Surveys from swing states conducted the day before the 2024 election indicated an extremely close contest between Trump and Harris. Trump held a slight lead over of Harris in the majority of swing states.

According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from white voters between the ages of 45 and 64. In comparison, 84 percent of Black voters between the ages of 18 and 29 reported voting for Kamala Harris.

On July 21, Biden announced he was ending his bid for reelection, later endorsing Kamala Harris, who is the official Democratic nominee as of the Democratic National Convention in August. Although approval of Harris was once generally low, favorability of the vice president has spiked since announcing her presidential bid. Although the race is certainly closer since Harris began her campaign, polling has fluctuated, with support for Trump increasing just days before the election. National polling indicated that the two presidential hopefuls were 0.1 percentage points apart on November 4, 2024, making it nearly impossible to predict the results. While presidential polls are generally reliable in measuring national trends, they are not infallible, particularly in close races or predictions of Electoral College outcomes.

2020 General Election: Trump vs. Biden | RealClearPolling

According to exit polling in the 2020 Presidential Election in the United States, 87 percent of surveyed Black voters reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, 57 percent of white voters reported voting for incumbent President Donald Trump.

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.

2016 General Election: Trump vs. Clinton | RealClearPolling

According to a 2024 survey, confidence in how accurately votes will be counted in the 2024 presidential election was relatively low across American voters, with nearly one-third of respondents stating that they were not confident that the votes would be counted correctly. However, 38 percent were very confident in the votes being counted accurately.

This layer contains voter polling locations utilized in past elections across the entire Commonwealth of Virginia. Data can be filtered by locality as well as election name, type, or date. Please note that the physical address and location information for each polling location is maintained by individual locality General Registrars. Addresses are geocoded using the VGIN Composite Geocoding Service.Please note that this layer currently only contains points for polling places dating back to the June 2024 Primary elections. Polling places for elections prior to the June 2024 Primaries can be found in tabular format on the Virginia Department of Elections website: https://www.elections.virginia.gov/resultsreports/registration-statistics/Individuals looking to confirm their assigned polling place for an upcoming election should refer to the Department's website for the most accurate, up-to-date information: Polling Place Lookup

As of November 2, 2020, incumbent Republican President Donald Trump was polling at 44 percent in the race to become the next president of the United States. The 2020 presidential election is set to take place on November 3, 2020.

In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias---and that these methods failed to adjust for important interactions between key variables like educational attainment, race, and geographic region. Finding calibration weights that account for important interactions remains challenging with traditional survey methods: raking typically balances the margins alone, while post-stratification, which exactly balances all interactions, is only feasible for a small number of variables. In this paper, we propose multilevel calibration weighting, which enforces tight balance constraints for marginal balance and looser constraints for higher-order interactions. This incorporates some of the benefits of post-stratification while retaining the guarantees of raking. We then correct for the bias due to the relaxed constraints via a flexible outcome model; we call this approach Double Regression with Post-stratification (DRP). We use these tools to to re-assess a large-scale survey of voter intention in the 2016 U.S. presidential election, finding meaningful gains from the proposed methods. The approach is available in the multical R package. Contains replication materials for "Multilevel calibration weighting for survey data", including raw data, scripts to clean the raw data, scripts to replicate the analysis, and scripts to replicate the simulation study.

2008 General Election: McCain vs. Obama | RealClearPolling

In 2022, there were 161.42 million people registered to vote in the United States. This is a decrease from the previous election, when 168.31 million people were registered to vote.

Voting requirements

While voting laws differ from state to state, the basic requirements are the same across the entire country. People are allowed to vote in elections in the United States if they are a U.S. citizen, meet their state’s residency requirements, are at least 18 years old before Election Day, and are registered to vote before the registration deadline.

Vote early and often

Generally, younger people are not registered to vote at the same rate as older individuals. Additionally, young people tend not to vote as much as older people, particularly in midterm elections. However, in the 2016 presidential election, a significant number of people across all age groups voted in the election, resulting in a high voter turnout.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The Netflix Prize was a competition devised by Netflix to improve the accuracy of its recommendation system. To facilitate this Netflix released real ratings about movies from the users (voters) of the system. Any set of movies can be transformed into an election via a process outlined by Mattei, Forshee, and Goldsmith.This data set includes all 5 candidate elections with at least 350 voters generated by this process from 300 randomly chosen movies. Extending beyond prior work by Mattei et al. we allow for weak preferences, i.e., a voter is indifferent between a set of movies if he assigns the same rating to each of them. Thus, there are 541 possibilities to rank a given set of five movies.The archive is gzip compressed and includes 165,672 elections in PrefLib.org's TOC file format (Orders with Ties - Complete List).

2024 Montana Senate - Sheehy vs. Tester | RealClearPolling

According to statistical districts (from 2015 onwards), the number of persons entitled to vote, the number of votes cast and the valid votes and the votes assigned to the candidates are presented for the mayors’ elections in Linz.

Polling stations and “Flying Commissions” were summarised under “Special Sprengel”.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Part of the Elections NWT mandate is to provide maps to all the Deputy Returning Officers and for candidate use during the periodic NWT Elections. To create these maps requires accurate boundaries of each of the 19 Electoral Districts of the NWTt. Previous versions of the required maps had been created by Earth Tec of Edmonton in Autocad format. These maps were not very detailed and within communities did not include the complete extent of the Electoral DistrictUsing Schedule A from the Legislative Assembly and Executive Council Act of the GNWT, the legal descriptions were used to create each Electoral District. The existing legal descriptions were reviewed and issues are detailed in the supplemental information. The Chief Electoral Officer did not believe that these would affect any potential voters so they will be addressed as part of the Electoral Boundary Commission.At the very end of the project, the decision was made to also include digital polling division boundaries. These boundaries were based on a document: 2011 Polling Divisions_Descriptions and Keys_24 June 2011.docx provided by Elections NWT. The polling division descriptions were created by each Electoral District and were not consistent or clear. The Chief Electoral Officer had to make interpretations on some boundary locations. This dataset only includes boundaries but no attributes.It was noted that Yellowknife, Hay River and Inuvik had street addresses as part of their community maps. Parcel and street number data as shapefiles was acquired from theCity of Yellowknife, Lot/parcel/block and street annotation was acquired from AECOM via the Town of Inuvik. The Earth Tech data was the best available for Hay River as it does not appear as though the Town of Hay River has any digital property related data.Hard copy version of the Hay River street addresses were compared to the Earth Tech data and there were not significant changes to the street names to update them. At the end of the project, an attempt was made to correct some street names where problems were known.Statistics NWT does have street addressing for all NWT Communities that do have street addresses. The data was reviewed, however attributes were not compatible with MACA attribute data.

Election results for all electoral districts at the 1979 parliamentary election in Sweden. Contains information on the total number of eligible voters, number of voters, number of valid and invalid ballots and the number of postal votes. In addition, there is information on the number of votes for the major parties.