The Urban Household Sample of the 1860 United States Census was designed to supplement the Bateman-Foust rural sample with observations from urban areas. The sample covers both northern and southern towns and cities and permits examination of female occupations and labor force participation rates. Information on individuals includes occupation, city of residence, age, sex, race, dollar value of real and personal property owned, whether American or foreign born, and literacy. The second release of this collection adds nine constructed variables, including several weight variables, collapsed occupation, ICPSR state code, region, and unique internal family and household identifier numbers.

Abstract

The survey on financial literacy among the citizens of Bosnia and Herzegovina was conducted within a larger project that aims at creating the Action Plan for Consumer Protection in Financial Services.

The conclusion about the need for an Action Plan was reached by the representatives of the World Bank, the Federal Ministry of Finance, the Central Bank of Bosnia and Herzegovina, supervisory authorities for entity financial institutions and non-governmental organizations for the protection of consumer rights, based on the Diagnostic Review on Consumer Protection and Financial Literacy in Bosnia and Herzegovina conducted by the World Bank in 2009-2010. This diagnostic review was conducted at the request of the Federal Ministry of Finance, as part of a larger World Bank pilot program to assess consumer protection and financial literacy in developing countries and middle-income countries. The diagnostic review in Bosnia and Herzegovina was the eighth within this project.

The financial literacy survey, whose results are presented in this report, aims at establishing the basic situation with respect to financial literacy, serving on the one hand as a preparation for the educational activities plan, and on the other as a basis for measuring the efficiency of activities undertaken.

Geographic coverage

Data collection was based on a random, nation-wide sample of citizens of Bosnia and Herzegovina aged 18 or older (N = 1036).

Analysis unit

Household, individual

Universe

Population aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

SUMMARY

In Bosnia and Herzegovina, as is well known, there is no completely reliable sample frame or information about universe. The main reasons for such a situation are migrations caused by war and lack of recent census data. The last census dates back to 1991, but since then the size and distribution of population has significantly changed. In such a situation, researchers have to combine all available sources of population data to estimate the present size and structure of the population: estimates by official statistical offices and international organizations, voters? lists, list of polling stations, registries of passport and ID holders, data from large random surveys etc.

The sample was three-stage stratified: in the first stage by entity, in the second by county/region and in the third by type of settlement (urban/rural). This means that, in the first stage, the total sample size was divided in two parts proportionally to number of inhabitants by entity, while in the second stage the subsample size for each entity was further divided by regions/counties. In the third stage, the subsample for each region/county was divided in two categories according to settlement type (rural/urban).

Taking into the account the lack of a reliable and complete list of citizens to be used as a sample frame, a multistage sampling method was applied. The list of polling stations was used as a frame for the selection of primary sampling units (PSU). Polling station territories are a good choice for such a procedure since they have been recently updated, for the general elections held in October 2010. The list of polling station territories contains a list of addresses of housing units that are certainly occupied.

In the second stage, households were used as a secondary sampling unit. Households were selected randomly by a random route technique. In total, 104 PSU were selected with an average of 10 respondents per PSU. The respondent from the selected household was selected randomly using the Trohdal-Bryant scheme.

In total, 1036 citizens were interviewed with a satisfactory response rate of around 60% (table 1). A higher refusal rate is recorded among middle-age groups (table 2). The theoretical margin of error for a random sample of this size is +/-3.0%.

Due to refusals, the sample structure deviated from the estimated population structure by gender, age and education level. Deviations were corrected by RIM weighting procedure.

MORE DETAILED INFORMATION

IPSOS designed a representative sample of approximately 1.000 residents age 18 and over, proportional to the adult populations of each region, based on age, sex, region and town (settlement) type.

For this research we designed three-stage stratified representative sample. First we stratify sample at entity level, regional level and then at settlement type level for each region.

