Pursuant to Local Laws 126, 127, and 128 of 2016, certain demographic data is collected voluntarily and anonymously by persons voluntarily seeking social services. This data can be used by agencies and the public to better understand the demographic makeup of client populations and to better understand and serve residents of all backgrounds and identities. The data presented here has been collected through either electronic form or paper surveys offered at the point of application for services. These surveys are anonymous. Each record represents an anonymized demographic profile of an individual applicant for social services, disaggregated by response option, agency, and program. Response options include information regarding ancestry, race, primary and secondary languages, English proficiency, gender identity, and sexual orientation. Idiosyncrasies or Limitations: Note that while the dataset contains the total number of individuals who have identified their ancestry or languages spoke, because such data is collected anonymously, there may be instances of a single individual completing multiple voluntary surveys. Additionally, the survey being both voluntary and anonymous has advantages as well as disadvantages: it increases the likelihood of full and honest answers, but since it is not connected to the individual case, it does not directly inform delivery of services to the applicant. The paper and online versions of the survey ask the same questions but free-form text is handled differently. Free-form text fields are expected to be entered in English although the form is available in several languages. Surveys are presented in 11 languages. Paper Surveys 1. Are optional 2. Survey taker is expected to specify agency that provides service 2. Survey taker can skip or elect not to answer questions 3. Invalid/unreadable data may be entered for survey date or date may be skipped 4. OCRing of free-form tet fields may fail. 5. Analytical value of free-form text answers is unclear Online Survey 1. Are optional 2. Agency is defaulted based on the URL 3. Some questions must be answered 4. Date of survey is automated

Abstract

Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. This survey covers topics about gender dynamics and norms, unpaid caregiving, and life during the COVID-19 pandemic. Aggregated data is available publicly on Humanitarian Data Exchange (HDX). De-identified microdata is also available to eligible nonprofits and universities through Facebook’s Data for Good (DFG) program. For more information, please email dataforgood@fb.com.

Geographic coverage

This survey is fielded once a year in over 200 countries and 60 languages. The data can help researchers track trends in gender equality and progress on the Sustainable Development Goals.

Analysis unit

• Public Aggregate Data on HDX: country or regional levels
• De-identified Microdata through Facebook Data for Good program: Individual level

Universe

The survey was fielded to active Facebook users.

Kind of data

Sample survey data [ssd]

Sampling procedure

Respondents were sampled across seven regions: - East Asia and Pacific; Europe and Central Asia - Latin America and Caribbean - Middle East and North Africa - North America - Sub-Saharan Africa - South Asia

For the purposes of this report, responses have been aggregated up to the regional level; these regional estimates form the basis of this report and its associated products (Regional Briefs). In order to ensure respondent confidentiality, these estimates are based on responses where a sufficient number of people responded to each question and thus where confidentiality can be assured. This results in a sample of 461,748 respondents.

The sampling frame for this survey is the global database of Facebook users who were active on the platform at least once over the past 28 days, which offers a number of advantages: It allows for the design, implementation, and launch of a survey in a timely manner. Large sample sizes allow for more questions to be asked through random assignment of modules, avoiding respondent fatigue. Samples may be drawn from diverse segments of the online population. Knowledge of the overall sampling frame allowed for more rigorous probabilistic sampling techniques and non-response adjustments than is typical for online and phone surveys

Mode of data collection

Internet [int]

Research instrument

The survey includes a total of 75 questions, split across into the following sections: - Basic demographics and gender norms - Decision making and resource allocation across household members - Unpaid caregiving - Additional household demographics and COVID-19 impact - Optional questions for special groups (e.g. students, business owners, the employed, and the unemployed)

Questions were developed collaboratively by a team of economists and gender experts from the World Bank, UN Women, Equal Measures 2030, and Ladysmith. Some of the questions have been borrowed from other surveys that employ alternative modes of administration (e.g., face-to-face, telephone surveys, etc.); this allows for comparability and identification of potential gaps and biases inherent to Facebook and other online survey platforms. As such, the survey also generates methodological insights that are useful to researchers undertaking alternative modes of data collection during the COVID-19 era.

In order to avoid “survey fatigue,” wherein respondents begin to disengage from the survey content and responses become less reliable, each respondent was only asked to answer a subset of questions. Specifically, each respondent saw a maximum of 30 questions, comprising demographics (asked of all respondents) and a set of additional questions randomly and purposely allocated to them.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error and nonresponse error.

