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 City of Norfolk is committed to using data to inform decisions and allocate resources. An important source of data is input from residents about their priorities and satisfaction with the services we provide. Norfolk last conducted a citywide survey of residents in 2022.

To provide up-to-date information regarding resident priorities and satisfaction, Norfolk contracted with ETC Institute to conduct a survey of residents. This survey was conducted in May and June 2024; surveys were sent via the U.S. Postal Service, and respondents were given the choice of responding by mail or online. This survey represents a random and statistically valid sample of residents from across the city, including each Ward. ETC Institute monitored responses and followed up to ensure all sections of the city were represented. Additionally, an opportunity was provided for residents not included in the random sample to take the survey and express their views. This dataset includes all random sample survey data including demographic information; it excludes free-form comments to protect privacy. It is grouped by Question Category, Question, Response, Demographic Question, and Demographic Question Response. This dataset will be updated every two years.

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999, the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The sample for the survey is selected to represent the U.S. population of all ages. Many of the NHANES 2007-2008 questions also were asked in NHANES II 1976-1980, Hispanic NHANES 1982-1984, NHANES III 1988-1994, and NHANES 1999-2006. New questions were added to the survey based on recommendations from survey collaborators, NCHS staff, and other interagency work groups. Estimates for previously undiagnosed conditions, as well as those known to and reported by survey respondents, are produced through the survey. In the 2003-2004 wave, the NHANES includes more than 100 datasets. Most have been combined into three datasets for convenience. Each starts with the Demographic dataset and includes datasets of a specific type. 1. National Health and Nutrition Examination Survey (NHANES), Demographic & Examination Data, 2003-2004 (The base of the Demographic dataset + all data from medical examinations). 2. National Health and Nutrition Examination Survey (NHANES), Demographic & Laboratory Data, 2003-2004 (The base of the Demographic dataset + all data from medical laboratories). 3. National Health and Nutrition Examination Survey (NHANES), Demographic & Questionnaire Data, 2003-2004 (The base of the Demographic dataset + all data from questionnaires) Variable SEQN is included for merging files within the waves. All data files should be sorted by SEQN. Additional details of the design and content of each survey are available at the NHANES website.

The 2019 Public Service Employee Survey (PSES) administered by Advanis, on behalf of the Office of the Chief Human Resources Officer, Treasury Board of Canada. The 2019 Public Service Employee Survey measured federal public servants’ opinions in relation to employee engagement, leadership, the workforce, the workplace, workplace well-being and compensation. The 2019 Public Service Employee Survey was conducted from July 22 to September 6, 2019. A total of 182,306 employees in 86 federal departments and agencies responded to the 2019 Public Service Employee Survey, for a response rate of 62.3%. The 2019 Public Service Employee Survey datasets contain the results of the survey by year (2019, 2018, 2017, 2014, 2011 and 2008) for the Public Service and departments/agencies, and the results broken down by demographic characteristics (e.g., age, gender) and organizational units. Results for 2018, 2017, 2014, 2011 and 2008 are only provided for questions repeated in the 2019 Public Service Employee Survey.

The STAMINA study examined the nutritional risks of low-income peri-urban mothers, infants and young children, 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 1 (English only).The survey questionnaire for survey 1 is available at 10.17028/rd.lboro.16825507.

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the county level..The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age;

B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by County Date of Coverage: 2016-2020

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

The City of Norfolk is committed to using data to help inform decisions and allocate resources. One important source of data is input from residents about their priorities and satisfaction with the services we provide. Norfolk last conducted a citywide survey of residents in 2014.

To provide up-to-date information regarding resident priorities and satisfaction, Norfolk contracted with ETC institute to conduct a survey of residents. This survey was conducted in the fall of 2022; surveys were sent via the U.S. Postal Service and respondents were given the choice of responding by mail, online, or by telephone. This survey represents a random and statistically valid sample of residents from across the city. ETC Institute monitored responses and followed up to ensure all sections of the city were represented. An opportunity was also provided for residents not included in the random sample to take the survey and express their views.

