This statistic depicts the percentage of prekindergarten children, ages 3 to 5 years, who were read to frequently by a family member in the U.S. in 2012, distinguished by race and Hispanic origin. In 2012, the percentage of non-Hispanic White children who were read to 3 or more times per week by a family member stood at 91 percent.

In 2018, more than three fourths (** percent) of people who identified as white surveyed in Brazil were deemed functionally literate – that is, minimally able to read and interpret memos, pieces of news, instructions, narratives, graphs, tables, ads, and other types of text. Among brown and black-skinned respondents, the functional literacy rate stood at ** and ** percent respectively. Less than half of Brazilians aged between 50 and 64 years were considered functionally literate.

About this dataset

Context

The dataset tabulates the Reading Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Reading, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Reading.

Key observations

Among the Hispanic population in Reading, regardless of the race, the largest group is of Mexican origin, with a population of 281 (65.35% of the total Hispanic population).

https://i.neilsberg.com/ch/reading-oh-population-by-race-and-ethnicity.jpeg" alt="Reading Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Reading
• Population: The population of the specific origin for Hispanic or Latino population in the Reading is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Reading total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Reading Population by Race & Ethnicity. You can refer the same here

Researchers have examined racial and gender patterns of intergenerational education mobility, but less attention has been given to the ways that race and gender interact to further shape these relationships. Based on data from the General Social Survey, this study examined the trajectories of education mobility among Blacks and Whites by gender over the past century. Ordinary least squares and logistic regression models revealed three noteworthy patterns. First, Black men and women have closed substantial gaps with their White counterparts in intergenerational education mobility. At relatively low levels of parental education, these gains have been experienced equally among Black men and women. However, Black men are most disadvantaged at the highest levels of parental education relative to Black women and Whites in general. Finally, the advantages in education mobility experienced by White men in the early and midpart of the 20th century have largely eroded. White women, in contrast, have made steady gains in education mobility across a variety of parental education levels.

This layer shows education level for adults (25+) by race by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of adults age 25+ who have a bachelor's degree or higher as their highest education level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002, C15002B, C15002C, C15002D, C15002E, C15002F, C15002G, C15002H, C15002I (Not all lines of these ACS tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Reading by race. It includes the distribution of the Non-Hispanic population of Reading across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Reading across relevant racial categories.

Key observations

Of the Non-Hispanic population in Reading, the largest racial group is White alone with a population of 172 (89.12% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Reading
• Population: The population of the racial category (for Non-Hispanic) in the Reading is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Reading total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Reading Population by Race & Ethnicity. You can refer the same here

Students from the Chinese ethnic group had the highest entry rate into higher education in every year from 2006 to 2024.

Editor’s Choice

• The Healthcare IT market size is expected to be worth around USD 1728 Bn by 2032
• According to a report by UNESCO, countries in South and South-West Asia have the highest number of illiterate adults in the world, estimated at 388 million.
• Approximately 36% of adult Americans possess only basic or below basic health literacy skills.
• Only 12% of Americans are considered proficient in their health literacy skills.
• Health literacy levels in China increased from 6.48% of the population in 2008 to 23.15% in 2020.
• A recent study analyzing global health literacy research from 1995 to 2020 identified the United States, Australia, and the United Kingdom as major contributors to the international collaboration network on health literacy.
• Mental health has been the most active research field in recent years in the context of health literacy.

Graph and download economic data for Expenditures: Education by Race: White and All Other Races, Not Including Black or African American (CXUEDUCATNLB0903M) from 2003 to 2023 about white, expenditures, education, and USA.

From 2020 to 2022, reading test scores declined for nine year olds of every race except for those who were Asian in the United States. Within this time period, the most significant decrease was for nine year olds who were two or more races, as their scores dropped from 224 in 2020 to 216 in 2022.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Reading by race. It includes the distribution of the Non-Hispanic population of Reading across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Reading across relevant racial categories.

Key observations

Of the Non-Hispanic population in Reading, the largest racial group is White alone with a population of 8,608 (86.32% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Reading
• Population: The population of the racial category (for Non-Hispanic) in the Reading is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Reading total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Reading Population by Race & Ethnicity. You can refer the same here

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

In the United States in 2023, Asian Americans spent an average of **** minutes reading per day. White readers spent the most time with books each day, whereas Hispanic Americans read for just *** minutes on average.

