The American Community Survey (ACS) is designed to estimate the characteristic distribution of populations and estimated counts should only be used to calculate percentages. They do not represent the actual population counts or totals. Beginning in 2019, the Washington Student Achievement Council (WSAC) has measured educational attainment for the Roadmap Progress Report using one-year American Community Survey (ACS) data from the United States Census Bureau. These public microdata represents the most current data, but it is limited to areas with larger populations leading to some multi-county regions*. *The American Community Survey is not the official source of population counts. It is designed to show the characteristics of the nation's population and should not be used as actual population counts or housing totals for the nation, states or counties. The official population count — including population by age, sex, race and Hispanic origin — comes from the once-a-decade census, supplemented by annual population estimates (which do not typically contain educational attainment variables) from the following groups and surveys: -- Washington State Office of Financial Management (OFM): https://www.ofm.wa.gov/washington-data-research/population-demographics -- US Census Decennial Census: https://www.census.gov/programs-surveys/decennial-census.html and Population Estimates Program: https://www.census.gov/programs-surveys/popest.html **In prior years, WSAC used both the five-year and three-year (now discontinued) data. While the 5-year estimates provide a larger sample, they are not recommended for year to year trends and also are released later than the one-year files. Detailed information about the ACS at https://www.census.gov/programs-surveys/acs/guidance.html

About Dataset

The dataset you provided, titled "Report Card Enrollment 2023-24 School Year," appears to be a comprehensive collection of information regarding student enrollment and demographics within educational institutions for the specified academic year. Here are some observations about the dataset:

1. Granularity: The dataset seems to be quite granular, providing detailed information not only about overall student enrollment but also about various demographic categories such as gender, race/ethnicity, English language learners, students with disabilities, and socioeconomic status.

2. Demographic Diversity: It captures the diversity of the student population by including counts for various racial/ethnic groups, as well as categories such as gender X, indicating a recognition of diverse gender identities.

3. Socioeconomic Indicators: The dataset includes indicators of socioeconomic status such as students in foster care, homeless students, and those from low-income families, which can provide insights into equity and access issues within the educational system.

4. Special Education and Gifted Programs: It tracks the enrollment of students with disabilities and those identified as highly capable, which are important metrics for evaluating the inclusivity and effectiveness of special education and gifted programs.

5. Geographical Context: The dataset includes information about the county, educational service district, and school district, providing a geographical context for the enrollment data.

6. Temporal Aspect: The "DataAsOf" column indicates that the data has a temporal aspect, suggesting that it may be periodically updated to reflect changes in enrollment and demographics throughout the academic year.

**columns : ** SchoolYear: Indicates the academic year for which the data is reported, in this case, it's 2023-24.

OrganizationLevel: Specifies the level of educational organization, which could be school, district, or any other relevant level within the educational system.

County: Refers to the county where the educational organization is located.

ESDName: Stands for Educational Service District Name, which represents the intermediate level of educational administration in some states.

ESDOrganizationID: ID assigned to the Educational Service District.

DistrictCode: Code assigned to the district within the educational system.

DistrictName: Name of the school district.

DistrictOrganizationId: ID assigned to the district organization.

SchoolCode: Code assigned to the school within the district.

SchoolName: Name of the school.

SchoolOrganizationID: ID assigned to the school organization.

CurrentSchoolType: Indicates the current type of the school, such as elementary, middle, or high school.

GradeLevel: Specifies the grade level(s) served by the school.

All Students: Total number of enrolled students in the school.

Female: Number of female students enrolled.

Gender X: Number of students who identify as gender X, indicating a non-binary or non-conforming gender identity.

Male: Number of male students enrolled.

American Indian/ Alaskan Native: Number of students identifying as American Indian or Alaskan Native.

Asian: Number of students identifying as Asian.

Black/ African American: Number of students identifying as Black or African American.

Hispanic/ Latino of any race(s): Number of students identifying as Hispanic or Latino of any race.

Native Hawaiian/ Other Pacific Islander: Number of students identifying as Native Hawaiian or other Pacific Islander.

Two or More Races: Number of students identifying as belonging to two or more races.

White: Number of students identifying as White.

English Language Learners: Number of students who are learning English as a second language.

Foster Care: Number of students in foster care.

Highly Capable: Number of students identified as highly capable or gifted.

Homeless: Number of students experiencing homelessness.

Low-Income: Number of students from low-income families.

Migrant: Number of students from migrant families.

Military Parent: Number of students with parents serving in the military.

Mobile: Number of students who frequently change residences.

