Raw Survey Research Articles

Bayesian Evidence Synthesis to Infer Unobserved Population Dynamics: An Application to International Migration Into the United States, 2000–2019

For the United States, detailed estimates of the number of resident migrants and the rates of migrant arrival are valuable for understanding population dynamics and for determining the impact of economic and political changes that influence migration. The goal of this analysis was to derive estimates of the U.S. foreign-born population and how this population has changed in recent years, as well as estimates of recent and historical immigration volumes. Using data from large population surveys (the 2000 U.S. decennial census and 2001–2019 American Community Survey (ACS)), a Bayesian evidence synthesis was conducted to pool survey data across years while accounting for various biases and logical constraints that apply to these data. This analysis produced highly disaggregated estimates of the foreign-born population residing in the United States over the period 2000–2019, as well as estimates of immigration volume for 1950–2019. These population estimates demonstrated high in- and out-of-sample predictive performance, with substantially greater precision than that for raw survey estimates. Estimated immigration flows tracked other available time series, although with higher precision and with the potential to include undocumented immigration not represented in other immigration data. This study documents immigration from 100 countries of origin into the United States and demonstrates how the results of repeated cross-sectional population surveys can be used to infer migration dynamics that are difficult to measure directly.

Estimates of disclosure and victimization rates for fishery observers in the maritime workplace

Seafarers working in remote ports and onboard fishing vessels often face isolated, high-risk environments, making them vulnerable to sexual harassment, intimidation, and assault. In the United States and other countries, scientists, called fishery observers, are required by the government to be deployed alongside fishing crews for extended periods to collect essential fisheries data and report potential fishing regulation violations they witness. Although many fishery observers who experience harassment submit official report statements, the true prevalence of the problem is unknown due to nondisclosure. This study uses anonymous responses from annual surveys distributed to North Pacific groundfish and halibut fishery observers to understand barriers to disclosure and estimate disclosure rates. By adjusting the annual counts of observers who submitted official harassment statements with these estimated disclosure rates, we provide the first estimates of the true number of victimized observers (prevalence) each year in a federal fisheries monitoring program in the United States. Model selection suggested that disclosure was influenced by the type of harassment experienced and not by observer demographics or employment year. Estimated disclosure rates (victimized observers who reported annually via official statement) were lowest for sexual harassment (0.18; 95% CI 0.11-0.29); higher for intimidation, coercion and hostile work environments (0.37; 95% CI 0.28-0.47); and highest for assault (0.57; 95% CI 0.41-0.73). Overall, 45% (95% CI 39-51%) of observers who experienced victimization disclosed harassment in a given year. We estimate that 22-38% of observers were victimized annually during the 2016-2022 study period, with rates of 24-60% for females and 12-24% for males. Victimization rates computed from raw survey summary statistics suffer from self-selection bias while rates derived solely from submission of official statements suffer from bias in underreporting. Supplementing official statements with estimates of disclosure rates from anonymous survey data provides a means of mitigating for these two forms of biases to obtain estimates of victimization untangled from fluctuations in reporting tendencies. When disclosure and victimization are teased apart, the effectiveness of risk reduction strategies can be better assessed over time.

HIV deathrate prediction by Gaidai multivariate risks assessment method.

HIV is a contagious disease with reportedly high transmissibility, being spread worldwide, with certain mortality, allegedly presenting a burden to public health worldwide. The main objective of this study was to determine excessive HIV death risks at any time within any region or country of interest. Current study presents a novel multivariate public health system bio-risk assessment approach that is particularly applicable to environmental multi-regional, biological, and public health systems, being observed over a representative period of time, yielding reliable long-term HIV deathrate assessment. Hence, the development of a new bio-statistical approach, that is, population-based, multicenter, and medical survey-based. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. Firstly, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case, - not to mention challenges with system dimensionality higher than 2D. Existing bio-statistical methods that process spatiotemporal clinical observations of multinational bio-processes often do not have the advantage of efficiently dealing with high regional dimensionalities and complex nonlinear inter-correlations between different national raw datasets. Hence, this study advocates the direct application of the novel bio-statistical Gaidai method to a raw unfiltered clinical data set. This investigation described the successful application of a novel bio-risk assessment approach, yielding reliable long-term HIV mortality risk assessments. The suggested risk assessment methodology may be utilized in various public bio and public health clinical applications based on available raw patient survey datasets.

