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Line transect aerial surveys are widely used for estimating abundance of biological populations, including threatened species. However, estimates obtained with data collected from aircraft are often underestimated because of visibility bias and bias in estimating group sizes from a fast-moving platform. An assessment of multiple sources of bias in aerial surveys were carried out in Brazilian coastal waters by experiments on multiple survey platforms (i.e., boat, airplane and helicopter). These studies focused on evaluating visibility bias (perception and availability bias) and potential differences in the estimation of group sizes from different types of platforms used in franciscana ( Pontoporia blainvillei ) abundance surveys. The ultimate goal was to develop correction factors to improve accuracy of estimates of density and population size for this threatened dolphin. Estimates of density and group sizes computed from boats were assumed to be unbiased and were compared to estimates of these quantities obtained from an airplane in the same area and period. In addition, helicopter surveys were conducted in two areas where water turbidity differed (clear vs. murky waters) to determine surfacing-diving intervals of franciscana groups and to estimate availability for aerial platforms. Abundance computed from the aerial survey data underestimated the true abundance by about 4-5 times, with ~70% of the total bias resulting from visibility bias (~80% from availability bias and ~20% from perception bias) and ~30% from bias in estimates of group size. The use of multiple survey platforms in contrasting habitats provided the opportunity to compute correction factors that can be used to refine range wide abundance estimates of the threatened franciscana given certain assumptions are met. Visibility bias and group size bias were substantial and clearly indicate the importance for accounting for such correction factors to produce unequivocal population assessment based on aerial survey data.

Self-control is positively associated with a host of beneficial outcomes. Therefore, psychological interventions that reliably improve self-control are of great societal value. A prominent idea suggests that training self-control by repeatedly overriding dominant responses should lead to broad improvements in self-control over time. Here, we conducted a random-effects meta-analysis based on robust variance estimation of the published and unpublished literature on self-control training effects. Results based on 33 studies and 158 effect sizes revealed a small-to-medium effect of g = 0.30, confidence interval (CI 95) [0.17, 0.42]. Moderator analyses found that training effects tended to be larger for (a) self-control stamina rather than strength, (b) studies with inactive compared to active control groups, (c) males than females, and (d) when proponents of the strength model of self-control were (co) authors of a study. Bias-correction techniques suggested the presence of small-study effects and/or publication bias and arrived at smaller effect size estimates (range: gcorrected =. 13 to. 24). The mechanisms underlying the effect are poorly understood. There is not enough evidence to conclude that the repeated control of dominant responses is the critical element driving training effects.

This paper analyzes 12 recent within-study comparisons contrasting causal estimates from a randomized experiment with those from an observational study sharing the same treatment group. The aim is to test whether different causal estimates result when a counterfactual group is formed, either with or without random assignment, and when statistical adjustments for selection are made in the group from which random assignment is absent. We identify three studies comparing experiments and regression-discontinuity (RD) studies. They produce quite comparable causal estimates at points around the RD cutoff. We identify three other studies where the quasi-experiment involves careful intact group matching on the pretest. Despite the logical possibility of hidden bias in this instance, all three cases also reproduce their experimental estimates, especially if the match is geographically local. We then identify two studies where the treatment and nonrandomized comparison groups manifestly differ at pretest but where the selection process into treatment is completely or very plausibly known. Here too, experimental results are recreated. Two of the remaining studies result in correspondent experimental and nonexperimental results under some circumstances but not others, while two others produce different experimental and nonexperimental estimates, though in each case the observational study was poorly designed and analyzed. Such evidence is more promising than what was achieved in past within-study comparisons, most involving job training. Reasons for this difference are discussed.

Healthy individuals typically show more attention to the left than to the right (known as pseudoneglect), and to the upper than to the lower visual field (known as altitudinal pseudoneglect). These biases are thought to reflect asymmetries in neural processes. Attention biases have been used to investigate how these neural asymmetries change with age. However, inconsistent results have been reported regarding the presence and direction of age-related effects on horizontal and vertical attention biases. The observed inconsistencies may be due to insensitive measures and small sample sizes, that usually only feature extreme age groups. We investigated whether spatial attention biases, as indexed by gaze position during free viewing of a single image, are influenced by age. We analysed free-viewing data from 4,243 participants aged 5–65 years and found that attention biases shifted to the right and superior directions with increasing age. These findings are consistent with the idea of developing cerebral asymmetries with age and support the hypothesis of the origin of the leftward bias. Age modulations were found only for the first seven fixations, corresponding to the time window in which an absolute leftward bias in free viewing was previously observed. We interpret this as evidence that the horizontal and vertical attention biases are primarily present when orienting attention to a novel stimulus – and that age modulations of attention orienting are not global modulations of spatial attention. Taken together, our results suggest that attention orienting may be modulated by age and that cortical asymmetries may change with age.

