Frauke Kreuter
Dados Biográficos
| ID | 79218 |
|---|---|
| NOME | Frauke Kreuter |
| PRENOMES | Frauke |
| SOBRENOME | Kreuter |
| ASSINATURA | KREUTER F |
| AFILIAÇÕES | University of Mannheim |
| ORCID | 0000-0002-7339-2645 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 58 |
| TOTAL DE CITAÇÕES | 623 |
| TOTAL COMO AUTOR | 54 |
| TOTAL COMO EDITOR | 4 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2000 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 12 |
Measuring public opinion towards artificial intelligence
The rapid proliferation of artificial intelligence (AI) has sparked both enthusiasm and ethical concerns in societies. As AI continues to permeate daily life, policymakers need to understand how it is perceived by diverse stakeholders and communities. To reliably measure attitudes towards AI of the general public, a short scale is essential for universal application. Existing scales face limitations in applicability due to their length, sub-stand…
Problem Solving Through Human–AI Preference-based Cooperation
While there is a widespread belief that artificial general intelligence—or even superhuman AI—is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human–AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty in keeping track of a complex solution artifact (e.g., a softwa…
Aapor Presidential Address
Simulating the Human in HCD with ChatGPT
Peer reviewed
Detecting Respondent Burden in Online Surveys
Online surveys are a widely used mode of data collection. However, as no interviewer is present, respondents face any difficulties they encounter alone, which may lead to measurement error and biased or (at worst) invalid conclusions. Detecting response difficulty is therefore vital. Previous research has predominantly focused on response times to detect general response difficulty. However, response difficulty may stem from different sources, su…
Bridging the gap
AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public…
Im Taktgefühl der Technik. Wie sich KI an unsere Welt anpasst
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…
Privacy Attitudes toward Mouse-Tracking Paradata Collection
Survey participants’ mouse movements provide a rich, unobtrusive source of paradata, offering insight into the response process beyond the observed answers. However, the use of mouse tracking may require participants’ explicit consent for their movements to be recorded and analyzed. Thus, the question arises of how its presence affects the willingness of participants to take part in a survey at all—if prospective respondents are reluctant to comp…
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…
From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making
Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a pr…
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 …
Social impacts of algorithmic decision-making
Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impac…
Protective and Risk Factors for Mental Distress and Its Impact on Health-Protective Behaviors during the Sars-CoV-2 Pandemic between March 2020 and March 2021 in Germany
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 sampl…
Global trends and predictors of face mask usage during the Covid-19 pandemic
The decision to wear a face mask in public settings is significantly associated with sociodemographic factors, risky social behaviors, and mask policies. This has important implications for health prevention policies and messaging, including the potential need for more targeted policy and messaging design
Predicting Question Difficulty in Web Surveys
Survey research aims to collect robust and reliable data from respondents. However, despite researchers’ efforts in designing questionnaires, survey instruments may be imperfect, and question structure not as clear as could be, thus creating a burden for respondents. If it were possible to detect such problems, this knowledge could be used to predict problems in a questionnaire during pretesting, inform real-time interventions through responsive …
Acceptability of App-Based Contact Tracing for 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 ef…
Big Data and Social Science
Interviewer Effects from a Total Survey Error Perspective
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…
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…
Mental Distress in the United States at the Beginning of the Covid-19 Pandemic
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…
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…
The Effect of Framing and Placement on Linkage Consent
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 questi…
Social Desirability Bias in Cati, IVR, and Web Surveys
Although it is well established that self-administered questionnaires tend to yield fewer reports in the socially desirable direction than do interviewer-administered questionnaires, less is known about whether different modes of self-administration vary in their effects on socially desirable responding. In addition, most mode comparison studies lack validation data and thus cannot separate the effects of differential nonresponse bias from the ef…
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…
Big Data in Survey Research
Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they a…
Theory and Practice in Nonprobability Surveys
Many in the survey research community have expressed concern at the growing popularity of nonprobability surveys. The absence of random selection prompts justified concerns about self-selection producing biased results and means that traditional, design-based estimation is inappropriate. This paper seeks to provide insight into the conditions under which nonprobability surveys can be expected to provide estimates free of selection bias. In fields…
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…
Nonresponse and Measurement Error in Employment Research
Journal Article Nonresponse and Measurement Error in Employment Research: Making Use of Administrative Data Get access Frauke Kreuter, Frauke Kreuter * *Address correspondence to Frauke Kreuter, University of Maryland, Joint Program in Survey Methodology, 1218 Lefrak Hall, College Park, MD 20742, USA; e-mail: [email protected]. Search for other works by this author on: Oxford Academic Google Scholar Gerrit Müller, Gerrit Müller Search for o…
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…
Assessing the Mechanisms of Misreporting to Filter Questions in Surveys
To avoid asking respondents questions that do not apply to them, surveys often use filter questions that determine routing into follow-up items. Filter questions can be asked in an interleafed format, in which follow-up questions are asked immediately after each relevant filter, or in a grouped format, in which follow-up questions are asked only after multiple filters have been administered. Most previous investigations of filter questions have f…
