Abstract

The Tanzania Demographic and Health Survey (TDHS) is part of the worldwide Demographic and Health Surveys (DHS) programme, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1999 TRCHS was to collect data at the national level (with breakdowns by urban-rural and Mainland-Zanzibar residence wherever warranted) on fertility levels and preferences, family planning use, maternal and child health, breastfeeding practices, nutritional status of young children, childhood mortality levels, knowledge and behaviour regarding HIV/AIDS, and the availability of specific health services within the community.1 Related objectives were to produce these results in a timely manner and to ensure that the data were disseminated to a wide audience of potential users in governmental and nongovernmental organisations within and outside Tanzania. The ultimate intent is to use the information to evaluate current programmes and to design new strategies for improving health and family planning services for the people of Tanzania.

Geographic coverage

National. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately.

Analysis unit

• Households
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The TRCHS used a three-stage sample design. Overall, 176 census enumeration areas were selected (146 on the Mainland and 30 in Zanzibar) with probability proportional to size on an approximately self-weighting basis on the Mainland, but with oversampling of urban areas and Zanzibar. To reduce costs and maximise the ability to identify trends over time, these enumeration areas were selected from the 357 sample points that were used in the 1996 TDHS, which in turn were selected from the 1988 census frame of enumeration in a two-stage process (first wards/branches and then enumeration areas within wards/branches). Before the data collection, fieldwork teams visited the selected enumeration areas to list all the households. From these lists, households were selected to be interviewed. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately. The health facilities component of the TRCHS involved visiting hospitals, health centres, and pharmacies located in areas around the households interviewed. In this way, the data from the two components can be linked and a richer dataset produced.

See detailed sample implementation in the APPENDIX A of the final report.

Mode of data collection

Face-to-face

Research instrument

The household survey component of the TRCHS involved three questionnaires: 1) a Household Questionnaire, 2) a Women’s Questionnaire for all individual women age 15-49 in the selected households, and 3) a Men’s Questionnaire for all men age 15-59.

The health facilities survey involved six questionnaires: 1) a Community Questionnaire administered to men and women in each selected enumeration area; 2) a Facility Questionnaire; 3) a Facility Inventory; 4) a Service Provider Questionnaire; 5) a Pharmacy Inventory Questionnaire; and 6) a questionnaire for the District Medical Officers.

All these instruments were based on model questionnaires developed for the MEASURE programme, as well as on the questionnaires used in the 1991-92 TDHS, the 1994 TKAP, and the 1996 TDHS. These model questionnaires were adapted for use in Tanzania during meetings with representatives from the Ministry of Health, the University of Dar es Salaam, the Tanzania Food and Nutrition Centre, USAID/Tanzania, UNICEF/Tanzania, UNFPA/Tanzania, and other potential data users. The questionnaires and manual were developed in English and then translated into and printed in Kiswahili.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview and children under five who were to be weighed and measured. Information was also collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, ownership of various consumer goods, and use of iodised salt. Finally, the Household Questionnaire was used to collect some rudimentary information about the extent of child labour.

The Women’s Questionnaire was used to collect information from women age 15-49. These women were asked questions on the following topics: · Background characteristics (age, education, religion, type of employment) · Birth history · Knowledge and use of family planning methods · Antenatal, delivery, and postnatal care · Breastfeeding and weaning practices · Vaccinations, birth registration, and health of children under age five · Marriage and recent sexual activity · Fertility preferences · Knowledge and behaviour concerning HIV/AIDS.

The Men’s Questionnaire covered most of these same issues, except that it omitted the sections on the detailed reproductive history, maternal health, and child health. The final versions of the English questionnaires are provided in Appendix E.

Before the questionnaires could be finalised, a pretest was done in July 1999 in Kibaha District to assess the viability of the questions, the flow and logical sequence of the skip pattern, and the field organisation. Modifications to the questionnaires, including wording and translations, were made based on lessons drawn from the exercise.

Response rate

In all, 3,826 households were selected for the sample, out of which 3,677 were occupied. Of the households found, 3,615 were interviewed, representing a response rate of 98 percent. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants were not at home despite of several callbacks.

In the interviewed households, a total of 4,118 eligible women (i.e., women age 15-49) were identified for the individual interview, and 4,029 women were actually interviewed, yielding a response rate of 98 percent. A total of 3,792 eligible men (i.e., men age 15-59), were identified for the individual interview, of whom 3,542 were interviewed, representing a response rate of 93 percent. The principal reason for nonresponse among both eligible men and women was the failure to find them at home despite repeated visits to the household. The lower response rate among men than women was due to the more frequent and longer absences of men.

