100+ datasets found
  1. U.S. Senators 1975-2023, by race and ethnicity

    • statista.com
    Updated Feb 25, 2025
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    Statista (2025). U.S. Senators 1975-2023, by race and ethnicity [Dataset]. https://www.statista.com/statistics/198430/senators-in-the-us-congress-by-ethnic-group-since-1975/
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    Dataset updated
    Feb 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    There are 100 Senators that serve in the United States Congress at any given time - two from each of the fifty states. As of the first day of the 118th Congress, there were three African American Senators, two Asian American Senators, and six Hispanic Senators.

  2. Senate, House, and Governor Race Candidates From Across the United States,...

    • icpsr.umich.edu
    • search.datacite.org
    ascii, delimited, sas +2
    Updated May 8, 2008
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    Latimer, Christopher (2008). Senate, House, and Governor Race Candidates From Across the United States, 2002 [Dataset]. http://doi.org/10.3886/ICPSR21000.v1
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    delimited, ascii, sas, stata, spssAvailable download formats
    Dataset updated
    May 8, 2008
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Latimer, Christopher
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/21000/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/21000/terms

    Time period covered
    2002
    Area covered
    United States
    Description

    This study considers the growing potential of the Internet in United States elections at the sub-presidential level and whether the Internet can be used as an effective tool in campaigns and elections. Internet sites for incumbents, challengers, and third-party candidates were closely examined and compared on several dimensions of quality. Using a sample of sites collected in the 2002 elections, a comprehensive tool was developed to assess Internet quality using both analytical criteria and statistical checks. Five dimensions were examined: content, interactivity, usability, transparency, and audience. This analysis of the 2002 United States election Web sites focuses on the contests for the House of Representatives, the Senate, and for governor in those states with scheduled elections. The dataset includes 111 separate races: 84 for the House, 12 for the Senate and 16 for governor. There are 245 individual House candidates, 62 gubernatorial candidates, and 45 individual Senate candidates. This dataset also explores the relationship between Internet quality and the political and demographic features of a district. Internet quality also is evaluated in relation to other significant resources in a candidate's campaign, e.g., years of service, incumbency, political party, competition, and campaign finance. House races were isolated in order to evaluate the relationship between Internet quality, these significant political resources, and demographic aspects of the districts. Shifting the level of analysis from the candidate to the district examined how short-term elements of campaigns, including a candidate's Web site, interact and correlate with political features of a contest and demographic features of a congressional district.

  3. U.S. number of women in the Senate 2025, by race and ethnicity

    • statista.com
    Updated Feb 25, 2025
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    Statista (2025). U.S. number of women in the Senate 2025, by race and ethnicity [Dataset]. https://www.statista.com/statistics/952975/number-women-color-us-senate-ethnicity/
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    Dataset updated
    Feb 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    United States
    Description

    The number of women in the United States Senate has been increasing in recent years. In 2025, there were 25 women serving in the United States Senate. Of those, 2 identified as Latina, and two as Black.

  4. U.S. number of Senate members 2024, by 2025

    • statista.com
    • ai-chatbox.pro
    Updated Jun 27, 2025
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    Statista (2025). U.S. number of Senate members 2024, by 2025 [Dataset]. https://www.statista.com/statistics/1361920/senators-age-share-us/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of 2025, the average age of senators in the 119th Congress was **. Of the total 100, ** members of the U.S. Senate were between the ages of ** and ** - more than any other age group. The minimum age requirement to be a member of the Senate is **, opposed to the House of Representatives which has a minimum age requirement of **. The average age of members of Congress from 2009 to 2023 can be found here.

  5. U.S. Senators in Congress 1975-2025, by gender

    • statista.com
    Updated Feb 24, 2025
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    Statista (2025). U.S. Senators in Congress 1975-2025, by gender [Dataset]. https://www.statista.com/statistics/198423/senators-in-the-us-congress-by-gender-since-1975/
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    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The 119th Congress began in January 2025. In this Congress, there were 26 women serving as Senators, and 74 men. The number of women has increased since the 1975 when there were no women in the Senate. The first female Senator was Rebecca Felton of Georgia who was sworn in 1922. A breakdown of women Senators by party can be found here.

  6. a

    OCACS 2017 Demographic Characteristics for State Senate Legislative...

