Analysis unit

Persons and households

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

UNIT DESCRIPTIONS: - Dwellings: The dwelling is a separate and independent place of residence. Regular dwellings (accommodations) are defined as housing units typical of the Republic (detached houses, flats, hostels) intended for private households to live in. - Households: The household is defined as a person, family or a group of families or persons permanently living in a given dwelling and having a common budget, with blood relationship between them not being compulsory. - Group quarters: Institutions (collective quarters) are places of residence intended for collective households, i.e. for groups of people cohabiting in the same housing unit (specialized institution), sharing common meals, but not having individual budgets or common consumer espenses, observing common rules and usualy not being related.

Universe

All inhabitants permanently residing in each housing unit, including persons who were temporarily absent at the census moment. Temporarily present citizens of the Republic of Belarus who reside in other places are to be only listed in the check census list. Children born after and persons who died before the census moment are not to be included in the census documents. Homeless (persons without a specific place of residence)

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Ministry of Statistics and Analysis of the Republic of Belarus

SAMPLE SIZE (person records): 990706.

SAMPLE DESIGN: Systematic sample of every 10th household after a random start, drawn by the National Statistical Office. Homeless (persons without a specific place of residence)

Mode of data collection

Face-to-face [f2f]

Research instrument

There are three documents. Form 2P contains directions for completing the list of those usually (permanently) living in the dwelling and their housing conditions. Form 3N III. Directions for recording answers to questions of the enumeration questionnaire. Form 4E directions for recording answers to questions of the enumeration questionnaire for those temporarily present on the territory of the Republic of Belarus.

Abstract

The main aim and objectives of the census is to provide benchmark statistics and a comprehensive profile of the population and households of Niue at a given time. This information obtained from the census is very crucial and useful in providing evidence to decision making and policy formulation for the Government, Business Community, Local Communities or Village Councils, Non Government Organisations of Niue and The International Communities who have an interest in Niue and its people.

Geographic coverage

National Coverage

Analysis unit

A Population and Household Census have the following units of analysis: - Households - Individuals/Persons - Members Overseas

Universe

All households in Niue and all persons in the household including those temporarily overseas and those absent for not more than 12 months.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable to a complete Enumeration Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionaire was published in English, a translated questionnaire was on hand when on demand by the respondent.

The questionnaire design differed slightly from the design of previous census questionnaires. As usual, government departments were asked to submit a list of questions on any specific topic they would like to add. Responses were not forthcoming in this census, although a few new questions were included.

There were two types of questionaires used in the census: the household questionaire and the individual questionnaire. An enumerator manual was prepared to assist the enumerators in their duties.

The questionnaire was pre-tested by the enumerators before they were to go out for field enumeration.

Cleaning operations

Census processing began as soon as questionaires were checked and coded. Forms were checked, edited and coded before being entered into the computer database.

Data processing was assisted by the Secretariat of the Pacific Community (SPC) using the computer software program CSPro for data entry and for generating tables. Tables were then exported to Excel for analysis.

Occupation and Industry were coded using the United Nations International Standard Classification of Occupation and International Standard Industrial Classification.

It is standard practice that as each area was completed the forms were first checked by the field supervisors for missing information and obvious inconsistencies. Omissions and errors identified at this stage were corrected by the enumerators.

The next stage was for the field supervisors to go through the completed forms again in the office to check in more detail for omissions and logical inconsistencies. Where they were found, the supervisors were responsible to take the necessary action.

Once the questionnaires had been thoroughly checked and edited, they were then coded in preparation for data processing.

Checking, editing and coding of the questionnaires in office were done after normal working hours as to ensure that the confidentiality of the survey is well observed.

Response rate

Complete enumeration of all households

Sampling error estimates

Not Applicable

The Department of Health requires nursing homes to complete electronic filing of each facility's licensed nursing home beds and availability by bed category on a weekly basis. All nursing homes are requested to submit their Weekly Bed Census between Wednesday and Friday of each week, based on the census at 12:00 AM on Wednesday night.

The Nursing Home Census- Historic data is the most comprehensive nursing home data available and is suitable for trending.

Available Bed and Occupancy information is self-reported, and is not audited by the NYSDOH.

For more information, check out http://nursinghomes.nyhealth.gov/, or go to the "About" tab.

Abstract

The five-yearly Census of Population and Dwellings is a very important item on Tokelau’s agenda. Its results provide the most authoritative data on how many people we have, what the composition of their households is, what education level they have, how they contribute to Tokelau’s economy, and so on. As a non-self- governing territory, Tokelau has a special constitutional relationship with New Zealand. This special relationship is strengthened by connections between the tiny Tokelau National Statistics Office (TNSO) and Statistics NZ. It is the latter organisation that has been largely responsible for the excellent Tokelau Censuses in 2006, 2011, and again in 2016.

Geographic coverage

National coverage. Tokelauan employees of the Tokelau Public Service based in Apia (and their immediate families), were also interviewed in Apia on census day.

Analysis unit

Individuals and Households.

Universe

The Census covers residents of the non-self-governing New Zealand territory of Tokelau and includes Tokelau public servants and their families who are employed in Apia, Samoa. While visitors to Tokelau on Census night are also included, the ultimate aim of the Census is to provide an accurate assessment of the de jure population. This has in the Censusus of 2006, 2011 and 2016 been done to an exact definition who is included. Previous definitions have been less precise which makes long-term time serie less reliable.

Kind of data

Census/enumeration data [cen]

Sampling procedure

N/A: Census.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questions matched the previous Censuses' format in Paper Assisted Personal Interview (PAPI) as much as possible. The "skips" in PAPI proved a big time saver, and the internal checks for suitability of answers made quality control much faster.

The questionnaire was published in English with the Tokelauan translation for each question. It was divided into two sections: - Dwelling questions - Individual questions.

Cleaning operations

Thanks to the Computer Assisted Personal Interview (CAPI) data collection method, it was possible to quality check census forms on census day as soon as the interviewers uploaded them. Supervisors helped the census management team to quality check every census form and if there were missing answers or errors found, the forms were sent back to the interviewers to fix. The ability to check the quality of answers was one of the major benefits of using tablets for data collection; it made the checking process faster and more thorough. This checking also ensured that the final population counts were able to be released only three weeks after census.

