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 complete a survey if additional measures are recorded, collecting paradata may do more harm than good. Previous research has found that other paradata collection modes reduce the willingness to participate, and that this decrease may be influenced by the specific motivation provided to participants for collecting the data. However, the effects of mouse movement collection on survey consent and participation have not been addressed so far. In a vignette experiment, we show that reported willingness to participate in a survey decreased when mouse tracking was part of the overall consent. However, a larger proportion of the sample indicated willingness to both take part and provide mouse-tracking data when these decisions were combined, compared to an independent opt-in to paradata collection, separated from the decision to complete the study. This suggests that survey practitioners may face a trade-off between maximizing their overall participation rate and maximizing the number of participants who also provide mouse-tracking data. Explaining motivations for paradata collection did not have a positive effect and, in some cases, even reduced participants' reported willingness to take part in the survey.

COVID-19, normative attitudes and pluralistic ignorance in employer-employee relationships

Employment relationships are embedded in a network of social norms that provide an implicit framework for desired behaviour, especially if contractual solutions are weak. The COVID-19 pandemic has brought about major changes that have led to situations, such as the scope of short-time work or home-based work in a firm. Against this backdrop, our study addresses three questions: first, are there social norms dealing with these changes; second, are there differences in attitudes between employees and supervisors (misalignment); and third, are there differences between respondents' average attitudes and the attitudes expected to exist in the population (pluralistic ignorance). We find that for the assignment of short-time work and of work at home, there are shared normative attitudes with only small differences between supervisors and nonsupervisors. Moreover, there is evidence for pluralistic ignorance; asked for the perceived opinion of others, respondents over- or underestimated the consensus in the (survey) population. Such pluralistic ignorance can contribute to the upholding of a norm even if individuals do not support the norm, with potentially far-reaching consequences for the quality of the employment relationship and the functioning of the organization. Our results show that, especially in times of change, social norms should be considered for the analysis of labour markets.

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 property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making.

Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool

Objectives: Real-time data analysis during a pandemic is crucial. This paper aims to introduce a novel interactive tool called Covid-Predictor-Tracker using several sources of COVID-19 data, which allows examining developments over time and across countries. Exemplified here by investigating relative effects of vaccination to non-pharmaceutical interventions on COVID-19 spread.

Methods: We combine >100 indicators from the Global COVID-19 Trends and Impact Survey, Johns Hopkins University, Our World in Data, European Centre for Disease Prevention and Control, National Centers for Environmental Information, and Eurostat using random forests, hierarchical clustering, and rank correlation to predict COVID-19 cases.

Results: Between 2/2020 and 1/2022, we found among the non-pharmaceutical interventions “mask usage” to have strong effects after the percentage of people vaccinated at least once, followed by country-specific measures such as lock-downs. Countries with similar characteristics share ranks of infection predictors. Gender and age distribution, healthcare expenditures and cultural participation interact with restriction measures.

Conclusion: Including time-aware machine learning models in COVID-19 infection dashboards allows to disentangle and rank predictors of COVID-19 cases per country to support policy evaluation. Our open-source tool can be updated daily with continuous data streams, and expanded as the pandemic evolves.

Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making

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.

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Investigates public acceptance of automated decision-making across four contexts

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Experimentally varies degree of automation, used data, and decision type and context

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Uses experimental data from a probability-based sample with large sample size

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Finds that respondents overall prefer human involvement

Public institutions and businesses increasingly rely on algorithmic support to make decisions about citizens. Promising enhanced efficiency, organizations use automated decision-making (ADM) for purposes ranging from content recommendations to credit or bail decisions. However, algorithms may potentially worsen social inequities by reproducing biases they find in the data. Moreover, citizens may feel uncomfortable being judged by a “machine,” but they also may distrust, e.g., the objectivity of human deciders. We present a survey experiment on citizens’ preferences for the degree of involvement of algorithms and human deciders across four highly relevant ADM contexts, while varying two other situational parameters. We find that respondents prefer the involvement of a human decider to purely automated decisions. Depending on context, ADM designers should therefore consider involving a human decider in the process.

Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries

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.

Changes in drinking days among United States adults during the COVID-19 pandemic

AIMS

To examine changes in drinking behavior among United States (US) adults between March 10 and July 21, 2020, a critical period during the COVID-19 pandemic.

DESIGN

Longitudinal, internet-based panel survey.

SETTING

The Understanding America Study (UAS), a nationally representative panel of US adults age 18 or older.

PARTICIPANTS

A total of 4298 US adults who reported alcohol use.

