The project “Building computational capacity among global data service staff” was funded for the UKRI's call on “."

Digital research infrastructures connect researchers, policymakers and innovators with the computers, data, tools techniques and skills to undertake ambitious and creative research. 

Social science data services worldwide play a key role in the digital research infrastructure by curating and managing access to many forms of social and economic data as well as promoting increased data literacy among the community. 

Recognising the growing importance of computational skills for data services staff in the social sciences, this project, led by academics affiliated with the UKDS, will address the critical need for training. 

The aim of this project is to build capacity within the international data services community, by providing upskilling opportunities for UK Data Service (UKDS) staff and developing foundational level data skills modules in computational social science for the wider global community. It will also establish a community of practice to provide enhanced support to users through the lifetime of the project and beyond. 

Direct beneficiaries of this project will include UKDS staff who will be given the opportunity to upskill in computational skills, as well as global data service staff who will be given access to a foundational-level online structured course(s) on computational social sciences. 

Through both upskilling mechanisms, this project will enhance data services capacity both in the UK and globally, enhance the careers of data service professionals, and through the establishment of a Community of Practice will contribute to a culture of lifelong learning.

Attendees explored how sociotechnical changes might shape the future of research, hearing from a variety of experts and collaborating with researchers and professionals from different sectors and disciplines. 

In addition to fostering vibrant discussions, Methods Con: Futures 2024 featured the launch of NCRM’s a series of publications reflecting on how sociotechnical changes might impact the way social science is conducted. 

The event was a resounding success, garnering excellent feedback for its interdisciplinary approach, valuable insights and providing an opportunity to engage with cutting-edge methods. 

The NCRM is proud to have facilitated such a dynamic exchange of ideas and looks forward to its continuing commitment to staying at the forefront of methodological innovation in the social sciences. 

During the month of September, University of Manchester colleagues at the UK Data Service (UKDS) delivered the following events: 

• (10 September, J. Kasmire, Louise Capener and Nadia Kennar) 
• (24 September, Nigel Denoronha and Placide Abasabanye) 
• (24 September, Alle Bloom, Pierre Walthery) 
• Presentation at RSS International Conference 2024: Coding in public: recognising vulnerability as a barrier to reproducibility (2 September, J. Kasmire) 
• Presentation at RSS International Conference 2024: Developing quantitative data driven research skills in social science students outside the classroom (2 September, Vanessa Higgins) 
• Presentation at NCRM methods showcase: The UK Data Service Data Skills Framework (11 September, Vanessa Higgins)

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the .

For information about upcoming events visit the . 

The Department of celebrates the publication of the report “Exploring the Intersection of Technology and Democracy”. 

Led by Professor , the 2023 Futures Summer Camp, funded by SPRITE+, was a collaborative effort with the Research Institute for Sociotechnical Cyber Security (RISCS) and facilitated by the School of International Futures (SOIF). 

The event aimed to foster future-focused collaborations across various disciplines to address challenges in digital security, identity, privacy, and trust.

The camp brought together 28 experts from academia, government, and industry for a two-day workshop to explore how technological advancements might impact democratic institutions over the next 5-15 years. 

Participants discussed potential threats and opportunities, considering actions to mitigate negative outcomes and maximize positive impacts.

Key themes and insights from the workshop will shape SPRITE+’s future initiatives, including a TIPs-focused expert meeting in May 2024 and a themed sandpit in June 2024 titled "Living in an Inauthentic World". 

This event underscores the importance of interdisciplinary collaboration in preparing for the future intersection of technology and democracy.

The report can be accessed .

On Thursday 16 May, the department of held its away day at , in central 91ֱ��. The event was a resounding success, fostering a collegial environment where important discussions flourished. 

Key themes included research collaborations within the department and support for early career academics. The day’s activities not only sparked meaningful conversations but also played a crucial role in shaping the future direction of the department.

During the month of April, University of Manchester colleagues at the UK Data Service (UKDS) delivered the following events:

• (9 April, J. Kasmire, Nadia Kennar and Louise Capener)
• (16 April, Louise Capener and Nadia Kennar)
• (17 April, J. Kasmire)
• SRMS Dissertation workshop: Locating Research Data and Data Access (18 April, Vanessa Higgins)
• (22 April, Nigel de Noronha)
• (30 April, J. Kasmire and Alle Bloom)

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the .