Sampling error estimates

Not applicable: Census.

Data appraisal

Given the small population size, no post-enumeration survey was done.

Abstract

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

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

Geographic coverage

National coverage

Universe

All inhabitants permanently residing in each housing unit, including persons who were temporarily absent at the census moment. Temporarily present citizens of the Republic of Belarus who reside in other places are to be only listed in the check census list. Children born after and persons who died before the census moment are not to be included in the census documents.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Population census

SAMPLE DESIGN: Every 10th household after a random start

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 990,706

Mode of data collection

Face-to-face [f2f]

Research instrument

There are three documents. Form 2P contains directions for completing the list of those usually (permanently) living in the dwelling and their housing conditions. Form 3N III. Directions for recording answers to questions of the enumeration questionnaire. Form 4E directions for recording answers to questions of the enumeration questionnaire for those temporarily present on the territory of the Republic of Belarus.

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Households and persons

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design

The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection

The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census:

Form A (Long Form): For conventional households and residential institutions

Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations

Form B2 (Short Form): For hotels/guesthouses

Form B3 (Short Form): For foreign missions/diplomatic corps

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.

Abstract

The primary mission of the 2006 Population and Housing Census (PHC) of Nigeria was to provide data for policy-making, evidence-based planning and good governance. The Government at all tiers, researchers, the academia, civil society organizations and the international agencies will find the sets of socio-demographic data useful in formulating developmental policies and planning. The 2006 data will certainly provide benchmarks for monitoring the Millennium Development Goals (MDGs). Enumeration in the 2006 PHC was conducted between March 21st and 27th 2006. It was designed to collect information on the quality of the population and housing, under the following broad categories: demographic and social, education, disability, household composition, economic activity, migration, housing and amenities, mortality and fertility. The results of the exercise are being released as per the Commission's Tabulation Plan which began with the release of the total enumerated persons by administrative areas in the country in the Official Gazette of the Federal Republic of Nigeria No.2, Vol 96 of February 2,2009 and followed with the release of Priority Tables that provide some detailed characteristics of the population of Nigeria by State and LGA.

Geographic coverage

National

Analysis unit

Individuals Households

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Census 2006 Processing: The Technology and Methodology:-

Unlike the data capture method used for the country’s previous censuses, where information from the census forms are typed into the computer system, data capture for census 2006 was carried out by OMR/OCR/ICR systems where questionnaires are scanned through high speed optical scanners. The choice of the scanning system was because it is faster and more accurate than the data keying method.

OMR/OCR/ICR Technology

Definition of terms

• OMR (Optical Mark Recognition) - This means the ability of the scanning machine to detect pencil marks made on the questionnaires by the Enumerators in accordance with the responses given by the respondents.
• OCR (Optical Character Recognition) - This means the ability of the scanning machine to recognize machine printed characters on the questionnaires.
• ICR (Intelligent Character Recognition) - This means the ability of the scanner to recognize characters hand written by the Enumerators in accordance with the responses given by the respondents.

Processing Procedures of Census 2006 at the DPCs:- Data processing took place in the Commission’s seven (7) Data Processing Centres located in different geographical zones in the country. There was absolute uniformity in the processing procedures in the seven DPCs.

(a) Questionnaire Retrieval/Archiving Questionnaires from the fields were taken directly from the Local Government Areas to designated DPCs. The forms on arrival at the DPCs were counted, archived and labeled. Retrieval of the questionnaires at the DPCs were carried out based on the EA frame received from the Cartography Department. Necessary Transmittal Forms are completed on receipt of the Forms at the DPCs. The Transmittal Forms are also used to keep track of questionnaires movement within the DPC.

(b) Forms Preparation The scanning machine has been designed to handle A4 size paper. And the Census form being twice that size has to be split into two through the dotted lines at the middle of the form. This forms preparation procedure is to get the questionnaires, for each Enumeration Areas (EAs), ready for scanning. There is a Batch Header to identify each batch.

(c) Scanning Each Batch on getting to the Scanning Room was placed on joggers (a vibrating machine)to properly align the forms, and get rid of dust or particles that might be on the forms.

The forms are thereafter fed into the scanner. There were security codes in form of bar codes on each questionnaire to identify its genuineness. There was electronic editing and coding for badly coded or poorly shaded questionnaires by the Data Editors. Torn, stained or mutilated forms are rejected by the scanner. These categories of forms were later manually keyed into the system.

Re-archiving of Scanned Forms:- Scanned forms were placed in their appropriate marked envelopes in batches, and thereafter returned to the Archiving Section for re-archiving.

Data Output from the Scanning Machine:- The OMR/OCR Software interprets the output from the scanner and translates it into an XML file from where it is further translated into the desired ASCII output that is compatible for use by the CSPro Package for further processing and tabulation.

Data back-up and transfer:- After being sure that the data are edited for each EA batch in an LGA, data then was exported to the SAN (Storage Area Network) of the Server. Two copies of images of the questionnaires for each EA copied to the LTO tapes as backup and then transferred to the Headquarters. The ASCII data files for each LGA are zipped and encrypted, and thereafter transfer to the Data Validation Unit (DVU) at the Headquarters in Abuja.

Data appraisal

Data collation and validation:- The Data Validation Unit at the Headquarters was responsible for collating these data into EAs, LGAs, States and National levels. The data are edited/validated for consistency errors and invalid entries. The Census and Survey Processing (CSPro) software is used for this process. The edited, and error free data are thereafter processed into desired tables.

Activities of the Data Validation unit (DVU):-

Decryption of each LGA Data File Concatenation/merging of Data Files Check each EA batch file for EA completeness within an LGA and State Check for File/Data Structure Check for Range and Invalid Data items Check for Blank and empty questionnaire Check for inter and intra record consistency Check for Skip Patterns Perform Data Validation and Imputation Generate Statistics Report of each function/activity Generate Statistical Tables on LGA, State and National levels.

