Abstract

This study is an experiment designed to compare the performance of three methodologies for sampling households with migrants:

• a stratified sample using the census to sample census tracts randomly, in which each household is then listed and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest;
• a snowball survey in which households are asked to provide referrals to other households with migrant members;
• an intercept point survey (or time-and-space sampling survey), in which individuals are sampled during set time periods at a prespecified set of locations where households in the target group are likely to congregate.

Researchers from the World Bank applied these methods in the context of a survey of Brazilians of Japanese descent (Nikkei), requested by the World Bank. There are approximately 1.2-1.9 million Nikkei among Brazil’s 170 million population.

The survey was designed to provide detail on the characteristics of households with and without migrants, to estimate the proportion of households receiving remittances and with migrants in Japan, and to examine the consequences of migration and remittances on the sending households.

The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Researchers can directly compare answers to the same questions across survey methodologies and determine the extent to which the intercept and snowball surveys can give similar results to the more expensive census-based survey, and test for the presence of biases.

Geographic coverage

Sao Paulo and Parana states

Analysis unit

Japanese-Brazilian (Nikkei) households and individuals

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity but instead asks questions on race, country of birth and whether an individual has lived elsewhere in the last 10 years. On the basis of these questions, a household is classified as (potentially) Nikkei if it has any of the following: 1) a member born in Japan; 2) a member who is of yellow race and who has lived in Japan in the last 10 years; 3) a member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China) and who did not live in a foreign country other than Japan in the last 10 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

1) Stratified random sample survey

Two states with the largest Nikkei population - Sao Paulo and Parana - were chosen for the study.

The sampling process consisted of three stages. First, a stratified random sample of 75 census tracts was selected based on 2000 Brazilian census. Second, interviewers carried out a door-to-door listing within each census tract to determine which households had a Nikkei member. Third, the survey questionnaire was then administered to households that were identified as Nikkei. A door-to-door listing exercise of the 75 census tracts was then carried out between October 13th, 2006, and October 29th, 2006. The fieldwork began on November 19, 2006, and all dwellings were visited at least once by December 22, 2006. The second wave of surveying took place from January 18th, 2007, to February 2nd, 2007, which was intended to increase the number of households responding.

2) Intercept survey

The intercept survey was designed to carry out interviews at a range of locations that were frequented by the Nikkei population. It was originally designed to be done in Sao Paulo city only, but a second intercept point survey was later carried out in Curitiba, Parana. Intercept survey took place between December 9th, 2006, and December 20th, 2006, whereas the Curitiba intercept survey took place between March 3rd and March 12th, 2007.

Consultations with Nikkei community organizations, local researchers and officers of the bank Sudameris, which provides remittance services to this community, were used to select a broad range of locations. Interviewers were assigned to visit each location during prespecified blocks of time. Two fieldworkers were assigned to each location. One fieldworker carried out the interviews, while the other carried out a count of the number of people with Nikkei appearance who appeared to be 18 years old or older who passed by each location. For the fixed places, this count was made throughout the prespecified time block. For example, between 2.30 p.m. and 3.30 p.m. at the sports club, the interviewer counted 57 adult Nikkeis. Refusal rates were carefully recorded, along with the sex and approximate age of the person refusing.

In all, 516 intercept interviews were collected.

3) Snowball sampling survey

The questionnaire that was used was the same as used for the stratified random sample. The plan was to begin with a seed list of 75 households, and to aim to reach a total sample of 300 households through referrals from the initial seed households. Each household surveyed was asked to supply the names of three contacts: (a) a Nikkei household with a member currently in Japan; (b) a Nikkei household with a member who has returned from Japan; (c) a Nikkei household without members in Japan and where individuals had not returned from Japan.

The snowball survey took place from December 5th to 20th, 2006. The second phase of the snowballing survey ran from January 22nd, 2007, to March 23rd, 2007. More associations were contacted to provide additional seed names (69 more names were obtained) and, as with the stratified sample, an adaptation of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved.

The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was three links.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Stratified sampling and snowball survey questionnaire

This questionnaire has 36 pages with over 1,000 variables, taking over an hour to complete.

If subjects refused to answer the questionnaire, interviewers would leave a much shorter version of the questionnaire to be completed by the household by themselves, and later picked up. This shorter questionnaire was the same as used in the intercept point survey, taking seven minutes on average. The intention with the shorter survey was to provide some data on households that would not answer the full survey because of time constraints, or because respondents were reluctant to have an interviewer in their house.

