Qualified Census Tract geometries within Baltimore City Limits. Based on data from HUD published September 2024.Change Log2022-2-15:- Added FY2022 data- Metadata added- Columns renamed to a standard format- Agency names reformatted with Workday conventions2024-8-27:update the dataset and metadata to reflect the current data and descriptionData Dictionaryfield_namedescriptiondata_typerange_of_possible_valuesexample_valuessearchableCensus Tract 2010 The ID of the US census tract from 2010 Census results. Only Qualified Census tracts are includedTextIDs of QCT's in Baltimore city range from 24510030100 to 24510280500, but not by regular intervals since only tracts designated QCT are listed. The values are not integers, they are numerical IDs.24510070200NogeometryMulti-polygon shapes for each census tractThese are shape polygons, thus don't have a single value or expected rangeNo To leave feedback or ask a question about this dataset, please fill out the following form: Baltimore City Qualified Census Tracts feedback form.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

A detailed description of the sampling methodology is available in appendix to the document "Basic Information Document".

The TLSS sample was designed to allow reliable estimation of poverty and most variables for a variety of other living standard indicators at the various domains of interest based on a representative probability sample on the level of: • Tajikistan as a whole
• Total urban and total rural areas • The five main administrative regions (oblasts) of the country: Dushanbe, Rayons of Republican Subordination (RRS), Sogd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)

The last census was conducted in 2000 and covered all five main administrative regions (oblasts) of the country (Dushanbe, RRS, Sogd, Khatlon, and GBAO). Each oblast was further subdivided into smaller areas called census section, instructor's sector and enumeration sector (ES). Each ES is either totally urban or rural. The list of ESs has census information on the population of each ES, and the ES lists were grouped by oblast.

In 2005, UNICEF implemented a Multiple Indicator Cluster Survey (MICS05) in Tajikistan during which an electronic database of the ES information was created. Information in this database included: oblast, rayon, jamoat, settlement type, city/village, ES code, and population. Information from this database was used in the sample design of the TLSS07.

The total number of clusters for the TLSS07 was established as 270 and total number of households per cluster was established as 18, resulting in a sample size of 4,860. The sample size was determined by taking into account: • The reliability of the survey estimates on both regional and national level • Quality of the data collected for the survey • Cost in time for the data collection • An oversample in 7 rayons in Khatlon

The final cluster allocation is as follows:

Region: Urban / Rural / Total Dushanbe 50 / 0 / 50 RRP 9 / 45 / 54 Sogd 18 / 38 / 56 Khatlon 12 / 59 / 71 GBAO 6 / 33 / 39 Total 95 / 175 / 270

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect information for the TLSS07: a household questionnaire, a female questionnaire for recording information about women of child bearing age, and a community questionnaire. These questionnaires were based on the TLSS questionnaires used in 2003, but had some changes. Questions were added to existing modules and new modules were added to collect information to be used for MICS analyses. These included HIV/AIDS awareness, and Immunizations and Anthropometric Measurements for children 0 to 5 years old. Other new modules on Migration, Financial Services, Subjective Poverty and Food Security, and Subjective Beliefs were also added. The Labor Market Module was changed substantially from 2003 to better look at the informal labor market. The food expenditures module included additional food products. The HIV/AIDS questions were removed from the female questionnaire and were applied to all household members 12 to 49 years old.

The Second Round Household Questionnaire was shorter and was used primarily to collect additional information that was not possible to collect in the First Round. Because the First Round questionnaire was very long, it was decided to collect some information in a second round of visits to the households. The Household Questionnaire was the main instrument used during the Second Round. The female questionnaire was only used if females were added to the household after the First Round and the community questionnaire was not repeated. In the Second Round Household Questionnaire, the time reference period for the Food Security module was reduced from 4 weeks to 2 weeks. This was done because in the households visited at the beginning of the Second Round, a 4 week period would have included the last portion of the Ramadan period.

Cleaning operations

Data Entry and Cleaning

The data entry program was designed using CSPro, a data entry package developed by the US Census Bureau. This software allows programs to be developed to perform three types of data checks: (a) range checks; (b) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (c) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data from the First Round were key entered at the Goskomstat headquarters in Dushanbe starting 4 October 2007 through 25 November 2007. The Second Round and Sughd data were key entered from 26 November 2007 through 12 December 2007. All of the data were double entered with both the First Round, Second Round and Sughd re-collection double entry being completed by 22 January 2008.

