10259 research outputs found
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Growing Up in Scotland: Cohort 1: Sweep 1, 2005-2006: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Growing Up in Scotland (GUS) study is a large-scale longitudinal social survey which follows the lives of several groups of Scottish children from infancy through childhood and adolescence. It aims to provide important information on children, young people and their families in Scotland. The study forms a central part of the Scottish Government's strategy for the long-term monitoring and evaluation of its policies for children and young people, with a specific focus on the early years. The study seeks both to describe the characteristics, circumstances and experiences of children in their early years in Scotland and, through its longitudinal design, to generate a better understanding of how children's start in life can shape their longer term prospects and developmentSince 2005 fieldwork has been undertaken by the Scottish Centre for Social Research. The survey design for Birth Cohort 1 consisted of recruiting the parents of an initial total of 5,217 children aged 10 months old in 2005 and interviewing them annually until their child reached age six. Further fieldwork was then undertaken at ages 8, 10, 12, 14 and 17-18 with a sample boost added at age 12.Data for sweeps 1-9 were collected via an in-home, face-to-face interview with self-complete sections. Fieldwork for sweep 10 was disrupted due to the COVID pandemic. As a result, the final portion of the data was collected via web and telephone questionnaires. Sweep 11 data were gathered via web, telephone and face-to-face surveys of cohort members and their parent/carer.Further information about the survey may be found on the Growing Up in Scotland website.In May 20205, data and documentation for Cohort 1, Sweeps 1-11 were released as individual studies (SNs 9373-9383 and 9386-9387). Previously they were held under one study (SN 5760) which has been withdrawn from the data catalogue.Main Topics:The questionnaire covered the following topics:household information non-resident parents and non-resident children the pregnancy and birththe first few monthscurrent situationparental supportparenting styles and activitiesparenting responsibilitieschildcarechild health and development self-completion section own employment, income and education partner's employment, income and education accommodationA topic overview covering all sweeps, is available on the GUS website.</div
Growing Up in Scotland: Cohort 1: Sweep 5, 2009-2010: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Growing Up in Scotland (GUS) study is a large-scale longitudinal social survey which follows the lives of several groups of Scottish children from infancy through childhood and adolescence. It aims to provide important information on children, young people and their families in Scotland. The study forms a central part of the Scottish Government's strategy for the long-term monitoring and evaluation of its policies for children and young people, with a specific focus on the early years. The study seeks both to describe the characteristics, circumstances and experiences of children in their early years in Scotland and, through its longitudinal design, to generate a better understanding of how children's start in life can shape their longer term prospects and developmentSince 2005 fieldwork has been undertaken by the Scottish Centre for Social Research. The survey design for Birth Cohort 1 consisted of recruiting the parents of an initial total of 5,217 children aged 10 months old in 2005 and interviewing them annually until their child reached age six. Further fieldwork was then undertaken at ages 8, 10, 12, 14 and 17-18 with a sample boost added at age 12.Data for sweeps 1-9 were collected via an in-home, face-to-face interview with self-complete sections. Fieldwork for sweep 10 was disrupted due to the COVID pandemic. As a result, the final portion of the data was collected via web and telephone questionnaires. Sweep 11 data were gathered via web, telephone and face-to-face surveys of cohort members and their parent/carer.Further information about the survey may be found on the Growing Up in Scotland website.In May 20205, data and documentation for Cohort 1, Sweeps 1-11 were released as individual studies (SNs 9373-9383 and 9386-9387). Previously they were held under one study (SN 5760) which has been withdrawn from the data catalogue.Main Topics:The main carer questionnaire covered the following topics:household informationnon-resident parentsfood and eatingparenting styles and responsibilitiestransition to pre-schooltransition to primary schoolchildcarechild health and developmentactivities with othersemployment and educationincome, expenditure and financial managementaccommodation and transportA topic overview covering all sweeps, is available on the GUS website.</div
Ofsted 'Big Listen' Exercise Data, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.Between 8 March 2024 and 31 May 2024, Ofsted carried out a ‘Big Listen’ exercise. The following information was provided to respondents, and summarises the purpose of the Big Listen exercise:"The Big Listen seeks views right across our work, from schools and children’s social care to teacher training and early years. Across our work, we want to explore 4 areas:
reporting how we report on our education and regulatory inspectionsinspection practice the shape of our education and regulatory inspections, our ways of working and the craft of inspectingculture and purpose the conduct of our inspections and the way we workimpact the consequences of our inspections for children, professionals, institutions and parents' choices
The Big Listen is structured to allow you to provide feedback on the areas of greatest interest and importance to you. You may only want to give us your views on schools. Or you might want to share views on how we report across further education and teacher training.
