This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

This data set shows the number and percentage of children graduating from high school in Travis County, including public, private, charter, home schools, and other high school equivalents. The data is from the Texas Education Agency (TEA) state agency that oversees primary and secondary public education in the state of Texas. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/n78t-2him

The 1989-90 Private School Universe Survey (PSS 1989-90) is a study that is part of the Private School Universe program; program data is available since 1989-1990 at . PSS 1989-90 (https://nces.ed.gov/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study was conducted using mail questionnaires and telephone follow-up of all private schools in the United States. The PSS includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. Key statistics produced from PSS 1989-90 are on the number of religiously affiliated schools, the number of private high school graduates, and the number of private school students and teachers.

The Private School Universe Survey, 2011-12 (PSS 2011-12), is a study that is part of the Private School Universe program. PSS 2011-12 (https://nces.ed.gov/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study was conducted using mail questionnaires, an internet response option and telephone and personal follow-up of all private schools in the United States. The PSS universe consists of a diverse population of schools. It includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. The study�s response rate is 91.8 percent. Key statistics produced from PSS 2011-12 are on the number of private schools, students, and teachers, the number of high school graduates, the length of the school year and school day.

This data set shows the number and percentage of children graduating from high school in Travis County, including public, private, charter, home schools, and other high school equivalents. The data is from the Texas Education Agency (TEA) state agency that oversees primary and secondary public education in the state of Texas.

View county-level data: https://data.austintexas.gov/Health-and-Community-Services/Strategic-Measure_Percentage-of-Students-Graduatin/djfu-26dw

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/n78t-2him

Datasets from the National Center for Education Statistics' Private School Universe Survey. A reproducible .Rmd script is included.From the National Center for Education Statistics:"With increasing concern about alternatives in education, the interest and need for data on private education has also increased. NCES has made the collection of data on private elementary and secondary schools a priority.The purposes of this data collection activity are; a) to generate biennial data on the total number of private schools, teachers, and students; and b) to build an accurate and complete list of private schools to serve as a sampling frame for NCES surveys of private schools. The PSS began with the 1989-90 school year and has been conducted every two years since."

This feature class/shapefile captures Private Schools defined by the Private School Survey (PSS) for the Homeland Infrastructure Foundation-Level Data (HIFLD) database. (https://gii.dhs.gov/HIFLD)This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA.

The number of students in regular programs for youth, general programs for adults, and vocational programs for youth and adults in public and private/independent schools, and home-schooling at the elementary-secondary level, by school type and program type.

The number of educators in private or independent elementary and secondary schools, by full-time and part-time work status, age group and sex.

The Private School Universe Survey, 2003-04 (PSS 2003-04), is a study that is part of the Private School Universe program. PSS 2003-04 (https://res1ncesd-o-tedd-o-tgov.vcapture.xyz/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study was conducted using mail questionnaires and telephone follow-up of all private schools in the United States. The PSS universe consists of a diverse population of schools. It includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. The study's unweighted and weighted response rates were both 94 percent. Key statistics produced from PSS 2003-04 are on the growth of religiously affiliated schools, the number of private high school graduates, the length of the school year for various private schools, and the number of private school students and teachers.

Philippines Enrolment Data

Examining Private and Public Schools

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides an interesting insight into the enrolment numbers in public and private schools across the Philippines. It covers all levels of enrolment – elementary, secondary, and post-secondary – as well as gender and urban/rural distinctions. This information is an invaluable asset for anyone looking to gain a comprehensive understanding of educational enrolment trends within the country in order to make informed decisions regarding resource allocation or policy implementations. However, keep in mind that due to differences in methodology and data collection techniques, caution should be taken when using this data as there may be inaccuracies or vague definitions applicable to specific age groups or subpopulations. Regardless, this dataset still serves as a valuable source of information for anyone wanting a proper picture of educational dynamics within the Philippines

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How to use the dataset

This dataset provides enrolment figures in public and private schools by level in the Philippines. With this data, users can explore disparities between public and private school enrolment and other potential inequalities associated with educational access.

In order to use this Kaggle dataset to analyze educational inequality in the Philippines, firstly one must understand which columns are included:

• Country: The name of the Philippine country
• School Level (Grouped): Groupings of school levels within primary/elementary and secondary level
• Enrolment Type: Public or Private
• Year: Time period of data collection

Now that you have an understanding about what this dataset contains, here are few ways you could use it for your analysis!

• Compare enrollment rates between genders - Use the 'School Level' column grouped into Primary/Elementary or Secondary fields along with 'Enrolment Type' (public vs. private) to sort out male/female enrollment differences from 2007 - 2018 at each grade level.
• Investigate discrepancies between urban vs rural areas - Look at where most students attend as reflected through the different divisions within provinces as defined by Commission on Elections (COMELEC). Depending if pupils mainly take up residence in urban or rural areas make sure to supplement this data with available measures towards educational disparities between these two settings such as infrastructure, resources etc.
• Analyze expansion trends over time - Using all columns within this dataset one could see how trends have changed over time since its inception year 2007 till recent year 2018 spanning different area types (such as mindanao through CAR etc.), school levels and regions across governance such provinces(NCR etc.).One could get additional insights such patterns around funding allocations too.

