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Latin America University Rankings 2020: methodology

The World University Rankings performance indicators are the heart of the Latin America University Rankings, but we give them some adjustments to take account of the area鈥檚 unique characteristics

Published on
June 29, 2020
Last updated
March 21, 2022
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罢丑别听罢贬贰听Latin America University Rankings will be published at 4pm BST on 7 July


The 探花视频 World University Rankings are the only global performance tables that judge research-intensive universities across all their core missions: teaching, research, knowledge transfer and international outlook. The Latin America University Rankings uses the same 13 carefully calibrated performance indicators to provide the most comprehensive and balanced comparisons, trusted by students, academics, university leaders, industry and even governments 鈥 but the weightings are specially recalibrated to reflect the characteristics of聽emerging economy universities.

The performance indicators are grouped into five areas: Teaching (the learning environment); Research (volume, income and reputation); citations (research influence); International outlook (staff, students and research); and industry income (knowledge transfer).

Latin America University Rankings 2020: methodology

罢别补肠丑颈苍驳听(the learning environment): 36%

The most recent Academic Reputation Survey (run annually) that underpins this category was carried out between November 2019 and March 2020. It examined the perceived prestige of institutions in teaching. We have worked hard to ensure a balanced spread of responses across disciplines and countries. Where disciplines or countries were over- or underrepresented, THE鈥檚 data team weighted the responses to more closely reflect the actual distribution of scholars. The 2020 data are combined with the results of the 2019 survey, giving more than 21,000 responses.

As well as giving a sense of how committed an institution is to nurturing the next generation of academics, a聽high proportion of postgraduate research students also suggests the provision of teaching at the highest level that is thus attractive to graduates and effective at聽developing them. This indicator is normalised to take account of a university鈥檚 unique subject mix, reflecting that the volume of doctoral awards varies by discipline.

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Institutional income is scaled against academic staff numbers and normalised for purchasing-power parity (PPP). It聽indicates an institution鈥檚 general status and gives a broad sense of the infrastructure and facilities available to students and staff.

The most prominent indicator in this category looks at a university鈥檚 reputation for research excellence among its peers, based on the responses to our annual Academic Reputation Survey (see below).

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搁别蝉别补谤肠丑听(volume, income and reputation): 34%

Research income is scaled against academic staff -numbers and adjusted for PPP. This is a controversial indicator because it can be influenced by national policy and economic circumstances. But income is crucial to the development of world-class research, and because much of it is subject to聽competition and judged by peer review, our experts suggested that it was a valid measure. This indicator is fully normalised to聽take account of each university鈥檚 distinct subject profile, reflecting the fact that research grants in science subjects are often bigger than those awarded for the highest-quality social science, arts and humanities research.

To measure productivity we count the volume of聽scholarly output including articles, reviews, conference proceedings, books and book chapters indexed by Elsevier鈥檚 Scopus database per scholar, scaled for institutional size and normalised for subject. This gives a sense of the university鈥檚 ability to get papers published in聽quality peer-reviewed journals.

颁颈迟补迟颈辞苍蝉听(research influence): 20%

Our research influence indicator looks at聽universities鈥 role in spreading new knowledge and ideas.

We examine research influence by聽capturing the average number of times a聽university鈥檚 published work is cited by scholars globally. This year, our bibliometric data supplier Elsevier examined more than 86聽million citations to 13.6聽million journal articles, article reviews, conference proceedings, books and book chapters published over five聽years. The data include more than 24,000 academic journals indexed by Elsevier鈥檚 Scopus database and all indexed publications between 2015 and 2019. Citations to these publications made in the six years from 2015 to 2020 are also collected.

The citations help to show us how much each university is contributing to the sum of聽human knowledge: they tell us whose research has stood out, has been picked up and built on by other scholars and, most importantly, has been shared around the global scholarly community to聽expand the boundaries of our under-standing, irrespective of discipline.

The data are normalised to reflect variations in citation volume between different subject areas. This means that institutions with high levels of research activity in subjects with traditionally high citation counts do not gain an unfair advantage.

In 2015-16, we excluded papers with more than 1,000 authors because they were having a disproportionate impact on the citation scores of a small number of univer-sities. In 2016-17, we designed a method for reincorporating these papers. Working with Elsevier, we have developed a聽new fractional counting approach that ensures that all universities where academics are authors of these papers will receive at least 5聽per cent of the value of the paper, and where those that provide the most contributors to the paper receive a聽proportionately larger contribution.

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International outlook聽(staff, students, research): 7.5%

The ability of a university to聽attract undergraduates, postgraduates and faculty from all over the planet is key to its success on the world stage.

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In the third international indicator, we calculate the proportion of a university鈥檚 total research publications that have at least one international co-author and reward higher volumes. This indicator is normalised to account for a聽university鈥檚 subject mix and uses the same five-year window as the 鈥淐itations: research influence鈥 category.

Industry income聽(knowledge transfer): 2.5%

A university鈥檚 ability to help industry with innovations, inventions and consultancy has become a core mission of聽the contemporary global academy. This category seeks to capture such knowledge-transfer activity by looking at聽how much research income an institution earns from industry (adjusted for PPP), scaled against the number of academic staff it聽employs.

The category suggests the extent to which businesses are聽willing to pay for research and a university鈥檚 ability to attract funding in the commercial marketplace 鈥 useful indicators of institutional quality.


Exclusions

Universities are excluded from the Latin America University Rankings if they do not teach under-graduates or if their research output amounted to fewer than 200 publications between 2015 and 2019. Universities can also be excluded if 80聽per cent or more of their research output is exclusively in one of our 11 subject areas.

Data collection

Institutions provide and sign off their institutional data for use in the rankings. On聽the rare occasions when a particular data point is not provided, we enter a conservative estimate. By doing this, we avoid penalising an institution too harshly with a 鈥渮ero鈥 value for data that it overlooks or does not provide, but we do not reward it for withholding them.

Getting to the final result

Moving from a series of specific data points to indicators, and finally to a total score for an聽institution, requires us to match values that represent fundamentally different data. To聽do this we use a standardisation approach for each indicator, and then combine the indicators in the proportions indicated to the right.

The standardisation approach we use is based on the distribution of data within a particular indicator, where we calculate a cumulative probability function, and evaluate where a particular institution鈥檚 indicator sits within that function.

For all indicators except for the Academic Reputation Survey we calculate the cumulative probability function using a version of Z-scoring. The distribution of the data in the Academic Reputation Survey requires us to add an exponential component.

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Reader's comments (1)

A la expectativa, que excelente trabajo!

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