Dataset on Financial Literacy, Financial Inclusion, Informal Financial Business Practices, and Intentions towards Formalization of Female Small Vendors in Lima, Peru

Abstract This article presents findings from a survey conducted in Lima, Peru, aimed at understanding the relationships between education, financial literacy, financial inclusion, and informal financial business practices among small female vendors. The study, which collected 118 valid responses, focused on the impact of these factors on vendors’ intentions toward formalization. Formality was assessed based on legal registration with tax authorities, emphasizing the informal practices viewed on a continuum. These practices were evaluated using a five-point gradation scale that depicted varying levels of formality. Financial literacy, financial inclusion, and formalization intentions were measured using a five-point Likert scale, while a dichotomous question captured the formality-informality of the businesses. The demographic variables included age, gender, business tenure, employee count, and business activity. Educational level, typically treated as demographic, was considered an antecedent to financial literacy. The dataset linked to this study included raw survey data. It serves as a valuable resource for researchers, industry representatives, public authorities, and stakeholders from developing countries to deal with informality and formalization. The survey methodology and data are adaptable for use in different national contexts, facilitating comparative analysis in developing countries.

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models' performance by expanding the dataset's size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the \textit{limited} survey data size using data augmentation technique(s). Next, we carry out data unification, followed by clustering for inferencing. We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. One is on features of students to be entrepreneurs, and the second is breast cancer survey data. We compare the results of the inference obtained from the raw survey data and the newly converted data. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.

Introducing ICBEN's new socio-acoustic survey archive ISAR

The International Committee on Biological Effects of Noise (ICBEN) has recently started an international archive of original survey response data from so called socio-acoustic noise surveys, named ISAR (ICBEN Socio-Acoustic Survey Archive). The ISAR archive caters to noise effects researchers around the globe and enables pooled analyses, establishment of generalized exposure-response relationships, trend analyses both spatially and temporally, as well as cross-cultural comparisons of self-reported noise reactions (noise annoyance and noise-induced sleep disturbances in particular). The data in the archive will be freely accessible. Data in the ISAR archive are fully anonymous in order to meet the pertinent data protection regulations in most countries. The ISAR archive is curated by ICBENs officers and the team chairs of Team 6 (Community response to noise and annoyance). The ISAR archive explicitly collects only raw survey response data, rather than data that have been aggregated on a higher level. We are constantly seeking to extend the archive with new study data and thus, every new contribution is highly welcome. In this talk, we would like to publicize and promote the archive and encourage our colleagues to share their survey data with the archive.

Semantic interpretation of raw survey vehicle sensory data for lane-level HD map generation

High-definition (HD) maps provide a complementary source of information for Advanced Driver Assistance Systems (ADAS), allowing them to better understand the vehicle’s surroundings and make more informed decisions. HD maps are also largely employed in virtual testing phases to evaluate the behavior of ADAS components under simulated conditions. With the advent of autonomous sensorized vehicles, raw machine-oriented data will be increasingly available. The proposed pipeline aims to provide a high-level semantic interpretation of raw vehicle sensory data to derive, in an automated fashion, lane-oriented HD maps of the environment. We first present RoadStarNet, a deep learning architecture designed to extract and classify road line markings from imagery data. We show how to obtain a semantic Bird’s-Eye View (BEV) mapping of the extracted road line markings by exploiting frame-by-frame localization information. Then, we present how to progress to a graph-based representation that allows modeling complex road line markings’ structures practically, as this representation can be leveraged to produce a Lanelet2 format HD map. Lastly, we experimentally evaluate the proposed approach in real-world scenarios in terms of accuracy and coverage performance.

Correction of China's income inequality for missing top incomes

AbstractAfter accounting for top incomes missing from the Chinese Household Income Project, this paper examines the income inequality trend in China during the 21st century. Our analysis, which involves fitting the upper tail of the income distribution to a power‐law model, reveals that the authoritative income survey data miss 0.68% of the population and 6.19% of aggregate income in 2018. Despite the most recent survey data providing better coverage of top incomes, our correction is still crucial. The raw survey data indicate a consistently increasing Gini coefficient between 2002 and 2018, but the corrected index starts to decline from 2013. Meanwhile, the revised top 1% income share increases from 7.01% in 2002 to 7.89% in 2013 and then slightly decreases to 7.64% in 2018, while the revised top 10% income share stabilises at around 33% throughout the period. Notably, China's revised top 10% and top 50% income shares in 2018 are close to those of the United Kingdom but are considerably lower than those of the United States.

SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency.

