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Cross-Tabs: October 2024 Times/Siena Poll of Hispanic Registered Voters

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Cross-Tabs: October 2024 Times/Siena Poll of Hispanic Registered Voters

How These Polls Were Conducted

Here are the key things to know about these polls:

• Interviewers spoke with 3,385 likely voters nationwide from Sept. 29 to Oct. 6, 2024.

• The national poll includes separate polls of 622 voters in Florida and 617 voters in Texas. The weight given to each of these groups in the national poll has been adjusted so that the overall results are reflective of the entire country.

• The poll also uses a polling technique to speak with more Black and Hispanic voters than the typical national poll. The technique, known as an oversample, enables more confident analysis of subgroups, such as Black men or younger Black voters. This method does not affect the top-level results of the final poll; in the overall poll of the nation, Black and Hispanic respondents are weighted down so that they represent the proper share of all voters and so their views are not overrepresented in the survey results.

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• Times/Siena polls are conducted by telephone, using live interviewers, in both English and Spanish. Overall, about 98 percent of respondents were contacted on a cellphone for these polls.

• Voters are selected for the survey from a list of registered voters. The list contains information on the demographic characteristics of every registered voter, allowing us to make sure we reach the right number of voters of each party, race and region. For these polls, interviewers placed nearly 365,000 calls to nearly 150,000 voters.

• To further ensure that the results reflect the entire voting population, not just those willing to take a poll, we give more weight to respondents from demographic groups that are underrepresented among survey respondents, like people without a college degree. You can see more information about the characteristics of our respondents and the weighted sample at the bottom of the page, under “Composition of the Sample.”

• The margin of sampling error among likely voters is plus or minus 2.4 points for the national poll and about plus or minus five points for each state poll. In theory, this means that the results should reflect the views of the overall population most of the time, though many other challenges create additional sources of error. When the difference between two values is computed, such as a candidate’s lead in a race, the margin of error is twice as large.

If you want to read more about how and why The Times/Siena Poll is conducted, you can see answers to frequently asked questions and submit your own questions here.

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Full Methodology

The New York Times/Siena College nationwide poll of 3,385 likely voters was conducted in English and Spanish on cellular and landline telephones from Sept. 29 to Oct. 6, 2024. The national poll includes separate polls of 622 voters in Florida and 617 voters in Texas. It uses a statistical technique known as an oversample to survey 589 Black voters, including 548 voters who identify as Black alone and 41 voters who identify as Black in combination with another race or ethnicity, and 902 voters of Hispanic descent, including 704 voters who identify as Hispanic or Latino alone and 198 voters who identify as Hispanic in combination. The weight given to each of these groups in the national poll has been adjusted so that the overall results are reflective of the entire country.

Nationally, the margin of sampling error is plus or minus 2.4 percentage points for the likely electorate and plus or minus 2.2 percentage points among registered voters. In Florida and Texas, the margin of sampling error among the likely electorate is 4.8 percentage points.

Among the sample of Hispanic voters, the margin of sampling error is plus or minus 4.5 points for the likely electorate and plus or minus 4.1 points among registered voters. For the Black sample, the margin of sampling error is plus or minus 5.6 points for the likely electorate and plus or minus 5.4 points for registered voters.

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Sample

The survey is a response-rate-adjusted stratified sample of registered voters taken from the voter file maintained by L2, a nonpartisan voter-file vendor, and supplemented with additional voter-file-matched cellular telephone numbers from Marketing Systems Group. The sample was selected by The New York Times in multiple steps to account for differential telephone coverage, nonresponse and significant variation in the productivity of telephone numbers by state.

To adjust for noncoverage bias, the L2 voter file for each state was stratified by statehouse district, party, race, gender, marital status, household size, turnout history, age and homeownership. The proportion of registrants with a telephone number and the mean expected response rate were calculated for each stratum. The mean expected response rate was based on a model of unit nonresponse in prior Times/Siena surveys. The initial selection weight was equal to the reciprocal of a stratum’s mean telephone coverage and modeled response rate. For respondents with multiple telephone numbers on the L2 file, or with differing numbers from L2 and Marketing Systems Group, the number with the highest modeled response rate was selected.

