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.

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• 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.

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.

IntroductionDespite the necessity for adults with lower literacy skills to undergo and succeed in high-stakes computer-administered assessments (e.g., GED, HiSET), there remains a gap in understanding their engagement with digital literacy assessments.MethodsThis study analyzed process data, specifically time allocation data, from the Program for the International Assessment of Adult Competencies (PIAAC), to investigate adult respondents’ patterns of engagement across all proficiency levels on nine digital literacy items. We used cluster analysis to identify distinct groups with similar time allocation patterns among adults scoring lower on the digital literacy assessment. Finally, we employed logistic regression to examine whether the groups varied by demographic factors, in particular individual (e.g., race/ethnicity, age) and contextual factors (e.g., skills-use at home).ResultsAdults with lower literacy skills spent significantly less time on many of the items than adults with higher literacy skills. Among adults with lower literacy skills, two groups of time allocation patterns emerged: one group (Cluster 1) exhibited significantly longer engagement times, whereas the other group (Cluster 2) demonstrated comparatively shorter durations. Finally, we found that adults who had a higher probability of Cluster 1 membership (spending more time) exhibited relatively higher literacy scores, higher self-reported engagement in writing skills at home, were older, unemployed, and self-identified as Black.DiscussionThese findings emphasize differences in digital literacy engagement among adults with varying proficiency levels. Additionally, this study provides insights for the development of targeted interventions aimed at improving digital literacy assessment outcomes for adults with lower literacy skills.

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

About this dataset

Context

The dataset tabulates the Reading town Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Reading town, 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 town.

Key observations

Among the Hispanic population in Reading town, regardless of the race, the largest group is of other Hispanic or Latino origin, with a population of 69 (75.99% of the total Hispanic population).

https://i.neilsberg.com/ch/reading-ma-population-by-race-and-ethnicity.jpeg" alt="Reading town 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 town
• Population: The population of the specific origin for Hispanic or Latino population in the Reading town is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Reading town 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 town Population by Race & Ethnicity. You can refer the same here

The highest literacy rates in Nigeria were registered in the southern regions of the country. In the South West, 89 percent of males and 80.6 percent of females were literate as of 2018. Also, the south zones showed the lowest percentage differences between male and female literacy. Female literacy rate in Nigeria is among the highest in West Africa. The highest female literacy rates were registered in Cabo Verde and Ghana, while Nigeria ranked third.

DRA2 measures the reading performance of students from Kindergarten through Grade 3. The subgroups reported are testing performance level, grade, race/ethnicity, English language learners, special education students, and students eligible for free or reduced-price meals. Students are assessed twice a year in Kindergarten and three times a year in Grades 1 through 3. Summer reading loss is calculated by subtracting the result of the fall administration from the result of the spring administration for the same calendar year (the prior school year). The DRA2 has been mandatory for all schools in the priority school districts (PSDs). SY2013-14 was a transition year during which the PSDs could use either the DRA2 or an alternate assessment from a menu created by Connecticut State Department of Education (SDE). Starting with SY2014-15, PSDs must use an assessment from the menu approved by the State Board of Education. SDE collects and CTdata.org carries annual reports of test results.

Grounded in a strength-based (asset) model, this study explores the racial disparities in students’ learning and well-being during the pandemic. Linking the U.S. national/state databases of education and health, it examines whole-child outcomes and related factors—remote learning and protective community. It reveals race/ethnicity-stratified, state-level variations of learning and well-being losses in the midst of school accountability turnover. This data file includes aggregate state-level data derived from the NAEP and NSCH datasets, including all 50 U.S. states' pre-pandemic and post-pandemic measures of whole-child development outcomes (academic proficiency, socioemotional wellness, and physical health) as well as environmental conditions (remote learning and protective community) among school-age children. Methods To address the research questions, this study examines repeated cross-sectional datasets with nation/state-representative samples of school-age children. For academic achievement measures, the National Assessment of Educational Progress (NAEP) 2019 and 2022 datasets are used to assess nationally representative samples of 4th-grade and 8th-grade students’ achievement in reading and math (http://www.nces.ed.gov/nationsreportcard). In 2019, the NAEP samples included: 150,600 fourth graders from 8,300 schools and 143,100 eighth graders from 6,950 schools. In 2022, the NAEP samples included: (1) for reading, 108,200 fourth graders from 5,780 schools and 111,300 eighth graders from 5,190 schools; (2) for math, 116,200 fourth graders from 5,780 schools and 111,000 eighth graders from 5,190 schools. Data are weighted to be representative of the US population of students in grades 4 and 8, each for the entire nation and every state. Results are reported as average scores on a 0 to 500 scale and as percentages of students performing at or above the NAEP achievement levels: NAEP Basic, NAEP Proficient, and NAEP Advanced. In this study, we focus on changes in the percentages of students at or above the NAEP Basic level, which is the minimum competency level expected for all students across the nation. As a supplement to the NAEP assessment data, this study uses the NAEP School Dashboard (see https://ies.ed.gov/schoolsurvey/mss-dashboard/), which surveyed approximately 3,500 schools each month at grades 4 and 8 each during the pandemic period of January through May 2021: 46 states/jurisdictions participated, and 4,100 of 6,100 sampled schools responded. This study uses state-level information on the percentages of students who received in-person vs. remote/hybrid instructional modes. The school-reported remote learning enrollment rate is highly correlated with the NAEP survey student-reported remote learning experience (during 2021) across grades and subjects (r = .82 for grade 4 reading, r = .81 for grade 4 math, r = .79 for grade 8 reading, r = .83 for grade 8 math). These strong positive correlations provide supporting evidence for the cross-validation of remote learning measures at the state level. For socioemotional wellness and physical health measures, the National Survey of Children’s Health (NSCH) data are used. The 2018/19 surveys involved about 356,052 households screened for age-eligible children, and 59,963 child-level questionnaires were completed. The 2020/21 surveys involved about 199,840 households screened for age-eligible children, and 93,669 child-level questionnaires were completed. Our analysis focuses on school-age children (ages 6-17) in the data. In addition, the NSCH data are also used to assess the quality of protective and nurturing environment for child development across family, school, and neighborhood settings (see Appendix).

