Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

Population and other demographic information is collected by the US Census Bureau.

View the US Census Bureau's Quick Facts page about Bloomington, Indiana at https://www.census.gov/quickfacts

The Demographic Profile and other data for Bloomington can be viewed or downloaded from the American FactFinder search tool: https://factfinder.census.gov/bkmk/cf/1.0/en/place/Bloomington city, Indiana/POPULATION/DECENNIAL_CNT

The Census Bureau is creating a new platform for data. This site is in a preview stage and some parts are under construction. Here is a link for Bloomington: https://data.census.gov/cedsci/results/all?q=Bloomington%20city,%20Indiana&g=1600000US1805860&ps=app*from@SINGLE_SEARCH

The City webpage for Census data contains other related information: https://bloomington.in.gov/about/census-data

About this dataset

Context

The dataset tabulates the Tool median household income by race. The dataset can be utilized to understand the racial distribution of Tool income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Tool, TX median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Tool, TX (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Tool median household income by race. You can refer the same here

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Dwelling and person

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

UNIT DESCRIPTIONS: - Dwellings: Every separate and independent structure that has been constructed or converted for use as temporary or permanent housing. This includes any class of fixed or mobile shelter used as a place of lodging at the time of enumeration. A dwelling can be a) a private house, apartment, floor in a house, room or group of rooms, ranch, etc. designed to give lodging to one person or a group of people or b) a boat, vehicle, railroad car, barn, shed, or any other type of shelter occupied as a place of lodging at the time of enumeration. - Households: All the occupying members of a family or private dwelling that live together as family. In most cases, a household is made up of a head of the family, relatives of this person (wife or partner, children, grand-children, nieces and nephews, etc.), close friends, guests, lodgers, domestic employees and all other occupants. Households with five or fewer lodgers are considered private,but households with six or more lodgers are considered a non-family group. - Group quarters: Accommodation for a group of people who are not usually connected by kinship ties who live together for reasons of discipline, healthcare, education, mlitary activity, religion, work or other dwellings such as reform schools, boarding schools, barracks, hopsitals, guest houses, nursing homes, workers camps, etc.

Universe

Population in private and communal housing

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 268,248

Mode of data collection

Face-to-face [f2f]

Research instrument

Single record that includes housing and population questionnaires

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.

This dynamic analysis tool allows you to examine National Crime Victimization Survey (NCVS) data on both violent and property victimization by select victim, household, and incident characteristics. The NCVS is the nation's primary source of information on criminal victimization. It is an annual data collection conducted by the U.S. Census Bureau for the Bureau of Justice Statistics. The NCVS collects information from a nationally representative sample of U.S. households on nonfatal crimes, reported and not reported to the police, against persons age 12 or older. Violent crimes measured by the NCVS include rape and sexual assault, robbery, aggravated assault, and simple assault. Property crimes include burglary/trespassing, motor-vehicle theft, and theft.

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

Abstract

The Population and Housing Census 2000 was prepared and conducted according to the recommendations of the United Nations Economic Commission for Europe and the Statistical Office of the European Communities (Eurostat), which guarantee that the census data are internationally comparable. Also the comparability with the data of previous censuses carried out in Estonia was taken into account. Census 2000 was carried out from March 31 to April 9.

The Statistical Office of Estonia was responsible for conducting the Census. The purpose of the Census was to collect data on the size, composition and distribution of the country's population and access housing stock and conditions. The moment of the Census was 00.00 on 31 March 2000; the data collected in the Census reflect the characteristics of housing and of the population as of the moment of the Census.

The content of the Census data and the data collection methods were developed in the Statistical Office in cooperation with the experts of different fields. Regulation of the Government of the Republic 5 March 1999 approved the Census questionnaires and Census rules.

Geographic coverage

The Census covered all country.

