The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

Terms of Access: By downloading the data, you agree to use the data only for academic research, agree not to share the data with outside parties, and agree not to attempt to re-identify individuals in the data set. We require this in order to protect the privacy of individuals in the data set and to comply with agreements made with TargetSmart. Abstract: We present the results of a large, $8.9 million campaign-wide field experiment, conducted among 2 million moderate and low-information “persuadable” voters in five battleground states during the 2020 US Presidential election. Treatment group subjects were exposed to an eight-month-long advertising program delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence the program increased or decreased turnout on average. We find evidence of differential turnout effects by modeled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (SE: 0.2pp) and decreased voting among Trump leaners by 0.3 percentage points (SE: 0.3pp), for a difference-in-CATES of 0.7 points that is just distinguishable from zero (t(1035571) = −2.09, p = 0.036, DIC = 0.7 points, 95% CI = [−0.014, −0.00]). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.

Electoral registrations for parliamentary and local government elections as recorded in electoral registers for England, Wales, Scotland and Northern Ireland.

Abstract: The Database on Ideology, Money in Politics, and Elections (DIME) is intended as a general resource for the study of campaign finance and ideology in American politics. The database was developed as part of the project on Ideology in the Political Marketplace, which is an on-going effort to perform a comprehensive ideological mapping of political elites, interest groups, and donors using the common-space CFscore scaling methodology (Bonica 2014). Constructing the database required a large-scale effort to compile, clean, and process data on contribution records, candidate characteristics, and election outcomes from various sources. The resulting database contains over 130 million political contributions made by individuals and organizations to local, state, and federal elections spanning a period from 1979 to 2014. A corresponding database of candidates and committees provides additional information on state and federal elections. The DIME+ data repository on congressional activity extends DIME to cover detailed data on legislative voting, lawmaking, and political rhetoric. (See http://dx.doi.org/10.7910/DVN/BO7WOW for details.) The DIME data is available for download as a standalone SQLite database. The SQLite database is stored on disk and can be accessed using a SQLite client or queried directly from R using the RSQLite package. SQLite is particularly well-suited for tasks that require searching through the database for specific individuals or contribution records. (Click here to download.) Overview: The database is intended to make data on campaign finance and elections (1) more centralized and accessible, (2) easier to work with, and (3) more versatile in terms of the types of questions that can be addressed. A list of the main value-added features of the database is below: Data processing: Names, addresses, and occupation and employer titles have been cleaned and standardized. Unique identifiers: Entity resolution techniques were used to assign unique identifiers for all individual and institutional donors included in the database. The contributor IDs make it possible to track giving by individuals across election cycles and levels of government. Geocoding: Each record has been geocoded and placed into congressional districts. The geocoding scheme relies on the contributor IDs to assign a complete set of consistent geo-coordinates to donors that report their full address in some records but not in others. This is accomplished by combining information on self-reported address across records. The geocoding scheme further takes into account donors with multiple addresses. Geocoding was performed using the Data Science Toolkit maintained by Pete Warden and hosted at http://www.datasciencetoolkit.org/. Shape files for congressional districts are from Census.gov (http://www.census.gov/rdo/data). Ideological measures: The common-space CFscores allow for direct distance comparisons of the ideal points of a wide range of political actors from state and federal politics spanning a 35 year period. In total, the database includes ideal point estimates for 70,871 candidates and 12,271 political committees as recipients and 14.7 million individuals and 1.7 million organizations as donors. Corresponding data on candidates, committees, and elections: The recipient database includes information on voting records, fundraising statistics, election outcomes, gender, and other candidate characteristics. All candidates are assigned unique identifiers that make it possible to track candidates if they campaign for different offices. The recipient IDs can also be used to match against the database of contribution records. The database also includes entries for PACs, super PACs, party committees, leadership PACs, 527s, state ballot campaigns, and other committees that engage in fundraising activities. Identifying sets of important political actors: Contribution records have been matched onto other publicly available databases of important political actors. Examples include: Fortune 500 directors and CEOs: (Data) (Paper) Federal court judges: (Data) (Paper} State supreme court justices: (Data) (Paper} Executives appointees to federal agencies: (Data) (Paper) Medical professionals: (Data) (Paper)

This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.