2) Intercept questionnaire

The questionnaire is four pages in length, consisting of 62 questions and taking a mean time of seven minutes to answer. Respondents had to be 18 years old or older to be interviewed.

Response rate

1) Stratified random sampling 403 out of the 710 Nikkei households were surveyed, an interview rate of 57%. The refusal rate was 25%, whereas the remaining households were either absent on three attempts or were not surveyed because building managers refused permission to enter the apartment buildings. Refusal rates were higher in Sao Paulo than in Parana, reflecting greater concerns about crime and a busier urban environment.

2) Intercept Interviews 516 intercept interviews were collected, along with 325 refusals. The average refusal rate is 39%, with location-specific refusal rates ranging from only 3% at the food festival to almost 66% at one of the two grocery stores.

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

Abstract

The 2003 Agriculture Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmer organisations, etc. As a result the dataset is both more numerous in its sample and detailed in its scope compared to previous censuses and surveys. To date this is the most detailed Agricultural Census carried out in Africa. The census was carried out in order to:

· Identify structural changes if any, in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in rural infrastructure and in the level of agriculture household living conditions; · Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stake holders. · Establish baseline data for the measurement of the impact of high level objectives of the Agriculture Sector Development Programme (ASDP), National Strategy for Growth and Reduction of Poverty (NSGRP) and other rural development programs and projects. · Obtain benchmark data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

Household, Individual

Universe

Small scale farmers, Large Scale Farmers, Community Level

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mainland sample consisted of 3,221 villages. These villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the 2002 Population and Housing Census. The total Mainland sample was 48,315 agricultural households. In Zanzibar a total of 317 EAs were selected and 4,755 agriculture households were covered. Nationwide, all regions and districts were sampled except three urban district (two from Mainland and one from Zanzibar). In both Mainland and Zanzibar, a stratified two stage sample was used. The number of villages/Enumeration Areas (EAs) were selected for the first stage with a probability proportional to the number of villages in each district. In the second stage, 15 households were selected from a list of farming households in each Village/EA using systematic random sampling. Table 1.1 gives the sample size of households, villages and districts for Tanzania Mainland and Zanzibar.

1.3.4 Questionnaire Design and Other Census Instruments The questionnaires were designed following user meetings to ensure that the questions asked were in line with users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data: · Where feasible all variables were extensively coded to reduce post enumeration coding error. · The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the farmer. · The responses to all questions were placed in boxes printed on the questionnaire, with one box per character. This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data entry. · Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent. · Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSPro, SPSS and the dissemination applications.

Three other instruments were used: · Village Listing Forms were used for listing households in the village and from this list a systematic sample of 15 agricultural households were selected. · A Training Manual which was used by the trainers for the cascade/pyramid training of supervisors and enumerators · Enumerator Instruction Manual which was used as reference material.

1.3.5 Field Pre-testing of the Census Instruments The Questionnaire was pre-tested in five locations (Arusha, Dodoma, Tanga, Unguja and Pemba). This was done to test the wording, flow and relevance of the questions and to finalise crop lists, questionnaire coding and manuals. In addition to this, several data collection methodologies had to be finalised, namely, livestock numbers in pastoralist communities, cut flower production, mixed cropping, use of percentages in the questionnaire and finalising skip patterns and documenting consistency checks.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census covered agriculture in detail as well as many other aspects of rural development and was conducted using three different questionnaires: · Small scale farm questionnaire · Community level questionnaire · Large scale farm questionnaire The small scale farm questionnaire was the main census instrument and includes questions related to crop and livestock production and practices; population demographics; access to services, resources and infrastructure; and issues on poverty, gender and subsistence versus profit making production units. The main topics covered were: · Household demographics and activities of the household members · Land access/ownership/tenure and use · Crop and livestock production and productivity · Access to inputs and farming implements · Access and use of credit · Access to infrastructure (roads, district and regional headquarters, markets, advisory services, schools, hospitals, veterinary clinics, etc...) · Crop marketing, storage and agro processing · Tree farming, agro-forestry and fish farming · Access and use of communal resources (grazing, communal forest, water for humans and livestock, beekeeping etc.) · Investment activities: Irrigation structures, water harvesting, erosion control, fencing, etc. · Off farm income and non agriculture related activities · Households living conditions (housing, sanitary facilities, etc.) · Labour use, livelihood constraints and subsistence versus non subsistence activities · Gender issues. The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantations and seasonal farm gate prices.