This CD consists of the TIGER/Line Census Files, 1990. The type of File is geographic. The 1990 Census TIGER/Line file is an extract of selected geographic and cartographic information from the Census Bureau's TIGER data base. The Census Bureau is releasing the 1990 Census TIGER/Line files to provide data users with the final 1990 census boundaries (including voting districts) and to support the 1990 Census Data Products Program. The 1990 Census TIGER/Line file provides digital data for all 1 990 census map features and boundaries, the associated 1990 census final tabulation geographic area codes (such as 1990 census block numbers), and the codes for the January 1, 1990 legal and statistical areas on both sides of each line segment of every mapped feature. This version also contains the final voting district codes and the 1990 census designated place codes. The 1990 Census TIGER/Line file contains basic information for 1990 census geographic area codes, basic map features and their names, and address ranges in the form of 12 'Record Types.' The record types are as follows: 1. Basic Data Records (Individual Feature Segment Records) 2. Shape Coordinate Points (Feature Shape Records) 3. Additional Decennial Census Geographic Area Codes 4. Index to Alternate Feature Names 5. Feature Name List 6. Additional Address Range and ZIP Code(2) Information 7. Landmark Features 8. Area Landmarks A. Additional Polygon Geographic Area Codes I. Area Boundaries P. Polygon Location R. Record Number Range Each segment record contains appropriate decennial census and, when appropriate, FIPS(1) geographic area codes, latitude/longitude coordinates for all line segments and point features, the name of the feature Geographic Coverage: The 1990 Census TIGER/Line files cover the entire United States, Puerto Rico, the Virgin Islands of the United States, American Samoa, Guam, the Northern Mariana Islands, Palau, the other Pacific entities that were part of the Trust Territo ry of the United States for the 1980 census (the Marshall Islands and the Federated States of Micronesia), and the Midway Islands (to provide complete mapping within the boundaries of the State of Hawaii). The data in the 1990 Census TIGER/Line files include information comparable to what was in the 1980 GBF/DIME-Files, which covered roughly 2 percent of the land area of the United States. The remaining 98 percent of the land area has been added using data that originated with the U.S. Geological Survey (USGS) based on the 1:100,000-scale USGS maps, supplemented with Census Bureau-compiled information and from other USGS map sheets. NOSB= Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Abstract

The main aim for the establishment census 2012 is to enumerate all of the economic establishments operating in Palestine in 2012, except for those establishments engaged in farming activities, and building a new updated a classified establishment register according to the geographical distribution, main economic activity according to international recommendations.

The goals for the Establishment Census could be summarized as follows: 1. Distribution of establishments by various economic activities. 2. Distribution of establishments by the Palestinian governorates. 3. The size of employment in various economic activities and its distribution by sex. 4. Distribution establishments in terms of economic organization, legal status, ownership and operation status. 5. The value of capital invested in establishments. 6. Distribution establishments in terms of registration status with the official authorities. 7. The rate of growth in the number of economic establishments.

Geographic coverage

The Establishment Census 2012 includes all of the establishments in Palestine, whether those of the government or international organizations and institutions, non-profit, and establishments engaged in economic activities in the markets or in factories and companies, or those that exercise an economic activity in houses and have the definition of an establishment, with the exception of those establishments engaged in the agriculture, forestry, fishing and animal husbandry

Analysis unit

Establishment

Universe

The establishment considered the statistical unit that the data collection was upon, which is an institution or part of it, which is located in one place and specializes mainly in one major activity (non Assistant) which will bring most of the added value, classified within the same activity (with probability of production of secondary activities) and for which data are available, allowing for calculating of operating surplus account, which provides data for both: workers, and expenses, production and revenue, and fixed assets. An establishment must provide the following requirements: 1.Participation in an economic activity, any establishment should provide good or service to the market. 2.The presence in a fixed place. 3.A holder of an establishment, whether an individual or a legal entity. 4.The presence of a single management of the establishment

Kind of data

Census/enumeration data [cen]

Sampling procedure

comprehensive census of all economic establishment in palestine

Mode of data collection

Face-to-face [f2f]

Research instrument

The establishment Census form consisted of two sections:

Part one: Identification data, which included basic information about the establishments, governorate, Locality, number of enumeration area, Building No. in the enumeration area, serial number of establishment in the enumeration area, establishment commercial name, name of holder or director, sex of the holder or director, phone number, location and description, including the name of the neighborhood and the street and the name of the building or the owner of the building, and the working status of the establishment.

Part two: data on operating establishments only, which include: (Description of the main economic activity, Ownership, Economic organization, Legal status, Establishment Year, Number of employees, Preparing of accounting records, Licensing and registration, No. workers, present value of capital, owner Identity No. or director of the of establishment).

Special form for Jerusalem Governorate area (J1) Due to the special situation in the Jerusalem governorate, specially J1 area (those parts of Jerusalem which were annexed by Israel in 1967) a short form for census questionnaire has been designed, which include the following questions: (Identification data for the establishment, working status, main economic activity, ownership of establishment, economic organization, establishment year, the number of employees in the establishment (paid, unpaid)).

Cleaning operations

The data processing stage includes editing, coding, data entry, reviewing lists and checking all previous operations of data entry for all enumeration areas. All procedures and instructions were conducted to check the consistency of the data and coding fields and ensure the entry of all enumeration areas and booklets and questionnaires, with their content of establishment data. As booklets and questionnaires required checking and moving from one operation site to another, a store was prepared for all the documents to be indexed and categorized and the store keeper controlled the flow of documents.

Coding manuals were prepared and examined beforehand, as well as the instructions for editing and coding procedures to check the consistency of the data and how to detect and correct errors. All editing and coding employees were selected from among the best fieldworkers who collected the data from establishments owners or manager. Training was conducted centrally to ensure uniform concepts and to eliminate disparities in fieldwork in all governorates. Editing, coding and testing the consistency of 100% of the questionnaires was conducted, in addition to desk reviewing, editing and coding (100%) in order to eliminate differences between individual editors and to discover and correct errors and circulate them daily.

