This layer shows the predominant level of insurance coverage for non-citizens in the USA. This is shown by county centroids. The data values are from the 2012-2016 American Community Survey 5-year estimate in the B27020 Table for health insurance coverage status and type by citizenship status. This map helps to answer a few questions:Do non-citizens have health insurance?Where are the non-citizens in the US?The color of the symbols represent the most common form of insurance held by foreign born non-citizens in the USA. This predominance map style compares the count of people who are insured or not insured, and returns the value with the highest count.Foreign born non-citizen without insuranceForeign born non-citizen with insuranceThe size of the symbol represents the count of all non-citizens in the area, which shows in the legend as "sum of categories". The strength of the color represents HOW predominant the form of insurance is for non-citizens. The stronger the symbol, the larger proportion of the non-citizens.This map is designed for a dark basemap such as the Human Geography Basemap or the Dark Gray Canvas Basemap. It helps show a regional pattern about the uninsured and insured non-citizen population. This data was downloaded from the United States Census Bureau American Fact Finder on March 1, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas. The data contains additional attributes that can be used for mapping and analysis. Nationally, the breakdown of insurance for the civilian noninstitutionalized population in the US is:Total:313,576,137+/-10,365Native Born:271,739,505+/-102,340With health insurance coverage246,142,724+/-281,131With private health insurance186,765,058+/-576,448With public coverage92,452,853+/-209,370No health insurance coverage25,596,781+/-190,502Foreign Born:41,836,632+/-109,590Naturalized:19,819,629+/-35,976With health insurance coverage17,489,342+/-42,261With private health insurance12,927,060+/-50,505With public coverage6,687,375+/-16,733No health insurance coverage2,330,287+/-20,148Noncitizen:22,017,003+/-118,842With health insurance coverage13,243,825+/-44,108With private health insurance9,320,483+/-26,031With public coverage4,459,972+/-34,270No health insurance coverage8,773,178+/-86,951Data note from the US Census Bureau:[ACS] data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

Abstract

The 2023 Jordan Population and Family Health Survey (JPFHS) is the eighth Population and Family Health Survey conducted in Jordan, following those conducted in 1990, 1997, 2002, 2007, 2009, 2012, and 2017–18. It was implemented by the Department of Statistics (DoS) at the request of the Ministry of Health (MoH).

The primary objective of the 2023 JPFHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the 2023 JPFHS: • Collected data at the national level that allowed calculation of key demographic indicators • Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality • Measured contraceptive knowledge and practice • Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and women age 15–49 • Conducted haemoglobin testing with eligible children age 6–59 months and women age 15–49 to gather information on the prevalence of anaemia • Collected data on women’s and men’s knowledge and attitudes regarding sexually transmitted infections and HIV/AIDS • Obtained data on women’s experience of emotional, physical, and sexual violence • Gathered data on disability among household members

The information collected through the 2023 JPFHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Jordan.

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2023 JPFHS was the 2015 Jordan Population and Housing Census (JPHC) frame. The survey was designed to produce representative results for the country as a whole, for urban and rural areas separately, for each of the country’s 12 governorates, and for four nationality domains: the Jordanian population, the Syrian population living in refugee camps, the Syrian population living outside of camps, and the population of other nationalities. Each of the 12 governorates is subdivided into districts, each district into subdistricts, each subdistrict into localities, and each locality into areas and subareas. In addition to these administrative units, during the 2015 JPHC each subarea was divided into convenient area units called census blocks. An electronic file of a complete list of all of the census blocks is available from DoS. The list contains census information on households, populations, geographical locations, and socioeconomic characteristics of each block. Based on this list, census blocks were regrouped to form a general statistical unit of moderate size, called a cluster, which is widely used in various surveys as the primary sampling unit (PSU). The sample clusters for the 2023 JPFHS were selected from the frame of cluster units provided by the DoS.

The sample for the 2023 JPFHS was a stratified sample selected in two stages from the 2015 census frame. Stratification was achieved by separating each governorate into urban and rural areas. In addition, the Syrian refugee camps in Zarqa and Mafraq each formed a special sampling stratum. In total, 26 sampling strata were constructed. Samples were selected independently in each sampling stratum, through a twostage selection process, according to the sample allocation. Before the sample selection, the sampling frame was sorted by district and subdistrict within each sampling stratum. By using a probability proportional to size selection at the first stage of sampling, an implicit stratification and proportional allocation were achieved at each of the lower administrative levels.

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2023 JPFHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Man’s Questionnaire, (4) the Biomarker Questionnaire, and (5) the Fieldworker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Jordan. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After all questionnaires were finalised in English, they were translated into Arabic.

Cleaning operations

All electronic data files for the 2023 JPFHS were transferred via SynCloud to the DoS central office in Amman, 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. Data editing was accomplished using CSPro software. During the duration of fieldwork, 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 and completed in September 2023.

Response rate

A total of 20,054 households were selected for the sample, of which 19,809 were occupied. Of the occupied households, 19,475 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 13,020 eligible women age 15–49 were identified for individual interviews; interviews were completed with 12,595 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 6,506 men age 15–59 were identified as eligible for individual interviews and 5,873 were successfully interviewed, yielding a response rate of 90%.

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 in 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 2023 Jordan Population and Family Health Survey (2023 JPFHS) 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 2023 JPFHS is only one of many samples that could have been selected from the same population, using the same design and sample 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 by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2023 JPFHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS 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.