The data cleaning process began in February 2008 and was completed at the end of May 2008.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

The census is Canada's largest and most comprehensive data source conducted by Statistics Canada every five years. The Census of Population collects demographic and linguistic information on every man, woman and child living in Canada.The data shown here is provided by Statistics Canada from the 2011 Census as a custom profile data order for the City of Vancouver, using the City's 22 local planning areas. The data may be reproduced provided they are credited to Statistics Canada, Census 2011, custom order for City of Vancouver Local Areas.Data accessThis dataset has not yet been converted to a format compatible with our new platform. The following links provide access to the files from our legacy site: Census local area profiles 2011 (CSV) Census local area profiles 2011 (XLS) Dataset schema (Attributes)Please see the Census local area profiles 2011 attributes page. NoteThe 22 Local Areas is defined by the Census blocks and is equal to the City'​s 22 local planning areas and includes the Musqueam 2 reserve.Vancouver CSD (Census Subdivision) is defined by the City of Vancouver municipal boundary which excludes the Musqueam 2 reserve but includes Stanley Park. Vancouver CMA (Census Metropolitan Area) is defined by the Metro Vancouver boundary which includes the following Census Subdivisions: Vancouver, Surrey, Burnaby, Richmond, Coquitlam, District of Langley, Delta, District of North Vancouver, Maple Ridge, New Westminster, Port Coquitlam, City of North Vancouver, West Vancouver, Port Moody, City of Langley, White Rock, Pitt Meadows, Greater Vancouver A, Bowen Island, Capilano 5, Anmore, Musqueam 2, Burrard Inlet 3, Lions Bay, Tsawwassen, Belcarra, Mission 1, Matsqui 4, Katzie 1, Semiahmoo, Seymour Creek 2, McMillian Island 6, Coquitlam 1, Musqueam 4, Coquitlam 2, Katzie 2, Whonnock 1, Barnston Island 3, and Langley 5. In 2011 Statistics Canada replaced the "long form" census with a voluntary National Household Survey. The result of the survey will not be directly comparable with previous census data. In 2006 there were changes made to the definition of households. A number of Single Room Occupancy and Seniors facilities were considered to be dwellings in 2001, and collective dwellings in 2006. The City believes a similar change occurred on some properties between 2006 and 2011. This would explain why the numbers of "Apartments under 5 stories" has fallen in some locations.Note that for the first time in 2011, three language questions (knowledge of official languages, home language and mother tongue) were included on the census questionnaire that was administered to 100% of the population.Language data and analysis published for all censuses since 1996 have been based almost exclusively on responses from the long-form census questionnaire administered to 20% of the population. However, Statistics Canada has observed changes in patterns of response to both the mother tongue and home language questions that appear to have arisen from changes in the placement and context of the language questions on the 2011 Census questionnaire relative to previous censuses. As a result, Canadians appear to have been less inclined than in previous censuses to report languages other than English or French as their only mother tongue, and also more inclined to report multiple languages as their mother tongue and as the language used most often at home. Data currencyThe data for Census 2011 was collected in May 2011. Data accuracyStatistics Canada is committed to protect the privacy of all Canadians and the confidentiality of the data they provide to us. As part of this commitment, some population counts of geographic areas are adjusted in order to ensure confidentiality. Counts of the total population are rounded to a base of 5 for any dissemination block having a population of less than 15. Population counts for all standard geographic areas above the dissemination block level are derived by summing the adjusted dissemination block counts. The adjustment of dissemination block counts is controlled to ensure that the population counts for dissemination areas will always be within 5 of the actual values. The adjustment has no impact on the population counts of census divisions and large census subdivisions. Websites for further information Statistics Canada 2011 Census Dictionary Local area boundary dataset

This web map is provides the data and maps used in the story map Population density and diversity in New Zealand, created by Stats NZ. It uses Statistical Area 1 (SA1) data collected and published as part of the 2018 Census. The web map uses a mapping technique called multi-variate dot density mapping. The data used in the map can be found at this web service - 2018 Census Individual part 1 data by SA1.For questions or comments on the data or maps, please contact info@stats.govt.nz Census Data Quality Notes:We combined data from the census forms with administrative data to create the 2018 Census dataset, which meets Stats NZ’s quality criteria for population structure information.We added real data about real people to the dataset where we were confident the people should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.Data quality for 2018 Census provides more information on the quality of the 2018 Census data.An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial Report of the 2018 Census External Data Quality Panel (September 2019), assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. Its second report 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables. In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables.The Quick guide to the 2018 Census outlines the key changes we introduced as we prepared for the 2018 Census, and the changes we made once collection was complete.The geographic boundaries are as at 1 January 2018. See Statistical standard for geographic areas 2018.2018 Census – DataInfo+ provides information about methods, and related metadata.Data quality ratings for 2018 Census variables provides information on data quality ratings.

