Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of College Corner by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of College Corner across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of female population, with 60.86% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for College Corner Population by Race & Ethnicity. You can refer the same here
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
The Farm to School Census measures USDA's progress toward improving access to local foods in schools. The web-based interface allows users to run customized searches using data from the Farm to School Census. From a total of 18,104 public, private, and charter school districts in the target list frame, 12,585 schools and school districts completed usable responses for a response rate of 70%. Visualizations display national and state level data, and explanatory notes for each portion of the survey questionnaire are provided. Users can focus their search by location/state/school district/zip code, participation level, local food purchased category (fruit, vegetables, fluid milk, other dairy, meat/poultry, eggs, seafood, plant-based protein, grains/flour, baked goods, herbs), and sources (purchased directly or through intermediary). Resources in this dataset:Resource Title: Census Data Explorer | USDA-FNS Farm to School Census. File Name: Web Page, url: https://farmtoschoolcensus.fns.usda.gov/census-results/census-data-explorer This searchable database allows users to run customized searches using data from the Farm to School Census.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors and removing implausible values. The study team linked the 2023 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located.Study date(s) and durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of further questions.Study spatial scale (size of replicates and spatial scale of study area)Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC.Level of true replicationUnknownSampling precision (within-replicate sampling or pseudoreplication)No sampling was involved in the collection of this data.Level of subsampling (number and repeat or within-replicate sampling)No sampling was involved in the collection of this data.Study design (before–after, control–impacts, time series, before–after-control–impacts)None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2023 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.)In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2023 Farm to School Census Report.The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the College Springs population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for College Springs. The dataset can be utilized to understand the population distribution of College Springs by age. For example, using this dataset, we can identify the largest age group in College Springs.
Key observations
The largest age group in College Springs, IA was for the group of age 60 to 64 years years with a population of 34 (15.67%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in College Springs, IA was the 55 to 59 years years with a population of 1 (0.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for College Springs Population by Age. You can refer the same here
https://www.icpsr.umich.edu/web/ICPSR/studies/3525/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3525/terms
The 1970 Census School District Data Tape (SDDT) User's Guide was designed to complement the 1970 Census User's Guide prepared by the United States Census Bureau. The School District Data Tape (SDDT) created by the National Center for Education Statistics is a recompilation of the 1970 Census Fourth Count Population data, providing data tables for each school district in the country with 300 or more students. The preparation of the School District Data Tape required three major steps: (1) overlaying school district boundaries on census maps, (2) creating a geo-reference tape indicating the percent of each census area falling within each school district, and (3) merging the geo-reference tape with the 1970 Census Fourth Count Population Files A (Traced Areas) and B (Minor Civil Divisions). Some of the major uses of the School District Data Tape include: allocation of federal funds, desegregation planning, bilingual and minority special education planning, preschool and child care planning, facility planning, redistricting, urban-suburban-rural analyses, mobility analysis, social and economic inequality among school districts, and school children profiles. In addition to these uses, most state education agencies will find data by school district of value in allocating federal and state aid to school districts and in the evaluation of the inequality of property taxes as a basis for financing elementary and secondary education. The School District Data Tape matches, as closely as possible, the format of the Fourth Count (Population) Summary tapes supplied by the Census Bureau.
