Professional organizations in STEM (science, technology, engineering, and mathematics) can use demographic data to quantify recruitment and retention (R&R) of underrepresented groups within their memberships. However, variation in the types of demographic data collected can influence the targeting and perceived impacts of R&R efforts - e.g., giving false signals of R&R for some groups. We obtained demographic surveys from 73 U.S.-affiliated STEM organizations, collectively representing 712,000 members and conference-attendees. We found large differences in the demographic categories surveyed (e.g., disability status, sexual orientation) and the available response options. These discrepancies indicate a lack of consensus regarding the demographic groups that should be recognized and, for groups that are omitted from surveys, an inability of organizations to prioritize and evaluate R&R initiatives. Aligning inclusive demographic surveys across organizations will provide baseline data that can be used to target and evaluate R&R initiatives to better serve underrepresented groups throughout STEM. Methods We surveyed 164 STEM organizations (73 responses, rate = 44.5%) between December 2020 and July 2021 with the goal of understanding what demographic data each organization collects from its constituents (i.e., members and conference-attendees) and how the data are used. Organizations were sourced from a list of professional societies affiliated with the American Association for the Advancement of Science, AAAS, (n = 156) or from social media (n = 8). The survey was sent to the elected leadership and management firms for each organization, and follow-up reminders were sent after one month. The responding organizations represented a wide range of fields: 31 life science organizations (157,000 constituents), 5 mathematics organizations (93,000 constituents), 16 physical science organizations (207,000 constituents), 7 technology organizations (124,000 constituents), and 14 multi-disciplinary organizations spanning multiple branches of STEM (131,000 constituents). A list of the responding organizations is available in the Supplementary Materials. Based on the AAAS-affiliated recruitment of the organizations and the similar distribution of constituencies across STEM fields, we conclude that the responding organizations are a representative cross-section of the most prominent STEM organizations in the U.S. Each organization was asked about the demographic information they collect from their constituents, the response rates to their surveys, and how the data were used. Survey description The following questions are written as presented to the participating organizations. Question 1: What is the name of your STEM organization? Question 2: Does your organization collect demographic data from your membership and/or meeting attendees? Question 3: When was your organization’s most recent demographic survey (approximate year)? Question 4: We would like to know the categories of demographic information collected by your organization. You may answer this question by either uploading a blank copy of your organization’s survey (linked provided in online version of this survey) OR by completing a short series of questions. Question 5: On the most recent demographic survey or questionnaire, what categories of information were collected? (Please select all that apply)

Disability status Gender identity (e.g., male, female, non-binary) Marital/Family status Racial and ethnic group Religion Sex Sexual orientation Veteran status Other (please provide)

Question 6: For each of the categories selected in Question 5, what options were provided for survey participants to select? Question 7: Did the most recent demographic survey provide a statement about data privacy and confidentiality? If yes, please provide the statement. Question 8: Did the most recent demographic survey provide a statement about intended data use? If yes, please provide the statement. Question 9: Who maintains the demographic data collected by your organization? (e.g., contracted third party, organization executives) Question 10: How has your organization used members’ demographic data in the last five years? Examples: monitoring temporal changes in demographic diversity, publishing diversity data products, planning conferences, contributing to third-party researchers. Question 11: What is the size of your organization (number of members or number of attendees at recent meetings)? Question 12: What was the response rate (%) for your organization’s most recent demographic survey? *Organizations were also able to upload a copy of their demographics survey instead of responding to Questions 5-8. If so, the uploaded survey was used (by the study authors) to evaluate Questions 5-8.

Includes questions written in Spanish pertaining to: race & ethnicitygenderagetribal affiliationdisabilityincomelanguagelocation

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

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 questions list for questionnaire – Demographics and basic work characteristics of survey respondents

Includes questions written in Spanish pertaining to: race & ethnicitygendersexual orientationagetribal affiliationdisabilityincomehouseholdlanguagelocationeducationhousing statustransportationemployment status

Includes questions pertaining to: race & ethnicitygenderagetribal affiliationdisabilityincomelanguagelocationeducation

Includes questions pertaining to: race & ethnicitygenderagetribal affiliationdisabilityincomelanguagelocation

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.

Abstract

The 2017-18 Albania Demographic and Health Survey (2017-18 ADHS) is a nationwide survey with a nationally representative sample of approximately 17,160 households. All women age 15-49 who are usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. Women 50-59 years old were interviewed with an abbreviated questionnaire that only covered background characteristics and questions related to noncommunicable diseases.