The Statistical Office of Estonia (SOE) launched the mapping programme for the 2000 Population and Housing Census in 1995. After completing the test areas the specifications for the digital Census maps were finalized. According to the Specification, 1:50 000 maps in rural areas and 1:5 000 maps in urban areas were drawn. The specification was optimized to create a cartographic basis for the Census planning (Census area (CA) delineation) and for the Census itself (maps for enumerators, maps for supervisors, etc.). The Census mapping process was outsourced from SOE. The work was done by two companies - one in urban, another in rural areas. The production methodology was different in urban and rural areas. In rural areas, paper maps of the 1989 Census were used as a base source material, digitized by the mapping company and updated by local governments. In urban areas, the existing maps and orthophotos were used as a base source and the maps were updated by the mapping company. For rural and urban areas the municipalities compiled household lists including the number of inhabitants in each building or apartment. The purpose of household lists was to provide information about the number of inhabitants for the delineation of enumeration areas (EA).

The borders of Census units were marked on digital Population Census maps and the maps were printed for Census purposes. SOE stores digital maps in urban areas in Mapinfo, in rural areas in ArcView software and household lists in Foxpro software. The Census maps were ready by December 1999. Digital Population Census maps with the registered borders of administrative and settlement units are the basis for presenting the Census results in a cartographic way and for the development of Census GIS.

Universe

The Census covered: - persons who were in the Republic of Estonia at the moment of the Census (March 31, at 00.00) (excluding the diplomatic staff of foreign diplomatic missions and consular posts and their family members and persons in active service in foreign army); - persons who resided in the Republic of Estonia but who were in foreign states temporarily for a term of up to one year; - diplomatic staff of diplomatic missions and consular posts of the Republic of Estonia and their family members, who were in a foreign state at the moment of the Census; - residential buildings and other buildings used for habitation, and apartments and other dwellings situated therein (excluding buildings of foreign diplomatic missions and consular posts and dwellings situated therein).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

PHC 2000 was conducted using two types of questionnaires - the Personal Questionnaire containing 31 questions, and the Housing Questionnaire with 12 questions. The Census questionnaires collected personal, household information as well as dwelling data.

1. Personal data include: 1.1. first and surname; personal identification code; 1.2. person’s and his/her parents’ place of birth, person’s permanent place of residence and location at the Census moment, person’s permanent place of residence on 12 January 1989, year of arrival in Estonia, address of the place of work; 1.3. sex, date of birth, citizenship, ethnic nationality, mother tongue, knowledge of languages (answering the question is voluntary), marital status, number of children given birth to, mother’s age at the time of birth of the first child; 1.4. main sources of subsistence, length of working week in the week preceding the Census (number of hours worked), social status (in military service, not working, actively seeking work, ready to start work, student (pupil), pensioner, homemaker, not working for other reasons), name of the main place of work / main employer (answering the question is voluntary), economic activity of the main place of work, employment status at the main place of work (employee with stable contract, other employee, entrepreneur-employer, farmer with salaried employees, self-employed person, freelancer, farmer without salaried employees, contributing family workers in a family enterprise, farm, member of commercial association), occupation at main place of work, length of usual working week; 1.5. level of curriculum that the person has completed or studies currently, highest level of vocational or professional education completed, highest level of general education completed; 1.6. long-term disability or illness determined by the medical commission of experts; 1.7. religious affiliation and faith confessed (answering the question is voluntary).

2. Household data describe: 2.1. type of institution; 2.2. list of household members, relationship of each household member to the reference person, family relationships between the household members, permanent and temporary members of the household, duration of absence of a permanent household member in months, duration of presence of a temporary household member; 2.3. legal basis for the use of the dwelling; 2.4. the links between the household and agricultural activity.

3. Data on dwelling include: 3.1. type, form of ownership, total area, number of rooms, existence of a kitchen, plumbing and heating (water supply system, sewage disposal system, hot water, bath (shower), sauna, flush toilet, electricity, gas, central heating, electric heating); 3.2. address, type and period of construction of the building containing dwellings.

