According to exit polling in ten key states of the 2024 presidential election in the United States, 46 percent of voters with a 2023 household income of 30,000 U.S. dollars or less reported voting for Donald Trump. In comparison, 51 percent of voters with a total family income of 100,000 to 199,999 U.S. dollars reported voting for Kamala Harris.

As of November 2024, 65.3 percent of the eligible voting population in the United States voted in the 2024 presidential election. Voter turnout was highest in New Jersey and Minnesota.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

Since 1964, voter turnout rates in U.S. presidential elections have generally fluctuated across all age groups, falling to a national low in 1996, before rising again in the past two decades. Since 1988, there has been a direct correlation with voter participation and age, as people become more likely to vote as they get older. Participation among eligible voters under the age of 25 is the lowest of all age groups, and in the 1996 and 2000 elections, fewer than one third of eligible voters under the age of 25 participated, compared with more than two thirds of voters over 65 years.

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

This statistic presents the voter turnout in the U.S. presidential elections from 1908 to 2012. Voter turnout in U.S. presidential elections in 1908 stood at **** percent. Additional information on voter turnout in the United States presidential elections Despite widespread societal and economic changes voter turnout has generally fluctuated between ** and ** percent. Turnouts above ** percent are rare and have not been witnessed since the middle of the 20th Century when American voter turnout was comparably higher. In the United States voting in presidential elections is not legally mandatory as it is in some countries such as Australia. Therefore, many American voters choose to remain absent from the ballot box every four years. In contrast to mid-term elections, years in which the bi-annual congressional elections take place in the absence of presidential campaigns, presidential elections enjoy a relatively larger degree of public attention. This difference could be seen to reflect a population who perceives the power of the president to be of greater influence than the country’s congressional house. This perception is particularly apparent among the millennial population of the United States. Although many millennials harbor a substantial level of mistrust toward many major political and civil institutions, the role of president is seen as relatively more trustworthy. This trend may however be subject to change following the departure of President Obama who proved himself popular among the millennial population in successive campaign victories.

This dataset provides detailed county-level returns for U.S. presidential general elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets and accompanied by machine-readable CSV files for additional administrative levels (county, congressional district, state). There are two codebooks for the this data collection describing variable names and meanings: one for the Congressional District level data and the other for County level data.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Data Sources – documentation of sources used to compile the dataset.For the 2016, 2020, and 2024 elections, additional Excel workbooks and CSV files are provided at the congressional district (CD) level, containing:Vote Data by Congressional District – vote totals by district for each candidate, with FIPS codes. Includes detailed allocations for counties that span multiple congressional districts.Data Sources – documentation of sources used to compile the dataset.Candidates – candidate names and national party ballot listings.Notes – state-level notes describing data compilation details.

This study contains files of Presidential election votes by State, County, and Town for each U.S. Presidential election year from 1964-2020. From Dave Leip, Atlas of U.S. Presidential Elections. Note: MIT posted similar publicly available data beginning with 1976 at https://doi.org/10.7910/DVN/42MVDX

Information available in each dataset

If you want to know what each Presidential Election dataset contains before downloading it, for easy reference, the CCSS Data Services team prepared a spreadsheet summarizing the contents of each dataset. You can view them in this Summary of contents and codebooks spreadsheet.

The summary spreadsheet contains the following: 1. A matrix table summarizing the information available in each Presidential election dataset 2. Codebook describing the variables in the Presidential Election vote data at the State level 3. Codebook describing the variables in the Presidential Election vote data at the County level 4. Codebook describing the variables in the Presidential Election vote data at the Town level 5. A matrix table listing the statistics and graphs included in each Presidential election dataset

Labels of the variables in the State, County, and Town data, as well as a description of each tab in the dataset, are also available here: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional years of data available going back to 1912 but at a fee.

Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Discovery and Replication Services. These files were last checked for updates in June 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

U.S. Presidential Election Constituency Returns (1976-2020)

  Dataset Summary

This dataset contains state-level constituency returns for U.S. presidential elections from 1976 to 2020, compiled by the MIT Election Data Science Lab. The dataset includes 4,287 observations across 15 variables, offering detailed insights into the voting patterns for presidential elections over four decades. The data sources include the biennially published document “Statistics of the… See the full description on the dataset page: https://huggingface.co/datasets/fdaudens/us-presidential-elections-with-electoral-college.

United States presidential elections are quadrennial elections that decide who will be the President and Vice President of the United States for the next four years. Voter turnout has ranged between 54 and 70 percent since 1964, with white voters having the highest voter turnout rate (particularly when those of Hispanic descent are excluded). In recent decades, turnout among black voters has got much closer to the national average, and in 2008 and 2012, the turnout among black voters was higher than the national average, exceeded only by non-Hispanic white voters; this has been attributed to Barack Obama's nomination as the Democratic nominee in these years, where he was the first African American candidate to run as a major party's nominee. Turnout among Asian and Hispanic voters is much lower than the national average, and turnout has even been below half of the national average in some elections. This has been attributed to a variety of factors, such as the absence of voting tradition in some communities or families, the concentration of Asian and Hispanic communities in urban (non-swing) areas, and a disproportionate number of young people (who are less likely to vote).