For information about upcoming events visit the .

(Social Statistics) has been awarded an ESRC grant for the project Enhancing Data Accessibility and Security through Innovative Data Synthesis (EDASIDA).

The EDASIDA project aims to transform both data accessibility and confidentiality through innovative data synthesis techniques. In essence, the project will develop a methodology for providing tailored teaching datasets and systematic disclosure risk assessment methods.

The new methodology involves leveraging cleared analytical outputs from data services as the basis for generating synthetic data using genetic algorithms. The goal is to provide trainees with data that not only closely resembles real-world data but also yields analytical output very similar to that of the real data, enhancing the training experience. 

A pilot study conducted in collaboration with Administrative Data Research UK, demonstrated the feasibility of generating synthetic teaching datasets with both high utility and no marginal disclosure risk. The pilot dataset (a synthetic version of the linked ASHE-census dataset) was successfully used in a ADR-UK training course in April 2024.

The approach also offers a route to formalise assessment the disclosure risk associated with analytical outputs from safe settings. By embodying statistical outputs in synthetic data, it enables a systematic evaluation of disclosure risk, addressing the informality and potential inconsistencies present in current output checking procedures. 

Finally, the project aims to bolster the federated services agenda by exploring the creation of synthetic linked data from using analytical outputs from data of multiple services. This approach expands the possibilities of data synthesis without the need for actual linkage and elaborate governance of infrastructure, such as trusted third parties.

On Wednesday 24 April, the Department of hosted the grand finale of the Data Science in Practice Series. 

Karolina Michalska from KANTAR's London office captivated an audience of eager students from the MSc Social Research Methods and Statistics program and the undergraduate Data Analytics pathways.

Karolina dazzled the attendees with an exhilarating presentation showcasing her groundbreaking work at KANTAR. 

She offered an in-depth and eye-opening overview of the diverse and dynamic roles available for data scientists within the company. Her presentation was not only highly informative but also sparked a vibrant and engaging discussion, leaving everyone inspired and buzzing with ideas.

On March 22, Dr Nikita Basov received laudation as the recipient of the Friedrich Wilhelm Bessel Research Award from The Alexander von Humboldt Foundation for outstanding research achievements, as part of the Symposium for Research Award Winners in Bamberg, Germany, March 21-24, 2024. 

The award was given for major scientific contributions over the past 10 years, including the pivotal role in building the field of socio-semantic network analysis and the pioneering input into understanding the dual relationship between culture and social structure via innovative methodological combinations of statistical network modelling and interpretive analysis of ethnographic data on creative collectives. 
 

During the month of March, University of Manchester colleagues at the UK Data Service (UKDS) delivered the following events:

• (11 March, Alle Bloom) 
• (12 March, Jools Kasmire, Nadia Kennar and Louise Capener) 
• (13 March, Jools Kasmire and Nigel de Noronha) 
• (19 March, Jools Kasmire) 
• (27 March, Jools Kasmire and Nigel de Noronha) 

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the . 
For information about upcoming events visit the . 
 

Chris Shumba, Head of Data Operations (Football) at 91ֱ�� United, visited the university on Wednesday, 13 March, to share insights with students enrolled in the "Data Analytics" pathways of the BASS/BAEcon programmes. 

In his talk, Shumba provided valuable perspectives on career pathways in data science and discussed the technologies shaping the field today. 

His presentation was well-received by the audience, sparking meaningful discussions and leaving attendees eager for future engagements with Shumba.

(Department of Social Statistics) has been awarded a British Academy Small Grant. The grant will fund the project titled "Statistical Modelling of Meaning Creation in Interaction: A Test of Core Social Theories.".

In this project, Dr. Basov will explore the capabilities of recently advanced network modelling methods to test two core social theories: symbolic interactionism and social constructivism. By doing so, he aims to provide the first comprehensive quantitative test of these theories.

These new methods allow the disentangling of 3-layer socio-cultural network structures that connect individuals, words, and material objects within small groups of collocated individuals. This, in turn, facilitates the cross-validation of the fundamental assumptions that both theories make about how cultural meanings are formed across different types of interactions, structural levels, and time spans.

The project will utilize a globally unique multi-dimensional dataset capturing socio-cultural dynamics in five groups of visual artists over two years.

The development of statistical models for analysing how cultural meaning is created in society will constitute a significant contribution to social science. The results of testing core social theories have the potential to catalyse fundamental changes in social sciences and beyond.