The response rates are lower in urban areas due to longer absence of respondents from their homes. One-member households are more common in urban areas and are more difficult to interview because they keep their houses locked most of the time. In urban settings, neighbours often do not know the whereabouts of such people.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TRCHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TRCHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

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

Note: See detailed sampling error calculation in the APPENDIX B

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

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

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 long-term presence of refugees in Chad and the reduction in funding to provide assistance in recent years have led the humanitarian community to reconsider the approach to assistance of these populations. WFP and UNHCR, the Government's main partners in providing assistance to refugees, had conducted a "socio-economic categorization" in 2014 and 2015 in some refugee camps, and an update was decided for 2017. This update was designed to go beyond a simple categorization and focuses on identifying profiles of refugee households that can be empowered in the short to medium term and the factors that can foster this empowerment. The assessment covers 87,724 refugee households in Chad and was carried out during June-August 2017.

Geographic coverage

Areas hosting refugees in Chad. This includes 19 refugee camps and 9 villages.

Analysis unit

Household and individual

Universe

All refugee households residing in Chad. UNHCR PPG: 1TCDA, 1TCDB, 1TCDD

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey's objective was to deliver representative data of all refugees living in Chad. The total population of refugees at the time of the survey was estimated at slightly below 90,000 households. These refugees were located in 19 refugee camps and 9 villages.

The survey applied a full-coverage (census) approach, i.e. no sample selection was made. The registration database served as the list frame. The total number of completed interviews was 87,724 households.

While the original data collection took a full-coverage approach, the public-release version of the dataset contains a systematically drawn sub-sample of this original data for reasons of statistical disclosure control. The total sample size in the dataset presented for public release is 8,772 households.

Sampling deviation

None.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

All questionaires are provided in section "external ressources".

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Moreover, it constitutes a sub-sample of the data originally collected.

Response rate

Information unavailable.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle) In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2016. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2016. Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016. Study spatial scale (size of replicates and spatial scale of study area) In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2016 is a participant dataset consisting of 8,815,472 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 8.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The nationally representative sample is composed of 60,003 records. The distribution by category is 5,449 pregnant women, 4,661 breastfeeding women, 3,904 postpartum women, 13,999 infants, and 31,990 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. 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 all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016.   Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: WIC Participant and Program Characteristics 2016. File Name: wicpc_2016_public.csvResource Description: The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.Resource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 Codebook. File Name: WICPC2016_PUBLIC_CODEBOOK.xlsxResource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data. File Name: WIC_PC_2016_SAS_SPSS_STATA_Files.zipResource Description: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 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 outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 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 and removing implausible values. The study team linked the 2023 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 durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of 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 replicationUnknownSampling 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-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach 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 2023 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 2023 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. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone

Abstract

In October 2001, South Africans were enumerated to collect information on persons and households throughout the country, using a uniform methodology. Household data collected included data on each household and each person present in the household on Census night, as well as data on services available to the household. Data on household residents, and residents of hostels and the other types of collective living quarters was also captured, as well as data on individuals who spent census night in institutions and hotels.

Geographic coverage

The South African Census 2001 has national coverage.

Analysis unit

The units of analysis for the South Africa Census 2001 were households and individuals

Universe

The South African Census 2001 covered every person present in South Africa on Census Night, 9-10 October 2001 including all de jure household members and residents of institutions.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The data in the South African Census 2001 dataset is a 10% unit level sample drawn from Census 2001 as follows: 1) Households: • A 10% sample of households in housing units, and • A 10% sample of collective living quarters (both institutional and non-institutional) and the homeless.

2) Persons: • A sample consisting of all persons in the households and collective living quarters, and the homeless, drawn for the samples described above

3) Mortality: • A sample consisting of all mortality information for the households in housing units drawn in the 10% sample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered for the South African Census 2001, questionnaire A (for persons in households), questionnaire B (for persons in institutions) and questionnaire C (for institutions). The Household questionnaire covered household characteristics, such as dwellling type, home ownership, household assets, access to services and energy sources. A component of the questionnaire captures fertility data. Both the household and persons in institutions questionnaires collected data on individuals' characteristics, including age, population group, language, religion, citizenship, migration, mortality and disability, as well as means of travel. Economic characteristics of individuals included employment activities and data on unemployment.