Tests were held for all applicants for data entry and those who performed best were trained centrally in a uniform procedure of data entry. During the first three days, all date entered were deleted and re-entered again to correct errors and inform employees so as to avoid such errors in the future. Certain procedures were adopted to ensure correct data entry: in the first stage a unique separate file was prepared for each enumeration area that included identification data (to ensure coverage), the number of establishments and the total number of booklets to ensure that all booklets and all households had been entered. Upon data entry, a thorough examination of the identification data and the range of each digital key question was conducted so that the computer did not accept any figure outside this range. For example, the operation status, sex and all the pre-coded questions in the establishment questionnaire, and the type of building in the buildings questionnaire. The remaining questions were exposed to a comprehensive re-examination of the range of each question after data entry and the extraction of error lists resulting from data inconsistency.

After data entry, certain lists were extracted to ensure the coverage of all enumeration areas, and establishments, and to examine the internal consistency of the data of each unit. The procedures used were to extract error lists that must be corrected or questioned These lists were submitted to the best reviewers under full supervision of the technical operations in the census directorate.

Specific programs previously prepared were used to detect errors according to the following procedures: 1. An instruction manual was prepared for desk editing and procedures for the establishments' questionnaire. A set of desk editing instructions were printed and the procedures for the questionnaire containing tests designed to ensure the coverage of data entry, to detect inconsistencies or to detect abnormal and rare cases. These were reviewed and printed with a name and number given to every error in the manual. 2. A list was extracted for each enumeration area, including the identification data of each establishment message (type of check) and the number printed in the manual. The auditor could then recognize the message name and type of error, location and procedures of editing and audit procedures patch, which consists of several checks on several stages. 3. Lists were submitted to the reviewers to return to the original booklets. If the error was caused by data entry, it would be corrected on the list. If the error was due to fieldwork, all associated questions should be considered for correction. For example, if the operation status of the establishment was closed, it must be no answers on the questions after it. The first check would be conducted through manual editing, then extracting the electronic lists after data entry for such types of tests, then they would be corrected manually on the original booklets and data re-entered correctly. As for the coverage test, there is a key reference that contains all enumeration areas and shows the number of booklets and establishments in the enumeration area to be entered on the computer. At this point, if there was a variation between the number of booklets and establishments actually entered and the total number of establishments in the file of each area, an error message appears to request correction. Through this method, we ensured that 100% of the establishments were entered.

All lists for the enumeration areas were extracted in this way and all kinds of tests. 1. Amended lists were sent back to data entry to be entered and corrected and a copy of the daily entered data was kept in several different places. 2. Previous stages were conducted twice or more until the data of each enumeration area became clean. 3. All files were compiled for enumeration areas for each locality and governorate. Then, all tables and any additional tests were conducted to test the data before the final tabulation in order to correct errors according to the aforementioned procedures.

Sampling error estimates

-

Data appraisal

There are two types of error that can occur: statistical errors and

This file consists of the four original sheets included in the District Profile Report download, as described on the About FY21 Report tab, an additional five sheets created for GIS use and documentation, and an About File tab.The original four sheets are as follows:1. The “District Profile Report” provides a list of statistics for a given school district under seven groupings listed above (A-G). There are six columns of data in this worksheet. The first three columns provide statistics for each data element for the selected district, similar districts average and statewide average. There are three additional columns that enable the user to look at the statistics of three other school districts for comparison. By clicking in the cell below the Comparison Districts 1, 2 and 3 labels, the user can choose three additional school districts to review simultaneously.2. The worksheet labeled “District Data” provides a downloadable file of school district data.3. The worksheet labeled “Similar Districts Data” provides a downloadable file of similar districts averages.4. The worksheet labeled “Statewide Data” provides statewide averages for every statistic in the report.The five new sheets are as follows:1. FY21_Data_Dictionary - contains the abbreviated column names for GIS use and their definitions as found on the original download page. (The link is on the About FY21 Report sheet)2. FY21_Join - contains the District Data formatted for use in a GIS in the following way: the attribute/column titles were abbreviated for use in GIS, column C - UNSDLEA21 was added to facilitate joining to the Census School District geography file, column D - COUNTY was added for geographical filtering purposes, column E - COUNTY_GEOID was added for geographical filtering purposes. All columns from F to BM remain in the same order as the original dataset3. About FY21 Report - contains the report description as found on the original download page. The link to that original download page is also on this sheet.4. Mismatches - contains the school districts where the Census UNSDLEA cannot be derived from the Ohio IRN for quality control purposes5. No Data - contains the school districts that are not in the Profile Report but are in the Census geography data, for a quality control check when performing a join