MEASUREMENTS

Changes in number of reported drinking days from March 11, 2020 through July 21, 2020 in the overall sample and stratified by sex, age, race/ethnicity, household structure, poverty status, and census region.

FINDINGS

Compared with March 11, the number of drinking days per week was significantly higher on April 1 by an average of 0.36 days (95% CI = 0.30, 0.43), on May 1 by an average of 0.55 days (95% CI = 0.47, 0.63), on June 1 by an average of 0.41 days (95% CI = 0.33, 0.49), and on July 1 by an average of 0.39 days (95% CI = 0.31, 0.48). Males, White participants, and older adults reported sustained increases in drinking days, whereas female participants and individuals living under the federal poverty line had attenuated drinking days in the latter part of the study period.

CONCLUSIONS

Between March and mid-July 2020, adults in the United States reported increases in the number of drinking days, with sustained increases observed among males, White participants, and older adults.

Universal adaptability: Target-independent inference that competes with propensity scoring

The gold-standard approaches for gleaning statistically valid conclusions from data involve random sampling from the population. Collecting properly randomized data, however, can be challenging, so modern statistical methods, including propensity score reweighting, aim to enable valid inferences when random sampling is not feasible. We put forth an approach for making inferences based on available data from a source population that may differ in composition in unknown ways from an eventual target population. Whereas propensity scoring requires a separate estimation procedure for each different target population, we show how to build a single estimator, based on source data alone, that allows for efficient and accurate estimates on any downstream target data. We demonstrate, theoretically and empirically, that our target-independent approach to inference, which we dub "universal adaptability," is competitive with target-specific approaches that rely on propensity scoring. Our approach builds on a surprising connection between the problem of inferences in unspecified target populations and the multicalibration problem, studied in the burgeoning field of algorithmic fairness. We show how the multicalibration framework can be employed to yield valid inferences from a single source population across a diverse set of target populations.

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

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.

Global trends and predictors of face mask usage during the COVID-19 pandemic

Background

Guidelines and recommendations from public health authorities related to face masks have been essential in containing the COVID-19 pandemic. We assessed the prevalence and correlates of mask usage during the pandemic.

Methods

We examined a total of 13,723,810 responses to a daily cross-sectional online survey in 38 countries of people who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last 7 days. The outcome was individual face mask usage in public settings, and the predictors were country fixed effects, country-level mask policy stringency, calendar time, individual sociodemographic factors, and health prevention behaviors. Associations were modeled using survey-weighted multivariable logistic regression.

Results

Mask-wearing varied over time and across the 38 countries. While some countries consistently showed high prevalence throughout, in other countries mask usage increased gradually, and a few other countries remained at low prevalence. Controlling for time and country fixed effects, sociodemographic factors (older age, female gender, education, urbanicity) and stricter mask-related policies were significantly associated with higher mask usage in public settings. Crucially, social behaviors considered risky in the context of the pandemic (going out to large events, restaurants, shopping centers, and socializing outside of the household) were associated with lower mask use.

Conclusion

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12175-9.

Human social sensing is an untapped resource for computational social science

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Trajectories of Mental Distress Among U.S. Adults During the COVID-19 Pandemic

Background

Cross-sectional studies have found that the coronavirus disease 2019 (COVID-19) pandemic has negatively affected population-level mental health. Longitudinal studies are necessary to examine trajectories of change in mental health over time and identify sociodemographic groups at risk for persistent distress.

Purpose

To examine the trajectories of mental distress between March 10 and August 4, 2020, a key period during the COVID-19 pandemic.

Methods

Participants included 6,901 adults from the nationally representative Understanding America Study, surveyed at baseline between March 10 and 31, 2020, with nine follow-up assessments between April 1 and August 4, 2020. Mixed-effects logistic regression was used to examine the association between date and self-reported mental distress (measured with the four-item Patient Health Questionnaire) among U.S. adults overall and among sociodemographic subgroups defined by sex, age, race/ethnicity, household structure, federal poverty line, and census region.

Results

Compared to March 11, the odds of mental distress among U.S. adults overall were 1.84 (95% confidence interval [CI] = 1.65–2.07) times higher on April 1 and 1.92 (95% CI = 1.62–2.28) times higher on May 1; by August 1, the odds of mental distress had returned to levels comparable to March 11 (odds ratio [OR] = 0.80, 95% CI = 0.66–0.96). Females experienced a sharper increase in mental distress between March and May compared to males (females: OR = 2.29, 95% CI = 1.85–2.82; males: OR = 1.53, 95% CI = 1.15–2.02).

Conclusions

These findings highlight the trajectory of mental health symptoms during an unprecedented pandemic, including the identification of populations at risk for sustained mental distress.