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

CHARACTERISTICS of an ENGAGING VIRTUAL WINE TASTING

Wine tasting rooms in the United States play an important role in the wine industry and the economic vitality of the regions that wine tourists visit, as wine tourists are generally well-educated and affluent, and they eagerly buy wine when they experience “pleasure” with their wine tourism experience (Bruwer & Rueger-Muck, 2019). When the COVID-19 pandemic forced many winery tasting rooms to shut down for months and operate under severe constraints once allowed to reopen, many wineries turned to virtual wine tastings to stay engaged with their consumers and attract new ones. This paper is an exploratory study of the features of a virtual wine tasting that participants in the U.S. find most engaging. We adopted the concept of winery tourism as a hedonic experience as the framework for our study of virtual wine tastings and apply the experiential view first applied to wine tourism by Bruwer and Alant (2009) to create an online survey employing the Best – Worst methodology first published by Finn and Louviere (1992). We collected 261 valid responses from people in the U.S. who participated in at least one virtual wine tasting. Using the classic agglomerative method, we performed unsupervised clustering on the raw survey response data to identify five main clusters of virtual wine tasting participant segments.

The Predicting Role of Emotional Intelligence in Job Satisfaction of Healthcare Practitioners in Pakistan

Healthcare had been dominated by stress-related literature for a long time. However, the contemporary approach focuses on doctors’ wellbeing. Since healthcare professionals’ wellbeing depends on their satisfaction with work, it is important to understand the factors that influence job satisfaction. Therefore, this study aimed to identify the predicting role of factors of Emotional Intelligence (EI), such as, perception of emotion, managing own emotions, managing others’ emotions, and usage of emotion in job satisfaction of healthcare practitioners in Pakistan while comparing managing own emotions in male and female healthcare practitioners and in surgeons and trainees. The study was quantitative, and 100 healthcare practitioners from public and private hospitals in Pakistan were sampled via convenient sampling. Descriptive statistics and multiple regression were used to interpret the raw survey data using SPSS. It was found that managing own emotions and usage of emotions positively predicted job satisfaction, with managing own emotions being the greater predictor. The perception of emotions and managing others’ emotions did not significantly predict job satisfaction. Moreover, it was found that managing own emotions was not a significant predictor for neither males nor females. It was also found that managing own emotions was not a significant predictor of job satisfaction for neither surgeons nor trainees. This is the first study in Pakistan exploring EI as a multidimensional construct, with each factor studied in relation to job satisfaction. The findings will play a pivotal role in designing training initiatives and outreach programs to enhance the job satisfaction of Pakistani healthcare practitioners for both government and private hospitals. Keywords : emotional intelligence, job satisfaction, healthcare practitioners. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.60.86

Preliminary results of the first lithic raw material survey in the piedmont zones of Kazakhstan

The study of raw materials was comprehensively studied in European and African Palaeolithic. However, systematic research of raw material sourcing has not been undertaken for the Palaeolithic of Kazakhstan, such surveys being embedded in reconnaissance works aimed at discovering new Palaeolithic sites. Our work presents preliminary results of the first lithic raw material survey in Kazakhstan. This study distinguishes the geographic patterns of land-use and their correlation with the stone tools from stratified sites. We describe primary and secondary sources of raw materials and compare macroscopically with the lithic assemblages. The survey results show a heterogeneous distribution of raw materials throughout the study regions. Macroscopic observations of lithic assemblages, and data extracted from literature suggest that hominins primarily selected local raw materials. Regional differences in the utilisation of a particular type of raw material which can be observed through the macroscopic examination of the lithic collections are confirmed by survey results.

Tracing the invisible rich: A new approach to modelling Pareto tails in survey data

This paper is concerned with the problem of modelling the tail of the wealth distribution with survey data when the data does not adequately cover the tail of the distribution. In order to deal with the problem post data collection, it is standard practice to either fit a Pareto tail to the data or to combine wealth survey data with observations from rich lists before fitting such a Pareto tail. This paper proposes a novel approach (‘rank correction’) to address such cases which does not require additional data-sources. Applying the rank correction approach to wealth survey data (HFCS, SCF, WAS) yields estimates of top wealth shares, which are closely in line with estimates from the World Inequality Database and therefore represent a significant improvement over the raw survey data. While the paper focuses on the distribution of wealth as a case in point the rank correction approach might generally prove useful in contexts, where the tail of a Pareto-distributed variable is not adequately covered by the available data.

