Multiple Biases Research Articles

The Effects of Iconicity and Conventionalization on Word Order Preferences.

Of the six possible orderings of the three main constituents of language (subject, verb, and object), two-SOV and SVO-are predominant cross-linguistically. Previous research using the silent gesture paradigm in which hearing participants produce or respond to gestures without speech has shown that different factors such as reversibility, salience, and animacy can affect the preferences for different orders. Here, we test whether participants' preferences for orders that are conditioned on the semantics of the event change depending on (i) the iconicity of individual gestural elements and (ii) the prior knowledge of a conventional lexicon. Our findings demonstrate the same preference for semantically conditioned word order found in previous studies, specifically that SOV and SVO are preferred differentially for different types of events. We do not find that iconicity of individual gestures affects participants' ordering preferences; however, we do find that learning a lexicon leads to a stronger preference for SVO-like orders overall. Finally, we compare our findings from English speakers, using an SVO-dominant language, with data from speakers of an SOV-dominant language, Turkish. We find that, while learning a lexicon leads to an increase in SVO preference for both sets of participants, this effect is mediated by language background and event type, suggesting that an interplay of factors together determines preferences for different ordering patterns. Taken together, our results support a view of word order as a gradient phenomenon responding to multiple biases.

Lack of correlation between school mask mandates and paediatric COVID-19 cases in a large cohort

ObjectivesTo expand upon an observational study published by the Centers for Disease Control (CDC) showing an association between school mask mandates and lower pediatric COVID-19 cases. We examine whether this association persists in a larger, nationally representative dataset over a longer period.MethodWe replicated the CDC study and extended it to more districts and a longer period, employing seven times as much data. We examined the relationship between mask mandates and per-capita pediatric cases, using multiple regression to control for observed differences.ResultsWe successfully replicated the original result using 565 counties; non-masking counties had around 30 additional daily cases per 100,000 children after two weeks of schools reopening. However, after nine weeks, cases per 100,000 were 18.3 in counties with mandates compared to 15.8 in those without them (p = 0.12). In a larger sample of 1832 counties, between weeks 2 and 9, cases per 100,000 fell by 38.2 and 37.9 in counties with and without mask requirements, respectively (p = 0.93).ConclusionsThe association between school mask mandates and cases did not persist in the extended sample. Observational studies of interventions are prone to multiple biases and provide insufficient evidence for recommending mask mandates.

Bias, the unfinished symphony.

In laboratory medicine, mathematical equations are frequently used to calculate various parameters including bias, imprecision, measurement uncertainty, sigma metric (SM), creatinine clearance, LDL-cholesterol concentration, etc. Mathematical equations have strict limitations and cannot be used in all situations and are not open to manipulations. Recently, a paper “Bias estimation for Sigma metric calculation: Arithmetic mean versus quadratic mean” was published in Biochemia Medica. In the paper, the author criticized the approach of taking the arithmetic mean of the multiple biases to obtain a single bias and proposed a quadratic method to estimate the overall bias using external quality assurance services (EQAS) data for SM calculation. This approach does not fit the purpose and it should be noted that using the correct equation in calculations is as important as using the correct reagent in the measurement of the analytes, therefore before using an equation, its suitability should be checked and confirmed.

Correlation of Employees’ Satisfaction, Employees’ Wellbeing and Workload between Work Life Balance

The idea of work life balance and upholding personal and professional accountability is directly related to thoughts on quality of life. Indirectly, employees will focus to complete their works during office hours, avoid procrastination and deliver works timely. Thus, it will increase the organizational performances. This study was aimed to identify the relationship of employees’ satisfaction, employees’ well-being and workload towards work life balance. Stratified sampling was utilised in this study to represents different groups or level of a population. Here, 113 respondents in a public sector in Kelantan are involved. For this analysis, SPSS was employed. Pearson’s correlation analysis and mean analysis were applied to this study to measure the relationships between independent and dependent variables. The finding of the study revealed that employees’ satisfaction, employees’ wellbeing and workload is the most dominant factor affecting work life balance. It is also shown that all factors were significantly related with work life balance. These results explained that the higher employees’ satisfaction, employees’ well-being and workload felt by the employees, will increase work life balance enjoyed by them. Several areas for improvement should be observed in future research. This study may be conducted in an organization in one particular area. This is done to ensure the focus group of respondents. Next the survey instrument used to assess employees’ satisfaction, employees’ wellbeing and workload may incorporate questions from multiple sources, eliminating errors or bias.

