Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

In California in 2022, 20.5 percent of students enrolled in K-12 public schools were white, 11.9 percent were Asian, and 56.2 percent were Hispanic. In the United States overall, 44.7 percent of K-12 public school students were white, 5.5 percent were Asian, and 28.7 percent were Hispanic.

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.

In the ******* school year, ** percent of K-12 public school students who were White attended a school where ** percent or more of the students were of their own race or ethnicity in the United States, in comparison to ** percent of students who were Hispanic and ** percent who were Black.

Demographic information on DOE schools, Grades 9-12

Report on Demographic Data in New York City Public Schools, 2017-18 in response to Local Law No. 59. Test results, as provided in this report, only count students who were actively enrolled as of October 31, 2017. Therefore, they do not match numbers publicly available elsewhere, which include all test takers.

Proportion of visible minorities, among the school-age population (ages 5 to 24), Canada and jurisdictions, in and out of census metropolitan areas (CMAs). Estimates and projections of population aged 0 to 29, by age group, Canada, provinces and territories. This table is included in Section A: A portrait of the school-age population: Cultural diversity of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

Research project

The project “Educational Success and Social Participation of Socially and Educationally Disadvantaged Students with Migration Background in Extended Education” (abbr. in German: GeLeGanz) was funded by the German Federal Ministry of Education and Research (BMBF) under the funding code 01JB211A-C from 2021 to 2025.

Traditionally, the German education system is organized as a “half-day”-system; instruction usually takes place in the morning. Many stakeholders see the conversion from half-day to all-day schooling as a way of overcoming the challenges facing the system, including those posed by immigration. High expectations are attached to the expansion of all-day schooling, in particular the strengthening of the educational success and social participation of socially and educationally disadvantaged students with a migration background. As yet however, these goals have not been sufficiently achieved in Germany. Education systems in other countries have established comparable offerings of high quality that appear to be effective. The GeLeGanz project aims to use findings and knowledge from other countries to better exploit the potential of all-day schools in Germany, particularly at the primary school level. The primary focus of the GeLeGanz project is on the potential of all-day primary schools to improve the educational opportunities of socially and educationally disadvantaged students, in particular those who live in a migrant family.

GeLeGanz is a collaborative project, carried out by three partners.

Freie Universität Berlin (FU):

• Prof. Dr. Marianne Schüpbach (Coordinator of the collaborative project, Co-head of the subproject FU Berlin): marianne.schuepbach@fu-berlin.de
• Dr. Nanine Lilla (Co-head of the subproject FU Berlin): nanine.lilla@fu-berlin.de
• Dr. Haiqin Ning (Researcher, Operational coordinator of the collaborative project): haiqin.ning@fu-berlin.de
• Jule Schmidt (Researcher): jule.swaantje.schmidt@fu-berlin.de

German Children and Youth Foundation (DKJS):

• Anna-Margarete Davis (Co-head of the subproject DKJS): anna-margarete.davis@dkjs.de
• Nicola Andresen (Co-Lead of the subproject DKJS and school counselling): nicola.andresen@dkjs.de

University of Hamburg (UHH):

• Prof. Dr. Dr. h.c. mult. Ingrid Gogolin (head of the subproject UHH): gogolin@uni-hamburg.de
• Luise Krejcik (Researcher): luise.krejcik@uni-hamburg.de

To achieve the objectives, the project was divided into the following phases:

Phase I: Expert interviews with researchers from the German and international research context on their perception of quality features and conditions for the successful design of all-day schools.

Phase II: The experts were interviewed again to evaluate and further specify the results with regard to the target group. For this, they were provided with a summary of the statements made by researchers from the German and international research context in Phase I.

Phase III: Focus group interviews with various practice-oriented actors from the German all-day school context, based on the results of expert interviews, to gain information and assessments related to the implementation of measures that might improve all-day schooling in Germany.

Phase IV: Based on the insights gained in the first three parts of the project, materials and concepts should be developed together with practice partner DKJS and transfer partners.

Project website: https://www.ewi-psy.fu-berlin.de/en/v/geleganz/index.html

Data set in UHH

The present data set comprises 30 expert interviews with 15 researchers from the German education research community, which were collected as part of the GeLeGanz project in phase I and II.

Experts: 15 researchers were interviewed twice (1x in phase I and 1x in phase II of the project). All were experts with relevant research experience, but different perspectives on the project’s guiding questions: all-day schools, informal and nonformal education, cultural and language diversity, social inequality and school development. The interview partners were identified via a review of empirical research on conditions of educational success of socially disadvantaged children with a migrant background and the potential advantages of all-day schools.

Interview procedure & topics: A sequential approach was chosen for conducting the interviews: In Phase I, interviewees were asked for

1. research-based assessments of features of high-quality all-day schools, especially for the support of socially disadvantaged children with a migration history,
2. factors that promote or hinder participation in all-day schools’ offers,
3. assessments of the current all-day school landscape in Germany.

In Phase II, the experts were interviewed again. They were provided with a summary of the statements made by the German and international experts in interviews of phase I. Experts were invited to prioritize the mentioned quality features and the potential for adaptation and implementation in the German context.

A semi-structured, problem-centred approach was used to conduct the interviews (Witzel, 2000). The guidelines included narrative-generating impulse questions, follow-up questions to promote understanding and narrative generation, and ad hoc questions on the topics discussed. The interviews were conducted in German by two trained interviewers (online or analogous). All interviews were recorded based on informed consent.

Period of the survey: The interviews were conducted from March to December 2022.