Data appraisal

Survey Limitations The survey only captures respondents who: (1) have access to the Internet (2) are Facebook users (3) opt to take this survey through the Facebook platform. Knowledge of the overall demographics of the online population in each region allows for calibration such that estimates are representative at this level. However, this means the results only tell us something about the online population in each region, not the overall population. As such, the survey cannot generate global estimates or meaningful comparisons across countries and regions, given the heterogeneity in internet connectivity across countries. Estimates have only been generated for respondents who gave their gender as male or female. The survey included an “other” option but very few respondents selected it, making it impossible to generate meaningful estimates for non-binary populations. It is important to note that the survey was not designed to paint a comprehensive picture of household dynamics but rather to shed light on respondents’ reported experiences and roles within households

Abstract We developed four online interfaces supporting citizen participation in decision-making. We included (1) learning loops (LLs), good practice in decision analysis, and (2) gamification, to enliven an otherwise long and tedious survey. We investigated the effects of these features on drop-out rate, perceived experience, and basic psychological needs (BPNs): autonomy, competence, and relatedness, all from self-determination theory. We also investigated how BPNs and individual causality orientation influence experience of the four interfaces. Answers from 785 respondents, representative of the Swiss German-speaking population in age and gender, provided insightful results. LLs and gamification increased drop-out rate. Experience was better explained by the BPN satisfaction than by the interface, and this was moderated by respondents’ causality orientations. LLs increased the challenge, and gamification enhanced the social experience and playfulness. LLs frustrated all three needs, and gamification satisfied relatedness. Autonomy and relatedness both positively influenced the social experience, but competence was negatively correlated with challenge. All observed effects were small. Hence, using gamification for decision-making is questionable, and understanding individual variability is a prerequisite; this study has helped disentangle the diversity of responses to survey design options. Data The directory data contains: - rq2_df_compl.csv: Anonymized data of participants that completed the whole survey. This is the basis data analyzed in the script. - rq2_df_compl_start_data.csv: Anonymized data of participants that completed at least the GCOS questionnaire. This data file is to carry out complementary analysis (shown in Supplementary Information). These data files are the result of the preprocessing pipeline contained and described in the data package https://doi.org/10.25678/0008WS (still to come, at the time of publishing the current data package). Analysis All models and figures in the paper were produced with R. The code is contained in Analysis_and plots.R. The plots for the investigation of the drop-out rates (see SI 7.7) are in Drop_out_analysis.R

Demographic breakdown of lay participants.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Abstract

In partnership with the Yale Program on Climate Change Communication, Facebook launched a Climate Change Opinion Survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors across 31 countries and territories. Aggregated data is available publicly on Humanitarian Data Exchange (HDX). De-identified microdata is also available to nonprofits and universities under a data license agreement through Facebook’s Data for Good (DFG) program. For more information please email dataforgood@fb.com.

Analysis unit

Public Aggregate Data on HDX: country or regional levels De-identified Microdata through Facebook Data for Good program: Individual level

Universe

The survey was fielded to active Facebook users ages 18+

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampled Facebook users saw an invitation to answer a short survey at the top of their Facebook Newsfeed and had the option to click the invitation to complete the survey on the Facebook platform. The sample was drawn from the population of Facebook monthly active users, defined as registered and logged-in Facebook users who had visited Facebook through the website or a mobile device in the last 30 days.

Within each country or territory surveyed, Facebook drew a sample in proportion to publicly available age and gender benchmarks. The sample population in the United States was drawn in proportion to the U.S. Census Bureau Current Population Survey 2018 March Supplement. All other countries and territories were sampled in proportion to data from the United Nations Population Division 2019 World Population Projections. Data were weighted separately for each country and territory using a multi-stage, pre- and post-survey weighting process based on census and nationally representative survey benchmarks, Facebook demographics, and Facebook engagement metrics, balanced to the total number of survey completions.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about people’s climate change knowledge, attitudes, policy preferences, and behaviors. The codebook with survey questions is available here.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design. Facebook provides survey weights to help make the sample more representative of each country or territory’s population.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error and nonresponse error.

Abstract

The 2023-24 Lesotho Demographic and Health Survey (2023-24 LDHS) is designed to provide data for monitoring the population and health situation in Lesotho. The 2023-24 LDHS is the 4th Demographic and Health Survey conducted in Lesotho since 2004.