Cleaning operations

Two scanners were used for optical data entry. The application software for data processing were worked out in co-operation with the company AS AboBase Systems and based on Oracle tools. The scanning of the Census questionnaires was performed in 2000 from 10 May to 22 September. During that period 3,505,451 questionnaires were scanned. 135 operators who had passed the training were engaged in the data processing.

Data appraisal

For evaluating the coverage of the Census and the quality of the Census data, a post-enumeration sample survey was organized. It covered about 1% of the population and a stratified random sample of enumeration areas was drawn. The post-enumeration survey was carried out from 14 to 19 April 2000 in 50 enumeration areas. Comparison of the Census data and the data collected in the post-enumeration survey showed that the undercoverage of the Census was on an average 1.2%.

Abstract

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

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

Geographic coverage

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

Republic of Palau State Hamlet/Village Enumeration District Block

Analysis unit

Individuals Families Households General Population

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

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

Cleaning operations

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

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

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

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

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

Sampling error estimates

Sampling Error is not applicable to full enumeration censuses.

Data appraisal

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

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

Abstract

The main objective of the PHC-2007 is to provide figures for the Palestinian population and their geographical distribution in accordance with a number of relatively stable basic characteristics to inform socioeconomic development purposes. Preparations for conducting censuses take usually 3-5 years for piloting, testing tools, work plans, human and physical needs and timetable. The census is one of the most important statistical activities as it provides statistical data on the distribution of population, and their demographic, social and economic characteristics in a certain reference period of time for all the individuals within the borders of the state.

Geographic coverage

West Bank and Gaza Strip

Analysis unit

individual, household

Universe

The PHC-2007 covered all individuals who were in the Palestinian Territory on the census reference night (30/11-1/12/2007) regardless of nationality and citizenship. It also covered all Palestinians who have usual residence in the Palestinian Territory (including those who were temporarily absent for less than one year for the purpose of visit, tourism, treatment, etc. while their households are still living at their permanent places of residence in the Palestinian Territory). All Palestinian students abroad while their households are still living at their permanent places of residence were also included, in addition to all prisoners and detainees in the Israeli jails regardless of the duration of detention. The census excluded all Palestinians holding identity cards who were absent for more than one year (except for the students), even if their households are still living in their places of residence in the Palestinian Territory

Kind of data

Census/enumeration data [cen]

Sampling procedure

It consists of all the enumerated households in the Census 2007 and who are staying in the west bank at the time of enumeration. We select a systematic random sample from each enumeration area in the Census, and we select a 20% of the total households concluding all the individuals in the household

Mode of data collection

Face-to-face [f2f]

Research instrument

The census questionnaire for the buildings is divided into four parts: Part one: includes identification data, such as: governorate, locality name, locality code, booklet no. in locality, total booklets in locality, no. of completed pages in booklet, enumeration area.
Part two: includes data for all buildings, such as: 1. Building Serial No. in the page 2. Building No. in Enumeration Area 3. Name of the Owner of the Building or Building Name and Address 4. Building Municipality No. 5. Type of Building 6. Type of Ownership 7. Material of External Walls 8. No. of Stories 9. Current Use of Building 10. Establishments Year 11. Total No. Houses in Building Part three: includes data for all Houses, such as: 1. No. of Houses in the Building 2. Current Use of Houses 3. The reason for Closed, Vacant and Deserted Houses Part four: includes data for all Houses used for habitation or work and habitation, such as: 1. Name of the Head of Household No. of Household Members

Cleaning operations

The data processing stage contain of the following operations: 1. Editing before Data Entry At this stage all booklets were edited in the office using the instructions previously prepared for checking to ensure consistent data. 2. Data Entry Program The data entry program was prepared and designed according to the census questionnaire. The program was prepared by using the Oracle database. 3. Data Entry After the completion of the design and the testing of the data entry program and training of data keyers, work began on data entry.