Content

Member countries are allocated votes at the time of membership and subsequently for additional subscriptions to capital. Votes are allocated differently in each organization.

Each member receives the votes it is allocated under IDA replenishments according to the rules established in each IDA replenishment resolution. Votes consist of subscription votes and membership votes.

Latest information about voting power is available at http://www.worldbank.org/en/about/leadership/votingpowers

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore World Bank's Financial Data using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under a Creative Commons Attribution 3.0 IGO license.

Cover photo by Brandon Mowinkel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under Creative Commons Attribution 3.0 IGO

This data set consists of all Fulton County Election results from April 2012 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.

The Dutch Parliamentary Election Study 2023 has been conducted by the Foundation of Electoral Studies in The Netherlands (Stichting Kiezeronderzoek Nederland; SKON). The Dutch Parliamentary Election Studies (DPES) are a series of national surveys carried out under the auspices of the Dutch Electoral Research Foundation (SKON). These surveys have been conducted since 1971. Many questions are replicated across studies, although fresh questions are included in each new round. The major substantive areas consistently covered include the respondents' attitudes toward and expectations of the government and its effectiveness in both domestic and foreign policy, the most important problems facing the people of the Netherlands, the respondents' voting behavior and participation history, and his/her knowledge of and faith in the nation's political leaders. The DPES data were collected via two different panels: via the LISS-panel monitored by CenterData and via the non-self subscribe panel from I&O-research. Both samples were used to build the DPES2023. The DPES2023 data consists of three datasets, which can be merged, but if done so, the data will not be representative anymore. 1. The standard DPES2023 dataset, aimed to be representative for the 2023 eligible voters. It includes weights to address population distortions. This dataset also includes items that were formulated by Young Scholars; these items become available from February 2025 onwards. 2. [not available yet] A dataset including eligible voters with a migration background 3. [not available yet] A dataset including eligible voters with an education other than tertiary higher vocational (hbo) or university

Total population, population density, migration, and voting age population by year. Includes population estimates and projections prepared by the NC State Demographer.

Our codes provide a tool for researchers using any part of the integrated datasets of the European Social Survey (European Social Survey Cumulative File, ESS 1-9, 2020) project to easily differentiate between respondents based on their political affiliation, dividing them into pro-government and pro-opposition groups. Individuals are coded as “government supporters”, “opposition supporters” and “non-identifiers” according to their survey response, while we excluded refusals. The database includes data for 422 985 respondents from eight data rounds between 2002 and 2020 from 33 European countries, organized all in all in 215 country-years.

There are two data files attached.

1. The “European Government-Opposition Voters (EGOV) Data Set” is a comma-separated values table (.csv format file) that includes three variables.

a. The variable “votedforwinner” differentiates between government voters (1), opposition voters (0) and non-voters (missing values); thus it defines the government-opposition status of European voters based on their last vote on the previous election.

b. The variable “closetowinner” differentiates between government partisans (1), opposition partisans (0) and non-partisans (missing values); thus it defines the government-opposition status of European party identifiers based on their partisan attachment.

c. The variable “cseqno” is a unique identification number for European Social Survey (ESS) respondents included in the integrated data sets of the ESS project.

1. The “EGOV – do file” is a do file that can be used to reproduce the content of the above table. These codes are annotated, that is, unusual changes in government composition and overlaps of elections and fieldwork periods are indicated.

The European Government-Opposition Voters Data Set has been produced by using the following pieces of information coming from the (European Social Survey Cumulative File, ESS 1-9, 2020), Comparative Political Data Sets (Armingeon, Isler, Knöpfel, Weisstanner, et al., 2016) and ParlGov (Döring and Manow, 2019) data sets.

•    partisan
  

  preferences, that is, respondents’ vote on the last general election (164 variables, ESS) and respondents’ partisan identity (167 variables, ESS)

•    date of
  

  the interview (year, month, day, ESS)

•    date of
  

  national elections and investitures in each country-case (CPDS and ParlGov)

•    cabinet
  

  composition (CPDS and ParlGov)

•    official
  

  sites on information on national elections for clarification, if necessary

This data is a recent survey data we collected by using Survey Monkey.