This CD consists of the TIGER/Line Census Files, 1990. The type of File is geographic. The 1990 Census TIGER/Line file is an extract of selected geographic and cartographic information from the Census Bureau's TIGER data base. The Census Bureau is releasing the 1990 Census TIGER/Line files to provide data users with the final 1990 census boundaries (including voting districts) and to support the 1990 Census Data Products Program. The 1990 Census TIGER/Line file provides digital data for all 1 990 census map features and boundaries, the associated 1990 census final tabulation geographic area codes (such as 1990 census block numbers), and the codes for the January 1, 1990 legal and statistical areas on both sides of each line segment of every mapped feature. This version also contains the final voting district codes and the 1990 census designated place codes. The 1990 Census TIGER/Line file contains basic information for 1990 census geographic area codes, basic map features and their names, and address ranges in the form of 12 'Record Types.' The record types are as follows: 1. Basic Data Records (Individual Feature Segment Records) 2. Shape Coordinate Points (Feature Shape Records) 3. Additional Decennial Census Geographic Area Codes 4. Index to Alternate Feature Names 5. Feature Name List 6. Additional Address Range and ZIP Code(2) Information 7. Landmark Features 8. Area Landmarks A. Additional Polygon Geographic Area Codes I. Area Boundaries P. Polygon Location R. Record Number Range Each segment record contains appropriate decennial census and, when appropriate, FIPS(1) geographic area codes, latitude/longitude coordinates for all line segments and point features, the name of the feature Geographic Coverage: The 1990 Census TIGER/Line files cover the entire United States, Puerto Rico, the Virgin Islands of the United States, American Samoa, Guam, the Northern Mariana Islands, Palau, the other Pacific entities that were part of the Trust Territory of the United States for the 1980 census (the Marshall Isl ands and the Federated States of Micronesia), and the Midway Islands (to provide complete mapping within the boundaries of the State of Hawaii). The data in the 1990 Census TIGER/Line files include information comparable to what was in the 1980 GBF/DIME-Files, which covered roughly 2 percent of the land area of the United States. The remaining 98 percent of the land area has been added using data that originated with the U.S. Geological Survey (USGS) based on the 1:100,000-scale USGS maps, supplemented with Census Bureau-compiled information and from other USGS map sheets. NOSB= Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This file consists of the four original sheets included in the District Profile Report download, as described on the About FY16 Report tab, an additional five sheets created for GIS use and documentation, and an About File tab.The original four sheets are as follows:1. The “District Profile Report” provides a list of statistics for a given school district under seven groupings listed above (A-G). There are six columns of data in this worksheet. The first three columns provide statistics for each data element for the selected district, similar districts average and statewide average. There are three additional columns that enable the user to look at the statistics of three other school districts for comparison. By clicking in the cell below the Comparison Districts 1, 2 and 3 labels, the user can choose three additional school districts to review simultaneously.2. The worksheet labeled “District Data” provides a downloadable file of school district data.3. The worksheet labeled “Similar Districts Data” provides a downloadable file of similar districts averages.4. The worksheet labeled “Statewide Data” provides statewide averages for every statistic in the report.The five new sheets are as follows:1. FY16_Data_Dictionary - contains the abbreviated column names for GIS use and their definitions as found on the original download page. (The link is on the About FY16 Report sheet)2. FY16_Join - contains the District Data formatted for use in a GIS in the following way: the attribute/column titles were abbreviated for use in GIS, column C - UNSDLEA16 was added to facilitate joining to the Census School District geography file, column D - COUNTY was added for geographical filtering purposes, column E - COUNTY_GEOID was added for geographical filtering purposes. All columns from F to BM remain in the same order as the original dataset3. About FY16 Report - contains the report description as found on the original download page. The link to that original download page is also on this sheet.4. Mismatches - contains the school districts where the Census UNSDLEA cannot be derived from the Ohio IRN for quality control purposes5. No Data - contains the school districts that are not in the Profile Report but are in the Census geography data, for a quality control check when performing a join

This study is a restricted data file of data from the L.A.FANS Neighborhood Observation Study, an in-person observational by trained L.A.FANS interviewers of the census blocks on which L.A.FANS respondents lived during L.A.FANS Wave 1. Interviewers were trained to walk each block face and record social and physical observations on precoded check sheets. Each block face was observed by several different interviewers working independently at different times of the day and week. These data are designed to be used with L.A.FANS Wave-1 survey interview data restricted versions 2.5 or 3 to provide data on the census block and census tract in which individual respondents lived. Users who apply for these restricted data must also be approved for using restricted version 2.5 or 3. Please note that L.A. FANS restricted data may only be accessed within the ICPSR Virtual Data Enclave (VDE) and must be merged with the L.A. FANS public data prior to beginning any analysis. The study is described in detail in the L.A.FANS Neighborhood Observations Codebook. Further information is available in: Jones, M., Pebley, A. R., and Sastry, N. (2011). Eyes on the block: Measuring urban physical disorder through in-person observation. Social Science Research, 40(2), 523-537.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Zambia Demographic and Health Survey (ZDHS) 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 2018 ZDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

Abstract

This was the second Agricultural Census to be conducted in Niue since the last one in 1989. As well as collecting information on agriculture, the census also included some detail information on the population to provide the Government with up-to-date information on some important population parameters. Although this as only the second agricultural census to be conducted in Niue, the country has a long history of Population activities and has gained experiences in data collection. Nevertheless, Food and Agricultural Organization of the United Nations (FAO) provided technical assistance under TCP/Niue/3101 through the services of an Agricultural Census Expert and a Data Processing Expert.

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 livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size. According to legal status, the holdings were classified as: (i) individual household on own account; (ii) in partnership; (iii) a village association; or (iv) institution.

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. The Enumeration The country was divided to 21 Enumeration Areas (EAs) for the enumeration purposes. This division was based on the Population and Household Census 2006. There were 30 EAs in the first Agriculture Census in 1989 and the decline of number of EAs to 21 this time was the direct result of the declining population and number of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Altogether, three questionnaires were used:

1 Household Form 2 Holding Form 3 Parcel Form.

The following information was required from all households:

(i) Location of household (ii) Date of birth, sex, Age, Decent, Country of Residence for all persons (iii) Educational Attainment, Main Activity, Hours worked in the Holding and Operator Status for persons 10 years and over (iv) Level of Agricultural Activity (v) Livestock, Poultry and Domestic Animals (vi) Household Fishing Activities ( fishing methods, Number of fishing trips, persons involved in fishing, proportion of catch sold, number of canoes ,dinghies and outboard motors owned or hired. (vii)Number of Uga caught and method of Catching (viii) Consumption of Major crops( drinking Nuts, Matured Coconuts, Green and Ripe Bananas, Taro, Cassava and Papaya (ix) Number of Coconuts for Feeding Animals.

Cleaning operations

a. Checking, Editing and Coding It is standard practice that as each enumeration area was completed the forms were first checked by the field supervisors for missing information and obvious inconsistencies. Omissions and errors identified at this stage were corrected by the enumerators. The next stage was for the field supervisors to go through the completed forms again in the office to check in more detail for omissions and logical inconsistencies. Where they were found, the supervisors were responsible to take the necessary action. Once the questionnaires had been thoroughly checked and edited, they were then coded in preparation for data processing. Checking, editing and coding of the questionnaires in office were done after normal working hours as to ensure that the confidentiality of the survey is well observed.

b. Data Processing The data was entered using two office computers of Statistics Niue with a custom designed CSPro database software by a computer programmer from The National Statistics office of the Philippines. Data entry was successfully done in a week. The next stage of processing, on line editing and cleaning in preparation for tabulation was not straight forward as expected because of these issues: the programmer assigned by FAO for the census was based in the Philippines and was only available on part time basis, the census expert (consultant) was based in Samoa and was also available on part time basis while the rest of the team was in the Niue office. The ‘distance’ between the parties, the day and time differences had became a hurdle to the smooth running of the final stages of data processing, cleaning and tabulation of the data and not to mention the difficulties in the communication systems. The progress was very much depended on the availability of internet communications and they were times it has broken down. These composite issues have delayed the final stages of data processing dramatically.