Associations Between Media Exposure and Mental Distress Among U.S. Adults at the Beginning of the COVID-19 Pandemic

INTRODUCTION

Exposure to disaster-related media may be a risk factor for mental distress, but this has not been examined in the context of the COVID-19 pandemic. This study assesses whether exposure to social and traditional media during the rise of the COVID-19 pandemic was associated with mental distress among U.S. adults.

METHODS

Data came from the Understanding America Study, conducted with a cross-sectional, nationally representative sample of adults who completed surveys online. Participants included 6,329 adults surveyed between March 10 and March 31, 2020. Regression analyses examined the associations of (1) self-reported average time spent on social media in a day (hours) and (2) number of traditional media sources (radio, TV, and newspaper) consulted to learn about COVID-19 with self-reported mental distress (4-item Patient Health Questionnaire). Data were analyzed in April 2020.

RESULTS

Participants responding at later survey dates reported more time spent on social media (β=0.02, 95% CI=0.01, 0.03), a greater number of traditional media sources consulted to learn about COVID-19 (β=0.01, 95% CI=0.01, 0.02), and greater mental distress (β=0.07, 95% CI=0.04, 0.09). Increased time spent on social media and consulting a greater number of traditional media sources to learn about COVID-19 were independently associated with increased mental distress, even after adjusting for potential confounders (social media: β=0.14, 95% CI=0.05, 0.23; traditional media: β=0.14, 95% CI=0.08, 0.20).

CONCLUSIONS

Exposure to a greater number of traditional media sources and more hours on social media was modestly associated with mental distress during the rise of the COVID-19 pandemic in the U.S.

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 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.

Mental distress during the COVID-19 pandemic among US adults without a pre-existing mental health condition: Findings from American trend panel survey

Most individuals in the United States have no history of a mental health condition yet are at risk for psychological distress due to the COVID-19 pandemic. The objective of this study was to assess the frequency and risk and protective factors of psychological distress, during the beginning of the COVID-19 pandemic, in this group. Data comes from the Pew Research Center's American Trends Panel (ATP), a probability-based online survey panel representative of the US adult population. The analytic sample consisted of 9687 individuals with no prior history of a mental health condition who completed the survey between March 19-24, 2020. Explanatory variables included sociodemographic factors and items related to behavior, perceptions, and experiences surrounding the pandemic. The outcome was psychological distress, measured by five items on symptoms of anxiety, depression, loneliness, sleep difficulties, and hyperarousal. A multivariable linear regression model was used to identify risk and protective factors for psychological distress. Fifteen percent of the sample experienced 2 psychological distress symptoms for at least 3 days over the past week; 13% had three or more symptoms. Risk factors for higher distress included searching online or using social media to post about coronavirus, reporting that the outbreak caused major changes to personal life, and perception that the virus was a threat to the US economy, the individual's personal health or finances. This has important implications for mental health service delivery.

Psychomorbidity, Resilience, and Exacerbating and Protective Factors During the SARS-CoV-2 Pandemic

BACKGROUND

The SARS-CoV-2 pandemic has caused mental stress in a number of ways: overstrain of the health care system, lockdown of the economy, restricted opportunities for interpersonal contact and excursions outside the home and workplace, and quarantine measures where necessary. In this article, we provide an overview of psychological distress in the current pandemic, identifying protective factors and risk factors.

METHODS

The PubMed, PsycINFO, and Web of Science databases were systematically searched for relevant publications (1 January 2019 - 16 April 2020). This study was registered in OSF Registries (osf.io/34j8g). Data on mental stress and resilience in Germany were obtained from three surveys carried out on more than 1000 participants each in the framework of the COSMO study (24 March, 31 March, and 21 April 2020).

RESULTS

18 studies from China and India, with a total of 79 664 participants, revealed increased stress in the general population, with manifestations of depression and anxiety, post-traumatic stress, and sleep disturbances. Stress was more marked among persons working in the health care sector. Risk factors for stress included patient contact, female sex, impaired health status, worry about family members and significant others, and poor sleep quality. Protective factors included being informed about the increasing number of persons who have recovered from COVID, social support, and a lower perceived infectious risk. The COSMO study, though based on an insufficiently representative population sample because of a low questionnaire return rate (<20%), revealed increased rates of despondency, loneliness, and hopelessness in the German population as compared to norm data, with no change in estimated resilience.

CONCLUSION

Stress factors associated with the current pandemic probably increase stress by causing anxiety and depression. Once the protective factors and risk factors have been identified, these can be used to develop psychosocial interventions. The informativeness of the results reported here is limited by the wide variety of instruments used to acquire data and by the insufficiently representative nature of the population samples.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study

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.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study

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.