The Effect of Benefit Wording on Consent to Link Survey and Administrative Records in a Web Survey
Survey data-collection costs are steadily increasing, forcing government agencies and survey organizations to consider more costeffective methods of acquiring high-quality information from respondents. Web surveys and administrative data linkage are two approaches often considered to help offset rising costs; however, these two approaches are rarely used jointly in the same survey. Consequently, little is known about how one should ask for linkag…
Evaluating the Measurement Error of Interviewer Observed Paradata
As survey researchers have begun exploiting paradata—for example, for the correction of nonresponse bias—the quality of these data has come into question. Inaccurate information is likely to affect the resulting statistics and conclusions drawn from such data. This paper focuses on one type of paradata, observations made by interviewers during the data-collection process, and assesses the quality of these observations by examining their measureme…
The Effects of Asking Filter Questions in Interleafed Versus Grouped Format
When filter questions are asked to determine respondent eligibility for follow-up items, they are administered either interleafed (follow-up items immediately after the relevant filter) or grouped (follow-up items after multiple filters). Experiments with mental health items have found the interleafed form produces fewer yeses to later filters than the grouped form. Given the sensitivity of mental health, it is unclear whether this is due to resp…
Mental Distress in the United States at the Beginning of the Covid-19 Pandemic
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…
The Framing of the Record Linkage Consent Question
Factors Affecting the Accuracy of Interviewer Observations
Interviewer observations of household characteristics can be useful variables for constructing post-survey nonresponse adjustments, particularly if the observations are good proxies of key variables collected later in the survey. The U.S. Census Bureau and other survey data-collection organizations now systematically design interviewer observations with this purpose in mind. However, because these observations are typically estimates or judgments…
Motivated Underreporting in Screening Interviews
Which Is the Better Investment for Nonresponse Adjustment
Survey methodologists are searching for covariates to use in nonresponse adjustment models, ultimately hoping to find variables that are highly correlated with both the outcomes of interest and the propensity to respond. These covariates can come from auxiliary data that provide information on both respondents and nonrespondents. Two such types of auxiliary data are interviewer observations (a form of paradata) and commercially available data on …
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…
The Effect of Framing and Placement on Linkage Consent
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 questi…
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…
The Effect of Differential Incentives on Attrition Bias
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 partic…
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 …
Latent class analysis of response inconsistencies across modes of data collection
Multiple Auxiliary Variables in Nonresponse Adjustment
Prior work has shown that effective survey nonresponse adjustment variables should be highly correlated with both the propensity to respond to a survey and the survey variables of interest. In practice, propensity models are often used for nonresponse adjustment with multiple auxiliary variables as predictors. These auxiliary variables may be positively or negatively associated with survey participation, they may be correlated with each other, an…
Confirmation Bias in Housing Unit Listing
Using an experimental repeated listing design, this article demonstrates the presence of confirmation bias in dependent housing unit listing. We find evidence that when provided with an initial listing to update in the field, listers tend not to add missing units or delete inappropriate units. The listers are biased toward confirming the initial list as correct. This finding has implications not only for surveys that use dependent listing to crea…
Social impacts of algorithmic decision-making
Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impac…
Untersuchungen zur Ursache unterschiedlicher Ergebnisse sehr ähnlicher Viktimisierungssurveys
Kriminalitätsfurcht
Die Messung von Kriminalitätsfurcht ist Gegenstand der vorliegenden Arbeit. Warum ist dieses Thema gesellschaftlich und wissenschaftlich relevant? Die Kriminalitätsfurcht der Bürger ist eine Größe, mi
Social Desirability Bias in Cati, IVR, and Web Surveys
Although it is well established that self-administered questionnaires tend to yield fewer reports in the socially desirable direction than do interviewer-administered questionnaires, less is known about whether different modes of self-administration vary in their effects on socially desirable responding. In addition, most mode comparison studies lack validation data and thus cannot separate the effects of differential nonresponse bias from the ef…
Nonresponse and Measurement Error in Employment Research
Journal Article Nonresponse and Measurement Error in Employment Research: Making Use of Administrative Data Get access Frauke Kreuter, Frauke Kreuter * *Address correspondence to Frauke Kreuter, University of Maryland, Joint Program in Survey Methodology, 1218 Lefrak Hall, College Park, MD 20742, USA; e-mail: [email protected]. Search for other works by this author on: Oxford Academic Google Scholar Gerrit Müller, Gerrit Müller Search for o…
Multiple Auxiliary Variables in Nonresponse Adjustment
Prior work has shown that effective survey nonresponse adjustment variables should be highly correlated with both the propensity to respond to a survey and the survey variables of interest. In practice, propensity models are often used for nonresponse adjustment with multiple auxiliary variables as predictors. These auxiliary variables may be positively or negatively associated with survey participation, they may be correlated with each other, an…
Confirmation Bias in Housing Unit Listing
Using an experimental repeated listing design, this article demonstrates the presence of confirmation bias in dependent housing unit listing. We find evidence that when provided with an initial listing to update in the field, listers tend not to add missing units or delete inappropriate units. The listers are biased toward confirming the initial list as correct. This finding has implications not only for surveys that use dependent listing to crea…