Fielding

The sample was stratified according to political party, race and region. Marketing Systems Group screened the sample to ensure that the cellular telephone numbers were active, and the Siena College Research Institute fielded the poll, with additional fieldwork by ReconMR, the Public Opinion Research Laboratory at the University of North Florida, the Institute for Policy and Opinion Research at Roanoke College, the Center for Public Opinion and Policy Research at Winthrop University in South Carolina and the Survey Center at University of New Hampshire. Interviewers asked for the person named on the voter file and ended the interview if the intended respondent was not available. Overall, 98 percent of respondents were reached on a cellular telephone.

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The questions were translated into Spanish by ReconMR. Bilingual interviewers began the interview in English and were instructed to follow the lead of the respondent in determining whether to conduct the survey in English or Spanish. Monolingual Spanish-speaking respondents who were initially contacted by English-speaking interviewers were recontacted by Spanish-speaking interviewers. Overall, 18 percent of interviews among respondents who self-reported as Hispanic alone were conducted in Spanish; among the weighted sample, the share is 19 percent among registered voters.

An interview was determined to be complete for the purposes of inclusion in the questions about whom the respondent would vote for if the respondent did not drop out of the survey after being asked the two self-reported variables used in weighting — age and education — and answered at least one of the questions about age, education or presidential-election candidate preference.

Weighting (registered voters)

The survey was weighted by The Times using the survey package in R in multiple steps.

First, the sample was adjusted for unequal probability of selection by stratum.

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Second, the Black, Hispanic and non-Black-or-Hispanic samples for Florida, Texas and the rest of the United States were weighted to match voter-file-based parameters for the characteristics of registered voters.

The following targets were used:

• Party (party registration if available in the state; if not, then classification based on participation in partisan primaries if available in the state; if not, then classification based on a model of vote choice in prior Times/Siena polls) by race. The national Hispanic sample was weighted to party by a classification of the strength of the respondent’s partisanship based on a model of vote choice in prior Times/Siena polls

• Age (self-reported age, or voter-file age if the respondent refused) by gender (L2 data)

• Education (four categories of self-reported education level, weighted to match NYT-based targets derived from Times/Siena polls, census data and the L2 voter file)

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• Race or ethnicity (L2 model), if part of the non-Black-or-Hispanic sample in Texas and Florida

• White/nonwhite race by college or noncollege educational attainment (L2 model of race weighted to match NYT-based targets for self-reported education), if part of the non-Black-or-Hispanic sample

• Marital status (L2 model)

• Homeownership (L2 model)

• Turnout history (NYT classifications based on L2 data)

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• Method of voting in the 2020 elections (NYT classifications based on L2 data)

• State region (NYT classifications), in Florida and Texas

• National region (NYT classifications), outside Florida and Texas

• Metropolitan status (2013 NCHS Urban-Rural Classification Scheme for Counties), if part of the national sample

• History of voting in the 2020 presidential primary (L2 data), if part of the national non-Black-or-Hispanic sample

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• Census block group density (A.C.S. 5-Year Census Block Group data), if part of the Florida or Texas non-Black-or-Hispanic sample

• Census block group density of Black residents (A.C.S. 5-Year Census Block Group data), if part of the national or Florida Black sample

• Census block group density of Hispanic residents (A.C.S. 5-Year Census Block Group data), if part of the national or Texas Hispanic sample

• Country of origin (L2 model), if part of the national or Florida Hispanic sample

Third, the sums of the weights were balanced so that each Florida and Texas represented the proper proportion of the national poll and so that the Black, Hispanic and non-Black-or-Hispanic samples represented the proper proportion of each state and the country.

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Finally, the sample of respondents who completed all questions in the survey was weighted identically as well as to the result for the general-election horse-race question (including voters leaning a certain way) on the full sample.

Weighting (likely electorate)

The survey was weighted by The Times using the R survey package in multiple steps.

First, the samples were adjusted for unequal probability of selection by stratum.

Second, the first-stage weight was adjusted to account for the probability that a registrant would vote in the 2024 election, based on a model of turnout in the 2020 election.

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Third, the sample was weighted to match targets for the composition of the likely electorate. The targets for the composition of the likely electorate were derived by aggregating the individual-level turnout estimates described in the previous step for registrants on the L2 voter file. The categories used in weighting were the same as those previously mentioned for registered voters.