The primary objective of the 2017-2018 ADHS was to provide estimates of basic sociodemographic and health indicators for the country as a whole and the twelve prefectures. Specifically, the survey collected information on basic characteristics of the respondents, fertility, family planning, nutrition, maternal and child health, knowledge of HIV behaviors, health-related lifestyle, and noncommunicable diseases (NCDs). The information collected in the ADHS will assist policymakers and program managers in evaluating and designing programs and in developing strategies for improving the health of the country’s population.

The sample for the 2017-18 ADHS was designed to produce representative results for the country as a whole, for urban and rural areas separately, and for each of the twelve prefectures known as Berat, Diber, Durres, Elbasan, Fier, Gjirokaster, Korce, Kukes, Lezhe, Shkoder, Tirana, and Vlore.

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), children age 0-4 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ADHS surveys were done on a nationally representative sample that was representative at the prefecture level as well by rural and urban areas. A total of 715 enumeration areas (EAs) were selected as sample clusters, with probability proportional to each prefecture's population size. The sample design called for 24 households to be randomly selected in every sampling cluster, regardless of its size, but some of the EAs contained fewer than 24 households. In these EAs, all households were included in the survey. The EAs are considered the sample's primary sampling unit (PSU). The team of interviewers updated and listed the households in the selected EAs. Upon arriving in the selected clusters, interviewers spent the first day of fieldwork carrying out an exhaustive enumeration of households, recording the name of each head of household and the location of the dwelling. The listing was done with tablet PCs, using a digital listing application. When interviewers completed their respective sections of the EA, they transferred their files into the supervisor's tablet PC, where the information was automatically compiled into a single file in which all households in the EA were entered. The software and field procedures were designed to ensure there were no duplications or omissions during the household listing process. The supervisor used the software in his tablet to randomly select 24 households for the survey from the complete list of households.

All women age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for individual interviews with the full Woman's Questionnaire. Women age 50-59 were also interviewed, but with an abbreviated questionnaire that left out all questions related to reproductive health and mother and child health. A 50% subsample was selected for the survey of men. Every man age 15-59 who was a usual resident of or had slept in the household the night before the survey was eligible for an individual interview in these households.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the ADHS, one for the household and others for women age 15-49, for women age 50-59, and for men age 15-59. In addition to these four questionnaires, a form was used to record the vaccination information for children born in the 5 years preceding the survey whose mothers had been successfully interviewed.

Cleaning operations

Supervisors sent the accumulated fieldwork data to INSTAT’s central office via internet every day, unless for some reason the teams did not have access to the internet at the time. The data received from the various teams were combined into a single file, which was used to produce quality control tables, known as field check tables. These tables reveal systematic errors in the data such as omission of potential respondents, age displacement, inaccurate recording of date of birth and age at death, inaccurate measurement of height and weight, and other key indicators of data quality. These tables were reviewed and evaluated by ADHS senior staff, which in turn provided feedback and advice to the teams in the field.

Response rate

A total of 16,955 households were selected for the sample, of which 16,634 were occupied. Of the occupied households, 15,823 were successfully interviewed, which represents a response rate of 95%. In the interviewed households, 11,680 women age 15-49 were identified for individual interviews. Interviews were completed for 10,860 of these women, yielding a response rate of 93%. In the same households, 4,289 women age 50-59 were identified, of which 4,140 were successfully interviewed, yielding a 97% response rate. In the 50% subsample of households selected for the male survey, 7,103 eligible men age 15-59 were identified, of which 6,142 were successfully interviewed, yielding a response rate of 87%.

Response rates were higher in rural than in urban areas, which is a pattern commonly found in household surveys because in urban areas more people work and carry out activities outside the home.

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 2017-18 Albania Demographic and Health Survey (ADHS) 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 2017-18 ADHS 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 2017-18 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor 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 final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The Turkey Demographic and Health Survey (DHS) 2008 has been conducted by the Haccettepe University Institute of Population Studies in collaboration with the Ministry of health General Directorate of Mother and Child Health and Family Planning and Undersecretary of State Planning Organization. The Turkey Demographic and Health Survey 2008 has been financed the scientific and Technological research Council of Turkey (TUBITAK) under the support program for Research Projects of Public Institutions.

