Bias In Surveys Research Articles

OSSOS. XXIX. The Population and Perihelion Distribution of the Detached Kuiper Belt

The detached trans-Neptunian objects (TNOs) are those with semimajor axes beyond the 2:1 resonance with Neptune that are neither resonant nor scattering. Using the detached sample from the Outer Solar System Origins Survey (OSSOS) telescopic survey, we produce the first studies of their orbital distribution based on matching the orbits and numbers of the known TNOs after accounting for survey biases. We show that the detached TNO perihelion (q) distribution cannot be uniform but is instead better matched by two uniform components with a break near q ≈ 40 au. We produce parametric two-component models that are not rejectable by the OSSOS data set and estimate that there are 36,000 detached TNOs with absolute magnitudes H r < 8.66 (D ≳ 100 km) and semimajor axes 48 au < a < 250 au (95% confidence limits). Although we believe that these heuristic two-parameter models yield a correct population estimate, we then use the same methods to show that the perihelion distribution of a detached disk created by a simulated rogue planet matches the q distribution even better, suggesting that the temporary presence of other planets in the early solar system is a promising model to create today’s large semimajor axis TNO population. This cosmogonic simulation results in a detached TNO population estimate of 48,000. Because this illustrates how difficult-to-detect q > 50 au objects are likely present, we conclude that there are (5 ± 2) × 104 dynamically detached TNOs, roughly twice as many as in the entire trans-Neptunian hot main belt.

Reducing vertical bias and error in tidal marsh digital elevation models with machine learning and LiDAR derivatives

The vertical bias and uncertainty of LiDAR-derived elevation data in tidal marshes is a major challenge for researchers in these critical ecosystems. Small positive bias errors in land surface elevation can lead to large underestimates of coastal inundation. Previous studies have shown that the bias and uncertainty of elevation data can be reduced using a variety of statistical techniques. However, many studies cover a relatively small extent, cover a large extent at coarse resolution, or rely on local data products that may not be widely available or are of unknown quality. This study aimed to develop and compare bias reduction approaches for digital elevation models (DEMs) using LiDAR derivatives in tidal marshes along Delaware Bay, Delaware, USA, a region that experiences high rates of relative sea-level rise and frequent coastal flooding. In this study, several statistical and machine-learning approaches were evaluated based on their ability to reduce vertical DEM error using field GPS survey data and LiDAR and terrain derivatives as inputs. The evaluated approaches for reducing DEM error included ensembles of multiple linear regressions, kernel-K Nearest Neighbors, gradient boosted regression trees, random forests, and deep neural networks (DNNs), which were compared based on statistical performance metrics. An ensemble of deep neural networks performed best at removing vertical DEM bias and reducing DEM uncertainty, though other approaches also performed well. GPS survey data indicated a mean absolute error, mean bias, and root mean square error in GPS surveys of 11.9 cm, 10.9, and 14.8 cm, respectively, in the original DEM. These error metrics were reduced to 3.5 cm, −0.2 cm, and 5.1 cm in the DNN ensemble-corrected DEM. The DNN ensemble was then applied to all tidal marshes throughout the study area. This corrected DEM clearly showed how interpretations of marsh platform inundation based on uncorrected DEM surfaces could be misguided. The approach used in this study only requires LiDAR-derived products and field surveys, so it may be applied readily in other regions.

The second set of pulsar discoveries by CHIME/FRB/Pulsar: 14 rotating radio transients and 7 pulsars

ABSTRACT The Canadian Hydrogen Mapping Experiment (CHIME) is a radio telescope located in British Columbia, Canada. The large field of view allows CHIME/FRB to be an exceptional pulsar and rotating radio transient (RRAT) finding machine, despite saving only the metadata of incoming Galactic events. We have developed a pipeline to search for pulsar/RRAT candidates using density-based spatial clustering of applications with noise (dbscan), a clustering algorithm. Follow-up observations are then scheduled with the more sensitive CHIME/Pulsar instrument capable of near-daily high-time resolution spectra observations. We have developed the CHIME/Pulsar Single Pulse Pipeline to automate the processing of CHIME/Pulsar search-mode data. We report the discovery of 21 new Galactic sources, with 14 RRATs, 6 isolated long-period pulsars, and 1 binary system. Owing to CHIME/Pulsar’s observations, we have obtained timing solutions for 8 of the 14 RRATs along with all the regular pulsars and the binary system. Notably, we report that the binary system is in a long orbit of 412 d with a minimum companion mass of 0.1303 solar masses and no evidence of an optical companion within 10″ of the pulsar position. This highlights that working synergistically with CHIME/FRB’s large survey volume CHIME/Pulsar can obtain arc second localizations for low-burst rate RRATs through pulsar timing. We find that the properties of our newly discovered RRATs are consistent with those of the presently known population. They tend to have lower burst rates than those found in previous surveys, which is likely due to survey bias rather than the underlying population.