Section 504: Number of students covered under Section 504 of the Rehabilitation Act, which provides accommodations for students with disabilities.

Students with Disabilities: Number of students with disabilities.

Non-English Language Learners: Number of students who are not learning English as a second language.

Non-Foster Care: Number of students who are not in foster care.

Non-Highly Capable: Number of students who are not identified as highly capable or gifted.

Non-Homeless: Number of students wh...

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

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.

Progression to HE by Ethnic Group - Explore Education Statistics data set Ethnicity from Widening participation in higher education

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, education, expenditures, and USA.

Provisional counts of deaths in the United States by race and educational attainment. The dataset includes cumulative provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

In the 6 years to July 2021, the number and percentage of White undergraduate entrants went down – they went up for all other ethnic groups.

The Fall Enrollment survey is conducted annually by the National Center for Education Statistics (NCES) as part of the Integrated Postsecondary Education Data System (IPEDS). The survey collects data that describe the status of student participation in various types of postsecondary institutions. The data are collected by sex for six racial/ethnic categories as defined by the Office for Civil Rights (OCR). There are two parts included in this survey. Part A, Enrollment Summary by Racial/Ethnic Status, provides enrollment data by race/ethnicity and sex and by level and year of study of the student. Part C, Clarifying Questions, supplies information on students enrolled in remedial courses, extension divisions, and branches of schools, as well as numbers of transfer students from in-state, out of state, and other countries.

In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

In 2022, there were approximately 107,700 students with American Indian or Alaskan Native heritage enrolled at a university in the United States. This is a slight increase from the previous year, when there were 106,600 students with American Indian or Alaska Native heritage enrolled in postsecondary education.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

In 2022, it was reported that 17.7 percent of the Mexican population lagged behind in education and was therefore considered socially vulnerable. Among the country's indigenous population, the rate of social vulnerability for lagging behind in education rose up to 35.3 percent. The figures have been increasing since 2016 and the ethnicity gap amounts to double of the non-indigenous population educational social vulnerability rate.

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.

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.

The numbers and proportions of young people in the state sector who reached Level 2/3 or Level 2 with English and maths and level in maths by age and characteristics (ethnicity by Free School Meal (FSM) eligibility and sex).

This dataset provides population 25 years and over estimates by sex, race and educational attainment for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Tables C15002A, C15002B, C15002C, C15002D, C15002E, C15002F, and C15002G. Sex categories: Male, Female, and Both. Race categories: White Alone, Black or African American Alone, American Indian and Alaska Native, Asian Alone, Native Hawaiian and Other Pacific Islander Alone, Some Other Race, and Two or More Races. Educational attainment categories: Less than High School, High School Graduate, Some College or Associates Degree, and Bachelors Degree or Higher.

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

The Impact Evaluation of Race to the Top and School Improvement Grants (RTT-SIG Impact Evaluation) is a study that is part of the Impact Evaluation of Race to the Top and School Improvement Grants (RTT-SIG Impact Evaluation) program. RTT-SIG Impact Evaluation (https://ies.ed.gov/ncee/projects/evaluation/other_racetotop.asp) is a cross-sectional survey that assesses the implementation of the Race to the Top (RTT) and School Improvement Grant (SIG) programs at the State, local education agency (LEA), and school levels, as well as whether the receipt of RTT and/or SIG funding to implement a school turnaround model has had an impact on outcomes for the lowest-achieving schools. Additionally, the study investigates whether RTT reforms were related to improvements in student outcomes and whether implementation of the four school turnaround models, and the strategies within those models, was related to improvement in outcomes for the lowest-achieving schools. The study was conducted using a combination of telephone interviews and web-based surveys targeted to school administrators at the state, LEA, and school levels. Key statistics produced from RTT-SIG Impact Evaluation include State, LEA, and school adoption levels of policies and practices promoted by RTT and SIG, as well as impacts on student outcomes of RTT and SIG funding.

The Civil Rights Data Collection, 2017-18 (CRDC 2017-18) is part of the Civil Rights Data Collection (CRDC) program; program data are available beginning with the 2000 collection at https://civilrightsdata.ed.gov/data. CRDC 2017-18 is a cross-sectional survey that collects data on key education and civil rights issues in the nation's public schools, which include student enrollment and educational programs and services, disaggregated by race/ethnicity, sex, limited English proficiency, and disability. LEAs submit administrative records about schools in the district. CRDC 2017-18 is a universe survey. Key statistics produced from CRDC 2017-18 can provide information about critical civil rights issues as well as contextual information on the state of civil rights in the nation, including enrollment demographics, advanced placement, school discipline, and special education services.