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

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.

Pursuant to Local Laws 126, 127, and 128 of 2016, certain demographic data is collected voluntarily and anonymously by persons voluntarily seeking social services. This data can be used by agencies and the public to better understand the demographic makeup of client populations and to better understand and serve residents of all backgrounds and identities.

The data presented here has been collected through either electronic form or paper surveys offered at the point of application for services. These surveys are anonymous.

Each record represents an anonymized demographic profile of an individual applicant for social services, disaggregated by response option, agency, and program. Response options include information regarding ancestry, race, primary and secondary languages, English proficiency, gender identity, and sexual orientation.

Idiosyncrasies or Limitations: Note that while the dataset contains the total number of individuals who have identified their ancestry or languages spoke, because such data is collected anonymously, there may be instances of a single individual completing multiple voluntary surveys. Additionally, the survey being both voluntary and anonymous has advantages as well as disadvantages: it increases the likelihood of full and honest answers, but since it is not connected to the individual case, it does not directly inform delivery of services to the applicant. The paper and online versions of the survey ask the same questions but free-form text is handled differently. Free-form text fields are expected to be entered in English although the form is available in several languages. Surveys are presented in 11 languages.
Paper Surveys 1. Are optional 2. Survey taker is expected to specify agency that provides service 2. Survey taker can skip or elect not to answer questions 3. Invalid/unreadable data may be entered for survey date or date may be skipped 4. OCRing of free-form tet fields may fail. 5. Analytical value of free-form text answers is unclear Online Survey 1. Are optional 2. Agency is defaulted based on the URL 3. Some questions must be answered 4. Date of survey is automated

Abstract

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 2022 Socio-Demographic and Economic Survey is a nationally representative household survey designed to provide information on population, migration, education, labour and employment, fertility, disability, household, and housing characteristics. The key objectives of the survey are:

-to generate essential key indicators as inputs in the preparation of national plans and programs for the well-being of the population -to monitor the progress of development programs as stipulated in the Sustainable Development Goals (SDGs), Medium Term Development Plans, Vision 2050 and other national policies/plans and priorities.

Geographic coverage

National coverage. 43 strata and 22 provinces were covered.

Analysis unit

Household and Individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

-Used a stratified, two-stage cluster sampling method, with a third stage in very large sample census units (CU, enumeration areas selected within the sample CUs).

-Produced 43 strata, 22 provinces by urban/rural (National Capital District has only urban areas).

-Allocation was done proportionately according to size (in terms of the number of households).

-Thus, 335 CUs / clusters were selected in the first- stage while a fixed number of 15 households per cluster were selected at the second stage resulting to a total sample size of 5,025 households.

Sampling deviation

Coverage: 95.8% (14 out of 335 clusters not accessed) due to security issues (tribal fights/lawlessness), and election related misconceptions.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was generated using the World Bank's software Survey Solutions. It contains a set of 47 questions covering several modules such as Employment, Fertility, Housing, Disability, Education. The questionnaire is provided in English in the External Resources section in this documentation.

Cleaning operations

-Checking of data submitted from field, identifying unique / valid households and removing invalid or duplicate households, coding of responses, consistency checks -Tabulations - generating tables for data analysis and generation of key indicators

n =  Numbers of responses. Test statistics for Wilcoxon rank sum test (W), Student's t-test (t), and χ2-test (χ2). Mean, median and ranges calculated from raw data before imputation.

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".

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.

Socio-demographic data of participants who completed the first follow-up period.