Details the statistics of the Electoral College vote and popular vote over time in both the Democratic Party and the Republican Party. Also shows the voting population participation trend over time.

Intro

Voter turnout is the percentage of eligible voters who cast a ballot in an election. Eligibility varies by country, and the voting-eligible population should not be confused with the total adult population. Age and citizenship status are often among the criteria used to determine eligibility, but some countries further restrict eligibility based on sex, race, or religion.

Context

The historical trends in voter turnout in the United States presidential elections have been determined by the gradual expansion of voting rights from the initial restriction to white male property owners aged 21 or older in the early years of the country's independence, to all citizens aged 18 or older in the mid-20th century. Voter turnout in United States presidential elections has historically been higher than the turnout for midterm elections. https://upload.wikimedia.org/wikipedia/commons/a/a7/U.S._Vote_for_President_as_Population_Share.png" alt="f">

Content

Turnout rates by demographic breakdown from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). This table are corrected for vote overreporting bias. For uncorrected weights see the source link.

Original source: https://data.world/government/vep-turnout

Immigration and demographic change have become highly salient in American politics, partly because of the 2016 campaign of Donald Trump. Previous research indicates that local influxes of immigrants or unfamiliar ethnic groups can generate threatened responses, but has either focused on non-electoral outcomes or has analyzed elections in large geographic units such as counties. Here, we examine whether demographic changes at low levels of aggregation were associated with vote shifts toward an anti-immigration presidential candidate between 2012 and 2016. To do so, we compile a novel, precinct-level data set of election results and demographic measures for almost 32,000 precincts in the states of Florida, Georgia, Michigan, Nevada, Ohio, Pennsylvania, and Washington. We employ regression analyses varying model specifications and measures of demographic change. Our estimates uncover little evidence that influxes of Hispanics or non-citizen immigrants benefited Trump relative to past Republicans, instead consistently showing that such changes were associated with shifts to Trump's opponent.

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.

This data collection contains voter registration and turnout surveys. The files contain summaries at state, town, and county levels. Each level of data include: total population, total voting-age population, total voter registration (excluding ND, WI), total ballots cast, total votes cast for president, and voter registration by party. Note: see the documentation for information on missing data.

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php lists the available data. Files are occasionally updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS. These files were last updated on 9 JUL 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the Data and Reproduction Archive Version number in your citations for the full dataset.

For additional information on file layout, etc. see https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_turnout.html.

Similar data may be available at https://www.electproject.org/election-data/voter-turnout-data dating back to 1787.

Since 1824, when the popular vote was first used to determine the overall winner in U.S. presidential elections, the share of the population who participate in these elections has gradually increased. Despite this increase, participation has never reached half of the total population; partly due to the share of the population below the voting age of eighteen, but also as many potential voters above the age of eighteen do not take part, or are ineligible to vote. For example, in the 2016 election, approximately twenty million U.S. adults were ineligible to vote, while over 94 million simply did not participate; in this election, Donald Trump won the electoral college with 63 million votes, which means that 19.4 percent of the total U.S. population (or 27.3 percent of eligible voters) voted for the winning candidate. Development throughout history While the figures for the 2016 election may appear low, over 42 percent of the total population participated in this election, which was the third highest participation rate ever recorded (after the 2008 and 2020 elections). In the first election decided by a popular vote in 1824, only 350 thousand votes were cast from a total population of 10.6 million, although this increased to over four million votes by the 1856 election, as restrictions that applied to non-property holding white males were gradually lifted. Participation levels then dropped during the Civil War and Reconstruction era, as those who lived in Confederate states could not vote in 1864, and many white southerners were restricted or discouraged in the following election. Although universal suffrage was granted to black males in the wake of the Civil War, the majority of black Americans lived in the southern states, where lawmakers introduced Jim Crow laws in the late 1800s to suppress and disenfranchise the black vote, as well as poor white voters. The next major milestone was the introduction of women's suffrage in 1920, which saw voter participation increase by seven million votes (or seven percent) between the 1916 and 1920 elections. Between the 1910s and 1970s, the Great Migration saw many black Americans move away from the south to northern and western states, where they faced fewer obstacles when voting and greater economic mobility. This period of black migration began to decline in the 1960s and 1970s, during which time many Jim Crow laws were repealed in the south, through legislation such as the Voting Rights Act of 1965. Female participation also grew gradually, and has exceeded male voting participation in all elections since the 1980s. The minimum voting age was lowered from 21 to 18 in all states in 1971, although this seemingly had a minimal impact on the short-term trajectory of voter participation. Recent elections The 1992 election was the first in which more than one hundred million votes were cast, which was almost 41 percent of the total population. All elections since 2004 have also had more than one hundred million votes cast, which has again been more than forty percent of the total population. Another key factor in the increase in voter participation is the fact that people are living longer than ever before, and that those aged 65 and over have had the highest turnout levels since 1992. While some figures may be subject to change, the 2020 election set new records for voter turnout. Despite the global coronavirus pandemic, which many thought could cause the lowest turnout in decades, a record number of voters cast their ballots early or by mail, setting a new record of votes just shy of 160 million. In the 2020 election, Joe Biden and Donald Trump received 81.3 million and 74.2 million votes respectively, both beating Barack Obama's previous record of 69.3 million ballots in 2008. 2024 then saw a decline in the number of votes cast, with over three million fewer votes than in 2020.