Cleaning operations

The following publication can be consulted for a detailed account of the editing undertaken for the South African Census 2001: Computer editing specifications / Statistics South Africa. Pretoria: Statistics South Africa, 2003 369p. [Report No. 03-02-43 (2001)]. ISBN 0-621-34566-0.

Data appraisal

As part of the quality check for Census 2001, a Post-Enumeration Survey (PES) was conducted in November 2001, approximately one month after the census. Fieldworkers re-visited a scientifically selected sample of almost 1% of the census enumeration areas, to do an independent recount. The published census results are adjusted for undercount according to the findings of the PES. In addition to the check on coverage, the PES also involved an independent re-measurement of the basic characteristics of the population. Details on this process are available in the publication:

Statistics South Africa. 2004. Census 2001: post-enumeration survey: results and methodology. Report no. 03-02-17 (2001).

Abstract

In discussing census objectives, it is useful to distinguish between short-term objectives, which basically entail the delivery of data for immediate uses, and long-term aims which point more towards the infrastructure and capacity building of the statistical system. Long Term Objectives: - The provision and maintenance of a time series of relevant population data at national and sub-national levels. A series of periodic censuses, at regular intervals, is important in assessing trends. The past can be appraised, the present assessed and the future estimated based on benchmark data from censuses. - The development of national capacity to undertake censuses and related statistical activities. Zimbabwe's capability to undertake censuses and surveys has improved over the years. For example, three professionals among those directly involved in the 1992 census are working on the 2002 Census. The long-term objective of capacity building entails: - Developing Central Statistical Office (CSO)'s capacity to produce and to co-ordinate the production and dissemination of relevant, accurate and timely statistics to meet the information needs of various agencies; - Improving its capability to advise other Government departments and agencies involved in the production and dissemination of statistics; - General strengthening of the infrastructure at CSO. In this process the census project constitutes one of the major ways through which such capacity is built. It involves acquisition of significant hardware for various purposes and acquisition of skills by CSO staff through on-the-job training provided by international consultants and formal training through fellowships provided by various agencies. The type of training involved covers a wide range of areas e.g. project planning and implementation, data processing, demographic analysis, sampling techniques, etc. - Provision of a frame for other statistical activities such as the household survey programme. Since 1982, the census has become an important data set for establishing sampling frames and weighting factors for Zimbabwe's National Household Surveys Capability programme. The frame and factors are to be revised after every population census e.g. the 2002 census. It is important to stress this linkage between the census and the survey programme. In essence, the census provides the baseline for the survey programme in terms of maps and household data, which are required in creating the master sample and sub-sample for the survey programme. Immediate Objectives: In general this involves the provision of current information on demographic and related socio-economic characteristics of the population at national level and various sub-national levels to facilitate effective planning and evaluation of various programmes of government, private sector etc. This needs to be performed in a manner that ensures effective application by the various agencies representing the main census data users.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Persons aged 10 and above
• Women aged 12 - 49

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Population Census 2002 is divided into the following sections: A Household identification B Household composition and individual characteristics - For all persons (except questions 12 and 13 for people 15 years and below) C Education - for persons 3 years and older and for persons 3-24 years D Economic activity - for persons 10 years and above E Births - for women aged 12-49 years F Living conditions G Deaths in the household H Total number of persons in the households

The questionnaire is in English, and is provided as external resources.

In order to report on its performance against the Protect pillar, the CRTC requires reliable trend data on the "Percentage of Canadians who consider that the CRTC is taking measures to enhance their safety and protection in the communication system" (the performance indicator). The initial survey took place in 2015. The questions posed in 2015 were replicated in this latest survey using the same methodology in order to be able to measure changes in public opinion over time. Two additional new survey questions were also posed. The survey was conducted by Environics Research Group and is based on 1,000 telephone interviews conducted from February 6 to 12, 2017. The sampling method was designed to complete 1,000 interviews with the target audience of Canadians 18 years of age and over living in private households. The sample was stratified across regions to ensure meaningful data and analysis across the province. The data are statistically weighted to ensure the sample is as representative of this population as possible (region, gender and age) according to the most recently available Census information. The margin of error for a sample of 1,000 is +/- 3.1 percentage points, in 19 out of 20 samples (margin of error is greater for subgroups).

