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 Somerville completed the first-ever Participatory Budgeting cycle in 2023. In Participatory Budgeting, residents submit ideas for how to improve Somerville with one million dollars; volunteers score and select the 20 best ideas; and residents vote on the final ideas. This dataset describes the demographics and satisfaction of residents who voted on the best ideas. After voting for the final Participatory Budgeting ideas, voters were invited to an optional survey to voluntarily submit their demographic information and indicate their satisfaction with the Participatory Budgeting process. Demographics were not connected to votes to preserve anonymity. Open text comments have also been removed from the open dataset for privacy.

In early February 2024, we will be retiring the Mpox Vaccinations Given to SF Residents by Demographics dataset. This dataset will be archived and no longer update. A historic record of this data will remain available.

A. SUMMARY This dataset represents doses of mpox vaccine (JYNNEOS) administered in California to residents of San Francisco ages 18 years or older. This dataset only includes doses of the JYNNEOS vaccine given on or after 5/1/2022. All vaccines given to people who live in San Francisco are included, no matter where the vaccination took place. The data are broken down by multiple demographic stratifications.

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR2), run by the California Department of Public Health (CDPH). Information on individuals’ city of residence, age, race, ethnicity, and sex are recorded in CAIR2 and are self-reported at the time of vaccine administration. Because CAIR2 does not include information on sexual orientation, we pull information from the San Francisco Department of Public Health’s Epic Electronic Health Record (EHR). The populations represented in our Epic data and the CAIR2 data are different. Epic data only include vaccinations administered at SFDPH managed sites to SF residents.

Data notes for population characteristic types are listed below.

Age * Data only include individuals who are 18 years of age or older.

Race/ethnicity * The response option "Other Race" is categorized by the data source system, and the response option "Unknown" refers to a lack of data.

Sex * The response option "Other" is categorized by the source system, and the response option "Unknown" refers to a lack of data.

Sexual orientation * The response option “Unknown/Declined” refers to a lack of data or individuals who reported multiple different sexual orientations during their most recent interaction with SFDPH.

For convenience, we provide the 2020 5-year American Community Survey population estimates.

C. UPDATE PROCESS Updated daily via automated process.

D. HOW TO USE THIS DATASET This dataset includes many different types of demographic groups. Filter the “demographic_group” column to explore a topic area. Then, the “demographic_subgroup” column shows each group or category within that topic area and the total count of doses administered to that population subgroup.

E. CHANGE LOG

• UPDATE 1/3/2023: Due to low case numbers, this page will no longer include vaccinations after 12/31/2022.

DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers

This repository covers the anonymized dataset + optional demographic information from the cultural evaluation study "DaKultur" (Müller-Eberstein et al., 2025). It includes the following fields:

input: The user input, prompting the models for knowledge of Danish culture. output_[ID]: The output generated by one of the evaluated models. judgment_[ID]: The user's judgment of the model's… See the full description on the dataset page: https://huggingface.co/datasets/NLPnorth/dakultur-demographics.

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.

Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.

Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent's e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option "other" for respondents who do not feel confident with the proposed classification:

Natural Sciences

Arts and Humanities or Social Sciences

Economics

Law

Medicine

Computer Sciences, Engineering, Technics

Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option "other" for respondents who do not feel confident with the proposed classification:

Professor

Special education teacher

Academic/scientific assistant or research fellow (research and teaching)

Academic staff (teaching)

Student assistant

Other

We chose to have a free text (numerical) for asking about a respondent's year of birth because we did not want to pre-classify respondents' age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents' age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim "aware". Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6th to March 3rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question No 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim "aware".

The main objective of a demographic household survey (DHS) is to provide estimates of a number of basic demographic and health variables. This is done through interviews with a scientifically selected probability sample that is chosen from a well-defined population.

The 2007 Nauru Demographic and Health Survey (2007 NDHS) was one of four pilot demographic and health surveys conducted in the Pacific under an Asian Development Bank ADB/ Secretariat of the Pacific Community (SPC) Regional DHS Pilot Project. The primary objective of this survey was to provide up-to-date information for policy-makers, planners, researchers and programme managers, for use in planning, implementing, monitoring and evaluating population and health programmes within the country. The survey was intended to provide key estimates of Nauru's demographics and health situation. The findings of the 2007 NDHS are very important in measuring the achievements of family planning and other health programmes. To ensure better understanding and use of these data, the results of this survey should be widely disseminated at different planning levels. Different dissemination techniques will be used to reach different segments of society.