Does Benefit Framing Improve Record Linkage Consent Rates? A Survey Experiment

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.

Trust and cooperative behavior: Evidence from the realm of data-sharing

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. We found that there is no relationship between trust and cooperation. This non-relationship may be rationalized in different ways which, in turn, provides important lessons for the study of the trust—behavior nexus beyond the particular situation we study empirically.

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 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.

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 nonconsent and nonparticipation. In the current study, we assess the circumstances under which smartphone users are willing to participate in passive mobile data collection. We surveyed 1,947 members of a German nonprobability online panel who own a smartphone using vignettes that described hypothetical studies where data are automatically collected by a research app on a participant's smartphone. The vignettes varied the levels of several dimensions of the hypothetical study, and respondents were asked to rate their willingness to participate in such a study. Willingness to participate in passive mobile data collection is strongly influenced by the incentive promised for study participation but also by other study characteristics (sponsor, duration of data collection period, option to switch off the app) as well as respondent characteristics (privacy and security concerns, smartphone experience).

Tree-based Machine Learning Methods for Survey Research

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.

Strategies for Increasing the Accuracy of Interviewer Observations of Respondent Features: Evidence from the U.S. National Survey of Family Growth

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 show that focusing on relevant correlates of the feature being observed and considering a diversity of cues are associated with increased observation accuracy.

Assessing the measurement error properties of interviewer observations of neighbourhood characteristics

Interviewer observations made during the process of data collection are currently used to inform responsive design decisions, to expand the set of covariates for nonresponse adjustments, to explain participation in surveys, and to assess nonresponse bias. However, little effort has been made to assess the quality of such interviewer observations. Using data from the Los Angeles Family and Neighbourhood Survey (L.A.FANS), this paper examines measurement error properties of interviewer observations of neighbourhood characteristics. Block level and interviewer covariates are used in multilevel models to explain interviewer variation in the observations of neighbourhood features.

Latent class analysis of response inconsistencies across modes of data collection

Latent class analysis (LCA) has been hailed as a promising technique for studying measurement errors in surveys, because the models produce estimates of the error rates associated with a given question. Still, the issue arises as to how accurate these error estimates are and under what circumstances they can be relied on. Skeptics argue that latent class models can understate the true error rates and at least one paper (Kreuter et al., 2008) demonstrates such underestimation empirically. We applied latent class models to data from two waves of the National Survey of Family Growth (NSFG), focusing on a pair of similar items about abortion that are administered under different modes of data collection. The first item is administered by computer-assisted personal interviewing (CAPI); the second, by audio computer-assisted self-interviewing (ACASI). Evidence shows that abortions are underreported in the NSFG and the conventional wisdom is that ACASI item yields fewer false negatives than the CAPI item. To evaluate these items, we made assumptions about the error rates within various subgroups of the population; these assumptions were needed to achieve an identifiable LCA model. Because there are external data available on the actual prevalence of abortion (by subgroup), we were able to form subgroups for which the identifying restrictions were likely to be (approximately) met and other subgroups for which the assumptions were likely to be violated. We also ran more complex models that took potential heterogeneity within subgroups into account. Most of the models yielded implausibly low error rates, supporting the argument that, under specific conditions, LCA models underestimate the error rates.

Are There Test Administrator Effects in Large-Scale Educational Assessments?

Abstract. In large-scale educational assessments such as the Third International Mathematics and Sciences Study (TIMSS) or the Program for International Student Assessment (PISA), sizeable numbers of test administrators (TAs) are needed to conduct the assessment sessions in the participating schools. TA training sessions are run and administration manuals are compiled with the aim of ensuring standardized, comparable, assessment situations in all student groups. To date, however, there has been no empirical investigation of the effectiveness of these standardizing efforts. In the present article, we probe for systematic TA effects on mathematics achievement and sample attrition in a student achievement study. Multilevel analyses for cross-classified data using Markov Chain Monte Carlo (MCMC) procedures were performed to separate the variance that can be attributed to differences between schools from the variance associated with TAs. After controlling for school effects, only a very small, nonsignificant proportion of the variance in mathematics scores and response behavior was attributable to the TAs (< 1%). We discuss practical implications of these findings for the deployment of TAs in educational assessments.