    • data-ocpw.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jan 22, 2020
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    OC Public Works (2020). OCACS 2017 Demographic Characteristics for State Senate Legislative Districts [Dataset]. https://data-ocpw.opendata.arcgis.com/maps/OCPW::ocacs-2017-demographic-characteristics-for-state-senate-legislative-districts
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    Dataset updated
    Jan 22, 2020
    Dataset authored and provided by
    OC Public Works
    Area covered
    Description

    US Census American Community Survey (ACS) 2017, 5-year estimates of the key demographic characteristics of State Senate Legislative Districts (Upper) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

  7. a

    OCACS 2018 Social Characteristics for State Senate Legislative Districts

    • data-ocpw.opendata.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Jun 19, 2020
    + more versions
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    OC Public Works (2020). OCACS 2018 Social Characteristics for State Senate Legislative Districts [Dataset]. https://data-ocpw.opendata.arcgis.com/datasets/ocacs-2018-social-characteristics-for-state-senate-legislative-districts
    Explore at:
    Dataset updated
    Jun 19, 2020
    Dataset authored and provided by
    OC Public Works
    Area covered
    Description

    US Census American Community Survey (ACS) 2018, 5-year estimates of the key social characteristics of State Senate Legislative Districts (Upper) geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2018 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

  8. CBS News/New York Times New York State Poll, April 2000

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Jul 28, 2009
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    Inter-university Consortium for Political and Social Research [distributor] (2009). CBS News/New York Times New York State Poll, April 2000 [Dataset]. http://doi.org/10.3886/ICPSR02981.v3
    Explore at:
    spss, stata, sas, delimited, asciiAvailable download formats
    Dataset updated
    Jul 28, 2009
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/2981/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/2981/terms

    Time period covered
    Apr 1, 2000 - Apr 5, 2000
    Area covered
    New York
    Description

    This special topic poll, fielded April 1-5, 2000, queried residents of New York State on the prospective Senate race between First Lady Hillary Rodham Clinton and New York City Mayor Rudolph Giuliani in 2000, and on a range of other political and social issues. Respondents were asked to give their opinions of President Bill Clinton, New York State governor George Pataki, Hillary Clinton, Rudolph Giuliani, and civil rights activist Al Sharpton. Regarding the upcoming Senate race, respondents were asked how much attention they were paying to the upcoming election, for whom they would vote, whether that decision was firm, and who they thought would win. Respondents were also asked which of the potential candidates cared more about people like the respondent, whether the candidates cared about the needs and problems of Black people, and whether the candidates were trying to bring together or divide various groups of New Yorkers. Respondents were asked whether they approved or disapproved of the way Giuliani was handling his job as mayor, and the way he was handling crime, education, and race relations. Regarding Mrs. Clinton, respondents were asked whether they approved of the way she was handling her role as First Lady. Opinions were also elicited on whether Hillary Clinton and Giuliani were spending more time explaining what they would do as senator or attacking each other. Respondents were asked to rate the performance of the New York City police department, whether the police should interfere in individuals' freedoms to make the city safer, and if the respondent had ever been insulted by an officer, felt in personal danger from a police officer, or felt safer because of a police officer. Other questions focused on whether racial profiling was widespread in New York City, whether racial profiling was justified, whether respondents had personally been racially profiled, and if the police favored whites over Blacks or Blacks over whites. In relation to the police shooting death of Patrick Dorismond, an unarmed Black male, outside of a Manhattan bar, respondents were asked how closely they had been following the shooting, how common brutality by the New York City police department against minorities was, how the policies of the Giuliani administration affected the amount of police brutality in New York City, whether the officer involved in the Dorismond shooting should face criminal charges, and whether the public comments made by Giuliani, Hillary Clinton, and Sharpton regarding the shooting made the situation better or worse. Background information on respondents includes voter registration and participation history, political party, political orientation, marital status, religion, education, age, sex, race, Hispanic descent, and family income.

  9. a

    Population (by Georgia Senate) 2017

    • opendata.atlantaregional.com
    Updated Jun 21, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Population (by Georgia Senate) 2017 [Dataset]. https://opendata.atlantaregional.com/datasets/population-by-georgia-senate-2017/explore?showTable=true
    Explore at:
    Dataset updated
    Jun 21, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show total population and change by Georgia Senate in the Atlanta region.

