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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 types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents’ data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.

Survey questionnaires often contain skip patterns, which let respondents skip over entire sections or a set of follow‐up questions that do not apply to them, and thus allow them to proceed through the interview faster. Survey designers might ask how filter and follow‐up questions can be presented to reduce the risk of motivated underreporting. We designed a series of experiments in which we varied the salience of the repetitive nature of filter and follow‐up questions. The results show that changes in topic removed the salience of the filtering patterns. Using slightly varied follow‐up questions and reducing the repetitiveness of the task increased endorsements to filter questions and thus successfully mitigated the effect of motivated underreporting. On the other hand, a visualization of the filtering by graying out items that no longer need to be answered reduced endorsements. Implications for questionnaire design involving filter questions are discussed.

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 used to working, so the assumptions often used to plan and manage a project may no longer hold. For example, social media data are so large that they may not be able to be processed on a single machine; they are in file formats with which many researchers are unfamiliar, and they require a level of data transformation and processing that has rarely been required when using more traditional data sources (e.g., survey data). Unfortunately, this type of information is often not obvious ahead of time as much of this knowledge is gained through word-of-mouth and experience. In this article, we attempt to document several challenges and opportunities encountered when working with Reddit, the self-proclaimed “front page of the Internet” and popular social media site. Specifically, we provide descriptive information about the Reddit site and its users, tips for using organic data from Reddit for social science research, some ideas for conducting a survey on Reddit, and lessons learned in merging survey responses with Reddit posts. While this article is specific to Reddit, researchers may also view it as a list of the type of information one may seek to acquire prior to conducting a project that uses any type of social media data.

Online-Befragungen sind zu einer ublichen und haufig praferierten Datenerhebungsmethode geworden. Die hohe Flexibilitat und Anpassbarkeit ist ein wesentlicher Vorteil dieser Technologie. Die technischen Moglichkeiten werden oft von Umfrageentwicklern verwendet, beispielsweise um die Befragten mithilfe automatischer Filter durch den Fragebogen zu fuhren. Andere Features wie beispielsweise Mausbewegungen konnen eingesetzt werden, um einzelne Befragte zu identifizieren, die besonderer Aufmerksamkeit bedurfen. Forscher aus verschiedenen Disziplinen haben insbesondere die zuruckgelegte Distanz, den Pfad der Maus und andere Bewegungsmuster analysiert, um damit Interesse, Unsicherheit und aufgetretene Schwierigkeiten beim Befragten zu messen. Die aktuelle Studie strebt die Entwicklung von Indikatoren und automatischen Prozeduren an, mit deren Hilfe Schwierigkeiten des Befragten diagnostiziert und quantifiziert werden sollen. Zu diesem Zweck wird auf vielversprechende Indikatoren aus der vorherigen Forschung und auf jungste methodologische Fortschritte aus der Psychologie zuruckgegriffen. Die psychologische Literatur schlagt vor, auf Basis von Mausbewegungen den kognitiven Zwiespalt zwischen einzelnen Antwortalternativen bzw. die Unsicherheit bei der Auswahl zu beurteilen.

Significance The US COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. As the largest public health survey conducted in the United States to date, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid revision to address emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing, and vaccination in relation to evolving high-priority policy questions over 12 mo of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting., The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Significance The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats., Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Human perceptions of fairness in (semi-)automated decision-making (ADM) constitute a crucial building block toward developing human-centered ADM solutions. However, measuring fairness perceptions is challenging because various context and design characteristics of ADM systems need to be disentangled. Particularly, ADM applications need to use the right degree of automation and granularity of data input to achieve efficiency and public acceptance. We present results from a large-scale vignette experiment that assessed fairness perceptions and the acceptability of ADM systems. The experiment varied context and design dimensions, with an emphasis on who makes the final decision. We show that automated recommendations in combination with a final human decider are perceived as fair as decisions made by a dominant human decider and as fairer than decisions made only by an algorithm. Our results shed light on the context dependence of fairness assessments and show that semi-automation of decision-making processes is often desirable.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is posing a global public health burden. These consequences have been shown to increase the risk of mental distress, but the underlying protective and risk factors for mental distress and trends over different waves of the pandemic are largely unknown. Furthermore, it is largely unknown how mental distress is associated with individual protective behavior. Three quota samples, weighted to represent the population forming the German COVID-19 Snapshot Monitoring study (24 March and 26 May 2020, and 9 March 2021 with &gt, 900 subjects each), were used to describe the course of mental distress and resilience, to identify risk and protective factors during the pandemic, and to investigate their associations with individual protective behaviors. Mental distress increased slightly during the pandemic. Usage of cognitive reappraisal strategies, maintenance of a daily structure, and usage of alternative social interactions decreased. Self-reported resilience, cognitive reappraisal strategies, and maintaining a daily structure were the most important protective factors in all three samples. Adherence to individual protective behaviors (e.g., physical distancing) was negatively associated with mental distress and positively associated with frequency of information intake, maintenance of a daily structure, and cognitive reappraisal. Maintaining a daily structure, training of cognitive reappraisal strategies, and information provision may be targets to prevent mental distress while assuring a high degree of individual protective behaviors during the COVID-19 pandemic. Effects of the respective interventions have to be confirmed in further studies.