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

The Population Research Laboratory (PRL), a member of the Association of Academic Survey Research Organizations (AASRO), seeks to advance the research, education and service goals of the University of Alberta by helping academic researchers and policy makers design and implement applied social science research projects. The PRL specializes in the gathering, analysis, and presentation of data about demographic, social and public issues. The PRL research team provides expert consultation and implementation of quantitative and qualitative research methods, project design, sample design, web-based, paper-based and telephone surveys, field site testing, data analysis and report writing. The PRL follows scientifically rigorous and transparent methods in each phase of a research project. Research Coordinators are members of the American Association for Public Opinion Research (AAPOR) and use best practices when conducting all types of research. The PRL has particular expertise in conducting computer-assisted telephone interviews (referred to as CATI surveys). When conducting telephone surveys, all calls are displayed as being from the "U of A PRL", a procedure that assures recipients that the call is not from a telemarketer, and thus helps increase response rates. The PRL maintains a complement of highly skilled telephone interviewers and supervisors who are thoroughly trained in FOIPP requirements, respondent selection procedures, questionnaire instructions, and neutral probing. A subset of interviewers are specially trained to convince otherwise reluctant respondents to participate in the study, a practice that increases response rates and lowers selection bias. PRL staff monitors data collection on a daily basis to allow any necessary adjustments to the volume and timing of calls and respondent selection criteria. The Population Research Laboratory (PRL) administered the 2012 Alberta Survey B. This survey of households across the province of Alberta continues to enable academic researchers, government departments, and non-profit organizations to explore a wide range of topics in a structured research framework and environment. Sponsors' research questions are asked together with demographic questions in a telephone interview of Alberta households. This data consists of the information from 1207 Alberta residence, interviewed between June 5, 2012 and June 27, 2012. The amount of responses indicates that the response rate, as calculated percentages representing the number of people who participated in the survey divided by the number selected in the eligible sample, was 27.6% for survey B. The subject ares included in the 2012 Alberta Survey B includes socio-demographic and background variables such as: household composition, age, gender, marital status, highest level of education, household income, religion, ethnic background, place of birth, employment status, home ownership, political party support and perceptions of financial status. In addition, the topics of public health and injury control, tobacco reduction, activity limitations and personal directives, unions, politics and health.

The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: Chinese General Social Survey (CGSS), Japanese General Social Survey (JGSS), Korean General Social Survey (KGSS), Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Survey information in this module focuses on family dynamics and includes demographic variables such as the number of family members, the number of younger and older siblings, the number of sons and daughters, and whether family members are alive or deceased. Respondents were also queried about specific information pertaining to family members and children not co-residing with them, such as, sex and birth order, age, marital status, residence status, contact frequency, employment status, and relation to the respondent. Other information collected includes attitudes toward financial support from family members and how frequently financial and personal support was provided. Questions also include opinions regarding household chores, lifestyle preferences, health of respondent and parents, as well as family obligations. Quality of life questions addressed how satisfied respondents were as well as overall marital happiness. Demographic information specific to the respondent and their spouse includes age, sex, marital status, education, employment status and hours worked, occupation, earnings and income, religion, class, size of community, and region.

A shift in scientific publishing from paper-based to knowledge-based practices promotes reproducibility, machine actionability and knowledge discovery. This is important for disciplines like social science, as study indicators are often social constructs such as race or education; hypothesis tests are challenging to compare in demographic research due to their limited temporal and spatial coverage; and natural language in research papers is often imprecise and ambiguous. Therefore, we present the MIRA-KG, consisting of: (1) an ontology for capturing social demography research, which links hypotheses and findings to evidence, (2) annotations of papers on health inequality in terms of the ontology, gathered by (i) prompting a Large Language Model to annotate paper abstracts using the ontology, (ii) mapping concepts to terms from NCBO BioPortal ontologies and GeoNames, and (iii) refining the final graph by a set of SHACL constraints, developed according to data quality criteria. The utility of the resource lies in its use for formally representing social demography research hypotheses, discovering research biases, discovery of knowledge, and the derivation of novel questions.This dataset was generated using the code available on Github at https://w3id.org/mira/ at version v1.0. It uses the following ontology: https://w3id.org/mira/ontology/.

Abstract

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

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

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

Geographic coverage

Kabul Province Kabul Districts Kabul Villages

Analysis unit

Individuals, households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

Cleaning operations

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

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

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.

Abstract copyright UK Data Service and data collection copyright owner. In 2016 the Centre for Time Use Research developed an online Click and Drag Diary Instrument (CaDDI), collecting population-representative (quota sample) time use diary data from Dynata’s large international market research panel across 9 countries. We fielded the same instrument using the UK panel across the COVID-19 pandemic: in May-June 2020 during the first lockdown; in late August 2020 following the relaxation of social restrictions; in November 2020 during the second lockdown; in January 2021 during the third lockdown; and in August/September 2021 after the lifting of restrictions.Each survey wave collected between 1-3 time use diaries per respondent, recording activities, location, co-presence, device use, and enjoyment across continuous 10-minute episodes throughout the diary day. The accompanying individual screening questionnaire included information on the standard socio-demographic variables, and a diary day questionnaire containing additional health and diary day related questions was added during wave 2. Overall, 6896 diaries were collected across the 6 waves, allowing analysis of behavioural change between a baseline (in 2016), three national lockdowns, and two intervening periods of the relaxation of social restrictions.The deposited data forms part of wider CTUR projects of ESRC-funded time use research - New Frontiers for Time Use Research, and Time Use Research for National Statistics. Information on time spent in the various activities of daily life provides a comprehensive and exhaustive basis for summarising the activities of a society, yet people in general do not know with any accuracy how much time they devote to their daily activities. For this reason, rather than asking a set of survey questions, such as "how much time did you spend last week in X activity", the time use diary instead asks people to record, in sequence, all their activities through the 24-hour day, with their start and end times. Further information both on these projects and the COVID-19 sequence data collection can be found on the CTUR website.Latest edition informationFor the fourth edition (May 2022), the data and documentation files were replaced with updated versions. Amendments include the replacement of questionnaires with final versions; changes to variable ordering in the questionnaires, dataset and codebook; and updated information on the GHQ questions. See the Summary of Changes document for further details.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