    The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

    The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    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)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    SumLevel

    Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

    GEOID

    Census tract Federal Information Processing Series (FIPS) code

    NAME

    Name of geographic unit

    Planning_Region

    Planning region designation for ARC purposes

    Acres

    # Area, Acres, 2017

    SqMi

    # Area, square miles, 2017

    County

    County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

    CountyName

    County Name

    TotPop_e

    # Total population, 2017

    TotPop_m

    # Total population, 2017 (MOE)

    rPopDensity

    Population density (people per square mile), 2017

    last_edited_date

    Last date the feature was edited by ARC

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  10. d

    Maryland Senate Districts Socioeconomic Characteristics - ACS 5-year...

    • catalog.data.gov
    • opendata.maryland.gov
    Updated Mar 16, 2024
    + more versions
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    opendata.maryland.gov (2024). Maryland Senate Districts Socioeconomic Characteristics - ACS 5-year Estimates (2018-2022) [Dataset]. https://catalog.data.gov/dataset/maryland-senate-districts-socioeconomic-characteristics-63bb3
    Explore at:
    Dataset updated
    Mar 16, 2024
    Dataset provided by
    opendata.maryland.gov
    Area covered
    Maryland
    Description

    Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates. The ACS 5-year period are period estimates that describe the average characteristics of the population and housing over the period of data collection (2018 through 2022). Data provides broad social, economics, housing, and demographics information by Maryland Senate Districts.

  11. a

    OCACS 2014 Demographic Characteristics for State Senate Legislative...

    • data-ocpw.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jan 17, 2020
    + more versions
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    OC Public Works (2020). OCACS 2014 Demographic Characteristics for State Senate Legislative Districts [Dataset]. https://data-ocpw.opendata.arcgis.com/datasets/67b7a826bf1c46bd954e1424e6b7cb80
    Explore at:
    Dataset updated
    Jan 17, 2020
    Dataset authored and provided by
    OC Public Works
    Area covered
    Description

    US Census American Community Survey (ACS) 2014, 5-year estimates of the key demographic characteristics of State Senate Legislative Districts (Upper) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2014 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

  12. a

    Demographic by Race (by Georgia Senate) 2018

    • opendata.atlantaregional.com
    Updated Mar 4, 2020
    + more versions
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    Georgia Association of Regional Commissions (2020). Demographic by Race (by Georgia Senate) 2018 [Dataset]. https://opendata.atlantaregional.com/datasets/08b41e29c8c34f21b6dcf598665d65fb
    Explore at:
    Dataset updated
    Mar 4, 2020
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

    The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

    The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). 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 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.

    For further explanation of ACS estimates and margin of error, visit Census ACS website.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

    Suffixes:

    _e18

    Estimate from 2014-18 ACS

    _m18

    Margin of Error from 2014-18 ACS

    _00_v18

    Decennial 2000 in 2018 geography boundary

    _00_18

    Change, 2000-18

    _e10_v18

    Estimate from 2006-10 ACS in 2018 geography boundary

    _m10_v18

    Margin of Error from 2006-10 ACS in 2018 geography boundary

    _e10_18

    Change, 2010-18

  13. U.S. midterm election Senate races with most money spent 2022

    • statista.com
    Updated Sep 15, 2022
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    Statista (2022). U.S. midterm election Senate races with most money spent 2022 [Dataset]. https://www.statista.com/statistics/1318120/midterm-senate-races-most-money-spent-us/
    Explore at:
    Dataset updated
    Sep 15, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of December 2022, the United States Senate race in Georgia spent the highest amount of funds in the midterm election cycle, followed by Pennsylvania and Florida. Pennsylvania and Georgia both played a key roll in securing Democrats a Senate majority after the 2022 midterms.

  14. American National Election Study: Pooled Senate Election Study, 1988, 1990,...

    • icpsr.umich.edu
    ascii, sas, spss
    Updated Mar 7, 2005
    + more versions
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    Miller, Warren E.; Kinder, Donald R.; Rosenstone, Steven J. (2005). American National Election Study: Pooled Senate Election Study, 1988, 1990, 1992 [Dataset]. http://doi.org/10.3886/ICPSR09580.v3
    Explore at:
    sas, ascii, spssAvailable download formats
    Dataset updated
    Mar 7, 2005
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Miller, Warren E.; Kinder, Donald R.; Rosenstone, Steven J.
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/9580/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/9580/terms

