The schools that create the most student debt

The top 10 schools for student loan debt in the United States

By Andy Kriebel [source]

About this dataset

This dataset contains information on the amount of student loan debt originated by schools in the United States for the 2020-2021 academic year. The data includes the school name, city, state, zip code, school type, loan type, number of recipients, number of loans originated, amount of money loaned, and number of disbursements

How to use the dataset

There are a few things to keep in mind when using this dataset:

• The data is for the 2020-2021 academic year.
• The data is for student loan debt originated by schools in the United States.
• The data is sorted by school.
• The columns of interest are: School, City, State, Zip Code, School Type, Loan Type, Recipients, # of Loans Originated, $ of Loans Originated, # of Disbursements, and $ of Disbursements

Research Ideas

• The dataset can be used to calculate the amount of loan debt originated by each type of school.
• The dataset can be used to calculate the amount of loan debt originated by each state.
• The dataset can be used to help students estimate their future student loan debt

Acknowledgements

The data for this visualization comes from the Common Origination and Disbursement (COD) System through the Department of Education

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Student Loan Debt by School 2020-2021.csv | Column name | Description | |:--------------------------|:-------------------------------------------------| | School | The name of the school. (String) | | City | The city where the school is located. (String) | | State | The state where the school is located. (String) | | Zip Code | The zip code of the school. (String) | | School Type | The type of school. (String) | | Loan Type | The type of loan. (String) | | Recipients | The number of recipients of the loan. (Integer) | | # of Loans Originated | The number of loans originated. (Integer) | | $ of Loans Originated | The amount of money originated in loans. (Float) | | # of Disbursements | The number of disbursements. (Integer) | | $ of Disbursements | The amount of money disbursed. (Float) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

College Scorecard

The College Scorecard dataset is provided by the U.S. Department of Education and contains information on nearly every college and university in the United States. The dataset includes data on student loan repayment rates, graduation rates, affordability, earnings after graduation, and more. The goal of this dataset is to help students make informed decisions about their college choice by providing them with clear and concise information about each school's performance

How to use the dataset

This dataset can help understand the cost of attending college in the United States, as well as the average debt load for students. It can also be used to compare different schools in terms of their graduation rates and repayment rates

Columns

• UNITID: Unit ID for institution
• INSTNM: Institution name
• CITY: City
• STABBR: State
• ZIP: Zip code
• OPEID: OPE ID for institution
• OPEID6: OPE ID for institution (6-digit)
• ACCREDAGENCY: Accrediting Agency
• INSTURL: Institution URL
• NPCURL: Net Price Calculator URL
• SCH_DEG: Highest degree awarded
• HCM2: Carnegie Classification 2010:** Basic
• MAIN: Carnegie Classification 2010:** Main
• NUMBRANCH: Number of branch campuses
• PREDDEG: Predominant degree awarded
• HIGHDEG: Highest degree awarded
• CONTROL: Control of institution
• ST_FIPS: State FIPS code
• REGION: Region
• LOCALE: Locale code
• LOCALE2: Locale code (multiple categories per state)
• CCBASIC: Carnegie Classification 2010:** Basic
• CCMAIN: Carnegie Classification 2010:** Main
• CCUGPROF: Carnegie Classification 2010:** Undergraduate Profile
• CCSIZSET: Carnegie Classification 2010:** Size and Setting
• HBCU: Historically Black College or University
• PBI: Predominantly Black Institution
• ANNHI: Tribal College or University
• TRIBAL: Tribal College or University (Public)
• AANAPII: Asian American and Native American Pacific Islander-Serving Institution
• HSIP: Hispanic-Serving Institution (HSI)
• NANTI: Native American-Serving Nontribal Institution
• MENONLY: Men only
• WOMENONLY: Women only
• RELAFFIL: Religious affiliation
• DISTANCEONLY: Distance-only
• CURROPER: Currently operating
• VETERAN: Veteran-supportive
• LIMDEP: Limited-degree-granting
• HIGHDEG_GRANTED: Highest degree granted
• PS: Predominantly two-year public
• UGRD_ENRL_TOTAL: Undergraduate total enrollment
• GRAD_ENRL_TOTAL: Graduate total enrollment
• UGRD_ENRL_ORIG_YR2_RT: Undergraduate, first-time, first-year retention rate (%)

Acknowledgements

This data was originally collected by the US Department of Education and made available on their website. Thank you to the US Department of Education for making this data available!