The primary objective of the 2023–24 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, awareness and behaviour regarding HIV and AIDS and other sexually transmitted infections (STIs), other health issues (including tuberculosis) and chronic diseases, adult mortality (including maternal mortality), mental health and well-being, and gender-based violence. In addition, the 2023–24 LDHS provides estimates of anaemia prevalence among children age 6–59 months and adults as well as estimates of hypertension and diabetes among adults.

The information collected through the 2023–24 LDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of Lesotho’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Lesotho.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2023–24 LDHS is based on the 2016 Population and Housing Census (2016 PHC), provided by the Lesotho Bureau of Statistics (BoS). The frame file is a complete list of all census enumeration areas (EAs) within Lesotho. An EA is a geographic area, usually a city block in an urban area or a village in a rural area, consisting of approximately 100 households. In rural areas, it may consist of one or more villages. Each EA serves as a counting unit for the population census and has a satellite map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2016 PHC. Lesotho is administratively divided into 10 districts; each district is subdivided into constituencies and each constituency into community councils.

The 2023–24 LDHS sample of households was stratified and selected independently in two stages. Each district was stratified into urban, peri-urban, and rural areas; this yielded 29 sampling strata because there are no peri-urban areas in Butha-Buthe. In the first sampling stage, 400 EAs were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was carried out in all of the selected sample EAs, and the resulting lists of households served as the sampling frame for the selection of households in the next stage.

In the second stage of selection, a fixed number of 25 households per cluster (EA) were selected with an equal probability systematic selection from the newly created household listing. All women age 15–49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Woman’s Questionnaire. In every other household, all men age 15–59 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Man’s Questionnaire. All households in the men’s subsample were eligible for the Biomarker Questionnaire.

Fifteen listing teams, each consisting of three listers/mappers and a supervisor, were deployed in the field to complete the listing operation. Training of the household listers/mappers took place from 28 to 30 June 2024. The household listing operation was carried out in all of the selected EAs from 5 to 26 July 2024. For each household, Global Positioning System (GPS) data were collected at the time of listing and during interviews.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2023–24 LDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Lesotho and were translated into Sesotho. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Cleaning operations

The survey data were collected using tablet computers running the Android operating system and Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A. English and Sesotho questionnaires were used for collecting data via CAPI. The CAPI programmes accepted only valid responses, automatically performed checks on ranges of values, skipped to the appropriate question based on the responses given, and checked the consistency of the data collected. Answers to the survey questions were entered into the tablets by each interviewer. Supervisors downloaded interview data to their tablet, checked the data for completeness, and monitored fieldwork progress.

Each day, after completion of interviews, field supervisors submitted data to the central server. Data were sent to the central office via secure internet data transfer. The data processing managers monitored the quality of the data received and downloaded completed data files for completed clusters into the system. ICF provided the CSPro software for data processing and technical assistance in the preparation of the data capture, data management, and data editing programmes. Secondary editing was conducted simultaneously with data collection. All technical support for data processing and use of the tablets was provided by ICF.

This item contains all data and statistical code to replicate the analysis presented in the preprint entitled "Using rapid online surveys to assess perceptions during infectious disease outbreaks: a cross-sectional survey on Covid-19 among the general public in the United States and United Kingdom".

