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.

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

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.

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).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) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 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.

Statistics on postsecondary graduates who owed money for their education to government-sponsored student loans at graduation, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of debt paid 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.

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.

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

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.

Context

The students in the USA are seeking Loans on a regular basis for their post-secondary education through certifications or college degrees. Different types of income status students’ pursue Federal Loans and grants like PELL for low-income students to fulfill their objectives. In deciding which institution is better for the students whether it is linked with affording, education, market opportunities or repaying loans after completion, the answer is the IPEDS numeric data. IPEDS is the Integrated Postsecondary Education Data System. It is a system of interrelated surveys conducted annually by the U.S. Department of Education’s National Center for Education Statistics (NCES). IPEDS gathers information from every college, university, and technical and vocational institution that participates in the federal student financial aid programs. All the IPEDS data is based on 5.05% interest rate. We will use the respective data from Loan perspective.

Inspiration

Predicting Loan applications as "Approved or Rejected" for the applicants. The focus is mostly on Low-income students (who have earnings up to 48000 dollars per annum) to check whether they will be able to pay their loans as they face difficulties in paying their tuition fees and other related expenses.

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

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included). The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. Data are revised on a wave by wave basis, as a result of backwards imputation from the current wave's data. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:“The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates.” Survey Periodicity - "Waves" to "Rounds" Due to the survey periodicity moving from “Waves” (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019. A Secure Access version of the WAS, subject to more stringent access conditions, is available under SN 6709; it contains more detailed geographic variables than the EUL version. Users are advised to download the EUL version first (SN 7215) to see if it is suitable for their needs, before considering making an application for the Secure Access version.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Occupation data for 2021 and 2022 data files The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.Latest edition informationFor the 18th edition (May 2023), the inheritance variables 'ivalb1r7' and 'ivalb1r7_i' which had been omitted in error have been added. Main Topics: The WAS questionnaire is divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This is completed by one person in the household (usually the head of household or their partner) and predominantly collects household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This is given to each adult in the household and asks questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Telephone interview Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LANDLORDS LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT AGE SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS TAX RELIEF TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WEALTH WILLS WINNINGS WORKPLACE property and invest...

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy. Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC). There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).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) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 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 5545 - Next Steps: Sweeps 1-9, 2004-2023 includes the main Next Steps survey data from Sweep 1 (age 14) to Sweep 9 (age 32).Latest edition informationFor the seventeenth edition (September 2024), data and documentation for Sweep 9 (Age 32) have been added to the study. Main Topics: The content of the Next Steps Sweep 9 (Age 32 Survey) covers the following topics: Household relationship - Module includes information on: current relationship, previous cohabiting relationships, children (previously reported and new to household), childcare, non-resident children, non-resident parents, other household members (previously reported and new to the household).Housing - Module includes information on: current and previous housing, homelessness.Activities and employment - Module includes information on: activity history, current activity, current employment, employment at age 25, employment details for first job after full-time education, second job, prospective employment, employment support, work attitudes, and partner employment.Finance - Module includes information on: current pay/salary main job, pay/salary from second job, debt, income from other sources, partner's salary, benefits, other income, household income, pensions, savings and investments, subjective disposable income and attitudes to debt/saving/pension and future planning.Educational qualifications - Module includes information on: current education, educational qualifications, fees paid while in education, partner educationHealth - Module includes information on: general health, height and weight, illness/disability, exercise, sleep, diet, Covid-19.Identity, attitudes, and social and political participation - Module includes information on: attitudes, ethnic group, leisure, national identity, partner ethnicity, politics, social networks and social support, trust.Self Completion - Module includes information on: age at menarche, cognitive assessment, crime, difficult events, domestic violence, drinking, drugs, financial circumstances, financial literacy, gender identity and sexual orientation, health, loneliness, mental health, migration, personality, pregnancy history, relationship quality, school, sexual behaviour, smoking and well-being. The content of the Next Steps Sweep 8 (Age 25 Survey) covers the following topics: Household relationships: This module included information on current relationship, previous cohabiting relationships (dating back to September 2006), children, childcare, non-resident children, non-resident parents, and other household members.Housing: This module covered current and previous housing (summary data is collected about the different addresses the study members have lived in since they were 16, if other than the parents' home).Employment: Included information about current activity, current employment, second job, prospective employment (for unemployed), activity history, employment details for first job after September 2006 (aged 16), employment support, work attitudes, and partner employment. Data on current economic activities and activity history was obtained back to the time of the last interview and no earlier than September 2006.Finance: This module captured current pay/salary main job, pay from second job, income from other jobs, partner's income, benefits, income from other sources, household income, pensions, and debt. Education and job training: The module included job training, education since previous interview/September 2006, current education, fees, and partner's education.Health and wellbeing: Included information on general health, height and weight, exercise, sleep, diet, accidents and injuries. Identity and participation: This module provided information on young people's ethnicity and religion, measures of trust, risk, patience, meritocratic beliefs, adult identity, leisure, politics, social networks and social media participation.Self-completion module: The self-completion module included data on gender identity, locus of control, overall life satisfaction, mental health, self-harm, crime and harassment, drinking and smoking behaviour, drugs, bullying, sexual behaviour, and pregnancy history. A key component of the Age 25 Survey sweep is data linkage to administrative records held about individuals by government departments. At Sweeps 1-4 information was gathered on: the young person's family background;parental socio-economic status;personal characteristics;attitudes, experiences and behaviours;attainment in education;parental employment;income and family environment as well as local deprivation;the school(s) the young person attends/has attended;the young person's post-16 plans. The questionnaires at Sweeps 5-7 consisted of two modules: Household Information Module: included questions on the young person's household situation details of any persons living with themYoung Person Module: topics included demographics, attitudes to local area, activity history and current activity, jobs and training, qualifications being studied, higher education, attitudes to work and debt, childcare and caring responsibilities, young people Not in Education Employment or Training (NEET), Apprenticeships, information, advice and guidance, risk behaviours, relationships and sexuality, and own children. The additional 'Monthly Main Activity' dataset takes responses to the Activity History section of the questionnaire at Sweeps 4-7 and synthesises this information into variables that represent a monthly time series running from September 2006 (two months after the respondents completed compulsory education) until May 2010 (the first month of interviews for Sweep 7). For each of the 45 months in this period, this file contains the respondent's derived 'main' activity which is classified as one of Education, Employment, Apprenticeship/Training or Unemployed/Inactive (NEET). Multi-stage stratified random sample Telephone interview Self-administered questionnaire: Computer-assisted (CASI) Face-to-face interview