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
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardization exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women
• Heaping in

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - 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 and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and 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, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

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 computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. 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 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

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 result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Indonesia Demographic and Health Survey (2017 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 2017 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 among 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 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for 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.

A more detailed description of estimates of sampling errors are presented in Appendix C of the survey final report.

Data appraisal

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

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

Abstract

The 2005 Republic of Palau Census of Population and Housing will be used to give a snapshot of Republic of Palau's population and housing at the mid-point of the decade. This Census is also important because it measures the population at the beginning of the implementation of the Compact of Free Association. The information collected in the census is needed to plan for the needs of the population. The government uses the census figures to allocate funds for public services in a wide variety of areas, such as education, housing, and job training. The figures also are used by private businesses, academic institutions, local organizations, and the public in general to understand who we are and what our situation is, in order to prepare better for our future needs.

The fundamental purpose of a census is to provide information on the size, distribution and characteristics of a country's population. The census data are used for policymaking, planning and administration, as well as in management and evaluation of programmes in education, labour force, family planning, housing, health, transportation and rural development. A basic administrative use is in the demarcation of constituencies and allocation of representation to governing bodies. The census is also an invaluable resource for research, providing data for scientific analysis of the composition and distribution of the population and for statistical models to forecast its future growth. The census provides business and industry with the basic data they need to appraise the demand for housing, schools, furnishings, food, clothing, recreational facilities, medical supplies and other goods and services.

Geographic coverage

A hierarchical geographic presentation shows the geographic entities in a superior/subordinate structure in census products. This structure is derived from the legal, administrative, or areal relationships of the entities. The hierarchical structure is depicted in report tables by means of indentation. The following structure is used for the 2005 Census of the Republic of Palau:

Republic of Palau State Hamlet/Village Enumeration District Block

Analysis unit

Individuals Families Households General Population

Universe

The Census covered all the households and respective residents in the entire country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2005 Palau Census of Population and Housing comprises three parts: 1. Housing - one form for each household 2. Population - one for for each member of the household 3. People who have left home - one form for each household.

Cleaning operations

Full scale processing and editing activiities comprised eight separate sessions either with or separately but with remote guidance of the U.S. Census Bureau experts to finalize all datasets for publishing stage.

Processing operation was handled with care to produce a set of data that describes the population as clearly and accurately as possible. To meet this objective, questionnaires were reviewed and edited during field data collection operations by crew leaders for consistency, completeness, and acceptability. Questionnaires were also reviewed by census clerks in the census office for omissions, certain inconsistencies, and population coverage. For example, write-in entries such as "Don't know" or "NA" were considered unacceptable in certain quantities and/or in conjunction with other data omissions.

As a result of this review operation, a telephone or personal visit follow-up was made to obtain missing information. Potential coverage errors were included in the follow-up, as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Subsequent to field operations, remaining incomplete or inconsistent information on the questionnaires was assigned using imputation procedures during the final automated edit of the collected data. Allocations, or computer assignments of acceptable data in place of unacceptable entries or blanks, were needed most often when an entry for a given item was lacking or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in previous censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in lace of blanks or unacceptable entries enhanced the usefulness of the data.

Another way to make corrections during the computer editing process is substitution. Substitution is the assignment of a full set of characteristics for a person or housing unit. Because of the detailed field operations, substitution was not needed for the 2005 Census.

Sampling error estimates

Sampling Error is not applicable to full enumeration censuses.

Data appraisal

In any large-scale statistical operation, such as the 2005 Census of the Republic of Palau, human- and machine-related errors were anticipated. These errors are commonly referred to as nonsampling errors. Such errors include not enumerating every household or every person in the population, not obtaining all required information form the respondents, obtaining incorrect or inconsistent information, and recording information incorrectly. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires.

To reduce various types of nonsampling errors, a number of techniques were implemented during the planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Race/Ethnicity, Educational Attainment, Unemployment, Health Insurance, Disability and Vehicle Access.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Race/Ethnicity· WPop: Total population of those who identify as white alone (B01001A).· PWPop: Percentage of total population that identifies as white alone (B01001A).· BPop: Total population of those who identify as black or African American alone (B01001B).· PWPop: Percentage of total population that identifies as black or African American alone (B01001B).· AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).· PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).· APop: Total population of those who identify as Asian alone (B01001D).· PAPop: Percentage of total population that identifies as Asian alone (B01001D).· PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).· PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).· OPop: Total population of those who identify as Some Other Race alone (B01001F).· POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).· MPop: Total population of those who identify as Two or More Races (B01001G).· PMPop: Percentage of total population that identifies as Two or More Races (B01001G).· WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).· PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).· LPop: Total population of those who identify as Hispanic or Latino (B01001I).· PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).Educational Attainment· EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).· PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).· EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).· PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).· EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).· PEdSC1824: Percentage of population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).· EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).· PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).· EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).· PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).· Ed912nD: Total population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).· PEd912nD: Percentage of population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).· EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).· PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).· EdSC: Total population ages 25 and over with some college education but no degree (S1501).· PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).· EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).· PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).· EdB: Total population ages 25 and over with bachelor’s degree (S1501).· PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).· EdG: Total population ages 25 and over with a graduate or professional degree (S1501).· PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).Unemployment, Health Insurance, Disability· UnempR: Unemployment rate among the population ages 16 and over (S2301).· UnIn: Total non-institutionalized population without health insurance (B27001).· PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).· Disab: Total non-institutionalized population with a disability (S1810).· PDisab: Percentage of non-institutionalized populations with a disability (B27001).Vehicle Access· OwnNV: Total number of owner-occupied households without a vehicle (B25044).· POwnNV: Percentage of owner-occupied households without a vehicle (B25044).· OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).· POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).· RentNV: Total number of renter-occupied households without a vehicle (B25044).· PRentNV: Percentage of renter-occupied households without a vehicle (B25044).· RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).· POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