This feature layer contains high school graduation rate data disaggregated by race for Gallatin County, MT sourced from the Growth and Enhancement of Montana Students (GEMS) interactive dashboard.Processing NotesData is retrieved from the GEMS interactive dashboard and imported into FME to create an AGOL Feature Service.No data present is an indication that the demographic has a count of 5 or fewer students and the information has been masked.Download Montana County's Graduation Rate dataAdditional LinksGrowth & Enhancement of Montana Students (GEMS)Navigation Guide for GEMS

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2022-2023 school year.

Student groups include:

Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races)

Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

The purpose of this data collection was to provide a more accurate measure of the racial/ethnic enrollment in postsecondary institutions in the United States than was previously available. The National Center for Education Statistics (NCES) collects racial/ethnic enrollment data from higher education institutions on an annual basis. Some institutions do not report these data, and their "unknown" categories have previously been distributed in direct proportion to the "knowns." This resulted in lower than accurate figures for the racial/ethnic categories. With the advent of the Integrated Postsecondary Education Data System (IPEDS), NCES has attempted to eliminate this problem by distributing all "race/ethnicity unknown" students through a two-stage process. First, the differences between reported totals and racial/ethnic details were allocated on a gender and institutional basis by distributing the differences in direct proportion to reported distributions. The second-stage distribution was designed to eliminate the remaining instances of "race/ethnicity unknown." The procedure was to accumulate the reported racial/ethnic total enrollments by state, level, control, and gender, calculate the percentage distributions, and apply these percentages to the reported total enrollments of institutional respondents (in the same state, level, and control) that did not supply race/ethnicity detail. In addition, the original "race/ethnicity unknown" data were also left unaltered for those who wish to review the numbers actually distributed. The racial/ethnic status was broken down into nonresident alien, Black non-Hispanic, American Indian or Alaskan Native, Asian or Pacific Islander, Hispanic, and White non-Hispanic. There are six data files. Part 1, Institutional Characteristics, includes variables on control and level of institution, religious affiliation, highest level of offering, Carnegie classification, and state FIPS code and abbreviation. Variables in Part 2 cover total original enrollment by race/ethnicity and sex and by level and year of study of student. Race/ethnicity data were not imputed for institutions that only reported total enrollment. The "race ethnicity unknown" category was not distributed among the race/ethnicity categories. In Part 3, enrollment data are presented by race/ethnicity and sex of student, and by level and year of study for the following selected major field of studies: architecture, education, engineering, law, biological/life sciences, mathematics, physical sciences, dentistry, medicine, veterinary medicine, and business management and administrative services. This file contains data for four-year institutions only. Part 4 provides summary enrollment data by adjusted race/ethnicity and sex of student and by level and year of study of student. The "race/ethnicity unknown" category data were distributed across all known race categories in this file. Also, race data were imputed for institutions that did not report enrollment by race. Part 5, Residence and Migration, contains enrollment data for first-time freshmen, by state of residence. Part 6, Clarifying Questions on Enrollments, provides information on students enrolled in remedial courses, extension divisions, and branches of schools, and numbers of transfer students from in-state, out of state, and other countries. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR02447.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Pupils from the Chinese ethnic group were most likely to meet both the expected and higher standards in reading, writing and maths in 2018/19.

About 71.1 percent of children under 18 years old of Asian ethnicity had at least one parent who had a Bachelor's degree or higher in the United States in 2021. In the same year, 28.7 percent of White students under the age of 18 had a parent with a Bachelor's degree.

County-level race and ethnicity estimates for populations 25 years of age and over, cross-tabulated with educational attainment estimates for populations that have less than a high school diploma. Race and ethnicity estimates include the following categories: White alone, Black or African American alone, American Indian or Alaska Native alone, Native Hawaiian or Other Pacific Islander alone, Some Other Race alone, Two or More Races, White alone and Not Hispanic or Latino, Hispanic or Latino, and people of color. Estimates are accompanied by margins of error, coefficients of variation, and percentages. Geometry source: 2020 Census. Attribute source: 2019-2023 American Community Survey 5-year estimates, tables B06009, C15002A, C15002B, C15002C, C15002D, C15002E, C15002F, C15002G, C15002H, and C15002I. Date of last data update: 2024-01-11 This is official RLIS data. Contact Person: Joe Gordon joe.gordon@oregonmetro.gov 503-797-1587 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3846 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

This graph shows the educational attainment of the U.S. population from in 2018, according to ethnicity. Around 56.5 percent of Asians and Pacific Islanders in the U.S. have graduated from college or obtained a higher educational degree in 2018.