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.

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 6^th to March 3^rd 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 N^o 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 1991 Indonesia Demographic and Health Survey (IDHS) is a nationally representative survey of ever-married women age 15-49. It was conducted between May and July 1991. The survey was designed to provide information on levels and trends of fertility, infant and child mortality, family planning and maternal and child health. The IDHS was carried out as collaboration between the Central Bureau of Statistics, the National Family Planning Coordinating Board, and the Ministry of Health. The IDHS is follow-on to the National Indonesia Contraceptive Prevalence Survey conducted in 1987.

The DHS program has four general objectives: - To provide participating countries with data and analysis useful for informed policy choices; - To expand the international population and health database; - To advance survey methodology; and - To help develop in participating countries the technical skills and resources necessary to conduct demographic and health surveys.

In 1987 the National Indonesia Contraceptive Prevalence Survey (NICPS) was conducted in 20 of the 27 provinces in Indonesia, as part of Phase I of the DHS program. This survey did not include questions related to health since the Central Bureau of Statistics (CBS) had collected that information in the 1987 National Socioeconomic Household Survey (SUSENAS). The 1991 Indonesia Demographic and Health Survey (IDHS) was conducted in all 27 provinces of Indonesia as part of Phase II of the DHS program. The IDHS received financial assistance from several sources.

The 1991 IDHS was specifically designed to meet the following objectives: - To provide data concerning fertility, family planning, and maternal and child health that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs; - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception; - To measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (BKKBN) has divided these provinces into three regions as follows:

• Java-Bali: Jakarta, West Java, Central Java, Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Kalimantan, South Kalimantan, North Sulawesi, South Sulawesi, and West Nusa Tenggara
• Outer Java-Bali II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timor, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya.

The 1990 Population Census of Indonesia shows that Java-Bali contains about 62 percent of the national population, while Outer Java-Bali I contains 27 percent and Outer Java-Bali II contains 11 percent. The sample for the Indonesia DHS survey was designed to produce reliable estimates of contraceptive prevalence and several other major survey variables for each of the 27 provinces and for urban and rural areas of the three regions.

In order to accomplish this goal, approximately 1500 to 2000 households were selected in each of the provinces in Java-Bali, 1000 households in each of the ten provinces in Outer Java-Bali I, and 500 households in each of the 11 provinces in Outer Java-Bali II for a total of 28,000 households. With an average of 0.8 eligible women (ever-married women age 15-49) per selected household, the 28,000 households were expected to yield approximately 23,000 individual interviews.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The DHS model "A" questionnaire and manuals were modified to meet the requirements of measuring family planning and health program attainment, and were translated into Bahasa Indonesia.

Cleaning operations

The first stage of data editing was done by the field editors who checked the completed questionnaires for completeness and accuracy. Field supervisors also checked the questionnaires. They were then sent to the central office in Jakarta where they were edited again and open-ended questions were coded. The data were processed using 11 microcomputers and ISSA (Integrated System for Survey Analysis).

Data entry and editing were initiated almost immediately after the beginning of fieldwork. Simple range and skip errors were corrected at the data entry stage. Secondary machine editing of the data was initiated as soon as sufficient questionnaires had been entered. The objective of the secondary editing was to detect and correct, if possible, inconsistencies in the data. All of the data were entered and edited by September 1991. A brief report containing preliminary survey results was published in November 1991.

Response rate

Of 28,141 households sampled, 27,109 were eligible to be interviewed (excluding those that were absent, vacant, or destroyed), and of these, 26,858 or 99 percent of eligible households were successfully interviewed. In the interviewed households, 23,470 eligible women were found and complete interviews were obtained with 98 percent of these women.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate analytically.

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of standard error of 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 one can reasonably be assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the IDHS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - 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.

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.

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

BackgroundSocial gatherings are frequent sources of COVID-19 infections, especially among youth. However, little is known about youth testing behaviors before and after gatherings. Our aim was to assess behaviors and perceptions of youth related to testing for COVID-19 before or after social gatherings in order to inform efforts to reduce disease spread.MethodsFive open-ended questions were texted to participants aged 14–24 throughout the United States via MyVoice. Using a content analysis approach, two investigators reviewed responses by question, developed a codebook, and independently applied codes. Discrepancies were resolved via discussion. Code frequency and demographic data were summarized using descriptive statistics.ResultsOf 1204 participants, 989 responded to at least one question (RR = 94.1%). The mean age was 20.2 years (SD: 2.4 years). Most participants (80.7%) reported testing for COVID-19 at least once. Most (70.6%) were likely to test following an event, especially “[i]f someone at the gathering tested positive,” while a smaller number (50.9%) endorsed testing prior to a gathering. Of youth who would not get tested, being vaccinated was the highest reported.ConclusionYouth in our nationwide sample are likely to test for COVID-19 after an event, though less likely if they are vaccinated. Their desire to test is primarily driven by symptoms, exposures, and requirements. Youth are interested in increased access to home testing. Youth-centered communications regarding testing recommendations and increased test availability for youth may reduce COVID-19 spread among young people and inform future pandemic recommendations.