Sample universe:

Population of B&H -18+; 1991 Census figures and estimated population dynamics, census figures of refugees and IDPs, 1996. Central Election Commision - 2008; CIPS - 2008;

Sampling frame:

Polling stations territory (approximate size of census units) within strata defined by regions and type of settlements (urban and rural) Polling stations territories are chosen to be used as primary units because it enables the most reliable sample selection, due to the fact that for these units the most complete data are available (dwelling register - addresses)

Type of sample:

Three stage random representative stratified sample

Definition and number of PSU, SSU, TSU, and sampling points

• PSU - Polling station territory Definition: Polling stations territories are defined by street(s) name(s) and dwelling numbers; each polling station territory comprises approximately 300 households, with exception of the settlements with less than 300 HH which are defined as one unite. Number of PSUs in sample universe: 4710
• SSU - Household Definition: One household comprises people living in the same apartment and sharing the expenditure for food
• TSU - Respondent Definition: Member of the HH , 18+ Number of TSUs in sample universe: = 2.966.766
• Sampling points Approximately 10 respondents per one PSU, total 104

Stratification, purpose and method

• First level strata: Federation of B&H Republika Srpska Brc ko District
• Second level strata: 10 cantons 2 regions -
• Third level strata: urban and rural settlements
• Purpose: Optimisation of the sample plan, and reducing the sampling error

• Method: The strata are defined by criteria of optimal geographical and cultural uniformity

• Selection procedure of PSU, SSU, and respondent Stratification, purpose and method

• PSU Type of sampling of the PSU: Polling station territory chosen with probability proportional to size (PPS) Method of selection: Cumulative (Lachirie method)

• SSU Type of sampling of the SSU: Sample random sampling without replacement Method of selection: Random walk - Random choice of the starting point

• TSU - Respondent Type of sampling of respondent: Sample random sampling without replacement Method of selection: TCB (Trohdal-Bryant scheme)

• Sample size N=1036 respondents

• Sampling error Marginal error +/-3.0%

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was modelled after the identical survey conducted in Romania. The questionnaire used in the Financial Literacy Survey in Romania was localized for Bosnia and Herzegovina, including adaptations to match the Bosnian context and methodological improvements in wording of questions.

Cleaning operations

Before data entry, 100% logic and consistency controls are performed first by local supervisors and once later by staff in central office.

Verification of correct data entry is assured by using BLAISE system for data entry (commercial product of Netherlands statistics), where criteria for logical and consistency control are defined in advance.

Response rate

• Nobody at home: 2,8%
• Eligible person is not home: 2,8%
• Refusal : 32,79%
• Given up after a minimum of two visits: 0,82%
• Other (excluded after control): 0,29%
• Finished: 60,5%

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The 60 years and over column of data refers to the age of the householder for the estimates of households, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines..The age specified on the population 15 years and over, population 25 years and over, population 30 years and over, civilian population 18 years and over, civilian population 5 years and over, population 1 years and over, population 5 years and over, and population 16 years and over lines refer to the data shown in the "Total" column while the second column is limited to the population 60 years and over..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The Census of Population and Housing is one of the most important surveys carried out by ISTAT. It is conducted every ten years from 1861, and the main objectives are: the count of the whole population and the recognition of its structural characteristics; updating and revision of civil registers; the definition of the legal population for juridical and electoral purposes; the collection of information about the number and structural characteristics of houses and buildings. The Census collects information about demographic and family structure of the population, the types of their households, their level of education, their employment status, and other informations on residents population. In 2011, for the first time, some information of socio-economic character were measured on a sample basis through the use of two types of questionnaire: one in a reduced form, with a few questions, including indispensable information for the production of the data required by the European Union with an high spatial detail, and one in complete form. In particular, Istat provides a 1% sample data (594,247 cases) released in two separate datasets: the first file (individui) refers to persons usually resident in private households and in Institutional households and the second one (alloggi) refers to living quarters. In urban areas with at least 20,000 inhabitants a sample was selected by a simple random sampling without replacement procedure of one third of the families. A complete version (long form) of the questionnaire has been sent to the sample, while a short version the questionnaire has been sent to all other inhabitants. web-based self-administered questionnaire (CAWI)