Whatever your focus, we have structured the Big Listen so you only need to comment on the things that matter to you.
Thank you for taking the time to help us improve and achieve our ambition of being a world-class inspectorate and regulator."
The dataset available from UKDS contains individual level responses to all quantitative questions. Free text responses are not included.Further information can be found on the Ofsted Big Listen: Supporting Documents webpage. It is planned that a podcast based on the research will be released in due course.Main Topics:Early years; schools; further education (FE) and skills; teacher development, which includes initial teacher education (ITE), early career framework (ECF) and national professional qualification (NPQ) programmes; special educational needs and/or disabilities (SEND) and alternative provision; children's social care.</div
Improving the Quality of Teaching and Student Learning in Secondary STEM Education in Rwanda, 2019-2023
No full textAbstract copyright UK Data Service and data collection copyright owner.Improving the Quality of Teaching and Student Learning in Secondary STEM Education in Rwanda, 2019-2023 includes data collected by a study aimed at generating evidence of improved teaching and learning for the Leaders in Teaching (LIT) initiative. The LIT initiative aims to improve the quality of teaching and learning outcomes in Rwandan secondary schools. The LIT initiative includes several interventions, centred around four pillars: recruit, train, lead, and motivate. The initiative is implemented in 14 out of 30 districts in Rwanda, selected based on lowest Lower Secondary Leaving Examination results, the highest gender gap in learners’ pass rates on these exams, and the highest percentage of dropouts. As learning partners, the REAL Centre and Laterite collected data (2019-2023) to track changes on schools affected by this program.
Five research instruments were used:
1. School leader survey: to understand and track changes in school leader inputs, motivation, perceptions of their own leadership and teaching quality, and views towards teaching in diverse settings and COVID-19 effects, and more.
2. Teacher survey: to understand and track changes in teacher inputs, motivation, perceptions of their own teaching quality, and views towards teaching, experience with continuous professional development and COVID-19 effects, and more.
3. Teacher Content and Pedagogical Knowledge Assessment: to ascertain content-specific and pedagogical content teacher knowledge. It was developed as a mock grading assignment for teachers.
4. Student Assessment: to understand and track improvements in student learning. The content is the same as the Learning Achievement in Rwandan Schools (LARS3) numeracy test. Additionally, the assessment also included a survey to collect background information on students.
5. Classroom Observations: to understand and track improvements in teaching processes and practices. It covers domains of teaching practice, e.g. classroom culture, instruction, socio-emotional skills.
Instrument 1 and 2 were deployed at 358 schools. Instruments 3-5 were applied to a subsample the 'learner schools' - of around 100 schools.Main Topics:Education; health; equity; gender; inclusion; student performance in LARS3; STEM teachers; S3 mathematics performance; school characteristics; teacher characteristics, beliefs and motivation; COVID-19 effects; pedagogy; continuous professional development; leaders in teaching.</p
Quarterly Labour Force Survey, October - December, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has 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. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2024 ReweightingIn February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to:age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent childfamily unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of familynationality and country of originfiner detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;health: including main health problem, and current and past health problemseducation and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeshipsindustry: including industry, industry class and industry group for main, second and last job, and industry made redundant fromoccupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant fromsystem variables: including week number when interview took place and number of households at addressother additional detailed variables may also be included.The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.Latest edition informationFor the second edition (May 2025), the variables DIFFHRS20 and YLESS20 were replaced with new versions, with previously missing imputed values for 'IOUTCOME=6' cases added.Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire.</div
Elevate Project: Participant Interview Data, 2023-2024
No full textThe dataset consists of professionally transcribed semi-structured interviews (n=110). The interviews were between 20 and 90 minutes long. They were recorded before (n=48) and after (n=55) month-long free loans of electric cargo bikes in suburbs of Leeds, Oxford and Brighton in Summer-Autumn 2023, and after (n=10) winter loans of the e-cargo bikes for between 3 and 6 months in Winter-Spring 2023-4.