Gender and racial biases in Press Ganey patient satisfaction surveys

Gender and racial biases in Press Ganey patient satisfaction surveys

DP01 Survey on the management of dysplastic naevus

Abstract Although dysplastic naevus (DN) is a frequent diagnosis, there is significant controversy on its definition and gradation and its relationship to melanoma risk. Therefore, there is no consensus over optimal management, and surveys from Australia, Canada and the USA have shown variation in clinical practice. Dysplastic naevus with involved histological margins has not been shown to progress to melanoma. To assess UK management of DN depending on the grade of atypia and reported excision margin, we circulated a questionnaire via email to dermatologists in Scotland, north Wales, Liverpool and Manchester. Seventy-two responded, of whom 46 (64%) were consultants. The cohort was generally highly experienced, with 10 (14%) responders with 1–5 years’ dermatology experience, 15 (21%) with 6–10 years, 27 (37%) with 11–20 years and 20 (28%) with &amp;gt; 20 years’ experience. Management of DN varied greatly. For mild DN excised with &amp;lt; 1-mm margin, 59 (82%) agreed that no further management was required. Opinions were divided when margins were involved in mild DN, with 14 (19%) agreeing that no further management was required, 42 (58%) choosing to re-excise and six (8%) to re-excising only any residual pigmentation. Most dermatologists reassured patients with moderately DN with margins &amp;lt; 1 mm (49, 68%); 18 (25%) preferred to re-excise. For moderate DN with involved margins, six respondents (8%) felt that no further management was necessary and 54 (75%) would re-excise. For severe DN with margins &amp;gt; 1 mm, 16 (22%) would still re-excise and six (8%) would agree management in the multidisciplinary team meeting (MDT). For severe DN with margins &amp;lt; 1 mm, 41 (57%) would re-excise and seven (10%) would agree management in the MDT. Lastly, for severe DN with involved margins, 62 (86%) would re-excise and seven (10%) would agree management in the MDT. This study has shown comparable results with those from Australia and the USA, whereby most dermatologists would re-excise moderate and severe DN with involved margins. Significant numbers indicated that they would re-excise in the case of close histological margins: this is unnecessary, and more standardized management may reduce expenditure. Limitations of the study include the usual potential sources of bias in a questionnaire survey; it should be noted that reported behaviour may differ from actual practice. However, this is the first UK study on this common clinical scenario. The move to report DN as low- or high-grade may help to reduce variation, but our survey suggests that some patients are currently undergoing unnecessary procedures.

Data weighting: An iterative process linking surveys, data synthesis, and population models to evaluate mis-specification

Integrated stock assessments specify a distribution for multiple data types, and these distributions control the relative leverage assigned to each datum. A decade of research has demonstrated that (1) proper data weighting is necessary to avoid bias resulting from overweighting noisy age- and length-composition data; (2) sampling data can be pre-processed to estimate the likely sampling variance for composition data; and (3) using random effects to estimate time-varying parameters can improve the fit to data while also changing statistical leverage, and thereby serve a similar role to reweighting data. However, there are also unresolved questions including: (A) Is it more appropriate to model age and length data as proportions-at-age and as an index for the total, or as a series of indices-at-age? (B) Are correlated residuals appropriately addressed via data weighting or do they require additional model changes (i.e., time-varying parameters)? (C) How to efficiently communicate information about sampling imprecision and model errors between sampling and stock-assessment teams? (D) How does model-based expansion of sampling data affect data weighting? and (E) How to address alternative hypotheses about factors driving poor fit to data? Here, we argue that stock assessment errors can be classified using four categories: sampling bias (e.g., changes in survey coverage), sampling imprecision (e.g., finite sample sizes), assessment model bias (e.g., incorrect demographic assumptions) and assessment model imprecision (e.g., random effects). This categorization has several implications with resulting practical recommendations. For example, we define Percent Excess Variance (PEV) from the ratio of input sample size (the measured variance of sampling imprecision) and effective sample sizes (the variance of assessment-model residuals). We propose calculating PEV as standardized diagnostic measuring the net effect of survey bias and assessment model bias and imprecision. We demonstrate PEV in a simulation experiment fitted using the Woods Hole Assessment Model (WHAM) conditioned upon Gulf of Alaska walleye pollock, where unacknowledged variation in fishery selectivity results in a PEV of 77 % and this is eliminated when correctly specifying a time-varying estimation model. We also argue that model-based expansion of data inputs using auxiliary information can mitigate sampling bias, while also measuring sampling imprecision for spatially unrepresentative surveys. Similarly, including random effects can mitigate model bias while increasing model imprecision when the demographic model has little explanatory power. Finally, we observe that down-weighting compositional data for a given fleet fails to propagate information about model residuals when interpreting abundance indices or reference points for that same fleet. When PEV is large for important fleets, we therefore encourage focused research to explain the sources of these errors rather than simply downweighting without propagating information about residuals. However, we acknowledge a continuing role for automated data weighting for less important fleets, although we recommend explicit hypotheses about potential sources of errors in those cases.