Statistical Consideration for Fit-for-Use Real-World Data to Support Regulatory Decision Making in Drug Development

ABSTRACT A Real-World Evidence (RWE) scientific working group of the American Statistical Association Biopharmaceutical Section has been reviewing the statistical considerations for the generation of real-world evidence to support regulatory decision making. As part of the effort, the working group is addressing the fitness-for-use of real-world data (RWD). RWD may be used in a variety of ways and study designs including in randomized studies, externally controlled studies, and purely observational studies. The use of RWD poses unique issues surrounding study integrity, transparency, and reproducibility. Rule-based methods and machine learning approaches can be used to extract key data elements from RWD sources. In some cases, multiple sources of data may be linked to obtain the necessary study data. Missing data may have unique considerations in the RWD sources, since data elements are collected for the practice of medicine and are not protocol driven. Lack or imperfect capture of some information in an RWD source may lead to multiple biases that threaten the fitness-for-use of an RWD source, including information bias, selection bias, and confounding. Validation studies and quantitative bias assessment can be used to assess the potential bias. The working group proposes a data-driven approach framework for determining the fit-for-use of RWD.

Existence and Control of Special Orbits around Asteroid 4 Vesta

This paper focuses on the existence and control of particular types of orbits around asteroid 4 Vesta, including Sun-synchronous orbits, orbits at the critical inclination, repeating ground-track orbits, and stationary orbits. J2, J3, and J4 terms are considered in the gravity model of Vesta. First, the inclination perturbation caused by solar gravitation is studied, and preset and multiple inclination bias methods are proposed to dampen the local time drift at the ascending node. Compared with Vesta, the control periods of the Sun-synchronous orbits of 21 Lutetia and 433 Eros are much longer. Second, Vesta’s orbits with a critical inclination depend on the semi-major axis and eccentricity. If the eccentricity is not greater than 0.2, inclination decreases slowly and monotonically concerning the semi-major axis. If the eccentricity is not smaller than 0.4, inclination increases rapidly and monotonically. Third, Sun-synchronous repeating ground-track circular orbits of Vesta, which do not exist for Lutetia and Eros, are investigated. Finally, the perturbations of stationary orbits caused by solar gravitation and solar radiation pressure are analyzed.

Further validation of the Cognitive Biases Questionnaire for psychosis

BackgroundCognitive biases are recognized as important treatment targets for reducing symptoms associated with severe mental disorders. Although cognitive biases have been linked to symptoms in most studies, few studies have looked at such biases transdiagnostically. The Cognitive Bias Questionnaire for psychosis (CBQp) is a self-reported questionnaire that assesses cognitive biases amongst individuals with a psychotic disorder, as well as individuals with other severe mental disorders. The current study aims to validate a French version of the CBQp and to explore transdiagnostic cognitive biases in individuals with psychotic disorders, individuals with depression, and in healthy controls.MethodsThe CBQp was translated into French following a protocol based on international standards. Discriminant validity and internal consistency were determined for total score and each subscale score. Confirmatory factor analyses were performed to test construct validity. Finally, cluster analyses were conducted to investigate cognitive biases across diagnostic groups.ResultsOur results were similar to those of the original authors, with the one-factor solution (assessment of a general thinking bias) being the strongest, but the two-factor solution (assessing biases within two themes relating to psychosis) and the five-factor solution (assessment of multiple distinct biases) being clinically more interesting. A six-cluster solution emerged, suggesting that individuals with similar diagnoses score differently on all cognitive biases, and that individuals with different diagnoses might have similar cognitive biases.ConclusionsThe current findings support the validity of the French translation of the CBQp. Our cluster analyses overall support the transdiagnostic presence of cognitive biases.

Analysis of Bias in NLP Models With Regression and Effect Sizes

In recent years, there has been an increasing awareness that many NLP systems incorporate biases of various types (e.g., regarding gender or race) which can have significant negative consequences. At the same time, the techniques used to statistically analyze such biases are still relatively simple. Typically, studies test for the presence of a significant difference between two levels of a single bias variable (e.g., male vs. female) without attention to potential confounders, and do not quantify the importance of the bias variable. This article proposes to analyze bias in the output of NLP systems using multivariate regression models. They provide a robust and more informative alternative which (a) generalizes to multiple bias variables, (b) can take covariates into account, (c) can be combined with measures of effect size to quantify the size of bias. Jointly, these effects contribute to a more robust statistical analysis of bias that can be used to diagnose system behavior and extract informative examples. We demonstrate the benefits of our method by analyzing a range of current NLP models on one regression and one classification tasks (emotion intensity prediction and coreference resolution, respectively).