The Australian Census Longitudinal Dataset (ACLD) brings together a 5% sample from the 2006 Census with records from the 2011 Census to create a research tool for exploring how Australian society is changing over time. In taking a longitudinal view of Australians, the ACLD may uncover new insights into the dynamics and transitions that drive social and economic change over time, conveying how these vary for diverse population groups and geographies. It is envisaged that the 2016 and successive Censuses will be added in the future, as well as administrative data sets. The ACLD is released in ABS TableBuilder and as a microdata product in the ABS Data Laboratory. \r \r The Census of Population and Housing is conducted every five years and aims to measure accurately the number of people and dwellings in Australia on Census Night. \r \r Microdata products are the most detailed information available from a Census or survey and are generally the responses to individual questions on the questionnaire. They also include derived data from answers to two or more questions and are released with the approval of the Australian Statistician.\r The following microdata products are available for this longitudinal dataset: \r •ACLD in TableBuilder - an online tool for creating tables and graphs. \r •ACLD in ABS Data Laboratory (ABSDL) - for in-depth analysis using a range of statistical software packages.\r \r

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

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 DHS is intended to serve as a primary source for international population and health information for policymakers and for the research community. In general, DHS has four objectives: - To provide participating countries with a database and analysis useful for informed choices, - To expand the international population and health database, - To advance survey methodology, and - To help develop in participating countries technical skills and resources necessary to conduct demographic and health surveys.

Apart from estimating fertility and contraceptive prevalence rates, DHS also covers the topic of child health, which has become the focus of many development programs aimed at improving the quality of life in general. The Indonesian DHS survey did not include health-related questions because this information was collected in the 1987 SUSENAS in more detail and with wider geographic coverage. Hence, the Indonesian DHS was named the "National Indonesian Contraceptive Prevalence Survey" (NICPS).

The National Indonesia Contraceptive Prevalence Survey (NICPS) was a collaborative effort between the Indonesian National Family Planning Coordinating Board (NFPCB), the Institute for Resource Development of Westinghouse and the Central Bureau of Statistics (CBS). The survey was part of an international program in which similar surveys are being implemented in developing countries in Asia, Africa, and Latin America.

The 1987 NICPS was specifically designed to meet the following objectives: - To provide data on the family planning and fertility behavior of the Indonesian population necessary for program organizers and policymakers in evaluating and enhancing the national family planning program, and - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and availability of contraception.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The 1987 NICPS sample was drawn from the annual National Socioeconomic Survey (popularly called SUSENAS) which was conducted in January and February 1987. Each year the SUSENAS consists of one set of core questions and several modules which are rotated every three years. The 1987 SUSENAS main modules covered household income, expenditure, and consumption. In addition, in collaboration with the Ministry of Health, information pertaining to children under 5 years of age was collected, including food supplement patterns, and measurement of height, weight, and arm circumference. In this module, information on prenatal care, type of birth attendant, and immunization was also asked.

This national survey covered over 60,000 households which were scattered in almost all of the districts. The data were collected by the "Mantri Statistik", a CBS officer in charge of data collection at the sub-district level. All households covered in the selected census blocks were listed on the SSN 87-LI form. This form was then used in selecting samples for each of the modules included in the SUSENAS. This particular form was also used to select the sample households in the 1987 NICPS.

Sample selection in the 1987 SUSENAS utilized a multistage sampling procedure. The first stage consisted of selecting a number of census blocks with probability proportional to the number of households in the block. Census blocks are statistical areas formed before the 1980 Population Census and contain approximately 100 households. At the second stage, households were selected systematically from each sampled census block.

Selection of the 1987 NICPS sample was also done in two stages. The first stage was to select census blocks from the those selected in the 1987 SUSENAS. At the second stage a number of households was selected systematically from the selected census block.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was used to record all members of the selected households who usually live in the household. The questionnaire was utilized to identify the eligible respondents in the household, and to provide the numerator for the computation of demographic measurements such as fertility and contraceptive use rates.

The individual questionnaire was used for all ever-married women aged 15-49, and consisted of the following eight sections:

Section 1 Respondent's Background

This part collected information related to the respondent and the household, such as current and past mobility, age, education, literacy, religion, and media exposure. Information related to the household includes source of water for drinking, for bathing and washing, type of toilet, ownership of durable goods, and type of floor.