US Presidential Campaign Logos

Color Diversity and Demographics

By Amber Thomas [source]

About this dataset

We conducted extensive research on popular election campaigns from 1968-2020 as compiled on Wikipedia's entry for each year. From this initial list, we excluded 32 candidates whose images could not be found--leaving us with a total of 271 primary and general party candidates across 14 electoral cycles during that period. In our search for campaign logo images, we prioritized official signs used at rallies, podiums, yards, posters, and bumper stickers with required Federal Election Commission disclaimers--resorting to using buttons only when absolutely necessary . We acknowledge that due to advances in technology, the printing process has significantly impacted the design aesthetics for modern logos compared to those made decades ago.

Using Chrome DevTools or Adobe Photoshop software programs; hexadecimal color values were retreived for each logo clipped from sources such as candidate websites or obtained through additional research efforts. To recognize RWB logos--those using only three colors of red white blue (RWB) --we also surveyed designs including accent tones paired with RWB palettes , two-color schemes (Red/Blue; Red/White; Blue/White), and multiple shades derived from a combination of any 3 primary or secondary RBW hues respectively.

In addition to visual elements associated with picture datasets , candidate demographics such as race , gender are indicated here as binary categories indicating whether a particular demographic is identifiable under one particular label ie either male / female or White / non White individuals . Candidates who fit into both these dual criteria are classfied under majority categories identified under binary labels ie ' whiteMale '. For greater census accuracy candidates classified simply as minority categorizations are merged sounding various Other labels including males belonging outidese racial definitions regardless if identifyingthemselves belonging within -- inclusion of them details belongs hereinunder :

name: The name of the candidate    (String);
party: The political party of thhe candiatate (String);              
white : Binary value indicating if thee candidiate is White     (Boolean);        male: Binary value indocating ffffthueee ccandidate is maille      (Boolean );           whitaeMaile :: Binary alula indicatig

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How to use the dataset

This dataset can be useful for understanding trends in campaign symbolism and visual rhetoric surrounding US presidential elections over time. This data could be used to evaluate how diversity amongst candidates is reflected in their campaign visuals by looking at changes in color usage or exploring differences between Democratic and Republican campaigns.

The data can also be visualized to create charts or maps that display possible trends or themes across different elections. This can help users more easily identify patterns between campaign logs for research purposes or simply make for an interesting comparison tool to explore different aspects of certain elections through visuals rather than text alone.

Using this data is easy! Start by familiarizing yourself with all the columns included; you will find information regarding RWB & non-RWB percentages, hexadecimal value breakdowns of each logo's colors & general candidate demographic information such as gender & race. Select desired columns to focus on and decide which analysis method works best; graphical representational options including line graphs, scatter graphs & pie charts are great ways to visually explore how various factors affect color usage both within an election cycle & across multiple cycles over time! Finally you can use these insights gleaned from your analysis to generate interesting questions regarding campaign symbolism design's relationship/influence on voting population demographics/politics!

Research Ideas

• Create an interactive map to show the color trends of presidential logos over the years.
• Use a machine learning algorithm to analyze how the logo colors correlate with primary and general elections.
• Analyze how diversity and inclusion in presidential campaigns has changed by comparing RWB versus non-RWB percentages for each year or election cycle

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

...

Description:

This dataset contains comprehensive voting data for the 2024 US elections, focusing on general ballot measures. This information includes voting results from various sources and tracking public opinion about political parties and candidates across states and demographic groups. Each item in the dataset represents a specific poll. Along with detailed information about the dates of the polls. Survey organization, sample size, margin of error, Percentage of respondents supporting each political party or candidates

Key Features:

Poll Date:The date when the poll was conducted.

Polling Organization: The name of the organization that conducted the poll.

Sample Size: The number of respondents in the poll.

Margin of Error: The statistical margin of error for the poll results.

Party/Candidate Support: Percentage of respondents who support each political party or candidate.

State/Demographics: Geographic and demographic breakdowns of the polling data.

Use Cases:

Analyzing trends in public opinion leading up to the 2024 U.S. elections. Comparing support for different political parties and candidates over time. Studying the impact of key events on voter preferences. Informing political strategies and campaign planning.

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.

Voter turnout is a crucial indicator of democratic health, yet forecasting turnout remains an understudied area in political science. This article presents two pioneering models for predicting U.S. presidential election turnout: The National Model and The State Model. The National Model, using data from 1868-2020, employs lagged turnout as its sole predictor. The State Model, covering 1984-2020, incorporates demographic and institutional variables to forecast state-level participation. The National Model predicts 65.3% turnout for 2024, while the State Model forecasts increased turnout in 41 states compared to 2020. The models' ability to generate early predictions offers valuable lead time for planning and resource allocation, which has implications for election administrators and political campaigns as well as for the vibrancy of civic engagement in America.