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

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.

About this dataset

Context

The dataset tabulates the population of Weston by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Weston across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.46% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Weston is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Weston total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Race & Ethnicity. You can refer the same here

This survey-based study examines health science scholars’ perceptions of collaborative research behavior and sharing open research data in university settings. A total of 362 health science scholars from U.S. universities participated in an online questionnaire consisting of 59 questions. Descriptive and inferential statistical analyses of the data included frequencies, cross-tabulations, descriptive ratio statistics, and the non-parametric Kruskal-Wallis one-way analysis of variance. Four open-ended questions were also analyzed to provide further insights into the survey findings. The study reveals that health scholars share their data with colleagues within their institution or project, demonstrating a lesser inclination toward open research data sharing practices through institutional repositories and journal supplements. Motivating factors and challenges influencing researchers’ decisions to share their research data were also identified. While scientific and knowledge advancement served as major incentives, health scholars working with human-related data expressed concerns about privacy and confidentiality breaches, which are primary barriers to data sharing. Some participants indicated that requirements and policies also influenced their willingness to share data. Disciplinary variations were observed regarding data-sharing practices through journal supplements, secondary data analysis, and personal communication. Furthermore, significant differences emerged between funded and non-funded scholars, impacting their practices, motivations, and challenges in sharing open research data. Important factors driving health science scholars to share open research data include resources, policy compliance, and requirements. This study contributes valuable insights for policy development by investigating factors that can foster openness and sharing of research data in the health sciences. The findings shed light on the complexities and considerations associated with open data-sharing practices, enabling stakeholders to develop effective strategies and frameworks.

Abstract

The primary objective of the 2012 Indonesia Demographic and Health Survey (IDHS) is to provide policymakers and program managers with national- and provincial-level data on representative samples of all women age 15-49 and currently-married men age 15-54.

The 2012 IDHS was specifically designed to meet the following objectives: • Provide data on fertility, family planning, maternal and child health, adult mortality (including maternal mortality), and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs; • Measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception; • Evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; • Assess married men’s knowledge of utilization of health services for their family’s health, as well as participation in the health care of their families; • Participate in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the areas of family planning, fertility, and health in general

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Ever married men age 15-54
• Never married men age 15-24

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 33 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts, and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The 2012 IDHS sample is aimed at providing reliable estimates of key characteristics for women age 15-49 and currently-married men age 15-54 in Indonesia as a whole, in urban and rural areas, and in each of the 33 provinces included in the survey. To achieve this objective, a total of 1,840 census blocks (CBs)-874 in urban areas and 966 in rural areas-were selected from the list of CBs in the selected primary sampling units formed during the 2010 population census.

Because the sample was designed to provide reliable indicators for each province, the number of CBs in each province was not allocated in proportion to the population of the province or its urban-rural classification. Therefore, a final weighing adjustment procedure was done to obtain estimates for all domains. A minimum of 43 CBs per province was imposed in the 2012 IDHS design.

Refer to Appendix B in the final report for details of sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 IDHS used four questionnaires: the Household Questionnaire, the Woman’s Questionnaire, the Currently Married Man’s Questionnaire, and the Never-Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49 in the 2012 IDHS, the Woman’s Questionnaire now has questions for never-married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey questionnaire.

The Household and Woman’s Questionnaires are largely based on standard DHS phase VI questionnaires (March 2011 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were adopted in the IDHS. In addition, the response categories were modified to reflect the local situation.

The Household Questionnaire was used to list all the usual members and visitors who spent the previous night in the selected households. Basic information collected on each person listed includes age, sex, education, marital status, education, and relationship to the head of the household. Information on characteristics of the housing unit, such as the source of drinking water, type of toilet facilities, construction materials used for the floor, roof, and outer walls of the house, and ownership of various durable goods were also recorded in the Household Questionnaire. These items reflect the household’s socioeconomic status and are used to calculate the household wealth index. The main purpose of the Household Questionnaire was to identify women and men who were eligible for an individual interview.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: • Background characteristics (marital status, education, media exposure, etc.) • Reproductive history and fertility preferences • Knowledge and use of family planning methods • Antenatal, delivery, and postnatal care • Breastfeeding and infant and young children feeding practices • Childhood mortality • Vaccinations and childhood illnesses • Marriage and sexual activity • Fertility preferences • Woman’s work and husband’s background characteristics • Awareness and behavior regarding HIV-AIDS and other sexually transmitted infections (STIs) • Sibling mortality, including maternal mortality • Other health issues

Questions asked to never-married women age 15-24 addressed the following: • Additional background characteristics • Knowledge of the human reproduction system • Attitudes toward marriage and children • Role of family, school, the community, and exposure to mass media • Use of tobacco, alcohol, and drugs • Dating and sexual activity

The Man’s Questionnaire was administered to all currently married men age 15-54 living in every third household in the 2012 IDHS sample. This questionnaire includes much of the same information included in the Woman’s Questionnaire, but is shorter because it did not contain questions on reproductive history or maternal and child health. Instead, men were asked about their knowledge of and participation in health-careseeking practices for their children.