The primary objective of the Turkey DHS 2008 is to provide data on fertility, contraceptive methods, maternal and child health. Detailed information on these issues is obtained through questionnaires, filled by face-to face interviews with ever-married women in reproductive ages (15-49).

Another important objective of the survey, with aims to contribute to the knowledge on population and health as well, is to maintain the flow of information for the related organizations in Turkey on the Turkish demographic structure and change in the absence of reliable vital registration system and ascertain the continuity of data on demographic and health necessary for sustainable development in the absence of a reliable vital registration system. In terms of survey methodology and content, the Turkey DHS 2008 is comparable with the previous demographic surveys in Turkey (MEASURE DHS+).

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Kind of data

Sample survey data

Mode of data collection

Face-to-face

Research instrument

Two main types of questionnaires were used to collect the TDHS-2008 data: a) The Household Questionnaire; b) The Individual Questionnaire for Ever-Married Women of Reproductive Ages.

The contents of these questionnaires were based on the DHS Model "A" Questionnaire, which was designed for the DHS program for use in countries with high contraceptive prevalence. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the DHS-2008 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2003 questionnaires, national and international population and health agencies were consulted for their comments.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, recent migration and residential mobility, employment, marital status, and relationship to the head of household of each person listed as a household member or visitor. The objective of the first part of the Household Questionnaire was to obtain the information needed to identify women who were eligible for the individual interview as well as to provide basic demographic data for Turkish households. The second part of the Household Questionnaire included questions on never married women age 15-49, with the objective of collecting information on basic background characteristics of women in this age group. The third section was used to collect information on the welfare of the elderly people. The final section of the Household Questionnaire was used to collect information on housing characteristics, such as the number of rooms, the flooring material, the source of water, and the type of toilet facilities, and on the household's ownership of a variety of consumer goods. This section also incorporated a module that was only administered in Istanbul metropolitan households, on house ownership, use of municipal facilities and the like, as well as a module that was used to collect information, from one-half of households, on salt iodization. In households where salt was present, test kits were used to test whether the salt used in the household was fortified with potassium iodine or potassium iodate, i.e. whether salt was iodized.

b) The Individual Questionnaire for ever-married women obtained information on the following subjects: - Background characteristics - Reproduction - Marriage - Knowledge and use of family planning - Maternal care and breastfeeding - Immunization and health - Fertility preferences - Husband's background
- Women's work and status - Sexually transmitted diseases and AIDS - Maternal and child anthropometry.

Cleaning operations

The questionnaires were returned to the Hacettepe Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all the selected households and eligible respondents were returned from the field.

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.

This data collection is comprised of responses from the March and April installments of the 2008 Current Population Survey (CPS). Both the March and April surveys used two sets of questions, the basic CPS and a separate supplement for each month.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment.In addition to the basic CPS questions, respondents were asked questions from the March supplement, known as the Annual Social and Economic (ASEC) supplement. The ASEC provides supplemental data on work experience, income, noncash benefits, and migration. Comprehensive work experience information was given on the employment status, occupation, and industry of persons 15 years old and older. Additional data for persons 15 years old and older are available concerning weeks worked and hours per week worked, reason not working full time, total income and income components, and place of residence on March 1, 2007. The March supplement also contains data covering nine noncash income sources: food stamps, school lunch program, employer-provided group health insurance plan, employer-provided pension plan, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Questions covering training and assistance received under welfare reform programs, such as job readiness training, child care services, or job skill training were also asked in the March supplement.The April supplement, sponsored by the Department of Health and Human Services, queried respondents on the economic situation of persons and families for the previous year. Moreover, all household members 15 years of age and older that are a biological parent of children in the household that have an absent parent were asked detailed questions about child support and alimony. Information regarding child support was collected to determine the size and distribution of the population with children affected by divorce or separation, or other relationship status change. Moreover, the data were collected to better understand the characteristics of persons requiring child support, and to help develop and maintain programs designed to assist in obtaining child support. These data highlight alimony and child support arrangements made at the time of separation or divorce, amount of payments actually received, and value and type of any property settlement.The April supplement data were matched to March supplement data for households that were in the sample in both March and April 2008. In March 2008, there were 4,522 household members eligible, of which 1,431 required imputation of child support data. When matching the March 2008 and April 2008 data sets, there were 170 eligible people on the March file that did not match to people on the April file. Child support data for these 170 people were imputed. The remaining 1,261 imputed cases were due to nonresponse to the child support questions. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. Data on employment and income refer to the preceding year, although other demographic data refer to the time at which the survey was administered.