The primary purpose of the 2007 NDHS was to furnish policy-makers and planners with detailed information on fertility, family planning, infant and child mortality, maternal and child health, nutrition, and knowledge of HIV and AIDS and other sexually transmitted infections.

NOTE: The only dissemination used was wide distribution of the report. A planned data use workshop was not undertaken. Hence there is some misconceptions and lack of awareness on the results obtained from the survey. The report is provided on the NBOS website free for download.

Version 1.0

• v1.0: Edited data, second version for internal use only

DHS questionnaire for women cover the following sections:

• Background characteristics (age, education, religion, etc)
• Reproductive history
• Knowledge and use of contraception methods
• Antenatal care, delivery care and postnatal care
• Breastfeeding and infant feeding
• Immunization, child health and nutrition
• Marriage and recent sexual activity
• Fertility preferences
• Knowledge about HIV/AIDS and other sexually transmitted infections
• Husbands background and women's work

The men's questionnaire covers the same except for sections 4, 5, 6 which are not applicable to men.

It was also recognized that some countries have a need for special information that is not contained in the core questionnaire. Separate questionnaire modules were developed on a series of topics. These topics are optional and include:

• maternal mortality
• pill-taking behaviour
• sterilization experience
• children's education
• women's status
• domestic violence
• health expenditures

• consanguinity

• Collection start: 2007

• Collection end: 2007

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

High-Value Consumer Contact Data for Strategic Market Penetration in New York State
Subject: Exclusive Opportunity: Acquisition of 13 Million New York State Consumer Contacts for Strategic Market Expansion (Including African Immigrants from the Diaspora) Now with Limited-Time Promotional Pricing! This proposal highlights a compelling and time-sensitive opportunity for companies to acquire a comprehensive database of consumer contact information for over 13 million residents of New York State, including 4 million within New York City. More importantly, includes Africans that have migrated from the continent to New York. This highvolume, cost-effective acquisition, now coupled with a significant promotional discount, presents an unparalleled chance to significantly deepen market penetration and capitalize on the immense potential of the financial capital of the world. The Opportunity: We are offering access to a meticulously compiled database that includes a combination of names, addresses, genders, dates of birth, with a high percentage of telephone numbers and/or email addresses. This data is subject to constant real-time changes based on individual consumer lifestyle activity, ensuring its ongoing relevance and value. Why New York State and New York City? • Financial Capital of the World: New York City's unparalleled economic influence and diverse population present a prime target for a wide range of services and products, particularly within the financial and business sectors. • Massive Untapped Potential: With over 13 million residents in New York State, this database offers a substantial pool of potential customers for various industries. • Strategic Expansion: For a global telecommunications, this data provides a direct avenue to explore and establish a strong foothold in a key Western market, diversifying your portfolio and opening new revenue streams. Key Advantages of Our Offering: • Unbeatable Promotional Price: We are offering this extensive database at a highly competitive rate of $0.01 (one cent) per contact. For a limited time, we are providing an exclusive offer of 50% off the purchase of the entire New York State database OR 50% off the purchase of the entire New York City listings. This is significantly below the industry standard of approximately $0.10 per contact, representing an exceptional return on investment. • Immediate Market Access: Gain instant access to a vast consumer base, enabling rapid deployment of targeted marketing campaigns and business development initiatives. • Resale Potential: One could leverage this acquisition not only for its own marketing efforts but also explore opportunities to resell portions of the data at higher data broker rates, generating additional revenue. • Versatile Application: This comprehensive list is invaluable for a wide array of industries, including but not limited to: o Real Estate: Identifying potential buyers, sellers, and investors. o List Buyers: For further segmentation and resale to specialized niches. o Developers: Pinpointing demographics for new projects. o Financial Services: Reaching potential clients for banking, investment, and insurance products. o NGOs: Engaging with communities for outreach and fundraising. o Marketing Agencies: Crafting highly effective and localized campaigns. o Health Care Providers: Connecting with patients and promoting services. o Debt Collectors: Locating individuals for recovery efforts. o And many more: The possibilities are extensive due to the broad nature of the data. • Future Expansion: Should there be serious interest in Ghana regarding the sales of New York Consumer Data, we are prepared to offer similar high-volume consumer contact databases for other major US states, including California, Florida, Texas, and even the entire United States of America's nearly 340 million consumers listings, at the same advantageous pricing structure. Data Details: • Total Contacts: Over 13,000,000 New York State residents. • New York City Contacts: Over 4,000,000 residents. • Included Data Points: Names, addresses, genders, dates of birth, with a high percentage including telephone numbers and/or email addresses. Pricing Structure (with Limited-Time Offer): • Per Contact Cost: $0.01 USD • Option 1: Purchase Entire New York State Database (13M+ Contacts) o Original Price: $130,000 USD o Promotional Price (50% Off): $65,000 USD • Option 2: Purchase Entire New York City Listings (4M+ Contacts) o Original Price: $40,000 USD o Promotional Price (50% Off): $20,000 USD