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by nousnou iwasaki on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

As the pandemic accelerated calls to provide relief to millions of student borrowers, President Biden announced executive action to cancel 10,000 dollars of student debt for most federal student loan holders. Both prior to and following his announcement, policymakers have debated the merits and details of student debt relief, focusing particular attention on the perceived deservingness of student loan borrowers. But we have little systematic evidence about how the public evaluates borrower deservingness, or whether elite arguments framing support or opposition to debt relief in terms of deservingness influence public preferences for student debt cancellation. This paper employs original conjoint and framing experiments conducted just prior to Biden’s announcement to explore each query. We find that, while certain borrower characteristics indicating need (e.g., amount of debt), responsibility for debt (e.g., type of institution attended), and reciprocity (e.g., time in repayment) can influence people’s evaluations of whether borrowers deserve debt relief, those results may not translate to broader deservingness arguments for or against student debt cancellation in a clear manner. Ultimately, our results shed light on a timely policy issue, while extending scholarly understandings of deservingness for a critical, and understudied, aspect of the American welfare state.

National Student Loan Data System

The dataset, sourced from the National Student Loan Data System, provides a comprehensive overview of student loan information for various educational institutions across the United States.

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The data covers loan recipients, origination and disbursement counts, as well as the corresponding monetary values. It spans the academic years from 2009 to 2010, including all four quarters of each year.

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The dataset is invaluable for understanding loan patterns, disbursement trends, and recipient demographics within different educational institutions during this period.

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The columns are :

OPE ID: - Definition: An 8-digit code uniquely identifying the school at its main branch. - Significance: Provides a specific identifier for each educational institution for accurate tracking and database management.

School: - Definition: The name of the educational institution associated with the OPE ID. - Significance: Identifies the specific school or university corresponding to the loan data, allowing for institution-specific analysis.

State: - Definition: The state where the main campus of the educational institution is located. - Significance: Offers geographical context, enabling regional comparisons and understanding loan dynamics in different states.

Zip Code: - Definition: The zip code of the main campus of the educational institution. - Significance: Provides specific location data, enhancing the granularity of the dataset and allowing for localized analysis.

School Type: - Definition: Indicates the control or ownership of the school (e.g., PRIVATE, PUBLIC). - Significance: Classifies schools based on ownership, enabling distinctions in loan trends between private and public institutions.

Recipients (Q1, Q2, Q3, Q4): - Definition: The number of loan recipients for the specified loan type during each quarter of the academic year. - Significance: Reflects the count of students or individuals receiving loans, providing a quarterly breakdown of loan recipients.

# of Loans Originated (Q1, Q2, Q3, Q4): - Definition: The number of loans initiated for the specified loan type during each quarter of the academic year. - Significance: Indicates the count of new loans originated during each quarter, offering insights into borrowing trends over time.

$ of Loans Originated (Q1, Q2, Q3, Q4): - Definition: The total dollar amount of loans initiated for the specified loan type during each quarter of the academic year. - Significance: Highlights the financial magnitude of loan originations for each quarter, showcasing the monetary aspects of borrowing.

# of Disbursements (Q1, Q2, Q3, Q4): - Definition: The number of disbursements made for the specified loan type during each quarter of the academic year. - Significance: Indicates the frequency of fund allocations, portraying the administrative workload related to disbursements each quarter.

$ of Disbursements (Q1, Q2, Q3, Q4): - Definition: The total dollar amount of disbursements made for the specified loan type during each quarter of the academic year. - Significance: Represents the cumulative disbursement amount for each quarter, providing insights into the financial distribution of student loans over time.

Many of your staff, grant recipients and partners may be eligible for loan forgiveness. Typically, to quality you must be employed by a U.S. federal, state, local, or tribal government, a 501(c)3 non-profit or a non-profit organization that provides a qualifying service (including military service). You can tailor these resources to spread the word about the PSLF program. Please consider sharing in your newsletters, social media feeds or at grant recipient convenings and conferences! Subject: Changes to Public Service Loan Forgiveness (PSLF) Program Offer More Options for Loan Forgiveness [INSERT STATE] Employees May Now Be Eligible The COVID-19 pandemic resulted in financial hardship for many, including members of the human services workforce. As a [INSERT STATE] employee, you may now be eligible for federal student loan forgiveness for your important public service, even if you were not eligible before. ACF has created a PSLF landing page that includes resources for you to share. It includes the March 31 webinar hosted by the Office of Early Childhood Development, in partnership with the Department of Education, attended by over 17,000 early educators. A webinar for the broader human services community was held on May 26th. Both recordings, as well as PDFs and Frequently Asked Questions, are housed on the site. Please help us share this news with the broader human services workforce, including all of you who work here at [INSERT STATE]. The Department of Education issued a waiver that allows you to get credit for past payments even if you didn’t make the payment on time, didn’t pay the full amount due, or weren’t on a the right repayment plan. Until Oct. 31, 2022, federal student loan borrowers can get credit for payments that previously didn’t qualify for Public Service Loan Forgiveness (PSLF). Many people in the human services sector (including those that work in government and nonprofits) qualify for this program but don’t know about it. See if you qualify . Because of the COVID-19 emergency, the U.S. Department of Education announced a change to Public Service Loan Forgiveness (PSLF) program rules. For a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for loan forgiveness. The waiver expires October 31, 2022. See if you qualify and apply today ! Did you know that for a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for the Public Service Loan Forgiveness program? Read the FAQs to learn more and see if you qualify. Click to Retweet to Twitter Click to Retweet to Twitter Click to Retweet to Twitter Metadata-only record linking to the original dataset. Open original dataset below.

