The 119th Congressional Districts dataset reflects boundaries from January 03, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

These data depict the 117th Congressional Districts and their representatives for the United States. Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Us House Congressional Representatives serving Macon-Bibb County.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

About this dataset

Context

The dataset tabulates the population of Congress by race. It includes the population of Congress across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Congress across relevant racial categories.

Key observations

The percent distribution of Congress population by race (across all racial categories recognized by the U.S. Census Bureau): 97.17% are white and 2.83% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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: This column displays the racial categories (excluding ethnicity) for the Congress
• Population: The population of the racial category (excluding ethnicity) in the Congress is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Congress total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Congress Population by Race & Ethnicity. You can refer the same here

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 118th Congress is seated from January 2023 through December 2024. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by August 31, 2022.

Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

Analysis of ‘C0109 - Population and Representatives Assigned to Each Local Electoral Area’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/316ca9c8-f81d-42e0-9a29-4105d5cd7cc6 on 14 January 2022.

--- Dataset description provided by original source is as follows ---

Population and Representatives Assigned to Each Local Electoral Area

--- Original source retains full ownership of the source dataset ---

Congressional districts are the 444 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable.The 119th Congress is seated from January 2023 through December 2024. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the congressional districts to cover all of the state or state equivalent area. In these areas with no congressional districts defined, the code "ZZ" has been assigned, which is treated as a single congressional district for purposes of data presentation. The TIGER/Line data for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by August 31, 2022.Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_legislative.gdb.zip Layer: Congressional_DistrictsMetadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/series_tl_2023_cd118.shp.iso.xml

This collection contains individual-level and 1-percent national sample data from the 1960 Census of Population and Housing conducted by the Census Bureau. It consists of a representative sample of the records from the 1960 sample questionnaires. The data are stored in 30 separate files, containing in total over two million records, organized by state. Some files contain the sampled records of several states while other files contain all or part of the sample for a single state. There are two types of records stored in the data files: one for households and one for persons. Each household record is followed by a variable number of person records, one for each of the household members. Data items in this collection include the individual responses to the basic social, demographic, and economic questions asked of the population in the 1960 Census of Population and Housing. Data are provided on household characteristics and features such as the number of persons in household, number of rooms and bedrooms, and the availability of hot and cold piped water, flush toilet, bathtub or shower, sewage disposal, and plumbing facilities. Additional information is provided on tenure, gross rent, year the housing structure was built, and value and location of the structure, as well as the presence of air conditioners, radio, telephone, and television in the house, and ownership of an automobile. Other demographic variables provide information on age, sex, marital status, race, place of birth, nationality, education, occupation, employment status, income, and veteran status. The data files were obtained by ICPSR from the Center for Social Analysis, Columbia University. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07756.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Congressional Districts are the 455 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the States based on census population counts, each State is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a State as practicable. The 115th Congress is seated from January 2015 to 2017. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts are provided to the Census Bureau through the Redistricting Data Program (RDP).

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

Analysis of ‘E2018 - Population and Representatives Assigned to Each Local Electoral Area 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/20c1c748-b305-4ba0-bb95-b60491836f77 on 15 January 2022.

--- Dataset description provided by original source is as follows ---

Population and Representatives Assigned to Each Local Electoral Area 2016

--- Original source retains full ownership of the source dataset ---

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1930 households: This dataset includes all households from the 1930 US census.

IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

Section 2

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1930 census data was collected in April 1930. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edite

Analysis of ‘US non-voters poll data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-non-voters-poll-datae on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains the data behind Why Many Americans Don't Vote.

Data presented here comes from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. The poll was conducted from Sept. 15 to Sept. 25 among a sample of U.S. citizens that oversampled young, Black and Hispanic respondents, with 8,327 respondents, and was weighted according to general population benchmarks for U.S. citizens from the U.S. Census Bureau’s Current Population Survey March 2019 Supplement. The voter file company Aristotle then matched respondents to a voter file to more accurately understand their voting history using the panelist’s first name, last name, zip code, and eight characters of their address, using the National Change of Address program if applicable. Sixty-four percent of the sample (5,355 respondents) matched, although we also included respondents who did not match the voter file but described themselves as voting “rarely” or “never” in our survey, so as to avoid underrepresenting nonvoters, who are less likely to be included in the voter file to begin with. We dropped respondents who were only eligible to vote in three elections or fewer. We defined those who almost always vote as those who voted in all (or all but one) of the national elections (presidential and midterm) they were eligible to vote in since 2000; those who vote sometimes as those who voted in at least two elections, but fewer than all the elections they were eligible to vote in (or all but one); and those who rarely or never vote as those who voted in no elections, or just one.

The data included here is the final sample we used: 5,239 respondents who matched to the voter file and whose verified vote history we have, and 597 respondents who did not match to the voter file and described themselves as voting "rarely" or "never," all of whom have been eligible for at least 4 elections.

If you find this information useful, please let us know.

License: Creative Commons Attribution 4.0 International License

Source: https://github.com/fivethirtyeight/data/tree/master/non-voters

This dataset was created by data.world's Admin and contains around 6000 samples along with Race, Q27 6, technical information and other features such as: - Q4 6 - Q8 3 - and more.

How to use this dataset

• Analyze Q10 3 in relation to Q8 6
• Study the influence of Q6 on Q10 4
• More datasets

Acknowledgements

If you use this dataset in your research, please credit data.world's Admin

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Content

A dataset of counties that are representative for Germany with regard to

• the average disposable income,
• the quota of divorces,
• the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,
• the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (1 Hour in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 76174 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the German population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

This dataset tracks the updates made on the dataset "Chart 1 3 X Total Number of Members Who Received ECM in Each Quarter by Population of Focus" as a repository for previous versions of the data and metadata.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

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National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).