Fourth, the initial likely electorate weight was adjusted to incorporate self-reported intention to vote. Four-fifths of the final probability that a registrant would vote in the 2024 election was based on the registrant’s ex ante modeled turnout score, and one-fifth was based on self-reported intentions, based on prior Times/Siena polls, including a penalty to account for the tendency of survey respondents to turn out at higher rates than nonrespondents. The final likely electorate weight was equal to the modeled electorate rake weight, multiplied by the final turnout probability and divided by the ex ante modeled turnout probability.

Finally, the sample of respondents who completed all questions in the survey was weighted identically as well as to the result for the general election horse-race question (including leaners) on the full sample.

The margin of error accounts for the survey’s design effect, a measure of the loss of statistical power due to survey design and weighting.

The design effect for the full sample is 1.97 for the nationwide likely electorate, 1.48 for the likely electorate in Florida, 1.48 for the likely electorate in Texas, 1.89 for the Black likely electorate and 1.92 for the Hispanic likely electorate.

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Among registered voters, the margin of sampling error is plus or minus 2.2 points nationwide, including a design effect of 1.78; 4.6 points in Florida, including a design effect of 1.36; plus or minus 4.5 points in Texas, including a design effect of 1.29; plus or minus 5.4 points for Black voters, including a design effect of 1.81; and plus or minus 4.1 for Hispanic voters, including a design effect of 1.57.

For the sample of completed interviews, among the likely electorate nationwide, the margin of sampling error is plus or minus 2.6 points, including a design effect of 1.93; plus or minus 5.6 points in Florida, including a design effect of 1.64; plus or minus 5.4 points in Texas, including a design effect of 1.5; plus or minus 6.3 points among Black voters, including a design effect of 1.86; and plus or minus 5.2 points among Hispanic voters, including a design effect of 1.96.

Historically, The Times/Siena Poll’s error at the 95th percentile has been plus or minus 5.1 percentage points in surveys taken over the final three weeks before an election. Real-world error includes sources of error beyond sampling error, such as nonresponse bias, coverage error, late shifts among undecided voters and error in estimating the composition of the electorate.

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With the white nationalist group Patriot Front, what you see is not what you get

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With the white nationalist group Patriot Front, what you see is not what you get

Members of the group Patriot Front ride the subway as a commuter looks on, in Washington, D.C., on July 4.

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The sight of hundreds of masked men roaming the streets of Washington, D.C., on July Fourth weekend, wearing khakis, blue shirts and uniform patches, was chilling to some of the city’s residents.

For many Americans, it was the first they heard about Patriot Front, a white nationalist organization that was born out of the deadly 2017 Unite the Right rally in Charlottesville, Va. A now-viral Reuters photo prompted reflections on the experience of a lone African American woman who was photographed in a Metro subway car, surrounded by white supremacists.

The planned demonstration of force was timed to bring a fringe group of extremists into public view as the nation marked 250 years of its independence. Indeed, the stunt succeeded in earning the group media coverage across mainstream outlets, amplifying its brand and potential to reach new recruits. On this occasion, the members refrained from engaging in violence and property damage, projecting an image of law-abiding, orderly activism.

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But those who are closely familiar with Patriot Front’s history and operations warn: Don’t believe what you see.

“That is not who they are in private,” said Len Kamdang, director of the Criminal Justice Project at the Lawyers’ Committee for Civil Rights Under Law. “Although they were on their best behavior [last] weekend, this is a dangerous group that commits acts of violence all over the country.”

Patriot Front’s history of violence and property damage

Kamdang’s organization sued members of Patriot Front for vandalizing a public mural dedicated to the tennis legend and Black activist Arthur Ashe in Richmond, Va., in 2021. Ashe, who was inducted into the International Tennis Hall of Fame in 1985, was born in Richmond and his legacy is a continuing source of pride to members of that community.

“A couple of Patriot Front members showed up under cover of night and vandalized the mural,” Kamdang said. “They painted white stencils all over. … They literally tried to whitewash him and they put their symbols of hate all over — their stencils, their slogans. And all the while they were caught on video. And that video leaked using some of the most horrible language that you can imagine.”

In many jurisdictions, law enforcement can seek additional hate crime charges or sentencing enhancements in cases where illegal acts appear to have been motivated by racial bias. But in this case, Kamdang said, Patriot Front members faced no criminal charges and their identities were only revealed when online activists later infiltrated the group and leaked internal records.