Addressing Non-response Bias in Surveys of Wealthy Households in Low- and Middle-Income Countries: Strategies and Implementation

Survey data is a crucial tool for understanding behaviours in low- and middle-income countries (LMICs), but it is often subject to various biases that impede statistical inference. One of these biases is the systematic non-response of wealthy populations, which poses a considerable risk to research on income and wealth inequality, entrepreneurship, consumer behaviour, housing markets, demand for urban services, education and career trajectories, and other topics relevant to these groups. This article offers a technical and methodological review of non-response bias inherent in surveys of wealthy households, encompassing unit non-response and item non-response. Based on a literature review, we suggest a range of strategies that can be used to minimize these biases, starting with strategies for implementing the survey itself and then proposing solutions for adjusting the sample after the survey. This article provides researchers and survey managers with a theoretical understanding of the issues at stake, as well as practical tools for mitigating biases specific to surveys of wealthy households in LMICs.

An attribution account of the effects of leaders’ gender and abusive supervision on employee insubordination

PurposeDrawing on attribution theory, this study aims to examine how and when abusive supervision affects insubordination, focusing on employees’ attribution bias related to leader gender.Design/methodology/approachTwo mixed-method studies were used to test the proposed research framework. Study 1 adopted a 2 (abusive supervision: low vs high) by 2 (leader gender: male vs female) by employee gender-leadership bias quasi-experiment. A sample of 173 US F&B employees completed Study 1. In Study 2, 116 hospitality employees responded to two-wave, time-lagged surveys. They answered questions on abusive supervision and gender-leadership bias in Survey 1. Two weeks later, they reported negative external attribution (embodied in injury initiation) and insubordination.FindingsHayes’ PROCESS macro results verified a three-way moderated mediation. The three-way interaction among abusive supervision, leader gender and gender-leadership bias affects external attribution, increasing insubordination. Employees with high leader–gender bias working under female leaders make more external attribution and engage in subsequent insubordination in the presence of abusive supervision.Originality/valueThis study is one of the first, to the best of the authors’ knowledge, that examines the mediating role of external attribution of abusive supervision. Second, this research explains the gender glass ceiling by examining employees’ attribution bias against female leaders.

Models for survey nonresponse and bias adjustment techniques

Survey statisticians have been dealing with the issues of nonresponse in sample surveys for many years. Due to the complex nature of the mechanism, so far it has not been easy to find a general solution to this problem. In this paper, several aspects of this topic will be elaborated on: the survey unit nonresponse bias has been examined alternatively by taking response amounts which are fixed initially and also by taking the response amounts as random variables. An overview of the components of the bias due to nonresponse will be performed. Nonresponse bias components are illustrated for each alternative approach and the amount of bias was computed for each case.

Sampling methodology influences habitat suitability modeling for chiropteran species.

Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are used independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (Lasiurus borealis, Lasiurus cinereus, and Perimyotis subflavus). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models preformed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.

Seven Decades of Gender Differences in German Voting Behavior

This article describes long-term changes in gender differences in voting behavior in Germany, using a globally unique data source: information from real ballots. Compared with self-reports in available surveys, actual votes counted by gender and age groups have three advantages: neither representativeness issues nor social desirability bias, a huge sample, and coverage of seven decades, beginning in 1953. Besides party-specific voting patterns, I analyze summary measures for gender dissimilarities, both overall and separated by age groups. The modern gender gap—women voting more left-wing than men—first appeared in Germany in the 2017 election, surprisingly late compared with previous international research. The speed and structure with which the modern gender gap grew suggest that it is due to period effects rather than cohort effects. The modern gender gap differs by age group and, in post-World War II Germany, women and men have never been as divided about politics as the youngest voters in 2021. These findings partly contrast with previous survey-based results. To explore whether this contrast stems from the smaller sample sizes of previous studies or gendered survey bias (e.g., more social desirability bias among women), I compare results from real ballots with estimates from two survey data sources. Findings suggest that large surveys might provide reasonable estimates at the aggregate level but might overestimate the gender gap for more radical parties such as the AfD (Alternative for Germany).