Prof. Arek Wisniowski published in February a new article in the prestigious journal “Migration Studies”. This new article explains that undercounting is a critical issue in migration statistics, resulting in bias. 

It typically arises from insufficient reporting requirements and problems with enforcing such requirements. The main sources of information on undercounting are the metadata accompanying official statistics and expert opinions. 

However, metadata and arbitrary expert opinions may be limited by overlooking important details in migration data shared by various countries. This includes potential oversight of changes in methodologies, definitions, or retrospective updates to the data following censuses.

In their paper, Prof. Wisniowski and his colleagues present a methodological solution with three objectives to address undercounting in international migration data. First, they provide an overview of available metadata and expert opinions on undercounting in European migration flows. 

Second, they propose a novel data-driven approach that incorporates year-specific and duration-of-stay-adjusted classifications. The proposed methodological solution relies on comparisons of flows in the same direction reported by a given country with high-quality data reported by another set of countries. 

They use bilateral migration data provided by Eurostat, UN and selected national statistical institutes. Duration-of-stay correction coefficients are derived through an optimization model or borrowed from the literature. Metadata and expert opinion scores can also be integrated to classify undercounting. Finally, they provide a dynamic classification of undercounting for 32 European countries (2002-2019), accessible through an online Shiny application, offering flexibility and adaptability.

Their findings highlight significant undercounting in new EU member states, particularly Bulgaria, Latvia, and Romania. Interestingly, other European countries, including those presumed to maintain reliable population statistics, also exhibit notable periods of undercounting.

The UK Data Service (UKDS) ran several events in the month of February.

During the month of February, University of Manchester colleagues at the UK Data Service (UKDS) delivered several training courses in partnership with the Office for National Statistics, the Home Office, and the Scottish Government, among others.
 

These included:

• , 6th at UCL, London (Sarah King-Hele)
• , 7th, online (Nigel de Noronha) 
• Introduction to Machine Learning, workshop for the University of Nottingham Data Fest, 7th, online (Jools Kasmire) 
• , 13th, online (Louise Capener and Nadia Kennar)
• , 13th, online (Nadia Kennar)
• , 14th, online (Sarah King-Hele)
• , 16th, online (Nadia Kennar)

For more information about these events and future courses, you can visit the website.

Aidan O’Brien and Zainab Kapasi (Data scientist at TalkTalk) came to the Department of Social Statistics to talk about their roles as Data Scientists at TalkTalk (a leading U.K. provider of mobile and internet services). 

They explained to our students how data science helps in their sector and what type of careers are available to aspiring data scientist.

A new article by (Social Statistics) has appeared in the leading international journal “P�DZ�پ�����”. 

The notes that in creative settings, people are often put together physically - to stimulate the exchange of ideas and practices. However, little is known of how exactly different spatial zones foster such creative sociality. 

The new paper by Basov et al. draws on a combination of interviews, observations, and surveys - analysed with an innovative mixture of abductive coding, computational space analysis, and statistical network modelling - to unveil how room sharing and object usage relate to friendships and collaborations in artistic residences. 

While social ties are indeed associated with joint material embeddedness, different types of spatial zones appear to encourage unexpected types of social ties. 

The findings inform the practice of creative space organising and the proposed approach enables explanatory analysis of the relation between material space and sociality in various contexts.
 

Are Children School Ready? Research by Dr. K. Purdam and colleagues from Social Statistics has highlighted the substantial differences in School Readiness at the individual, school and local area levels in England. 

The educational attainment levels of children in state-funded schools in England are lower than in many countries with comparable levels of economic development. There are also striking differences at the local level across England. To understand these differences it is important to examine children’s development in their early years. 

This research uses multilevel analysis of the National Pupil Database to investigate child development at ages 4 and 5 years old at the individual, school and local levels including within a case study urban area. Child development is assessed using teachers’ observations to measure what is termed School Readiness. This is based on a child’s communication, literacy and numeracy skills and their physical, personal and social development. 

The findings reveal substantial differences in School Readiness at the individual, school and local area levels including in terms of sex, ethnic background, age in the school year, welfare benefit entitlement and local area income deprivation level. Such differences are also evident across the separate Early Learning Goals that are used to assess School Readiness. 