Current State of Archiving Archaeological Reports in Bulgaria: still more physical than digital

This article provides an overview of the current state of archaeological archiving in Bulgaria. It briefly outlines the legislation that regulates archaeological fieldwork activities. Although the national legislation regulates the non-destructive and destructive activities equally, differences occur owing to the existence of the 'Archaeological Map of Bulgaria', a national 'sites and monuments' type of archaeological information system. Currently, online storage of brief data and information is possible for different archaeological sites. Its next version will be based on GIS and geographic features that allow storage of raw field survey data. According to the regulations, paper/digital reports of all fieldwork activities are collected yearly and copies of all of them are stored at the 'Scientific Archive' section at the National Archaeological Institute with Museum at the Bulgarian Academy of Sciences. Although their digitisation is still in the early stages, the good news is that the centralised archaeological workflow regulates their storage in one place, which makes them more easily accessible. The annual publication of the 'Archaeological Discoveries and Excavations' series, with summaries for all fieldwork activities in the year, is also very useful for keeping track of the archaeological work and results.

Consensus revisited: quantifying scientific agreement on climate change and climate expertise among Earth scientists 10 years later

The scientific consensus on human-caused global warming has been a topic of intense interest in recent decades. This is in part due to the important role of public perception of expert consensus, which has downstream impacts on public opinion and support for mitigation policies. Numerous studies, using diverse methodologies and measures of climate expertise, have quantified the scientific consensus, finding between 90% and 100% agreement on human-caused global warming with multiple studies converging on 97% agreement. This study revisits the consensus among geoscientists ten years after an initial survey of experts, while exploring different ways to define expertise and the level of agreement among these groups. We sent 10 929 invitations to participate in our survey to a verified email list of geosciences faculty at reporting academic and research institutions and received 2780 responses. In addition to analyzing the raw survey results, we independently quantify how many publications self-identified climate experts published in the field of climate change research and compare that to their survey response on questions about climate change. As well as a binary approach classifying someone as ‘expert’ or ‘non-expert’, we also look at expertise as a scale. We find that agreement on anthropogenic global warming is high (91% to 100%) and generally increases with expertise. Out of a group of 153 independently confirmed climate experts, 98.7% of those scientists indicated that the Earth is getting warmer mostly because of human activity such as burning fossil fuels. Among those with the highest level of expertise (independently confirmed climate experts who each published 20+ peer reviewed papers on climate change between 2015 and 2019) there was 100% agreement that the Earth is warming mostly because of human activity.

The evolution of the galaxy stellar-mass function over the last 12 billion years from a combination of ground-based and HST surveys

ABSTRACT We present a new determination of the galaxy stellar-mass function (GSMF) over the redshift interval 0.25 ≤ z ≤ 3.75, derived from a combination of ground-based and Hubble Space Telescope (HST) imaging surveys. Based on a near-IR-selected galaxy sample selected over a raw survey area of 3 deg2 and spanning ≥4 dex in stellar mass, we fit the GSMF with both single and double Schechter functions, carefully accounting for Eddington bias to derive both observed and intrinsic parameter values. We find that a double Schechter function is a better fit to the GSMF at all redshifts, although the single and double Schechter function fits are statistically indistinguishable by z = 3.25. We find no evidence for significant evolution in M⋆, with the intrinsic value consistent with $\log _{10}(M^{\star }/\hbox{$\rm \, M_{\odot }$})=10.55\pm {0.1}$ over the full redshift range. Overall, our determination of the GSMF is in good agreement with recent simulation results, although differences persist at the highest stellar masses. Splitting our sample according to location on the UVJ plane, we find that the star-forming GSMF can be adequately described by a single Schechter function over the full redshift range, and has not evolved significantly since z ≃ 2.5. In contrast, both the normalization and the functional form of the passive GSMF evolve dramatically with redshift, switching from a single to a double Schechter function at z ≤ 1.5. As a result, we find that while passive galaxies dominate the integrated stellar-mass density at z ≤ 0.75, they only contribute ≲10 per cent by z ≃ 3. Finally, we provide a simple parametrization that provides an accurate estimate of the GSMF, both observed and intrinsic, at any redshift within the range 0 ≤ z ≤ 4.