Objectives: To examine the association of non-pharmaceutical interventions (NPIs) with anxiety and depressive symptoms among adults and determine if these associations varied by gender and age.Methods: We combined survey data from 16,177,184 adults from 43 countries who participated in the daily COVID-19 Trends and Impact Survey via Facebook with time-varying NPI data from the Oxford COVID-19 Government Response Tracker between 24 April 2020 and 20 December 2020. Using logistic regression models, we examined the association of [1] overall NPI stringency and [2] seven individual NPIs (school closures, workplace closures, cancellation of public events, restrictions on the size of gatherings, stay-at-home requirements, restrictions on internal movement, and international travel controls) with anxiety and depressive symptoms.Results: More stringent implementation of NPIs was associated with a higher odds of anxiety and depressive symptoms, albeit with very small effect sizes. Individual NPIs had heterogeneous associations with anxiety and depressive symptoms by gender and age.Conclusion: Governments worldwide should be prepared to address the possible mental health consequences of stringent NPI implementation with both universal and targeted interventions for vulnerable groups.

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey – over 20 million responses in its first year of operation – allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.Significance statementThe U.S. COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. The largest public health survey ever conducted in the United States, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid response to emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing and vaccination in relation to evolving high-priority policy questions over 12 months of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting.

Simultaneously tracking the global COVID-19 impact across multiple populations is challenging due to regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide reliable and standardized data streams to support monitoring and decision-making world-wide, in real time, and with limited local resources. The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, invites daily cross-sectional samples from the social media platform’s active users to participate in the survey since launch April 23, 2020. COVID-19 indicators through December 20, 2020, from N=31,142,582 responses representing N=114 countries, weighted for nonresponse and adjusted to basic demographics, were benchmarked with government data. COVID-19-related signals showed similar concordance with reported benchmark case and test positivity. Bonferroni significance and minimal Spearman correlation strength thresholds were met in the majority. Light Gradient Boost machine learning trained on national and pooled global data verified known symptom indicators, and predicted COVID-19 trends similar to other signals. Risk mitigation behavior trends are correlated with, but sometimes lag, risk perception trends. In regions with strained health infrastructure, but active social media users, we show it is possible to define suitable COVID-19 impact trajectories. This syndromic surveillance public health tool is the largest global health survey to date, and, with brief participant engagement, can provide meaningful, timely insights into the COVID-19 pandemic and response in regions under-represented in epidemiological analyses.Significance StatementThe University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 23, 2020, is the largest remote global health monitoring system. This study includes about 30 million UMD-CTIS responses over 34 weeks (through December 2020) from N=114 countries with survey-weights to adjust for nonresponse and demographics. Using limited self-reported data, sampled daily from an international cohort of Facebook users, we demonstrate validity and utility for COVID-19 impacts trends, even in regions with scant or delayed government data. We predict COVID-19 cases in the absence of testing, and characterize perceived COVID-19 risk versus risk-lowering measures. The UMD-CTIS has the potential to support existing monitoring systems for the COVID-19 pandemic, as well as other new, as-yet-undefined global health threats.

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 investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.

The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.