Ethical and Legal Considerations We understand that the acquisition and utilization of consumer data require strict adherence to privacy regulations and ethical guidelines. We operate with the understanding of evolving data privacy landscapes, particularly in the United States. While the New York Privacy Act (NYPA) and other comprehensive privacy laws are still under legislative consideration in New York (with th...

Background: The Coronavirus disease (COVID-19) has emphasised the critical need to investigate the mental well-being of healthcare professionals working during the pandemic. It has been highlighted that healthcare professionals display a higher prevalence of mental distress and research has largely focused on frontline professions. Social restrictions were enforced during the pandemic that caused rapid changes to the working environment (both clinically and remotely). The present study aims to examine the mental health of a variety of healthcare professionals, comparing overall mental wellbeing in both frontline and non-frontline professionals and the effect of the working environment on mental health outcomes.

Method: A cross-sectional mixed methods design, conducted through an online questionnaire. Demographic information was optional but participants were required to complete: (a) Patient Health Questionnaire, (b) Generalised Anxiety Disorder, (c) Perceived Stress Scale, and (d) Copenhagen Burnout Inventory. The questionnaire included one open-ended question regarding challenges experienced working during the pandemic.

Procedure:
Upon ethical approval the online questionnaire was advertised for six weeks from 1st May 2021 to 12th June 2021 to maximise the total number of respondents able to partake. The survey was hosted on the survey platform “Online Surveys”. It was not possible to determine a response rate because identifying how many people had received the link was unattainable information. The advert for the study was placed on social media platforms (WhatsApp, Instagram, Facebook and Twitter) and shared through emails.

Participants were recruited through the researchers’ existing professional networks and they shared the advertisement and link to questionnaire with colleagues. The information page explained the purpose of the study, eligibility criteria, procedure, costs and benefits of partaking and data storage. Participants were made aware on the information page that completing and submitting the questionnaire indicated their informed consent. It was not possible to submit complete questionnaires unless blank responses were optional demographic data. Participants were informed that completed questionnaires could not be withdrawn due to anonymity.

The questionnaire consisted of four sections: demographic data, mental health information and the four psychometric tools, PHQ-9, GAD-7, PSS-10 and CBI. Due to the sensitive nature of this research, only the psychometric measures required an answer for each question, thus all demographic information was optional to encourage participant contentment. Once participants had completed the questionnaire and submitted, they were automatically taken to a debrief page. This revealed the hypothesis of the questionnaire and rationalised why it was necessary to conceal this prior to completion. Participants were signposted to mental health charities and a self-referral form for psychological support. Participants could contact the researcher via email to express an interest in the results. It was explained that findings would be analysed using descriptive statistics to investigate any correlations or patterns in the responses. Data collected was stored electronically, on a password protected laptop. It will be kept for three years and then destroyed.

Instruments: PHQ-9, GAD-7, PSS-10 and CBI.

Other questions included:

Thank you for considering taking part in the questionnaire! Please remember by completing and submitting the questionnaire you are giving your informed consent to participate in this study.

Demographic:

Gender: please select one of the following:

Male Female Non-binary Prefer not to answer

Age: what is your age?

Open question: Prefer not to answer

What is your current region in the UK?