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Graham Platner makes it official in Maine, submitting paperwork to leave Senate race

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Graham Platner makes it official in Maine, submitting paperwork to leave Senate race

Now-former Democratic Senate candidate Graham Platner speaks at his primary election night event on June 9 in Blue Hill, Maine. Platner officially dropped out of the race July 10 following rape allegations from a former romantic partner that he denies.

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CJ Gunther/Getty Images

Graham Platner, Maine’s Democratic nominee for Senate, is officially out of the race.

The Maine Secretary of State said Platner filed the necessary paperwork to withdraw his candidacy two days after he announced he planned to do so following an accusation of rape by a former romantic partner. Platner denies the allegation.

The Maine Democratic Party has until July 27 to pick Platner’s replacement.

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In his withdrawal notice, Platner said “people are desperate for change” and that’s why they voted “for a new kind of politics” by making him the Democratic nominee. He expressed gratitude for those who supported his campaign and said that he will continue to fight for “the movement we have built together and the future we believe in.”

He ended his notice with a strong statement aligned with the progressive platform.

“F*ck ICE. Free Palestine. Up the Hearts.”

Platner announced his plan to withdraw from the race in an 11-minute video he posted to social media on July 8. He said he had no choice but to suspend his campaign, citing it was no longer viable financially.

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“We are going to lose our ability to fundraise. We are going to lose our ability to access voter data. We are going to lose all of the things that any campaign needs on the basic level simply to function,” he said.

Platner added that dropping out was not an admission of guilt. Rather, the decision, he said, is to keep the progressive movement in Maine alive to defeat Republican Sen. Susan Collins in November. Platner blamed the “political establishment” for his downfall and argued the goal was to force him out of the race.

“We built a campaign. We engaged in electoral politics. We motivated people. We banded together. We did it the way that we were told we are supposed to make change and we won. And now they are not going to let us have it. Not if it’s me,” he said.

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Waymo called the cops on teen riders, raising privacy concerns

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Waymo called the cops on teen riders, raising privacy concerns

A Waymo robotaxi drives in San Francisco’s North Beach neighborhood this week.

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Police in San Mateo, Calif., posted Monday on social media that they had apprehended a pair of teenagers from a Waymo driverless robotaxi after the company alerted authorities to suspected criminal activity. It’s the latest incident involving video surveillance of passengers and others by autonomous vehicles — raising questions about the limits of privacy in such vehicles.

The Facebook post by the San Mateo County Police said: “Parents do you know where your teens are? @waymo does!”

The 15-year-olds were allegedly drinking alcohol and shooting toy guns from the car, according to the police. They said Waymo’s systems detected behavior that then triggered a safety response, after which the company disabled the vehicle and contacted police.

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Waymo’s cars, equipped with an array of cameras, microphones and other sensors to monitor passengers and other nearby vehicles, are becoming more common in cities across the United States. Experts say the detention of the two teens in San Mateo highlights a potential — but not inevitable — trade-off between privacy and convenience. It also questions the extent to which companies similar to Waymo are required to hand over private data, including audio and video of passengers, in situations where a crime is suspected.

NPR reached out to Waymo, which is owned by Alphabet, the parent company of Google, for comment on the details of the San Mateo incident and how the company responded, but did not hear back. But on its website, the company says that as many as 29 cameras in its autonomous cars provide an all-around view and “are designed with high dynamic range and thermal stability, to see in both daylight and low-light conditions, and tackle more complex environments.”

“There already exist laws that govern duty to report or even duty to protect” for carriers such as Waymo, according to Alessandro Acquisti, a professor of information technology at the MIT Sloan School of Management. “The privacy problems arise when and if driverless carrier companies used such laws or ethical obligations as a pretext for blanket, indiscriminate accumulation of identifiable data for unspecified future purposes.”

That includes not just monitoring people inside the cars, but outside too. Take, for example, a hit-and-run investigation last year in Los Angeles. Media reported that the police inquiry was aided by video captured by a Waymo taxi that had a clear view of the crime. Critics suggested at the time that authorities were using the company’s vehicles as a mobile surveillance platform. And during 2025 protests in Los Angeles against Immigration and Customs Enforcement crackdowns, demonstrators vandalized Waymos, apparently angry that video recorded by the vehicles could be used by police, although there is no evidence that happened.

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