More and more surveys are conducted online. While web surveys are generally cheaper and tend to have lower measurement error in comparison to other survey modes, especially for sensitive questions, potential advantages might be offset by larger nonresponse bias. This article compares the data quality in a web survey administration to another common mode of survey administration, the telephone. The unique feature of this study is the availability of administrative records for all sampled individuals in combination with a random assignment of survey mode. This specific design allows us to investigate and compare potential bias in survey statistics due to 1) nonresponse error, 2) measurement error, and 3) combined bias of these two error sources and hence, an overall assessment of data quality for two common modes of survey administration, telephone and web. Our results show that overall mean estimates on the web are more biased compared to the telephone mode. Nonresponse and measurement bias tend to reinforce each other in both modes, with nonresponse bias being somewhat more pronounced in the web mode. While measurement error bias tends to be smaller in the web survey implementation, interestingly, our results also show that the web does not consistently outperform the telephone mode for sensitive questions.

In times of increasing digitization, the protection of individual data privacy becomes more important than ever before. To craft privacy policies that do not only meet legal requirements, but also address the public’s concerns, understanding individual privacy attitudes is key. Previous research suggests that privacy attitudes depend on a set of parameters related to the data type, data collector and other situational characteristics. However, the importance of single situational characteristics may possibly be altered by changes in the environment. This circumstance becomes apparent and even more important with the outbreak of the COVID-19 pandemic. The pandemic constitutes an exceptional situation in which individuals may be willing to transmit more personal data than usual for the sake of public health and safety. In this study, we analyze how attitudes towards acceptable data use shift in times of crisis. In July 2019, long before the pandemic, we conducted a survey in Germany in which we measured respondents’ acceptance of the collection and use of health data for public health purposes including preventing the spread of a virus. As the pandemic set in, we replicated this survey in the spring of 2020 to investigate changes in respondents’ willingness to share data for public health purposes in response to the crisis. Using data from 3,502 respondents, we demonstrate and quantify the shift in privacy attitudes with situational characteristics. Public acceptance of the use of personal health data to combat an infectious disease outbreak increased notably, while acceptance of personal data use in several other scenarios barely changed over time. We conclude that policymakers need to carefully consider the intended purpose of and appropriate limitations on data use for public health and argue that the design of data collection tools should meet both public health and privacy concerns.

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 transparency and allow for punishment in cases in which the trustee violates trust. The introduction of the General Data Protection Regulation (GDPR) in May 2018 – a wide-reaching regulation in EU law on data protection and privacy that covers millions of individuals in Europe – provides a unique setting to study the impact of privacy regulation on trust in data collectors. We collected survey panel data in Germany around the implementation date and ran a survey experiment with a GDPR information treatment. Our observational and experimental evidence does not support the hypothesis that the GDPR has positively affected trust. This finding and our discussion of the underlying reasons are relevant for the wider research field of trust, privacy, and big data.

Smartphones sind fur viele Menschen zu einem selbstverstandlichen Bestandteil des Alltags geworden. Sie werden neben der Nutzung zur Kommunikation, Unterhaltung und Information auch bei der Jobsuche und im Arbeitsalltag genutzt (Perrin 2017). Dies bietet Moglichkeiten Smartphones als Datenerhebungsinstrument fur die wissenschaftliche Forschung einzusetzen.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.

Objectives. To assess the impact of the COVID-19 pandemic on mental distress in US adults. Methods. Participants were 5065 adults from the Understanding America Study, a probability-based Internet panel representative of the US adult population. The main exposure was survey completion date (March 10–16, 2020). The outcome was mental distress measured via the 4-item version of the Patient Health Questionnaire. Results. Among states with 50 or more COVID-19 cases as of March 10, each additional day was significantly associated with an 11% increase in the odds of moving up a category of distress (odds ratio = 1.11; 95% confidence interval = 1.01, 1.21; P = .02). Perceptions about the likelihood of getting infected, death from the virus, and steps taken to avoid infecting others were associated with increased mental distress in the model that included all states. Individuals with higher consumption of alcohol or cannabis or with history of depressive symptoms were at significantly higher risk for mental distress. Conclusions. These data suggest that as the COVID-19 pandemic continues, mental distress may continue to increase and should be regularly monitored. Specific populations are at high risk for mental distress, particularly those with preexisting depressive symptoms.