Application of enzymes in anaerobic digestion

Owing to the very low economic value of brewer's spent grains, its utilisation for biogas production is very promising. The hydrolysis of ligno-cellulose is the rate limiting step in anaerobic digestion. Enzymatic pre-treatment promotes the hydrolysis of ligno-cellulose, breaking it down to lower molecular weight substances which are ready to be utilised by the bacteria. A cheap raw multi-enzyme produced by a solid state fermentation (SSF) process is a good substitute for expensive conventional enzyme. The SSF enzyme application to spent grain has been investigated by carrying out enzymatic solubility tests, hydrolytic experiments and two-step anaerobic fermentation of spent grain. Gas chromatograph analysis was conducted to quantify fatty acids concentrations, while CH(4), CO(2), O(2), H(2) and H(2)S were measured to determine biogas quality by means of a gas analyser. DS, oDS, pH were also measured to analyse the anaerobic digestion. The result shows that enzyme application promotes the hydrolysis of ligno-cellulose, indicated by higher enzymatic solubility and fatty acid concentration in a hydrolytic bioreactor. Moreover, biogas production is also increased. The quality of the gases produced is also enhanced. Since the anaerobic digestion can be operated in a stable performance, it can also be concluded that SSF enzyme is compatible with anaerobic digestion.

Antidepressive effect and pharmacokinetics of amitriptyline with consideration of unbound drug and 10-hydroxynortriptyline plasma levels

In 27 inpatients with primary affective disorder the urinary excretion of 3-methoxy-4-hydroxyphenylglycol (MHPG) was measured prior to a 4-week treatment with 150 mg amitriptyline (AT)/day. Ratings according to the Hamilton depression scale were performed before therapy and repeated after 2 and 4 weeks. Plasma levels of AT, nortriptyline (NT), and E-10-hydroxynortriptyline (OHNT) were assayed weekly, and binding of AT to plasma proteins was determined in one sample. Better therapeutic results were obtained at intermediate, as compared to low and high concentrations of AT or AT plus NT. Independent evaluation of AT and metabolite levels revealed that patients with AT of 50--125 ng/ml responded particularly well when NT did not exceed 95 ng/ml or when NT plus OHNT was below 150 ng/ml. Outside this "therapeutic window' the outcome was markedly poorer. Interindividual variation of AT binding was much smaller than variation of total concentrations. Evaluation of free, instead of total levels did not help to clarify the relationship between clinical and pharmacokinetic variables. Plasma levels within the optimal ranges were found in more patients with high than with low MHPG excretion. The free fraction of OHNT in plasma of healthy subjects was about 35%.

Do urinary MHPG and plasma drug levels correlate with response to amitriptyline therapy?

Twenty-nine inpatients with primary affective disorder were treated with 150 mg amitriptyline (AT) daily for 28 days. Pretreatment urinary excretion of 3-methoxy-4-hydroxyphenylglycol (MHPG) was measured in two or three 24-h urine samples. Plasma levels of AT and nortriptyline (NT) were determined after 14, 21, and 28 days of treatment. MHPG excretion was significantly correlated with clinical response to treatment. Responders defined by two different methods showed higher pretreatment MHPG excretion than nonresponders. Correspondingly, high MHPG excretors (median split) showed significantly more improvement than low excretors. These relationships were even more apparent when possibly incomplete urine samples (creatinine excretion below 1000 mg/24h) were excluded. The high and low MHPG subgroups did not significantly differ from each other in their plasma levels of AT, NT, or AT plus NT. A significant rank correlation between clinical response and plasma levels of AT and/or NT did not exist, but there was a trend towards lower levels in responders.

[Cerebrospinal fluid passage of therapeutic immunoglobulins of the IgG class in infectious inflammatory disease of the CNS]

Immunoglobulins are often used as an optimizing therapy in cases of infectious diseases of the central nervous system. To clarify the question of whether or not an intravenously administered compound of the IgG class is able to penetrate the cerebrospinal fluid barrier despite its high molecular weight, 12 anti-HBs negative patients received 20 ml each of a beta-Propiolacton treated IgG compound with a high anti-HBs titre (1 : 115 000) used as a marker. Four patients having an inconspicuous fluid condition were consulted for control. Five patients were suffering from slight disturbances and three other patients had severe disorders of the blood-cerebrospinal fluid barrier function resulting from inflammatory diseases of the central nervous systems. Cerebrospinal fluid was produced by way of lumbar puncture resp. drainage for the determination of anti-HBs. Simultaneously, the concentration of antibodies in serum was determined. In all patients having barrier disturbances, anti-HBs was evident in the cerebrospinal fluid, the transfer of intravenously administered immunoglobulins to cerebrospinal fluid increasing in correlation with the degree of the barrier disorder. The therapeutical importance of immunoglobulin therapy in treating infections of the central nervous system is pointed out.