Estimating Vaccine Effectiveness by Linking Population-Based Health Registries: Some Sources of Bias.

The COVID-19 pandemic has underscored the importance of observational studies of real-world vaccine effectiveness to help answer urgent public health questions. One approach to rapidly answering questions about real-world vaccine effectiveness relies on linking data from a population-based registry of vaccinations with a population-based registry of health outcomes. Here we consider some potential sources of bias in linked registry studies including: incomplete reporting to the registries; errors in linking individuals between registries; and errors in the assumed population size of the catchment area of the registries. We show that the direction of the bias resulting from one source of error by itself is predictable. However, if multiple sources of error are present, the direction of the bias can be either upward or downward. The biases can be so strong as to make harmful vaccines appear effective. We provide explicit formulas to quantify and adjust for multiple biases in estimates of vaccine effectiveness which could be used in sensitivity analyses. While this work was motivated by COVID-19 vaccine questions, the results are generally applicable to studies that link population-based exposure registries with population-based case registries to estimate relative risks of exposures.

We Are All Behavioural, More, or Less: A Taxonomy of Consumer Decision-Making

Abstract We examine how 17 behavioural biases relate to each other, to three standard measures of risk and time preferences, to cognitive skills, personality, and demographics, and to outcomes in household finance, well-being, and health. Most consumers in our nationally representative panel data exhibit multiple biases, with substantial cross-person heterogeneity. Biases are positively correlated within person, especially after adjusting for measurement error. From that correlation structure, we reduce our 20 bias and standard preference measures to four behavioural common factors. Each BCF reflects a group of related biases regarding beliefs, decision quality, discounting, or risk/uncertainty attitudes. The first two BCFs also strongly correlate with each other (positively) and cognitive skills (negatively). The first three BCFs and cognitive skills strongly correlate with various outcomes in the expected directions. Our results support processing-based models where basic limitations in cognition and/or attention produce multiple biases, and they have several other implications for theory and practice.

THE ENTROPY OF PREDICTING PERSONALITY FROM FUNNINESS SCORES

This investigation is a correlation and entropy study of two models of personality: The Big Five (B5) psychometric personality traits and the Age Trend Classification (ATC) model of artificial personality. ATC is based on a nonlinear dynamic model of animal motivation; the classification is computed a posteriori from funniness scores of arbitrary selected 100 humorous sentences. The purpose of the study is to assess the prediction of B5 traits by linear regression from funniness scores. The performance of prediction is measured by the Kullback-Leibler divergence (Kld) entropy of information loss.The analysis is implemented in stages, the first computes the correlation of participants mean funniness score with B5 traits. The mean is a measure of personal bias in assessing humour and includes contributions from multiple biases, such as mood, insecurity, personality attributes and emotional states.The analysis of the second stage is performed on scaled funniness scores and sentences with opposite orientation are aggregated and keyed, as is done in B5 assessment questionnaires. The graphic results show a repeating pattern of profiles not observed before.Stage three involves splitting the data among age groups, the graphs roughly showed a general trend of declining correlation with age, which suggests that the influence of motivation over traits in general also declines with age.The ATC model of artificial personality is intended as a counterpart of the personality of the user in a man-machine interface, where the machine could complement the user’s personality in order to achieve higher learning, more entertainment and/or improve productivity.

Range Dependence of Polarimetric Radar Estimates for Extreme Flood-Producing Rainfall in Urban Watersheds

Abstract We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, KDP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that KDP can provide major improvements in estimating extreme rainfall, but the advantages of KDP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

Coherence of attention and memory biases in currently and previously depressed women

ABSTRACT Previous research has found that depression is characterised by biased processing of emotional information. Although most studies have examined cognitive biases in isolation, simultaneous examination of multiple biases is required to understand how they may interact and influence one another to produce depression vulnerability. In this study, the attention and memory biases of currently depressed, previously depressed, and never depressed women were examined using the same stimuli and a unified methodology. Participants viewed negative, positive, and neutral words while their eye gaze was tracked and recorded. After a distraction task, participants completed an incidental recognition test that included words from the eye-tracking task and new words. The results supported the hypothesised mediation model for positive words: currently depressed women had a reduced attention bias for positive words and, in turn, had poorer memory for positive words relative to never depressed women. Previously depressed women, however, showed a lack of coherence between attention and memory biases for positive words. The groups did not differ in their attention or memory biases for negative words. The findings provide novel evidence in support of a causal link between the absence of protective attention and memory biases for positive information in clinical depression.