Using all these different analyses offered one can gain a better understanding about evolving disparities around education access in particular region or even countrywide!

Research Ideas

• Comparing enrolment statistics between public and private schools to identify more effective approaches in either sector.
• Identifying regions or areas which may benefit from additional investment in education infrastructure and resources.
• Visualizing enrolment rates at different levels of schooling to understand the relative level of educational attainment within a certain geographical area or region over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: education-nscb-xls-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Humanitarian Data Exchange.

The 1999-2000 Private School Universe Survey (PSS 1999-2000) is a study that is part of the Private School Universe program; program data is available since 1989-1990 at . PSS 1999-2000 (https://nces.ed.gov/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment, race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study was conducted using mail questionnaires and telephone follow-up of all private schools in the United States. The PSS includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. Key statistics produced from PSS 1999-2000 are on the number of religiously affiliated schools, the number of private high school graduates, and the number of private school students and teachers.

This dataset contains geocoded location data for private schools, produced by the National Center for Education Statistics (NCES) Education Demographic and Geographic Estimates (EDGE) program. Geocodes for private schools are based on data collected by the NCES Private School Survey (PSS). The PSS is a biennial collection of private elementary and secondary schools that provides data related to enrollment, staffing, type of program, and other basic administrative features. Additional information about the PSS collection and data resources for private schools are available at https://nces.ed.gov/surveys/pss/index.asp.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

The Private School Universe Survey, 2005-06 (PSS 2005-06), is a study that is part of the Private School Universe program. PSS 2005-06 (https://nces.ed.gov/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study was conducted using mail questionnaires and telephone follow-up of all private schools in the United States. The PSS universe consists of a diverse population of schools. It includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. The study's unweighted and weighted response rates were both 94 percent. Key statistics produced from PSS 2005-06 are on the growth of religiously affiliated schools, the number of private high school graduates, the length of the school year for various private schools, and the number of private school students and teachers.

Private school (elementary, secondary, and combined*) enrolment numbers are organized by student gender and school level for each private school. The number captures the enrolment as of October 31st for the given school year. To be included, a student must be actively enrolled to attend the private school as their main school as of October 31.

Data includes:

• academic year
• school number
• school name
• school level
• elementary male enrolment
• elementary female enrolment
• secondary male enrolment
• secondary female enrolment
• total male enrolment
• total female enrolment

Source: As reported by private schools in the Ontario School Information System (OnSIS), October submission.

Data includes private, First Nations, overseas, secondary and combined schools.

*Combined schools offer both elementary and secondary education.

Data does not include publicly funded elementary and secondary schools, hospital and provincial schools and care, treatment and correctional facilities.

Small cells have been suppressed:

• where fewer than 10 students are in a given category, the data is depicted with (<10)
• suppressed totals are depicted with (SP)
• the report may not be used in any way that could lead to the identification of an individual

Note:

• starting 2018-2019, enrolment numbers have been rounded to the nearest five.
• where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

The Private School Universe Survey, 2007-08 (PSS 2007-08), is a study that is part of the Private School Universe Survey program. PSS 2007-08 (https://res1ncesd-o-tedd-o-tgov.vcapture.xyz/surveys/pss/) is a cross-sectional survey that builds an accurate and complete universe of private schools to serve as a sampling frame for NCES surveys of private schools and generates biennial data on the total number of private schools, teachers, and students. The study was conducted using surveys of administrative personnel. The study's response rate was 91.8 percent. Key statistics produced from PSS 2007-08 are religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program.

The Ministry of Educations' - Basic Education Statistical Booklet captures national statistics for the Education Sector in totality in the year 2014. This Dataset shows the Private Secondary School ratios for English, Kiswahili, Maths, Biology, Chemistry and Physics subjects text books across the 47 counties.

Source - The Ministry of Educations' 2014 Basic Education Statistical Booklet, Table 83: Private Secondary Schools Text Book Ratios Selected Subjects.

Germany DE: School Enrollment: Secondary: Private: % of Total Secondary data was reported at 10.013 % in 2022. This records an increase from the previous number of 9.973 % for 2021. Germany DE: School Enrollment: Secondary: Private: % of Total Secondary data is updated yearly, averaging 8.688 % from Dec 1999 (Median) to 2022, with 24 observations. The data reached an all-time high of 10.013 % in 2022 and a record low of 6.536 % in 1999. Germany DE: School Enrollment: Secondary: Private: % of Total Secondary data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Education Statistics. Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;