharing Data Collected with Smartphone Sensors
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. Resear…
Understanding Willingness to Share Smartphone-Sensor Data
The growing smartphone penetration and the integration of smartphones into people’s everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipa…
Augmenting Surveys With Data From Sensors and Apps: Opportunities and Challenges
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…
Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources
Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical eviden…
Early and Late Participation during the Field Period: Response Timing in a Mixed-Mode Probability-Based Panel Survey
Reluctance of respondents to participate in surveys has long drawn the attention of survey researchers. Yet, little is known about what drives a respondent’s decision to answer the survey invitation early or late during the field period. Moreover, we still lack evidence on response timing in longitudinal surveys. That is, the questions on whether response timing is a rather stable respondent characteristic and what—if anything—affects change in r…
Augmenting Surveys with Paradata, Administrative Data, and Contextual Data
Monitoring Attitudes Over Time: Real Change or the Result of Repeated Interviewing
Panel data are often used to study change and stability in social patterns. However, repeated interviewing may affect respondents' attitudes in a panel study by triggering reflection processes on the surveyed topics (cognitive stimulus hypothesis) . Using data from a survey experiment within a probability-based and a nonprobability panel in Germany, we investigate change-and the mechanisms underlying change-in respondents' abortion attitudes over…
Respondent Conditioning in Online Panel Surveys: Results of Two Field Experiments
In this article, we investigate changes in survey reporting due to prior interviewing. Two field experiments were implemented in a probability-based online panel in which the order of the questionnaires was switched. Although experimental methods for studying panel conditioning are favorable, experiments in longitudinal studies are rare. Studies on conditioning demand additional resources and might influence respondents’ answers. Panel conditioni…
Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany: The Gesis Panel
Various open probability-based panel infrastructures have been established in recent years, allowing researchers to collect high-quality survey data. In this report, we describe the processes and deliverables of setting up the GESIS Panel, the first probability-based mixed-mode panel infrastructure in Germany open for data collection to the academic research community. The reference population for the GESIS Panel is the German-speaking population…
Does panel conditioning affect data quality in ego-centered social network questions
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…
A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research
There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about proba…
Augmenting Surveys With Data From Sensors and Apps: Opportunities and Challenges
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…
Understanding Willingness to Share Smartphone-Sensor Data
The growing smartphone penetration and the integration of smartphones into people’s everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipa…
Sharing Data Collected with Smartphone Sensors
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. Resear…
Early and Late Participation during the Field Period: Response Timing in a Mixed-Mode Probability-Based Panel Survey
Reluctance of respondents to participate in surveys has long drawn the attention of survey researchers. Yet, little is known about what drives a respondent’s decision to answer the survey invitation early or late during the field period. Moreover, we still lack evidence on response timing in longitudinal surveys. That is, the questions on whether response timing is a rather stable respondent characteristic and what—if anything—affects change in r…
Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources
Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical eviden…
Augmenting Surveys with Paradata, Administrative Data, and Contextual Data
Fulfilling data access obligations: How could (and should) platforms facilitate data donation studies
Research into digital platforms has become increasingly difficult. One way to overcome these difficulties is to build on data access rights in EU data protection law, which requires platforms to offer users a copy of their data. In data donation studies, researchers ask study participants to exercise this right and donate their data to science. However, there is increasing evidence that platforms do not comply with designated laws. We first discu…
Best practices for studies using digital data donation
Digital trace data form a rich, growing source of data for social sciences and humanities. Data donation offers an innovative and ethical approach to collect these digital trace data. In data donation studies, participants request a copy of the digital trace data a data controller (e.g., large digital social media or video platforms) collected about them. The European Union’s General Data Protection Regulation obliges platforms to provide such a …
Can tailored recruitment messaging increase digital trace data donation compliance
Monitoring Attitudes Over Time: Real Change or the Result of Repeated Interviewing
Panel data are often used to study change and stability in social patterns. However, repeated interviewing may affect respondents' attitudes in a panel study by triggering reflection processes on the surveyed topics (cognitive stimulus hypothesis) . Using data from a survey experiment within a probability-based and a nonprobability panel in Germany, we investigate change-and the mechanisms underlying change-in respondents' abortion attitudes over…
Meaningful informed consent? How participants experience and understand data donation
Data donation makes it possible to invite participants to request and share their data from digital platforms for research purposes. While it is a user-centric approach to digital trace data collection, little is known about how participants experience and understand the data donation process. This study therefore asked twenty participants to verbalize their thoughts and actions as they went through data donation (from Google, YouTube, Facebook, …
How Much Data Should I Request? Balancing Richness and Compliance in Digital Trace Data Donations
Digital trace “data donation” studies offer researchers a unique opportunity to collect high-quality behavioral data, but decisions about the scope of requested data can impact both dataset richness and participant compliance. This paper examines the tradeoffs between requesting larger data packages, which include more extensive historical records, and participants’ willingness to donate. In a randomized experiment with Facebook and Instagram dat…
Computer Science (15 works) · Psychology (10 works) · Survey Methodology and Nonresponse (10 works) · Data collection (8 works) · Internet privacy (8 works) · Statistics (8 works) · Mathematics (7 works) · Sociology (7 works) · Medicine (6 works) · Data science (5 works)