Data appraisal

Overall, the standard of enumeration was high. A PES was conducted to evaluate the accuracy of the data. The PES used objective measurement techniques (compass and tape measure) to measure the physical area of the selected parcels of land. The results of the survey revealed differences between the areas recorded in the census interview and the physical area as measured.

Abstract

The Agricultural Census of the Republic of Lithuania was carried out on 3 May–30 September 2010. It was the second Agricultural Census after the restoration of Independence of Lithuania (the first one was conducted in 2003 and the first in Lithuania as a member of the European Union. The Agricultural Census 2003 took place from 1 to 30 June 2003. A pilot census was carried out on 3–14 June 2002. The census was carried out by interviewers. They visited every holding and filled in questionnaires. There were about 6800 interviewers. All farmers and land users growing agricultural products were enumerated. The Agricultural Census 2003 questionnaire was completed for each agricultural holding with the agricultural land area of more than 1 ha or with the agricultural land area of less than 1 ha but income from sales of agricultural production not less than LTL 5000 per year – approximately EUR 1448 per year. Holdings with the agricultural land area of less than 1 ha and income less than the abovementioned were included in the household enumeration list. The definition of enumeration units in the Agricultural Census was harmonised with the recommendations of the Statistical Office of the European Union (Eurostat) and the United Nations Food and Agriculture Organization (FAO). Totally 364414 have been surveyed – 199913 agricultural holdings (646 agricultural companies and enterprises and 199267 farmers’ and family farms) and 164501 small farms.

Objective: The Agricultural Census 2010 Census was carried out for the following purposes: - to record changes taking place in agriculture; to find out the number of producers of agricultural products in Lithuania; - to obtain accurate statistical data needed for the analysis of the development of Lithuanian agriculture; - to predict the opportunities of agricultural development; - to assess the effectiveness of the European Union aid and future needs thereof.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex Ito the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, as either its primary or secondary activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame: Prior to the AC 2010, lists of respondents by municipality were prepared using the following statistical and administrative sources: the Integrated Administration and Control System Register, the Livestock Register, and the AC 2003 database updated with data from other statistical surveys. The quality of the list was checked and some corrections were made by a specialist in municipalities.

b. Sample size: The FSS 2005 sample size was 65579. During the survey 330 new farms were found. Altogether, data were provided by 62333 farms - 61791 farmers' and family farms and 542 agricultural companies and enterprises. According to the FSS 2005 data it was estimated that the total number of farms was 252946.

Mode of data collection

Computer Assisted Web Interview (CAWI)

Research instrument

Two questionnaires were used in the AC 2010: (i) the main questionnaire, for enterprises and family farms that met the EU minimum thresholds for agricultural activity, with two annexes (the annex had to be filled in if the land and/or the livestock of the holding were in different municipalities and the annex on farm buildings and machinery); and (ii) the Small Units Questionnaire, for very small farms that were below the minimum thresholds.

Main Questionnaire (1):

I. DATA OF AGRICULTURAL HOLDING II. LAND OF THE HOLDING III. LIVESTOCK OF HOLDING NUMBER IV. FARM LABOR FORCE V. RURAL DEVELOPMENT VI. SUPPORT FOR RURAL DEVELOPMENT VII. AGRICULTURAL PRODUCTION METHODS VIII. FARM BUILDINGS, MACHINES AND EQUIPMENT

Other Questionnnare (2):

I. FARM BUILDINGS II. FARM MACHINES AND EQUIPMENT III. LAND OF THE HOLDING VI. LIVESTOCK OF HOLDING NUMBER

The AC 2010 covered all 16 core items recommended in the WCA 2010. See questionnaires in external materials tab.

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The statistical data collected in the municipalities (ward offices) were submitted to Statistics Lithuania for data processing. ABBYY Form Filler 2.5 software was used for entering statistical data into laptop computers and to fill in the web questionnaire. A special programme created using Oracle software was used for statistical data processing at Statistics Lithuania. SAS was used to link statistical data from several sources according to the selected criterion and for the calculation of derived statistical indicators. The results were transferred into Microsoft Excel worksheet tables. Data on organic farming, taken from the Organic Farming Register, were loaded directly onto the database.

b. CENSUS DATA QUALITY There were 317 different logical and arithmetic controls for the main questionnaire and 26 for the small unit questionnaire. The final data check was performed using administrative sources and agricultural statistics surveys (crop production, livestock survey, etc.).

Data appraisal

A press release with the first census results was published in December 2010. Another press release, on the main preliminary results of the AC 2010 and Excel files with preliminary results, were published in February 2011. The provisional results of the AC 2010 were delivered to Eurostat. Three publications on final census results were published. The first publication (with results at country level) was published in July 2012; the second publication (by county and municipality) was released in September 2012 and the third publication (by ward) in December 2012.

The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

The Data

The data is split into several files, with the main one being Data.csv. The Data.csv contains all the variables of interest in this dataset, while the others are lists of references and general nation-by-nation information.

Data.csv contains the following fields:

Data.csv

• Country.Name: the name of the country
• Country.Code: the country's code
• Indicator.Name: the name of the variable that this row represents
• Indicator.Code: a unique id for the variable
• 1960 - 2016: one column EACH for the value of the variable in each year it was available

The other files

I couldn't find any metadata for these, and I'm not qualified to guess at what each of the variables mean. I'll list the variables for each file, and if anyone has any suggestions (or, even better, actual knowledge/citations) as to what they mean, please leave a note in the comments and I'll add your info to the data description.