Transcription & anonymization: The transcripts were initially computer-generated, then completely revised manually according to established transcription and anonymization rules (Rädiker and Kuckartz, 2019, p. 44f).

Contents of the data set UHH:

• Collected storage of the 30 interview transcripts in a single MAXQDA project (version 2022, MX22-file)
• Individual file of the 30 interview transcripts in Excel format (xls) and html format
• Anonymization and transcription rules, data naming scheme (pdf)

Note: The dataset is stored in the ZFMD repository of the University of Hamburg in both an open-access (DOI 10.25592/uhhfdm.14815) and a restricted-access version (DOI 10.25592/uhhfdm.14771). Both datasets are available from January 1, 2026. In the open access dataset, research-related data such as research projects and studies of the respondents are anonymized in addition to personal and school-related data. In the restricted access dataset, only the respondents' personal and school-related data are anonymized.

References:

Rädiker, S., & Kuckartz, U. (2019). Analyse qualitativer Daten mit MAXQDA: Text, Audio und Video. Springer Fachmedien.

Witzel, A. (2000). Das problemzentrierte Interview [25 Absätze]. Forum: Qualitative Social Research, 1(1), Article 22. http://nbnresolving.de/urn:nbn:de:0114-fqs0001228

Enrollment counts are based on the October 31st Audited Register for 2015. Data on students with disabilities, English language learners and students poverty status are as of February 2nd 2016. Due to missing demographic information in rare cases, demographic categories do not always add up to citywide totals. In order to view all data there is an excel file attached which you would select to open.

Demographic information on DOE schools with grades K-8

Resources for the Systematic Research Projects Review (SRPR) about European research projects on school inclusion and diversity. The SRPR is related to the project "Gamified Values Education For Fostering Migrant Integration at Schools (GAMIGRATION)" funded by Erasmus+ programme of the European Union (ref. 2021-1-ES01-KA220-SCH-000032607).

Search conducted on CORDIS and Erasmus+ platform.

Analysis of ‘2018 Diversity Report - Grades K-8 School’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/99274f14-8cee-464a-a077-8e305683e288 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Report on Demographic Data in New York City Public Schools, 2017-18 in response to Local Law No. 59. Test results, as provided in this report, only count students who were actively enrolled as of October 31, 2017. Therefore, they do not match numbers publicly available elsewhere, which include all test takers.

--- Original source retains full ownership of the source dataset ---

This dataset tracks annual diversity score from 2017 to 2023 for Detroit Public Schools Community School District vs. Michigan

Analysis of ‘2014-2015 Diversity Report - K-8 & Grades 9-12 District, Schools, Special Programs, Diversity Efforts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e7dc14b8-c671-4c2f-b501-44f13ec6f1d5 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Enrollment counts are based on the October 31st Audited Register for 2014.
Data on students with disabilities, English language learners and students poverty status are as of February 2nd 2015. Due to missing demographic information in rare cases, demographic categories do not always add up to citywide totals. In order to view all data there is an excel file attached which you would select to open.

--- Original source retains full ownership of the source dataset ---

This dataset is accessed from https://www.kaggle.com/jessemostipak/college-tuition-diversity-and-pay and was downloaded on August 4, 2021.

The following excerpt is from Kaggle regarding the sources of this dataset:

The data this week comes from many different sources but originally came from the US Department of Education.

Tuition and fees by college/university for 2018-2019, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Diversity by college/university for 2014, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Example diversity graphics from Priceonomics. Average net cost by income bracket from TuitionTracker.org. Example price trend and graduation rates from TuitionTracker.org Salary potential data comes from payscale.com.

This dataset included the following files:

1. diversity_school.csv

2. historical_tuition.csv

3. salary_potential.csv

4. tuition_cost.csv

5. tuition_income.csv

After data cleaning, the data in diversity_school.csv and tuition_cost.csv were merged and the data in salary_potential.csv and tuition_income.csv were merged. The combined datasets were then split based on the US Census Regions into West, Midwest, Northeast and South (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).

This dataset tracks annual diversity score from 1992 to 2023 for George Washington High School vs. West Virginia and Kanawha County Schools School District

This bulletin summarises the diversity of students with a language background other than English (LBOTE) who are enrolled in New South Wales government schools.

Data source:

• Statistical publications. Centre for Education Statistics and Evaluation.

This bulletin summarises the diversity of students with a language background other than English (LBOTE) who are enrolled in New South Wales government schools. Data source: Statistical publications. Centre for Education Statistics and Evaluation. This bulletin summarises the diversity of students with a language background other than English (LBOTE) who are enrolled in New South Wales government schools. Data source: Statistical publications. Centre for Education Statistics and Evaluation.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

Polling data routinely indicates broad support for the concept of diverse schools, but integration initiatives—both racial and socioeconomic—regularly encounter significant opposition. We leverage a nationally-representative survey experiment to provide novel evidence on public support for integration initiatives. Specifically, we present respondents with a hypothetical referendum where we provide information on two policy options for assigning students to schools: 1) A residence-based assignment option and 2) An option designed to achieve stated racial/ethnic or socioeconomic diversity targets, with respondents randomly assigned to the racial/ethnic or socioeconomic diversity option. After calculating public support and average willingness-to-pay, our results demonstrate a clear plurality of the public preferring residence-based assignment to the racial diversity initiative, but a near-even split in support for residence-based assignment and the socioeconomic integration initiative. Moreover, we find that the decline in support for race-based integration, relative to the socioeconomic diversity initiative, is entirely attributable to white and Republican respondents.