Between local areas children with similar backgrounds can vary considerably in their likelihood of being categorised as School Ready. Many children face multiple disadvantages as a consequence of different interlinked factors including where they live. The gap in the levels of School Readiness has long-term implications for the individuals themselves and for society more widely. 

Whilst increasing the levels of School Readiness is a key target in the UK Government’s Levelling Up policy, tackling the stark inequalities will take considerable investment, highly targeted support and engagement across the home and school learning environments. 

The research is available to read .

Research by , and from Social Statistics has highlighted new methods to improve the accuracy of cross-national surveys. 

Cross-national surveys run the risk of differential survey errors, where data collected vary in quality from country to country. 

Responsive and adaptive survey designs (RASDs) have been proposed as a way to reduce survey errors, by leveraging auxiliary variables to inform fieldwork efforts, but have rarely been considered in the context of cross-national surveys. 

Using data from the European Social Survey, we simulate fieldwork in a repeated cross-national survey using RASD where fieldwork efforts are ended early for selected units in the final stage of data collection. 

Demographic variables, paradata (interviewer observations), and contact data are used to inform fieldwork efforts. 

Eight combinations of response propensity models and selection mechanisms are evaluated in terms of sample composition (as measured by the coefficient of variation of response propensities), response rates, number of contact attempts saved, and effects on estimates of target variables in the survey. 

We find that sample balance can be improved in many country-round combinations. Response rates can be increased marginally and targeting high propensity respondents could lead to significant cost savings associated with making fewer contact attempts. 

Estimates of target variables are not changed by the case prioritizations used in the simulations, indicating that they do not impact nonresponse bias. 

We conclude that RASDs should be considered in cross-national surveys, but that more work is needed to identify suitable covariates to inform fieldwork efforts.

The research is available .

, a lecturer in Social Statistics, has been honoured with the Outstanding Digital Enhanced Teaching award for the 2022/2023. 

A cornerstone of her teaching methodology is the creation of reproducible teaching materials using a combination of R and RMarkdown. The approach empowers students to engage with course content in a meaningful way and also ensures that educational materials can be easily replicated, contributing to a more effective learning process. 

A workshop focused on reproducible research techniques is also being held. The workshop will use a specially developed reproducible dedicated to advancing the principles of reproducibility in research. 

The workshop and the learning resources will be a valuable resource for researcher practitioners seeking to enhance their research methodologies. For more information and to register for the workshop, please visit the .

The University of Manchester is one of five partner organisations who together have been awarded £37.5 million from The Economic and Social Research Council . Of this award £7 million is to support our team at the Cathie Marsh Institute (CMI), now 13-strong, where Vanessa Higgins and Debora Price lead the national Training and User Support functions of the service.
 

Welcoming the award, , Director of Research, School of Social Science said:

The UK Data Service provides access to the largest collection of economic, social and population data in Europe. It hosts over 9,000 datasets including the UK Census, Understanding Society and the Labour Force Survey. There are 50,000 registered users, from 148 countries.
 

, Deputy Director of the Service and lead Investigator at the University of Manchester, said:

The cross-site Training and User Support service led by the CMI team at 91ֱ�� has grown significantly in recent years with an outstanding track record of providing appropriate training programmes. 

Last year, they ran over 100 live training events and there is a large and growing range of online materials, from interactive data skills modules, to written guides and how-to videos for all levels of user. Feedback from users is excellent with 97% of respondents last year saying they would recommend their training event to others.
 

, Co-Investigator at the University of Manchester, and national Service Director for Training and User Support said:

Our research has examined the inequalities in health using Office for National Statistics (ONS) data on life expectancy. The differences in life expectancy between local areas in the UK can be as high as 25 years. 

The deprivation levels of an area and premature mortality are strongly associated, and the differences in life expectancy between economically deprived and prosperous areas are increasing. In some areas the long-term increases in life expectancy have stalled or even reversed. 

A number of interrelated factors are associated with lower life expectancy. These include access to health care, income, employment, smoking and alcohol consumption, diet, exercise, the local environment, social status and social isolation.
 

What’s on offer?

How does where you live affect your life expectancy? What can we do about it? 

Find out at this pop-up information session, based around the striking differences in life expectancy at key points along the River Mersey and the River Thames. 

The maps are using the River Mersey and the River Thames to link life expectancy data to well known places. The researchers will be on hand to answer questions and leaflets will also be given out with information about the research and the findings.
 

