Crowdsourced Samples Research Articles

Supplemental Material for Alcohol-Related Stimuli Disrupt Inhibitory Control in Heavy but Not Light Drinkers in a Crowdsourced Sample

Supplemental Material for Alcohol-Related Stimuli Disrupt Inhibitory Control in Heavy but Not Light Drinkers in a Crowdsourced Sample

Self-Reported Spite Predicts Spiteful Behavior in an Online Crowd-Sourced Sample

Spiteful behavior, where one is willing to harm oneself to harm another, is a common social behavior that is associated with social competition. However, there is currently only one well-used psychological measure that claims to measure spitefulness, the Spitefulness Scale (Marcus et al., 2014). At present, there is little research investigating whether spite, as measured by this scale, is associated with spiteful decision-making. To test this relationship, we asked a UK sample (N = 113; 51% female, Mage = 38) to play a Joy of Destruction game before completing the Spitefulness Scale. Participant scores on the scale predicted whether participants spent any non-zero amount on destroying the resources of others and the amount spent by those who chose destruction. The results add further support to the validity of the Spitefulness Scale as a predictor of spiteful behavior, and suggests its utility can expand to data collected via online crowd-sourcing.

Mental Health and Mobile Communication Profiles of Crowdsourced Participants.

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and burdensome. To increase mental health screening rates, the digital health research community has been exploring the ability to augment self reporting instruments with digital logs. Crowdsourced workers are being increasingly recruited for behavioral health research studies as demographically representative samples are desired for later translational applications. Overshadowed by predictive modeling, descriptive modeling has the ability to expand knowledge and understanding of the clinical generalizability of models trained on data from crowdsourced participants. In this study, we identify mobile communication profiles of a crowdsourced sample. To achieve this, we cluster features derived from time series of call and text logs. The psychiatric, behavioral, and demographic characteristics were notably different across the four identified mobile communication profiles. For example, the profile that had the lowest average depression and anxiety screening scores only shared incoming text logs. This cluster had statistically significantly different depression and anxiety screening scores in comparison to the cluster that shared the most outgoing text logs. These profiles expose important insights regarding the generalizability of crowdsourced samples to more general clinical populations and increase understanding regarding the limitations of crowdsourced samples for translational mental health research.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

Linkages Between Mineral Element Composition of Soils and Sediments With Hyporheic Zone Dissolved Organic Matter Chemistry Across the Contiguous United States

AbstractThe hyporheic zone is a hotspot for biogeochemical cycling where interactions with mineral metals preserve the release and biodegradation of organic matter (OM). A small fraction of OM can still be exchanged between localized sediments and the overlying water column, and recent evidence suggests there exists a longitudinal structuring in sediment dissolved OM (DOM) chemistry across the continental United States (CONUS). In this study, we tested a hypothesis that water extractable sediment DOM chemistry could be explained by sediment metal contents and integrative watershed scale features at the CONUS scale. Crowdsourced samples were characterized for high resolution mass spectrometry and coupled with sediment metals determined via x‐ray fluorescence as well as with land cover and soil elemental information obtained from national databases. Our results highlight weak relationships between DOM chemistry and elemental composition at the CONUS scale indicating limited transferability of organo‐metal linkages into multi‐scale hydrobiogeochemical models.

Initial evidence of reliability and validity of an implicit association test assessing attitudes toward individuals who use substances

ABSTRACT Background: Substance use disorders (SUDs) are stigmatized conditions, with individual biases driving poor health outcomes. There are surprisingly few validated measures of bias or stigma toward individuals who use substances. Bias can be classified as explicit (self-report) or implicit (behaviorally based). Objectives: The goal of the present study was to establish preliminary indices of reliability and validity of an implicit association test (IAT) designed to measure implicit bias toward individuals who use substances. Methods: A large United States-based, crowd-sourced sample (n = 394, 51.5% male, 45.4% female, 2.5% nonbinary) completed the IAT and a small battery of survey instruments that assessed social distance to mental illness (including heroin use), attitude toward and perceived controllability of injection drug use, perception of public stigma, and social desirability. Results: Nearly all (92%; n = 363) scores on the IAT indicated greater negative than positive attitudes toward those who use substances. Spearman-Brown corrected split-half reliability on the IAT scores was excellent, r = .953. Controlling for social desirability, IAT scores positively correlated with all included measures pertaining to substance use as well as social distance for heroin and schizophrenia (but not diabetes). A principal component analysis resulted in two interpretable components representing disapproval (perceived controllability and negative attitudes) and perceived stigma (social stigma and social distance). Scores on the IAT positively correlated to scores on both components, again, controlling for social desirability. Conclusion: These results provide compelling preliminary evidence of validity of an IAT designed to measure bias toward individuals who use substances.

