Descriptive Statistics of Socio-demographic variables (n = 25,501).

The study assessed the knowledge, attitude and practice of undergraduate students regarding family planning methods. A descriptive quantitative design was used. The population was undergraduate students, and the sample size was four hundred (400) students. The study was conducted in a selected institution of higher learning in the Tshwane district of the Gauteng Province. A questionnaire was used to collect data, and descriptive statistics analysis was used to analyze the data. The data collected were entered into Microsoft Office 2019. The IBM SPSS Statistics version 28 was used to perform the analysis. Test for associations the Pearson Chi-square test was performed.

Note: Scale ranges are: SIAS = 0 (least anxious) to 80; STAI-T = 20 (least anxious) to 80; CFMT-total and CCMT = 24 (chance) to 72 (100% correct); CFMT-novel = 10 (chance) to 30 (100% correct).

Dataset of the demographic analysis conducted by Econsult Solutions, Inc. (ESI) for DVRPC's study, "Development Matters: Understanding the Opportunities and Impacts of Multifamily Development" published in September 2020, along with methodology. For more information about the study, visit the Community Impacts of Multifamily Development webpage.

Descriptive statistics for key demographic variables in winners (n = 28) and losers (n = 27).

The Nonemployer Statistics by Demographics (NES-D): Company Summary estimates provide economic data classified by sex, ethnicity, race, and veteran status of nonemployer firms. The NES-D is not a survey; rather, it leverages existing administrative records to assign demographic characteristics to the universe of nonemployer businesses. The nonemployer universe is comprised of businesses with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries), and filing IRS tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series). Data for all firms are also presented. These estimates are produced by combining estimates for nonemployer firms from the Nonemployer Statistics by Demographics (NESD) and employer firms from the Annual Business Survey (ABS).

The dataset is a .csv file consisting of 10 columns: "subject" is the number assigned to each of the adolescents in the study, 1,...,55; "lecture_type" is either cc (compare-contrast), ce (cause-effect), or n (narrative), and each of the 55 subjects have a row for each lecture type; "development_type" is collected at the subject level, and is either TD (typically developing) or TBI (traumatic brain injury); "sex," (Male/Female) "age," (13-19) and "ses" (a summary of socioeconomic status; a standardized "z-value") are also collected at the subject level; "U" (>=1) is the total number of utterances in the discourse; "C" (>=U) is the total number of clauses in the discourse; "W" (>=C) is the total number of words in the discourse; and "D" (<=W) is the total number of distinct words in the discourse.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Sex for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01A.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Sex Female Male Equally male-owned and female-owned Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. ...

Background: Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

Methods: A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pretested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles' households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Results: Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x2 = 20.392, df=3), educational status(x2 = 19.358, df=4), source of income (x2 = 21.777, df=3), monthly income (x2 = 13.322, df=3), availability of additional facilities (x2 = 98.144, df=7), cleanness status (x2 =42.979, df=4), scarcity of water (x2 = 5.1388, df=1) and family size (x2 = 9.934, df=2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

Conclusion: The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore, ; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

Contains resident demographic data at a summary level. The Resident Data Book is compiled to serve as an information source for queries involving resident demographic as well as a source of data for internal analysis. Statistics are compiled via HUD mandated annual income reviews involving NYCHA Staff and residents. Data is then aggregated and compiled by development. Each record pertains to a single public housing development.

Descriptive statistics for demographic information.

Sex and Age of the Household Head, Summary Statistics of Demographic Characteristics, 1995/96 – 2010/11, Nepal Living Standard Survey 2011.

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.

Prior research documents strong associations between health limitations and well-being outcomes. However, less is known about how levels of health limitations differ across cultures and across demographic groups within those different cultures. This study presents an in-depth, cross-national exploration of self-rated mental health across cultures, and its variations across key demographic groups. Using a diverse and international dataset of approximately 200,000 individuals from 22 countries, we will examine relationships between health limitations and key demographics, including: age, gender, marital status, employment status, religious service attendance, education, and immigration status. Our descriptive results will also present the ordered means of health limitations in life across countries. We will be mindful of potential interpretation challenges due to varying cultural contexts and response scales used. Our work will illuminate the distributions and descriptive statistics of health limitations across demographic features, offer insight into country-specific variations in health limitations, and lay a valuable foundation for future investigations into sociocultural influences that might shape health limitations.

