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

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.

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.

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

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.

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.

Description:

This dataset combines data from three sources to provide a comprehensive overview of county-level socioeconomic indicators, educational attainment, and voting outcomes in the United States. The dataset includes variables such as unemployment rates, median household income, urban influence codes, education levels, and voting percentages for the 2020 U.S. presidential election. By integrating this data, the dataset enables analysis of how factors like income, education, and unemployment correlate with political preferences, offering insights into regional voting behaviors across the country.

References:

The following reference datasets were used to construct this dataset.

[1] Harvard Dataverse, Voting Data Set by County. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/VOQCHQ

[2] USDA Economic Research Service, Educational Attainment and Un- employment Data. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/county-level-data-sets-download-data/

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, 2024) 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, 2024 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (24 November 2025).; 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).

Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%. Margins of error are estimated at the 90% confidence level.

Data Source: Current Population Survey (CPS) Voting Supplement, 2020

Why This Matters

Voting is one of the primary ways residents can have their voices heard by the government. By voting for elected officials and on ballot initiatives, residents help decide the future of their community.

For much of our nation’s history, non-white residents were explicitly prohibited from voting or discriminated against in the voting process. It was not until the Voting Rights Act of 1965 that the Federal Government enacted voting rights protections for Black voters and voters of color.

Nationally, BIPOC citizens and especially Hispanic and Asian citizens have consistently lower voter turnout rates and voter registration rates. While local DC efforts have been taken to remove these barriers, restrictive voter ID requirements and the disenfranchisement of incarcerated and returning residents act as institutionally racist barriers to voting in many jurisdictions.

The District's Response

The DC Board of Elections has lowered the barriers to participate in local elections through online voter registration, same day registration, voting by mail, and non-ID proof of residence.

Unlike in many states, incarcerated and returning residents in D.C. never lose the right to vote. Since 2024, DC has also extended the right to vote in local elections to residents of the District who are not citizens of the U.S.

Although DC residents pay federal taxes and can vote in the presidential election, the District does not have full representation in Congress. Efforts to advocate for DC statehood aim to remedy this.

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.

Abstract (en): This instructional package includes a student manual containing six exercises, an instructor's guide, and four subsets of data required for use in conjunction with the manual's exercises. The package's major purpose is to enable students to examine certain substantive questions about electoral behavior through analysis of political survey data. The manual avoids instruction in methodology, per se, hence the student is taken no further than the analysis of straightforward variables in percentagized tables with and without controls, and is introduced to epsilon, the percentage difference measures based on 2 X 2 tables, but offered no elaborate discussion of measures of association. The six structured exercises introduce the basic analytic techniques necessary for coping with survey data in the expectation that the students will then move on to their own topics. The datasets were designed to be both substantively and analytically interesting, as students are forced continually to make choices and judgments about which variables to use and how to combine code categories. Beyond this, the exercises serve a more complex purpose: to help the student gain a better understanding of the existing scholarly literature by going through steps similar to those of the original analysts. In some instances, the students can readily appreciate how close their work is to the analysis in assigned reading. The instructor's guide has two purposes: first, to help instructors use the student manual effectively, and second, to suggest various ways to depart from the six exercises and to develop essentially new manuals. The subsets (Parts 1-4) contain data from every presidential election survey that was conducted by the Survey Research Center (SRC) and Center for Political Studies (CPS) (at the University of Michigan) from 1952 to 1980. Part 4 contains an extensive set of variables drawn exclusively from the CPS's AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763). This is the only deck needed to complete the exercises in Exercises l-5. Part 1 includes small sets of comparable variables from each SRC/CPS presidential election study from 1952-1972. The variables in these decks were selected with the intention of providing students with a range of interesting possibilities for original research topics for term papers. Part 2 includes variables and respondents from panel surveys contained in AMERICAN NATIONAL ELECTION SERIES: 1972, 1974, 1976 (ICPSR 7607). This dataset may be used with Exercise 6. Supplementing the panel file is the data in Part 3, based on the cross-section survey, AMERICAN NATIONAL ELECTION STUDY, 1976 (7381). It repeats the variables from the 1976 component of the panel, with a much larger N. The AMERICAN NATIONAL ELECTION STUDY, 1976 (7381) may be used independently, as with the AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763), or it may be used in exercises comparing cross-section with panel data. Data used for the exercises were made available by ICPSR. The major analyses of these data have appeared in two publications: (1) University of Michigan. Survey Research Center. THE AMERICAN VOTER. New York, NY: Wiley, 1960, and (2) Campbell, Angus, Philip Converse, Warren Miller, and Donald Stokes. ELECTIONS AND THE POLITICAL ORDER. New York, NY: Wiley, 1966. 2006-01-12 All files were removed from dataset 6 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads. The codebooks, Student Manual for All Parts and the Guide to Instruction for All Parts, are provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

This file shows all election related data by state and county (i.e. total votes, Republican votes, Democratic votes, Republican voting percentage, Democratic voting percentage) for both the 2020 and 2024 U.S. Presidential Elections.

How does the context in which a person lives affect his or her political behavior? I exploit an event in which demographic context was exogenously changed, leading to a significant change in voters' behavior, and demonstrating that voters react strongly to changes in an outgroup population. Between 2000 and 2004, the reconstruction of public housing in Chicago caused the displacement of over 25,000 African Americans, many of whom had previously lived in close proximity to white voters. After the removal of their African American neighbors, the white voters' turnout dropped by over ten percentage points. Consistent with psychological theories of racial threat, their change in behavior was a function of the size and proximity of the outgroup population. Proximity was also related to increased voting for conservative candidates. These findings strongly suggest that racial threat occurs because of attitude change rather than selection.