The large scale farm questionnaire was administered to large farms which were either privately or corporately managed. Some data from the large scale farm questionnaire are incorporated in this report, however an in depth analysis of large scale farms is presented in a separate report.

Cleaning operations

Data processing consisted of the following processes: · Data entry · Data structure formatting · Batch validation · Tabulation

Data Entry Scanning and ICR data capture technology for the small holder questionnaire were used on the Mainland. This not only increased the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to track errors during the verification process. The scanning operation was so successful that it is highly recommended for adoption in future censuses/surveys. In Zanzibar all data was entered manually using CSPro.

Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification and good handwriting. A score was given to each questionnaire based on the legibility and the completeness of enumeration. This score will be used to assess the quality of enumeration and supervision in order to select the best field staff for future censuses/surveys.

CSPro was used for data entry of all Large Scale Farm and community based questionnaires due to the relatively small number of questionnaires. It was also used to enter data from the 2,880 small holder questionnaires that were rejected by the ICR extraction application.

Data Structure Formatting A program was developed in visual basic to automatically alter the structure of the output from the scanning/extraction process in order to harmonise it with the manually entered data. The program automatically checked and changed the number of digits for each variable, the record type code, the number of questionnaires in the village, the consistency of the Village ID Code and saved the data of one village in a file named after the village code.

Batch Validation A batch validation program was developed in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to the more complex checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaires. After the long process of data cleaning, tabulations were prepared based on a pre-designed tabulation plan.

Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while ArcView and Freehand were used for the maps.

Analysis and Report Preparation The analysis in this report focuses on regional comparisons, time series and national production estimates. Microsoft Excel was

Abstract

The 2007/08 Agricultural Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmers' organizations, and others. The dataset is both more numerous in its sample and detailed in its scope and coverage so as to meet the user demand.

The census was carried out in order to:

• Identify any structural changes,in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in the rural infrastructure and the level of agricultural households living conditions;

-Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stakeholders; and

• Obtain data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing and service delivery.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

Community, Household, Individual

Universe

Small scale farmers, Large Scale Farmers, Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mainland sample consisted of 3,192 villages. The total Mainland sample was 47,880 agricultural households while in Zanzibar, a total of 317 EAs were selected and 4,755 agricultural households were covered.

The villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the previous 2002 Population and Housing Census.

The numbers of villages/Enumeration Areas (EAs) were selected for the first stage with a probability proportional to the number of villages/EAs in each district. In the second stage, 15 households were selected from a list of agricultural households in each village/EA using systematic random sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census used three different questionnaires: - Small scale farm questionnaire - Community level questionnaire - Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it included questions related to crop and livestock production and practices; population demographics; access to services, community resources and infrastructure; issues on poverty and gender. The main topics covered were:

• Household demographics and activities of the household members
• Land access, ownership, tenure and use
• Crop and livestock production and productivity
• Access to inputs and farming implements
• Access and use of credit
• Access to infrastructure (roads, district and regional headquarters, markets, advisory services, schools, hospitals).
• Crop marketing, storage and agro processing
• Tree farming, agro-forestry, and fish farming
• Access and use of communal resources (grazing land, communal forests, water for humans and livestock, beekeeping)
• Investment activities ( irrigation structures, water harvesting, erosion control, fencing)
• Off farm income and non agricultural related activities
• Households living conditions (housing, sanitary facilities )
• Livelihood constraints
• Poverty Indicators

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The Large Scale Farm questionnaire was administered to large farms either privately or corporately managed.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: - Manual cleaning exercisePrior to scanning. (Questionnaires found dirty or damaged and generally unsuitable for scanning were put aside for manual data entry ) - CSPro was used for data entry of all Large Scale Farms and Community based questionnaires - Scanning and ICR data capture technology for the smallholder questionnaire - There was an Interactive validation during the ICR extraction process. - The use of a batch validation program developed in CSPro. This was used in order to identify inconsistencies within a questionnaire. - Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations - Microsoft Excel was used to organize the tables, charts and compute additional indicators -Arc GIS (Geographical Information System) was used in producing the maps. - Microsoft Word was used in compiling and writing up the reports