South West, East of England, South East, East Midlands, Yorkshire and the Humber, North West, West Midlands, North East, London, Scotland, Wales, Northern Ireland Prefer not to answer

Ethnicity: please select one of the following:

White English, Welsh, Scottish, Northern Irish or British Irish Gypsy or Irish Traveller Any other White background Mixed or Multiple ethnic groups White and Black Caribbean White and Black African White and Asian Any other Mixed or Multiple ethnic background Asian or Asian British Indian Pakistani Bangladeshi Chinese Any other Asian background Black, African, Caribbean or Black British African Caribbean Any other Black, African or Caribbean background Other ethnic group Arab Option for other please specify Prefer not to answer

Employment/environment:

What was your employment status in 2020 prior to COVID-19 pandemic?

Please select the option that best applies. Employed Self-employed Unpaid work (homemaker/carer) Out of work and looking for work Out of work but not currently looking for work Student Volunteer Retired Unable to work Prefer not to answer Option for other please specify

What is your current employment status?

Please tick the option that best applies. Employed Self-employed Unpaid work (homemaker/carer) Out of work and looking for work Out of work but not currently looking for work Student Volunteer Retired Unable to work Prefer not to answer Option for other please specify

What is your healthcare profession/helping profession?

Please state your job title. Open question

How often did you work from home before the COVID-19 pandemic?

Not at all, rarely, some, most, everyday Option for N/A

How often did you work from home during the first UK national lockdown for COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often did you work from home during the second UK national lockdown during COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often have you worked from home during the third UK national lockdown during COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often are you currently working from home during the COVID-19 pandemic?

Not at all, rarely, some, most, everyday Option for N/A

Mental health:

How would you describe your mental health leading up to the COVID-19 pandemic?

Excellent, Very good, Good, Fair, Poor

How would you describe your mental health during the COVID-19 pandemic?

Excellent, Very good, Good, Fair, Poor

What have been the main challenges working as a healthcare professional/helping profession during COVID-19 pandemic? Open question

Data analysis: Firstly, any missing data was checked by the researcher and noted in the results section. The data was then analysed using a statistical software package called Statistical Package for the Social Sciences version 28 (SPSS-28). Descriptive statistics were collected to organise and summarise the data, and a correlation coefficient describes the strength and direction of the relationship between two variables. Inferential statistics were used to determine whether the effects were statistically significant. Responses to the open-ended question were coded and examined for key themes and patterns utilising the Braun and Clarke (2006) thematic analysis approach.

Ethical considerations: The study was approved by the Health Science, Engineering and Technology Ethical Committee with Delegated Authority at the University of Hertfordshire.

The potential benefits and risks of partaking in the research were contemplated and presented on the information page to promote informed consent. Precautions to prevent harm to participants included eligibility criteria, excluding those under eighteen years older or experiencing mental health distress. As the questionnaire was based around employment and the working environment, another exclusion involved experiencing a recent job change which caused upset.

An anonymous questionnaire and optional input of demographic data fostered the participants’ right to autonomy, privacy and respect. Specific employment and organisation or company information were not collected to protect confidentiality. Although participants were initially deceived regarding the hypotheses, they were provided with accurate information about the purpose of the study. Deceit was appropriate to collect unbiased information and participants were subsequently informed of the hypotheses on the debrief page.

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

Results of On-Line and Paper Surveys provided to recipients of select City services.

"Why: Legislation? Data is collected to determine the demographics of persons voluntarily seeking social services from NYC with the goal of improving services to populations served and informing outreach to populations that are undeserved. The data can be used by agencies to strengthen the cultural competence of their services or to devise strategies to reach undeserved populations.

How: The data is collected via electronic and paper surveys offered at the point of application for services.

What does each record represent: Each record represents a demographic profile of the applicant.

How can this data be used: To gain insight into populations using City services.

Idiosyncrasies or Limitations: The limitations are that survey completion is voluntary. The fact that it is anonymous cuts both ways: increasing the likelihood of full and honest answers, but by not being connected to the case does not directly inform delivery of services to the applicant. The paper and on-line 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 in several languages. Surveys are presented in 11 languages.
Paper Survey 1. They are optional 2. Survey taker is expected to specify agency that provides service 2. Survey taker can ignore questions 3. Invalid/unreadable data may be entered for survey date or date may be skipped 4. OCRing of free-form text fields may fail. 5. Analytical value of free-form text answers is questionable Online Survey 1. Optional 2. Agency is defaulted based on the URL 3. Some questions must be answered 4. Date of survey is automated"

Welcome to BatchData, your trusted source for comprehensive US homeowner data, contact information, and demographic data, all designed to empower political campaigns. In the fast-paced world of politics, staying ahead and targeting the right audience is crucial for success.