We asked how much people will vote Pete Buttigieg as President of the US, if he is nominee, and asked many reasons by scalar-bar questions which is created by us based on the initial open question survey.

This survey is completely original, not related with his campaign.

Insight Survey of Pete Buttigieg https://www.surveymonkey.com/r/L3H3CKD

We are looking for a data scientist or a causal analyst who has great ability to extract the insights from this type of data format. The winner of the best result will be honored by a spinning out company who will focus on commercial delivery of this analysis. Marketing Research has been struggling this type of open and close questions why people like a brand and products.

Find WHYs.

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset.

CSES Variable List The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module.

Themes: MICRO-LEVEL DATA:

Identification and study administration variables: weighting factors;election type; date of election 1st and 2nd round; study timing (post election study, pre-election and post-election study, between rounds of majoritarian election); mode of interview; gender of interviewer; date questionnaire administered; primary electoral district of respondent; number of days the interview was conducted after the election

Demography: age; gender; education; marital status; union membership; union membership of others in household; current employment status; main occupation; employment type - public or private; industrial sector; occupation of chief wage earner and of spouse; household income; number of persons in household; number of children in household under the age of 18; attendance at religious services; religiosity; religious denomination; language usually spoken at home; race; ethnicity; region of residence; rural or urban residence

Survey variables: respondent cast a ballot at the current and the previous election; respondent cast candidate preference vote at the previous election; satisfaction with the democratic process in the country; last election was conducted fairly; form of questionnaire (long or short); party identification; intensity of party identification; political parties care what people think; political parties are necessary; recall of candidates from the last election (name, gender and party); number of candidates correctly named; sympathy scale for selected parties and political leaders; assessment of the state of the economy in the country; assessment of economic development in the country; degree of improvement or deterioration of economy; politicians know what people think; contact with a member of parliament or congress during the past twelve months; attitude towards selected statements: it makes a difference who is in power and who people vote for; people express their political opinion; self-assessment on a left-right-scale; assessment of parties and political leaders on a left-right-scale; political information items

DISTRICT-LEVEL DATA:

number of seats contested in electoral district; number of candidates; number of party lists; percent vote of different parties; official voter turnout in electoral district

MACRO-LEVEL DATA:

founding year of parties; ideological families of parties; international organization the parties belong to; left-right position of parties assigned by experts; election outcomes by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percent of seats in upper house received by parties in current lower house/upper house election; percent of votes received by presidential candidate of parties in current elections; electoral turnout; electoral alliances permitted during the election campaign; existing electoral alliances; most salient factors in the election; head of state (regime type); if multiple rounds: selection of head of state; direct election of head of state and process of direct election; threshold for first-round victory; procedure for candidate selection at final round; simple majority or absolute majority for 2nd round victory; year of presidential election (before or after this legislative election); process if indirect election of head of state; head of government (president or prime minister); selection of prime minister; number of elected legislative chambers; for lower and upper houses was coded: number of electoral segments; number of primary districts; number of seats; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; compulsory voting; votes cast; voting procedure; electoral formula; party threshold; parties can run joint lists; requirements for joint party lists; possibility of apparentement; types of apparentement agreements; multi-party endorsements; multi-party endorsements on ballot; ally party support; constitutional prerogatives of the head of state; constitutional powers of prime minister; methods of cabinet dismissal; dissolution of legislature

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

The National Elections across Democracy and Autocracy (NELDA) dataset provides detailed information on all election events from 1960-2006. To be included, elections must be for a national executive figure, such as a president, or for a national legislative body, such as a parliament, legislature, constituent assembly, or other directly elected representative bodies. In order for an election to be included, voters must directly elect the person or persons appearing on the ballot to the national post in question. Voting must also be direct, or “by the people” in the sense that mass voting takes place. That voting is “by the people” does not imply anything about the extent of the franchise: some regimes may construe this to mean a small portion of the population. However, when voting takes place by committee, institution or a coterie, the “election” is not included. By-elections are not counted as elections for the purpose of this project, unless they take the form of midterm elections occurring within a pre-established schedule. In federal systems, only elections to national-level bodies are included. Cases in which any portion of the seats in a national legislative body are filled through voting are included. Beyond these basic requirements, elections may or may not be competitive, and may have any number of other ostensible flaws. In fact, this last feature of the dataset is what separates NELDA most clearly from other available datasets on elections.