The demographic indicators of the People’s Republic of China, Hong Kong, Macao, and Taiwan were compiled from (1) the World Bank United Nations (UN) Population Division, World Population Prospects: 2022 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) UN Statistical Division. Population and Vital Statistics Report (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Program. The dataset consists of descriptive demographic statistics of the People’s Republic of China, Hong Kong, Macao, and Taiwan and includes the following indicators: (1) total population, (2) population by broad age groups, (3) annual rate of population change, (4) crude birth rate and crude death rate, (5) annual number of births and deaths, (6) total fertility, (7) mortality under age 5, (8) life expectancy at birth by sex, (9) life expectancy at birth (both sexes combined), (10) annual natural change and net migration, (11) population by age and sex: 2101, (12) annual number of deaths per 1,000 population, and (13) annual number of deaths.

Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors which include hyperinsulinaemia, impaired glucose tolerance, hypertension, central obesity, and dyslipidaemia. MetS could lead to a greater risk of developing type 2 diabetes, non-alcoholic fatty liver disease, and vascular conditions including coronary artery disease, peripheral vascular diseases and stroke. Worldwide, prevalence of MetS is around 20% to 60% in adult population. Increasing the risks of MetS means rising the health burden and health care costs by higher morbidity of non-communicable diseases (NCDs). People with unhealthy lifestyle are vulnerable to developing MetS and its complication of NCDs. Early identification of MetS and prevention of risk factors are important for controlling NCDs in elder population as Myanmar also facing an increasing number of senior citizens. Despite some evidences could be available regarding prevalence of MetS in Myanmar children and adolescents, there is a scarcity of research studies in adult population. Therefore, this study aims to identify the prevalence and risk factors of MetS in adult population in the community. After achieving the approval from Ethics and Research Committee of University of Nursing (Yangon), a public-based cross sectional study will be performed to fulfill the objectives of the study. A township from Yangon Region will be chosen based on the morbidity and mortality of NCDs for this study. Components of MetS such as blood pressure, waist circumference, lipid profiles, fasting blood glucose, height and weight of 400 participants will be measured by home visitings. Trained data collectors and health professionals will be used for standardized data collection procedure to abstain from inter-observer bias. Confidentiality of the participants’ data will be maintained throughout the study. Lifestyle behaviors will be assessed by structured questionnaire which include information about smoking, alcohol drinking, betel chewing, physical activity, exercise status, and sleeping and eating pattern. Information about demographic, socioeconomic and lifestyle factors will be presented by descriptive statistics and association of MetS with lifestyle and anthropometric measurements will be illustrated by inferential statistics. The output of the study will be presented to corresponding health officials to recognize the status of upcoming public health challenges. Full study output will be reported to Department of Medical Research (Yangon) and publishing on international health journal will be performed to be accessible for world wide readers. Academic presentation at national or international conference will be conducted to discuss the the findings of this study and to share the existing knowledge with local and global experts.

Analysis of ‘NYCHA Resident Data Book Summary’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9841c0c7-a1db-4d72-89e1-8fde4f7491d1 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Contains resident demographic data at a summary level as of January 1, 2019. The Resident Data Book is compiled to serve as an information source for queries involving resident demographic as well as a source of data for internal analysis. Statistics are compiled via HUD mandated annual income reviews involving NYCHA Staff and residents. Data is then aggregated and compiled by development. Each record pertains to a single public housing development.

--- Original source retains full ownership of the source dataset ---

Descriptive statistics for sample demographic variables.

Replication file for Learning Together: Experimental Evidence on Promoting Connections in Remote Classes ** Notes ** Set the working directory to the folder where ReadMe.txt is located. Datasets * REPLICATION_Fall2020_Dataset.dta * File containing student responses and demographic information for Fall 2020 pre and post survey * REPLICATION_Spring_2021.dta * File containing student responses and demographic information for Spring 2021 pre and post survey * CUBoulder_Campus_Survey.dta * File containing student responses and demographic information for a campus wide survey conducted in the spring of 2021 ** .do files ** * REPLICATION_DescriptiveStats.do * File with code for descriptive statistics for Fall 2020 and Spring 2021 and code for Fall 2020 regression model * REPLICATION_CampusSurvey.do * File with code for descriptive statistics for CU Boulder campus wide survey (non-representative)

DemoEnPoC2016.csv/DemoEnPoC2006.csv:

This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ).

DGRwithLable:

This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer.

Aggregation and SS Determination:

This script contains code for:

• Aggregating postal code level data to the Dissemination Area (DA) level.
• Eliminating rural DAs.
• Converting environmental data into ordinal categories using quartile and even break methods.
• Identifying sweet and sour spots for each DA based on these methods.

SSEJ Analysis:

This script includes code for:

• Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).
• Performing linear regression analyses between environmental categories and demographic variables.

SS Heatmap:

This script comprises code for:

• Summarizing the results of the linear regression analyses.
• Assessing changes in inequities among demographic groups between 2006 and 2016.
• Visualizing regression coefficients through heatmaps.