This dataset includes all survey data (including demographics questions and responses), with the exception of free form comments and the Ward and Superward that the respondent lived in at the time of the survey. This dataset will be updated every two years.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, Round 6 (2014-2015) 36 countries, and Round 7 (2016-2018) 34 countries. The survey covered 34 countries in Round 8 (2019-2021).

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens aged 18 years and above excluding those living in institutionalized buildings.

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Gabon - Sample size: 1,200 - Sampling Frame: Recensement Général de la Population et des Logements (RGPL) de 2013 réalisée par la Direction Générale de la Statistique et des Etudes Economiques - Sample design: Representative, random, clustered, stratified, multi-stage area probability sample - Stratification: Province, Department, and urban-rural location - Stages: Primary sampling unit (PSU), start points, households, respondents - PSU selection: Probability Proportionate to Population Size (PPPS) - Cluster size: 8 households per PSU - Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval - Respondent selection: Gender quota to be achieved by alternating interviews between men and women; potential respondents (i.e. household members) of the appropriate gender are listed, then the computer chooses the individual random

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 8 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data.

The questionnaire consists of three parts: 1. Part 1 captures the steps for selecting households and respondents, and includes the introduction to the respondent and (pp.1-4). This section should be filled in by the Fieldworker. 2. Part 2 covers the core attitudinal and demographic questions that are asked by the Fieldworker and answered by the Respondent (Q1 – Q100). 3. Part 3 includes contextual questions about the setting and atmosphere of the interview, and collects information on the Fieldworker. This section is completed by the Fieldworker (Q101 – Q123).

Response rate

Outcome rates: - Contact rate: 99% - Cooperation rate: 92% - Refusal rate: 3% - Response rate: 91%

Sampling error estimates

+/- 3% at 95% confidence level

Data presented in this paper related to Malaysian university reaerch-based students’ perceptions that affect their psychological health during the COVID-19 pandemic. A sample of 384 was drawn from approximately 193,570 population both Ph.D. and research-based Master students who are currently studying in Malaysia during the COVID-19 pandemic. A simple random sampling technique was used to collect the data. Data were collected through an online survey questionnaire. The surveys were administered to the Ph.D. and research-based master’s students between June 15 and June 29, 2020, with the support of Internet platforms (Institutional Email, Google Form, WhatsApp), and resulted in valid 103 responses. The response rate is 26.82%. Demographic information data were collected by using 11 items. Psychological impact data were collected by using the 7-item Generalized Anxiety Disorder Scale (GAD-7), and research progress, academic life and daily life related data were collected by using 3 items.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Abstract copyright UK Data Service and data collection copyright owner.

The Citizenship Survey (known in the field as the Communities Study) ran from 2001 to 2010-2011. It began as the 'Home Office Citizenship Survey' (HOCS) before the responsibility moved to the new Communities and Local Government department (DCLG) in May 2006. The survey provided an evidence base for the work of DCLG, principally on the issues of community cohesion, civic engagement, race and faith, and volunteering. The survey was used extensively for developing policy and for performance measurement. It was also used more widely, by other government departments and external stakeholders to help inform their work around the issues covered in the survey. The survey was conducted on a biennial basis from 2001-2007. It moved to a continuous design in 2007 which means that data became available on a quarterly basis from April of that year. Quarter one data were collected between April and June; quarter two between July and September; quarter three between October and December and quarter four between January and March. Once collection for the four quarters was completed, a full aggregated dataset was made available, and the larger sample size allowed more detailed analysis.