About this dataset

Context

The dataset tabulates the United States population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of United States across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of United States was 333,287,557, a 0.38% increase year-by-year from 2021. Previously, in 2021, United States population was 332,031,554, an increase of 0.16% compared to a population of 331,511,512 in 2020. Over the last 20 plus years, between 2000 and 2022, population of United States increased by 51,125,146. In this period, the peak population was 333,287,557 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the United States is shown in this column.
• Year on Year Change: This column displays the change in United States population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for United States Population by Year. You can refer the same here

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

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Group quarters: A collective household is a group of persons that does not live in an ordinary household, but lives in a collective establishment, sharing meal times.

Universe

Residents of France, of any nationality. Does not include French citizens living in other countries, foreign tourists, or people passing through.

Kind of data

Census/enumeration data [cen]

Sampling procedure

SAMPLE UNIT: Private dwellings and individuals for group quarters and compte a part

SAMPLE FRACTION: 5%

SAMPLE UNIVERSE: The microdata sample includes mainland France and Corsica.

SAMPLE SIZE (person records): 2,934,758

Mode of data collection

Face-to-face [f2f]

Research instrument

Form 1A for dwelling consists of (1) dwelling characteristics, (2) List A. permanent occupants of the dwelling, (3) List B. household members who do not live in the dwelling of enumeration, and (4) building characteristics; Form 2B. Individual form.

The 1940 Census Public Use Microdata Sample Project was assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology at the University of Wisconsin. The collection contains a stratified 1-percent sample of households, with separate records for each household, for each "sample line" respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), standard metropolitan areas (SMAs), and state economic areas (SEAs). Accompanying the data collection is a codebook that includes an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. Also included is a procedural history of the 1940 Census. Each of the 20 subsamples contains three record types: household, sample line, and person. Household variables describe the location and condition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, wage deductions for Social Security, and occupation. Person records also contain variables describing demographic characteristics including nativity, marital status, family membership, education, employment status, income, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08236.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

About this dataset

Context

The dataset tabulates the Florida population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Florida across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Florida was 22,244,823, a 1.91% increase year-by-year from 2021. Previously, in 2021, Florida population was 21,828,069, an increase of 1.10% compared to a population of 21,589,602 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Florida increased by 6,198,675. In this period, the peak population was 22,244,823 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Florida is shown in this column.
• Year on Year Change: This column displays the change in Florida population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

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

Households and Group Quarters

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes

UNIT DESCRIPTIONS: - Households: Dwelling places excluding institutions and transient quarters. - Group quarters: No threshold was applied; in order for a household to be considered group quarters in 2000, it had to be on the list of group quarters that is continuously maintained by the Census Bureau.

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: 5%

SAMPLE SIZE (person records): 189,828

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 census used a long form questionnaire. Long Form Sampling Entities (LFSEs) were used to determine sampling rates. If the smallest LFSE that included all or any part of a block had an estimated housing unit count of less than 800, the housing units in the block were sampled at a 1-in-2 rate. If it had an estimated housing unit count of 800 or more but less than 1,200, units were sampled at a 1-in-4 rate. If a block was not in either of the two previous categories, and was part of an interim census tract with 2,000 or more estimated housing units, units were sampled at a 1-in-8 rate. Housing units in all remaining blocks were sampled at a 1-in-6 rate. When all sampling rates were taken into account across the nation, approximately 1 out of every 6 housing units was included in the Census 2000 sample.

Response rate

UNDERCOUNT: No official estimates

About this dataset

Context

The dataset tabulates the Watts population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Watts across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Watts was 277, a 1.09% increase year-by-year from 2021. Previously, in 2021, Watts population was 274, an increase of 0.74% compared to a population of 272 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Watts decreased by 51. In this period, the peak population was 344 in the year 2008. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Watts is shown in this column.
• Year on Year Change: This column displays the change in Watts population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2014 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..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..Source: U.S. Census Bureau, 2014 American Community Survey 1-Year Supplemental Estimates

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

Abstract

The Estimating the Size of Populations through a Household Survey (EPSHS), sought to assess the feasibility of the network scale-up and proxy respondent methods for estimating the sizes of key populations at higher risk of HIV infection and to compare the results to other estimates of the population sizes. The study was undertaken based on the assumption that if these methods proved to be feasible with a reasonable amount of data collection for making adjustments, countries would be able to add this module to their standard household survey to produce size estimates for their key populations at higher risk of HIV infection. This would facilitate better programmatic responses for prevention and caring for people living with HIV and would improve the understanding of how HIV is being transmitted in the country.