Who’s leading the event?

Event Booking details:

• Turn up on the day - no need to book
   

For more information about this event, visit the .

The main objective of the event was to provide students and the younger generation with the appropriate assistance for their exploration of quantitative analysis at the early stages of their careers.

The Social Statistics Hackathon attracted a wide and diverse group of participants from industry to three different universities. The programme consisted of a mixture of talks, teamwork and presentation competitions.

The groups of participants were asked to conduct analyses on data from the UK Census 2021 to find hidden associations between a set of variables.

At the end of the Hackathon, the head of the Department of Social Statistics, Dr Arkadiusz Wisniowksi presented awards to the final winners of the prizes. A group comprised of two students from The University of Manchester and 91ֱ�� Metropolitan University won the first prize.

New research into the affordability of funerals in the UK by K. Purdam, G. Chatzi and A.M. Nicoriciu from the University of Manchester has been highlighted by the .

The research drew on official survey data and interviews with older people aged 50 years and older living on low incomes. Substantial numbers of older people did not have the financial resources to be able to afford the costs of an expense such as a funeral, particularly those people living on low incomes and those in poor health.

There were widespread concerns about the costs of funerals and the burden on the families people leave behind. 

As one interviewee aged 86 stated: 

“I save £2 coins for my funeral.” 

Many older people had not made a plan for their funeral. As one interviewee aged 75 commented: 

“It’s the height of irresponsibility, putting the burden on someone who is grieving.” 
 

It is important that funerals are affordable, and that people are supported during a time of grief and vulnerability. Those people who die without family or close friends should also be treated with respect. The full research findings will be published shortly.

New research by staff from Social Statistics has reviewed the methodology of one of the most comprehensive indices of gender equality, the Gender Equality Index by the European Institute for Gender Equality (EIGE). 

The research shows that in addition to the use of the correcting coefficient, other methodological choices (such as the use of ratios and geometric means) result in an unjustified penalisation of lower-GDP countries and the reinforcing of biased assumptions about gender equality progress.

The research calls for greater transparency around theory, method and the relationship between the two while also proposing methodological improvements. 

These changes would bring the EIGE index closer to fulfilling its potential to provide a nuanced understanding of gender equality levels in the European Union and effectively inform policy development toward social change.

The full research article is available here:

Schmid, C.B. and Elliot, M., 2023. . Social Indicators Research.

The UK Data Service and Social Statistics staff co-led two data user conferences with the Office for National Statistics, the Department for Work and Pensions, NatCen and University College London. 

For more information see:

Alle Bloom has presented the work of the UK Data Service at the European Survey Research Association conference in Milan. Further details are available .

Big data and how important insights can be gained from the vast quantities of data we generate in our everyday lives was highlighted by Dr Julia Kasmire from the UK Data Service on BBC Radio 4. More details .

The UK Data Service is delighted to announce the winners of this year’s UK Data Service Dissertation Award. 

The award celebrates undergraduate dissertations based on the use of archived data available through the UK Data Service. 

The winners were:

• Aiste Timukaite, BA (Hons) Criminology, University of Manchester. 
  A quantitative analysis of victim’s rational decision to report violent crime: does trust in the police matter?
• Caitlin Lin, Population Health Sciences (Data Science), University College. London (UCL). 
  A quantitative study into the control of diabetes in older age: who goes on to have eye problems?
• Finlay Yates, Economics (BA), Durham University. 
  The impact of the minimum wage on employment: An assessment of the effect of employer concentration.

Further information can be found .

Social Statistics staff led a session on children’s wellbeing for researchers and policy makers at the Royal Statistical Society Annual Conference. 

The event highlighted research on: School Readiness, Covid-19 and the impact on wellbeing and attainment and young people’s mental health. 

The various data sources for researching wellbeing were presented by the UK Data Service and new innovative approaches for linking data were also highlighted.

You can read more about the conference and the research here:

Social Statistics and UK Data Service staff recently presented new research at the World Statistics Congress. spoke about the importance of data literacy and the value of work placements. 

discussed the and the results of a qualitative research project to explore how Social Sciences, Arts and Humanities (SHAPE) students participate in UK Data Service training events to develop their data literacy skills. 

The session was chaired by Professor Natalie Shlomo. You can read more about the work here:

Jingwen Zhang from Social Statistics at the University of Manchester recently presented her research at the British Gerontology Conference. The research examines ageing and internal migration in China. 