The table ND-Voter-History-2024-10-02 is part of the dataset L2 Voter and Demographic Dataset, available at https://redivis.com/datasets/t6qv-ad1vt3wqf. It contains 422433 rows across 280 variables.

In the fall of 2006 the American National Election Studies (ANES) carried out a pilot study after the 2006 mid-term elections in the United States. The 2006 ANES Pilot Study was conducted for the purpose of testing new questions and conducting methodological research to inform the design of future ANES studies. As such, it is not considered part of the ANES time series that has been conducted since 1948, and the pilot study only includes time series questions necessary to evaluate the new content. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. This full release dataset contains all 675 interviews, with the survey portion of the interview lasting just over 37 minutes on average. The study had a re-interview rate of 56.25 percent. Respondents were asked questions over a variety of topics. They were queried on need for closure in various situations including unpredictable ones, how fast important decisions were made, and how often they could see that both people can be right when in disagreement. Respondents were asked many questions pertaining to their values. Some questions dealt with optimism and pessimism. Respondents were asked if they felt that were generally optimistic, pessimistic, or neither in regard to the future. They were asked specifically how they felt about the future of the United States. Respondents were also asked about their social networks, about who they talked to in the last six months, and how close they felt to them. Respondents were further queried about how many days in the last six months they talked to these people, their political views, interest in politics, and the amount of time it would take to drive to their homes. Other questions sought respondents' political attitudes including attentiveness to following politics, ambivalence, efficacy, and trust in government. Respondents were asked questions related to the media such as how much time and how many days during a typical week they watched or read news on the Internet, newspaper, radio, or television. Questions that dealt with abortion consisted of giving respondents various scenarios and asking if they favored or opposed it being legal for the women to have an abortion in that circumstance. The issue of justice was also included by asking respondents what percent of people of different backgrounds who are suspected of committing a crime in America are treated fairly. Respondents were also asked to give their opinion on gender in politics, specifically, whether gender played a role in how the respondent would vote for various political offices. Respondents were also queried on whether they would vote for Bill Clinton or George W. Bush and whether they had voted in the elections in November. Respondents were also asked if they approved of the way George W. Bush was handling his job as president, the way he was handling relations with foreign countries, and the way he was dealing with terrorism. Respondents were also asked how upsetting the thought of their own death was, and how likely it was that a majority of all people on Earth would die at once during the next 100 years because of a single event. Demographic variables include age, party affiliation, sex, religious preference, and political party affiliation.