The specific objectives of the ESPHS were: 1. To assess the feasibility of the network scale-up method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 2. To assess the feasibility of the proxy respondent method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 3. To estimate the population size of MSM, FSW, IDU, and clients of sex workers in Rwanda at a national level; 4. To compare the estimates of the sizes of key populations at higher risk for HIV produced by the network scale-up and proxy respondent methods with estimates produced using other methods; and 5. To collect data to be used in scientific publications comparing the use of the network scale-up method in different national and cultural environments.

Geographic coverage

National

Analysis unit

• Household
• Individual

Sampling procedure

The Estimating the Size of Populations through a Household Survey (ESPHS) used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC), which was conducted in 2012; it was provided by the National Institute of Statistics of Rwanda (NISR).

The sampling frame was a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means "to know" someone.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The Estimating the Size of Populations through a Household Survey (ESPHS) used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

A total of 2,125 households were selected in the sample, of which 2,120 were actually occupied at the time of the interview. The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99 percent.

From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98 percent. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98 percent. The response rates do not significantly vary by type of questionnaire or residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made to minimize this type of error during the implementation of the Rwanda ESPHS 2011, non-sampling 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 ESPHS 2011 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 errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the ESPHS 2011 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 ESPHS 2011 is a SAS program. This program uses the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

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

Population: County: Age 65 and Above: Guizhou data was reported at 2.794 Person th in 2022. This records a decrease from the previous number of 2.830 Person th for 2021. Population: County: Age 65 and Above: Guizhou data is updated yearly, averaging 1.945 Person th from Dec 1997 (Median) to 2022, with 26 observations. The data reached an all-time high of 2,675.335 Person th in 2020 and a record low of 1.455 Person th in 1999. Population: County: Age 65 and Above: Guizhou data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: By Age and Region: Rural.

Abstract

The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population.

The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development.

OBJECTIVES

The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: - assess the overall demographic situation in Kenya, - assist in the evaluation of the population and health programmes in Kenya, - advance survey methodology, and - assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

KEY FINDINGS

The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence.

Geographic coverage

The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1993 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons.

As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level.

After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda).

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

b) The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers.

c) Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS.

d) The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster.

Cleaning operations

All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

About this dataset

Context

The dataset tabulates the Valdese population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Valdese across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Valdese was 4,687, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Valdese population was 4,687, an increase of 0.02% compared to a population of 4,686 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Valdese decreased by 29. In this period, the peak population was 4,716 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Valdese is shown in this column.
• Year on Year Change: This column displays the change in Valdese population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset tabulates the Orem population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Orem across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Orem was 95,910, a 2.28% decrease year-by-year from 2021. Previously, in 2021, Orem population was 98,150, a decline of 0.28% compared to a population of 98,424 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Orem increased by 11,521. In this period, the peak population was 98,424 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Orem is shown in this column.
• Year on Year Change: This column displays the change in Orem population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health. The primary objective of the Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, fertility preferences, as well as maternal and child health and nutrition that can be used by program managers and policy makers to evaluate and improve existing programs. In addition, the JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional or crossnational studies.

The content of the 2002 JPFHS was significantly expanded from the 1997 survey to include additional questions on women’s status, reproductive health, and family planning. In addition, all women age 15-49 and children less than five years of age were tested for anemia.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result 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 2002 JPFHS 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 2002 JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Mode of data collection

Face-to-face

Research instrument

The 2002 JPFHS used two questionnaires – namely, the Household Questionnaire and the Individual Questionnaire. Both questionnaires were developed in English and translated into Arabic. The Household Questionnaire was used to list all usual members of the sampled households and to obtain information on each member’s age, sex, educational attainment, relationship to the head of household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. The Household Questionnaire was also used to identify women who are eligible for the individual interview: ever-married women age 15-49. In addition, all women age 15-49 and children under five years living in the household were measured to determine nutritional status and tested for anemia.