Survey researchers are increasingly seeking opportunities to link interview data with administrative records. However, obtaining consent from all survey respondents (or certain subgroups) remains a barrier to performing record linkage in many studies. We experimentally investigated whether emphasizing different benefits of record linkage to respondents in a telephone survey of employee working conditions improves respondents’ willingness to consent to linkage of employment administrative records relative to a neutral consent request. We found that emphasizing linkage benefits related to “time savings” yielded a small, albeit statistically significant, improvement in the overall linkage consent rate (86.0) relative to the neutral consent request (83.8 percent). The time savings argument was particularly effective among “busy” respondents. A second benefit argument related to “improved study value” did not yield a statistically significant improvement in the linkage consent rate (84.4 percent) relative to the neutral request. This benefit argument was also ineffective among the subgroup of respondents considered to be most likely to have a self-interest in the study outcomes. The article concludes with a brief discussion of the practical implications of these findings and offers suggestions for possible research extensions.

This chapter focuses on a different type of paradata that could provide information about breakdowns of the survey response process: post-survey interviewer observations of respondents and their behaviors during the interviewing process. This chapter analyzes interviewer observations from two surveys – the European Social Survey (ESS) and the National Survey of Family Growth (NSFG). In the ESS, interviewers recorded the five post-survey interviewer observations on a five-point scale (ranging from “never” to “very often”). In the NSFG, the dependent variables measuring indirect indicators of data quality included one paradata variable and four proxy indicators of measurement error. In the NSFG, the types of observations contributed to defining the quality classes, suggesting that both respondent behaviors and the interviewing environment can affect response quality; this makes sense given the sensitive subject matter about sexual health. In the ESS, only the observations of respondent behaviors were found to vary across the derived quality classes.

Interviewer Effects from a Total Survey Error Perspective presents a comprehensive collection of state-of-the-art research on interviewer-administered survey data collection. Interviewers play an essential role in the collection of the high-quality survey data used to learn about our society and improve the human condition. Although many surveys are conducted using self-administered modes, interviewer-administered modes continue to be optimal for surveys that require high levels of participation, include difficult-to-survey populations, and collect biophysical data. Survey interviewing is complex, multifaceted, and challenging. Interviewers are responsible for locating sampled units, contacting sampled individuals and convincing them to cooperate, asking questions on a variety of topics, collecting other kinds of data, and providing data about respondents and the interview environment. Careful attention to the methodology that underlies survey interviewing is essential for interviewer-administered data collections to succeed. In 2019, survey methodologists, survey practitioners, and survey operations specialists participated in an international workshop at the University of Nebraska-Lincoln to identify best practices for surveys employing interviewers and outline an agenda for future methodological research. This book features 23 chapters on survey interviewing by these worldwide leaders in the theory and practice of survey interviewing. Chapters include: The legacy of Dr. Charles F. Cannell's groundbreaking research on training survey interviewers and the theory of survey interviewing Best practices for training survey interviewers Interviewer management and monitoring during data collection The complex effects of interviewers on survey nonresponse Collecting survey measures and survey paradata in different modes Designing studies to estimate and evaluate interviewer effects Best practices for analyzing interviewer effects Key gaps in the research literature, including an agenda for future methodological research Written for managers of survey interviewers, survey methodologists, and students interested in the survey data collection process, this unique reference uses the Total Survey Error framework to examine optimal approaches to survey interviewing, presenting state-of-the-art methodological research on all stages of the survey process involving interviewers. Acknowledging the important history of survey interviewing while looking to the future, this one-of-a-kind reference provides researchers and practitioners with a roadmap for maximizing data quality in interviewer-administered surveys.

Survey data are important for establishing new insights in many disciplines such as sociology, economics, and others. This chapter provides a broad overview of statistical methods for identifying interviewer falsification and demonstrating promising statistical identification strategies using data from a large-scale refugee survey in Germany that includes confirmed falsifications. In practice, non-statistical strategies for detecting falsifications are usually part of standard quality control methods. Some approaches are used during the field period, while others are used after all interviews have been conducted. Interview monitoring, mainly applied in telephone surveys, is another commonly used procedure for identifying falsifiers, which also serves as a deterrent to interviewers. To illustrate the statistical identification tools, the following indicators are considered: acquiescent response style, extreme response style, interview duration, middle response style, recency effects, semi-open responses, and stereotyping. Statistical identification methods have been demonstrated to be effective and are becoming increasingly popular tools for identifying falsified interviews in surveys.