Abstract

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

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

The American Community Survey (ACS) is a relatively new survey conducted by the U.S. Census Bureau. It uses a series of monthly samples to produce annually updated estimates for the same small areas (census tracts and block groups) formerly surveyed via the decennial census long-form sample. Initially, five years of samples were required to produce these small-area data. Once the Census Bureau, released its first 5-year estimates in December 2010; new small-area statistics now are produced annually. The Census Bureau also will produce 3-year and 1-year data products for larger geographic areas. The ACS includes people living in both housing units (HUs) and group quarters (GQs). The ACS is conducted throughout the United States and in Puerto Rico, where it is called the Puerto Rico Community Survey (PRCS).

Geographic coverage

National coverage

Analysis unit

• Households

UNIT DESCRIPTIONS: - Households: Dwelling places excluding institutions and transient quarters. - Group quarters: A place where people live or stay, in a group living arrangement, that is owned or managed by an entitiy or organization providing housing and/or services for the residents. This is not a typical household-type living arrangement. These services many include custodial or medical care as well as other types of assistance, and residency is commonly restricted to those receiving these services. People living in group quarters are usually not related to each other.

Universe

Residents of Puerto Rico.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: U.S. Census Bureau

SAMPLE UNIT: Household

SAMPLE FRACTION: 1%

SAMPLE SIZE (person records): 36,032

Mode of data collection

Face-to-face [f2f]

Response rate

UNDERCOUNT: No official estimates

Abstract

The 2013 NDHS is designed to provide information on fertility, family planning, and health in the country for use by the government in monitoring the progress of its programs on population, family planning and health.

In particular, the 2013 NDHS has the following specific objectives: • Collect data which will allow the estimation of demographic rates, particularly fertility rates and under-five mortality rates by urban-rural residence and region. • Analyze the direct and indirect factors which determine the level and patterns of fertility. • Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. • Collect data on health, immunizations, prenatal and postnatal check-ups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever and acute respiratory infections among children below five years old. • Collect data on environmental health, utilization of health facilities, health care financing, prevalence of common non-communicable and infectious diseases, and membership in the National Health Insurance Program (PhilHealth). • Collect data on awareness of cancer, heart disease, diabetes, dengue fever and tuberculosis. • Determine the knowledge of women about AIDS, and the extent of misconception on HIV transmission and access to HIV testing. • Determine the extent of violence against women.

Geographic coverage

National coverage

Analysis unit

• Household
• Individuals/ persons
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selection methodology for the 2013 NDHS is based on a stratified two-stage sample design, using the 2010 Census of Population and Housing (CPH) as a frame. The first stage involved a systematic selection of 800 sample enumeration areas (EAs) distributed by stratum (region, urban/rural). In the second stage, 20 sample housing units were selected from each sample EA, using systematic random sampling.

All households in the sampled housing units were interviewed. An EA is defined as an area with discern able boundaries consisting of contiguous households. The sample was designed to provide data representative of the country and its 17 administrative regions.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2013 NDHS used three questionnaires: Household Questionnaire, Individual Woman’s Questionnaire, and Women’s Safety Module. The development of these questionnaires resulted from the solicited comments and suggestions during the deliberation in the consultative meetings and separate meetings conducted with the various agencies/organizations namely: PSA-NSO, POPCOM, DOH, FNRI, ICF International, NEDA, PCW, PhilHealth, PIDS, PLCPD, UNFPA, USAID, UPPI, UPSE, and WHO. The three questionnaires were translated from English into six major languages - Tagalog, Cebuano, Ilocano, Bicol, Hiligaynon, and Waray.

The main purpose of the Household Questionnaire was to identify female members of the sample household who were eligible for interview with the Individual Woman’s Questionnaire and the Women’s Safety Module.

The Individual Woman’s Questionnaire was used to collect information from all women aged 15-49 years.

The Women’s Safety Module was used to collect information on domestic violence in the country, its prevalence, severity and frequency from only one selected respondent from among all the eligible women who were identified from the Household Questionnaire.

Cleaning operations

All completed questionnaires and the control forms were returned to the PSA-NSO central office in Manila for data processing, which consisted of manual editing, data entry and verification, and editing of computer-identified errors. An ad-hoc group of thirteen regular employees from the DSSD, the Information Resources Department (IRD), and the Information Technology Operations Division (ITOD) of the NSO was created to work fulltime and oversee data processing operation in the NDHS Data Processing Center that was carried out at the NSO-CVEA Building in Quezon City, Philippines. This group was responsible for the different aspects of NDHS data processing. There were 19 data encoders hired to process the data who underwent training on September 12-13, 2013.

Data entry started on September 16, 2013. The computer package program called Census and Survey Processing System (CSPro) was used for data entry, editing, and verification. Mr. Alexander Izmukhambetov, a data processing specialist from ICF International, spent two weeks at NSO in September 2013 to finalize the data entry program. Data processing was completed on December 6, 2013.