Demographics (Sex %, Race % and Age–Mean (SD)).

Question: What share of adult citizens as defined by statute has the legal right to vote in national elections? Clarification: This question does not take into consideration restrictions based on age, residence, having been convicted for crime, or being legally incompetent. It covers legal de jure restrictions, not restrictions that may be operative in practice de facto. The adult population as defined by statute is defined by citizens in the case of independent countries or the people living in the territorial entity in the case of colonies. Universal suffrage is coded as 100%. Universal male suffrage only is coded as 50%. Years before electoral provisions are introduced are scored 0%. The scores do not reflect whether an electoral regime was interrupted or not. Only if new constitutions, electoral laws, or the like explicitly introduce new regulations of suffrage, the scores were adjusted accordingly if the changes suggested doing so. If qualifying criteria other than gender apply such as property, tax payments, income, literacy, region, race, ethnicity, religion, and/or 'economic independence', estimates have been calculated by combining information on the restrictions with different kinds of statistical information on population size, age distribution, wealth distribution, literacy rates, size of ethnic groups, etc., secondary country-specific sources, and --- in the case of very poor information --- the conditions in similar countries or colonies. The scores reflect de jure provisions of suffrage extension in percentage of the adult population. If the suffrage law is revised in a way that affects the extension, the scores reflect this change as of the calendar year the law was enacted. Scale: Interval, from low to high (0-1).

This dataset tracks annual two or more races student percentage from 2021 to 2023 for Reading Elementary School vs. Vermont and Mountain Views Unified Union School District #76

The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle school and high school exams, respectively. RACE-M has 28,293 questions and RACE-H has 69,574. Each question is associated with 4 candidate answers, one of which is correct. The data generation process of RACE differs from most machine reading comprehension datasets - instead of generating questions and answers by heuristics or crowd-sourcing, questions in RACE are specifically designed for testing human reading skills, and are created by domain experts.

The National Center for Early Development and Learning (NCEDL) Multi-State Study of Pre-Kindergarten examined the pre-kindergarten programs of six states: California, Illinois, New York, Ohio, Kentucky, and Georgia. For this study, pre-kindergarten (pre-k) included center-based programs for four-year-olds that are fully or partially funded by state education agencies and that are operated in schools or under the direction of state and local education agencies. The study had two primary purposes: To describe the variations of experiences for children in pre-kindergarten and kindergarten programs in school-related settings (public schools and state-funded pre-k classrooms in community-based settings), and To examine the relationships between variations in pre-kindergarten/kindergarten experiences and children's outcomes in early elementary school. The study addressed six primary groups of research questions: What is the nature and distribution of education and experience of teachers and teacher assistants in pre-k public school programs? What is the nature and distribution of global quality and specific practices in key areas such as literacy, math, and teacher-child relationships in a diverse sample of pre-k public school programs for four-year-olds as well as in a similarly diverse sample of kindergarten classes? How do quality and practices vary as a result of child and teacher characteristics (e.g., child gender, race, home language, family income, and teacher's years of education) and classroom, program, community, and state structural variables (e.g., teacher-child ratio, funding base of the program, teacher salary, and degree of state regulation) for children with different demographic characteristics (e.g., race, gender, home language, and family income)? Do quality and practice vary in relation to combinations of these variables? For example, are quality and practice a function of family poverty and teacher pay or education? Can children's outcomes at the end of their pre-kindergarten year be predicted by the children's experiences in pre-k programs? Are the various dimensions of quality and/or practice differentially related to outcomes? Are these relationships constant across a population of children with different characteristics (e.g., race, gender, home language, and family income)? Do pre-kindergarten program quality and practices predict children's transitions to kindergarten and children's skills at the end of the kindergarten year? Are these transitions moderated by children's characteristics, like race, gender, and family income? The six states in the study were selected based on the significant amount of resources they have committed to pre-k initiatives. States were also selected to maximize the diversity in geography, program settings (public school or community), program intensity (full day versus part day), and educational requirements for teachers. Within each state, a random sample of 40 centers/schools was selected. One classroom in each center/school was selected at random for observation, and four children in each classroom were selected for individual assessment. The children were followed from the beginning of pre-k through the end of kindergarten. In five of the six states, families were also visited in their homes. Classroom Services and Specific Instructional Practices Within the 40 classrooms in each participating state, carefully trained data collectors conducted classroom observations twice each year, while additional surveys were used to gather information from administrators/principals, teachers, and parents. Data were gathered on program services, (e.g., healthcare, meals, and transportation), program curriculum, teacher training and education, teachers' opinions of child development, and their instructional practices on subjects such as language, literacy, mathematics concepts, and social-emotional competencies. Data were also collected as to what types of steps were taken to aid children in their transitions from pre-k to kindergarten. Children Within each participating pre-k classroom, four randomly selected children were assessed using a battery of individual instruments to measure language, literacy, mathematics, and related concept development, as well as social competence. A panel of expert reviewers aided the researchers in selecting a variety of standardized and nonstan