In an increasing number of surveys, interviewers are tasked with collecting blood, saliva, and other biomeasures, and asking survey respondents for consent to link survey data to administrative records. Errors introduced by interviewers can take the form of bias or variance. Early research found that interviewers vary in how they administer survey questions and that their effects were similar to sample clusters in both face-to-face and telephone surveys. Given the nesting of respondents within interviewers, hierarchical or random effects models have long been used for the study of interviewer effects. Multilevel models are flexible and can be used to infer whether interviewer effects differ across subgroups of items, respondents, and interviewers. At the bare minimum, an anonymized interviewer ID variable on data files would allow analysts to estimate interviewer variance components. Additional data on interviewers, extending beyond simply demographics and experience, would facilitate understanding the mechanisms by which interviewers affect survey data.

Background The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. Objective The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. Methods We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. Results We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. Conclusions Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

BACKGROUND The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. OBJECTIVE The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. METHODS We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. RESULTS We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. CONCLUSIONS Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

Accessing and combining large amounts of data is important for quantitative social scientists, but increasing amounts of data also increase privacy risks. To mitigate these risks, important players in official statistics, academia, and business see a solution in the concept of differential privacy. In this opinion piece, we ask how differential privacy can benefit from social-scientific insights, and, conversely, how differential privacy is likely to transform social science. First, we put differential privacy in the larger context of social science. We argue that the discussion on implementing differential privacy has been clouded by incompatible subjective beliefs about risk, each perspective having merit for different data types. Moreover, we point out existing social-scientific insights that suggest limitations to the premises of differential privacy as a data protection approach. Second, we examine the likely consequences for social science if differential privacy is widely implemented. Clearly, workflows must change, and common social science data collection will become more costly. However, in addition to data protection, differential privacy may bring other positive side effects. These could solve some issues social scientists currently struggle with, such as p-hacking, data peeking, or overfitting; after all, differential privacy is basically a robust method to analyze data. We conclude that, in the discussion around privacy risks and data protection, a large number of disciplines must band together to solve this urgent puzzle of our time, including social science, computer science, ethics, law, and statistics, as well as public and private policy.

Die Nachfrage nach gut ausgebildeten DatenwissenschaftlerInnen, die sowohl die Fahigkeiten besitzen, Daten auf „traditionellem Weg“ zu erheben und auszuwerten und ebenso mit grosen semi- oder gar unstrukturierten Datensatzen zu arbeiten, steigt kontinuierlich an. In diesem Beitrag beschreiben wir, welche Kompetenzen Sozial- und MarktforscherInnen heutzutage benotigen, um am Arbeitsmarkt erfolgreich zu sein. Wir diskutieren Herausforderungen und Chancen im Bereich der Lehre dieser neuen Inhalte und deren Potenzial, den steigenden Bedarf an Fachkraften im Bereich Datenerhebung und Datenanalyse in den kommenden Jahren zu decken.

Numerous within-household selection methods have been tested in general population surveys since the advent of telephone interviewing. However, very few selection studies, if any, have been conducted with a dual frame (landline and cell phone) sample. Landline and cell phone frames are known to represent demographically different groups of respondents, and selection methods that may result in more representative demographics in a landline frame may actually skew the results when combined with the cell phone frame. This study tested 11 different within-household selection methods with approximately 11,000 landline respondents. A parallel cell phone sample was also collected with 1,000 respondents, and the frames were combined for analysis. The selection methods tested included one probability-based method, four quasi-probability methods and six nonprobability methods. The methods were evaluated on four criteria: response rates, accuracy, demographic representation and substantive results. The demographic representativeness of each method was examined for the landline frame only and for the dual (landline and cell phone) frame combination. The probability method had the lowest response rate, while the nonprobability at-home methods had the highest. Accuracy rates were lowest for the quasi-probability birthday methods. There were few demographic differences between selection methods, and no substantive differences, when combined with the cell phone sample.

This article examines the influence of interviewers on the estimation of regression coefficients from survey data. First, we present theoretical considerations with a focus on measurement errors and nonresponse errors due to interviewers. Then, we show via simulation which of several nonresponse and measurement error scenarios has the biggest impact on the estimate of a slope parameter from a simple linear regression model. When response propensity depends on the dependent variable in a linear regression model, bias in the estimated slope parameter is introduced. We find no evidence that interviewer effects on the response propensity have a large impact on the estimated regression parameters. We do find, however, that interviewer effects on the predictor variable of interest explain a large portion of the bias in the estimated regression parameter. Simulation studies suggest that standard measurement error adjustments using the reliability ratio (i.e., the ratio of the measurement-error-free variance to the observed variance with measurement error) can correct most of the bias introduced by these interviewer effects in a variety of complex settings, suggesting that more routine adjustment for such effects should be considered in regression analysis using survey data.