Response rate

For the 2013 NDHS sample, 16,732 households were selected, of which 14,893 were occupied. Of these households, 14,804 were successfully interviewed, yielding a household response rate of 99.4 percent. The household response rates in urban and rural areas are almost identical.

Among the households interviewed, 16,437 women were identified as eligible respondents, and the interviews were completed for 16,155 women, yielding a response rate of 98.3 percent. On the other hand, for the women’s safety module, from a total of 11,373 eligible women, 10,963 were interviewed with privacy, translating to a 96.4 percent response rate. At the individual level, urban and rural response rates showed no difference. The principal reason for non-response among women was the failure to find individuals at home, despite interviewers’ repeated visits to the household.

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 2013 National Demographic and Health Survey (NDHS) 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 2013 NDHS 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 the results of all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey data.

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 2013 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2013 NDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replications method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of weighted cases in the group or subgroup under consideration.

Further details on sampling errors calculation are given in Appendix B of the final report.

Data appraisal

Data quality tables were produced to review the quality of the data: - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: The tables are presented in APPENDIX C of the final report.

Abstract

The survey was specifically designed to meet the following objectives: -to assess the current situation in Moldova concerning fertility, abortion, contraception and various other reproductive health issues; -to enable policy makers, program managers, and researchers to evaluate and improve existing programs and to develop new strategies; -to measure changes in fertility and contraceptive prevalence rates and study factors that affect these changes, such as geographic and socio-demographic factors, breast-feeding patterns, use of induced abortion, and availability of family planning; -to provide data necessary to develop sex education and health promotion programs; -to obtain data on knowledge, attitudes, and behavior of young adults 15-24 years of age; -to provide information on the level of knowledge about AIDS transmission and prevention; -to identify and focus further reproductive health studies toward high risk groups.

The survey provides data that will assist the Moldovan Government in improving services related to the health of women and children and was proposed in conjunction with the UNFPAsponsored reproductive health (RH) activities in Moldova, which consist of several components intended to increase the use of effective contraception, reduce the reliance on induced abortion as a means of fertility control, and, more generally, to improve RH. Specific projects supported by UNFPA in Moldova include ongoing support to the Government for developing a national RH plan, provisions of contraceptives, and training of family planning providers. In addition, the national RH plan is receiving support from USAID (family planning logistics management, information/ education/communication activities), IPPF (provision of contraceptives), and UNICEF.

Geographic coverage

The 1997 MRHS was designed to collect information from a representative sample of women of reproductive age throughout Moldova.

Universe

The universe from which the respondents were selected included all females between the ages of 15 and 44, regardless of marital status, who were living in Moldova when the survey was carried out.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a three-stage probability sample design and successfully interviewed 5,412 (98%) of 5,543 women identified in sample households as eligible for interview.

The survey employed a three-stage sampling design using two sampling frames (one for urban areas and one for rural areas) provided by the MSDS. The urban sampling frame was based on the 1989 census, whereas the rural sampling frame consisted of a list of the 1,607 villages in the country, recently updated for household composition in January-April 1997 for an agricultural registry.

In the first stage, 128 census sectors in urban areas and 122 villages were selected as Primary Sampling Units (PSUs) with probability proportional to the number of households in each census sector/village. In the second stage of sampling, clusters of households were randomly selected in each census sector/village chosen in the first stage. Before second-stage selection in urban areas, the Census Division of the MSDS redefined each 1989 census sector selected as a PSU for street boundaries, converted the maps and listings from Russian to Moldavian, and updated the sector's household composition in collaboration with personnel from the local health care units. A cluster of households was randomly selected from the updated sector lists of the PSUs in urban areas and from the household listings in the villages selected as PSUs in the first stage. (Since there were roughly equal numbers of urban and rural households, the sample was designed to be geographically self-weighting.) In each sample strata, urban and rural, the third stage consisted of the random selection of one woman if there were two or more eligible women (aged 15-44 years) living in the same household.

Cluster size determination was based on the number of households required to obtain an average of 20 interviews per cluster. The total number of households in each cluster took into account estimates of unoccupied households, average number of women 15-44 per household, the interview of only one woman per household, and an estimated response rate of 90% in urban areas and 92% in rural areas. In urban areas, the cluster size with a yield of 20 interviews, on average, was determined to be 45 households. In rural areas, because the average number of women 15-44 per household varies considerably by raion, the average number of households needed to obtain 20 complete interviews varied from 42 to 60.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was first drafted by CDC/DRH consultants based on a core questionnaire used in the 1993 Romanian Reproductive Health Survey. This core questionnaire was reviewed and modified by Moldovan experts in reproductive health and family planning, as well as by USAID and UNFPA. Based on these reviews, a pretest questionnaire was developed and field-tested in April 1997. The questionnaire, developed in Romanian, was translated into Russian after the pretest. All interviewers spoke these two languages.

The questionnaire had two components: (1) A short household questionnaire used to collect residential and geographic information, select information about all women of childbearing age living in sampled households, and information on interview status. This module was also used to randomly select one respondent when there was more than one eligible woman in the household; (2) The longer individual questionnaire collected information on the topics mentioned above.