Response rate

100%

Sampling error estimates

Not Applicaple

Abstract

The population and housing census (PHC) is the unique source of reliable and comprehensive data about the size of population and also on major socio-economic & socio-demographic characteristics of the country. It provides data on geographic and administrative distribution of population and household in addition to the demographic and socio-economic characteristics of all the people in the country. Generally, it provides for comparing and projecting demographic data, social and economic characteristics, as well as household and housing conditions at all levels of the country’s administrative units and dimensions: national, regional, districts and localities. The data from the census is classified, tabulated and disseminated so that researchers, administrators, policy makers and development partners can use the information in formulating and implementing various multi-sectorial development programs at the national and community levels. Data on all key variables namely area, household, population, economic activity, literacy and education, fertility and child survival, housing conditions and sanitation are collected and available in the census data. The 2021 PHC in Ghana had an overarching goal of generating updated demographic, social and economic data, housing characteristics and dwelling conditions to support national development planning activities.

Geographic coverage

National Coverage , Region , District

Analysis unit

• Individuals
• Households
• Emigrants
• Absentee population
• Mortality
• Type of residence (households and non household)

Universe

All persons who spent census night (midnight of 27th June 2021) in Ghana

Kind of data

Census/enumeration data [cen]

Sampling procedure

This 10% sample data for the 2021 PHC is representative at the district/subdistrict level and also by the urban rural classification.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

GSS developed two categories of instruments for the 2021 PHC: the listing form and the enumeration instruments. The listing form was only one, while the enumeration instruments comprised six questionnaires, designated as PHC 1A, PHC 1B, PHC 1C, PHC 1D, PHC 1E and PHC 1F. The PHC 1A was the most comprehensive with the others being its subsets.

1. Listing Form: The listing form was developed to collect data on type of structures, level of completion, whether occupied or vacant and use(s) of the structures. It was also used to collect information about the availability, number and types of toilet facilities in the structures. It was also used to capture the number of households in a structure, number of persons in households and the sex of the persons residing in the households if occupied. Finally, the listing form was used to capture data on non-household populations such as the population in institutions, floating population and sex of the non-household populations.

2. PHC 1A: The PHC 1A questionnaire was used to collect data from all households in the country. Primarily, it was used to capture household members and visitors who spent the Census Night in the dwelling of the household, and their relationship with the head of the household. It was also used to collect data on homeless households. Members of the households who were absent were enumerated at the place where they had spent the Census Night. The questionnaire was also used to collect the following household information: emigration; socio-demographic characteristics (sex, age, place of birth and enumeration, survival status of parents, literacy and education; economic activities; difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

3. PHC 1B: The PHC 1B questionnaire was used to collect data from persons in stable institutions comprising boarding houses, hostels and prisons who were present on Census Night. Other information that was captured with this instrument are socio-demographic characteristics, literacy and education, economic activities, difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

4. PHC 1C: The PHC 1C questionnaire was used to collect data from persons in “unstable” institutions such as hospitals and prayer camps who were present at these places on Census Night. The instrument was used to capture only the socio-demographic characteristics of individuals.

5. PHC 1D: The PHC 1D questionnaire was used to collect data from the floating population. This constitutes persons who were found at airports, seaports, lorry stations and similar locations waiting for or embarking on long-distance travel, as well as outdoor sleepers on Census Night. The instrument captured the socio-demographic information of individuals.

6. PHC 1E: All persons who spent the Census Night at hotels, motels and guest houses were enumerated using the PHC 1E. The content of the questionnaire was similar to that of the PHC 1D.

7. PHC 1F: The PHC 1F questionnaire was administered to diplomats in the country.

Cleaning operations

The Census data editing was implemented at three levels: 1. data editing by enumerators and supervisors during data collection 2. data editing was done at the regional level by the regional data quality monitors during data collection 3. Final data editing was done at the national level using the batch edits in CSPro and STATA Data editing and cleaning was mainly digital.

Response rate

100 percent

Data appraisal

A post Enumeration Survey (PES) was conducted to assess the extent of coverage and content error.