Section 2 Reproduction

This part gathered information on all children ever born, sex of the child, month and year of birth, survival status of the child, age when the child died, and whether the child lived with the respondent. Using the information collected in this section, one can compute measures of fertility and mortality, especially infant and child mortality rates. With the birth history data collected in this section, it is possible to calculate trends in fertility over time. This section also included a question about whether the respondent was pregnant at the time of interview, and her knowledge regarding women's fertile period in the monthly menstrual cycle.

Section 3 Knowledge and Practice of Family Planning

This section is one of the most important parts of the 1987 NICPS survey. Here the respondent was asked whether she had ever heard of or used any of the family planning methods listed. If the respondent had used a contraceptive method, she was asked detailed questions about the method. For women who gave birth to a child since January 1982, questions on family planning methods used in the intervals between births were also asked. The section also included questions on source of methods, quality of use, reasons for nonuse, and intentions for future use. These data are expected to answer questions on the effectiveness of family planning use. Finally, the section also included questions about whether the respondent had been visited by a family planning field worker, which community-level people she felt were most appropriate to give family planning information, and whether she had ever heard of the condom, DuaLima, the brand being promoted by a social marketing program.

Section 4 Breastfeeding

The objective of this part was to collect information on maternal and child health, primarily that concerning place of birth, type of assistance at birth, breastfeeding practices, and supplementary food. Information was collected for children born since January 1982.

Section 5 Marriage

This section gathered information regarding the respondent's age at first marriage, number of times married, and whether the respondent and her husband ever lived with any of their parents. Several questions in this section were related to the frequency of sexual intercourse to determine the respondent's risk of pregnancy. Not all of the data collected in this section are presented in this report; some require more extensive analysis than is feasible at this stage.

Section 6 Fertility Preferences

Intentions about having another child, preferred birth interval, and ideal number of children were covered in this section.

Section 7 Husband's Background and Respondent's Work

Education, literacy and occupation of the respondent's husband made up this section of the questionnaire. It also collected information on the respondent's work pattern before and after marriage, and whether she was working at the time of interview.

Section 8 Interview Particulars

This section was used to record the language used in the interview and information about whether the interviewer was assisted by an interpreter. The individual questionnaire also included information regarding the duration of interview and presence of other persons at particular points during the interview. In addition to the questionnaires, two manuals were developed. The manual for interviewers contained explanations of how to conduct an interview, how to carry out the field activity, and how to fill out the questionnaires. Since information regarding age was vital in this survey, a table to convert months from Javanese, Sundanese and Islamic calendar systems to the Gregorian calendar was attached to the 1987 NICPS manual for the interviewers.

Response rate

The NICPS covered a sample of nearly 15,000 households to interview 11,884 respondents. Respondents for the individual interview were ever-married women aged 15-49. During the data collection, 14,141 out of the 14,227 existing households and 11,884 out of 12,065 eligible women were successfully interviewed. In general, few problems were encountered during interviewing, and the response rate was high--99 percent for households and 99 percent for individual respondents.

Note: See APPENDIX A in the report for more information.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) non-sampling error and (2) sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and

The National Household Survey (NHS) was conceived to replace the mandatory long-form census questionnaire. The content of the NHS 2011 is similar to the past long-form questionnaire, although some questions and sections have changed. NHS Data Tables provide statistical information about people in Canada by their demographic, social and economic characteristics as well as information about the housing units in which they live. Geography levels include: 1) Canada, provinces and territories 2) Census metropolitan areas and census agglomerations

Abstract

During October 1996 Statistics South Africa recorded the details of people living in more than nine million households in South Africa, as well as those in hostels, hotels and prisons. Census 1996 was the first nation wide census since the splitting up of the country under apartheid after 1970 and sought to apply the same methodology to everyone: visiting the household, and obtaining details about all its members from a representative who was either interviewed, or else filled in the questionnaire in their language of choice.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered households and household members in households in the nine provinces of South Africa.

Kind of data

Sample survey data

Sampling procedure

A sample of 1600 Enumerator Areas (EA's) was produced in conjunction with the sample for the 1996 Population Census post-enumeration survey. A two stage sampling procedure was applied in the following manner.