The household and women’s questionnaires were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Jordan, using experience gained from the 1990 and 1997 Jordan Population and Family Health Surveys. For each evermarried woman age 15 to 49, information on the following topics was collected:

1. Respondent’s background
2. Birth history
3. Knowledge and practice of family planning
4. Maternal care, breastfeeding, immunization, and health of children under five years of age
5. Marriage
6. Fertility preferences
7. Husband’s background and respondent’s employment
8. Knowledge of AIDS and STIs

In addition, information on births and pregnancies, contraceptive use and discontinuation, and marriage during the five years prior to the survey was collected using a monthly calendar.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding of the open-ended questions.

Data entry and verification started after one week of office data processing. The process of data entry, including one hundred percent re-entry, editing and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by the end of October 2002. A data processing specialist from ORC Macro made a trip to Jordan in October and November 2002 to follow up data editing and cleaning and to work on the tabulation of results for the survey preliminary report. The tabulations for the present final report were completed in December 2002.

Response rate

A total of 7,968 households were selected for the survey from the sampling frame; among those selected households, 7,907 households were found. Of those households, 7,825 (99 percent) were successfully interviewed. In those households, 6,151 eligible women were identified, and complete interviews were obtained with 6,006 of them (98 percent of all eligible women). The overall response rate was 97 percent.

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

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result 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 2002 JPFHS 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 2002 JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir (for central sample), (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household, Individual

Universe

The following rules regarding the population to be covered were applied in listing of households and persons:

1. Under-trial prisoners in jails and indoor patients of hospitals, nursing homes etc., are to be excluded, but residential staff therein will be listed while listing is done in such institutions. The persons of the first category will be considered as normal members of their parent households and will be counted there. Convicted prisoners undergoing sentence will be outside the coverage of the survey.

2. Floating population, i.e., persons without any normal residence will not be listed. But households residing in open space, roadside shelter, under a bridge, etc., more or less regularly in the same place, will be listed.

3. Foreign nationals will not be listed, nor their domestic servants, if by definition the latter belong to the foreign national's household. If, however, a foreign national becomes an Indian citizen for all practical purposes, he or she will be covered.

4. Persons residing in barracks of military and paramilitary forces (like police, BSF, etc.) will be kept outside the survey coverage due to difficulty in conduct of survey therein. However, civilian population residing in their neighbourhood, including the family quarters of service personnel, are to be covered. Permission for this may have to be obtained from appropriate authorities.

5. Orphanages, rescue homes, ashrams and vagrant houses are outside the survey coverage. However, persons staying in old age homes, students staying in ashrams/ hostels and the residential staff (other than monks/ nuns) of these ashrams may be listed. For orphanages, although orphans are not to be listed, the persons looking after them and staying there may be considered for listing.

DEFINITION OF A HOUSEHOLD:

A group of persons normally living together and taking food from a common kitchen will constitute a household. It will include temporary stay-aways (those whose total period of absence from the household is expected to be less than 6 months) but exclude temporary visitors and guests (expected total period of stay less than 6 months). Even though the determination of the actual composition of a household will be left to the judgment of the head of the household, the following procedures will be adopted as guidelines.

(i) Each inmate (including residential staff) of a hostel, mess, hotel, boarding and lodging house, etc., will constitute a single-member household. If, however, a group of persons among them normally pool their income for spending, they will together be treated as forming a single household. For example, a family living in a hotel will be treated as a single household.

(ii) In deciding the composition of a household, more emphasis is to be placed on 'normally living together' than on 'ordinarily taking food from a common kitchen'. In case the place of residence of a person is different from the place of boarding, he or she will be treated as a member of the household with whom he or she resides.