Selection biases in crowdsourced big data applied to tourism research: An interpretive framework

Crowdsourced big data has faced growing criticism due to its quality issues, particularly selection biases. We propose an interpretive framework for understanding selection biases in crowdsourced big data applied to tourism research. Inspired by medical terminology, the framework was structured according to external manifestations, internal causes, and potential influencing factors. Using illustrative case data from six websites, the framework demonstrates the emergence and impact of selection biases. Specifically, crowdsourcing-based tourism analysis can be notably affected by online platforms and destination contexts. Crowdsourced samples may not provide a perfect representation of actual travelers due to skewness in gender, age, origin, etc. Tourism researchers and stakeholders are urged to acknowledge selection biases and respond judiciously in their academic and practical efforts. Our research addresses a timely data science issue and offers insights for advancing knowledge innovation and technological improvements in tourism.

Borderline personality disorder features are associated with inflexible social interpretations

BackgroundBorderline personality disorder (BPD) is thought to involve aberrant social learning, including impaired revision of social interpretations with new evidence (social interpretation inflexibility). However, this topic has received little empirical attention outside of specific literatures, such as moral inference or behavioral economics. Further, the contribution of comorbid depression to BPD-related interpretation inflexibility has not yet been assessed. MethodsIn two independent samples (Study 1: N = 213; Study 2: N = 210, oversampled for BPD features), we assessed the associations between BPD symptoms, depressive symptoms, and task-based measures of social interpretation flexibility. ResultsWe found that BPD symptoms, particularly volatility of identity and relationships, were associated with less revision of social interpretations with both positive and negative evidence. Meanwhile, depressive symptoms were associated with a pattern of less revision of social interpretations with positive versus negative information. LimitationsThe use of cross-sectional, crowd sourced samples limits causal interpretations. Translation to clinical populations should be assessed in future studies. ConclusionsResults suggest that inflexible social interpretations across valences may be feature of BPD-related pathology, and could be connected to symptoms involving volatility in social contexts. Future studies should investigate whether treatments geared toward increasing the flexibility of social interpretations are effective in treating BPD symptoms, especially those involving interpersonal difficulties.

Maximum respiration rates in hyporheic zone sediments are primarily constrained by organic carbon concentration and secondarily by organic matter chemistry

Abstract. River corridors are fundamental components of the Earth system, and their biogeochemistry can be heavily influenced by processes in subsurface zones immediately below the riverbed, referred to as the hyporheic zone. Within the hyporheic zone, organic matter (OM) fuels microbial respiration, and OM chemistry heavily influences aerobic and anaerobic biogeochemical processes. The link between OM chemistry and respiration has been hypothesized to be mediated by OM molecular diversity, whereby respiration is predicted to decrease with increasing diversity. Here we test the specific prediction that aerobic respiration rates will decrease with increases in the number of unique organic molecules (i.e., OM molecular richness, as a measure of diversity). We use publicly available data across the United States from crowdsourced samples taken by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our continental-scale analyses rejected the hypothesis of a direct limitation of respiration by OM molecular richness. In turn, we found that organic carbon (OC) concentration imposes a primary constraint over hyporheic zone respiration, with additional potential influences of OM richness. We specifically observed respiration rates to decrease nonlinearly with the ratio of OM richness to OC concentration. This relationship took the form of a constraint space with respiration rates in most systems falling below the constraint boundary. A similar, but slightly weaker, constraint boundary was observed when relating respiration rate to the inverse of OC concentration. These results indicate that maximum respiration rates may be governed primarily by OC concentration, with secondary influences from OM richness. Our results also show that other variables often suppress respiration rates below the maximum associated with the richness-to-concentration ratio. An important focus of future research will identify physical (e.g., sediment grain size), chemical (e.g., nutrient concentrations), and/or biological (e.g., microbial biomass) factors that suppress hyporheic zone respiration below the constraint boundaries observed here.