This study was designed to collect college student victimization data to satisfy four primary objectives: (1) to determine the prevalence and nature of campus crime, (2) to help the campus community more fully assess crime, perceived risk, fear of victimization, and security problems, (3) to aid in the development and evaluation of location-specific and campus-wide security policies and crime prevention measures, and (4) to make a contribution to the theoretical study of campus crime and security. Data for Part 1, Student-Level Data, and Part 2, Incident-Level Data, were collected from a random sample of college students in the United States using a structured telephone interview modeled after the redesigned National Crime Victimization Survey administered by the Bureau of Justice Statistics. Using stratified random sampling, over 3,000 college students from 12 schools were interviewed. Researchers collected detailed information about the incident and the victimization, and demographic characteristics of victims and nonvictims, as well as data on self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 3, School Data, the researchers surveyed campus officials at the sampled schools and gathered official data to supplement institution-level crime prevention information obtained from the students. Mail-back surveys were sent to directors of campus security or campus police at the 12 sampled schools, addressing various aspects of campus security, crime prevention programs, and crime prevention services available on the campuses. Additionally, mail-back surveys were sent to directors of campus planning, facilities management, or related offices at the same 12 schools to obtain information on the extent and type of planning and design actions taken by the campus for crime prevention. Part 3 also contains data on the characteristics of the 12 schools obtained from PETERSON'S GUIDE TO FOUR-YEAR COLLEGES (1994). Part 4, Census Data, is comprised of 1990 Census data describing the census tracts in which the 12 schools were located and all tracts adjacent to the schools. Demographic variables in Part 1 include year of birth, sex, race, marital status, current enrollment status, employment status, residency status, and parents' education. Victimization variables include whether the student had ever been a victim of theft, burglary, robbery, motor vehicle theft, assault, sexual assault, vandalism, or harassment. Students who had been victimized were also asked the number of times victimization incidents occurred, how often the police were called, and if they knew the perpetrator. All students were asked about measures of self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 2, questions were asked about the location of each incident, whether the offender had a weapon, a description of the offense and the victim's response, injuries incurred, characteristics of the offender, and whether the incident was reported to the police. For Part 3, respondents were asked about how general campus security needs were met, the nature and extent of crime prevention programs and services available at the school (including when the program or service was first implemented), and recent crime prevention activities. Campus planners were asked if specific types of campus security features (e.g., emergency telephone, territorial markers, perimeter barriers, key-card access, surveillance cameras, crime safety audits, design review for safety features, trimming shrubs and underbrush to reduce hiding places, etc.) were present during the 1993-1994 academic year and if yes, how many or how often. Additionally, data were collected on total full-time enrollment, type of institution, percent of undergraduate female students enrolled, percent of African-American students enrolled, acreage, total fraternities, total sororities, crime rate of city/county where the school was located, and the school's Carnegie classification. For Part 4, Census data were compiled on percent unemployed, percent having a high school degree or higher, percent of all persons below the poverty level, and percent of the population that was Black.
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets
Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 14 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school.
https://www.icpsr.umich.edu/web/ICPSR/studies/38579/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38579/terms
This study contains measures of neighborhood-school gap for 2009-2010 and 2015-2016. Neighborhood-school gap (NS gap) refers to the discrepancy between the demographics of a public school and its surrounding community. For example, if 60 percent of a school's student body is Black, but 30 percent of the neighborhood population is Black, the school has a positive Black neighborhood-school gap. These datasets measure gaps in race and poverty between elementary school student populations and the census tracts and ZIP code tabulation areas (ZCTAs) that those elementary schools serve. Data is at the census tract and ZCTA level. Supplemental data containing component variables used to calculate NS gap at the school and block group level is also available.
This dataset allows users to drill-down into the data from the USDA Farm to School Census. Once you’ve conducted your query, you can easily download your results in an excel file.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the College Place population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for College Place. The dataset can be utilized to understand the population distribution of College Place by age. For example, using this dataset, we can identify the largest age group in College Place.
Key observations
The largest age group in College Place, WA was for the group of age 20 to 24 years years with a population of 1,306 (13.29%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in College Place, WA was the 80 to 84 years years with a population of 134 (1.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for College Place Population by Age. You can refer the same here
The Healthy, Hunger-Free Kids Act of 2010 (HHFKA) formally established a Farm to School Program within USDA to improve access to local foods in schools. In order to establish realistic goals with regard to increasing the availability of local foods in schools, in 2013, USDA conducted the first nationwide Farm to School Census (the Census). In 2015, USDA conducted a second Farm to School Census to measure progress towards reaching this goal.