Abstract. Because survey response rates are consistently declining worldwide, survey researchers strive to obtain as much auxiliary information on sampled units as possible. Surveys using in-person interviewing often request that interviewers collect observations on key features of all sampled units, given that interviewers are the eyes and ears of the survey organization. Unfortunately, these observations are prone to error, which decreases the effectiveness of nonresponse adjustments based on the observations. No studies have investigated the strategies being used by interviewers tasked with making these observations, or examined whether certain strategies improve observation accuracy. This study is the first to examine the associations of observational strategies used by survey interviewers with the accuracy of observations collected by those interviewers. A qualitative analysis followed by multilevel models of observation accuracy shows that focusing on relevant correlates of the feature being observed and considering a diversity of cues are associated with increased observation accuracy.

Standardized interviewing (SI) and conversational interviewing are two approaches to collect survey data that differ in how interviewers address respondent confusion. This article examines interviewer–respondent interactions that occur during these two techniques, focusing on requests for and provisions of clarification. The data derive from an experimental study in Germany, where the face-to-face interviews were audio-recorded. A sample of 111 interviews was coded in detail. We find that conversational interviewers do make use of the ability to clarify respondent confusion. Although the technique improved response accuracy in the main study compared to SI, conversational interviewers seem to provide clarifications even when there is no evidence of respondent confusion. This may lengthen administration time and potentially increase data collection costs. Conversational interviewers also employ neutral probes, which are generally associated with standardized interviews, at an unexpectedly high rate. We conclude with suggestions for practice and directions for future research.

Recent methodological studies have attempted to decompose the interviewer variance introduced in interviewer-administered surveys into its potential sources, using the Total Survey Error framework. These studies have informed the literature on interviewer effects by acknowledging interviewers’ dual roles as recruiters and data collectors, thus examining the relative contributions of nonresponse error variance and measurement error variance among interviewers to total interviewer variance. However, this breakdown may depend on the interviewing technique: some techniques emphasize behaviors designed to reduce variation in the answers collected by interviewers more so than other techniques. The question of whether the contributions of these error sources to total interviewer variance change for different interviewing techniques remains unanswered. Addressing this gap in knowledge has important implications for interviewing practice because the technique used could alter the relative contributions of variance in these error sources to total interviewer variance. This article presents results from an experimental study mounted in Germany that was designed to answer this question about two specific interviewing techniques. A national sample of employed individuals was first selected from a database of official administrative records, then randomly assigned to interviewers who themselves were randomized to conduct either conversational interviewing (CI) or standardized interviewing (SI), and finally measured face-to-face on a variety of cognitively challenging survey questions with official values also available for verifying the accuracy of responses. We find that although nonresponse error variance does exist among interviewers for selected measures (especially respondent age in the CI group), measurement error variance tends to be the more important source of total interviewer variance, regardless of whether interviewers are using CI or SI.

Respondent incentives are widely used to increase response rates, but their effect on nonresponse bias has not been researched as much. To contribute to the research, we analyze an incentive experiment embedded within the third wave of the German household panel survey “Panel Labor Market and Social Security” conducted by the German Institute for Employment Research. Our question is whether attrition bias differs in two incentive plans. In particular, we want to study whether an unconditional €10 cash incentive yields less attrition bias in self-reported labor income and other sociodemographics than a conditional lottery ticket incentive. We find that unconditional cash incentives are more effective than conditional lottery tickets in reducing attrition bias in income and several sociodemographic variables.

Summary Currently, most surveys ask for occupation with open-ended questions. The verbal responses are coded afterwards, which is error prone and expensive. We present an alternative approach that allows occupation coding during the interview. Our new technique uses a supervised learning algorithm to predict candidate job categories. These suggestions are presented to the respondent, who in turn can choose the most appropriate occupation. 72.4% of the respondents selected an occupation when the new instrument was tested in a telephone survey, entailing potential cost savings. To aid further improvements, we identify some factors for how to increase quality and to reduce interview duration.