(iii) A resident employee, or domestic servant, or a paying guest (but not just a tenant in the household) will be considered as a member of the household with whom he or she resides even though he or she is not a member of the same family.

(iv) When a person sleeps in one place (say, in a shop or in a room in another house because of space shortage) but usually takes food with his or her family, he or she should be treated not as a single member household but as a member of the household in which other members of his or her family stay.

(v) If a member of a family (say, a son or a daughter of the head of the family) stays elsewhere (say, in hostel for studies or for any other reason), he/ she will not be considered as a member of his/ her parent's household. However, he/ she will be listed as a single member household if the hostel is listed.

Kind of data

Sample survey data [ssd]

Sampling procedure

Outline of sample design: A stratified multi-stage design has been adopted for the 64th round survey. The first stage units (FSU) was the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. However, for the newly declared towns and out growths (OGs) in census 2001 for which UFS had not yet been done, each individual town/ OG was considered as an FSU. The ultimate stage units (USU) was be households in both the sectors. In case of large FSUs i.e. villages/ towns/ blocks requiring hamlet-group (hg)/ sub-block (sb) formation, one intermediate stage was the selection of two hgs/ sbs from each FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (Panchayat wards for Kerala) constitute the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks and for non-UFS towns list of such towns/ OGs was considered as the sampling frame.

Stratification: Within each district of a State/ UT, generally speaking, two basic strata were formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, if there were one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district was considered as another basic stratum. For a few districts, particularly in case of Tamil Nadu, if total number of towns in the district for which UFS was not yet done exceeds certain number, all such towns taken together formed another basic stratum. Otherwise, they were merged with the UFS towns for stratification.

Sub-stratification in the Rural sector: If "r" be the sample size allocated for a rural stratum, the number of sub-strata formed is "r/4?. The villages within a district as per frame were first arranged in ascending order of population. Then sub-strata 1 to "r/4" were demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population.

Sub-stratification in the Urban sector: If "u" be the sample size for a urban stratum, "u/4" number of sub-strata were formed. The towns within a district, except those with population 10 lakhs or more and also the non-UFS towns, were first arranged in ascending order of population. Next, UFS blocks of each town were arranged by IV unit no. × block no. in ascending order. From this arranged frame of UFS blocks of all the towns, "u/4? number of sub-strata were formed in such a way that each sub-stratum had more or less equal number of FSUs. For towns with population 10 lakhs or more, the urban blocks were first arranged by IV unit no. × block no. in ascending order. Then "u/4? number of sub-strata were formed in such a way that each sub-stratum had more or less equal number of blocks. All non-UFS towns taken together within the district formed one sub-stratum.

Total sample size (FSUs): 12688 FSUs for central sample and 13624 FSUs for state sample have been allocated at all-India level.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to population as per census 2001 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators had been kept in view.

Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample was allocated between two sectors in proportion to population as per census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 8 FSUs was allocated to each state/ UT separately for rural and urban areas. Further the State level allocation for both rural and urban have been adjusted marginally in a few cases to ensure that each stratum gets a minimum allocation of 4 FSUs.

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More information on the sampling methodology is available in the document " Instructions to Field Staff - Volume-I"

Mode of data collection

Face-to-face [f2f]

Research instrument

In the 64th round survey, a separate schedule on employment and unemployment (Schedule 10.2), with provision for collecting information on migration particulars, will be canvassed.

The broad structure of the employment and unemployment part of Schedule 10.2 will be the same as that of the schedule canvassed during the NSS 60th round with the following modifications: a) Information on vocational training will not be collected. b) Particulars of persons unemployed on all the 7 days will not be collected in the present round.

The scope for collecting information on migration particulars has been enlarged with the provision for collecting information on: a) Migration particulars of the households which migrated to the place of enumeration during the last 365 days, such as location of last usual residence, pattern of migration and reason for migration. b) Particulars of out-migrants who migrated out to other village/ town, from the household, any time in the past, such as present place of residence, reason for migration, period