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(SLMBP) Student Loan Market: Will Series B Preferred Stock Ride the Wave?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Student Loan Meltdown? (SLM)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included).The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:"The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates."Survey Periodicity - "Waves" to "Rounds"Due to the survey periodicity moving from "Waves" (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Users should note that issues with linking have been reported and the WAS team are currently investigating.Secure Access WAS dataThe Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:WardsParliamentary Constituency Areas for Wave 1 onlyCensus Output AreasLower Layer Super Output AreasLocal AuthoritiesLocal Education AuthoritiesProspective users of the Secure Access version of the WAS will need to fulfil additional requirements, including completion of face-to-face training, and agreement to the Secure Access User Agreement and Licence Compliance Policy, in order to obtain permission to use that version (see 'Access' section below). Users are therefore strongly encouraged to download the EUL version (SN 7215) to see if it contains sufficient detail for their needs, before considering making an application for the Secure Access version.Latest Edition InformationFor the ninth edition (October 2022), the Round 7 person and household data have been updated. The Round 7 Wave 1 Variable Catalogue Excel file has also been updated. Main Topics: The WAS questionnaire was divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This was completed by one person in the household (usually the head of household or their partner) and predominantly collected household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This was given to each adult in the household and asked questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ALIMONY ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BEDROOMS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS EXPENDITURE FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FREQUENCY OF PAY FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCOME TAX INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INTEREST FINANCE INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LAND VALUE LANDLORDS LIFE INSURANCE LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT RETIREMENT AGE ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS SURVIVORS BENEFITS TAX RELIEF TAXATION TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WAR VETERANS BENEFITS WEALTH WILLS WINNINGS WORKPLACE property and invest...