    Time period covered
    1988
    Area covered
    United States
    Description

    This data collection, focusing on Senate elections, combines data from a three-part series (1988, 1990, 1992) of Senate studies. Over the course of these three elections voters in each of the 50 states were interviewed, and data were gathered on citizen evaluations of all senators at three stages of their six-year election cycles. Both survey data and contextual data for all 50 states are included. The survey data facilitate the comparison of House of Representatives and Senate races through the use of questions that generally parallel those questions used in election studies since 1978 concerning respondents' interaction with and evaluation of candidates for the House of Representatives. However, because of redistricting in the early 1990s, the congressional districts for the 1992 respondents could not be pre-identified. The survey instrument was, therefore, redesigned to some degree, cutting some of the House-related content for the 1992 survey. The 50-state survey design also allows for the comparison of respondents' perceptions and evaluation of senators who were up for re-election with those in the second or fourth years of their terms. Topics covered include respondent's recall and like/dislike of House and Senate candidates, issues discussed in the campaigns, contact with House and Senate candidates/incumbents, respondent's opinion of the proper roles for senators and representatives, a limited set of issue questions, liberal/conservative self-placement, party identification, media exposure, and demographic information. Contextual data presented include election returns for the Senate primary and general elections, voting indices for the years 1983-1992, information about the Senate campaign such as election outcome predictions, campaign pollster used, and spending patterns, and demographic, geographic, and economic data for the state. Also included are derived measures that reorganize the House of Representatives and Senate variables by the party and incumbency/challenger status of the candidate and, for Senate variables only, by proximity to next election. Additionally, a number of analytic variables intended to make analyses more convenient (e.g., Senate class number and whether the respondent voted for the incumbent) are presented.

  15. f

    Sex and Age (by Georgia Senate) 2017

    • gisdata.fultoncountyga.gov
    Updated Jun 22, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Sex and Age (by Georgia Senate) 2017 [Dataset]. https://gisdata.fultoncountyga.gov/datasets/GARC::sex-and-age-by-georgia-senate-2017/data
    Explore at:
    Dataset updated
    Jun 22, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population by sex and age by Georgia Senate in the Atlanta region.

    The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

    The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    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)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here). Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  16. Connecticut Senate Race

    • realclearpolling.com
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    Real Clear Polling, Connecticut Senate Race [Dataset]. https://www.realclearpolling.com/polls/senate/general/2006/connecticut/lamont-vs-lieberman-vs-schlesinger
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    Dataset provided by
    RealClearPoliticshttps://realclearpolitics.com/
    Authors
    Real Clear Polling
    Area covered
    Connecticut
    Description

    Connecticut Senate Race | RealClearPolling

  17. a

    Race/Ethnicity (by Georgia Senate) 2019

    • opendata.atlantaregional.com
    • hub.arcgis.com
    Updated Feb 25, 2021
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    Georgia Association of Regional Commissions (2021). Race/Ethnicity (by Georgia Senate) 2019 [Dataset]. https://opendata.atlantaregional.com/datasets/race-ethnicity-by-georgia-senate-2019/about
    Explore at:
    Dataset updated
    Feb 25, 2021
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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

  18. a

    Race/Ethnicity (by Georgia Senate) 2018

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • opendata.atlantaregional.com
    Updated Mar 4, 2020
    + more versions
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    Georgia Association of Regional Commissions (2020). Race/Ethnicity (by Georgia Senate) 2018 [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/GARC::race-ethnicity-by-georgia-senate-2018
    Explore at:
    Dataset updated
    Mar 4, 2020
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

    The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

    The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). 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 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.

    For further explanation of ACS estimates and margin of error, visit Census ACS website.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

    Suffixes:

    _e18

    Estimate from 2014-18 ACS

    _m18

    Margin of Error from 2014-18 ACS

    _00_v18

    Decennial 2000 in 2018 geography boundary

    _00_18

    Change, 2000-18

    _e10_v18

    Estimate from 2006-10 ACS in 2018 geography boundary

    _m10_v18

    Margin of Error from 2006-10 ACS in 2018 geography boundary

    _e10_18

    Change, 2010-18

  19. f

    Population Change 2010 (State Senate Districts)