Sexual, romantic, and related orientations across all institutions, based on the queered survey (n = 1932).

Includes questions pertaining to: race & ethnicitygenderpreferred pronounssexual orientationagetribal affiliationdisabilityincomehouseholdlanguagelocationeducationhousing statustransportationemployment status

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

Universe

The non-institutional population.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Population Census Organization

SAMPLE DESIGN: Approximately 24 thousand blocks were selected out of 75 thousand in the country. A sample of households would be taken from each block to yield 300,000 households. Urban households were oversampled relative to rural. Roughly 15% of households do not have a head and appear to be fragments. *NOTE: The sample excludes 4 districts in the North-West Frontier Province: Chitral, Dir, Swat, and Malakand Agency.

SAMPLE UNIT: Household

SAMPLE FRACTION: 2%

SAMPLE SIZE (person records): 1,453,332

Mode of data collection

Face-to-face [f2f]

Research instrument

The HED sample survey was a second phase of the 1972 Census administered to 300,000 households. The first phase was a full-count census in September 1972 that used a seven-question short form. The HED questionnaire contains two parts. Part I asks questions on housing characteristics and household facilities for both urban and rural areas. Part II asks questions particulars of household member.

About this dataset

Context

The dataset tabulates the population of Weston by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Weston. The dataset can be utilized to understand the population distribution of Weston by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Weston. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Weston.

Key observations

Largest age group (population): Male # 55-59 years (36) | Female # 55-59 years (28). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Weston population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Weston is shown in the following column.
• Population (Female): The female population in the Weston is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Weston for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Weston Population by Gender. You can refer the same here

The STAMINA study examined the nutritional risks of low-income peri-urban mothers, infants and young children (IYC), and households in Peru during the COVID-19 pandemic. The study was designed to capture information through three, repeated cross-sectional surveys at approximately 6 month intervals over an 18 month period, starting in December 2020. The surveys were carried out by telephone in November-December 2020, July-August 2021 and in February-April 2022. The third survey took place over a longer period to allow for a household visit after the telephone interview.The study areas were Manchay (Lima) and Huánuco district in the Andean highlands (~ 1900m above sea level).In each study area, we purposively selected the principal health centre and one subsidiary health centre. Peri-urban communities under the jurisdiction of these health centres were then selected to participate. Systematic random sampling was employed with quotas for IYC age (6-11, 12-17 and 18-23 months) to recruit a target sample size of 250 mother-infant pairs for each survey.Data collected included: household socio-demographic characteristics; infant and young child feeding practices (IYCF), child and maternal qualitative 24-hour dietary recalls/7 day food frequency questionnaires, household food insecurity experience measured using the validated Food Insecurity Experience Scale (FIES) survey module (Cafiero, Viviani, & Nord, 2018), and maternal mental health.In addition, questions that assessed the impact of COVID-19 on households including changes in employment status, adaptations to finance, sources of financial support, household food insecurity experience as well as access to, and uptake of, well-child clinics and vaccination health services were included.This folder includes the questionnaire for survey 3 in both English and Spanish languages.The corresponding dataset and dictionary of variables for survey 3 are available at 10.17028/rd.lboro.21741014

Abstract

The Bangladesh Demographic and Health Survey (BDHS) is the first of this kind of study conducted in Bangladesh. It provides rapid feedback on key demographic and programmatic indicators to monitor the strength and weaknesses of the national family planning/MCH program. The wealth of information collected through the 1993-94 BDHS will be of immense value to the policymakers and program managers in order to strengthen future program policies and strategies.

The BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: - asses the overall demographic situation in Bangladesh, - assist in the evaluation of the population and health programs in Bangladesh, and - advance survey methodology.