https://www.icpsr.umich.edu/web/ICPSR/studies/3523/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3523/terms
This data file contains school district-level data from the following two sources: (1) the National Institute of Education (NIE) Special Tabulations of 1970 Census data, retabulated to 1973-1974 school district boundaries, and (2) the 1970 Census of Population and Housing Fifth Count data. The data in this collection were extracted from the 1976-1977 Merged Federal File produced by AUI Policy Research. Since some districts on the 1976-1977 Merged Federal File had consolidated by 1978-1979, NIE Special Tabulations data for these districts were combined. The Census data file was created in three steps. First, a skeleton file was created, containing one record for each school district on the 1978-1979 Merged Federal File. Each record on the skeleton file contained those data items in the School District Identification segment on the Merged Federal File. Second, the NIE Special Tabulations data were merged by the Office of Education (OE) state code and Local Education Agency (LEA) code to the skeleton file. Finally, Census Fifth Count records were merged by OE state code and LEA code to the skeleton file.
Population by Ethnicity by Community College District from the 2020 Decennial Census
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Note: For information on data collection, confidentiality protection, nonsampling error, subject definitions, and guidance on using the data, visit the 2020 Census 118th Congressional District Summary File (CD118) Technical Documentation webpage..To protect respondent confidentiality, data have undergone disclosure avoidance methods which add "statistical noise" - small, random additions or subtractions - to the data so that no one can reliably link the published data to a specific person or household. The Census Bureau encourages data users to aggregate small populations and geographies to improve accuracy and diminish implausible results..For 2020 Group Quarters Definitions and Code List, see Appendix B in the "2020 Census 118th Congressional District Summary File (CD118) Technical Documentation.".Source: U.S. Census Bureau, 2020 Census 118th Congressional District Summary File (CD118)
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Consumer Unit Characteristics: Percent College by Region: Residence in the Midwest Census Region (CXU980310LB1103M) from 1984 to 2023 about Midwest Census Region, consumer unit, tertiary schooling, education, residents, percent, and USA.
Population by 5-year Age Groups by Community College District from the 2020 Decennial Census
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This application displays the buildings in State College borough in 1930 as polygon features. The buildings are linked to a table with the contents of the 1930 Census of State College. Click on a building to bring up information about its physical features, such as building material or number of floors, as well as its address and associated land use. If the building contained residents listed on the Census, scroll down within the info box and click on the link below "Related Tables" to bring up a list of the residents. Clicking on a resident in the list will open that resident's entry in the Census table, which includes socioeconomic information such as their name, age, nationality, marital status, and occupation. Residents can also be searched for by name in the Query box that appears on the left side of the screen. Data Sources- Scanned copies of the U.S. Census for various years (including 1920 and 1930) available from Ancestry Library Edition database.- Sanborn shapefiles were created by Bednar student interns at Penn State's Pattee/Paterno Library. They are based on the collection of PA Sanborns housed in the Maps Collection at the library.
The Farm to School Census is a dataset created by the USDA Food and Nutrition Service (FNS) to track farm-to-school initiatives nationwide. Launched in 2013 under the Healthy, Hunger-Free Kids Act of 2010, it surveys school food authorities (SFAs)—entities managing school meal programs—to assess their participation in farm-to-school activities. The dataset includes responses from over 11,800 SFAs across all 50 states and U.S. territories, detailing local food purchasing practices, types of activities (e.g., school gardens, farm partnerships, direct purchases), and challenges/opportunities faced. Its primary purpose is to monitor program growth, inform policy, and support efforts to increase local food access in schools. Key features include its comprehensive geographic coverage, quadrennial updates (with datasets available from 2013, 2015, 2019, and 2023), and interactive tools for exploring trends in local food procurement and program participation. Unique aspects include its role as the sole national data source on farm-to-school efforts and the availability of downloadable data for analysis. The census serves researchers, policymakers, and stakeholders aiming to strengthen school food systems and local agriculture economies.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of College Corner by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of College Corner across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of female population, with 60.86% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for College Corner Population by Race & Ethnicity. You can refer the same here