Summary Several studies have shown that conversational interviewing (CI) reduces response bias for complex survey questions relative to standardized interviewing. However, no studies have addressed concerns about whether CI increases intra-interviewer correlations (IICs) in the responses collected, which could negatively impact the overall quality of survey estimates. The paper reports the results of an experimental investigation addressing this question in a national face-to-face survey. We find that CI improves response quality, as in previous studies, without substantially or frequently increasing IICs. Furthermore, any slight increases in the IICs do not offset the reduced bias in survey estimates engendered by CI.

From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government employees in data analytics. Second we need a careful rethinking of the rules and technical systems that protect data in order to expand access to linked individual-level data across agencies and jurisdictions, while maintaining privacy. Here, we describe a program that has been run for the last three years by the University of Maryland, New York University, and the University of Chicago, with partners such as Ohio State University, Indiana University/Purdue University, Indianapolis, and the University of Missouri. The program—which trains government employees on how to perform applied data analysis with confidential individual-level data generated through administrative processes, and extensive project-focused work—provides both online and onsite training components. Training takes place in a secure environment. The aim is to help agencies tackle important policy problems by using modern computational and data analysis methods and tools. We have found that this program accelerates the technical and analytical development of public sector employees. As such, it demonstrates the potential value of working with individual-level data across agency and jurisdictional lines. We plan to build on this initial success by creating a larger community of academic institutions, government agencies, and foundations that can work together to increase the capacity of governments to make more efficient and effective decisions.Keywords: training programs, evidence-based policy, confidential data, administrative data research facility, government data

Numerous surveys link interview data to administrative records, conditional on respondent consent, in order to explore new and innovative research questions. Optimizing the linkage consent rate is a critical step toward realizing the scientific advantages of record linkage and minimizing the risk of linkage consent bias. Linkage consent rates have been shown to be particularly sensitive to certain design features, such as where the consent question is placed in the questionnaire and how the question is framed. However, the interaction of these design features and their relative contributions to the linkage consent rate have never been jointly studied, raising the practical question of which design feature (or combination of features) should be prioritized from a consent rate perspective. We address this knowledge gap by reporting the results of a placement and framing experiment embedded within separate telephone and Web surveys. We find a significant interaction between placement and framing of the linkage consent question on the consent rate. The effect of placement was larger than the effect of framing in both surveys, and the effect of framing was only evident in the Web survey when the consent question was placed at the end of the questionnaire. Both design features had negligible impact on linkage consent bias for a series of administrative variables available for consenters and non-consenters. We conclude this research note with guidance on the optimal administration of the linkage consent question.

Trust is praised by many social scientists as the foundation of functioning social systems owing to its assumed connection to cooperative behavior. The existence of such a link is still subject to debate. In the present study, we first highlight important conceptual issues within this debate. Second, we examine previous evidence, highlighting several issues. Third, we present findings from an original experiment, in which we tried to identify a "real" situation that allowed us to measure both trust and cooperation. People's expectations and behavior when they decide to share (or not) their data represents such a situation, and we make use of corresponding data. Our study yields insights that are relevant for the trust --- behavior nexus beyond the particular situation we study empirically.

A key goal of survey interviews is to collect the highest quality data possible from respondents. In practice, however, it can be difficult to achieve this goal because respondents do not always understand particular survey questions as designers intended. Researchers have used a variety of indicators to identify and predict respondent confusion and difficulty in answering questions in different modes. In web surveys, it is possible to automatically detect response difficulty in real time. The research to date has focused on response latencies—mostly long response times—as evidence of difficulty. In addition to response latencies, however, web surveys offer rich behavioral data, which may predict respondent confusion and difficulty more directly than response times. This article focuses on one such behavior, mouse movements. We examine a set of mouse movements participants engage in when answering questions about experimental scenarios whose difficulty has been manipulated (as confirmed by respondent ratings). This approach makes it possible to determine which movements are general movements, demonstrating how a person interacts with a computer, and which movements are related to response difficulty. We find not only that certain mouse movements are highly predictive of difficulty but also that such movements add considerable value when used in conjunction with response times. The approach developed in this article may be useful in delivering help to confused respondents in real time and as a diagnostic tool to identify confusing questions.