The first stratification was done by province, as well as by type of EA (formal or informal urban areas, commercial farms, traditional authority areas or other non-urban areas). Originally eight hundred EA's were allocated to each strata by province proportionately. Later some adjustments were made to ensure adequate representation of smaller provinces such as the Northern Cape. Independent systematic samples of EA's were drawn for each stratum within each province. The sampling frame that was used was constructed from the preliminary database of EA's which was established during the demarcation and listing phase of the 1996 population census. In the second phase 10 households were drawn from each EA on the western and eastern side of the EA drawn for the post enumeration survey. This meant 10 households per EA in 1600 different EA's, that is 16 000 households in total.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data files in the October Household Survey 1996 (OHS 1996) correspond to the following sections in the questionnaire:

House: Data from FLAP, Section 1 and Section 7 Person: Data from Section 2 Worker: Data from Section 3 Migrant: Data from Section 4 Death: Data from Section 5 Births: Data from Section 6 - This data had a considerable number of problems and will not be published. Income: Data from Section 7 (included in House) Domestic: Data from Section 8

Data appraisal

Questionnaire: The October Household Survey 1996 questionnaire had incorrect FLAP data. No Population Group question was indicated on the FLAP. DataFirst notified Statistics SA who supplied a corrected questionnaire which is the one now available with the dataset.

Household IDs: In the previous version of the 1996 October Household Survey dataset archived by DataFirst the HHID were not unique. This was corrected in the first version disseminated by DataFirst, version 1. Version 1.1 keeps this correction, but data users should check versions not obtained from DataFirst and replace these with the latest version available from DataFirst.

Linking Files: The Metadata for the OHS 1996 provides an explanation for merging the files in the files in the OHS 1996 dataset: "The data from different files can be linked on the basis of the record identifiers. The record identifiers are composed of the first few fields in each file. Each record contains the three fields Magisterial District, Enumeration area, and Visiting point number. These eleven digits together constitute a unique household identifier. All records with a given household identifier, no matter which file they are in, belong to the same household. For individuals, a further two digits constituting the Person number, when added to the household identifier, creates a unique individual identifier. Again, these can be used to link records from the PERSON and WORK files. The syntax needed to merge information from different files will differ according to the statistical package used (October Household Survey 1996: Metadata: General Notes: 2).” According to the above, to generate household IDs it is necessary to use a combination of magisterial district number (mdnumber), enumeration area number (eanumber) and visiting point number (vpnumber). To generate person IDs it is necessary to use the above with the person number (personnu).

These variables are named as such in the OHS 1996 House, OHS 1996 Births, OHS 1996 Migrant, OHS 1996 Deaths, OHS 1996 Household Income Other, OHS 1996 Other, OHS 1996 Domestic and OHS 1996 Flap data files. However, in the OHS 1996 Worker and OHS 1996 Person data files the variable for magisterial district number is “distr”, the variable for Enumeration Area is “ea” and the variable for visiting point number is called "visp”. The variable for person number in these files is called “respno”.

The metadata provided to DataFirst with this dataset does not discuss these changes.

October Household Survey 1996 Births file: Births data was collected by Section 6 of the OHS 1996 questionnaire, completed for all women younger than 55 years who had ever given birth. The metadata for this survey from Statistics SA states that “This data had a considerable number of problems and will not be published” The dataset provided by DataFirst therefore does not include the original “births” file. Those in possession of this file from unofficial versions of the dataset should note the following problems with the data in the OHS 1996 births file:

Variable name: eegender Question 6.2: Is/was (the child) a boy or a girl? Valid range: 1 (boy) - 2 (girl) Data quality issue: There is a third response value of 0 with no description

Variable name: livinghh Question 6.4: If alive: Is (the child) currently living with this household? Valid range: 1 (yes) - 2 (no) Data quality issue: This variable has an additional response value (0), which has no description

Variable name: agealive Question 6.5: If alive: How old is he/she? This question was asked of all women younger than 55 years who have ever given birth to provide the age of their living children. Data quality issue: responses range from 0-77 for age of child (assuming age 99 is for missing responses) which is outside the plausible range.

Variable name: agenaliv Question 6.6: If dead: How old was (the child) when he/she died? Data quality issue: The format of the age at death variable is not clear

Variable name: datebirt Question 6.7: [All children]: In what year and month was (the child) born? Data quality issue: There are problems with the format of the date of birth variable

Variable name: wherebor Question 6.8: [All children]: Where was (the child) born? Data quality issue: There are only three options for the place of birth in the questionnaire (in a hospital, in a clinic and elsewhere), but the data has 10 response values (0-9) with no explanation for this in the metadata.