At BatchData, we understand the importance of having the most accurate, up-to-date, and relevant data to help you make informed decisions and connect with your constituents effectively. With our robust data offerings, political campaign agencies can easily reach both homeowners and renters, using direct contact information such as cell phone numbers, emails, and mailing addresses.

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Public perception regarding diagnostic sample types as well as personal experiences can influence willingness to test. As such, public preferences for specific sample type(s) should be used to inform diagnostic and surveillance testing programs to improve public health response efforts. To understand where preferences lie, we conducted an international survey regarding the sample types used for SARS-CoV-2 tests. A Qualtrics survey regarding SARS-CoV-2 testing preferences was distributed via social media and email. The survey collected preferences regarding sample methods and key demographic data. Python was used to analyze survey responses. From March 30th to June 15th, 2022, 2,094 responses were collected from 125 countries. Participants were 55% female and predominantly aged 25–34 years (27%). Education and employment were skewed: 51% had graduate degrees, 26% had bachelor’s degrees, 27% were scientists/researchers, and 29% were healthcare workers. By rank sum analysis, the most preferred sample type globally was the oral swab, followed by saliva, with parents/guardians preferring saliva-based testing for children. Respondents indicated a higher degree of trust in PCR testing (84%) vs. rapid antigen testing (36%). Preferences for self- or healthcare worker-collected sampling varied across regions. This international survey identified a preference for oral swabs and saliva when testing for SARS-CoV-2. Notably, respondents indicated that if they could be assured that all sample types performed equally, then saliva was preferred. Overall, survey responses reflected the region-specific testing experiences during the COVID-19. Public preferences should be considered when designing future response efforts to increase utilization, with oral sample types (either swabs or saliva) providing a practical option for large-scale, accessible diagnostic testing.

Responses from the 2021 open participation (non-probability) survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The open participation survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

USA States (Generalized) provides 2017 boundaries for the States of the United States in the 50 states and the District of Columbia. The linework has been generalized for increased performance and best viewed at smaller scales.Attribute fields include estimated 2017 total population, 2010 U.S. Census demographic information, and 2012 Census of Agriculture information for the USA States.

Census tracts as of 2018."Census Tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity that are updated by local participants prior to each decennial census as part of the Census Bureau's Participant Statistical Areas Program. The Census Bureau delineates census tracts in situations where no local participant existed or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.Census tract boundaries generally follow visible and identifiable features. They may follow nonvisible legal boundaries, such as minor civil division (MCD) or incorporated place boundaries in some states and situations, to allow for census-tract-to-governmental-unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. Tribal census tracts are a unique geographic entity defined within federally recognized American Indian reservations and off-reservation trust lands and can cross state and county boundaries. Tribal census tracts may be completely different from the census tracts and block groups defined by state and county.Census Tract Codes and Numbers—Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively."- US Census Bureau For information about US Census Bureau geographies, click here. USE CONSTRAINTS: The Alaska Department of Commerce, Community, and Economic Development (DCCED) provides the data in this application as a service to the public. DCCED makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided on this site. DCCED shall not be liable to the user for damages of any kind arising out of the use of data or information provided. DCCED is not the authoritative source for American Community Survey data, and any data or information provided by DCCED is provided "as is". Data or information provided by DCCED shall be used and relied upon only at the user's sole risk.

Most preferred sample type for diagnostic testing, by demographic group.

Archived as of 5/30/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. In August 2018, 10 optional questions were added to all online applications through the state for health coverage, the Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance for Needy Families (TANF). It does not represent anyone who applied in-person, by telephone, by main, or any other method. In 2019, 79% of those who applied for SNAP, TANF, or health coverage applied online. The assessment does not impact eligibility for SNAP, TANF, or health coverage. Applications are filed at a household level and may represent several individuals. The application includes demographic information for the person who applied and not all members of the household. An individual may complete an assessment every time they apply for health coverage, SNAP or TANF. If an individual completed the survey more than once with multiple applications for assistance, each set of survey responses is represented on the dashboard. If an individual completes more than one assessment when applying for multiple programs, only one assessment will be represented in the data. To ensure personally identifiable information is protected, all data are presented in aggregate and data representing 20 or fewer individuals in any county will not be displayed (the demographic field will show as 0). Because some survey responses are not included in the individual race categories shown here, total counts from the individual race categories add up to less than the total for the "All" race category.