In January 2011, the DCLG announced that the Citizenship Survey was to close. As part of the drive to deliver cost savings across government and to reduce the fiscal deficit, research budgets were closely scrutinised to identify where savings can be made. For this reason, and the belief that priority data from this survey could either be dropped; collected less frequently; or collected via other means, the survey was cancelled. Fieldwork concluded on 31 March 2011, followed by publication of reports in the months after analysis of that data. Further information about the survey, including links to publications, can be found on the National Archives webarchive page for the Citizenship Survey. The Consultation outcome: the future of the citizenship survey statement can be viewed on the gov.uk website. The Community Life Survey, (held under GN 33475), which began in 2012-2013 and is conducted by the Cabinet Office, incorporates a small number of priority measures from the Citizenship Survey, in order that trends in these issues
can continue to be tracked over time. For these measures the Community Life Survey findings are comparable to the Citizenship Survey findings.


UK Data Archive holdings: End User Licence and Secure Access
The Archive holds standard End User Licence (EUL) versions of the complete Citizenship Survey series from 2001-2011, held under SNs 4754, 5087, 5367, 5739, 6388, 6733 and 7111, and Secure Access versions of the 2005, 2007-2008, 2008-2009 and 2009-2010 and 2010-2011 waves (all held under SN 7403). The Secure Access datasets include extra variables that are not available in the standard EUL versions. They cover: more detailed and extensive household and demographic information; more detailed geographies, including Police Force Area, Local Authority Districts, Wards, Middle Layer Super Output Areas (MSOA) and Lower Layer Super Output Areas (LSOA); more detailed responses to questions covering violent extremism, immigration, and religion; and more detailed administrative variables. Prospective users of the Secure Access version of the Citizenship Survey will need to agree to rigorous Terms and Conditions, including applying for ESRC Accredited Researcher Status and attending a training session, in order to obtain permission to use that version Therefore, users are encouraged to download and inspect the EUL versions of the data prior to ordering the Secure Access versions.


A research project was carried out by the National Centre for Social Research in partnership with the Institute for Volunteering Research in 2006/7, as a follow-up to the Citizenship Survey, 2005. The resulting study, the National Survey of Volunteering and Charitable Giving, 2006-2007, is held at the UKDA under SN 5793.
Main Topics:

The 2005 survey questionnaire covered:

• social networks
• respondents' feelings about their commmunities, including community cohesion
• trust and influence
• volunteering
• civil renewal
• race and religious prejudice and discrimination
• rights and responsibilities
• demographic and geodemographic information

Standard Measures:
The questionnaire used established questions and classifications where appropriate:

• demographic questions were mostly taken from the Office for National Statistics (ONS) harmonised question booklets
• questions on family relationships were adapted from the General Household Survey (GHS) family relationship grid (the GHS is held at the UK Data Archive (UKDA) under GN 33090)
• the hierarchy...

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Company Cars (Module 1a): questions about the number of company cars in the household; total mileage and total business mileage; age of car and value of car when new; engine size. Mortgage Arrears (Module 2): source of mortgage, if any; whether behind in payments, and if so reasons for falling behind. Also question on whether bought from a Right to Buy scheme. Head of Household Information (Module 70a): occupation and supervisory status of head of household. GP Accidents (Module 78): accidents in previous three months that resulted in seeing a doctor or going to hospital; where accident happened; whether saw a GP or went straight to hospital. Arrears and Repossessions (Module 79): questions about mortgage arrears and repossessions or voluntary surrenders of accommodation as a result of falling behind with mortgage payments. Marital Status and Cohabitation (Module 90): marital status and marital history; reasons for getting married if living together before marrying; history of previous cohabitation relationships that did not lead to marriage. The data for module 90 are under embargo and are therefore not currently available. Multi-stage stratified random sample Face-to-face interview

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.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

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, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

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

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: 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 Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) 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 2022 Ghana Demographic and Health Survey (2022 GDHS) 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 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical 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 between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A 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 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization 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.

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

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of

Abstract

The Gallup Poll Social Series (GPSS) is a set of public opinion surveys designed to monitor U.S. adults' views on numerous social, economic, and political topics. The topics are arranged thematically across 12 surveys. Gallup administers these surveys during the same month every year and includes the survey's core trend questions in the same order each administration. Using this consistent standard allows for unprecedented analysis of changes in trend data that are not susceptible to question order bias and seasonal effects.