    • gisdata.fultoncountyga.gov
    Updated Jun 12, 2018
    + more versions
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    Georgia Association of Regional Commissions (2018). Population Change 2010 (State Senate Districts) [Dataset]. https://gisdata.fultoncountyga.gov/datasets/a09bbeb222e74e1ab310eeda69458a24
    Explore at:
    Dataset updated
    Jun 12, 2018
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from 2010 U.S. Census to show population change by state Senate district for the state of Georgia.Attributes: DISTRICT = GA Senate District POPULATION = District Population (2010 Census) Population_2010 = Population, 2010 Population_2000 = Population, 2000 Population_Change_2000_2010 = Population Change, 2000-2010Pct_Population_Change_2000_2010 = % Population Change, 2000-2010Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)profile_url = Web address of ARC district profile- - - - - -Pop_under_age_19_2010 = Population under age 19, 2010 Pop_ages_20_34_2010 = Population ages 20-34, 2010 Pop_ages_35_44_2010 = Population ages 35-44, 2010 Pop_ages_45_64_2010 = Population ages 45-64, 2010 Pop_ages_65_over_2010 = Population ages 65 and over, 2010 Pct_Pop_under_age_19_2010 = % Population under age 19, 2010 Pct_Pop_ages_20_34_2010 = % Population ages 20-34, 2010 Pct_Pop_ages_35_44_2010 = % Population ages 35-44, 2010 Pct_Pop_ages_45_64_2010 = % Population ages 45-64, 2010 Pct_Pop_ages_65_over_2010 = % Population ages 65 and over, 2010 Pop_under_age_19_2000 = Population under age 19, 2000 Pop_ages_20_34_2000 = Population ages 20-34, 2000 Pop_ages_35_44_2000 = Population ages 35-44, 2000 Pop_ages_45_64_2000 = Population ages 45-64, 2000 Pop_ages_65_over_2000 = Population ages 65 and over, 2000 Pct_Pop_under_age_19_2000 = % Population under age 19, 2000 Pct_Pop_ages_20_34_2000 = % Population ages 20-34, 2000 Pct_Pop_ages_35_44_2000 = % Population ages 35-44, 2000 Pct_Pop_ages_45_64_2000 = % Population ages 45-64, 2000 Pct_Pop_ages_65_over_2000 = % Population ages 65 and over, 2000 Chg_Pop_Under_19 = Change in Population Under 19 (2000-2010) Chg_Pct_Pop_Under_19 = Change in Percent Population Under 19 (2000-2010) Chg_pop_ages_20_34 = Change in population ages 20-34 (2000-2010) Chg_Pct_pop_ages_20_34 = Change in Percent population ages 20-34 (2000-2010) Chg_pop_ages_35_44 = Change in population ages 35-44 (2000-2010) Chg_Pct_pop_ages_35_44 = Change in Percent population ages 35-44 (2000-2010) Chg_pop_ages_45_64 = Change in population ages 45-64 (2000-2010) Chg_Pct_pop_ages_45_64 = Change in Percent population ages 45-64 (2000-2010) Chg_pop_ages_65_over = Change in population ages 65 and over (2000-2010) Chg_Pct_pop_ages_65_over = Change in Percent population ages 65 and over (2000-2010)Non_Hisp_White_2010 = Non-Hispanic White, 2010 Non_Hisp_Black_2010 = Non-Hispanic Black, 2010 Non_Hisp_AsianPI_2010 = Non-Hispanic Asian/Pacific Islander, 2010 Non_Hisp_Other_Biracial_2010 = Non-Hispanic Other Races (includes biracial), 2010 Hisp_All_races_2010 = Hispanic, All races, 2010 Pct_Non_Hisp_White_2010 = % Non-Hispanic White, 2010 Pct_Non_Hisp_Black_2010 = % Non-Hispanic Black, 2010 Pct_Non_Hisp_AsianPI_2010 = % Non-Hispanic Asian/Pacific Islander, 2010 Pct_Non_Hisp_Other_Bi_2010 = % Non-Hispanic Other Races (includes biracial), 2010 Pct_Hisp_All_races_2010 = % Hispanic, All races, 2010 Non_Hisp_White_2000 = Non-Hispanic White, 2000 Non_Hisp_Black_2000 = Non-Hispanic Black, 2000 Non_Hisp_AsianPI_2000 = Non-Hispanic Asian/Pacific Islander, 2000 Non_Hisp_Other_Biracial_2000 = Non-Hispanic Other Races (includes biracial), 2000 Hisp_All_races_2000 = Hispanic, All races, 2000 Pct_Non_Hisp_White_2000 = % Non-Hispanic White, 2000 Pct_Non_Hisp_Black_2000 = % Non-Hispanic Black, 2000 Pct_Non_Hisp_AsianPI_2000 = % Non-Hispanic Asian/Pacific Islander, 2000 Pct_Non_Hisp_Other_Bi_2000 = % Non-Hispanic Other Races (includes biracial), 2000 Pct_Hisp_All_races_2000 = % Hispanic, All races, 2000 Chg_Non_Hisp_White = Change in Non-Hispanic White Population (2000-2010) Chg_Non_Hisp_Black = Change in Non-Hispanic Black Population (2000-2010) Chg_Non_Hisp_AsianPI = Change in Non-Hispanic Asian/Pacific Islander Population (2000-2010) Chg_Non_Hisp_Other_Biracial = Change in Non-Hispanic Other (includes biracial) Population (2000-2010) Chg_Hisp_Population = Change in Hispanic Population (2000-2010) Chg_Pct_Non_Hisp_White = Change in Percent Non-Hispanic White (2000-2010) Chg_Pct_Non_Hisp_Black = Change in Percent Non-Hispanic Black (2000-2010) Chg_Pct_Non_Hisp_AsianPI = Change in Percent Non-Hispanic Asian/Pacific Islander (2000-2010) Chg_Pct_Non_Hisp_Other_Biracial = Change in Percent Non-Hispanic Other (includes biracial) (2000-2010) Chg_Pct_Hisp_Population = Change in Percent Hispanic Population (2000-2010)