Between Oct. 14, 2014, and May 21, 2015, Pew Research Center, with generous funding from The Pew Charitable Trusts and the Neubauer Family Foundation, completed 5,601 face-to-face interviews with non-institutionalized adults ages 18 and older living in Israel.

The survey sampling plan was based on six districts defined in the 2008 Israeli census. In addition, Jewish residents of West Bank (Judea and Samaria) were included.

The sample includes interviews with 3,789 respondents defined as Jews, 871 Muslims, 468 Christians and 439 Druze. An additional 34 respondents belong to other religions or are religiously unaffiliated. Five groups were oversampled as part of the survey design: Jews living in the West Bank, Haredim, Christian Arabs, Arabs living in East Jerusalem and Druze.

Interviews were conducted under the direction of Public Opinion and Marketing Research of Israel (PORI). Surveys were administered through face-to-face, paper and pencil interviews conducted at the respondent's place of residence. Sampling was conducted through a multi-stage stratified area probability sampling design based on national population data available through the Israel's Central Bureau of Statistics' 2008 census.

The questionnaire was designed by Pew Research Center staff in consultation with subject matter experts and advisers to the project. The questionnaire was translated into Hebrew, Russian and Arabic, independently verified by professional linguists conversant in regional dialects and pretested prior to fieldwork.

The questionnaire was divided into four sections. All respondents who took the survey in Russian or Hebrew were branched into the Jewish questionnaire (Questionnaire A). Arabic-speaking respondents were branched into the Muslim (Questionnaire B), Christian (Questionnaire C) or Druze questionnaire (D) based on their response to the religious identification question. For the full question wording and exact order of questions, please see the questionnaire.

Note that not all respondents who took the questionnaire in Hebrew or Russian are classified as Jews in this study. For further details on how respondents were classified as Jews, Muslims, Christians and Druze in the study, please see sidebar in the report titled "http://www.pewforum.org/2016/03/08/israels-religiously-divided-society/" Target="_blank">"How Religious are Defined".

Following fieldwork, survey performance was assessed by comparing the results for key demographic variables with population statistics available through the census. Data were weighted to account for different probabilities of selection among respondents. Where appropriate, data also were weighted through an iterative procedure to more closely align the samples with official population figures for gender, age and education. The reported margins of sampling error and the statistical tests of significance used in the analysis take into account the design effects due to weighting and sample design.

In addition to sampling error and other practical difficulties, one should bear in mind that question wording also can have an impact on the findings of opinion polls.

Abstract

The 1993 KMPS was carried out under the direction of researchers from the University of North Carolina at Chapel Hill, Paragon Research International, Inc., and the Institute of Sociology of the Russian Academy of Sciences.3 The government of the Kyrgyz Republic has recently established an open access policy in regards to the data collected in the KMPS (for details, see appendix A). The potential uses of this data set are quite broad given the multi-topic nature of the data and the fact that it was carried out at the national level.

The purpose of this paper is to provide detailed documentation of the KMPS in order to:

a) simplify its use for potential users thereby lowering start-up costs to analysts; b) ensure that the procedures used in the design, implementation and initial analysis of the survey are chronicled accurately.

Such documentation will serve both to facilitate use of the data set and to prevent misuse of the data due to misunderstandings of the sample and/or field work procedures.

Geographic coverage

The whole country.

Analysis unit

• Individuals
• Households
• Municipals

Universe

In this study, "household" was defined as a group of people who live together in a given domicile, who keep house together, and share common income and expenditures. Judging from the 1989 census, there were about 856'000 families containing 4'258'000 individuals living in Kyrgyzstan at that time and an average of about five members per family. The questionnaires are address to:

• Family in the households about their income and expenditures, and the health of all its members;
• Adult of the household from14 years and older, preferably privately;
• Children under the age of 14 years;
• Community abouts basic information on community services, infrastructure and economic structure;

Kind of data

Sample survey data [ssd]

Sampling procedure

According to the 1989 Census, there were about 856,000 families and 4,258,000 individuals living in the Kyrgyz Republic at that time (an average of about five members per family). Though the definition of 'household' used in the KMPS differs from the Census definition of 'family', this figure provided an estimate of the number of households from which the sample was to be drawn. Note that the sampling methodology assumes that any growth in the number of households since 1989 was equally distributed across regions. The target household sample size was 2,000. To allow for an estimated non-response rate of about five percent, a sample of 2,100 households was drawn. The actual number of completed household interviews was 1,938, reflecting a non response rate of 7.7 per cent. The response rate for individuals is more difficult to calculate, since some household members (eg. students under 18 studying elsewhere) could not be interviewed.