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

Context

This data comes from the Associated Press - the AP has been tracking vote counts in US elections since 1848 and their data is widely considered to be accurate.

Content

The variables in this dataset are: - state: State to which the vote count corresponds - state_abr: Two-letter abbreviation of state name - trump_pct: Percentage of the vote won by Donald Trump - biden_pct: Percentage of the vote won by Joe Biden - trump_win: Binary variable denoting whether Donald Trump won the vote in a state - biden_win: Binary variable denoting whether Joe Biden won the vote in a state

Key Table Information.Table Title.Median Household Income for Households With a Citizen, Voting-Age Householder (in 2024 Inflation-Adjusted Dollars).Table ID.ACSDT1Y2024.B29004.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates o...

This instructional package includes a student manual containing six exercises, an instructor's guide, and four subsets of data required for use in conjunction with the manual's exercises. The package's major purpose is to enable students to examine certain substantive questions about electoral behavior through analysis of political survey data. The manual avoids instruction in methodology, per se, hence the student is taken no further than the analysis of straightforward variables in percentagized tables with and without controls, and is introduced to epsilon, the percentage difference measures based on 2 X 2 tables, but offered no elaborate discussion of measures of association. The six structured exercises introduce the basic analytic techniques necessary for coping with survey data in the expectation that the students will then move on to their own topics. The datasets were designed to be both substantively and analytically interesting, as students are forced continually to make choices and judgments about which variables to use and how to combine code categories. Beyond this, the exercises serve a more complex purpose: to help the student gain a better understanding of the existing scholarly literature by going through steps similar to those of the original analysts. In some instances, the students can readily appreciate how close their work is to the analysis in assigned reading. The instructor's guide has two purposes: first, to help instructors use the student manual effectively, and second, to suggest various ways to depart from the six exercises and to develop essentially new manuals. The subsets (Parts 1-4) contain data from every presidential election survey that was conducted by the Survey Research Center (SRC) and Center for Political Studies (CPS) (at the University of Michigan) from 1952 to 1980. Part 4 contains an extensive set of variables drawn exclusively from the CPS's AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763). This is the only deck needed to complete the exercises in Exercises l-5. Part 1 includes small sets of comparable variables from each SRC/CPS presidential election study from 1952-1972. The variables in these decks were selected with the intention of providing students with a range of interesting possibilities for original research topics for term papers. Part 2 includes variables and respondents from panel surveys contained in AMERICAN NATIONAL ELECTION SERIES: 1972, 1974, 1976 (ICPSR 7607). This dataset may be used with Exercise 6. Supplementing the panel file is the data in Part 3, based on the cross-section survey, AMERICAN NATIONAL ELECTION STUDY, 1976 (7381). It repeats the variables from the 1976 component of the panel, with a much larger N. The AMERICAN NATIONAL ELECTION STUDY, 1976 (7381) may be used independently, as with the AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763), or it may be used in exercises comparing cross-section with panel data. Data used for the exercises were made available by ICPSR. The major analyses of these data have appeared in two publications: (1) University of Michigan. Survey Research Center. THE AMERICAN VOTER. New York, NY: Wiley, 1960, and (2) Campbell, Angus, Philip Converse, Warren Miller, and Donald Stokes. ELECTIONS AND THE POLITICAL ORDER. New York, NY: Wiley, 1966.

This dataset is collected from 1824 to 2020: 1. Year: Description: The year in which the U.S. election took place. Type: Numeric (Integer) Example: 1824, 1860, 1920, 2020

1. Candidate: Description: The name of the candidate participating in the election. Type: String (Candidate's name) Example: John Adams, Abraham Lincoln, Franklin D. Roosevelt, Joe Biden

2. Party: Description: The political party affiliation of the candidate. Type: String (Party name or abbreviation) Example: Democratic, Republican, Whig, Libertarian

3. Popular Vote: Description: The total number of votes that the candidate received in the popular vote. Type: Numeric (Integer) Example: 500,000, 5,000,000, 70,000,000

4. Result: Description: The outcome of the election for the specified candidate. Type: String (e.g., "Winner," "Runner-up," "Withdrew") Example: Winner, Runner-up, Withdrew, Conceded

5. Percentage: Description: The percentage of the total popular vote received by the candidate. Type: Numeric (Float) Example: 25.3%, 49.8%, 60.5%

This dataset appears to capture essential information about U.S. elections over time, including details about the candidates, their political party affiliations, the number of popular votes they received, the outcome of the election, and the percentage of the total popular vote they secured. This comprehensive dataset allows for the analysis of historical U.S. election trends and outcomes.

Context

In a United States presidential election, the popular vote is the total number or percentage of votes cast for a candidate by voters in the 50 states and Washington, D.C.; the candidate who gets the most votes nationwide is said to have won the popular vote. However, the popular vote is not used to determine who is elected as the nation's president or vice president. Thus it is possible for the winner of the popular vote to end up losing the election, an outcome that has occurred on five occasions, most recently in the 2016 election. This is because presidential elections are indirect elections; the votes cast on Election Day are not cast directly for a candidate, but for members of the Electoral College. The Electoral College's electors then formally elect the president and vice president.The Twelfth Amendment to the United States Constitution provides the procedure by which the president and vice president are elected.

Reason behind the dataset

With upcomming elections let's see what and how results were during various elections and how seats and parties changed.

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.