Background: Given the extensive time needed to conduct a nationally representative household survey and the commonly low response rate in phone surveys, rapid online surveys may be a promising method to assess and track knowledge and perceptions among the general public during fast-moving infectious disease outbreaks. Objective: To apply rapid online surveying to determine knowledge and perceptions of coronavirus disease 2019 (Covid-19) among the general public in the United States (US) and the United Kingdom (UK). Methods: An online questionnaire was administered to 3,000 adults residing in the US and 3,000 adults residing in the UK who had registered with Prolific Academic to participate in online research. Strata by age (18 - 27, 28 - 37, 38 - 47, 48 - 57, or 58 years), sex (male or female), and ethnicity (White, Black or African American, Asian or Asian Indian, Mixed, or “Other”), and all permutations of these strata, were established. The number of participants who could enrol in each of these strata was calculated to reflect the distribution in the US and UK general population. Enrolment into the survey within the strata was on a first-come, first-served basis. Participants completed the questionnaire between February 23 and March 2 2020. Results: 2,986 and 2,988 adults residing in the US and the UK, respectively, completed the questionnaire. 64.4% (1,924/2,986) of US and 51.5% (1,540/2,988) of UK participants had a tertiary education degree. 67.5% (2,015/2,986) of US participants had a total household income between $20,000 and $99,999, and 74.4% (2,223/2,988) of UK participants had a total household income between £15,000 and £74,999. US and UK participants’ median estimate for the probability of a fatal disease course among those infected with SARS-CoV-2 was 5.0% (IQR: 2.0% – 15.0%) and 3.0% (IQR: 2.0% – 10.0%), respectively. Participants generally had good knowledge of the main mode of disease transmission and common symptoms of Covid-19. However, a substantial proportion of participants had misconceptions about how to prevent an infection and the recommended care-seeking behavior. For instance, 37.8% (95% CI: 36.1% – 39.6%) of US and 29.7% (95% CI: 28.1% – 31.4%) of UK participants thought that wearing a common surgical mask was ‘highly effective’ in protecting them from acquiring Covid-19. 25.6% (95% CI: 24.1% – 27.2%) of US and 29.6% (95% CI: 28.0% – 31.3%) of UK participants thought it prudent to refrain from eating at Chinese restaurants. Around half (53.8% [95% CI: 52.1% – 55.6%] of US and 39.1% [95% CI: 37.4% –40.9%] of UK participants) thought that children were at an especially high risk of death when infected with SARS-CoV-2. Conclusions: The distribution of participants by total household income and education followed approximately that of the general population. The findings from this online survey could guide information campaigns by public health authorities, clinicians, and the media. More broadly, rapid online surveys could be an important tool in tracking the public’s knowledge and misperceptions during rapidly moving infectious disease outbreaks.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Abstract

The 2022 Nepal Demographic and Health Survey (NDHS) is the sixth survey of its kind implemented in the country as part of the worldwide Demographic and Health Surveys (DHS) Program. It was implemented by New ERA under the aegis of the Ministry of Health and Population (MoHP) of the Government of Nepal with the objective of providing reliable, accurate, and up-to-date data for the country.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.

The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2022 NDHS is an updated version of the frame from the 2011 Nepal Population and Housing Census (NPHC) provided by the National Statistical Office. The 2022 NDHS considered wards from the 2011 census as sub-wards, the smallest administrative unit for the survey. The census frame includes a complete list of Nepal’s 36,020 sub-wards. Each sub-ward has a residence type (urban or rural), and the measure of size is the number of households.

In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.

Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nepal. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.

Cleaning operations

Data capture for the 2022 NDHS was carried out with Microsoft Surface Go 2 tablets running Windows 10.1. Software was prepared for the survey using CSPro. The processing of the 2022 NDHS data began shortly after the fieldwork started. When data collection was completed in each cluster, the electronic data files were transferred via the Internet File Streaming System (IFSS) to the New ERA central office in Kathmandu. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were immediately communicated to the field teams for review so that problems would be mitigated going forward. Secondary editing, carried out in the central office at New ERA, involved resolving inconsistencies and coding the open-ended questions. The New ERA senior data processor coordinated the exercise at the central office. The NDHS core team members assisted with the secondary editing. The paper Biomarker Questionnaires were compared with the electronic data file to check for any inconsistencies in data entry. The pictures of vaccination cards that were captured during data collection were verified with the data entered. Data processing and editing were carried out using the CSPro software package. The concurrent data collection and processing offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed by July 2022, and the final cleaning of the data set was completed by the end of August.

Response rate

A total of 14,243 households were selected for the sample, of which 13,833 were found to be occupied. Of the occupied households, 13,786 were successfully interviewed, yielding a response rate of more than 99%. In the interviewed households, 15,238 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 14,845 women, yielding a response rate of 97%. In the subsample of households selected for the men’s survey, 5,185 men age 15-49 were identified as eligible for individual interviews and 4,913 were successfully interviewed, yielding a response rate of 95%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors result from mistakes made in implementing data collection and in data processing, such as failing to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and entering the data incorrectly. Although numerous efforts were made during the implementation of the 2022 Nepal Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the