Sampling deviation

The sample is designed to be fully representative of all households in the Kyrgyz Republic in the second half of 1993. Stratification was based on information on the population provided in the 1989 Census (since results from the 1994 microcensus were not available at the time of the survey). A stratified, multi-stage sampling procedure was used, with the number of stages dependent on whether households were being drawn from urban or rural areas.13 The following is a brief description of the sampling process (summarized in table below).

                          Stages of the sampling process
                                            Non self-representing strata

Stage Self-representing strata Urban areas Rural areas 1st microcensus enumeration urban settlements rural settlements districts (cities) (villages) 2nd households microcensus household enumeration districts 3rd household

Mode of data collection

Face-to-face [f2f]

Research instrument

Explanation of the five questionnaires of this study:

• The household questionnaire was administered to the person who best knew the business and concerns of the family, its income and expenditures, and the health of all its members. This respondent may not necessarily have been the head of the household, however the household questionnaire was not to be administered to a child.
• The adult individual questionnaire was administered personally to every member of the household 14 years and older, preferably privately. Interviewers were not permitted to fill out an adult questionnaire based on answers provided by another member of the household.
• The child individual questionnaire was completed for every member of the household under the age of 14 years. The questionnaire was administered to the adult member of the household who was responsible for caring for the child.
• The "Survey of Availability and Prices of Food Products and Fue"l contains three sections of information relating to retail outlets7 selling food products in the 'local area'8 of the households participating in the survey.
• Basic information on community services, infrastructure and economic structure were collected in this survey. A community, or 'immediate place of residence' is defined as the microcensus enumeration district in urban areas and the settlement (village) in rural areas. The survey includes preliminary identification data and details the raion, settlement identifier, microcensus enumeration district, date of survey and also the name of the person conducting the survey.

Cleaning operations

• The local supervisors were required to examine the questionnaires to locate problems which could be remedied in the field. Such problems included missing key demographic information and problem with household and individual identification numbers. All questionnaires were then sent to Bishkek, where they were again checked for identification number problems and then to Moscow, where yet another ID check was performed.

• Open-ended questions (eg. occupation and nationality questions) were not immediately coded. Instead, the responses were entered into the data set in text, to be coded at a later date. Codes for all open-ended questions except occupation were made available in midFebruary. Occupation codes were made available in June 1994.

• Data entry and verification of the household questionnaires was completed by a private data entry firm by January 25. All other data entry was handled in-house using the SPSS data program. The first entry of the 10,000 child and adult questionnaires began on December 20, 1993; the verification pass began on January 20 and was completed by February 2. Entry of the community and price surveys began in late January and was completed in two weeks.

Response rate

To allow for an estimated non-response rate of about five percent, a sample of 2,100 households was drawn. The actual number of completed household interviews was 1,938, reflecting a non response rate of 7.7 per cent. The response rate for individuals is more difficult to calculate, since some household members (eg. students under 18 studying elsewhere) could not be interviewed.

The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM(r) classification, census block group, and latitude-longitude. PRIZM(r) classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM(r) classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describing US Census geographies, including those provided by the US Census. This database includes data only for environmental improvement: In regard to the following environmental and quality of life issues, I'd like you to tel... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F48%2F580 for complete metadata about this dataset.

Background: In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis.FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2020 Participant and Program Characteristics (PC2020) is the 17th to be completed using the prototype PC reporting system. In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Processing methods and equipment used: Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2020. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency. Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2020.Study spatial scale (size of replicates and spatial scale of study area): In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Level of true replication: UnknownSampling precision (within-replicate sampling or pseudoreplication):State Agency Data Submissions. PC2020 is a participant dataset consisting of 7,036,867 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data.PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public.National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 70,368 records. The distribution by category is 5,469 pregnant women, 6,131 breastfeeding women, 4,373 postpartum women, 16,817 infants, and 37,578 children.Level of subsampling (number and repeat or within-replicate sampling): The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample.Study design (before–after, control–impacts, time series, before–after-control–impacts): None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertaken: Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers.Description of any gaps in the data or other limiting factors: All State agencies provided data on a census of their WIC participants.Resources in this dataset:Resource Title: WIC PC 2020 National Sample File Public Use Codebook.; File Name: PC2020 National Sample File Public Use Codebook.docx; Resource Description: WIC PC 2020 National Sample File Public Use CodebookResource Title: WIC PC 2020 Public Use CSV Data.; File Name: wicpc2020_public_use.csv; Resource Description: WIC PC 2020 Public Use CSV DataResource Title: WIC PC 2020 Data Set SAS, R, SPSS, Stata.; File Name: PC2020 Ag Data Commons.zipResource; Description: WIC PC 2020 Data Set SAS, R, SPSS, Stata One dataset in multiple formats

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out the Socio-Economic Survey 2018. The survey round covered a total sample of about 9926 households.

