Browse the full Impact Rankings 2020 results

This ranking focuses on universities¡¯ research on social inequalities, their policies on discrimination and their commitment to recruiting staff and students from under-represented groups.

Please view the methodology for the Impact Rankings 2020 to find out how these data are used in the overall ranking.

Metrics

Research on reducing inequalities (27%)

• Proportion of papers in the top 10 per cent of journals as defined by Citescore (10%)
• Field-weighted citation index of papers produced by the university (10%)
• Number of publications (7%)

This focuses on research that is relevant to reducing inequalities. The field-weighted citation index is a subject-normalised score of the citation performance of publications.

The data are provided by Elsevier¡¯s Scopus dataset, based on a query of keywords associated with SDG 10 (reduced inequalities). The dataset includes all indexed publications between 2014 and 2018. The data are normalised across the range using Z-scoring.

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First-generation students (15.5%)

To see how the university is addressing economic inequality we measure the number of students starting a first (bachelor¡¯s) degree who identify as being the first person in their immediate family to attend university, divided by the total number of students starting a first (bachelor¡¯s) degree. All data are provided as full-time equivalents.

The data were provided directly by universities and normalised across the range using Z-scoring.

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Students from developing countries (15.5%)

This is defined as the proportion of international students at all degree levels who are from low-income and lower-middle-income countries, as defined by the World Bank. In order to be included, these students must be receiving financial aid that significantly supports them. All data are provided as full-time equivalents.

The data were provided directly by universities and normalised across the range using Z-scoring.

Students and staff with disabilities (23%)

• Proportion of students with disabilities (11.5%)
• Proportion of employees with disabilities (11.5%)

The student metric is defined as the number of students with disabilities at all degree levels, divided by the total number of students at all degree levels. The employee metric is defined as the number of employees with disabilities, divided by the total number of employees. All data are provided as full-time equivalents.

The data were provided directly by universities and normalised across the range using Z-scoring.

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Measures against discrimination (19%)

• Non-discriminatory admissions policy (1.9%)
• Track application and admission rates of under-represented groups (1.9%)
• Programmes to recruit students and staff from under-represented groups (1.9%)
• Anti-discrimination and anti-harassment policies for staff and students (1.9%)
• Existence of a diversity and equality committee, office or officer (1.9%)
• Mentoring, counselling or peer support programmes aimed at students and staff from under-represented groups (1.9%)
• Accessible facilities for people with disabilities (1.9%)
• Support services for people with disabilities (1.9%)
• Access schemes for people with disabilities (1.9%)
• Accommodation policy or strategy for people with disabilities, including adequate funding (1.9%)

The data and evidence were provided directly by universities. The evidence was evaluated and scored by Times ÖйúAƬ and is not normalised.

Evidence

When we ask about policies and initiatives, our metrics require universities to provide the evidence to support their claims. Evidence is evaluated against a set of criteria and decisions are cross validated where there is uncertainty. Evidence is not required to be exhaustive ¨C we are looking for examples that demonstrate best practice at the institutions concerned.

Timeframe

Unless otherwise stated, the data used refer to the closest academic year to January to December 2018.

Exclusions

Universities must teach undergraduates and be validated by a recognised accreditation body to be included in the ranking.

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Data collection

Institutions provide and sign off their institutional data for use in the rankings. On the rare occasions when a particular data point is not provided, we enter a value of zero.