Introduced in 2001, the GPSS is the primary method Gallup uses to update several hundred long-term Gallup trend questions, some dating back to the 1930s. The series also includes many newer questions added to address contemporary issues as they emerge.

The dataset currently includes responses from up to and including 2025.

Methodology

Gallup conducts one GPSS survey per month, with each devoted to a different topic, as follows:

January: Mood of the Nation

February: World Affairs

March: Environment

April: Economy and Finance

May: Values and Beliefs

June: Minority Rights and Relations (discontinued after 2016)

July: Consumption Habits

August: Work and Education

September: Governance

October: Crime

November: Health

December: Lifestyle (conducted 2001-2008)

The core questions of the surveys differ each month, but several questions assessing the state of the nation are standard on all 12: presidential job approval, congressional job approval, satisfaction with the direction of the U.S., assessment of the U.S. job market, and an open-ended measurement of the nation's "most important problem." Additionally, Gallup includes extensive demographic questions on each survey, allowing for in-depth analysis of trends.

Interviews are conducted with U.S. adults aged 18 and older living in all 50 states and the District of Columbia using a dual-frame design, which includes both landline and cellphone numbers. Gallup samples landline and cellphone numbers using random-digit-dial methods. Gallup purchases samples for this study from Survey Sampling International (SSI). Gallup chooses landline respondents at random within each household based on which member had the next birthday. Each sample of national adults includes a minimum quota of 70% cellphone respondents and 30% landline respondents, with additional minimum quotas by time zone within region. Gallup conducts interviews in Spanish for respondents who are primarily Spanish-speaking.

Gallup interviews a minimum of 1,000 U.S. adults aged 18 and older for each GPSS survey. Samples for the June Minority Rights and Relations survey are significantly larger because Gallup includes oversamples of Blacks and Hispanics to allow for reliable estimates among these key subgroups.

Gallup weights samples to correct for unequal selection probability, nonresponse, and double coverage of landline and cellphone users in the two sampling frames. Gallup also weights its final samples to match the U.S. population according to gender, age, race, Hispanic ethnicity, education, region, population density, and phone status (cellphone only, landline only, both, and cellphone mostly).

Demographic weighting targets are based on the most recent Current Population Survey figures for the aged 18 and older U.S. population. Phone status targets are based on the most recent National Health Interview Survey. Population density targets are based on the most recent U.S. Census.

Usage

The year appended to each table name represents when the data was last updated. For example, January: Mood of the Nation - 2025** **has survey data collected up to and including 2025.

For more information about what survey questions were asked over time, see the Supporting Files.

Bulk Data Access

Data access is required to view this section.

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...

Our Demographics package in the USA offers data pertaining to the households of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Household Data

• Household Statistics: Number of Households and Age of Children in Households.
• Marriage Status: Married, Divorced, Never Married and Widowed.
• Household Types: Single-Person Households, Households with Children etc.
• Language Spoken in Households: English, Limited English etc.

• • Household Statistics: Marketers can use this data to understand the size and composition of households, allowing for targeted campaigns. For instance, companies offering family-oriented products can tailor promotions based on the presence and age of children in households, ensuring that their messaging resonates with the specific needs of different family structures.
  • Marriage Status: This data provides valuable insights into the marital status of individuals, enabling marketers to tailor their messaging based on relationship status. For example, businesses offering wedding-related services may focus on advertising to engaged or newly married individuals, while financial institutions may customize their promotions based on the unique financial considerations of divorced or widowed individuals.
  • Household Types: Understanding the various household types helps marketers create targeted campaigns. For example, real estate agencies may target single-person households with advertisements for apartments or studios, while companies offering family-friendly products can focus on households with children.
  • Language Spoken in Households: This data variable assists marketers in tailoring their communication strategies based on the predominant language spoken in households. For instance, businesses can create multilingual advertising content to effectively reach and engage diverse linguistic demographics, ensuring that their messages are understood and resonate with the target audience.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