Height-related detection bias in camera trap surveys: Insights for combining data sets

No abstract available

High prevalence of self-reported sexually transmitted infections among older adults in Tanzania: results from a list experiment in a population-representative survey

PurposeSelf-report of sensitive or stigmatized health states is often subject to social desirability and interviewer biases. To reduce such biases, we estimated the rate of sexually transmitted infections (STIs) using a list experiment. MethodsThis population-representative study was nested within the Dar es Salaam Urban Cohort Study, a Health and Demographic Surveillance System (HDSS) in the Ukonga ward of Dar es Salaam, Tanzania. Men and women aged ≥40years were randomly assigned to receive a list of either four control items (i.e., the control group) or four control items plus an additional item on having had a disease through sexual contacts in the past 12months (i.e., the treatment group). We calculated the mean difference in the total number of items to which respondents answered “yes” in the treatment versus control group and compared this prevalence estimate to the one measured by the direct question. ResultsA total of 2310 adults aged ≥40years were enrolled in the study: 32% were male and 48% were aged 40–49years. The estimated prevalence of having STIs in the past 12months was 17.8% (95% confidence interval [CI] 12.3–23.3) in the list experiment, almost 10 times higher than the estimated prevalence of 1.8% (95%CI 1.3–2.4) based on the direct question (P < .001). STI prevalence remained high after adjusting for age, the number of lifetime sex partners, alcohol consumption and smoking in multivariate linear regression (15.6%; 95% CI 7.3–23.9). ConclusionsWe found a substantially higher prevalence of STIs among older adults in urban Tanzania when we based our estimation on a list experiment rather than a direct question in a population-representative survey. List experiments should be considered to elimnate social desirability and interviewer biases in surveys of sensitive or stigmatized health states. The very high prevalence of STIs highlights the need for improved access to STI screening, prevention and treatment for older adults in urban Africa.

Institutional approaches to evaluation of learning and teaching: A sector scan of Australasian universities

ABSTRACT Teaching evaluation is deeply entrenched in institutional quality assurance and is a feature of a range of policies including academic recruitment, promotion, and performance management. Universities must ensure that evaluation practices meet regulatory requirements, while balancing student voice and wellbeing. There is extensive literature examining the validity, reliability, and bias of student surveys, but limited focus on institutional evaluation strategies and what constitutes best practice. This study analyses and reports on the teaching evaluation strategies and practices of Australian and New Zealand universities. All 29 participating institutions use standardised and centrally deployed surveys to evaluate teaching and participated in external student experience surveys to benchmark nationally. Comparisons are provided between the strategic intent of evaluation; survey practices, technology, analysis, and reporting; and the use of other nonsurvey methods. The study informs higher education institutions’ evaluation strategy, policies, and practice and provides an agenda for reframing approaches to evaluation of teaching.

DETERMINANTS OF LAWSUITS RELATED TO THE FINANCIAL RIGHTS OF HEALTH PRACTITIONERS A RETROSPECTIVE COHORT STUDY IN THE ASEER REGION

Background&#x0D; The employee’s sense of entitlement to the allowance despite the absence of a legal document; thinking about obtaining the financial allowance through the judiciary; lack of confidence in the financial and human resources staff at their workplace, low salaries, work pressure, and distinction from others; delay in promotions, courses, and development programs; ignoring the responsible person for responding to employees feeling injustice; and hearing unpleasant news and views from their society. Employees receive additional benefits, bonuses, or gratuities. The study uses data from court records of financial rights-related lawsuits filed by health practitioners in the Aseer Region over one year. The study identifies and analyzes the demographic and professional characteristics of the health practitioners involved in these lawsuits and the factors associated with the lawsuits, such as the type of health facility, the nature of the dispute, and the amount of money involved.&#x0D; &#x0D; Method&#x0D; The Aseer Health Research Ethics Committee approved this one-year retrospective cohort study under REC-19-11-2022. After the official request, the law department provided one year of data for all lawsuits in the Aseer health record. This study focused on litigation numbers, percentages, and types. All variables and data sources were changed to eliminate survey bias. We studied all financial rights lawsuits in the year we grouped. The statistical analysis employed frequency and percentage for all study variables. All correlated variables were subjected to bivariate analyses.&#x0D; &#x0D; Result&#x0D; In one year, there were 2,782 cases, 1,552 of which were infection risk and harm allowance (55.8%), 472 were normal exchange allowance (17%), and 7% were employee complaints. 2018 national issues involve total lawsuits yearly bonus 4%, roughly 2.2% to 3.5% request change or cancellation.&#x0D; &#x0D; Conclusion&#x0D; Overall, the research findings highlight the importance of creating safe and fair work environments, offering appropriate compensation and benefits, and ensuring effective organizational communication and management practices. Failure to do so can result