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy. Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC). There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).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) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 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 8656 - Next Steps: Sweeps 1-9, 2004-2023: Secure Access includes sensitive variables from the main Next Steps survey from Sweep 1 (age 14) to Sweep 9 (age 32). A version of these variables were previously available under SN 7104. University identifiers for Sweeps 6 and 7 have also been reinstated and are available in the young person file. International Data Access Network (IDAN)These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.Latest edition informationFor the third edition (September 2024), sensitive variables for Sweep 9 (Age 32) have been added to the study. The main interview file includes unit group (4-digit) SOC2010, SOC20 and SIC2007 codes for current job, first job, job at age 25 and partner's job; university from which obtained undergraduate and post-graduate degrees; detailed long standing illness, national identity, gender and day date variables. In addition there is a separate pregnancy histories dataset which includes variables on type of fertility treatment received. Main Topics: The Safeguarded (EUL) version of Next Steps is provided alongside the Secure Access version. Additional variables which are available under Secure Access are from the following categories: date of interview (detailed)date of birth (detailed)detailed disabilitiesfull or detailed SOC/SIC codeschild care arrangementshigher Education identifierspotential school identifiers In this revised deposit, the variable denoting number of rooms in the house has been top-coded and there are truncated versions of SOC and SIC codes, longstanding illness and subject studied at university, The original version of the variables containing the detailed values is available under Secure Access. Multi-stage stratified random sample Face-to-face interview Telephone interview

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

what percentage of your gross salary does the consumer financial protection bureau suggest your student loan payment be in order to be affordable and limit your risk of delinquency and default?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).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) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 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. Users are only allowed one Geographical Identifiers Census Boundaries study, either SN 8189 (2001 Census Boundaries) or SN 8190 (2011 Census Boundaries).International Data Access Network (IDAN)These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).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) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 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), England, 2005-2009: 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.