Parenting Concerns During the COVID-19 Pandemic: Results From a Canadian Crowdsourced Sample

Early research suggests that the COVID-19 pandemic may impact parental functioning. The purpose of this study was to examine parents’ concerns during the pandemic and to explore the characteristics of families who may be particularly vulnerable to parenting concerns. Data were drawn from an online crowdsourcing survey of parents of children under age 15 in Canada ( N = 32,228). Many parents were very or extremely concerned about managing child behaviour (61%) and having less patience with, scolding, or yelling at their children (46%). Family characteristics, including working from home, age of children, number of adults in the home, and child disability were associated with these parenting concerns in logistic regression models. Results are discussed in the context of the known impacts of stress on parenting and families, highlighting family-level and pandemic-specific stressors.

Supplemental Material for Examining the Validity of the Addictions Neuroclinical Assessment Domains in a Crowdsourced Sample of Adults With Current Alcohol Use

Supplemental Material for Examining the Validity of the Addictions Neuroclinical Assessment Domains in a Crowdsourced Sample of Adults With Current Alcohol Use

IWFUCIA: An integrated model for Wi-Fi fingerprints radio map updating and localization on changed impact analysis

In Wi-Fi fingerprinting indoor localization, automating radio map database maintenance is one of the crucial issues, as it is a labour-intensive and long-term task for collecting and filtering samples to keep an up-to-date and accurate database. In particular, those access points (APs) newly installed in the environment should update radio maps and be included in the database to improve localization performance. This study presents an IWFUCIA system that automates indoor radio map database maintenance (RMapDM) using crowdsourced samples without accurate location annotation. The IWFUCIA incorporates the newly installed APs detection and identification, the significant APs feature selection, fingerprint integration updating, and online localization algorithms. After collecting new crowdsourced samples, we apply Willmott’s index of agreement (WIA) based on the Supported Vector Machine (SVM) regression to detect and identify a newly installed AP and the original existing ones. After getting the new APs, we propose a correlated coefficient and t-test score algorithm to select only those significant AP-based feature samples. We also proposed a fingerprint integration model to fuse original existing and new APs to update the database. Extensive experiments have been conducted in our teaching building to validate and evaluate the effectiveness of IWFUCIA. The results show that our IWFUCIA is robust for long-term maintenance and updating the outdated radio map database server. The average localization accuracy achieves 0.466 m, which significantly outperforms the localization positioning approaches with the original radio map by 84.96%, outdated radio maps by the changed APs powers removed, increased and decreased by 26.32%, 55.36%, and 73.14%, respectively.

Determining the biogeochemical transformations of organic matter composition in rivers using molecular signatures

Inland waters are hotspots for biogeochemical activity, but the environmental and biological factors that govern the transformation of organic matter (OM) flowing through them are still poorly constrained. Here we evaluate data from a crowdsourced sampling campaign led by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to investigate broad continental-scale trends in OM composition compared to localized events that influence biogeochemical transformations. Samples from two different OM compartments, sediments and surface water, were collected from 97 streams throughout the Northern Hemisphere and analyzed to identify differences in biogeochemical processes involved in OM transformations. By using dimensional reduction techniques, we identified that putative biogeochemical transformations and microbial respiration rates vary across sediment and surface water along river continua independent of latitude (18°N−68°N). In contrast, we reveal small- and large-scale patterns in OM composition related to local (sediment vs. water column) and reach (stream order, latitude) characteristics. These patterns lay the foundation to modeling the linkage between ecological processes and biogeochemical signals. We further showed how spatial, physical, and biogeochemical factors influence the reactivity of the two OM pools in local reaches yet find emergent broad-scale patterns between OM concentrations and stream order. OM processing will likely change as hydrologic flow regimes shift and vertical mixing occurs on different spatial and temporal scales. As our planet continues to warm and the timing and magnitude of surface and subsurface flows shift, understanding changes in OM cycling across hydrologic systems is critical, given the unknown broad-scale responses and consequences for riverine OM.