The major reproductive health topics on which information was collected were: pregnancies and childbearing (a complete history of all pregnancies, including planning status of pregnancies in the last five years, a detailed history of abortions within the last five years, including postabortion counseling, and the history of all births within the last five years, including the patterns of utilization of health services during pregnancy, maternal morbidity, infant health and breast-feeding); family planning (knowledge and history of use of methods of preventing pregnancy, current use of contraception, source of contraception, reasons for not using, reasons for use of less effective methods of contraception, future fertility preferences and intentions to use voluntary sterilization); women's health (health behavior and use of women's health services, tobacco and alcohol use); reproductive health knowledge and attitudes (especially regarding birth control pills, condoms, and IUDs); knowledge about HIV/AIDS transmission and prevention; domestic violence, including violence during the most recent pregnancy; history of sexual abuse; and socioeconomic characteristics of women and their husbands/families. The young women (15-24 years of age) were asked additional questions on sex education, age and contraceptive use at first sexual intercourse, and sexual behaviors.

Most issues have been examined by geographic, demographic, and socio-economic characteristics, making it possible to identify the segments of the population with specific health needs or problems.

Response rate

Of the 11,506 households selected, 5,543 were found to include at least one 15-44 year-old woman. Of these women, 5,412 were successfully interviewed, for a response rate of 97.6%. Less than one percent of selected women refused to be interviewed, while another 1.3% could not be located. Response rates were slightly better in rural areas (98%) than in municipalities and other urban areas (97%). In Chisinau (not shown), the response rate was 96%; nearly 3% of women selected in the sample could not be located.

Data appraisal

The geographic distribution of the sample, by residence and region, is very close to official figures of the population distribution for 1996, estimated by the Moldovan State Department for Statistics.

The percent distribution of women in the sample by five-year age groups is compared with the 1994 official estimates (the most recent estimates by age group) in Table 2.3. Compared with these estimates, the survey sample has slightly over-represented adolescent women (15-19 yearolds) and under-represented women aged 40-44 by about two percentage points. However, several factors may have contributed to the differences observed: first, there is a three-year difference between the time the official estimates were calculated and the survey was implemented; second, the official estimates are projections of the age composition recorded by the 1989 census and thus dependent on assumptions used in projecting the aging of a cohort; finally, official estimates include any possible age misreporting that occured in the census.

Abstract

The 2009 MDHS was designed to provide data to monitor the population and health situation in Maldives. Specifically, the MDHS collected information on fertility levels and preferences, marriage, sexual activity, knowledge and use of family planning methods, breastfeeding practices, nutrition status of women and young children, childhood mortality, maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted infections. At the household level, the survey collected information on domains of physical disability among those age 5 and older, developmental disability among young children, support for early learning, children at work, the impact of the tsunami of 2004, health expenditures, and care and support for physical activity of adults age 65 and older. At the individual level, the survey assessed additional features of blood pressure, diabetes, heart attack, and stroke.

Geographic coverage

National

Analysis unit

• Households
• Children age under 5
• Women age 15-49
• Men age 15-64

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The population of the republic of Maldives is distributed on 195 inhabited islands among a total of 202 inhabited islands; seven islands have no residents (MPND, 2008). Each inhabited island is an administrative unit with an island office that handles island-based affairs. The islands are regrouped to form atolls, a higher-level administrative unit with an atoll office and an atoll chief. There are 20 atolls in total in the republic. The capital city of Malé and the two surrounding islands, Villingili and Hulhumale, form a special atoll. The 21 atolls are regrouped to form six geographic regions according to their location. Malé atoll alone forms a region. In Maldives, there is no urbanrural designation for residential households within an atoll. All residential households in the 20 atolls outside of Malé are considered rural; all residential households in Malé are considered urban.

The 2009 Maldives DHS is based on a probability sample of 7,515 households. The sample was designed to produce representative data on households, women, and children for the country as a whole, for urban and rural areas, for the six geographical regions, and for each of the atolls of the country. The male and youth surveys were designed to produce representative results for the country as a whole, for urban and rural areas, and for each of the six geographical regions.

The 2006 Maldives Population and Housing Census provided the sampling frame for the 2009 MDHS. The MDHS sample was a stratified multistage sample selected in two stages from the census frame. In the first stage, 270 census blocks were selected using a systematic selection, with probability proportional to the number of residential households residing in the block. Stratification was achieved by treating each of the 21 atolls as a sampling stratum. Samples were selected independently in each stratum according to an appropriate allocation.

In the second stage of sampling, residential households were selected in each of the selected census blocks. Household selection involved an equal probability systematic selection of a fixed number of households: 28 households per block. Households were selected from the household listings created in the census, but to allow all households an opportunity to be included in the sample, listings were sent to island offices for updating prior to making household selections for the MDHS.

All ever-married women age 15-49 in the total sample of MDHS households, who were either usual residents of the household or visitors present in the household on the night before the survey, were eligible to be interviewed. In half of the households selected for the ever-married sample of women, all ever-married men age 15-64, who were either usual residents of the household or visitors present in the household on the night before the survey, were eligible to be interviewed. In the same half of households selected for the ever-married sample of men, never-married women and nevermarried men age 15-24, who were either usual residents of the household or visitors present in the household on the night before the survey, were also eligible to be interviewed. The MDHS was for the most part limited to Maldivian citizens; non-Maldivians were included in the survey only if they were the spouse, son, or daughter of a Maldivian.