The questionnaire for never-married men age 15-24 includes the same questions asked to nevermarried women age 15-24.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computeridentified errors. Data processing activities were carried out by a team of 58 data entry operators, 42 data editors, 14 secondary data editors, and 14 data entry supervisors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2012 IDHS.

Response rate

The response rates for both the household and individual interviews in the 2012 IDHS are high. A total of 46,024 households were selected in the sample, of which 44,302 were occupied. Of these households, 43,852 were successfully interviewed, yielding a household response rate of 99 percent.

Refer to Table 1.2 in the final report for more detailed summarized results of the of the 2012 IDHS fieldwork for both the household and individual interviews, by urban-rural residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Indonesia Demographic and Health Survey (2012 IDHS) 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 2012 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is 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 2012 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2012 IDHS is a SAS program. This program used the Taylor linearization method

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.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

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 2018 Zambia Demographic and Health Survey (ZDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 ZDHS 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 2018 ZDHS 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 linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey 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 - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

About this dataset

Context

The dataset tabulates the population of Powers by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Powers. The dataset can be utilized to understand the population distribution of Powers by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Powers. 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 Powers.

Key observations

Largest age group (population): Male # 85+ years (40) | Female # 35-39 years (67). 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 Powers population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Powers is shown in the following column.
• Population (Female): The female population in the Powers 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 Powers 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 Powers Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Marked Tree by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Marked Tree. The dataset can be utilized to understand the population distribution of Marked Tree by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Marked Tree. 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 Marked Tree.

Key observations

Largest age group (population): Male # 55-59 years (146) | Female # 25-29 years (136). 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 Marked Tree population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Marked Tree is shown in the following column.
• Population (Female): The female population in the Marked Tree 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 Marked Tree 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 Marked Tree Population by Gender. You can refer the same here

Abstract

The IDHS is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health.

The main objective of 2007 IDHS was to provide detailed information on population, family planning, and health for policymakers and program managers. The 2007 IDHS was conducted in all 33 provinces in Indonesia. The survey collected information on respondents’ socioeconomic background, fertility levels, marriage and sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, childhood and adult mortality including maternal mortality, maternal and child health, and awareness and behavior regarding HIV/AIDS and other sexually-transmitted infections.

The 2007 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality, and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs; - Measure trends in fertility and contraceptive prevalence rates, analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception.; - Evaluate achievement of goals previously set by the national health programs, with special focus on maternal and child health; - Assess men’s participation and utilization of health services, as well as of their families; - Assist in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the area of family planning, fertility, and health in general.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

Administratively, Indonesia is divided into 33 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The 2007 IDHS sample is designed to provide estimates with acceptable precision for the following domains: - Indonesia as a whole; - Each of 33 provinces covered in the survey, and - Urban and rural areas of Indonesia

The census blocks (CBs) are the primary sampling unit for the 2007 IDHS. The sample developed for the 2007 National Labor Force Survey (Sakernas) was used as a frame for the selection of the 2007 IDHS sample. Household listing was done in all CBs covered in the 2007 Sakernas. This eliminates the need to conduct a separate household listing for the 2007 IDHS.

A minimum of 40 CBs per province has been imposed in the 2007 IDHS design. Since the sample was designed to provide reliable indicators for each province, the number of CBs in each province was not allocated proportional to the population of the province nor proportional by urban-rural classification. Therefore, a final weighing adjustment procedure was done to obtain estimates for all domains.

The 2007 IDHS sample is selected using a stratified two-stage design consisting of 1,694 CBs. Once the number of households was allocated to each province by urban and rural areas, the number of CBs was calculated based on an average sample take of 25 selected households. All evermarried women age 15-49 and all unmarried persons age 15-24 in these households are eligible for individual interview. Eight households in each CB selected for the women sample were selected for male interview.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2007 IDHS used three questionnaires: the Household Questionnaire (HQ), the Ever-Married Women’s Questionnaire (EMWQ) and the Married Men’s Questionnaire (MMQ). In consultation with BKKBN and MOH, BPS made a decision to base the 2007 IDHS survey instruments largely on the questionnaires used in the 2002-03 IDHS to facilitate trend analysis. Input was solicited from other potential data users, and several modifications were made to optimize the draft 2007 IDHS instruments to collect the needs for population and health data. The draft IDHS questionnaires were also compared with the most recent version of the standard questionnaires used in the DHS program and minor modifications incorporated to facilitate international comparison.