Country-Series.csv

• CountryCode
• SeriesCode
• DESCRIPTION

Country.csv

• Country.Code
• Short.Name
• Table.Name
• Long.Name
• 2-alpha.code
• Currency.Unit
• Special.Notes
• Region
• Income.Group
• WB-2.code
• National.accounts.base.year
• National.accounts.reference.year
• SNA.price.valuation
• Lending.category
• Other.groups
• System.of.National.Accounts
• Alternative.conversion.factor
• PPP.survey.year
• Balance.of.Payments.Manual.in.use
• External.debt.Reporting.status
• System.of.trade
• Government.Accounting.concept
• IMF.data.dissemination.standard
• Latest.population.census
• Latest.household.survey
• Source.of.most.recent.Income.and.expenditure.data
• Vital.registration.complete
• Latest.agricultural.census
• Latest.industrial.data
• Latest.trade.data
• Latest.water.withdrawal.data

FootNote.csv

• CountryCode
• SeriesCode
• Year
• DESCRIPTION

Series-Time.csv

• SeriesCode
• Year
• DESCRIPTION

Series.csv

• Series.Code
• Topic
• Indicator.Name
• Short.definition
• Long.definition
• Unit.of.measure
• Periodicity
• Base.Period
• Other.notes
• Aggregation.method
• Limitations.and.exceptions
• Notes.from.original.source
• General.comments
• Source
• Statistical.concept.and.methodology
• Development.relevance
• Related.source.links
• Other.web.links
• Related.indicators
• License.Type

Acknowledgements

This dataset was downloaded from The World Bank's Open Data project. The summary of the Terms of Use of this data is as follows:

• You are free to copy, distribute, adapt, display or include the data in other products for commercial and noncommercial purposes at no cost subject to certain limitations summarized below.

• You must include attribution for the data you use in the manner indicated in the metadata included with the data.

• You must not claim or imply that The World Bank endorses your use of the data by or use The World Bank’s logo(s) or trademark(s) in conjunction with such use.

• Other parties may have ownership interests in some of the materials contained on The World Bank Web site. For example, we maintain a list of some specific data within the Datasets that you may not redistribute or reuse without first contacting the original content provider, as well as information regarding how to contact the original content provider. Before incorporating any data in other products, please check the list: Terms of use: Restricted Data.

-- [ed. note: this last is not applicable to the Gender Statistics database]

• The World Bank makes no warranties with respect to the data and you agree The World Bank shall not be liable to you in connection with your use of the data.

• This is only a summary of the Terms of Use for Datasets Listed in The World Bank Data Catalogue. Please read the actual agreement that controls your use of the Datasets, which is available here: Terms of use for datasets. Also see World Bank Terms and Conditions.

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

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 The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

Abstract

The Census of Agriculture & Fisheries (AGC 2011) is a national government operation geared towards the collection and compilation of statistics in the agriculture sector of the country. The collected data will constitute the bases from which policymakers and planners will formulate plans for the country's development.

The first Census of Agriculture (CoA) in the Cook Islands was conducted in 1988 and the second in 2000. Both censuses were supported technically by FAO. The Cook Islands also has a long history of population census taking at 5-yearly intervals in years ending in 1 and 6. Traditionally the Census of Population and Dwellings (CoPD) has included questions on agricultural activity at the household level, types of crops grown, livestock numbers, farm machinery and involvement in fishing and pearl farming activities. Section 3 of this report looks at data collected in the CoPD 2011 related to agricultural, fishing and pearl farming activities

Geographic coverage

National coverage.

Analysis unit

Household; Holding; Parcel; Individual.

Universe

The census covered all households, agricultural operators, agricultural establishments, fishing operators and pearl farmers.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The census of population and dwellings had 4 categories of agricultural activity, namely: subsistence only, commercial only, subsistence and commercial and no agriculture. For those engaged in agricultural activity a further breakdown was collected, namely: vegetables, fruit, flowers and other. The census of agriculture also had 4 categories but for crop growing only, namely, non-agricultural, minor agricultural, subsistence and commercial. The differences in these classifications and the types of agriculture included make comparisons difficult, however, it is useful to evaluate the two sets of data and draw conclusions as to the extent of agricultural activity in the cook islands from these two sources.

The questionnaires used for the census of agriculture 2000 and the census of population and dwellings 2006, related to agriculture, were reviewed and efforts made to avoid duplication. In particular, the question on the numbers of livestock kept by the household was dropped from the census of population and dwellings as this data was being collected in the census of agriculture. Likewise, information on machinery and equipment was dropped from the census of agriculture as this was being collected in the census of population and dwelling. Questions on the extent of involvement in agricultural activity at the household level were maintained in both censuses as was the extent of involvement in fishing and pearl farming. This provided a useful coverage check for the census of agriculture, in particular, although it was noted that there were definitional differences between the two censuses especially related to flower cultivation which was considered an agricultural activity in the census of population and dwellings but not in the census of agriculture. At the individual level, data on labour inputs was recorded in the census of agriculture by age and sex but other data at the individual level has then to be obtained through linkages to the census of population and dwellings through the person and household number.

The household questionnaire was administered in each household, which collected various information on levels of agricultural activity, holdings detail (including name of operator, total area, number of separate parcels, location), crops currently growing and/or harvested (including crops currently growing, total area, number of plants,crops planted and/or harvested, total area, number of plants), proportion of income from agriculture, loans for agriculture purposes, fertilizers, agricultural chemicals, improved varieties, other selected activities during the last 12 months (including bee keeping, hydroponic, floriculture, handicrafts), traditional methods on food storage and planting, travelling with locally grown food, water usage

In addition to a household questionnaire, questions were administered in each household for holding which collected various information on holding iidentification, parcel details during the lasts 12 months (including location, area, land tenure, land use, months used), scattered plants/trees (including number of plants), labour input for persons 15 years and over working during the last month (including sex, age, status, type, average hours worked per week, wages per month, benefits and other paid job)

In addition to a holding questionnaire, questions were administered for parcels which collected various information (during the last 12 months) on plot details (including proportion to parcel area, crops grown, method of planting, number of plants and proportion for sale), crops planted and harvested (including area harvested, number of plants and proportion for sale)