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

Data on the average amount of OSAP debt owed by students. The data is specific to those who attended programs with typical durations. Data is for: * 4-year undergraduate university students * 2-year college diploma students * 1-year private career college students The data fields are: * academic year of completion * postsecondary sector (university, publicly-assisted college, or private career college) * program duration (1 year, 2 years or 4 years) * average repayable debt after loan forgiveness applied through the Ontario Student Opportunity Grant Debt is in nominal dollars with no adjustment for inflation. *[OSAP]: Ontario Student Assistance Program

Earnings and Loan Repayment in US Four-Year Colleges
From the College Scorecard, this data set contains by-college-by-year data on how students who attended those colleges are doing.

• A data frame with 48,445 rows and 8 variables: | Column | Description | | --- | --- | | unitid | College identifiers | | inst_name | Name of the college or university | | state_abbr | Two-letter abbreviation for the state the college is in | | pred_degree_awarded_ipeds | Predominant degree awarded. 1 = less-than-two-year, 2 = two-year, 3 = four-year+ | | year | Year in which outcomes are measured | | earnings_med | Median earnings among students (a) who received federal financial aid, (b) who began as undergraduates at the institution ten years prior, (c) with positive yearly earnings | | count_not_working | Number of students who are (a) not working (not necessarily unemployed), (b) received federal financial aid, and (c) who began as undergraduates at the institution ten years prior | | count_working | Number of students who are (a) working, (b) who received federal financial aid, and (c) who began as undergraduates at the institution ten years prior |

Details

This data is not just limited to four-year colleges and includes a very wide variety of institutions.

Note that the labor market (earnings, working) and repayment rate data do not refer to the same cohort of students, but rather are matched on the year in which outcomes are recorded. Labor market data refers to cohorts beginning college as undergraduates ten years prior, repayment rate data refers to cohorts entering repayment seven years prior.

Data was downloaded using the Urban Institute's educationdata package.

This data was used in the Describing Variables chapter of The Effect by Huntington-Klein

Source Education Data Portal (Version 0.4.0 - Beta), Urban Institute, Center on Education Data and Policy, accessed June 28, 2019. https://educationdata.urban.org/documentation/, Scorecard.

References Huntington-Klein. 2021. The Effect: An Introduction to Research Design and Causality. https://theeffectbook.net.

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Dataset Description

The dataset includes 1,000 records with information about loan applications, including variables related to the applicant's financial status, credit history, and loan details. The goal is to analyze patterns in credit risk or build models to predict loan defaults.

Columns:

• checking_balance: Customer's current account balance in deutschmarks, classified as < 0 DM (negative balance), 1 - 200 DM, > 200 DM, or unknown (unknown).
• months_loan_duration: Duration of the loan in months.
• credit_history: Credit history of the applicant.
• purpose: Purpose of the loan.
• amount: Loan amount.
• savings_balance: Savings account balance.
• employment_duration: Length of employment.
• percent_of_income: Percentage of income allocated to loan repayment.
• years_at_residence: Years at the current residence.
• age: Applicant's age.
• other_credit: Presence of other credit agreements.
• housing: Housing status (e.g., rent, own).
• existing_loans_count: Number of existing loans.
• job: Job type or classification.
• dependents: Number of dependents.
• phone: Availability of a telephone.
• default: Target variable indicating loan default ("yes" or "no").

Inspiration

This dataset can be used for: - Building predictive models for loan default. - Exploring relationships between financial variables and credit risk. - Enhancing your understanding of credit risk analysis.

License

This dataset is published under the CC BY-NC-SA 4.0 license: - Permitted: Educational, research, and personal use. - Restricted: Commercial use is not allowed. - Attribution: Credit to Universidad de Santiago de Chile is required. - Sharing: Derivative works must use the same license.

This dataset was originally provided by the Universidad de Santiago de Chile as part of the course "Machine Learning for Management". I am not the original creator of the data, and my role is solely to share this resource for educational and research purposes. All rights to the original data belong to the university and/or the original authors.

This dataset may not be used for commercial purposes or in contexts that violate the copyright or policies of the institution that created it. Users are responsible for complying with the terms of use specified in the accompanying license and should ensure they provide appropriate credit.

Additional Notes If you are a student or researcher interested in using this dataset, please make sure to give proper credit to the original source in your publications or projects.