The research found that not only current migration status, but also the timing of migration and the sequence of migration destination, are associated with health in later life. 

Migrating in early adulthood is associated with better mental health in later life, whereas internal migrants who subsequently return to rural settings have similar mental health outcomes to rural residents, who have comparably poorer health outcomes. 

Migration and health in later life is highly gendered. For example, migrating before marriage has an empowering effect for rural women and they are more likely have better physical and mental health outcomes as they age. For married couples, the relationship between migration trajectories and later-life health for men and women also depends on the migration trajectories of their spouses. 

You can read more about the research and findings here:

Professor Jackie Carter talks about her passion for helping young people overcome barriers and how they can pursue their careers in research and policymaking. Her recent book is also available from .

The full Times Higher Education article is available .

New research led by Arkadiusz Wiśniowski, Ruth Allen, Andrea Aparicio-Castro, Wendy Olsen Wendy and Maydul Islam from the University of Manchester has documented the economic impacts of the Covi-19 Pandemic on ethnic minorities in 91ֱ��. 

The research utilised multiple reporting sources to explore various dimensions of the economic shock in the UK, linking this to studies of pre-Covid-19 economic and ethnic composition. Many neighbourhoods in the city were always going to be less resilient to the economic shock caused by the pandemic compared with other, less-deprived, areas. 

Particular challenges for 91ֱ�� include the high rates of poor health, low-paid work, low qualifications, poor housing conditions and overcrowding. Ethnic minority groups faced disparities long before the onset of the pandemic. Within the UK, ethnic minorities were found to be most disadvantaged in terms of employment and housing - particularly in large urban areas containing traditional settlement areas for ethnic minorities. 

Unprecedented cuts to public spending as a result of austerity have also disproportionately affected women of an ethnic minority background alongside disabled people, the young and those with no or low-level qualifications. This environment has created and sustained a multiplicative disadvantage for 91ֱ��'s ethnic minority residents. The full article is available .

New research highlights the relationship between expert knowledge and social ties in local communities exposed to flood risks. As part of a cross-European project led by Dr. Nikita Basov, from Social Statistics at the University of Manchester, new research highlights how communities' knowledge responds to flooding and the role of multiple social networks and expert knowledge in this response. 

This includes how information moves through different social networks in communities, the interface with expert knowledge and official agencies, and the dependencies between these different and overlapping sources of information.

Innovative methods developed by the research team allow for revealing how framing and language used by professionals are adapted rather than merely adopted by the public integrating professional knowledge into locally embedded practice-driven knowledge. The research opens new paths for understanding the creation, reproduction and transformation of knowledge in society, with possible applications ranging from public discourse to organizational cultures, social entrepreneurship, and many other areas. The full article is available .

Dr. Nikita Basov, a senior lecturer in Social Statistics, has received the Friedrich Wilhelm Bessel Research Award from the German Alexander von Humboldt Foundation. It is granted to internationally recognized scientists to recognize their outstanding research achievements. Dr Basov was awarded his PhD in from St. Petersburg University in 2009 and for 10 years led the Centre for German and European Studies at St. Petersburg University.

His research is known for advancing multidimensional socio-semantic and socio-material network analyses, with a particular focus on mixing qualitative methods with computational methods and statistical network modelling. See for more details.

New research on the affordability of funerals has been highlighted at the ESRC Festival of Social Science. The research draws on survey data analysis and interviews with older people across Greater 91ֱ��. The initial findings highlight peoples concerns about funeral costs and how those people on low incomes need more financial help. The full findings will be published in early 2023. You can find out more details on .

The new Data Analytics and Social Statistics master’s degree will begin in September 2023 and promises to be a pioneering course that prioritises data veracity and analysis.

, Course Director, Senior Lecturer in the Department of Social Statistics and the chair of the Social Statistics Section of the Royal Statistical Society, says:

“In a time of disinformation campaigns, this master’s is well-placed to promote data literacy and inform the next generation of data scientists with the skills to tackle such issues.

This master’s will enable students to confidently analyse real-world data by using the latest statistical software and methods. The course covers all the stages of working with data from data collection, importing, wrangling and visualisation to analysis and presentation.”

As data continues to improve in scope and specificity, we need to develop the next generation of data scientists to harness this information effectively and create actionable insights that will improve our world. 