The numbers contained in this study are released pursuant to the order of the United States Court of Appeals for the Ninth Circuit in Carter v. Department of Commerce, 307 F.3d 1084. These numbers are not official Census 2000 counts. These numbers are estimates of the population based on a statistical adjustment method, utilizing sampling and modeling, applied to the official Census 2000 figures. The estimates utilized the results of the Accuracy and Coverage Evaluation (A.C.E.), a sample survey intended to measure net over- and undercounts in the census results. The Census Bureau has determined that the A.C.E. estimates dramatically overstate the level of undercoverage in Census 2000, and that the adjusted Census 2000 data are, therefore, not more accurate than the unadjusted data. On March 6, 2001, the Secretary of Commerce decided that unadjusted data from Census 2000 should be used to tabulate population counts reported to states and localities pursuant to 13 U.S.C. 141(c) (see 66 FR 14520, March 13, 2001). The Secretary's decision endorsed the unanimous recommendation of the Executive Steering Committee for A.C.E. Policy (ESCAP), a group of 12 senior career professionals within the Census Bureau. The ESCAP, in its recommendation against the use of the statistically adjusted estimates, had noted serious reservations regarding their accuracy. In order to inform the Census Bureau's planned October 2001 decision regarding the potential use of the adjusted estimates for non-redistricting purposes, the agency conducted extensive analyses throughout the summer of 2001. These extensive analyses confirmed the serious concerns the agency had noted earlier regarding the accuracy of the A.C.E. estimates. Specifically, the adjusted estimates were determined to be so severely flawed that all potential uses of these data would be inappropriate. Accordingly, the Department of Commerce deems that these estimates should not be used for any purpose that legally requires use of data from the decennial census and assumes no responsibility for the accuracy of the data for any purpose whatsoever. The Department, including the U.S. Census Bureau, will provide no assistance in the interpretation or use of these numbers. The collection contains four tables: (1) a count of all persons by race (Table PL1), (2) a count of Hispanic or Latino and a count of not Hispanic or Latino by race of all persons (Table PL2), (3) a count of the population 18 years and older by race (Table PL3), and (4) a count of Hispanic or Latino and a count of not Hispanic or Latino by race for the population 18 years and older (Table PL4).

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.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Geographic coverage

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

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design Outline of sample design: A stratified multi-stage design has been adopted for the 60th round survey. The first stage units (FSU) will be the 1991 census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) will be households in both the sectors. In case of large villages/blocks requiring hamlet-group (hg)/sub-block (sb) formation, one intermediate stage will be the selection of two hgs/sbs from each FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of Census 1991 villages (panchayat wards for Kerala) and Census 1981 villages for J & K will constitute the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks will be considered as the sampling frame.

Stratification Rural sector: Two special strata will be formed at the State/ UT level, viz.

Stratum 1: all FSUs with population between 0 to 50 and Stratum 2: FSUs with population more than 15,000.

Special stratum 1 will be formed if at least 50 such FSUs are found in a State/UT. Similarly, special stratum 2 will be formed if at least 4 such FSUs are found in a State/UT. Otherwise, such FSUs will be merged with the general strata.

From FSUs other than those covered under special strata 1 and 2, general strata will be formed and its numbering will start from 3. Each district of a State/UT will normally be treated as a separate stratum. However, if the census rural population of the district is greater than or equal to 2.5 million as per population census 2001 or 2 million as per population census 1991, the district will be split into two or more strata, by grouping contiguous tehsils to form strata. However, in Gujarat, some districts are not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region will constitute a separate stratum.

Urban sector: In the urban sector, strata will be formed within each NSS region on the basis of size class of towns as per Population Census 2001. The stratum numbers and their composition (within each region) are given below.

stratum 1: all towns with population less than 50,000 stratum 2: all towns with population 50,000 or more but less than 2 lakhs stratum 3: all towns with population 2 lakhs or more but less than 10 lakhs stratum 4, 5, 6,...: each town with population 10 lakhs or more

The stratum numbers will remain as above even if, in some regions, some of the strata are not formed.

Total sample size (FSUs): 7612 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 8260 for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample is allocated between two sectors in proportion to provisional population as per Census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. Earlier practice of giving double weightage to urban sector has been modified considering the fact that there has been considerable growth in urban population. A minimum of 8 FSUs will be allocated to each state/UT separately for rural and urban areas.