Note: See detailed sample implementation information in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Four questionnaires were used for the 2009 MDHS: the Household Questionnaire, the Women’s Questionnaire, the Men’s Questionnaire, and the Youth Questionnaire. The contents of the Household, Women’s, and Men’s questionnaires were based on model questionnaires developed by the MEASURE DHS programme. The DHS model questionnaires were modified to reflect concerns pertinent to the Maldives in the areas of population, women and children’s health, family planning, and others. Questionnaires were translated from English into Dhivehi.

The Household Questionnaire was used to list all the usual members and visitors in the selected households and to identify women and men who were eligible for the individual interview. Basic information was collected on the characteristics of each person listed, including their age, sex, education, and relationship to the head of the household. The Household Questionnaire was also designed to collect information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, water shortage, materials used for the floor and roof of the house, and ownership of various durable goods. In addition, height and weight measurements of ever-married women age 15-49 and children age 6-59 months were recorded in the Household Questionnaire to assess their nutritional status.

Topics added to the Household Questionnaire to reflect issues relevant in the Maldives include physical disability among those age 5 and older, developmental disability among young children, support for early learning, children at work, the tsunami of 2004, health expenditures, and care and support for physical activities of adults age 65 and older.

The Women’s Questionnaire was used to collect information from ever-married women age 15-49. These women were asked questions on the following topics: - Background characteristics (education, media exposure, etc.) - Reproductive history - Knowledge and use of family planning methods - Fertility preferences - Antenatal and delivery care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Infant and child feeding practices - Childhood mortality - Awareness and behaviour about AIDS and other sexually transmitted infections (STIs) - Knowledge of blood pressure, diabetes, heart attack, and stroke

The Men’s Questionnaire was administered to all ever-married men age 15-64 living in every second household in the MDHS sample. The Men’s Questionnaire collected much of the same information as the Women’s Questionnaire, but it was shorter because it did not contain questions on reproduction, maternal and child health, and nutrition.

The Youth Questionnaire was administered to all never-married women and men age 15-24 living in every second household in the MDHS sample (the same one-half selected for the Men’s survey). The Youth Questionnaire focuses on priorities of the MOHF that pertain to young adults: reproductive health, knowledge and attitudes about HIV/AIDS, sexual activity, and tobacco, alcohol, and drug use.

Response rate

A total of 7,515 households were selected in the sample, of which 7,137 were found to be occupied at the time of data collection. The difference between the number of households selected and the number occupied usually occurs because some structures are found to be vacant or non-existent. The number of occupied households successfully interviewed was 6,443, yielding a household response rate of 90 percent.

In the households interviewed in the survey, a total of 8,362 ever-married women were identified as eligible for the individual interview; interviews were completed with 7,131 women, yielding a female response rate of 85 percent. In the one-half sub-sample of MDHS households, a total of 3,224 evermarried men age 15-64 were identified as eligible for the individual interview; interviews were completed with 1,727 men, yielding a male response rate of 54 percent. In the same sub-sample of households, a total of 3,205 never-married women and men age 15-24 (youth) were identified as eligible for individual interview; interviews were completed with 2,240 youth, yielding a youth response rate of 70 percent. The response rate was higher for female youth (80 percent) than male youth (61 percent).

The urban household response rate of 83 percent is lower than the 92 percent response rate among rural households. The same is true for individual interviews with ever-married respondents; response rates are somewhat lower among urban women (79 percent) and men (47 percent) than among their rural counterparts (87 percent and 55 percent, respectively). The difference in response rates between urban and rural youth is negligible.

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

Sampling error estimates

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

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 primary objective of the 2017-18 Jordan Population and Family Health Survey (JPFHS) is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2017-18 JPFHS: - Collected data at the national level that allowed calculation of key demographic indicators - Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunisation coverage among children, the prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery among ever-married women - Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and ever-married women age 15-49 - Conducted haemoglobin testing on children age 6-59 months and ever-married women age 15-49 to provide information on the prevalence of anaemia among these groups - Collected data on knowledge and attitudes of ever-married women and men about sexually transmitted infections (STIs) and HIV/AIDS - Obtained data on ever-married women’s experience of emotional, physical, and sexual violence - Obtained data on household health expenditures

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017-18 JPFHS is based on Jordan's Population and Housing Census (JPHC) frame for 2015. The current survey is designed to produce results representative of the country as a whole, of urban and rural areas separately, of three regions, of 12 administrative governorates, and of three national groups: Jordanians, Syrians, and a group combined from various other nationalities.

The sample for the 2017-18 JPFHS is a stratified sample selected in two stages from the 2015 census frame. Stratification was achieved by separating each governorate into urban and rural areas. Each of the Syrian camps in the governorates of Zarqa and Mafraq formed its own sampling stratum. In total, 26 sampling strata were constructed. Samples were selected independently in each sampling stratum, through a two-stage selection process, according to the sample allocation. Before the sample selection, the sampling frame was sorted by district and sub-district within each sampling stratum. By using a probability-proportional-to-size selection for the first stage of selection, an implicit stratification and proportional allocation were achieved at each of the lower administrative levels.

In the first stage, 970 clusters were selected with probability proportional to cluster size, with the cluster size being the number of residential households enumerated in the 2015 JPHC. The sample allocation took into account the precision consideration at the governorate level and at the level of each of the three special domains. After selection of PSUs and clusters, a household listing operation was carried out in all selected clusters. The resulting household lists served as the sampling frame for selecting households in the second stage. A fixed number of 20 households per cluster were selected with an equal probability systematic selection from the newly created household listing.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2017-18 JPFHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect population and health issues relevant to Jordan. After all questionnaires were finalised in English, they were translated into Arabic.