Hyperbolic modeling and assessment of hypothetical health behaviors during a viral outbreak using crowdsourced samples.

The COVID-19 pandemic provided an opportunity to investigate factors related to public response to public health measures, which could help better prepare implementation of similar measures for inevitable future pandemics. To understand individual and environmental factors that influence likelihood in engaging in personal and public health measures, three crowdsourced convenience samples from Amazon Mechanical Turk (MTurk) completed likelihood-discounting tasks of engaging in health behaviors given a variety of hypothetical viral outbreak scenarios. Experiment 1 assessed likelihood of mask wearing for a novel virus. Experiment 2 assessed vaccination likelihood based on efficacy and cost. Experiment 3 assessed likelihood of seeking health care based on number of symptoms and cost of treatment. Volume-based measures and three-dimensional modeling were used to analyze hypothetical decision making. Hypothetical public and personal health participation increased as viral fatality increased and generally followed a hyperbolic function. Public health participation was moderated by political orientation and trust in science, whereas treatment-seeking was only moderated by income. Analytic methods used in this cross-sectional study predicted population-level outcomes that occurred later in the pandemic and can be extended to various health behaviors.

Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples

Tree planting has been suggested as a potentially effective solution for mitigating climate change. China has implemented the world’s largest afforestation and reforestation project since the 1970s, but high-resolution maps of China’s planted forests remain unavailable. In this study, we explored the use of multi-source remote sensing images and crowdsourced samples to produce the first high-resolution (30-m) map of China’s planted forests. We constructed a Google Earth Engine (GEE)-based mapping framework using spectral, temporal, structural, textural and topographic features derived from Landsat and Sentinel-1 time series imagery, Digital Elevation Model (DEM) and Chinese Forest Canopy Height (CFCH) data. Over 300,000 high-quality crowdsourced samples were collected for training the mapping pipeline. Validation against independent field samples indicated an accuracy of 84.93 % and an F1 score of 0.85. The uncertainty map of each pixel was also constructed and showed that the areas of low and medium uncertainties accounted for 38.27 % and 50.98 % of the total area, respectively, indicating the high estimation reliabilities of the planted forest map. We show that China’s planted forests in the year of 2020 had a total area of 769853.01 km2, accounting for 31.30 % of the world’s total planted forests. The majority (77.45 %) of China’s planted forests were located in the Eastern, Center-South, and Southwestern regions. By further assessing the performance of the image features used to map the planted forests, we found that temporal features are key to identifying the planted forests in East and Center-South of China, where they are mainly timber plantations. However, structural and textural features were more useful for locating the planted forests in North and Northeast of China, where are dominated by planted shelterbelts. Our study demonstrated that combining crowdsourced samples with high-resolution satellite images allows mapping planted forests with unprecedented resolution (30-m) across large areas. Our map could contribute to the sustainable management of China’s forests and a more accurate quantification of the carbon balance of China’s natural ecosystems.

Outcome sequences and illusion of control – part II: the effect on post-loss speeding

ABSTRACT When gambling, people tend to speed up after losses. This ’post-loss speeding’ is in contrast with ’post-error slowing’, which is often observed in behavioral tasks in experimental psychology. Importantly, participants can control the outcome in most behavioral tasks, but not in gambling tasks. To test whether perceived controllability over the outcome influences response speed after negative outcomes when gambling, we ran two online studies in which we created an illusion of control without changing the nature of the chance-determined gamble. Using the manipulation by Langer and Roth (1975), whose effect is replicated in Part I, we presented three groups of healthy participants (N = 600 per experiment, crowdsourced samples) with three different sequences of outcomes in a coin-tossing task. We replicated that participants presented with more wins at the beginning of a sequence estimated their ability to predict the outcome of a coin-toss higher than participants presented with more losses at the beginning, or those presented with a random sequence. Additionally, participants generally responded more quickly after a loss than after a win. However, the illusion of control did not influence post-loss speeding. This result is not consistent with several theoretical accounts for changes in response speed after sub-optimal outcomes.