Allocation to strata: Within each sector of a State/UT, the respective sample size will be allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level will be adjusted to a multiple of 4 with a minimum sample size of 4.

Selection of FSUs: FSUs will be selected with Probability Proportional to Size With Replacement (PPSWR), size being the population as per Population Census 1991 in all the strata for rural sector except for stratum 1. In stratum 1 of rural sector and in all the strata of urban sector, selection will be done using Simple Random Sampling Without Replacement (SRSWOR). Within each stratum, samples will be drawn in the form of two independent sub-samples in both the rural and urban sectors.

Note: Detail sampling procedure is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 10: Employment and Unemployment

Block 0- Descriptive identification of sample household: This block is meant for recording descriptive identification particulars of the sample household and the sample village/block to which the sample household belongs.

Block 1- Identification of sample household: The identification particulars of the sample household are to be recorded against items 1, 5 to 15.

Block 2- Particulars of field operation: The identity of the Investigator, Assistant Superintendent and Superintendent associated, date of survey/inspection/scrutiny of Schedules, despatch, etc., will be recorded in this block against the appropriate items in the relevant columns.

Block 3- Household characteristics: Certain household characteristics, such as, household size, household type, religion, social-group, household industry, household occupation, monthly household consumer expenditure, land possessed as on the date of survey (code) etc., will be recorded in this block.

Block 4- Demographic and usual activity particulars of household members: This block is meant to record the demographic particulars like sex, age, marital status, educational level etc. and usual principal activity and usual subsidiary activity particulars of all the household members.

Block 5- Time disposition of members during the week: This block is meant for recording the time disposition for all the 7 days preceding the date of survey, the current weekly status based on the 7 days time disposition, wage and salary earnings during the week, etc.

Block 6- Follow-up questions for persons unemployed on all the seven days of the week: This block is meant for collecting information on persons who are found to be unemployed on all the seven days of the week preceding the date of survey.

Block 7- Particulars of vocational training received by household members: Particulars of formal vocational training received will be collected in respect of all the household members who are in the age group 15-29 with minimum general education level middle and above but below graduate (i.e with codes 05 to 08 in column 7, block 4) and for those who are graduate in vocational courses within the age group 15-29.

Block 8- Household consumer expenditure: This block is meant for collecting household consumer expenditure information which is the sum total of monetary values of all goods and services consumed (out of purchase or procured otherwise) by the household on domestic account during a specific reference period.

Block 9- Remarks by investigator: Any remark which is considered necessary for explaining any peculiarity in the consumption pattern of the household or any other item-specific unusual feature of the household or of any member thereof will be noted here.

Block 10- Comments by supervisory officer(s): The supervisory officers should note their views on any aspect pertaining to the characteristics under enquiry in this schedule relating to the household or any member thereof.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes* - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: yes*

UNIT DESCRIPTIONS: - Dwellings: no - Households: Yes - Group quarters: A collective household is a group of persons that does not live in an ordinary household, but lives in a collective establishment, sharing meal times.

Universe

Residents in France, of any nationality. Does not include French citizens living in other countries, foreign tourists, or people passing through. The microdata sample includes mainland France and Corsica. Reintegrated persons: Persons living in group quarters or without a fixed address but having a usual home elsewhere (i.e., enumerated away from their usual residence). During data processing, most of these people are reintegrated into their usual households, except in the case of persons in psychiatric hospitals and prisons. Legal population refers to de jure population plus population compte a part.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: INSEE (Institut National de la Statisque et des Etudes Economiques)

SAMPLE SIZE (person records): 2320901.

SAMPLE DESIGN: Systematic manual sorting into lots with different sample units, according to target population. Reintegrated persons: Persons living in group quarters or without a fixed address but having a usual home elsewhere (i.e., enumerated away from their usual residence). During data processing, most of these people are reintegrated into their usual households, except in the case of persons in psychiatric hospitals and prisons. Legal population refers to de jure population plus population compte a part.

Mode of data collection

Face-to-face [f2f]

Research instrument

Separate forms for buildings, group quarters (collective households), group quarters (compte a part), private households, and boats. Four forms for individuals (living in group quarters and private dwellings; two different forms for people compte a part; living in boats).