Surveys of wellbeing, nature connectedness and pro-nature conservation behaviour scores from adult human participants before and after taking part in nature-based activities, including citizen science, in 2020 are presented. Participants were recruited via a public campaign and were randomly allocated into groups: citizen science, noticing nature (three good things in nature activity), combined citizen science and three good things in nature, and a wait list control. They were invited to take part in activities up to five times in the following eight days. Online surveys of wellbeing and nature connectedness were undertaken at people’s sign up to the project and after the eight days of activities. Demographic characteristics and people’s engagement with the project and responses to the pathways to nature connectedness were recorded after the eight days of activities. The research was carried out to investigate concern about the negative impacts of COVID-19 movement restrictions and social distancing on people's wellbeing and mental health. Research was funded through NERC grant NE/V009656/1 - COVID 19 - Does nature-based citizen science enhance well-being and mitigate negative effects of social isolation? Full details about this dataset can be found at https://doi.org/10.5285/56d4b055-c66b-42b9-8962-a47dfcf3b8b0

Data from: American Community Survey, 5-year SeriesKing County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010 of over 50 attributes of the most requested data derived from the U.S. Census Bureau's demographic profiles (DP02-DP05). Also includes the most recent release annually with the vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, <a href='https://www.census.gov/programs-surveys/acs/news/data-releases/2023/release.html#5yr' style='font-family:inherit;' target='_blank' rel='nofollow ugc noopener noreferr

Participants completed an online survey about their preferences over ways of reducing their risks of dying over time such that they obtained gains in life expectancy. The dataset includes the options they faced and their choices. It also includes some demographic information and other related preference questions (e.g. time preferences, risk preferences, sequence preferences).

A key role of the UK government is to address causes of premature fatality. In the UK, air pollution leads to the loss of 340,000 years of life each year and workplace cancers led to the loss of over 140,000 years of life in 2010. Government policies can address the many causes of premature fatality, but these policies need to be evaluated to ensure they make the best use of public money. The question then becomes: what is the value of increasing a person's life expectancy?

To address this question, researchers have introduced the concept of the Value Of a Life Year (VOLY). This VOLY is used in government policy evaluations as a measure of the benefits of policies including air pollution mitigation and workplace safety regulation, and thus it is crucial it is measured accurately.

The VOLY is estimated using surveys of members of the public, in which people state how much they would pay for a given reduction in their risk of dying, or for a given increase in their life expectancy. The benefits being valued occur in the future. Crucially then, a key component of the VOLY is the effect of timing. Put simply, the further in the future something is, the less we tend to care about it. So a reduction in our risk of dying this year might be more valuable than a reduction in our risk of dying in the future, even if the effect on our overall life expectancy is the same. Unless we understand the influence of this 'discounting' for changes in life expectancy, we cannot accurately disentangle it from the true VOLY. This is the problem we aim to solve with our research.

To solve it, our team of experimental economists will use an innovative mixture of experiments and surveys. Participants will play experimental games designed to include simplified models of the air pollution policies, so our team can learn the best ways to describe and measure discounting as it relates to delayed changes in risk. The survey will use the insights from the experiment and elicit individuals' preferences for reductions in their risks at different points in the future. Taken together, the experiments and survey will provide the first major investigation into how people discount their future life expectancy in the context of the VOLY.

Our results will be important for policymakers in two ways. First, unless we can account for the effects of discounting on the VOLY, then policy estimates of the VOLY taken from current surveys might be wrong. If these incorrect estimates are used in the evaluation of policies aimed at improving life expectancy, then the value of the policies will be over- or under-estimated, which means public money is likely to be spent on the wrong policies. Second, when the government is evaluating policies where improvements in life expectancy happen in the future, as is the case for air pollution policies, they have to apply discounting to the value of the benefits. Our research will provide evidence about how governments should discount future gains in life expectancy, to make sure that public preferences are reflected in policymaking.

Our research is also academically cutting-edge. It combines models from economics with insights from psychology to generate new methodological and empirical evidence about how discounting influences preferences for changes in risk, both for money outcomes (in the experiments) and for fatality risks (in the surveys). It also forges a new methodological agenda, which is the incorporation of incentivised experiments into policy-driven research projects.

Overall, our research aims to provide the basis for changing the VOLY used in government policy, challenge existing guidance for discounting fatality risk reductions, and ultimately change how government money is spent, so that the policies implemented are those that improve the wellbeing of society.

