3 datasets found
  1. m

    Data from: Annual and State-Level Data on U.S. Total Fertility Rates,...

    • data.mendeley.com
    Updated Jan 27, 2025
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    Gregori Galofré-Vilà (2025). Annual and State-Level Data on U.S. Total Fertility Rates, 1931–Present [Dataset]. http://doi.org/10.17632/52xszfstsd.1
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    Dataset updated
    Jan 27, 2025
    Authors
    Gregori Galofré-Vilà
    License

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

    Area covered
    United States
    Description

    This dataset reconstructs U.S. fertility trends with state-level Total Fertility Rates (TFR) from 1931 to the present, calculated annually. Birth data by maternal age and race (white and non-white) were digitized from the “Vital Statistics of the United States.” TFR calculations use interpolated census data on women aged 14–49. The dataset highlights regional shifts in fertility during the Baby Boom, later declines, and recent fluctuations, influenced by socio-economic and cultural factors. It provides a valuable tool for researchers and policymakers analyzing fertility determinants and their broader implications.

  2. Data from: Age-by-Race Specific Crime Rates, 1965-1985: [United States]

    • catalog.data.gov
    • icpsr.umich.edu
    Updated Nov 14, 2025
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    National Institute of Justice (2025). Age-by-Race Specific Crime Rates, 1965-1985: [United States] [Dataset]. https://catalog.data.gov/dataset/age-by-race-specific-crime-rates-1965-1985-united-states-b16aa
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    Dataset updated
    Nov 14, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    United States
    Description

    These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

  3. Generalized linear models of depressive symptoms and fitness components.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Sep 30, 2024
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    Christopher I. Gurguis; Renée A. Duckworth; Nicole M. Bucaro; Consuelo Walss-Bass (2024). Generalized linear models of depressive symptoms and fitness components. [Dataset]. http://doi.org/10.1371/journal.pone.0310598.t002
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    xlsAvailable download formats
    Dataset updated
    Sep 30, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Christopher I. Gurguis; Renée A. Duckworth; Nicole M. Bucaro; Consuelo Walss-Bass
    License

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

    Description

    Generalized linear models of depressive symptoms and fitness components.

  4. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Close
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Gregori Galofré-Vilà (2025). Annual and State-Level Data on U.S. Total Fertility Rates, 1931–Present [Dataset]. http://doi.org/10.17632/52xszfstsd.1

Data from: Annual and State-Level Data on U.S. Total Fertility Rates, 1931–Present

Related Article
Explore at:
Dataset updated
Jan 27, 2025
Authors
Gregori Galofré-Vilà
License

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

Area covered
United States
Description

This dataset reconstructs U.S. fertility trends with state-level Total Fertility Rates (TFR) from 1931 to the present, calculated annually. Birth data by maternal age and race (white and non-white) were digitized from the “Vital Statistics of the United States.” TFR calculations use interpolated census data on women aged 14–49. The dataset highlights regional shifts in fertility during the Baby Boom, later declines, and recent fluctuations, influenced by socio-economic and cultural factors. It provides a valuable tool for researchers and policymakers analyzing fertility determinants and their broader implications.

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