This study was conducted under the auspices of the Center for Studies in Demography and Ecology at the University of Washington. It is a nationally representative sample of the population of the United States in 1900, drawn from the manuscript returns of individuals enumerated in the 1900 United States Census. Household variables include region, state and county of household, size of household, and type and ownership of dwelling. Individual variables for each household member include relationship to head of household, race, sex, age, marital status, number of children, and birthplace. Immigration variables include parents' birthplace, year of immigration and number of years in the United States. Occupation variables include occupation, coded by both the 1900 and 1950 systems, and number of months unemployed. Education variables include number of months in school, whether respondents could read or write a language, and whether they spoke English.

About this dataset

Context

The dataset tabulates the Spray population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Spray.

Content

The dataset constitues the following two datasets across these two themes

• Spray, OR Population Breakdown by Gender
• Spray, OR Population Breakdown by Gender and Age

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

2013-2023 Virginia Population by Race by Census Block Group. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B03002 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

The 1961 Census Microdata for Great Britain: 9% Sample: Secure Access dataset was created from existing digital records from the 1961 Census. It comprises a larger population sample than the other files available from the 1961 Census (see below) and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions. See Access section for further details.

The file was created under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1961 data, the team worked on files from the 1971 Census and 1981 Census.

The original 1961 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details).

Three other 1961 Census datasets have been created; users should obtain the other datasets in the series first to see whether they are sufficient for their research needs before considering making an application for this study (SN 8275), the Secure Access version:

• SN 8272 - 1961 Census Microdata Individual File for Great Britain: 5% Sample, which contains information on individuals in larger local authorities;
• SN 8273 - 1961 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context. SNs 8272 and 8273 are both available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions); and
• SN 8274 - 1961 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence.

Analysis unit

Persons, households, and dwellings Low ages grouped into categories

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Dwelling refers to a room (or set of rooms) that is: regularly dedicated to residential use; separate (i.e. surrounded by walls and covered by a roof); independent (with at least one external access that is either independent or through shared entry areas - road, courtyard, stairs, landings, common balconies, terraces, etc. - access, in other words, does not require passing through other dwellings); incorporated in a building (or constitutes a building). - Households: Group of persons who are cohabiting as usual residents of a dwelling - Group quarters: Collective residential structure refers to facilities used to house large groups of people and/or one or more families. This category includes hotels, hospitals, rest homes for senior citizens and reception centers and institutes of various kinds (religious, healthcare-related, welfare support, educational, etc.).

Universe

All citizens or foreigners registered to stay in Italy

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE SIZE (person records): 2968065.

SAMPLE DESIGN: 5% sample drawn by national statistics office

Mode of data collection

Face-to-face [f2f]

Research instrument

Two forms for dwellings and persons: a short form (CP.1B) and a long form (CP.1). One third of families in population centers from municipalities with at least 20,000 inhabitants or provincial capitals received the long form. Persons living in cohabitation received a separate form (CP.2).

Abstract

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

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

Geographic coverage

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

Republic of Palau State Hamlet/Village Enumeration District Block

Analysis unit

Individuals Families Households General Population

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

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

Cleaning operations

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

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

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

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

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

Sampling error estimates

Sampling Error is not applicable to full enumeration censuses.

Data appraisal

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

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

The Public Use Microdata Samples (PUMS) from the 1980 Census contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. The B Sample contains information for each state, and for households and persons residing in metropolitan areas that are too small to be separately identified and/or that cross state boundaries. Standard Metropolitan Statistical Areas (SMSAs) and county groups are defined differently here than in the A Sample [CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE (A SAMPLE): 5-PERCENT SAMPLE (ICPSR 8101)]. Most states cannot be identified in their entirety. As a percentage of the l-Percent Public Use Microdata Sample (B Sample) [CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE (B SAMPLE): 1-PERCENT SAMPLE (ICPSR 8170)], this file constitutes a 1-in-1000 sample, and contains all household- and person-level variables from the original B Sample. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. Person-level variables include sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education. (Source: retrieved from ICPSR 06/15/2011)

Data from: American Community Survey, 5-year Series

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Building or any construction structures including boat, houseboat, and truck at which a person can live. - Households: A household refers to the living one person or many persons in the same house or the same construction structure. They seek for, consume, and utilize all facilities together for their benefit, regardless of whether they are related or not. - Group quarters: Household which compose of several people living together because of having certain rule or regulation which indicated that those people must live together or needed to stay together for their own benefit. There are two kinds of collective households: institutions and other collective households [also called 'special households' in this sample]

Universe

All Thai nationals residing in Thailand on the census date; foreign civilians who normally reside in Thailand or who temporarily reside in Thailand 3 months or more before the census date; any individual who has normally resided in Thailand but was away for military training, sailing, or temporarily travelling abroad; and Thai civil/military/diplomatic officers and their families who normally have their offices in foreign countries.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Statistical Office

SAMPLE SIZE (person records): 604519.