Abstract

Facebook partners with academic institutions to support COVID-19 research and to help inform public health decisions. The COVID-19 Trends and Impact Survey (CTIS) is designed to help researchers better monitor and forecast the spread of COVID-19. Facebook invites app users in the United States to take the survey collected by faculty at Carnegie Mellon University (CMU) Delphi Research Center and users in more than 200 countries and territories globally to take the survey collected by faculty at the University of Maryland (UMD) Joint Program in Survey Methodology (JPSM). Sampled users see the invitation at the top of their News Feed, but the surveys are collected off the Facebook app and the Facebook company does not collect or receive survey responses. UMD and CMU (“survey host universities”) each partnered with the broader public health community to design the survey. The survey includes questions about COVID-19 vaccine acceptance, barriers to getting a vaccine, symptoms, preventive behaviors, access to care, social distancing behavior, mental health issues, socio-demographic characteristics and financial constraints. This information may help health systems plan where resources are needed and potentially when, where, and how to reopen parts of society.

CMU and UMD aggregate survey responses at a subnational level and then publish the data publicly in APIs -- one for the United States and one for the rest of the world. Microdata is also available to nonprofits and universities through Facebook’s Data for Good program.

Geographic coverage

The surveys are fielded daily in over 200 countries and territories.

Analysis unit

• Public Aggregate Data: Subnational levels
• Microdata through Facebook Data for Good program: Individual level

Universe

The survey was fielded to active Facebook users ages 18 and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

CMU and UMD design, collect, and analyze the survey data. Facebook provides assistance with questionnaire translation, survey sampling and recruitment, and statistical bias correction. Facebook invites a new sample of adult users on the Facebook App to take the survey each day. These users see an invitation at the top of their Facebook News Feed to an optional, off Facebook survey. The sampled users can then choose whether or not to consent to the survey. If they consent, they are redirected to a Qualtrics survey hosted by UMD or CMU. The surveys are daily repeated cross-sections. Sampled users may be invited to take the survey again in either a few weeks or months, depending on the density of their area.

We stratify the sample using administrative boundaries within countries and territories to provide geographic coverage. We are constantly working with the survey host universities to optimize the sampling design, including incorporating adaptive sampling, which could improve statistical power for local area estimates in priority areas as the pandemic progresses.

The responses of sampled users who participate more than once will not be linked longitudinally. In order to enable an agile public health response, we aim to provide data that can detect either outbreaks or successful containment over time rather than cumulative or overall prevalence alone.

Mode of data collection

Internet [int]

Research instrument

CTIS includes questions about COVID-19 vaccine acceptance, barriers to getting a vaccine, symptoms, preventive behaviors, access to care, social distancing behavior, mental health issues, socio-demographic characteristics and financial constraints. The survey instruments are owned by the survey host universities and are available, along with their translations, with the data at the following links:

https://gisumd.github.io/COVID-19-API-Documentation/docs/survey_instruments.html https://cmu-delphi.github.io/delphi-epidata/symptom-survey/coding.html https://umdsurvey.umd.edu/jfe/preview/SV_2mWYHEMq5ZoUBNj?Q_CHL=preview&Q_JFE=qdg https://cmu.ca1.qualtrics.com/jfe/preview/SV_cT2ri3tFp2dhJGZ?Q_SurveyVersionID=current&Q_CHL=preview

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Facebook provides analytic weights that adjust for non-response and coverage biases. By non-response bias, Facebook means that some sampled users are more likely to respond to the survey than others. To adjust for this, Facebook calculates the inverse probability that sampled users complete the survey using their self-reported age and gender as well as other characteristics we know correlate with non-response. They then use these inverse probabilities to create weights for responses, after which the survey sample reflects the active adult user population on the Facebook app.

By coverage bias, Facebook means that not everyone in every country has a Facebook app account or uses their account regularly. To adjust for this, Facebook adjusts the weights created in the first step even further so that the distribution of age, gender, and administrative region of residence in the survey sample reflects that of the general population. Making adjustments using the weights ensures that the sample more accurately reflects the characteristics of the target population represented.