Overview

• The Magic, Memory, Curiosity (MMC) dataset contains data from 50 healthy human adults incidentally encoding 36 videos of magic tricks inside the MRI scanner across three runs.
• Before and after incidental learning, a 10-min resting-state scan was acquired.
• The MMC dataset includes contextual incentive manipulation, curiosity ratings for the magic tricks, as well as incidental memory performance tested a week later using a surprise cued recall and recognition test .
• Working memory and constructs potentially relevant in the context of motivated learning (e.g., need for cognition, fear of failure) were additionally assessed.

Stimuli

The stimuli used here were short videos of magic tricks taken from a validated stimulus set (MagicCATs, Ozono et al., 2021) specifically created for the usage in fMRI studies. All final stimuli are available upon request. The request procedure is outlined in the Open Science Framework repository associated with the MagicCATs stimulus set (https://osf.io/ad6uc/).

Participant responses

Participants’ responses to demographic questions, questionnaires, and performance in the working memory assessment as well as both tasks are available in comma-separated value (CSV) files. Demographic (MMC_demographics.csv), raw questionnaire (MMC_raw_quest_data.csv) and other score data (MMC_scores.csv) as well as other information (MMC_other_information.csv) are structured as one line per participant with questions and/or scores as columns. Explicit wordings and naming of variables can be found in the supplementary information. Participant scan summaries (MMC_scan_subj_sum.csv) contain descriptives of brain coverage, TSNR, and framewise displacement (one row per participant) averaged first within acquisitions and then within participants. Participants’ responses and reaction times in the magic trick watching and memory task (MMC_experimental_data.csv) are stored as one row per trial per participant.

Preprocessing

Data was preprocessed using the AFNI (version 21.2.03) software suite. As a first step, the EPI timeseries were distortion-corrected along the encoding axis (P>>A) using the phase difference map (‘epi_b0_correct.py’). The resulting distortion-corrected EPIs were then processed separately for each task, but scans from the same task were processed together. The same blocks were applied to both task and resting-state distortion-corrected EPI data using afni_proc.py (see below): despiking, slice-timing and head-motion correction, intrasubject alignment between anatomy and EPI, intersubject registration to MNI, masking, smoothing, scaling, and denoising. For more details, please refer to the data descriptor (LINK) or the Github repository (https://github.com/stefaniemeliss/MMC_dataset).

afni_proc.py -subj_id "${subjstr}" \
  -blocks despike tshift align tlrc volreg mask blur scale regress \
  -radial_correlate_blocks tcat volreg \
  -copy_anat $derivindir/$anatSS \
  -anat_has_skull no \
  -anat_follower anat_w_skull anat $derivindir/$anatUAC \
  -anat_follower_ROI aaseg anat $sswindir/$fsparc \
  -anat_follower_ROI aeseg epi $sswindir/$fsparc \
  -anat_follower_ROI FSvent epi $sswindir/$fsvent \
  -anat_follower_ROI FSWMe epi $sswindir/$fswm \
  -anat_follower_ROI FSGMe epi $sswindir/$fsgm \
  -anat_follower_erode FSvent FSWMe \
  -dsets $epi_dpattern \
  -outlier_polort $POLORT \
  -tcat_remove_first_trs 0 \
  -tshift_opts_ts -tpattern altplus \
  -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \
  -align_epi_strip_method 3dSkullStrip \
  -tlrc_base MNI152_2009_template_SSW.nii.gz \
  -tlrc_NL_warp \
  -tlrc_NL_warped_dsets $sswindir/$anatQQ $sswindir/$matrix $sswindir/$warp \
  -volreg_base_ind 1 $min_out_first_run \
  -volreg_post_vr_allin yes \
  -volreg_pvra_base_index MIN_OUTLIER \
  -volreg_align_e2a \
  -volreg_tlrc_warp \
  -volreg_no_extent_mask \
  -mask_dilate 8 \
  -mask_epi_anat yes \
  -blur_to_fwhm -blur_size 8 \
  -regress_motion_per_run \
  -regress_ROI_PC FSvent 3 \
  -regress_ROI_PC_per_run FSvent \
  -regress_make_corr_vols aeseg FSvent \
  -regress_anaticor_fast \
  -regress_anaticor_label FSWMe \
  -regress_censor_motion 0.3 \
  -regress_censor_outliers 0.1 \
  -regress_apply_mot_types demean deriv \
  -regress_est_blur_epits \
  -regress_est_blur_errts \
  -regress_run_clustsim no \
  -regress_polort 2 \
  -regress_bandpass 0.01 1 \
  -html_review_style pythonic