MRILoc: Multiresolution Indoor Localization from crowdsourced samples

The demand of Wi-Fi fingerprinting-based indoor localization has been growing steadily for its open-source and low-cost infrastructure. Since site survey is tedious and costly, crowdsourcing is becoming a typical practice for fingerprint database construction. However, erroneous location annotation and non-uniform sample density need to be addressed in sample crowdsourcing. Furthermore, the traditional massive fingerprint databases in large and complex indoor environments require longer processing time and extensive computational complexity, limiting their efficiency and scalability. We propose a Multiresolution Indoor Localization from crowdsourced samples (MRILoc) system to address these challenges. The MRILoc consists of three major offline modules and a new online hierarchical and multiresolution localization algorithm. Firstly, we designe a Sample Reliability Measure Algorithm (SRMA) to identify reliable crowdsourced samples. Secondly, we construct weighted surfaces using reliable fingerprints and downsample fingerprints at the center of grids to resolve the issue of the non-uniform sample density. Thirdly, we construct hierarchical training databases for multiresolution localization. Our online algorithm integrates the K-Nearest Neighbors (KNN) to classify a test sample in different resolution subareas and uses XGBoost regression for the final exact localization approximation if necessary. To evaluate the performance of the proposed method, we conduct experiments on two field measurement datasets. The experiment results show a high average hitting rate and 19% localization accuracy improvement over the peer schemes.

Supplemental Material for Next-Day Responsibilities Attenuate Demand for Alcohol Among a Crowdsourced Sample of Community Adults

Supplemental Material for Next-Day Responsibilities Attenuate Demand for Alcohol Among a Crowdsourced Sample of Community Adults

Evaluating and improving the quality of survey data from panel and crowd-sourced samples: A practical guide for psychological research.

The use of crowd-sourced and panel survey data in addiction research has become widespread. However, the validity of data obtained from newer panels such as Qualtrics has not been extensively evaluated. Furthermore, few addiction researchers appear to employ previously recommended guidelines for maximizing the quality of data obtained from panel samples. The goals of the present study were as follows: (a) to evaluate the quality of survey data obtained from Qualtrics including an evaluation of the company's internal data screening process and (b) to provide a practical implementation guide for data screening practices that maximize the quality of data obtained via panel and crowd-sourced samples. To address the goals, two panel samples evaluating vaping and video gaming behaviors were recruited in Canada via Qualtrics and underwent Qualtrics's internal data screening process before being rigorously rescreened by the authors. The results demonstrate that while Qualtrics's paid internal data quality process flags and removes many low-quality participants, there is still a large portion of participants presented by Qualtrics as high-quality that are likely low-quality responses that need to be screened out. The presented methodology provides a rigorous data screening protocol, including step-by-step application, for crowd-sourced samples in addictive behavior research for maximizing data quality. Researchers should be cautious in the use of Qualtrics data for administration of addiction survey research and are encouraged to use additional data screening procedures to maximize data quality. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The validity of qualtrics panel data for research on video gaming and gaming disorder.

Crowdsourced samples are increasing in popularity, particularly within psychological and addictive behaviors research. The trend has resulted in significant interest in the use of panel samples for the examination of behavioral and substance addictions. One newer panel platform, Qualtrics, has seen an increase in usage in recent years despite lack of research examining the validity of Qualtrics-produced data for addictive behaviors. The goal of the present study was to evaluate the validity of Qualtrics-obtained data for the most recently classified behavioral addiction-video gaming. The evaluation compared a Qualtrics-recruited video gamers sample (n = 586) to traditional community (n = 108) and student (n = 217) samples on demographics and key outcomes relevant to gaming disorder researchers (average playtime, frequency of gaming, and gaming disorder risk scores) to evaluate the generalizability of Qualtrics panel data. The results revealed that Qualtrics samples were comparable to a traditionally recruited community sample, but different from a student sample on gaming frequency (p < .001) and risk for gaming disorder (p < .001). The Qualtrics sample also had longer durations of average gaming time relative to the student sample (p = .01), with some differences in demographics between the all three sources of recruitment. The findings suggest that Qualtrics may provide a suitable method of convenience panel recruitment, generalizable to the broader North American community, for research examining video gaming behaviors and gaming disorder. (PsycInfo Database Record (c) 2022 APA, all rights reserved).