The GIS shapefile "Census summary of southern sea otter 2017" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2017 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2017). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California. References: Tinker, M. T., Doak, D. F., Estes, J. A., Hatfield, B. B., Staedler, M. M. and Bodkin, J. L. (2006), INCORPORATING DIVERSE DATA AND REALISTIC COMPLEXITY INTO DEMOGRAPHIC ESTIMATION PROCEDURES FOR SEA OTTERS. Ecological Applications, 16: 2293–2312, https://doi.org/10.1890/1051-0761(2006)016[2293:IDDARC]2.0.CO;2 Tinker, M. T. , D. P. Costa , J. A. Estes , and N. Wieringa . 2007. Individual dietary specialization and dive behaviour in the California sea otter: using archival time–depth data to detect alternative foraging strategies. Deep Sea Research II 54: 330–342, https://doi.org/10.1016/j.dsr2.2006.11.012

Abstract

The 2008 Sudan Population and Housing Census is the 5th Sudan Population and Housing Census conducted, and one of the most important censuses in the history of Sudan. It is based on the comprehensive peace agreement. It provides hope for Sudanese people to build a new Sudan, with a fair share in power, resources, services and development. To achieve these goals a population census with a high accuracy and a full coverage is a necessity.

Geographic coverage

National

Analysis unit

• Households;
• Individual.

Universe

The de facto method is applied for the enumeration of the population.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

As mentioned above the census data is to be collected in two forms. A short form to be used for 90% of EAs with a minimum number of questions ( 11 questions ) and to satisfy the basic population data needed for the election and other basic demographic needs. A long form to be administered in10% of the enumeration areas (EAS) and will provide all other standard social and economic information. The details of these questionnaires are following closely the UN principles and recommendations for censuses as decided by the TWG. That had put sometimes the TWG in conflicts with the governing councils and politicians at the national and regional levels. For e.g. the MOC had requested the deletion of the questions on ethnicity after its endorsement by the PCC in its second meeting. The PCC decided to raise it to the Presidency as the TWG had reconfirmed its technical importance. Based on the understanding that ethnicity and religion are causes of conflicts in Sudan, the Presidency decided to delete these questions. It was suggested as a compromise to use the question on previous residence to give information about Southern people living in the North. The South Sudan Population Census Council (SSPCC) requested an amplification of the question to reflect household origin from the nine 1956 Provinces (Northern, Khartoum, Central, Eastern, Kordofan, Darfur, Upper Nile, Bahr Elghazal and Equatoria) in stead of (north/south). But that was not accepted by many members of the PCC and some politicians in the north who believe that it is another way of bringing back the ethnicity question. The SSPCC then insisted on the re-inclusion of the ethnicity and religion questions. That led to a lot of delays in printing the questionnaires. In order to get out of this dilemma the TWG with support of UNFPA had decided to stick firmly to the UN standards. That is to stick to the previous residence question (origin) which is core one and to neglect the ethnicity question which is an optional one.

Cleaning operations

For census data entry the Technical Working Group (TWG) decided with endorsement of the PCC that the data entry was to be decentralized. Nine centers were suggested. These are the capitals of old British provinces. The TWG also decided that the short and long forms to be scanned using optical mark recognition (OMR) technology. That decision was based on the field visits to some African countries which used the same technology in their censuses. For quality assurance a high level team from both CBS and SSCCSE were sent to DRS Company in UK to ensure that the forms were correctly printed in both Arabic and English so as to avoid occurrence of any errors or faults during enumeration and the scanning process. It was decided that the census data was to be processed, the results produced and the tabulation prepared centrally. The national and regional tabulation to be analyzed and published using different data dissemination methods such as:-printed reports, electronic media (websites, Emails), data archiving, seminars and workshops. The use of internet as another tool for data dissemination was also suggested.

Abstract

The main purpose of this report is to provide a short descriptive analysis and related tables on main thematic areas covered in the 2022 Population and Housing Census. Areas covered include population and household characteristics, social and economic activities. Other reports in the series of Census publications include Regional Demographic and Socio-Economic Profiles and Thematic Reports.