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 shortfalls 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 1998 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 1998 GDHS 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 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 percent 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 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Note: See detailed tables in APPENDIX C of the survey report.

The Ontario Material Deprivation Survey (OMDS) was conducted on behalf of the Ontario Government. The OMDS was fielded in March and April 2009 as a supplement to the Labour Force Survey. The aim was to gauge the ability of families to satisfy basic material needs such as food, clothing, housing, as well as social needs of participation and leisure. The survey included questions about ten items deemed to be necessities, due to lack of resources, as well as questions on income. The list of ten items came from a 2008 study by the Daily Bread Food Bank of Toronto. The survey was designed to produce estimates of the incidence of missing two or more items (out of ten) for Ontario and various geographic subgroups. It was also designed to provide contextual data on demographic and socioeconomic characteristics of Ontario families and individuals.

This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation from 21 to 27 October 2022.

The survey includes face-to-face interviews with 2,018 respondents aged 18 years and older. The survey was conducted in 25 out of Ukraine’s 26 regions (not including Crimea). The theoretical error in sampling does not exceed 2.3%. At the same time, additional systematic deviations in sampling may be due to the consequences of Russian aggression, in particular the forced evacuation of millions of citizens.

This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire is also included in this data collection as separate pdf-file. Additionally, the data collection contains the file “DIF_10.2022-selected-results.pdf" which documents some major results of the survey in the form of descriptive statistics in English.

The major thematic blocs refer to language usage (questions numbered with X) and ecology/ environmental issues and policies (questions numbered with Q). Additionally, there are questions about the socio-demographic status of the respondents (questions numbered with D). Questions X13-15, X17 as well as Q24-25 directly refer to the Russian war of aggression against Ukraine. Please note that some questions about socio-demographic status (questions numbered with D) also include effects of the war (i.e. place of residence before the war) and about language use (language spoken at home).

Data on ecology/ environmental issues could be included in this data collection thanks to permission by the International Charity Organization “Environment-People-Law” (EPL) (Lviv).

Full edition for scientific use. Since 1983 labour force surveys (LFS) are conducted annually in all European Union (EU) member states. The LFS serve as a basis for internationally compatible (in terms of definition and survey method) data on employment and unemployment for the European Commission. In Austria the LFS is conducted in full annually. The chosen month therefore is March because in this month the Microcensus-quarterly-survey which is most suitable in terms of scheduling for the LFS is performed. Central questions for the assessment of the number of employed and unemployed persons (and as a result for the calculation of the unemployment rate according to international standards) are in addition (since 1994) asked quarterly in the Microcensus standard survey. The survey conducted in March always relates to the week before the interview and includes the whole population, which means everybody who has their main residence in Austria. Data for persons not found have to be added via a substitution method so that results for the whole population can be provided. In Austria (as well as in several other states) the LFS is only conducted among the population in private households; people who live in institutions (retirement homes, boarding homes, and the like) are not included in the survey (the Microcensus special surveys are not conducted in institutional households due to organisational problems and problems with performing the surveys there). These are topics of the LFS: -> immigrants with and without the Austrian citizenship (4 questions) -> features of the first job (21 questions) -> statements on part-time jobs (6 questions) -> previous employments of unemployed persons (7 questions) -> job-seeking (13 questions) -> situation of unemployed persons (3 questions) -> school and professional education (9 questions) -> situation one year previous to the survey (7 questions) Furthermore, there are questions on the demographic background, providing information, evidence and the like. In the Microcensus, the annual special LFS survey contains 70 questions in addition to the questions of the standard survey which is concerned with standard LFS topics. The questions have remained more or less the same over the years. The only questions that have been changed slightly were those on education. Missing information is substituted with information from persons with similar socio-demographic variables (imputation), so that there are no unknown cases.