The main objective of collecting data on the socio-economic and its components, including demographic charachteristics, employment, unemployment, is to provide basic information on the size and structure of the Palestinian households, as well as other data on the status of housing and characteristics of individuals, the family and living conditions.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all Palestinian households and individuals who habitually reside with their households in Palestine for the reference period of the survey, which is 2018, and there was a focus on individuals of the age group 18 years and over to complete an additional form for individuals in this category.

---> Sampling Frame: The sampling frame consists of a comprehensive sample selected from the Population, Housing and Establishments Census 2007: This comprehensive sample consists of geographical areas with an average of 124 households, and these are considered as enumeration areas used in the census and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 9926 households in 2018.

---> Sample Design The sample is two stage stratified cluster sample with three stages : First stage: a systematic random sample of 337 enumeration areas for the whole round was selected. Second stage: the same households that were visited in the previous survey session 2015, which are about 25 households from each enumeration area were visited in 2018. Households that changed their place of residence and address available in the previous database to another place to complete the questionnaire are tracked. Individuals from the previous session who were separated from their household and formed new households or joined new households. Third stage: a male or female individual was selected from all sample families (old and new) in the second stage of individuals 18 years and over using check tables to complete the form for individuals 18 years and over (quality of life model). Taking into account that the household whose number is even in the enumeration area sample, we choose a female from it, and the household whose number is odd, from which we choose a male.

---> Sample strata: The population was divided by: 1. The governorate (16 governorates in the West Bank, including Jerusalem J1 governorate, which the Israeli occupation annexed to it by force after its occupation of the West Bank in 1967 as Tabqa and the Gaza Strip). 2. Type of settlement (urban, rural, camp). 3. Area C (class C, non-C) as an implicit class.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is the key tool for data collection. It must be conforming to the technical characteristics of fieldwork to allow for data processing and analysis. The survey questionnaire comprised the following parts: - Part one: Identification data. - Part two: Quality control - Part three: Data of households’ members and social data. - Part four: Housing unit data - Part five: Assistance and Coping Strategies Information - Part six: Expenditure and Consumption - Part seven: Food Variation and Facing Food Shortage - Part eight: Income - Part nine: Agricultural and economic activities. - Part ten: Freedom of mobility - In addition to a questionnaire for individuals (18 years old and above): Questions on suffering and life quality, assessment of health, education, administration (Ministry of the Interior) services and information technology.

Cleaning operations

---> Raw Data PCBS started collecting data since 27/8/2018 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 11,008 households of which 9,926 households completed the interview; whereas 5,898 households from the West Bank and 4,028 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in Palestine reached 90.2%.

Sampling error estimates

---> Sampling Errors Those errors result from studying part (sample) of the society and not all society units. Since the socio-economic conditions survey 2018 was conducted on a sample, sampling errors are expected to occur. To minimize sampling errors, a properly designed probability sample was used to calculate errors throughout the process. This means that for every unit of the society there is a probability to be selected in the sample. The variance was calculated to measure the impact on sample design for Palestine.

---> Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. Those are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course. Non-sampling errors in the survey resulted from the private data it collected and that some households considered this as interference in the very details of their private life. They refused to cooperate with data. Several methods were used to convince households provide answers and minimize non-response.

Data appraisal

Concept of data quality covers many aspects, starting from the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are seven dimensions of the statistical quality: relevance, accuracy, timeliness, accessibility, comparability, coherence, and completeness.

Topics covered in the 2021 UK Census included:

• demography and migration
• ethnic group, national identity, language and religion
• labour market and travel to work
• housing
• education
• health, disability, and unpaid care
• Welsh and other languages
• UK armed forces veterans
• sexual orientation and gender identity.