In addition to a household questionnaire, questions were administered in each household for livestock which collected various information on type and number of livestock, type of operation, nature of disposal during the last 12 months (including kind of livestock, number disposed (including home use, feast/gifts, sold, slaughtered, live)

In addition to a household questionnaire, questions were administered in each household for fishing which collected various information on household members engaged, main purpose of fishing activity, household members (including average hours spent per week), details of fishing activities (including forms of fishing, number of people fishing, location, average number of fishing trips, average hours per fishing trip), boat details (including type of boat, length, engine), proportion of fish caught/collected and sold, proportion consumed

In addition to a household questionnaire, questions were administered in each household for pearl farming which collected various information (during the last 12 months) on farming details (including farm lines, spat collector lines, spat details, number of farm shells, labour input (including person number, sex, age, status, type, average hours worked per week, wages per month, benefits received, other paid job) , boat operation (including times used per week), type of equipment and facility, number of times per week, number owned, hired, borrowed), shelling details, proportion of income, loan details

The questionnnaires, that were developed in English, contain was divided into 5 forms: -Household Form: Levels of agricultural activity, List of agricultural holdings, Crops, Income from agricultural activities, Loans, Fertilizers, Other relevant questions. -Holding Form: Parcel details, Scattered plants/trees, Labour inputs. -Parcel Form: Number of sepearate plots, Plot details, Crops. -Livestock Form: Livestock details, Type of operation, Nature of disposal. -Fishing & Pearl Farming Form: Fisheries activities details, Pearl farm information, Labour inputs, Boats and other equipment used, Other relevant information.

Cleaning operations

The length and complexity of the census of agriculture forms made the exercise much more time consuming and virtually all records had to be edited. The data capture and data cleaning exercise for the census of agriculture took the best part of 12 months, including the adjustments following the re-enumeration of Aitutaki. Tabulation also proved to be challenging because of the need for considerable internal computation of areas and numbers of plants. The final database was then split up into a number of smaller databases designed for each set of tables. The tabulation was done using Microsoft EXCEL and ACCESS

In interpreting the results of the census of agriculture, account needs to be taken of the fact that households classified as having no agricultural or fishing activities in the census of population and dwellings were excluded from the census of agriculture, especially on Rarotonga. Other definitional differences between the two censuses should also be noted. The census of population and dwellings defined agricultural activity as crops, livestock and floriculture whereas the ensus of agriculture definition was primarily crops. Livestock and poultry raising was treated separately in the census of agriculture and flower growing was only included in the census of agriculture if it was a commercial activity or was carried out in conjunction with food crop activities.

Abstract

A Census of Population and Housing is the single most extensive statistical undertaking of a country. In order to plan and implement programmes and activities, statistics are needed by the Government administrators of various levels, private users, research organizations and the general public.

The 2001 Census was conducted under the Census Ordinance, which was amended by the Census Act No 55 of 2000. Census Ordinance places the legal obligation upon the public to give accurate information to the Census officers. The ordinance also guarantees the confidentiality of the information collected at individual level. The CPH 2001 has been designed to collect various information about the characteristics of the population, housing units and the households in Sri Lanka.

The CHP2001 provides:

a. Reliable and detailed benchmark statistics on the size, distribution and composition of population. b. Information pertaining to the characteristics of the housing units. c. Information on the characteristics of the households d. Information pertaining to the characteristics of the disable persons.

Geographic coverage

National coverage The 2001 census enumeration was able to be carried out completely in 18 districts. These include all the 17 districts in Western, Central, Southern, North Western, North Central, Uva and Sabaragamuwa Provinces and Amparai district in the Eastern Province. Due to the disturbed conditions in Northern and Eastern provinces of Sri Lanka, however, certain areas could not be enumerated completely.

Analysis unit

(1) Individual (2) Household

Universe

CPH 2001 covered all residents in each household and all units in each census block. The census did not cover diplomats.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

I) Population and Housing Schedule (F3): This schedule was used to collect 24 items from individuals pertaining to demographic and economic characteristics such as General information, Migration patterns, Educational characteristics, Economic characteristics, Nuptiality and Fertility and additional 9 items on Housing unit characteristics such as Occupancy status, Number of households in the unit, Number of occupants in the unit, Construction material of wall, floor, roof, Type of structure, Year of construction, Unit usage, Availability of rooms and Number of rooms and 7 items on Household such as Number of occupants in the household, Availability of toilet, Type of toilet, Source of drinking water, Type of lighting, Type of cooking fuel and Tenure

II) Schedule for Disabled Persons (F4): This schedule was used to collect information pertaining to 6 types of disabilities such as Vision, Hearing / Speaking, Manual/walking, Mental and Other Physical disabilities. [This is dealt with as a special census project and archived separately].

Data Collection Forms:

F1 - List of all the building units located in a Census block F2 - Administrative/Technical form (Summary of F1) F3 - Population and Housing Schedule (all information of the population, housing and household information). F4 - Schedule for disabled persons F5 - Special schedule for Tourists and Foreign visitors Schedule for post enumeration survey

Cleaning operations

Data processing consisted of two major phases: (1) Manual editing and coding, (2) Computer processing such as fixes while data entry, structure checking and completeness and secondary editing

Manual editing was confined in the field to simple checks such as verification of area identification codes and the codes for certain questions (e.g. district of birth). Coding was required only in respect of three questions, namely educational attainment, occupation and industry.

Data were entered for the second time to verify the original keyed data which is called the verification process. When the administrators fell that the overall error rate is diminishing, the verification process was mitigated step by step assuming that the operators are progressively improving in entering the questionnaires correctly.

A series of computer edit checks were carried out and records containing errors were printed for visual verification. These edit checks included both range and consistency checks. Finally limited number of imputations was done before the tabulation of data.

Processing was done on IBM S390 integrated server 3006 model B01 and several personal computers. Keyboard to disk type data entry was adopted for data capture.

The software Integrated Micro Computer Processing System (IMPS) developed by U.S. Bureau of Census was used to data processing activities including data entry.

Data appraisal

The Districtwise data files were analysed. the breakups of the analysis such as

1. Male/female totals
2. Frequencies
3. Housing unit types

were filed as standard benchmarks for each district to be used to compare various District Table figures.