The course was developed to help address key world challenges, such as inequality and data privacy, and to produce impact in various contexts including policymaking, education, healthcare, medicine and voluntary work.

, Lecturer in Social Statistics, says: “Using real-world social data can really make a big impact in terms of social problems. It helps with bigger picture, if more people understand the context of the data and how it’s used, people can create an impact.”

The new Master’s in Data Analytics and Social Statistics was designed with these issues at the forefront and with the need to combat fake news and disinformation in mind. Students will be taught by experts in social sciences and data sciences, working with real-world data in this highly practical course.

Tailored for busy working professionals, this master’s is 100% online, allowing students to easily fit it into their schedules amongst their other commitments. Aimed at individuals from industries as wide as education, health, business analytics and non-profits, this course will empower students to leverage data for predictive purposes.

Prospective students will also be able to apply for an Equity and Merit Scholarship, created to promote the United Nations Sustainable Development Goals. These scholarships are open to applicants from Ethiopia, Malawi, Tanzania, Uganda and Zimbabwe with the potential to deliver solutions to encourage sustainable development.

Based firmly in practical techniques such as data analysis using R, students already working within data analysis will be able to apply their learning directly into their daily work. Newcomers to the field will be able to upskill and use this degree as a conversion to a new career.

Topics covered in this course include data visualisation, statistical modelling, survey methods, research skills and more.

This course was specifically designed to help expand data literacy and encourage students to question the purpose of how statistics are presented and why. The context of data, including how it is used, is a key focus.

With the dissertation, students on this course can focus on a contemporary research topic of interest to showcase their quantitative and analytical skills. Students will be taught by researchers who conduct these activities in their professional work, further enhancing the practical and real-world dimension of the course.

Dr Alexandru Cernat comments:

“The dissertation will help bring all these skills together to create an analytics report or research paper that answers real-world problems using advanced statistical methods and complex data.”

The first class will begin in September 2023. Learn more about the new postgraduate course in Data Analytics and Social Statistics.

A new Head of Department for Social Statistics at the University of Manchester has been appointed. joined the Social Statistics Department in 2015 and is now a Senior Lecturer. He also leads a Statistical Modelling Research Group at the Cathie Marsh Institute, is a Visiting Fellow at the Research School of Social Sciences, Australian National University, Adjunct Associate Professor at the Asian Demographic Research Institute, University of Shanghai, and Associate Member at the ESRC Centre for Population Change, University of Southampton. He holds a PhD in Economics and two MSc degrees from the Warsaw School of Economics. His research concentrates on combining traditional and new forms of data as well as developing statistical methods for analysing demographic change, with a particular focus on migration and mobility.

Compiled by Professor Jackie Carter, from Social Statistics at the University of Manchester, new case studies from Social Science alumni highlight how their degrees from the University of Manchester - Bachelor’s, Master’s or PhDs - gave them the skills, knowledge and experience to enter graduate careers in policy. Their analytical, research and professional skills are mapped onto frameworks developed by Professor Carter drawing on the British Academy’s The Right Skills Report, and LinkedIn and McKinsey Industry reports. The alumni now work in local government, central government, international charities and leading businesses. For more details see .

Research has highlighted the value of Mixed Methods Research for conducting challenging and complex research. The study examined the importance of Mixed Methods Research for understanding the value of emotional support for older people living with cancer. The narrative Mixed Methods Research methodology is a tool for putting stories at the centre of the research process. Read the full article which is available .

New research has examined how the COVID-19 pandemic impacted on population mental health. The findings suggest that the psychological impact of the pandemic evolved in the early phase of lockdown. COVID-related anxiety may represent the mechanism through which economic consequences of the pandemic are associated with psychiatric symptoms. Find out more information .

The high quality and impact of Social Statistics research has been recognised in the Research Excellence Framework. Researchers from the University of Manchester consistently make an important contribution to solving the world’s most urgent problems, with the University ranking 9th in the world for delivering against the UN Sustainable Development Goals in the Times Higher Education Impact Rankings. For more information see:

/discover/news/first-uk-wide-evaluation-since-2014-confirms-university-of-manchester-is-a-research-powerhouse/

The , on Wednesday, 4 May, released an independent evaluation of the . Currently Q-Step operates in 17 universities across the UK, including the University of Manchester, and was established in 2013 as a strategic response to the shortage of quantitatively skilled social science graduates for careers in research and other data-led professions.