SAMPLE DESIGN: A stratified two-stage sample was adopted. 5 strata were Bangkok and the four regions (Central, North, Northeastern, South), and each stratum was divided into municipal areas and non-municipal areas. Then, the sample was selected in two stages. In stage one, a number of sample enumeration districts (EDs) were selected systematically in each sub-stratum with sampling fraction of 1 in 20. In stage two, a sample of households was selected systematically from each sample ED as follows. For private households, one-fifth of households in each ED were selected. For collective households, one-fifth of special households and one fiftth of institutional households were selected in each sub-stratum (municipal and non-municipal areas.)

Mode of data collection

Face-to-face [f2f]

Research instrument

The population was enumerated with Form 2, which consists of three parts. Part 1 identifies the location of the household. Part 2 contains questions on population including questions on demography (S1-S16) and questions on detail of population (L17-L27). Part 3 contains housing questions that are asked of the sample private households only. Note: (i) Only Part 1 and questions on demography (S1-S16) of Part 2 in Form 2 were asked of the private households that have not been selected as sample households. (ii) For the private households that have been selected as sample private households (20%), all questions in Form 2 were asked. (iii) All collective households were enumerated using Form 2 on Part 1 (location of household) and Part 2 (questions on demography and on details of population), but questions on housing were not asked.

Abstract

The CSES is a household survey with questions to households and the household members. In the household questionnaire there are a number of modules with questions relating to the living conditions, e.g. housing conditions, education, health, expenditure/income and labour force. It is designed to provide information on social and economic conditions of households for policy studies on poverty, household production and final consumption for the National Accounts and weights for the CPI.

The main objective of the survey is to collect statistical information about living standards of the population and the extent of poverty. Essential areas as household production and cash income, household level and structure of consumption including poverty and nutrition, education and access to schooling, health and access to medical care, transport and communication, housing and amenities and family and social relations. For recording expenditure, consumption and income the Diary Method was applied for the first time. The survey also included a Time Use Form detailing activities of household members during a 24-hour period.

Another main objective of the survey is also to collect accurate statistical information about living standards of the population and the extent of poverty as an essential instrument to assist the government in diagnosing the problems and designing effective policies for reducing poverty, and in evaluating the progress of poverty reduction which are the main priorities in the "Rectangular Strategy" of the Royal Government of Cambodia.

Geographic coverage

National Phnom Penh/Other Urban/Other Rural Provinces/Groups of provinces

Analysis unit

Households

Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

In this section the sampling design and the sample selection for CSES 2009, is described. The sampling design for the 2009 survey is the same as that used for the CSES 2004. The sampling design for the 2004 CSES is described in for instance National Institute of Statistics (2005a).

The sampling frame for the 2009 survey is based on preliminary data from the General Population Census conducted in 2008. The sample is selected as a three stage cluster sample with villages in the first stage, enumeration areas in the second stage and households in the third.

The Sampling Frame

Preliminary data from the General Population Census 2008 was used to construct the sampling frame for the first stage sampling, i.e. sampling of villages. All villages except 'special settlements' were included in the frame. In all, the first stage sampling frame of villages consisted of 14,073 villages, see Appendix 1. Compared to previous years the frame used for the 2009 survey based on the census 2008 was more up to date than in previous surveys which were based on the population census 1998.

The following variables were used from the census; Province code, province name, district code, district name, commune code, commune name, village code, village name, urban-rural classification of villages, the number of households per village and, the number of enumeration areas in the village.

In the second-stage Enumeration Areas (EA) are selected in each selected village. In most villages only one EA was selected but in some large villages more than one was selected.

For the third stage, the sampling of households, a frame was constructed in field. For selected EAs the census map of the village, including EAs and residences, was given to enumerator who updated the map and listed the households in the selected EA. A sample of households was then selected from the list.