Abstract copyright UK Data Service and data collection copyright owner. The Inventing Adulthoods (IA) archive currently consists of qualitative interview transcripts for 30 young people interviewed up to six times. Transcripts for a further 20 young people will be included in the archive in the near future. Inventing Adulthoods is a qualitative longitudinal (QL) study that 'walked alongside' young people as they moved from early teenage years to young adulthood in five contrasting areas of England and Northern Ireland. This dataset showcases the biographical material collected between 1998 and 2004, providing a unique window on many aspects of young people's lives at the turn of the 21st century. The case data for each of these young people comprise biographical data that illustrate change over time in most aspects of their lives: home and family, leisure, education, work, relationships, identity and adulthood. Interview 1: focuses more specifically on moral development Interview 2: includes perceptions of life chances and the future, as well as reflections on the research process Interview 3: if young people had responded to the memory book research method, this involves data based on discussion of the content of the memory book. If not, the content reflects that of Interview 2 Interview 4: includes material on social perceptions and responses to issues of social exclusion and reflections on the research process Interview 5: includes material on perceptions of community, networks and social change Interview 6: includes material on spirituality and responses to the researcher's interpretations of the case narrative The focus for investigation shifted from values, to adulthood, to social capital across these three studies. However, a consistent concern was to investigate agency and the 'reflexive project of self'; values and the construction of adult identity; how the social and material environment in which young people grow up acts to shape the values and identities that they adopt; and the impact of globalisation on the individual. Working with the complexity of young people's accounts, the study focused on the dynamic interplay between the individual, the resources available to them and the structuring effects of time, locality, class and gender. The study also offers considerable methodological potential not only for the further development of prospective QL methodology and biographical and case history approaches but also for application to policy and practice. Further information is available at the project's site, Inventing Adulthoods. In September 2011, the title was shorted to Inventing Adulthoods, 1996-2006 to describe the current data collection. For the third edition (July 2011), 44 semi-structured interview transcripts with 10 new, young female respondents were added to the data collection (5777int108 to 5777int151). Main Topics: Teenagers, young adulthood, growing up. Volunteer sample Face-to-face interview 1996 2006 ACADEMIC ACHIEVEMENT ACHIEVEMENT ADOLESCENCE ADOLESCENTS AGE GROUPS ALCOHOL USE ALCOHOLISM AMPHETAMINES ARTISTIC ACTIVITIES ASIANS ASPIRATION ATTITUDES TO SAVING AUTHORITY BALL GAMES BEREAVEMENT BINGE DRINKING BIRTH CONTROL BROKEN FAMILIES CANNABIS CHILDBIRTH CLASS CONFLICT CLASS CONSCIOUSNESS CLUBS COCAINE COHABITATION COMMUNITIES COMMUNITY IDENTIFIC... COMMUNITY PARTICIPA... CREATIVITY CULTURAL ACTIVITIES CULTURAL BEHAVIOUR CULTURAL PARTICIPATION CULTURAL VALUES CULTURE Community Compulsory and pre ... Conflict DEBTS DECISION MAKING DEPENDENCY RELATION... DISABILITIES DISABLED PERSONS DIVORCE DRINKING BEHAVIOUR DROPPING OUT EDUCATION DRUG ABUSE DRUG SIDE EFFECTS DRUG TRAFFICKING DRUG USE ECSTASY DRUG EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL EXPECTA... EMPLOYMENT ENTERTAINMENT ETHNIC GROUPS ETHNIC MINORITIES EXAMINATIONS EXTENDED FAMILY REL... EXTRACURRICULAR ACT... Education England FAMILIES FAMILY COHESION FAMILY ENVIRONMENT FAMILY INFLUENCE FAMILY MEMBERS FASHION FRIENDS FRIENDSHIP Family life and mar... GENERATIONS AGE GLOBALIZATION Gender and gender r... HEALTH HETEROSEXUAL RELATI... HOBBIES HOLIDAYS HOMELESSNESS HOMOSEXUALITY Health behaviour Health care service... Higher and further ... History Housing IDENTITY ILL HEALTH IMAGE INDIVIDUAL DEVELOPMENT INFIDELITY INTERNAL MIGRATION INTERNET USE INTERPERSONAL ATTRA... INTERPERSONAL INFLU... INTERPERSONAL RELAT... ISLAM JOB HUNTING JOB SEEKER S ALLOWANCE LEADERSHIP LEAVING HOME YOUTH LEISURE TIME ACTIVI... LIFE EVENTS LIFE HISTORIES LISTENING TO MUSIC LONELINESS LOVE LSD DRUG Labour and employment MAGIC MUSHROOMS MALE HOMOSEXUALITY MARRIAGE MARRIAGE DISSOLUTION MASS CULTURE MIDDLE CLASS MORAL VALUES MOTIVATION MUSIC EDUCATION Minorities NATIONAL CULTURES NATIONAL IDENTITY NETWORKING Northern Ireland OCCUPATIONAL CHOICE OCCUPATIONS PARENT CHILD RELATI... PARENTAL DEPRIVATION PARENTAL ENCOURAGEMENT PARENTAL SUPERVISION PARTNERSHIPS PERSONAL PEER GROUP PRESSURE PEER GROUP RELATION... PERFORMANCE PERFORMING ARTS PERSONAL APPEARANCE PERSONAL DEBT REPAY... PERSONAL EFFICACY PERSONAL FASHION GOODS PERSONAL IDENTITY PHYSICAL ACTIVITIES PHYSICAL DISABILITIES PHYSICALLY DISABLED... POVERTY PREMARITAL SEX PRESTIGE PUBLIC IMAGE Political behaviour... QUALIFICATIONS RACE RELATIONS RACIAL PREJUDICE RACISM REGIONAL IDENTITY RELATIVE DEPRIVATION RELIGION RELIGIOUS AFFILIATION RELIGIOUS BEHAVIOUR RELIGIOUS BELIEFS RELIGIOUS EXPERIENCE RESIDENTIAL MOBILITY RESPONSIBILITY RURAL AREAS Religion and values SAFE SEX SAME SEX RELATIONSHIPS SAVINGS SCHOOL DISCIPLINE SCHOOL LEAVING SCHOOL LEAVING AGE SECONDARY EDUCATION SECONDARY SCHOOL LE... SECTARIANISM SELF DISCIPLINE SELF ESTEEM SEX EDUCATION SEXUAL ABSTINENCE SEXUAL AWARENESS SEXUAL BEHAVIOUR SEXUALITY SIBLING RELATIONSHIP SMOKING SOCIAL ACTIVITIES L... SOCIAL ALIENATION SOCIAL ATTITUDES SOCIAL BEHAVIOUR SOCIAL CAPITAL SOCIAL CLASS SOCIAL DISADVANTAGE SOCIAL EXCLUSION SOCIAL INFLUENCE SOCIAL MOBILITY SOCIAL NETWORKS SOCIAL RESPONSIBILITY SOCIAL SUCCESS SOCIAL VALUES SPORT SPOUSES STATE RESPONSIBILITY STUDENT LEISURE STUDENT LOANS STUDENTS STUDENTS COLLEGE SUBCULTURAL GROUPS SUBSTANCE USE SUPERVISION Social and occupati... Social behaviour an... Society and culture Specific social ser... TEETOTALISM TRANSITION FROM SCH... UNDERAGE DRINKING UNDERAGE SEX UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS URBAN AREAS Unemployment Vocational educatio... WHITE PEOPLE WORKING CLASS YOUNG ADULTS YOUTH YOUTH UNEMPLOYMENT Youth security and peace urban and rural life