0485 Experience and Attitudes About Prescription Insomnia Medications: Results from an Online Survey of Individuals with Sleeping Difficulties and Insomnia

Abstract Introduction This survey explored several topics related to living with insomnia/sleeping difficulties. Reported here are patient experience, attitudes, and perspectives about and understanding of prescription medications for insomnia. Methods The online survey was conducted by The Harris Poll in the U.S. between February 14 and March 8, 2019. Survey respondents (“patients”) were adults age ≥18y who had been diagnosed with insomnia (11% of respondents), or had experienced sleeping difficulties (defined as difficulty falling asleep or staying asleep for ≥3 nights/week for ≥3 months; 89% of respondents). Raw survey data were weighted by relevant factors to be representative of the total U.S. adult population with insomnia/sleeping difficulties. Results Among 525 patients (mean age 46y; 55% female) who completed the survey, 83 were currently using prescription medication, 45 used prescription medication previously, and 397 had no prescription medication history. The majority of all patients “somewhat” or “strongly” agreed they were “concerned about the safety risks of sleep medications currently available by prescription” (79%); felt “there have got to be better medications that help people sleep” (74%); and that they “wish there were more medications to choose from” (67%). Within the group of respondents with current/past prescription history (n=128), 63%, 23%, and 14% had tried 1-2, 3-4, or ≥5 different prescription medications, respectively. Among reasons for missing/skipping a dose, ~20% of respondents with current/past prescription history selected for each response that they “do not feel my medication is effective”; “do not like the way my medication makes me feel when I wake up the next morning”; and “prefer not to take my medication every night unless absolutely necessary.” Conclusion Results from this online survey provide insights into patient attitudes toward pharmacotherapy and indicate that a significant number of insomnia patients feel dissatisfied with medication treatment options, including concerns regarding safety and side effects. Support Eisai Inc.

PIH71 RISK FACTORS ASSOCIATED WITH FALLS AMONG THE ELDERLY IN U.S.: AN ANALYSIS OF MEDICARE CURRENT BENEFICIARY SURVEY DATA

Falls are the second leading cause of accidental or unintentional injury and deaths worldwide, with elderly population suffering the greatest number of fatal falls. However, falls are often not reported to a clinician if there is no obvious injury associated with the fall, making it difficult to devise programs to prevent falls. This study aims to evaluate the risk factors associated with falls among elderly Medicare beneficiaries and also evaluate the proportion of patients seeking medical care for falls. This retrospective study analyzed data from the Medicare Current Beneficiary Survey, a nationally representative sample of the Medicare population. The primary outcome measure in the study was self-reported falls over the last 12-months. Demographics, social status, comorbidities, residence type, general health, and functioning were among those evaluated as potential risk factors for falls. Univariate, bivariate, and logistic regression models were conducted. Raw survey estimates and weighted estimates with 95% confidence intervals are reported. Of the 13,822 Medicare beneficiaries included in analysis, 27.41% (n=3,788) reported a fall during the previous year. Forty-eight percent were ≥75-years old, 57% were female, and 34% were obese. Falls were more frequently associated with race (odds ratio (OR) for African-Americans = 0.59, 95% CI: 0.52-0.69), marital status (divorced/separated = 1.17, 1.03– 1.32), general health (poor general health = 1.88, 1.54-2.30), functional limitations (5-6 activity limitations = 3.57, 2.86-4.46), genitourinary problems (0.56, 0.52-0.62), dementia or Alzheimer’s disease (1.24, 1.04-1.47). Only 27.77% of patients sought medical care for the falls. Falls are a significant health event among elderly patients and imposes a significant economic burden on society. This analysis suggests that risk of falls increased 3.5-fold with severe activity limitations and 2-fold with poor general health. Less than 1/3rd of patients with fall reported seeking medical care, which is an addressable gap.

Piketty’s team on inequality in Russia: A collection of statistical artifacts

The paper provides a critical assessment of an authoritative study “From Soviets to oligarchs” (2017) on evolution of economic inequality in Russia by F. Novokmet, T. Piketty and G. Zucman where Russia is portrayed as a country with abnormally polarized income distribution by international standards. The author examines main quantitative results obtained by Piketty’s team, describes peculiar methodological features of their measuring procedure and analyzes how they dissect available empirical data sets. A general conclusion is that the trio uses an unconventional methodology that does not allow to apply equivalence scales; their argument suffers from internal inconsistencies (different units of observation and different definitions of income are used); they misunderstand the nature of data that form a base for their calculations (simulated estimates are perceived as raw survey data, post-tax incomes as pretax ones, etc.); deduction and declaration coefficients that they impose on tax data are empirically improbable; their final estimates of income inequality for Russia are higher than empirically realistic ones approximately by one third (Gini coefficients 0.5—0.6 instead of 0.3—0.4 by other researchers).

The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.