Cleaning operations

All electronic data files for the 2017-18 JPFHS were transferred via IFSS to the DOS central office in Amman, 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. Data editing was accomplished using CSPro software. During the duration of fieldwork, 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 October 2017 and completed in February 2018.

Response rate

A total of 19,384 households were selected for the sample, of which 19,136 were found to be occupied at the time of the fieldwork. Of the occupied households, 18,802 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 14,870 women were identified as eligible for an individual interview; interviews were completed with 14,689 women, yielding a response rate of 99%. A total of 6,640 eligible men were identified in the sampled households and 6,429 were successfully interviewed, yielding a response rate of 97%. Response rates for both women and men were similar across urban and rural areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Jordan Population and Family Health Survey (JPFHS) 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 2017-18 JPFHS is only one of many samples that could have been selected from the same population, using the same design and sample 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 by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017-18 JPFHS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF International. These programmes 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.

The Taylor linearisation method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

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

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

Abstract

The 2011 Ethiopia Demographic and Health Survey (EDHS) was conducted by the Central Statistical Agency (CSA) under the auspices of the Ministry of Health.

The principal objective of the 2011 Ethiopia Demographic and Health Survey (EDHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, use of maternal and child health services, knowledge of HIV/AIDS, and prevalence of HIV/AIDS and anaemia. The specific objectives are these: - Collect data at the national level that will allow the calculation of key demographic rates; - Analyse the direct and indirect factors that determine fertility levels and trends; - Measure the levels of contraceptive knowledge and practice of women and men by family planning method, urban-rural residence, and region of the country; - Collect high-quality data on family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under ge five, and maternity care indicators, including antenatal visits and assistance at delivery; - Collect data on infant and child mortality and maternal mortality; - Obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to assess the nutritional status of women and children; - Collect data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluate patterns of recent behaviour regarding condom use; - Conduct haemoglobin testing on women age 15-49 and children 6-59 months to provide information on the prevalence of anaemia among these groups; - Carry out anonymous HIV testing on women and men of reproductive age to provide information on the prevalence of HIV.

This information is essential for informed policy decisions, planning, monitoring, and evaluation of programmes on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Agency to plan, conduct, process, and analyse data from complex national population and health surveys.

Moreover, the 2011 EDHS provides national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries and to Ethiopia’s two previous DHS surveys, conducted in 2000 and 2005. Data collected in the 2011 EDHS add to the large and growing international database of demographic and health indicators.

The survey was intentionally planned to be fielded at the beginning of the last term of the MDG reporting period to provide data for the assessment of the Millennium Development Goals (MDGs).

The survey interviewed a nationally representative population in about 18,500 households, and all women age 15-49 and all men age 15-59 in these households. In this report key indicators relating to family planning, fertility levels and determinants, fertility preferences, infant, child, adult and maternal mortality, maternal and child health, nutrition, women’s empowerment, and knowledge of HIV/AIDS are provided for the nine regional states and two city administrations. In addition, this report also provides data by urban and rural residence at the country level.

Major stakeholders from various government, non-government, and UN organizations have been involved and have contributed in the technical, managerial, and operational aspects of the survey.

Geographic coverage

A nationally representative sample of 17,817 households was selected.

Universe

All women 15-49 who were usual residents or who slept in the selected households the night before the survey were eligible for the survey. A male survey was also conducted. All men 15-49 who were usual residents or who slept in the selected households the night before the survey were eligible for the male survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the 2011 EDHS was designed to provide population and health indicators at the national (urban and rural) and regional levels. The sample design allowed for specific indicators, such as contraceptive use, to be calculated for each of Ethiopia's 11 geographic/administrative regions (the nine regional states and two city administrations). The 2007 Population and Housing Census, conducted by the CSA, provided the sampling frame from which the 2011 EDHS sample was drawn.

Administratively, regions in Ethiopia are divided into zones, and zones, into administrative units called weredas. Each wereda is further subdivided into the lowest administrative unit, called kebele. During the 2007 census each kebele was subdivided into census enumeration areas (EAs), which were convenient for the implementation of the census. The 2011 EDHS sample was selected using a stratified, two-stage cluster design, and EAs were the sampling units for the first stage. The sample included 624 EAs, 187 in urban areas and 437 in rural areas.

Households comprised the second stage of sampling. A complete listing of households was carried out in each of the 624 selected EAs from September 2010 through January 2011. Sketch maps were drawn for each of the clusters, and all conventional households were listed. The listing excluded institutional living arrangements and collective quarters (e.g., army barracks, hospitals, police camps, and boarding schools). A representative sample of 17,817 households was selected for the 2011 EDHS. Because the sample is not self-weighting at the national level, all data in this report are weighted unless otherwise specified.

In the Somali region, in 18 of the 65 selected EAs listed households were not interviewed for various reasons, such as drought and security problems, and 10 of the 65 selected EAs were not listed due to security reasons. Therefore, the data for Somali may not be totally representative of the region as a whole. However, national-level estimates are not affected, as the percentage of the population in the EAs not covered in the Somali region is proportionally very small.