Comparing estimates of household expenditures between pictorial diaries and surveys in three low- and middle-income countries.

In most low- and middle-income countries (LMICs), household out-of-pocket (OOP) health spending constitutes a major source of healthcare financing. Household surveys are commonly used to monitor OOP health spending, but are prone to recall bias and unable to capture seasonal variation, and may underestimate expenditure-particularly among households with long-term chronic health conditions. Household expenditure diaries have been developed as an alternative to overcome the limitations of surveys, and pictorial diaries have been proposed where literacy levels may render traditional diary approaches inappropriate. This study compares estimates for general household and chronic healthcare expenditure in South Africa, Tanzania and Zimbabwe derived using survey and pictorial diary approaches. We selected a random sub-sample of 900 households across urban and rural communities participating in the Prospective Urban and Rural Epidemiology study. For a range of general and health-specific categories, OOP expenditure estimates use cross-sectional survey data collected via standardised questionnaire, and data from these same households collected via two-week pictorial diaries repeated four times over 2016-2019. In all countries, average monthly per capita expenditure on food, non-food/non-health items, health, and consequently, total household expenditure reported by pictorial diaries was consistently higher than that reported by surveys (each p<0.001). Differences were greatest for health expenditure. The share of total household expenditure allocated to health also differed by method, accounting for 2% in each country when using survey data, and from 8-20% when using diary data. Our findings suggest that the choice of data collection method may have significant implications for estimating OOP health spending and the burden it places on households. Despite several practical challenges to their implementation, pictorial diaries offer a method to assess potential bias in surveys or triangulate data from multiple sources. We offer some practical guidance when considering the use of pictorial diaries for estimating household expenditure.

The Potential of New LiDAR Datasets for Archaeology in Switzerland

LiDAR and its derived elevation models have revolutionized archaeological research in forested areas around the globe. Almost a third of Switzerland is covered in forests. The number of archaeological sites recorded in forests in Switzerland is, however, limited. Given these circumstances, it is surprising how underutilized LiDAR data are in archaeological research in the country. As the Federal Office of Topography swisstopo is finalizing the acquisition of new LiDAR datasets, increasing the covered area and allowing for limited time series analyses, these data should be used to the fullest extent. This article describes the open access datasets and elaborates on their potential for archaeological research and cultural heritage management. By employing LiDAR data on a large scale, Swiss archaeological research would likely substantially increase the number of recorded heritage sites. Additionally, this will have the effect of complementing the palimpsests of past anthropogenic activity throughout the landscape while reducing survey biases in the archaeological record.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey.

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic. Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges. We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data. We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors. Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

From scanner to court: A neuroscientifically informed "reasonable person" test of trademark infringement.

Many legal decisions center on the thoughts or perceptions of some idealized group of individuals, referred to variously as the "average person," "the typical consumer," or the "reasonable person." Substantial concerns exist, however, regarding the subjectivity and vulnerability to biases inherent in conventional means of assessing such responses, particularly the use of self-report evidence. Here, we addressed these concerns by complementing self-report evidence with neural data to inform the mental representations in question. Using an example from intellectual property law, we demonstrate that it is possible to construct a parsimonious neural index of visual similarity that can inform the reasonable person test of trademark infringement. Moreover, when aggregated across multiple participants, this index was able to detect experimenter-induced biases in self-report surveys in a sensitive and replicable fashion. Together, these findings potentially broaden the possibilities for neuroscientific data to inform legal decision-making across a range of settings.

Gender Gaps in Financial Literacy: A Multi-Arm Rct to Break the Response Bias in Surveys

Gender Gaps in Financial Literacy: A Multi-Arm Rct to Break the Response Bias in Surveys