Content

Source ID: FL313066220.Q

For more information about the Flow of Funds tables, see: https://www.federalreserve.gov/apps/fof/Default.aspx

For a detailed description, including how this series is constructed, see: https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL313066220&t=

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1945-10-01

• Observation End : 2019-04-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Michael on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

Data for the Student Loan Marketing Association (Sallie Mae) are included in the Federal government sector until the completion of Sallie Mae's privatization in 2004:Q4 and in the Finance company sector thereafter.

For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1977-01-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Jamie Street on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The State Loan Repayment Program helps HRSA provide grant funding for states and territories to operate their own loan repayment programs. Through SLRP each state and territory can design programs that address the most pressing health care needs of their residents. Primary medical, mental/behavioral, and dental clinicians who receive awards through SLRP-funded programs pay off their student debt in exchange for working in areas with provider shortages.HRSA programs provide equitable health care to people who are geographically isolated and economically or medically vulnerable. This includes programs that deliver health services to people with HIV, pregnant people, mothers and their families, those with low incomes, residents of rural areas, American Indians and Alaska Natives, and those otherwise unable to access high-quality health care. HRSA programs also support health infrastructure, including through training of health professionals and distributing them to areas where they are needed most, providing financial support to health care providers, and advancing telehealth. Location and data was provided by the Health Resources and Services Administration in October 2022. Update Frequency: Annual

Since 2002, AmeriList has been the nation’s premier provider of student-marketing data, offering a broad suite of ethically compiled, highly accurate, and deliverable mailing, email, and telemarketing lists targeting families, high-school students, college-bound freshmen, enrolled college students, and adult learners for continuing education

Comprehensive Dataset Overviews • Parents of Students / Households with Children – Reach parents alongside teens and pre-teens, ideal for programs like prom services, tutoring, summer camps, and private school admissions • High-School Students – Access ~5 million U.S. students and their parents, with robust selects including GPA, class rank, SAT/GED scores, arts/athletic interests, intended college, school year, and more • College-Bound Students Database – Tap into over 3–4 million incoming freshmen making major purchases (electronics, school supplies, dorm essentials, apparel), with segmentation by college attending, GPA, sports interest, geography, income, credit usage, and more • College Students Mailing List – Access ~24.4 million enrolled college students, segmented by class year, gender, field of study, hobbies, buying habits, and more for highly targeted outreach • Adult Learners / Continuing Education – Reach over 30 million individuals who have completed some college or are interested in continuing education, vocational or trade programs

How the Data Is Compiled & Maintained AmeriList uses a rigorous, ethical data-collection methodology, aggregating information from direct responses, internet and telephone surveys, public records, club memberships, purchase history, self-reported data, and proprietary sources.

All lists undergo monthly updates and data hygiene processes, including: - CASS-certification for address standardization - DPV (Delivery Point Validation) removal of unverifiable addresses - NCOALink, LACSLink, and Address Change processing for forwarding accuracy - Do-Not-Call, DMA suppression, in-house suppression for compliance - Deceased-record scrubbing via internal and third-party checks

Recommended Uses • Parents & High-School Campaigns – Promote private schooling, test prep, student loans, scholarships, events like prom or summer camps, trade schools, teen retail, or electronics • College-Bound Freshmen – Ideal for marketing student loans, scholarships, credit cards, dorm suppliers, school supplies, electronics, study aids, and apparel • Enrolled College Students – Excellent for textbook vendors, academic supplies, coupons, food delivery, financial aid, campus services, tech products, and lifestyle brands • Adult Learners / Continuing Ed – Perfect for vocational schools, certificate programs, online learning, re-enrollment, or career enhancement marketing

With data that is fresh, accurate, and ethically sourced, AmeriList gives you the tools to launch smarter, more impactful campaigns across mail, email, and telemarketing channels. Backed by two decades of expertise, proven results, and unmatched audience coverage, AmeriList is the trusted partner for organizations that want to connect with the student market and drive measurable growth.

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

Latest edition information
For the nineteenth edition (October 2025), data and documentation from the Next Steps 2019 Web Survey have been added to the study. The Longitudinal File has also been updated.

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

SN 7104 - Next Steps: Linked Education Administrative Datasets (National Pupil Database - KS2-KS5), England, 1997-2009: Secure Access includes linked National Pupil Database records on pupils’ attainment at KS2, KS3, KS4 and KS5 and data about the pupil such as free school meal eligibility and Special Education Needs (SEN) status. Information is also available about the school attended at the sampling stage.

For the sixth edition (August 2020), the study has been updated to only include the Linked Education Administrative Datasets (National Pupil Database), England, 2005-2009. The main Next Steps survey sensitive variables, previously available as part of this study, have moved to a new study (SN 8656) or are now available under EUL as part of SN 5545. The 'next_steps_redeposit_dictionary.xlsx' available under both SN 5545 and SN 8656 should be consulted for the location of specific variables.