The Effects of Asking Filter Questions in Interleafed Versus Grouped Format
When filter questions are asked to determine respondent eligibility for follow-up items, they are administered either interleafed (follow-up items immediately after the relevant filter) or grouped (follow-up items after multiple filters). Experiments with mental health items have found the interleafed form produces fewer yeses to later filters than the grouped form. Given the sensitivity of mental health, it is unclear whether this is due to resp…
Latent class analysis of response inconsistencies across modes of data collection
Motivated Underreporting in Screening Interviews
Improving Surveys with Paradata
Improving Surveys with Paradata
Practical Tools for Designing and Weighting Survey Samples
Undercoverage Rates and Undercoverage Bias in Traditional Housing Unit Listing
Many face-to-face surveys use field staff to create lists of housing units from which samples are selected. However, housing unit listing is vulnerable to errors of undercoverage: Some housing units are missed and have no chance to be selected. Such errors are not routinely measured and documented in survey reports. This study jointly investigates the rate of undercoverage, the correlates of undercoverage, and the bias in survey data due to under…
Facing the Nonresponse Challenge
This article provides a brief overview of key trends in the survey research to address the nonresponse challenge. Noteworthy are efforts to develop new quality measures and to combine several data sources to enhance either the data collection process or the quality of resulting survey estimates. Mixtures of survey data collection modes and less burdensome survey designs are additional steps taken by survey researchers to address nonresponse
Factors Affecting the Accuracy of Interviewer Observations
Interviewer observations of household characteristics can be useful variables for constructing post-survey nonresponse adjustments, particularly if the observations are good proxies of key variables collected later in the survey. The U.S. Census Bureau and other survey data-collection organizations now systematically design interviewer observations with this purpose in mind. However, because these observations are typically estimates or judgments…
Evaluating the Measurement Error of Interviewer Observed Paradata
As survey researchers have begun exploiting paradata—for example, for the correction of nonresponse bias—the quality of these data has come into question. Inaccurate information is likely to affect the resulting statistics and conclusions drawn from such data. This paper focuses on one type of paradata, observations made by interviewers during the data-collection process, and assesses the quality of these observations by examining their measureme…
A Note on Mechanisms Leading to Lower Data Quality of Late or Reluctant Respondents
Survey methodologists worry about trade-offs between nonresponse and measurement error. Past findings indicate that respondents brought into the survey late provide low-quality data. The diminished data quality is often attributed to lack of motivation. Quality is often measured through internal indicators and rarely through true scores. Using administrative data for validation purposes, this article documents increased measurement error as a fun…
Which Is the Better Investment for Nonresponse Adjustment
Survey methodologists are searching for covariates to use in nonresponse adjustment models, ultimately hoping to find variables that are highly correlated with both the outcomes of interest and the propensity to respond. These covariates can come from auxiliary data that provide information on both respondents and nonrespondents. Two such types of auxiliary data are interviewer observations (a form of paradata) and commercially available data on …
Assessing the Mechanisms of Misreporting to Filter Questions in Surveys
To avoid asking respondents questions that do not apply to them, surveys often use filter questions that determine routing into follow-up items. Filter questions can be asked in an interleafed format, in which follow-up questions are asked immediately after each relevant filter, or in a grouped format, in which follow-up questions are asked only after multiple filters have been administered. Most previous investigations of filter questions have f…
The Effect of Benefit Wording on Consent to Link Survey and Administrative Records in a Web Survey
Survey data-collection costs are steadily increasing, forcing government agencies and survey organizations to consider more costeffective methods of acquiring high-quality information from respondents. Web surveys and administrative data linkage are two approaches often considered to help offset rising costs; however, these two approaches are rarely used jointly in the same survey. Consequently, little is known about how one should ask for linkag…
The Framing of the Record Linkage Consent Question
A Note on Improving Process Efficiency in Panel Surveys with Paradata
Call scheduling is a challenge for surveys around the world. Unlike cross-sectional surveys, panel surveys can use information from prior waves to enhance call-scheduling algorithms. Past observational studies showed the benefit of calling panel cases at times that had been successful in the past. This article is the first to experimentally assign panel cases to previously beneficial call windows. The results from a large-scale national survey in…
Big Data in Survey Research
Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they a…
A Practical Technique for Improving the Accuracy of Interviewer Observations of Respondent Characteristics
Face-to-face household surveys sometimes ask field interviewers to record observations about selected characteristics of all sampled housing units. Some surveys ask interviewers to record judgments about potential respondents to serve as proxy measures of key variables. Past studies have shown that these judgments are prone to error, which has negative implications for survey estimators in terms of the bias and variance introduced by nonresponse …
Using Mouse Movements to Predict Web Survey Response Difficulty
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 automatica…
Computer Science (40 obras) · Survey Methodology and Nonresponse (34 obras) · Psychology (32 obras) · Statistics (25 obras) · Mathematics (22 obras) · Data collection (15 obras) · Sociology (15 obras) · Political science (14 obras) · Data science (12 obras) · Medicine (12 obras)