According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of surveyed white voters reported voting for Donald Trump. In contrast, ** percent of Black voters reported voting for Kamala Harris.

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

According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from men between the ages of ** and **. In comparison, ** percent of women between the ages of ** and ** reported voting for Kamala Harris.

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.

According to exit polling in *** key states of the 2024 presidential election in the United States, almost ********** of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with ******** degrees reported voting for Kamala Harris.

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.

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

Context

The election of Donald Trump has taken the world by surprise and is fuelling populist movements in Europe, e.g. in Italy, Austria and France. Understanding populism and assessing the impact of the “Trump effect” on Europe is a tremendous challenge, and Dalia wants to help pool brainpower to find answers.

The goal is to find out where the next wave of populism could hit in Europe by comparing and contrasting US and EU voter profiles, opinions of Trump vs Clinton voters, Brexit vs. Bremain voters, and future expectations.

Content

Expanding Dalia’s quarterly "EuroPulse" omnibus survey to the USA, Dalia has conducted a representative survey with n=11.283 respondents across all 28 EU member countries and n=1.052 respondents from the United States of America. To find out where the next wave of populism could hit Europe, Dalia’s survey traces commonalities in social and political mindsets (like authoritarianism, prejudice, open-mindedness, xenophobia, etc.), voting behaviour and socio-demographic profiles on both sides of the Atlantic.

Inspiration

The sources of our inspirations are many, but to name a few who influenced the way we asked questions: we were very inspired by the 'angry voter' profile laid out by Douglas Rivers, the influence of political and moral attitudes pointed out by Jonathan Haidt and the profile of "America's forgotten working class" by J. D. Vance.

Researchers should apply the necessary logic, caution and diligence when analysing and interpreting the results.

2024 General Election: Trump vs. Biden | RealClearPolling

2016 General Election: Trump vs. Clinton vs. Johnson vs. Stein | RealClearPolling

US election season is here, which means it's time to analyze some polling data! Less than four years ago, Donald Trump was elected president of the United States, and on November 2nd, 2020, he will run for re-election against a Democratic challenger. Kernels may answer a variety of questions, including: "How accurate were the polls in 2016?", "Which Democratic challenger will fare best against Trump in 2020 according to the polls?", "Which states are anybody's game?", and of course the ultimate question, "Will Trump win again in 2020?" Then, after November 2nd, we can ask ourselves what happened in 2020!

The two datasets included here include:

• US 2016 General Election Results by State and County (from Data World)
• US 2020 General Election Polling Data (from FiveThirtyEight)

This data should be used in conjunction with the 2016 General Election Polling Data from this link: https://www.kaggle.com/fivethirtyeight/2016-election-polls

The 2020 polling data will be updated regularly until the election on November 2nd, 2020. Then, I will upload a dataset of 2020 general election final results. As time goes on and we get closer to the election, we will acquire higher quantities of data and data that is more representative of what might happen on November 2nd. Have fun!

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed voters making less than 50,000 U.S. dollars reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of voters with an income of 100,000 U.S. dollars or more reported voting for incumbent President Donald Trump.

Generalized linear regression estimates adjusted for demographic variables and unhealthy component.

Demographic and political preferences of participants.

Context

The 2020 Presidential election is disputed. The mainstream media won't cover it. The states seem intent to cover up any potential fraud. If there was fraud, can it be hidden from data analysts? There are a lot of great findings online, but try to see if you can find some yourself. I wanted to see if I could reproduce some results I had seen online.

Content

****Original Data**** state timestamp (Zulu time) votes - Integer eevp (est. vote percentage) trumpd (% of "votes" for Trump) - Floating point with only 3 significant digits ?? bidenj (% of "votes" for Biden)

****Added columns**** TrumpVotes (votes * trumpd) BidenVotes (votes * bidenj) 3rdVotes (votes * (1-trumpd - bidenj) Increment (ordered timestamps by state) Tinc (vote increase for Trump) Binc (vote increase for Biden) 3inc (vote increase for 3rd Party)

Acknowledgements

Thanks to US voters, Edison tabulators, and NY Times for posting.

Inspiration

There are a lot of statistically suspect issues with this data. Why are votes calculated real numbers, rather than counted integers? Why do vote ratios often stay consistent for dozens of batches, coming from different precincts reporting all over a state (check any swing state)? Why does the Biden to Trump vote ratio in swing states creep up at such a constant, non-random rate? Why did swing states have massive Biden vote dumps in the middle of the night after the counting had been stopped? Hey, it's the data used to determine our President, no big deal :)

This survey was conducted among Wisconsin voters likely to vote in the 2016 general election.

2024 General Election: Trump vs. Biden vs. Kennedy vs. West vs. Stein | RealClearPolling

These data files contain election results for both the 2012 and 2016 US Presidential Elections, include proportions of votes cast for Romney, Obama (2012) and Trump, Clinton (2016).

The election results were obtained from this Git repository: https://github.com/tonmcg/County_Level_Election_Results_12-16

The county facts data was obtained from another Kaggle election data set: https://www.kaggle.com/benhamner/2016-us-election

2016 North Carolina: Trump vs. Clinton | RealClearPolling

Baseline demographics of county-level data*.