Variable name: regstere Question 6.9 [All children] Was the birth registered? Valid range: 1(yes) - 2 (no) Data quality issue: There are 4 response values (0-3) for this variable

Description: The questions contained in SASAS questionnaires one and two for 2007 were asked of a half sample of approximately 3500 respondents each. The data set contains 2907 records and 116 variables. Topics included in the questionnaires are: democracy, intergroup relations, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: child poverty, poverty, household expenditure, women, childcare and work (client module)climate change / global warming, soccer world cup, service delivery, Batho Pele principles, International Social Surveys Programme (ISSP) module: leisure time and sport and smoking and tobacco behaviour (client module). Abstract: The primary objective of the South African Social Attitudes Survey (SASAS) is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa. In meeting this objective, the HSRC is carefully and consistently monitoring and providing insight into changes in attitudes among various socio-demographic groupings. SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how its changing political and institutional structures interact over time with changing social attitudes and values. The survey has been designed to yield a national representative sample of adults aged 16 and older, using the Human Sciences Research Council's (HSRC) second Master Sample, which was designed in 2007 and consists of 1000 primary sampling units (PSUs). These PSUs were drawn, with probability proportional to size from a pre-census 2001 list of 80780 enumerator areas (EAs). As the basis of the 2007 SASAS round of interviewing, a sub-sample of 500 EAs (PSUs) was drawn from the second master sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. The survey is conducted annually and the 2007 survey is the fifth wave in the series. To accommodate the wide variety of topics included in the survey, two questionnaires are administered simultaneously. Apart from the standard set of demographic and background variables, each version of the questionnaire contained a harmonised core module. The questions contained in the core modules of the two SASAS questionnaires (demographics and core thematic issues) were asked of 7000 respondents, while the remaining rotating modules were asked of a half sample of approximately 3500 respondents each. The core module remains constant for with the aim of monitoring change and continuity in a variety of socio-economic and socio-political variables. In addition, a number of themes are accommodated in rotation. The rotating element of the survey consists of two or more topic-specific modules in each round of interviewing and is directed at measuring a range of policy and academic concerns and issues that require more detailed examination at a specific point in time than the multi-topic core module would permit. Topics included in the questionnaires are: democracy, national identity, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: child poverty, poverty, household expenditure, climate change / global warming, Soccer World Cup, service delivery, Batho Pele principles and smoking and tobacco behaviour. International Social Survey Programme. (ISSP web page:www.issp.org/) The International Social Survey Programme (ISSP) is run by a group of research organisations, each of which undertakes to field annually an agreed module of questions on a chosen topic area. SASAS 2003 represents the formalisation of South Africa's inclusion in the ISSP, the intention being to include the module in one of the SASAS questionnaires in each round of interviewing. Each module is chosen for repetition at intervals to allow comparisons both between countries (membership currently stands at 48) and over time. In 2007, the chosen subject was the leisure time and sport and the module was carried in version two of the questionnaire (Qs.1-60). This data can be accessed through the ISSP data portal (see link above).

The objectives of the Kiribati Census changed over time shifting from earlier years where they were essentially household registrations and counts, to now where a national population census stands supreme as the most valuable single source of statistical data for Kiribati.

Census data is now widely used to evaluate: - The availability of basic household needs in key sectors, to identify disadvantaged areas and help set priorities for action plans; - Benefits of development programmes in particular areas, such as literacy, employment and family planning; In addition, census data is useful to asses manpower resources, identify areas of social concern and for the improvement in the social and economic status of women by giving more information and formulating housing policies and programmes and investment of development funds.

The census objective is to make a quick and sweeping count to avoid double counting—or under-counting for that matter. This is the main reason why the questions are often restricted to a manageable size—i.e. not to wantonly list any question one thinks of. The whole purpose of the questionnaire design is to ensure the most needed questions are asked, in addition to the count, structure and distribution of the population.

v01 - Cleaned and labelled version of the Master file.

-Population: Population's relationship, marital status, religion, residence, origins, education, work status, women's characteristics (on children given birth to)
-Housing: Living quarters and its conditions, water and electricity access, sanitation, waste disposal, household durables and livestock & pets

• Collection start: 2015
• Collection end: 2015

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September of 2021. It can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas. We encourage all data users to download and review the following resources provided as attachments to this dataset (click on "Show More" to view): 1) 2021 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2021 CEV dataset 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) 2021 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta) and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps 4) 2021 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 5) 2021 CEV Nonresponse Analysis – technical report produced by U.S. Census Bureau 6) 2021 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

Abstract

The 2009 Census falls within the 2010 Round of Pacific Census, ten years after the 1999 census.