Recent research has shown that race can influence perceptions of men’s size and strength. Across two studies (Study 1: N = 1,032, Study 2: N = 303) examining men and women from multiple racial groups (Asian, Black, and White adults), we found that although race does impact judgments of size and strength, raters’ judgments primarily track targets’ objective physical features. In some cases, racial stereotypes actually improved group-level accuracy, as these stereotypes aligned with racial-group differences in size and strength according to nationally representative data. We conclude that individuals primarily rely on individuating information when making physical judgments but do not completely discount racial stereotypes, which reflect a combination of real group-level differences and culturally transmitted beliefs.

Background Autism spectrum disorder (ASD) is one of the serious developmental disorders that is usually diagnosed below the age of three years. Although the severity of the disease’s symptoms varies from patient to patient, the ability to communicate with others is affected in all forms of ASD. This study aimed to determine the prevalence of ASD in high-risk groups by continent. Methods The present study was conducted by systematic review and meta-analysis from 2008 to July 2021. Databases such as Science Direct, PubMed, Scopus, SID, Magiran, Web of Science (WoS), and Google Scholar from 2008 to July 2021 were searched to find related studies. Data were analysed using Comprehensive Meta-Analysis software (Version 2). Results A total of 74 studies with 30,212,757 participants were included in this study. The prevalence of ASD in the world was 0.6% (95% confidence interval: 0.4–1%). Subgroup analyses indicated that the prevalence of ASD in Asia, America, Europe, Africa and Australia was 0.4% (95% CI: 0.1–1), 1% (95% CI: 0.8–1.1), 0.5% (95% CI: 0.2–1), 1% (95% CI: 0.3–3.1), 1.7% (95% CI: 0.5–6.1) respectively. Conclusion ASD imposes a heavy health burden on communities around the world. Early detection of ASD can reduce the incidence of developmental disorders and improve patients’ communication skills. Therefore, health policymakers need to be aware of the prevalence and increasing trend of ASD to implement appropriate planning and interventions to reduce its consequences.

Failure to reach data saturation has an impact on the quality of the research conducted and hampers content validity. The aim of a study should include what determines when data saturation is achieved, for a small study will reach saturation more rapidly than a larger study. Data saturation is reached when there is enough information to replicate the study when the ability to obtain additional new information has been attained, and when further coding is no longer feasible. The following article critiques two qualitative studies for data saturation: Wolcott (2004) and Landau and Drori (2008). Failure to reach data saturation has a negative impact on the validity on one’s research. The intended audience is novice student researchers.

An analysis of institutional data to understand the outcome of obstacles faced by students from historically disadvantaged backgrounds is important in order to work toward promoting equity and inclusion. We use 10 years of institutional data at a large public research university to investigate the grades earned by students categorized on four demographic characteristics: gender, race/ethnicity, low-income status, and first-generation college student status. We find that on average across all years of study, underrepresented minority (URM) students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students. Moreover, the URM students with additional disadvantages due to socioeconomic status or first-generation college status were further penalized in their average GPA. These inequitable outcomes point to systemic inequities in higher education for students with historically disadvantaged backgrounds and the need to dismantle institutional inertia to support them.

Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced. Although previous studies proposed several methods to deal with the imbalanced class problem, most of them focused on the technical details of how to improve each technique, while only a few focused on the application aspect, especially for the application of data with different imbalance ratios. In this study, we compared several sampling techniques to handle the different ratios of the class imbalance problem (i.e., moderately or extremely imbalanced classifications) using the High School Longitudinal Study of 2009 dataset. For our comparison, we used random oversampling (ROS), random undersampling (RUS), and the combination of the synthetic minority oversampling technique for nominal and continuous (SMOTE-NC) and RUS as a hybrid resampling technique. We used the Random Forest as our classification algorithm to evaluate the results of each sampling technique. Our results show that random oversampling for moderately imbalanced data and hybrid resampling for extremely imbalanced data seem to work best. The implications for educational data mining applications and suggestions for future research are discussed.

People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society. Lee et al. show people's biases in social perception can be explained merely by the structure of their social networks, without assuming biased cognition. Social perception biases can be explained by homophily of personal networks and minority-group size.