The 2022 PHC results are for integrated plans and sustainable development of the country and will increase awareness and transparency in allocation of resources at all levels of administration based on the actual population. The results will be used by the Government and stakeholders in monitoring and evaluating various national, regional and international development frameworks including the Tanzania Development Vision 2025 and Zanzibar Development Vision 2050; the Third National Five -Year Development Plan 2021/22 - 2025/26 and Zanzibar Development Plan 2021/22 - 2025/26; the East African Community Vision 2050; Southern and African Development Community Vision 2050 and the African Development Agenda 2063.

Furthermore, the results will enable the country to evaluate the progress of implementation of Sustainable Development Goals (United Nations Sustainable Development Agenda 2030); goals that aim at achieving equality and eradicating poverty of all kinds including extreme poverty by 2030 by ensuring no one is left behind. The census data will also provide a basis for the computation of several indicators such as enrolment and literacy rates, infant and maternal mortality rates, unemployment rate and others.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

Household and Individual

Universe

Entire population of the country

Kind of data

Census/enumeration data [cen]

Sampling procedure

The country was devided in Hamlets and only one type of Questionnaire long questionnaire was mainly used. There were other questionnaires (community; persons on transit, hotel/lodge residents and hospital in-patients; and persons with no fixed Residence) which were administered during enumeration. Apart from the main Census questionnaire there was also building questionnare for counting the number of existing buildings in Tanzania

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2022 PHC had three main digital tools for data collection.

• The first one was a community questionnaire, which collected information on all social amenities; land use patterns and environmental or natural features and available community infrastructure.

• The second tool was the main census questionnaire which collected detailed information on demographics, including fertility, mortality, migration, orphanhood, and disabilities; possession of national documents, education level and economic activities. It also collected information on land ownership and information related to ICT ownership and use, housing, utilities, ownership of assets and agriculture.

• The third tool was a questionnaire for special population groups such as diplomats and travellers.

All queationnaires are published in English and Kiswahili Language

Cleaning operations

Data editing started during the data collection time where by data with some gaps were fixed at the field level after been noted by the Headquater team. This was done by notifying enumarators at the field to make follow and fix the gap. After the enumeration of all data, data were downloaded from the server and then office editing started. The CsPro editing program waswas used followed by the SPSS for further cleaning and tabulation exercse.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Tool. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Tool, the median household income for the households where the householder is White increased by $5,334(9.29%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $57,410 in 2011 and $62,744 in 2021.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2011 and 2021.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/tool-tx-median-household-income-by-race-trends.jpeg" alt="Tool, TX median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Tool.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Tool median household income by race. You can refer the same here

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: Yes (Homeless, refugees, camps)

UNIT DESCRIPTIONS: - Dwellings: A building is an independent free-standing structure irrespective of its construction material, composed of one or more rooms. - Households: A household consists of a person or a group of persons who live together in the same housing unit or part of it and who consider themselves as one unit in terms of the provision of food and/or other essentials of living for the group. When most of the members of such a group are related by blood (i.e., biologically) the group shall be referred to as a Private Household for the purpose of the census. On the other hand when the group (i.e., household as defined earlier) consists of members who are not related by blood and they are more than 10, they will be considered as Non-Institutional Collective Household. Note that if the group consists of 10 or less members, it should be considered a private household. - Group quarters: An institution is usually a set of premises used to house a large number of people who are not related by blood or marriage but bound together by a common objective or personal interest (e.g., universities, boarding houses, hospitals, army barracks, camps, prisons, hotels, etc.)

Universe

Residents of Sudan

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE DESIGN: Long form questionnaire for sedentary households (selected enumeration areas) and a sample of nomad households.

SAMPLE UNIT: Household

SAMPLE FRACTION: 16.6%

SAMPLE SIZE (person records): 5,066,530

Mode of data collection

Face-to-face [f2f]

Research instrument

Two forms: Long Questionnaire (for a sample of areas) and Short Questionnaire (for the rest of the country). The information used here is based on the long form questionnaire.