The results of the 2009 census will be required to:

a. help produce high-quality information for planning, decision-making, and monitoring of development progress in Solomon Islands. This implies very heavy data requirements and these requirements are continuously increasing, particularly towards development planning, implementation monitoring and evaluation of Government policies outlined in NERDEP and the current Medium Term Development Strategies.

b. The data from the Census will also be used for monitoring the achievement of the Millennium Development Goals (MDG's) and other goals included in the International Conference for Population & Development (ICPD).

c. check whether the population policies, which were put in place after the 1986 census on the basis of 1976-86 population trends and then as reviewed in the early 2000s in respect of the 1999 population trends, proved effective, and

d. Establish a new benchmark and a new set of post-1999 population trends on which to base a reconsideration of existing (population) policies in the framework of sustained and sustainable development.

e. Also, the results of this census will help facilitate updating of constituencies in preparation to the 2010 national election of Solomon Islands.

f. Further to these, the results of the census will provide a sample Frame from which further household capability surveys which include a household income expenditure in 2010/2011, a second demographic and health survey (DHS) 2011/2012 and a Labour Force Survey before the next census can be undertaken.

g. The 2009 census will also provide the much needed village level data on population, resources and infrastructure for government's bottom-up approach development policy initiative.

Accepting the notion that a new census is required and that a number of overseas aid organisations will be able to support the government on an undertaking similar to the 1999 census, the following points are considered in more detail in this project proposal.

It is recommended that the present census interval should not exceed ten years and that the same month should be selected in 2009, for the period of enumeration as in 1999, mainly to ensure that seasonal factors would not reduce the comparability of the information provided by the two censuses. As a result of this recommendation, 22nd November 2009 is therefore proposed as the new census date. This date will be formally announced by the Prime Minister in line with the Census Act.

For making current administrative decisions and prepare longer term socio-economic development policies governments and private organisations need reliable up-to-date knowledge about available natural and human resources. In a country like Solomon Islands one of the most important statistical systems for obtaining the required socio-economic information is the population census. This does not only provide a numerical description of the population at a given census date - through comparison with previous census results - but also of the ongoing trends in a sustained and sustainable development of certain population characteristics such as changes in population growth, age composition, direction of mobility and levels of urbanisation, economic activities and educational status. Such knowledge may allow the development planner to devise policies that will stem the flow of trends considered not in line with development aims. Alternatively, trends considered fitting can be identified and fostered by the introduction of appropriate policies. The success thereof can then be assessed when a next census is held some ten years later.

Geographic coverage

The 2009 Population and Housing Census Covers 100% of geography as in Urban and Rural Areas for the Entire Country :

The Solomon Islands as a whole by:

• 10 Provinces
• Constituencies
• Wards
• Enumeration Areas
• Household Level

Analysis unit

1. Population ( Urban and Rural )
2. Household ( Urban and Rural )
3. Provincial records
4. National Records
5. Geography

Universe

The National Population and Housing Census 2009,covers the entire Population,the ones in the Hotels,Motels,Ships which was collected when all ship arrived at wharf during the Census times. It covers all overseas people living in and aorund Solomon Islands,Urban and Rural,excluded the Diplomats. In overroll:- This is any individual member of the household or institution who is present on the census night and is therefore counted in the census. This includes every young and old, male of female, expatriates or residents, tourist and locals alike.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Census - Not applicable for complete enumeration survey

This section only apply for Sample Surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. QUESTIONNAIRE AND SCANNING

The need to set up the questionnaire in terms of suitability for local printing have done, using a software package called in-design, or whatever is most appropriate, which will then allow “optimisation ” for scanning with check boxes, drop-out colours (colours which are then filtered out by the scanner) etc. It is important that the questions are laid out correctly to make sure the results of the scan are possible and legible and eligible or recorded. Prior to the pilot census, the questionnaire needs to be finalised and come up with something everyone is happy with, finalise it and then make sure it works (if questions/formatting needs amendments as a result of the pilot, such changes will of course be done).