Derivatives

The anat folder contains derivatives associated with the anatomical scan. The skull-stripped image created using @SSwarper is available in original and ICBM 2009c Nonlinear Asymmetric Template space as sub-[group][ID]_space-[space]_desc-skullstripped_T1w.nii.gz together with the corresponding affine matrix (sub-[group][ID]_aff12.1D) and incremental warp (sub-[group][ID]_warp.nii.gz). Output generated using @SUMA_Make_Spec_FS (defaced anatomical image, whole brain and tissue masks, as well as FreeSurfer discrete segmentations based on the Desikan-Killiany cortical atlas and the Destrieux cortical atlas) are also available as sub-[group][ID]_space-orig_desc-surfvol_T1w.nii.gz, sub-[group][ID]_space-orig_label-[label]_mask.nii.gz, and sub-[group][ID]_space-orig_desc-[atlas]_dseg.nii.gz, respectively.

The func folder contains derivatives associated with the functional scans. To enhance re-usability, the fully preprocessed and denoised files are shared as sub-[group][ID]_task-[task]_desc-fullpreproc_bold.nii.gz. Additionally, partially preprocessed files (distortion corrected, despiked, slice-timing/head-motion corrected, aligned to anatomy and template space) are uploaded as sub-[group][ID]_task-[task]_run-[1-3]_desc-MNIaligned_bold.nii.gz together with slightly dilated brain mask in EPI resolution and template space where white matter and lateral ventricle were removed (sub-[group][ID]_task-[task]_space-MNI152NLin2009cAsym_label-dilatedGM_mask.nii.gz) as well as tissue masks in EPI resolution and template space (sub-[group][ID]_task-[task]_space-MNI152NLin2009cAsym_label-[tissue]_mask.nii.gz).

The regressors folder contains nuisance regressors stemming from the output of the full afni_proc.py preprocessing pipeline. They are provided as space-delimited text values where each row represents one volume concatenated across all runs for each task separately. Those estimates that are provided per run contain the data for the volumes of one run and zeros for the volumes of other runs. This allows them to be regressed out separately for each run. The motion estimates show rotation (degree counterclockwise) in roll, pitch, and yaw and displacement (mm) in superior, left, and posterior direction. In addition to the motion parameters with respect to the base volume (sub-[group][ID]_task-[task]_label-mot_regressor.1D), motion derivatives (sub-[group][ID]_task-[task]_run[1-3]_label-motderiv_regressor.1D) and demeaned motion parameters (sub-[group][ID]_task-[task]_run[1-3]_label-motdemean_regressor.1D) are also available for each run separately. The sub-[group][ID]_task-[task]_run[1-3]_label-ventriclePC_regressor.1D files contain time course of the first three PCs of the lateral ventricle per run. Additionally, outlier fractions for each volume are provided (sub-[group][ID]_task-[task]_label-outlierfrac_regressor.1D) and sub-[group][ID]_task-[task]_label-censorTRs_regressor.1D shows which volumes were censored because motion or outlier fraction exceeded the limits specified. The voxelwise time course of local WM regressors created using fast ANATICOR is shared as sub-[group][ID]_task-[task]_label-localWM_regressor.nii.gz.