SAMPLING FRAME

The sampling frame used for 2011 EDHS is the Population and Housing Census (PHC) conducted in 2007 provided by the Central Statistical Agency (CSA, 2008). CSA has an electronic file consisting of 81,654 Enumeration Areas (EA) created for the 2007 census in 10 of its 11 geographic regions. An EA is a geographic area consisting of a convenient number of dwelling units which served as counting unit for the census. The frame file contains information about the location, the type of residence, and the number of residential households for each of the 81,654 EAs. Sketch maps are also available for each EA which delimitate the geographic boundaries of the EA. The 2007 PHC conducted in the Somali region used a different methodology due to difficulty of access. Therefore, the sampling frame for the Somali region is in a different file and in different format. Due to security concerns in the Somali region, in the beginning it was decided that 2011 EDHS would be conducted only in three of nine zones in the Somali region: Shinile, Jijiga, and Liben, same as in the 2000 and 2005 EDHS. However, a later decision was made to include three other zones: Afder, Gode and Warder. This was the first time that these three zones were included in a major nationwide survey such as the 2011 EDHS. The sampling frame for the 2011 EDHS consists of a total of 85,057 EAs.

The sampling frame excluded some special EAs with disputed boundaries. These EAs represent only 0.1% of the total population.

Ethiopia is divided into 11 geographical regions. Each region is sub-divided into zones, each zone into Waredas, each Wareda into towns, and each town into Kebeles. Among the 85,057 EAs, 17,548 (21 percent) are in urban areas and 67,509 (79 percent) are in rural areas. The average size of EA in number of households is 169 in an urban EA and 180 in a rural EA, with an overall average of 178 households per EA. Table A.2 shows the distributions of households in the sampling frame, by region and residence. The data show that 81 percent of the Ethiopia’s households are concentrated in three regions: Amhara, Oromiya and SNNP, while 4 percent of all households are in the five smallest regions: Afar, Benishangul-Gumuz, Gambela, Harari and Dire Dawa.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2011 EDHS used three questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from model survey instruments developed for the MEASURE DHS project to reflect the population and health issues relevant to Ethiopia. Issues were identified at a series of meetings with the various stakeholders. In addition to English, the questionnaires were translated into three major languages—Amharigna, Oromiffa, and Tigrigna.

The Household Questionnaire was used to list all the usual members and visitors of selected households. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The data on the age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various consumer

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Dwellings, households and persons

UNITS IDENTIFIED: - Dwellings: yes - Vacant units: yes - Households: yes - Individuals: yes - Group quarters: yes - Special populations: no

UNIT DESCRIPTIONS: - Dwellings: A space or structure delimited by walls and roofs of any material with an independent entrance that is used as lodging. Any place where one or more persons live is considered a dwelling even if it was not intended for habitation when constructed. - Households: Person or a group of people that share their living expenses and reside under the same roof. - Group quarters: A dwelling that is intended for habitation by a group of people without family ties who live together due to health, work, religion, study, specific discipline, as guests, etc.

Universe

Habitual residents: individuals who had resided in the country for six or more months at the time of enumeration or who intended at the time of enumeration to reside in the country for six or more months. Foreign diplomats and their families were not enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Centro Latinoamericano de Demografia (CELADE)

SAMPLE DESIGN: Systematic sample of every 10th household given a random start. Sample drawn by MPC.

SAMPLE UNIT: Households

SAMPLE FRACTION: 10.0%

SAMPLE SIZE (person records): 943,784

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form containing six sections: I) Geographic location; II) Dwelling characteristics; III) Household identification; IV) Household characteristics; V) List of Household members; VI) Characteristics of permanent household members. The form was processed by optical reader.

Response rate

COVERAGE: Unknown

Abstract

The primary objective of the Adolescent Reproductive Health component (ARH) 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 never married women and men age 15-24.

Specifically, the ARH component of the 2012 IDHS was designed to: • Measure the level of knowledge of adolescents concerning reproductive health issues • Examine the attitudes of adolescents on various reproductive health issues • Measure the level of tobacco use, alcohol consumption, and drug use among adolescents • Measure the level of sexual activity among adolescents • Explore adolescents’ awareness of HIV/AIDS and other sexually transmitted infections

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Never married woman age 15-49
• Never married man age 15-54

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 was 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.

For further details on sample design and implementation, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 IDHS used four questionnaires: the Household Questionnaire, the Woman’s Questionnaire, the 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 had questions added for never-married women age 15-24. These questions had previously been a part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. Questions asked of never-married women age 15-24 assessed additional background characteristics; knowledge of the human reproductive system; attitudes toward marriage and having children; the role of family, school, community, and media; use of smoking tobacco, alcohol, and drugs; and dating and sexual activity.

Cleaning operations

Data processing activities, which included editing and coding open-ended questions, were carried out by a team of 58 data entry operators, 42 data editors, 14 secondary data editors, and 14 data entry supervisors. Census and Survey Processing System (CSPro) software was used to process the survey data.

Response rate

A total of 46,024 households were selected in the sample, of which 44,302 were occupied. Of the households found in the survey, 43,852 were successfully interviewed, yielding a very high response rate (99 percent).

In the interviewed households, 9,442 never-married female and 12,381 never-married male respondents age were identified for an individual interview. Of these, completed interviews were conducted with 8,902 women and 10,980 men, yielding response rates of 94 and 89 percent, respectively. These response rates are higher than those of the 2007 IYARHS, which were 90 and 86 percent, respectively.

Sampling error estimates

Detailed description of estimates of sampling errors are presented in Appendix C of the survey report.