  20. CBS News/New York Times New Jersey State Survey, October 2002

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Apr 29, 2009
    + more versions
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    Inter-university Consortium for Political and Social Research [distributor] (2009). CBS News/New York Times New Jersey State Survey, October 2002 [Dataset]. http://doi.org/10.3886/ICPSR03709.v3
    Explore at:
    stata, delimited, ascii, sas, spssAvailable download formats
    Dataset updated
    Apr 29, 2009
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/3709/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3709/terms

    Time period covered
    Oct 2002
    Area covered
    New Jersey, United States
    Description

    This special topic poll is part of a continuing series of monthly surveys that solicit public opinion on the presidency and a range of other political and social issues. The study was conducted in part to assess respondents' interest in and opinions about the 2002 elections in New Jersey. Residents of that state were asked to give their opinions of President George W. Bush and his handling of the presidency, as well as their views of United States Senators Jon Corzine and Robert Torricelli, New Jersey governor Jim McGreevey, and former United States Senator Frank Lautenberg. Those queried were asked whether they intended to vote in the November 5, 2002, elections, and for whom they would vote if the election for United States Senator were held that day, given a choice between Lautenberg (Democratic Party) and Douglas Forrester (Republican Party). Respondents were also asked if Lautenberg and Forrester had spent more time during the campaign attacking each other or explaining what they would do if elected, whether they found the Senate race interesting or dull, what they considered to be the most important issue in deciding how to vote, and whether they considered their vote as a vote for or against Bush. Those polled answered sets of questions comparing Lautenberg and Forrester as Senate candidates in terms of their experience, honesty, integrity, age, political orientation, position on Iraq, and their potential decisions on United States Supreme Court nominees. A series of questions addressed the withdrawal of Torricelli from the Senate race and Lautenberg's replacement of him: whether Torricelli did the right thing by withdrawing, whether it was fair that the Democrats replaced him on the ballot, whether the New Jersey Supreme Court made the right decision by allowing his replacement, and whether that decision had made a difference in how the respondent intended to vote. Respondents' views were sought on the use of tax dollars to pay for abortions for indigent women, increased restrictions on the sale of handguns, whether the sentence for a murder conviction should be the death penalty or life in prison without parole, whether companies responsible for major pollution problems should be held accountable for the clean-up costs, and whether the government should cover losses incurred by individuals who chose to invest their Social Security taxes in the stock market. Additional questions probed respondents' views on corruption in New Jersey politics, the importance of which political party controls the United States Congress, the influence of Lautenberg and Forrester campaign advertisements, and whether the respondent would vote for musician Bruce Springsteen if he were a candidate for United States Senator from New Jersey. Background information on respondents includes age, gender, political party, political orientation, voter registration and participation history, handgun ownership, education, religion, marital status, Hispanic descent, race, years in community, and household income.

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Statista (2025). U.S. Senators 1975-2023, by race and ethnicity [Dataset]. https://www.statista.com/statistics/198430/senators-in-the-us-congress-by-ethnic-group-since-1975/
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U.S. Senators 1975-2023, by race and ethnicity

Explore at:
Dataset updated
Feb 25, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

There are 100 Senators that serve in the United States Congress at any given time - two from each of the fifty states. As of the first day of the 118th Congress, there were three African American Senators, two Asian American Senators, and six Hispanic Senators.

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