Investigation of AlN/Si based heterogeneous Junction using inter-digitated electrodes for enhanced UV light detection

A 200 nm thick AlN layer is fabricated by Hydride Vapor Phase Epitaxial (HVPE) technique onto the n-type Silicon < 111 > wafer. After epitaxial growth the AlN is masked and 100 nm thick interdigitated electrodes of Silver are deposited by the atomistic scaled Physical Vapor Deposition (PVD) system. These interdigitated electrodes are further examined with five possible circuit configurations of AlN/Silicon under multiple biases and temperatures. Different electrical and electro-optical characterization such as Current-Voltage, Charge Deep Level Transient Spectroscopy and Kinetics of Dark and UV current; have been performed for each configuration. For all such cases the Ultra-Violet (UV) light has been sensed and found that the reversed biased silicon coupled with interdigitated AlN schemes may provide largest responsivity under both photovoltaic and photoconductive modes. Also, the photo-conversion gain (ratio of UV Current / Dark Current) of each case under different physical conditions have also been emulated and found that the same reverse biased configuration leads higher extent of photo-conversion gain. The charge based transient analysis using (Q-DLTS) have been performed for all said configurations and it is found that the same reverse biased scheme offers largest magnitude of capture cross section i.e. ~ 10−18 cm2, which may capture larger extent of free charge carriers in UV mode and could be opted for enhanced UV light detection.

Effects of contraction bias on the decision process in the macaque prefrontal cortex.

Our representation of magnitudes such as time, distance, and size is not always veridical because it is affected by multiple biases. From a Bayesian perspective, estimation errors are considered to be the result of an optimization mechanism for the behavior in a noisy environment by integrating previous experience with the incoming sensory information. One influence of the distribution of past stimuli on perceptual decisions is represented by the regression toward the mean, a type of contraction bias. Using a spatial discrimination task with 2 stimuli presented sequentially at different distances from the center, we show that this bias is also present in macaques when comparing the magnitude of 2 distances. We found that the contraction of the first stimulus magnitude toward the center of the distribution accounted for some of the changes in performance, even more so than the effect of difficulty related to the ratio between stimulus magnitudes. At the neural level in the dorsolateral prefrontal cortex, the coding of the decision after the presentation of the second stimulus reflected the effect of the contraction bias on the discriminability of the stimuli at the behavioral level.

Estimation of the COVID-19 mean incubation time: Systematic review, meta-analysis, and sensitivity analysis.

Providing sensible estimates of the mean incubation time for COVID‐19 is important yet complex. This study aims to provide synthetic estimates of the mean incubation time of COVID‐19 by capitalizing on available estimates reported in the literature and exploring different ways to accommodate heterogeneity involved in the reported studies. Online databases between January 1, 2020 and May 20, 2021 are first searched to obtain estimates of the mean incubation time of COVID‐19, and meta‐analyses are then conducted to generate synthetic estimates. Heterogeneity of the studies is examined via the use of Cochran's Q statistic and Higgin's & Thompson's I2 statistic, and subgroup analyses are conducted using mixed effects models. The publication bias issue is assessed using the funnel plot and Egger's test. Using all those reported mean incubation estimates for COVID‐19, the synthetic mean incubation time is estimated to be 6.43 days with a 95% confidence interval (CI) [5.90, 6.96], and using all those reported mean incubation estimates together with those transformed median incubation estimates, the estimated mean incubation time is 6.07 days with a 95% CI [5.70, 6.45]. The reported estimates of the mean incubation time of COVID‐19 vary considerably due to multiple reasons, including heterogeneity and publication bias. To alleviate these issues, we take different angles to provide a sensible estimate of the mean incubation time of COVID‐19. Our analyses show that the mean incubation time of COVID‐19 between January 1, 2020 and May 20, 2021 ranges from 5.68 to 8.30 days.

Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements

Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.

Spain is not different: teaching quantitative courses can also be hazardous to one's career (at least in undergraduate courses).