Abstract

The 2016-16 Malawi Demographic and Health Survey (2015-16 MDHS) was conducted between October 2015 and February 2016 by the National Statistical Office (NSO) of Malawi in joint collaboration with the Ministry of Health (MoH) and the Community Health Services Unit (CHSU). Malawi conducted its first DHS in 1992 and again in 2000, 2004, and 2010. The 2015-16 MDHS is the fifth in the series. The survey is based on a nationally representative sample that provides estimates at the national and regional levels and for urban and rural areas with key indicator estimates at the district level. The survey included 26,361 households, 24,562 female respondents, and 7,478 male respondents.

The primary objective of the 2015-16 MDHS is to provide current estimates of basic demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in Malawi. More specifically, the 2015-16 MDHS: - collected data that allow the calculation of key demographic indicators, particularly fertility and under 5 and adult mortality rates - provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality - measured the levels of contraceptive knowledge and practice - obtained data on key aspects of family health, such as immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators that include antenatal visits and assistance at delivery - obtained data on child feeding practices including breastfeeding - collected anthropometric measures that assess nutritional status, and conducted anaemia testing for all eligible children under age 5 and women age 15-49 - collected data on knowledge and attitudes of women and men about sexually-transmitted diseases (STDs) and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviours and condom use) and coverage of HIV Testing and Counselling (HTC) and other key HIV programmes - collected dried blood spot (DBS) specimens for HIV testing from women age 15-49 and men age 15-54 to provide information on the prevalence of HIV among the adult population in the prime reproductive ages.

The micronutrient component of the 2015-16 MDHS was designed to: (1) determine the prevalence of micronutrient deficiencies (vitamin A, B, iron, iodine, zinc) and anaemia among pre-school and school-age children, women, and men of child-bearing age; (2) estimate micronutrient supplementation and fortification coverage; and (3) assess the knowledge and practices in maternal and child nutrition.

The information collected in the 2015-16 MDHS will assist policy makers and programme managers in evaluating and designing programmes and strategies that can improve 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 15-54

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2015-16 MDHS is the frame of the Malawi Population and Housing Census (MPHC), conducted in Malawi in 2008, and provided by the Malawi National Statistical Office (NSO). The census frame is a complete list of all census standard enumeration areas (SEAs) created for the 2008 MPHC. A SEA is a geographic area that covers an average of 235 households. The sampling frame contains information about the SEA location, type of residence (urban or rural), and the estimated number of residential households.

Administratively, Malawi is divided into 28 districts. The sample for the 2015-16 MDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the 28 districts.

The 2015-16 MDHS sample was stratified and selected in two stages. Each district was stratified into urban and rural areas; this yielded 56 sampling strata. Samples of SEAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

In the first stage, 850 SEAs, including 173 SEAs in urban areas and 677 in rural areas, were selected with probability proportional to the SEA size and with independent selection in each sampling stratum.

In the second stage of selection, a fixed number of 30 households per urban cluster and 33 per rural cluster were selected with an equal probability systematic selection from the newly created household listing.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2015-16 MDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Malawi. Input was solicited from stakeholders who represented government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the definitive questionnaires in English, the questionnaires were then translated into Chichewa and Tumbuka languages. All four questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection, and to offer the option to choose either English, Chichewa or Tumbuka for each questionnaire.

Cleaning operations

All electronic data collected in the 2015-16 MDHS were received via IFSS at the NSO central office in Zomba, where the data 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 four individuals who took part in the fieldwork training, and were supervised by two senior staff from NSO. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2015 and completed in March 2016.

Response rate

A total of 27,516 households were selected for the sample, of which 26,564 were occupied. Of the occupied households, 26,361 were successfully interviewed, for a response rate of 99%.

In the interviewed households, 25,146 eligible women were identified for individual interviews. Interviews were completed with 24,562 women, for a response rate of 98%. In the subsample of households selected for the male survey, 7,903 eligible men were identified and 7,478 were successfully interviewed, for 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 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 2015-16 Malawi Demographic and Health Survey (2015-16 MDHS) 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 year acronym 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 2015-16 MDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed by SAS programs developed by ICF International. 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