The questionnaire was finalised and a reliable printer to print the questionnaires was sought in advance through the tender bidding process. There are a whole series of things the Census office need to check here to make sure that the job gets done to a sufficient standard and that the scanning works well (good quality machines, paper, ink, air conditioned operating environment etc). There was no printing company in Honiara who can do this thus the printing done in Australia

In addition the questionnaire develop and were all in English language as people normally understand the english reading than the Solomons pidgin.The quetionnaire was design in Adobe Illustrator as to make sure the lines and writtings all well linned and parallel to what had written.Hence the census form have to have the right color which the scannning has to read and can easily collect the characters and values. As such the census forms had been well protected while in field and properly manage in a way which the forms will not distroyed easily by rain or sea. Hence,the census questionnaire covers Households and Housing.All Persons and GPS,more detailed in Scope section.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) After Scanning data exported to CSPro4.0 edited done by data proccessing officer. b) Secondly the Data proccessing officer pass the data to Data verifiers c) Structure checking and completeness by verifiers in terms of wrong written numbers and spellings

d) Batch editing: - Variables out of range - Fertility Questions - Coding and Value sets - Editing of Variables..eg.age,date of birth and etc.

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Sampling error estimates

Not apply for Census

Data appraisal

The 2009 Census data was involved people from SPC and SINSO for checking and assisting in terms of cleaning,and verifying.After Census dataset cleaned on 19/09/2011,Census dataset has checked my running tabulation on Male and female by villages,and checking Villages were all coded and no village coded with zero "0".mean makesure all villages has values and makesure the villages with same name coded with unique code where they located by their on provinces.

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

Abstract

The 2022 Nepal Demographic and Health Survey (NDHS) is the sixth survey of its kind implemented in the country as part of the worldwide Demographic and Health Surveys (DHS) Program. It was implemented by New ERA under the aegis of the Ministry of Health and Population (MoHP) of the Government of Nepal with the objective of providing reliable, accurate, and up-to-date data for the country.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.

The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-49, 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 2022 NDHS is an updated version of the frame from the 2011 Nepal Population and Housing Census (NPHC) provided by the National Statistical Office. The 2022 NDHS considered wards from the 2011 census as sub-wards, the smallest administrative unit for the survey. The census frame includes a complete list of Nepal’s 36,020 sub-wards. Each sub-ward has a residence type (urban or rural), and the measure of size is the number of households.

In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.

Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nepal. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.

Cleaning operations

Data capture for the 2022 NDHS was carried out with Microsoft Surface Go 2 tablets running Windows 10.1. Software was prepared for the survey using CSPro. The processing of the 2022 NDHS data began shortly after the fieldwork started. When data collection was completed in each cluster, the electronic data files were transferred via the Internet File Streaming System (IFSS) to the New ERA central office in Kathmandu. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were immediately communicated to the field teams for review so that problems would be mitigated going forward. Secondary editing, carried out in the central office at New ERA, involved resolving inconsistencies and coding the open-ended questions. The New ERA senior data processor coordinated the exercise at the central office. The NDHS core team members assisted with the secondary editing. The paper Biomarker Questionnaires were compared with the electronic data file to check for any inconsistencies in data entry. The pictures of vaccination cards that were captured during data collection were verified with the data entered. Data processing and editing were carried out using the CSPro software package. The concurrent data collection and processing offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed by July 2022, and the final cleaning of the data set was completed by the end of August.

Response rate

A total of 14,243 households were selected for the sample, of which 13,833 were found to be occupied. Of the occupied households, 13,786 were successfully interviewed, yielding a response rate of more than 99%. In the interviewed households, 15,238 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 14,845 women, yielding a response rate of 97%. In the subsample of households selected for the men’s survey, 5,185 men age 15-49 were identified as eligible for individual interviews and 4,913 were successfully interviewed, yielding a response rate of 95%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors result from mistakes made in implementing data collection and in data processing, such as failing to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and entering the data incorrectly. Although numerous efforts were made during the implementation of the 2022 Nepal Demographic and Health Survey (2022 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 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the

The National Household Survey (NHS) was conceived to replace the mandatory long-form census questionnaire. The content of the NHS 2011 is similar to the past long-form questionnaire, although some questions and sections have changed. This profile presents information from the 2011 National Household Survey (NHS) for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, dissemination areas, federal electoral districts, and forward sortation areas. The forward sortation areas profile was created as a custom tabulation by the University of Toronto, and subsequently shared with ODESI and the DLI. NHS data topics include: Immigration and Ethnocultural Diversity; Aboriginal Peoples; Education and Labour; Mobility and Migration; Language of work; Income and Housing. 2011 Census data topics include: Population and dwelling counts; Age and sex; Families, households and marital status; Structural type of dwelling and collectives; and Language. The Aboriginal Population Profile presents information on the Aboriginal identity population from the 2011 National Household Survey (NHS). The profile for the NHS Special Collection for 13 Indian reserves and Indian settlements in Northern Ontario provides information from a special data collection following the 2011 National Household Survey (NHS).

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.