Stratification

The sampling frame of villages was stratified by province and urban and rural. There are 24 provinces and each village is classified as either urban or rural which means that in total we have 48 strata, see Appendix 1. Each stratum of villages was sorted by district, commune and village code.

Sampling

The sampling design in the CSES 2009 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two. The sampling designs used in the three stages were:

Stage 1. A systematic pps sample of villages, Primary Sampling Units (PSUs) was selected from each stratum,

i.e. without replacement systematic sampling with probabilities proportional to size. The size measure used was the number of households in the village according to the sampling frame.

Stage 2. One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

As mentioned above, in a few large villages more than one EA was selected.

Stage 3. In each selected EA a sample of households was selected by systematic sampling.

The selection of villages and EAs were done at NIS while the selection of households in stage three was done in field. As mentioned in section 1.1 all households in selected EAs were listed by the enumerator. The sample of households was then selected from the list.

Sample sizes and allocation

The sample size of PSUs, were, as in the 2004 survey, 720 villages (or EAs). In urban villages 10 households were selected and in rural 20 households. In all 12,000 households were selected.

Urban and rural villages were treated separately in the allocation. The allocation was done in two steps. First the sample sizes for urban and rural villages in the frame were determined and then sample sizes for the provinces within urban and rural areas were determined, i.e. the strata sample sizes.

The total sample size was divided into to two, one sample size for urban villages and the other for rural villages. The calculation of the sample sizes for urban and rural areas were done using the proportion of consumption in the two parts of the population. Data on consumption from the CSES 2007 survey was used. The resulting sample sizes for urban villages was 240 and for rural 480. (Some adjustments of the calculated sample sizes were done, resulting in the numbers 240 and 480).

Allocation of the total sample size on the strata within urban and rural areas respectively, was done in the following way. The sample size, i.e. the number of PSUs, villages, selected from stratum h, is proportional to the number of households in stratum h, i.e.

n(Ih)=n1(Mh/Sum of Mh) (1.1)

where,

is the sample size in stratum h, i.e. the number villages selected in stratum h,

is the total sample size of villages for urban or rural villages,

H is the number of strata in urban or rural areas,

is the number of households in stratum h according to the frame.

As mentioned above, the sample size calculations are done separately for urban and rural villages, i.e. for strata with urban villages (1.1) is used with nI = 240 and is the number of households in urban villages in the frame and for rural villages (1.1) is used with nI = 480 and is the

number of households in rural villages in the frame.

Monthly samples

In section 1.3 the selection of the annual sample was described. The annual sample was divided into 12 monthly samples of equal sizes. The monthly samples consisted of 20 urban and 40 rural villages. The division of the annual sample into monthly samples was done so that as far as possible each province would be represented in each monthly sample. Since the sample size of villages in some provinces is smaller than 12, all provinces were not included in all monthly samples. Also, the outline of the fieldwork with teams of 4 enumerators and one supervisor puts constraints on how to divide the annual sample into monthly samples. The supervisors must travel between the villages in a team and therefore the geographical distance between the villages surveyed by a team cannot be too large.

Estimation

Totals, ratios such as means or proportions were estimated for the population or for subgroups of population, i.e. domains of study. The domains were defined by e.g. region or sex. Means and proportions were estimated by first estimating totals and then calculating the ratio of two estimated totals. To estimate totals from a sample survey weights are needed.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four different questionnaires or forms were used in the survey:

1. Household listing form

The Household listing and mapping were done prior to the sampling. During the household listing the enumerator recorded household information on e.g. location, number of members and principal economic activity.

1. Village questionnaire

The Village questionnaire was used to gather basic common information on:

    1. Demographic information

    2. Economy & Infrastructure

    3. Rainfall & Natural disasters

    4. Education

    5. Health

    6. Retail prices (food and non-food items)

    7. Employment & Wages

    8. Access to common property resources during the last 5 years

    9. Sale prices of agricultural land in the village

    10. Recruitment of children for work outside the village 

1. Household questionnaire

The following modules were included in the Household questionnaire:

1. Initial visit

01A. List of household member

01B. Food, beverages and tobacco consumption during the last 7 days

01C. Recall non-food expenditures

01D. Vulnerability

1. Education & Literacy

2. Information on migration (includes past and current migration)

   1. Housing

3. Household economic activities

05A.Land ownership

05B.Production of

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections: Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)