The primary findings of the evaluation demonstrate that Q-Step is helping to address the shortage of graduates with the skills to apply quantitative methods in both research and professional settings. 15 months after graduation 46% of Q-Step trained graduates earn over £25k per year, compared to 30% of non-Q-Step graduates and 72% of Q-Step trained graduates are employed in highly skilled jobs, compared to 59% of non-Q-Step graduates.

Q-Step was found to equip students with a deep and secure grasp of the skills needed to make sense of data and a grounding in the ways that data can be used to better understand society.

The evaluation also highlighted an increased quantitative teaching capacity at participating universities and has prompted a range of further investment and initiatives designed to boost quantitative methods across these institutions, as well as in other universities and educational organisations. Student satisfaction levels for Q-Step programmes are high as a result of good quality teaching and work placements.

Prof Jackie Carter and Dr Mark Brown, Co-Directors of the University of Manchester have been integral in developing the pioneering programme of living-wage, paid work placements, through opening the world of work for students, connecting them to employers such as the World Bank, the Home Office, the Department for International Development, YouGov, Santander, the BBC, The Times and Sunday Times, and Ipsos MORI.

Q-Step is transforming the way social science undergraduates are taught. Q-Step is teaching students to crunch the data needed to answer research questions of academic and public policy interest and enabling them to apply these skills to real-world environments such as social and political research, consultancy, business and marketing.

Prof Jackie Carter says: "This programme is about giving all social sciences students – whatever their backgrounds – access to professions based on their talent, and we have seen our students grow into data analysts. This programme is a real differentiator for the University." The Q-Step programme at 91ֱ�� aims to challenge traditional pipelines into data careers, 25% of our Q-Step work placement students are from Widening Participation backgrounds and upwards of 70% are women.

Prof Carter’s recently published book, , draws on several student voices from the Q-Step programme to showcase how you can use a work placement to develop your research and professional skills. The book demonstrates how you can transfer and grow skills from your academic training to the workplace and maximise the benefits of learning by doing - giving you key employability and workplace skills. 

, Research Associate, 

Experimenting on animals is a big part of biomedical research and also a big concern for those interested in animal welfare. Scientists are encouraged to use as few animals as possible in their research, but reducing animal numbers also reduces how precise a researcher can be about the results of a study. If a treatment only has a small effect, lots of animals are needed to see it.

However, a new machine-learning technique (a type of AI) could reduce the number of animals needed in certain studies and still give precise results.

The method, called linear Poisson modelling, developed at the University of Manchester, was created to help planetary scientists automate the inspection of images of . It was used to learn the different textures of planetary terrain, then measure how much of those same textures appeared elsewhere on the same planet. The method also helped the citizen science project count craters on the moon.

Now back on Earth, it has found a new use as a tool for cancer researchers to inspect medical images of tumours implanted in lab mice. And compared with traditional statistical methods, early tests show that using this method up to 16 times fewer animals may be needed to detect the effects of treatments in cancer research.

From the moon to cancer tumours.

The machine learning method can describe the differences between groups of tumours that have been treated and those that have not. The medical images that were used in a we carried out used a type of scanning that measures the random motion of water molecules. This is useful because treated tumours become more watery as cell tissue breaks down.

Looking for these changes can be difficult because treatments take time to have an effect and tumours change over time whether they have been treated or not. Plus, there are lots of different ways that tumours can change and no two tumours are exactly alike. This complex behaviour can now be learnt using the machine learning method, allowing treatment effects to be precisely measured in individual tumours.

The more traditional approach, using what statisticians call a , often requires using a dozen or more animals before a precise result can be seen.

A more sensitive method

Cancer treatments can work better in certain combinations and can work better or worse depending upon the genetics of a tumour. Finding out which combinations of treatment work best for different tumours is an ongoing challenge.

The number of genetic differences between tumours and the number of treatment combinations that are possible leads to many thousands of experiments that need to be performed. This new method has the potential to significantly reduce the number of animals needed for large experiments. The sensitivity of the method may also allow shorter experiments to be performed, thereby reducing the discomfort and distress of the animals involved.

Our proof-of-concept study is the first step to fully realising the benefits of the new approach, but it must first be demonstrated that similar results can be repeated on other tumour images. We are optimistic that they will.

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