Abstract

Carrying out of GAC in Romania is ’to ensure statistical data for substantiating national agricultural politics in agreement with the acquis communautaire in order to ensure comparable data at international level, needed for Romania’s participation process to Commune Agricultural Politics (CAP) by registering utilised agriculture areas, livestock, agriculture machinery and equipment, labour force and rural development”. The strategic objectives of the Romanian GAC 2010 were the following:

• Obtaining comparable statistical data at international level, essential to the process of Romania’s participation in CAP
• Substantiating measures needed to elaborate the National Development Plan, the National Rural Development Programme and other programmes concerning agriculture and rural development
• Strengthening the agriculture statistics system by obtaining new data and information on the structural characteristics of agricultural holdings
• Updating the Farm Register, as a sampling base for other surveys in agriculture
• Supplying data and information necessary for the development of FADN
• To ensure comparable data on agricultural activities geo-referenced, covering the entire EU, according to EU legislation
• To set up the typology and economic dimension of agricultural holdings based on census data and on 2007 Standard Output Coefficients

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined according to Eurostat requirements and FAO recommendations, such 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

Frame: The census frame was established using the Agricultural Administrative Registers (AARs), held in each commune. The AAR is the official register of information on agricultural households (natural persons) and/or legal units that own or use agricultural land and/ or keep livestock. All holdings in scope were enumerated.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was used for census data collection, comprising also the characteristics on agricultural production methods. The GAC 2012 questionnaire covered all 16 core items recommended in the WCA 2010. The following indicators were included:

• Number of agricultural holdings
• Arable land
• Cultivated area with common wheat and spelt, durum wheat and maize
• Kitchen gardens
• Pastures and meadows
• Permanent crops
• UAA (in Hectares, average by an agricultural holding and by categories of use)
• Unutilised agricultural area
• Total area of the agricultural holding
• Livestock, by main species (bovines, sheep, goats, pigs, poultry, horses, bee families)
• Labour force in agriculture, number of worked days and Annual Working Units (AWU)

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Data capture was done in a decentralized manner, with manual data entry, at the level of each territorial statistical office (42 offices). For this activity, 450 computer operators were temporarily hired. After data entry and after resolving all the errors identified at the micro (record) level, the data were transferred to the Central Technical Secretariat. The Secretariat reviewed the data at the aggregate level (both at national and county level) to identify possible inconsistencies. Depending on the type of errors found, they were solved by individual correction at local level (the territorial statistical offices), and through automatic corrections, applied at the central level. Imputations to complete the missing data were also used. According to the Law of national archives No. 16/1996 and Government Decision No. 1370/2009, after validation, archiving and publication of the results, the census questionnaires were destroyed.

b. CENSUS DATA QUALITY A PES was carried out in February 2011 to check the quality and coverage of information collected in the GAC 2010.

Data appraisal

The census results were disseminated through printed publications, CD-ROMs and the NIS website. Preliminary estimates with information on 20 characteristics were released on December 2011 (in a printed publication and CD-ROM). Final results were released in July 2012 (printed publication, CD-ROM and through the NIS website).

The GIS shapefile "Census summary of southern sea otter 2016" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2016 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are ca ...

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 programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household based of fish farmers

Universe

The survey covered all de jure household members (usual residents), who were into fish production

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Fishing farming housing units were systematically selected and canvassed .

Sampling deviation

There was deviations from the original sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC fishery questionnaire was divided into the following sections: - Holding identification: This is to identify the holder through HU serial number, HH serial number, and demographic characteristics. - Type of fishing sites used by holder. - Sources and quantities of fishing inputs. - Quantity of aquatic production by type. - Quantity sold and value of sale of aquatic products. - Funds committed to fishing by source and others

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collated and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

Both Enumeration Area (EA) and Fish holders' level Response Rate was 100 per cent.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data