The HQ was used to list all the usual members and visitors in the selected households. Basic information collected on each person listed includes: age, sex, education, and relationship to the head of the household. The main purpose of the HQ was to identify women and men who were eligible for the individual interview. Information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, construction materials used for the floor and outer walls of the house, and ownership of various durable goods were also recorded in the HQ. These items reflect the household’s socioeconomic status.

The EMWQ was used to collect information from all ever-married women age 15-49. These women were asked questions on the following topics:: - Background characteristics (marital status, education, media exposure, etc.) - Knowledge and use of family planning methods - Reproductive history and fertility preferences - Antenatal, delivery and postnatal care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Practices related to the malaria prevention - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Infant’s and children’s feeding practices - Childhood mortality - Awareness and behavior regarding AIDS and other sexually transmitted infections (STIs) - Sibling mortality, including maternal mortality.

The MMQ was administered to all currently married men age 15-54 living in every third household in the IDHS sample. The MMQ collected much of the same information included in the EMWQ, but was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition and maternal mortality. Instead, men were asked about their knowledge and participation in health-care-seeking practices for their children.

Cleaning operations

All completed questionnaires for the IDHS, accompanied by their control forms, were returned to the BPS central office in Jakarta for data processing. This consisted of office editing, coding of openended questions, data entry, verification, and editing computer-identified errors. A team of 42 data entry clerks, data editors and data entry supervisors processed the data. Data entry and editing was carried using a computer package program called CSPro, which was specifically designed to process DHS-type survey data. During the preparation of the data entry programs, a BPS staff spent several weeks at ORC Macro offices in Calverton, Maryland. Data entry and editing activities, which began in September, 2007 were completed in March 2008.

Response rate

In general, the response rates for both the household and individual interviews in the 2007 IDHS are high. A total of 42,341 households were selected in the sample, of which 41,131 were occupied. Of these households, 40,701 were successfully interviewed, yielding a household response rate of 99 percent.

In the interviewed households, 34,227 women were identified for individual interview and of these completed interviews were conducted with 32,895 women, yielding a response rate of 96 percent. In a third of the households, 9,716 eligible men were identified, of which 8,758 were successfully interviewed, yielding a response rate of 90 percent. The lower response rate for men was due to the more frequent and longer absence of men from the household.

Note: See summarized response rates by place of residence in Table 1.2 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 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 2007 Indonesia Demographic and Health Survey (IDHS) 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 2007 IDHS 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.

Public parks and other green areas are crucial components of urban development. Urban management in emerging countries such as Bangladesh faces major challenges, especially because of the socio-environmental impacts of urbanization. Urban management initiatives in developing countries sometimes neglect crucial services for university students, such as study environments and recreational facilities. This study aimed to investigate students’ perceptions of urban green space (UGS) and its potential benefits to our daily lives at Shahjalal University of Science and Technology (SUST) in Sylhet, Bangladesh, We collected data from 438 respondents by using a survey questionnaire-based stated preference approach as a methodological tool, using non-monetary assertions as the basis for the method. The survey included questions about respondents’ social background, the frequency with which they visited green spaces, the benefits of urban green spaces and their perception of urban green spaces. We performed statistical analysis both descriptive and inferential statistics. Our findings suggest that 71.7% of students primarily use street trees and peace gardens as their main sites for urban green spaces. The study suggests that the advantages of urban green spaces, which include physical, mental, and environmental benefits, are strongly associated with criteria such as gender, academic level, and vulnerable to home locality for climate change (CC) or extreme weather events (EWEs). The Binary Logistic regression analysis identified urban life as the most influential factor. The correct classification rate was approximately 74.7%, indicating the model’s strong accuracy in classification. Students who have lived in urban areas for more than 20 years have a reduced awareness of urban green spaces at 5% level of significance. Planning and policymaking for the creation and administration of urban green spaces, considering aspects like land use and environmental sustainability, could benefit from this study.

About this dataset

Context

The dataset tabulates the population of New Site by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Site. The dataset can be utilized to understand the population distribution of New Site by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Site. 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 New Site.

Key observations

Largest age group (population): Male # 75-79 years (64) | Female # 30-34 years (35). 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 New Site population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Site is shown in the following column.
• Population (Female): The female population in the New Site 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 New Site 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 New Site Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Midland County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Midland County. The dataset can be utilized to understand the population distribution of Midland County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Midland County. 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 Midland County.

Key observations

Largest age group (population): Male # 60-64 years (3,125) | Female # 55-59 years (3,086). 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 Midland County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Midland County is shown in the following column.
• Population (Female): The female population in the Midland County 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 Midland County 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 Midland County Population by Gender. You can refer the same here