Leeds pre-summer interviews (n=16)
Leeds post-summer interviews (n=18)
Leeds post-winter interviews (n=3)
Oxford pre-summer interviews (n-16)
Oxford post-summer interviews (n-19)
Oxford post-winter interviews (n-3)
Brighton pre-summer interviews (n=17)
Brighton post-summer interviews (n=18)
Brighton post-winter interviews (n=4)
These interviews collected pre-trial everyday travel routines and expectations of use of the e-cargo bikes with the post-trial reality. This enabled us to explore: which trips had been undertaken with the e-cargo bike, and why; which had not, and why; which travel routines suited the e-cargo bike; which unexpected trips were generated with its availability; the subjective, affective and embodied experiences of e-cargo bike usage, as well as the post-trial intention to buy, and recommendations to policy-makers about how to make e-cargo bike usage and ownership more attractive. Some interviews involved multiple householders, including children.The UK transport sector lags behind all other sectors in its achievement of energy diversification and carbon emission reductions to date, with emissions from transport essentially unchanged since the benchmark year of 1990. The Committee on Climate Change have been very critical of this failure and identified electrically-assisted scooters and bikes as part of solutions that need to be urgently accelerated. Indeed, the UK lags behind other countries in the uptake of a range of innovative light vehicles for both passenger and freight applications. Examples include electrically-assisted: bicycles, cargo bicycles, push scooters, skateboards, trikes, quadricycles, hoverboards etc. These involve some electrical assistance, as well as some energy expenditure by the user. Hence, we class these vehicles as light electric vehicles for active travel (LEVATs). They enable people to cycle, scoot, skate or otherwise travel more easily or enjoyably than conventional walking or cycling. Their power source provides the opportunity to link to a variety of digital technologies - from unlocking shared vehicles, to 'track-and-trace' systems for delivery companies, to map systems or health feedback tools for users - what ELEVAVTE refers to as 'digital' travel. Innovation at the interface of e-mobility and digital technologies plays a key role for the uptake of these novel modes, with energy, IT and transport industries as key players.
Increased uptake of these vehicles has significant potential for reducing mobility-related energy demand and carbon emissions, especially when users switch from non-active modes such as cars or vans. The aim of this project is to better understand these opportunities - the technological and business options and specifications, where and who they might appeal to, what trips they could be used for, how far they could replace conventional motor vehicle trips - and some of the challenges that accompany them - such as overall energy usage, safety and regulatory issues, digital integration, physical environment design, battery standardisation and behavioural inertia. After developing typologies and technology assessments based on multiple criteria, the empirical end user research will consist of surveys (aiming for 1,200 responses), demonstration days (aiming to engage at least 300 people) and longer trials with at least 60 private individuals in 3 cities in England throughout 2020 and 2021. Quantitative surveys and in-depth interviews will be undertaken with participants before and after usage to understand changes in user perceptions and experience, triangulated with GPS tracking of the trial vehicles and contextual data (e.g. weather, hilliness). As part of the work, we will develop new safety training resources for each mode, drawing on, and adapting, existing UK initiatives and international experience and working towards certified schemes. Freight applications in the logistics industry will be analysed through expert interviews and case studies. A number of technology and demand scenarios will assess the whole lifecycle health and environmental impacts. This will include work with the World Health Organization expert group to extend the HEAT tool (which enables users without expertise in impact assessment to conduct economic assessments of the health impacts of walking or cycling) to include these types of vehicle.
