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

The data sheet contains socio demographic information, item wise analysis for internet use and factors for internet addiction.

Abstract

SDES in Kabul was launched in June 2013, jointly by the Central Statistics Organization (CSO) and the United Nations Population Fund (UNFPA) where the latter provided the technical assistance to the entire survey operations. SDES data serve as the benchmark for demographic information at the district level and to some extent, group of villages/enumeration areas. It is the only survey that addresses the need of local development planners for information at the lower level of disaggregation. There are other surveys that CSO has conducted but these are available only at the national and provincial levels.

To achieve a responsive and appropriate policymaking, statistics plays a vital role. In Afghanistan, there has been a longstanding lack of reliable information at the provincial and district levels which hinders the policy making bodies and development planners to come up with comprehensive plans on how to improve the lives of Afghans. With SDES data, though it is not complete yet for the whole country, most of the important indicators in monitoring the progress towards the achievement of Afghanistan's Millennium Development Goals (MDGs) are being collected.

The main objectives of the survey were: · Gathering data for evidence based decision making, policy, planning and management · Providing data for business and industries · Providing policy and planning for residence housing · Providing data about vulnerable populations · Providing data for the basis of humanitarian assistance · Availability of data for research and analysis

Geographic coverage

Kabul Province Kabul Districts Kabul Villages

Analysis unit

Individuals, households

Universe

The survey covered all de jure household members (usual residents)

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey consisted of two related activities: a) the extensive listing and mapping of houses, establishments and institutions (conducted before the household survey) and b) the household survey.

The listing and mapping covered all houses, businesses and institutions in every village and urban area in Kabul Province and included the preparation of sketch maps on which the physical location of each building structure was marked during the canvassing. The locations of important public services, establishments and institutions such as schools, hospitals, banks, etc., were pinpointed using global positioning system (GPS) devices at a later date.

The surveyors used the mapping outputs to guide them in conducting the survey and ensure complete coverage. In total, 16 nahias, and around 843 villages in 14 districts in Kabul Province were canvassed, divided into 3,068 enumeration areas.

The survey first involved a listing of every household in each village. Half of these listed households (i.e. every other household) were taken as samples and asked questions on education, literacy, employment, migration, functional difficulty, fertility, mortality, parents’ living status, birth registration and household and housing characteristics.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect the survey data. - Listing sheet for village/enumeration area - Household questionnaire - Summary sheets for village/enumeration area

Cleaning operations

Central Statistics Organization (CSO) and UNFPA technical staff were responsible for editing the questionnaires, spot-checking, re-interviewing and recording observations during household interviews in all 16 nahias and 14 districts. This helped to ensure errors were corrected at an early stage of enumeration.

Data encoding and cleaning were also done in Karte-char where 178 encoders were hired and four CSO supervisors were detailed to oversee the whole data processing stage.

Sample socio-demographic profile.

The dataset presented provides the compilation of extensive socio-demographic profile variables as age, gender, family income measurements and so on. The purpose of this data is to analyze if the panelists of Ortiz, et al. participatory Delphi methodology correctly represents the diverse community of USFQ. This data supports the variables taken into consideration in the research design, process, and analysis.

Selected demographic, social, economic, and housing estimates data by community district/PUMA (Public Use Micro Data Sample Area). Three year estimates of population data from the Census Bureau's American Community Survey

The Stata data file "CAP_Demographics_Jumla_Kavre_recoded.dta” and equivalent excel file of the same name comprises data collected by adolescent secondary school students during a "Citizen Science" project in the district of Kavre in the central hills of Nepal during April 2022 and in the district of Jumla in the remote mountains of West Nepal during June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project. The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. Researchers from KLL and UCL trained the adolescents to record basic socio-demographic information about themselves and their households including caste/ethnicity, religion, education, water sources, assets, household characteristics, and income sources. The form also asked about their access to mobile phones or other devices and internet and their concerns with respect to climate change. The resulting data describe the participants in the citizen science project, but their names and addresses have been removed. The app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Tables 2 and 3 of this paper.

Socio-demographic & clinical characteristics of participants in the study.