Voting behavior. Causes of non-voting. Political attitudes. Political knowledge. Topics: Political interest; voting for mainstream parties in last federal or state election (recall); frequency of voting for Union parties in federal or state elections (recall); intention to vote and type of voting (polling station, postal vote); current reasons for not voting (open); intention to vote (Sunday question); alternative voting intention; acceptance of electoral norm; satisfaction with democracy; right-left scale: Ranking parties; right-left scale: self-ranking; political knowledge: Recall: top candidate Union 2005; political knowledge: recall: top candidate SPD 2005; political knowledge: Government coalition 2005; reasons for not voting in 2005 (open); rating of parties (scalometer: CDU, CSU, SPD, FDP, LINKE, B90/Grüne, Piraten); with which politician would one which politician would you like to have a drink with; change of party preference in previous elections; self-classification of voter type (regular voter, swing voter, non-voter); Reasons for not voting (prevented, unimportant decision, clear winner, preferred party had no chance, dissatisfaction with own party, parties show that it can´t go on like this, previous party not electable, no differences between parties, satisfied as it is, basically doesn´t go to vote, no politician, to whom one wanted to give the vote, own party clear winner, only Europe is still in charge, does not feel informed, state as a whole does not like, parties and politicians do what they want, better forms to get involved politically, does not feel connected to any party, no party stands up for things that are important); party identification (direction, strength, stability); reasons for current CDU/CSU distance (devout Christian, no home for Christians, no longer advocates the problems of the little people, no longer holds to conservative virtues and values, can no longer believe promises, not currently voting, could vote if there were more politicians like Angela Merkel, divided, no longer represents own political convictions); request: lack of representation of convictions (open). Non-voters only 2005: participation and type of voting in the 2009 Bundestag election (recall); voting behavior in the 2009 Bundestag election (recall); satisfaction with voting decision in 2009. Demography: sex; age; highest level of schooling; occupation; profession; religious denomination; frequency of church attendance. Additionally coded were: political municipality size, BIK, state; region; weighting factors. Wahlverhalten. Ursachen der Nichtwahl. Politische Einstellungen. Politisches Wissen. Themen: Politisches Interesse; Wahl von Volksparteien bei der letzten Bundes- oder Landtagswahl (Recall); Häufigkeit der Wahl von Unionsparteien bei Bundes- oder Landtagswahlen (Recall); Wahlbeteiligungsabsicht und Art der Stimmabgabe (Wahllokal, Briefwahl); aktuelle Gründe für Nichtwahl (offen); Wahlabsicht (Sonntagsfrage); alternative Wahlabsicht; Akzeptanz der Wahlnorm; Demokratiezufriedenheit; Rechts-Links-Skala: Einstufung Parteien; Rechts-Links-Skala: Selbsteinstufung; politisches Wissen: Erinnerung Spitzenkandidat Union 2005; politisches Wissen: Erinnerung Spitzenkandidat SPD 2005; politisches Wissen: Regierungskoalition 2005; Gründe der Nichtwahl 2005 (offen); Bewertung der Parteien (Skalometer: CDU, CSU, SPD, FDP, LINKE, B90/Grüne, Piraten); mit welcher Politikerin bzw. welchem Politiker würden man gerne einmal etwas trinken gehen; Wechsel der Parteipräferenz bei bisherigen Wahlen; Selbsteinstufung Wählertyp (Stammwähler, Wechselwähler, Nichtwähler); Gründe für Nichtwahl (verhindert, unwichtige Entscheidung, klarer Gewinner, präferierte Partei hatte keine Chance, Unzufriedenheit mit eigener Partei, Parteien zeigen, dass es nicht so weitergehen kann, bisherige Partei nicht wählbar, keine Unterschiede zwischen den Parteien, zufrieden wie es ist, geht grundsätzlich nicht wählen, kein Politiker, dem man die Stimme geben wollte, eigene Partei klarer Gewinner, nur noch Europa hat das Sagen, fühlt sich nicht informiert, Staat als Ganzes gefällt nicht, Parteien und Politiker machen was sie wollen, bessere Formen, sich politisch zu engagieren, fühlt sich keiner Partei verbunden, keine Partei setzt sich für Dinge ein, die wichtig sind); Parteiidentifikation (Richtung, Stärke, Stabilität); Gründe für aktuelle CDU/CSU-Distanz (gläubiger Christ, keine Heimat für Christen, setzt sich nicht mehr für die Probleme der kleinen Leute ein, hält nicht mehr an konservativen Tugenden und Werten fest, kann Versprechungen nicht mehr glauben, derzeit nicht wählen, wählbar, wenn es mehr Politiker wie Angela Merkel gäbe, zerstritten, vertritt nicht mehr eigene politische Überzeugung); Nachfrage: fehlende Vertretung von Überzeugungen (offen). Nur Nichtwähler 2005: Teilnahme und Art der Stimmabgabe bei der Bundestagswahl 2009 (Recall); Wahlverhalten bei der Bundestagswahl 2009 (Recall); Zufriedenheit mit der Wahlentscheidung 2009. Demographie: Geschlecht; Alter; höchster Schulabschluss; Berufstätigkeit; Beruf; Konfession; Kirchgangshäufigkeit. Zusätzlich verkodet wurde: Politische Gemeindegröße, BIK, Bundesland; Region; Gewichtungsfaktoren. Probability: MultistageProbability.Multistage Wahrscheinlichkeitsauswahl: Mehrstufige ZufallsauswahlProbability.Multistage

The table WV-Voter-History-2024-10-17 is part of the dataset L2 Voter and Demographic Dataset, available at https://redivis.com/datasets/t6qv-ad1vt3wqf. It contains 1125864 rows across 990 variables.