This project is supported by a range of partners - including the three local authorities, Sustrans and the World Health Organization - and will be guided by an advisory panel. We will also engage with a range of industry stakeholders, through the Transport Systems Catapult, Clean Growth UK and other means. We also envisage international engagement in the work, given the rapidly evolving and growing nature of the topic, and the lack of a substantial academic literature on the implications of these innovative light vehicles for energy demand, mobility and climate change.</p
Labour Force Survey Two-Quarter Longitudinal Dataset, July - December, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.
Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.New reweighting policyFollowing the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.
LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.
Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.
Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.
An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email [email protected].
Occupation data for 2021 and 2022 data filesThe ONS has 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. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust. Main Topics:The two-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey.<br
Survey of Moto-taxi Riders in Kampala, Uganda, 2022
No full textSurvey data from 334 moto-taxi riders in Kampala, Uganda, generated using computer-assisted personal interviewing (CAPI) software between January and February 2022. Content includes: respondents' backgrounds and employment histories; means of motorcycle access; incomes and expenditures; working conditions; experiences of digital (ride-hail) work; interactions with law enforcers; and perceptions of politics and governance.Doctoral research carried out at the London School of Economics, which looked at the rise of new digital and financial technologies in Uganda's moto-taxi sector over the past decade. Focusing primarily on digital ride-hailing platforms, the study examined the ways in which 'platformisation' has reshaped work, livelihoods and politics in the sector, whilst also underlining the limits of such a process in transforming pre-existing systems of labour informality. Data collection for this study involved in-depth interviews with 112 respondents, a survey of 370 moto-taxi riders (deposited here), and six months' worth of in-person observations and 'ride-alongs'.</p
Everyday Digital Life: Participatory Mapping Workshops, 2023
No full textThis data deposit is based upon a year-long ethnographic study of the digital lives of asylum seekers in the UK.
The aim of the study was to understand the role that smartphones now play in the asylum application process and broader parts of everyday life (making friends, staying connected with family, navigating cities etc.).
The mapping part of the project was co-designed with a charity in the NE England and Vodafone in their ‘charity.connected’ partnership.This data deposit is based upon a year-long ethnographic study of the digital lives of asylum seekers in the UK. The aim of the study was to understand the role that smartphones now play in the asylum application process and broader parts of everyday life (making friends, staying connected with family, navigating cities etc.). This project was co-designed with a charity in the NE England, and the participatory mapping workshops were developed and implemented together.</p
Millennium Cohort Study: Linked Education Administrative Datasets (Ofsted), England, 2005-2019: Secure Access
No full textAbstract copyright UK Data Service and data collection copyright owner.Background:The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will requireto provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)to collect information on previously neglected topics, such as fathers' involvement in children's care and developmentto focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may beto emphasise intergenerational links including those back to the parents' own childhoodto investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when availableAdditional objectives subsequently included for MCS were:to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of EnglandFurther information about the MCS can be found on the Centre for Longitudinal Studies web pages.The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website. The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.Safeguarded versions of MCS studies:The Safeguarded versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.Polygenic IndicesPolygenic indices are available under Special Licence SN 9437. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped. Sub-sample studies:Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).Release of Sweeps 1 to 4 to Long Format (Summer 2020)To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation. How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasets:Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard Safeguarded Licence or Special Licence (see 'Access data' tab above).Secure Access versions of the MCS include:detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;Banded Distances to English Grammar Schools for MCS5 held under SN 8394linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030linked Hospital of Birth data held under SN 5724.The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).
SN 9436 - Millennium Cohort Study: Linked Education Administrative Datasets (Ofsted), England, 2005-2019: Secure AccessEducation administrative records from the publicly available Ofsted reporting of school inspections in England have been linked to school data for cohort members in the MCS. The main aim of this data linkage exercise is to enhance the research potential of the study, by combining administrative records with the rich information collected in the surveys.Ofsted results are available on an anonymised school identifier which can either be matched directly to NPD data, or to survey data using the school lookup included in the deposit</p