ALLBUS (GGSS - the German General Social Survey) is a biennial trend survey based on random samples of the German population. Established in 1980, its mission is to monitor attitudes, behavior, and social change in Germany. Each ALLBUS cross-sectional survey consists of one or two main question modules covering changing topics, a range of supplementary questions and a core module providing detailed demographic information. Additionally, data on the interview and the interviewers are provided as well. Key topics generally follow a 10-year replication cycle, many individual indicators and item batteries are replicated at shorter intervals. The present data set contains socio-demographic variables from the ALLBUS 2021, which were harmonized to the standards developed as part of the KonsortSWD sub-project “Harmonized Variables” (Schneider et al., 2023). While there are already established recommendations for the formulation of socio-demographic questionnaire items (e.g. the “Demographic Standards” by Hoffmeyer-Zlotnik et al., 2016), there were no such standards at the variable level. The KonsortSWD project closes this gap and establishes 32 standard variables for 19 socio-demographic characteristics contained in this dataset.

The STAMINA study examined the nutritional risks of low-income peri-urban mothers, infants and young children (IYC), and households in Peru during the COVID-19 pandemic. The study was designed to capture information through three, repeated cross-sectional surveys at approximately 6 month intervals over an 18 month period, starting in December 2020. The surveys were carried out by telephone in November-December 2020, July-August 2021 and in February-April 2022. The third survey took place over a longer period to allow for a household visit after the telephone interview.The study areas were Manchay (Lima) and Huánuco district in the Andean highlands (~ 1900m above sea level).In each study area, we purposively selected the principal health centre and one subsidiary health centre. Peri-urban communities under the jurisdiction of these health centres were then selected to participate. Systematic random sampling was employed with quotas for IYC age (6-11, 12-17 and 18-23 months) to recruit a target sample size of 250 mother-infant pairs for each survey.Data collected included: household socio-demographic characteristics; infant and young child feeding practices (IYCF), child and maternal qualitative 24-hour dietary recalls/7 day food frequency questionnaires, household food insecurity experience measured using the validated Food Insecurity Experience Scale (FIES) survey module (Cafiero, Viviani, & Nord, 2018), and maternal mental health.In addition, questions that assessed the impact of COVID-19 on households including changes in employment status, adaptations to finance, sources of financial support, household food insecurity experience as well as access to, and uptake of, well-child clinics and vaccination health services were included.This folder includes the dataset and dictionary of variables for survey 3 (English only).The survey questionnaire for survey 3 is available at 10.17028/rd.lboro.21740921.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

The dataset is a part of the survey conducted in Ho Chi Minh City and Dong Nai the Southeast region of Vietnam in 2020 to collect information for research on fertility. The main research purpose is to identify the socioeconomic determinants of low fertility in the Southeast. In total 808 individuals in the main reproductive age were interviewed, including 382 cases from Dong Nai and 426 cases from Ho Chi Minh City, or 273 unmarried persons and 535 married women. Information about family size desires and socio-demographic characteristics of 535 married men were asked when interviewing their spouses. As such, the survey collected information on the family size desires of 1343 individuals. The dataset has been converted to SPSS format (version 26.0). For data analysis, the dataset need to be weighted (WEI variable) as individuals were not selected with equal probability.

The U.S. Census Bureau conducts the Island Areas Censuses in partnership with the governments of American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands to comply with the legal requirements set forth in Title 13 of the United States Code and to meet the specific data needs of the Island Areas. The 2020 Island Areas Censuses counted people living in the U.S. Island Areas using a long-form questionnaire to meet the Island Areas' data needs for demographic, social, economic, and housing unit information. This long-form questionnaire was similar to the American Community Survey questionnaire used in the 50 states, the District of Columbia, and Puerto Rico. With the release of the 2020 IAC Demographic Profile, the Census Bureau provides summary statistics for the Island Areas, including selected demographic and housing characteristics for places and minor civil divisions (MCDs).

Ipsos Global @dvisor wave 62 was conducted on September 2 and September 16, 2014. It included the following question sections: [add section letter and name, e.g., A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, JS: Taking Surveys.

Socio-demographic characteristics of the respondents included in the study.

This county geography dataset includes selected indicators (2011-2015 5-Year Averages) pertaining to population, age, race/ethnicity, language, housing, poverty/income, education, disability, health insurance, employment, and age*race*gender groups. This dataset is assembled annually from the U.S. Census American Community Survey American Factfinder website and is maintained by the Colorado Department of Public Health and Environment.

Socio-demographic profile of participants (n = 45).

Abbreviations: ICD-10 = International Classification of Diseases (10th edition); EURO-D = EURODEP Concerted Action Programme common depression symptoms scale NCDs = Non-communicable diseases.

Income of Immigrant taxfilers, by sex, immigrant admission category, socio-demographic profile, admission year and tax year, for Canada and provinces, 2022 constant dollars.

Income of Immigrant taxfilers, by sex, immigrant admission category, socio-demographic profile, admission year and tax year, for Canada and provinces, 2021 constant dollars.