Student evaluations of teaching (SETs) have become a widely used tool for assessing teaching in higher education. However, numerous investigations have shown that SETs are subject to multiple biases, one of which is particularly relevant, namely, the area of knowledge to which the subject belongs. This article aims to replicate the article by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299) in a different educational context to verify whether the negative bias toward instructors who teach quantitative courses found by the authors in the US also appears in the Spanish university system. The study was conducted at the Business and Law School of the Universidad Pontificia Comillas, a private Spanish university, using two different samples. First, we analyzed undergraduate courses using a sample of 80,667 SETs in which 2,885 classes (defined as a single semester-long course taught by an individual instructor to a specific group of students), 488 instructors, and 322 different courses were evaluated over a time period of four academic years (2016/2017–2019/2020). Second, in the same period, 16,083 SETs corresponding to master’s degree courses were analyzed, which involved the study of 871 classes, 275 instructors, and 155 different courses. All the data included in the analysis were obtained from official university surveys developed by a team of professionals specialized in teaching quality responsible for ensuring the reliability of the information. At the degree level, the results show that despite the considerable cultural and temporal difference between the samples, the results are very similar to those obtained by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299); i.e., professors teaching quantitative courses are far more likely to obtain worse SETs than instructors in other areas. There are hardly any differences at the master’s degree level, regardless of whether nearly 75% of master’s degree instructors also teach at the undergraduate level. This leads us to three different conclusions. (1) Evidence suggests that the reason for these differences is not due to faculty teaching quantitative courses being less effective than faculty teaching in some other fields. Our results indicate that the same instructor is evaluated very differently depending on whether he or she teaches at the undergraduate or master’s level. (2) It is essential to avoid comparisons of SETs between different areas of knowledge, at least at the undergraduate level. (3) A significant change in the use and interpretation of SETs is imperative, or its replacement by other evaluation mechanisms should be considered. If this does not occur, it is possible that in the future, there will be an adverse selection effect among professors of quantitative methods; i.e., only the worst professionals in quantitative methods will opt for teaching since the good professionals will prefer other jobs.

The Impact of Quadratic Biases on Cosmic Shear

In this paper we revisit potential biases in cosmic shear power spectra caused by bias terms that multiply up to quadratic powers of the shear. Expanding the multiplicative bias field as a series of independent spin-$s$ fields we find terms $m_s$ that multiply integer and half-integer powers of the shear. We propagate these biases into shape measurement statistics and the cosmic shear power spectrum. We find that such biases can be measured by performing regression on calibration data. We find that for integer powers of shear the impact of quadratic order terms on the power spectrum is an additional bispectrum dependency; ignoring quadratic terms can lead to biases in cosmological parameters of up to $2(m_2+m_{-2}-m_6)0.4\sigma$ for Stage-IV dark energy experiments, but that the susceptibility to them can be decreased by using methods to remove small-scale sensitivity. We also find, for half-integer powers of the shear that, for a Stage-IV experiment, biases are required to be known to better than approximately $\sigma[m_0+15(m_1+m_3)+0.1(m_{-1}+m_5)]\leq 0.01$. In future, Stage-IV dark energy experiments should seek to measure and minimise such all such bias terms.

A Single-Session Online Cognitive Bias Modification of Interpretations Modified for Adults With Anxiety and Depressive Symptoms

Anxiety and depression are common, co-occurring, and costly mental health disorders. Cognitive bias modification aims to modify biases to reduce associated symptoms. Few studies have targeted multiple biases associated with both anxiety and depression, and those that have lacked a control condition. This study piloted a single-session online cognitive bias modification (known as CBM-IA) designed to target two biases associated with anxiety and depression—interpretation bias and attribution style—in adults with varying levels of anxiety and depressive symptoms. Participants (18–26 years) with at least mild levels of anxiety/stress and depressive symptoms on the DASS-21 were randomly allocated to an intervention (n = 23) or a control (n = 22) condition. The training consisted of a single-session online CBM-IA to encourage positive interpretations and a positive attribution style.Interpretation bias, attribution style, anxious and depressive mood states, and anxiety, stress and depressive symptoms improved at posttraining and at follow-up, irrespective of condition. Changes in interpretation bias from pre- to posttraining were significantly associated with changes in anxious mood state. CBM-IA, as implemented in this single-session pilot study, did not significantly reduce targeted biases and symptoms compared to a control condition. This adds to the mixed evidence on the efficacy of single-session CBM-I for altering biases and symptoms.