The weights will be available for the United States as well as 114 other countries and territories globally where we are able to generate high-quality weights. The current set of weighted countries and territories are listed on the next page. The set of countries and territories for which weights are available will be revised over the course of data collection as Facebook and the survey host universities evaluate sample coverage within each country. For more details about the weighting methodology and the general population benchmarks used, please see this weighting documentation: https://arxiv.org/abs/2009.14675

The Before Soonest column includes responses where participants indicated they thought the event had already occurred.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Zambia Demographic and Health Survey (ZDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

Survey to help identify whether Octopus might help researchers produce high quality, open research In order to complement in-depth, but necessarily small sample size, interviews, an online survey allowed us to reach a broader population to gain some quantitative data on the current research culture and barriers to best practice, as well as whether the Octopus platform might help overcome them. The finalised survey was implemented on EUSurvey. It is a fully open source online survey platform developed and administered by the European Commission, adhering to relevant privacy regulations (e.g. the General Data Protection Regulation (GDPR)). The survey was open from 17 January to 5 February 2023. Details on the implementation of this survey is published on Octopus.

U.S. Census Bureau 2020 block groups within the City of Seattle with American Community Survey (ACS) 5-year series data of frequently requested topics. Data is pulled from block group tables for the most recent ACS vintage. Seattle neighborhood geography of Council Districts, Comprehensive Plan Growth Areas are also included based on block group assignment.The census block groups have been assigned to a neighborhood based on the distribution of the total population from the 2020 decennial census for the component census blocks. If the majority of the population in the block group were inside the boundaries of the neighborhood, the block group was assigned wholly to that neighborhood.Feature layer created for and used in the Neighborhood Profiles application.The attribute data associated with this map is updated annually to contain the most currently released American Community Survey (ACS) 5-year data and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintages: 2023ACS Table(s): Select fields from the tables listed here.Data downloaded from: Census Bureau's Explore Census Data <div style='font-family:inher

Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of two modules: MODULE 1 "Trick of the Traits. An experimental study on trait ownership and mediated leader effects", MODULE 2 "An Experimental Assessment of Approval and Evaluative Voting". Fieldwork was conducted by MARKETAGENT.

MODULE 1: Trick of the Traits. An experimental study on trait ownership and mediated leader effects (Loes Aalerding, Sophie Lecheler)
This study tests, by means of a survey experiment, how leader perceptions are affected by media portrayals of party leaders in terms of their leadership traits, and to what extent partisan stereotypes and trait ownership moderates this relationship. Research has shown that citizens’ subjective party leader perceptions, especially in terms of leadership traits, affect voting behavior (e.g., Bittner, 2011; Aarts, Blais, & Schmitt, 2013). What remains a largely unresolved question, however, is which trait evaluations matter most. The main goal of this study is to test how media messages of party leaders in terms of their leadership traits affects voters’ perception of those party leaders and to what extent trait ownership moderates this relation. The contribution of the study is threefold. First, it takes into account that current political life is highly mediatized by focusing on mediated leader effects. Second, it strengthens the causal claim of (the conditionality) of leader effects by using an experimental research design as opposed to correlational data. Third, it is the first to test the theory of trait ownership in Austria and therefore (completely) outside the two-party context of the US.

MODULE 2: An Experimental Assessment of Approval and Evaluative Voting (Philipp Harfst, Jean-Francois Laslier, Damien Bol)
In our PUMA module, we ran an online survey experiment in which we asked a representative sample of the Austrian population to cast a vote. We created a ballot to similar to the one of the 2017 election of the National Council. The respondents saw on their screen the main parties and the main candidates of these parties. Then, they had to indicate their preference for one of the parties and for 15 individual candidates within this party. The experimental treatment is the type of preference vote the respondents could cast to express their preference for individual candidates. A third of the respondents (randomly selected) could choose to approve each of the candidates or not [0,1]. This binary system is often called Approval Voting (AV). Another third of the respondents (randomly selected) could give 0, 1, or 2 points to each of the candidates. The last third of the respondents could give a positive, a negative, or no points to each of the candidates [-1,0,1]. These last two systems are two different versions to what is usually referred to as Evaluative Voting (EV). The goal of our research is to study the effect of the type of preference voting on voters’ decisions. The survey was fielded in June 2018 and targeted the population of eligible Austrian voters. The sample size is 700 respondents, and is representative of the Austrian population in terms of gender, age and education. The survey was conducted online, which is the best survey model for this type of study. Unlike telephone interviews, online surveys allow for a visualisation of the ballot, which helps improve the quality of responses. Also, this way of asking for respondents’ vote choice has already been successfully implemented in other contexts (Laslier et al. 2015).