More specifically, the BDHS was designed to: - provide data on the family planning and fertility behavior of the Bangladesh population to evaluate the national family planning programs, - measure changes in fertility and contraceptive prevalence and, at the same time, study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding patterns, and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Bangladesh.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into five administrative divisions, 64 districts (zillas), and 489 thanas. In rural areas, thanas are divided into unions and then mauzas, an administrative land unit. Urban areas are divided into wards and then mahallas. The 1993-94 BDHS employed a nationally-representative, two-stage sample. It was selected from the Integrated Multi-Purpose Master Sample (IMPS), newly created by the Bangladesh Bureau of Statistics. The IMPS is based on 1991 census data. Each of the five divisions was stratified into three groups: 1) statistical metropolitan areas (SMAs) 2) municipalities (other urban areas), and 3) rural areas. In rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 census frame, the units for the BDHS were sub-selected from the IMPS with equal probability to make the BDHS selection equivalent to selection with probability proportional to size. A total of 304 primary sampling units were selected for the BDHS (30 in SMAs, 40 in municipalities, and 234 in rural areas), out of the 372 in the IMPS. Fieldwork in three sample points was not possible, so a total of 301 points were covered in the survey.

Since one objective of the BDHS is to provide separate survey estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal Division und for municipalities relative to the other divisions, SMAs, and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.

After the selection of the BDHS sample points, field staffs were trained by Mitra and Associates and conducted a household listing operation in September and October 1993. A systematic sample of households was then selected from these lists, with an average "take" of 25 households in the urban clusters and 37 households in rural clusters. Every second household was identified as selected for the husband's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed the husband of any woman who was successfully interviewed. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 4,200 of their husbands.

Note: See detailed in APPENDIX A of the survey final report.

Sampling deviation

Data collected for women 10-49, indicators calculated for women 15-49. A total of 304 primary sampling units were selected, but fieldwork in 3 sample points was not possible.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Husbands' Questionnaire, and a Service Availability Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings with representatives of various organizations, including the Asia Foundation, the Bangladesh Bureau of Statistics, the Cambridge Consulting Corporation, the Family Planning Association of Bangladesh, GTZ, the International Centre for Diarrhoeal Disease Research (ICDDR,B), Pathfinder International, Population Communications Services, the Population Council, the Social Marketing Company, UNFPA, UNICEF, University Research Corporation/Bangladesh, and the World Bank. The questionnaires were developed in English and then translated into and printed in Bangla.

The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

The Women's Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.), - Reproductive history, - Knowledge and use of family planning methods, - Antenatal and delivery care, - Breastfeeding and weaning practices, - Vaccinations and health of children under age three, - Marriage, - Fertility preferences, and - Husband's background and respondent's work.

The Husbands' Questionnaire was used to interview the husbands of a subsample of women who were interviewed. The questionnaire included many of the same questions as the Women's Questionnaire, except that it omitted the detailed birth history, as well as the sections on maternal care, breastfeeding and child health.

The Service Availability Questionnaire was used to collect information on the family planning and health services available in and near the sampled areas. It consisted of a set of three questionnaires: one to collect data on characteristics of the community, one for interviewing family welfare visitors and one for interviewing family planning field workers, whether government or non-governent supported. One set of service availability questionnaires was to be completed in each cluster (sample point).

Cleaning operations

All questionnaires for the BDHS were returned to Dhaka for data processing at Mitra and Associates. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing inconsistencies found by the computer programs. One senior staff member, 1 data processing supervisor, questionnaire administrator, 2 office editors, and 5 data entry operators were responsible for the data processing operation. The data were processed on five microcomputers. The DHS data entry and editing programs were written in ISSA (Integrated System for Survey Analysis). Data processing commenced in early February and was completed by late April 1994.

Response rate

A total of 9,681 households were selected for the sample, of which 9,174 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant, or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 9,255 households that were occupied, 99 percent were successfully interviewed. In these households, 9,900 women were identified as eligible for the individual interview and interviews were completed for 9,640 or 97 percent of these. In one-half of the households that were selected for inclusion in the husbands' survey, 3,874 eligible husbands were identified, of which 3,284 or 85 percent were interviewed.

The principal reason for non-response among eligible women and men was failure to find them at home despite repeated visits to the household. The refusal rate was very low (less than one-tenth of one percent among women and husbands). Since the main reason for interviewing husbands was to match the information with that from their wives, survey procedures called for interviewers not to interview husbands of women who were not interviewed. Such cases account for about one-third of the non-response among husbands. Where husbands and wives were both interviewed, they were interviewed simultaneously but separately.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey final report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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

Abstract

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

Geographic coverage

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

Analysis unit

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

Universe

The survey was fielded to active Facebook users.

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Internet [int]

Research instrument

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

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

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

Response rate

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

Sampling error estimates

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

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

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

Data appraisal

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