The 2021 Census: Safeguarded Household Microdata Sample dataset consists of a random sample of 1% of households from the 2021 Census and contains records for all individuals within these sampled households. It includes records for 263,729 households and 606,210 persons. These data cover England and Wales only. This sample allows linkage between individuals in the same household.  The lowest level of geography is Wales and regions within England. It contains 56 variables and a low level of detail. This is a new ONS product following user feedback from the 2011 Census.

Census Microdata

Microdata are small samples of individual records from a single census from which identifying information have been removed. They contain a range of individual and household characteristics and can be used to carry out analysis not possible from standard census outputs, such as:

• creating tables using bespoke variable combinations
• investigating specific combinations of variables or categories in a high level of detail
• conducting non-tabular statistical analyses on record-level data.

The microdata samples are designed to protect the confidentiality of individuals and households. This is done by applying access controls and removing information that might directly identify a person, such as names, addresses and date of birth. Record swapping is applied to the census data used to create the microdata samples. This is a statistical disclosure control (SDC) method, which makes very small changes to the data to prevent the identification of individuals. The microdata samples use further SDC methods, such as collapsing variables and restricting detail. The samples also include records that have been edited to prevent inconsistent data and contain imputed persons, households, and data values. To protect confidentiality, imputation flags are not included in any 2021 Census microdata sample.

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

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

Abstract

Côte d'Ivoire has just carried out its third agricultural census called Census of Farmers and Agricultural Holdings i.e. Recensement des Exploitants et Exploitations Agricoles (REEA) 2015/2016. The special feature of this census is the exhaustive and systematic survey of all the farms in the country, agricultural households, rural villages, professional agricultural or livestock organisations (OPA/OPE) and modern farms in the country using new technologies. The method CAPI (computer-assisted personal interview) coupled with contact details using the Global Positioning System (GPS) have made it possible to carry out this census. The implementation of the REEA followed the modular approach recommended by the World Programme for the Census of Agriculture (PMRA) 2010.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as an economic unit of agricultural production under single management, comprising all land used wholly or partly for agricultural production and all livestock kept, without regard to title, legal form or size. The following types of agricultural holdings were covered in the census: (i) family farm ("agricultural household") (ii) crop/livestock production organization (OPA/OPE).

Rural villages were statistical units for the community survey conducted together with the REEA.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census Although a modular approach was planned for conducting the REEA, only the core census module comprising the agricultural households and the OPA/OPE were implemented, because of budget constraints. A community survey was implemented along with the core module.

ii. Frame A listing operation was conducted during census enumeration to identify the agricultural households. The EA maps from the Population and Housing Census (PHC) 2014 were used for this operation. For the frame of the OPA/OPE and modern agricultural holdings, different sources were used: information available at the National Institute of Statistics (NIS), and information provided by the regional directorates of the ministries involved in the REEA. In addition, local administrations provided a list of new modern agricultural holdings.

iii. Complete and/or sample enumeration methods The core module was conducted on a complete enumeration basis.

iv. Sample design Not applied

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The collection of data was done using electronic questionnaires. Specific questionnaires were administered for the REEA 2015/2016:

(i) a core module questionnaire for family farms; (ii) an OPA/OPE questionnaire; (iii) a community survey questionnaire.

The census questionnaires covered all 16 core items recommended in the WCA 2010, namely;

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING Two modes of data transmission were adopted under the REEA: (i) the routing of data via the Internet (specifically, via Dropbox) and (ii) physical transmission (on hard disk, USB key and paper supports). The data processing plan covered the following aspects: (i) equipment preparation; (ii) file clearance; (iii) data validation; and (iv) tabulation. Computer processing was done centrally, in Abidjan. The software used to process the REEA data was CSPro version 6.1. The clearance phase was an iterative process of cleaning up the database and producing clean files. To ensure undistorted data after clearance, the tabulation2 was done under CSPro on both versions of the database: with both raw and clean data. The analysis of census results was undertaken from March to April 2017. All data collected and documents produced (reports, methodology, manuals) were archived in a database the administration of which was entrusted to the Directorate of Statistics, Documentation and Informatics (DSDI) of MINAGRI and a backup copy was made.

(b) CENSUS DATA QUALITY Technical arrangements were made at different levels to ensure the quality of field data collection. The first provision was to incorporate consistency checks into the data entry programme for the different questionnaires, to minimize data entry errors, inconsistencies, and incomplete data. Two key programmes were designed and used: the Data Consistency Control Programme and the Team Tracking Programme. The latter programme made it possible to monitor the mobility of teams in the field.