This dataset tracks annual two or more races student percentage from 2011 to 2023 for Level Creek Elementary School vs. Georgia and Gwinnett County School District

The National Science Foundation (NSF) Surveys of Public Attitudes monitored the general public's attitudes toward and interest in science and technology. In addition, the survey assessed levels of literacy and understanding of scientific and environmental concepts and constructs, how scientific knowledge and information were acquired, attentiveness to public policy issues, and computer access and usage. Since 1979, the survey was administered at regular intervals (occurring every two or three years), producing 11 cross-sectional surveys through 2001. Data for Part 1 (Survey of Public Attitudes Multiple Wave Data) were comprised of the survey questionnaire items asked most often throughout the 22-year survey series and account for approximately 70 percent of the original questions asked. Data for Part 2, General Social Survey Subsample Data, combine the 1983-1999 Survey of Public Attitudes data with a subsample from the 2002 General Social Survey (GSS) (GENERAL SOCIAL SURVEYS, 1972-2002: [CUMULATIVE FILE] [ICPSR 3728]) and focus solely on levels of education and computer access and usage. Variables for Part 1 include the respondents' interest in new scientific or medical discoveries and inventions, space exploration, military and defense policies, whether they voted in a recent election, if they had ever contacted an elected or public official about topics regarding science, energy, defense, civil rights, foreign policy, or general economics, and how they felt about government spending on scientific research. Respondents were asked how they received information concerning science or news (e.g., via newspapers, magazines, or television), what types of television programming they watched, and what kind of magazines they read. Respondents were asked a series of questions to assess their understanding of scientific concepts like DNA, probability, and experimental methods. Respondents were also asked if they agreed with statements concerning science and technology and how they affect everyday living. Respondents were further asked a series of true and false questions regarding science-based statements (e.g., the center of the Earth is hot, all radioactivity is manmade, electrons are smaller than atoms, the Earth moves around the sun, humans and dinosaurs co-existed, and human beings developed from earlier species of animals). Variables for Part 2 include highest level of math attained in high school, whether the respondent had a postsecondary degree, field of highest degree, number of science-based college courses taken, major in college, household ownership of a computer, access to the World Wide Web, number of hours spent on a computer at home or at work, and topics searched for via the Internet. Demographic variables for Parts 1 and 2 include gender, race, age, marital status, number of people in household, level of education, and occupation.

This dataset tracks annual two or more races student percentage from 2013 to 2023 for Reading Sr. High School vs. Pennsylvania and Reading School District

Historical Dataset of Lubbock High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2007-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2007-2023),Asian Student Percentage Comparison Over Years (2007-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (2007-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2007-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2007-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2012-2023)

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

Funded by the same public sources, charter schools offer primary- and secondary-level programs more flexible in curriculum design and implementation than traditional public schools. Charter schools receive nearly all their funding from federal, state and local governments, so revenue can fluctuate significantly based on budgetary decisions tied to tax income. However, many charter schools are funded per pupil, enabling schools to drive up enrollment without worrying about financial support. Sustained long-term growth in charter school enrollment saw its first setback in 2022 as alternatives became much more attractive, though growth in per-pupil funding helped offset shrinking student bodies. Overall, industry revenue is anticipated to climb at a CAGR of 0.5% to an estimated $49.6 billion through the end of 2024, including a 0.3% increase in 2024 alone. As of 2024, 45 states have charter school legislation. Charter schools are overwhelmingly located in urban areas, and the rising percentage of the population living in cities will drive up demand for charter schools. Employment has surged through the current period as administrators seek to lower student-to-teacher ratios and improve educational outcomes. While all charter schools are nonprofit, these strong jumps in wage costs have dampened surpluses and forced some schools to cut their spending on discretionary services, like professional development programs. An anticipated expansion in parent choice programs will drive enrollment and make room for new charter schools to open. While some studies have shown charter schools to be more effective than traditional public schools, they still have room to prove their superiority. Competition from highly regarded private schools will be challenging for charter schools to overcome, especially as parents become better able to afford them. Charter schools are expected to become more specialized as they focus on specific demographics to help their students succeed better and secure a reputation for academic rigor. Over the five years to 2029, industry revenue is anticipated to swell at a CAGR of 0.2% to reach $50.2 billion.