Research on research evaluation

Hemlin, Sven

doi:10.1080/02691729608578815

Cited by 40 publications

(26 citation statements)

References 95 publications

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“…With respect to size, the existence of a correlation between the output and the impact of publications has been identified since long time (Hemlin, 1996) Basically, most rankings use absolute numbers of publications and citations as the main element.…”

Section: Dependence On University Size and Subject MIXmentioning

confidence: 99%

Rankings and university performance: A conditional multidimensional approach

Daraio

Bonaccorsi

Simar

2015

European Journal of Operational Research

128

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University rankings are the subject of a paradox: the more they are criticized by social scientists and experts on methodological grounds, the more they receive attention in policy making and the media. In this paper we attempt to give a contribution to the birth of a new generation of rankings, one that might improve on the current state of the art, by integrating new kind of information and using new ranking techniques. Our approach tries to overcome four main criticisms of university rankings, namely: monodimensionality; statistical robustness; dependence on university size and subject mix; lack of consideration of the input-output structure. We provide an illustration on European universities and conclude by pointing on the importance of investing in data integration and open data at European level both for research and for policy making. University rankings are the subject of a paradox: the more they are criticized by social scientists and experts on methodological grounds, the more they receive attention in policy making and the media. In this paper we attempt to give a contribution to the birth of a new generation of rankings, one that might improve on the current state of the art, by integrating new kind of information and using new ranking techniques. Our approach tries to overcome four main criticisms of university rankings, namely: monodimensionality; statistical robustness; dependence on university size and subject mix; lack of consideration of the input-output structure. We provide an illustration on European universities and conclude by pointing on the importance of investing in data integration and open data at European level both for research and for policy making. I N S T I T U T D E S T A T I S T I Q U E B I O S T A T I S T I Q U E E T S C I E N C E S A C T U A R I E L L E S ( I S B A )UNIVERSITÉ

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Section: Dependence On University Size and Subject MIXmentioning

confidence: 99%

Rankings and university performance: A conditional multidimensional approach

Daraio

Bonaccorsi

Simar

2015

European Journal of Operational Research

128

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“…In this last article, Holmes and Oppenheim point out that, despite the many criticisms of citation counting, citation studies consistently prove to be statistically significant when correlated with other measures of eminence. In an overview of research evaluation, Hemlin also confirms a strong positive correlation between citation counts and other means of measuring research excellence, despite concerns over the validity of citation counting [82].…”

Section: Isi Databases Comprising the Science Citation Index (Sci) mentioning

confidence: 97%

The Influence of Peer Review on the Research Assessment Exercise

Bence

Oppenheim

2004

Journal of Information Science

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“…in Hemlin (1996). Specific "quali-quantitative" approaches may be requested for the assessment of interdisciplinary research, see e.g.…”

Section: Methodsmentioning

confidence: 99%

“…The main object of a research evaluation is represented by the results of given research activities, which can be considered the research effort (Hemlin, 1996). The outputs of a given research activity are the result of a complex set of interacting characteristics and activities that involve, but are not limited to: ability, talents, social aspects, luck, incentives, motivations, trade-offs, commitment, financial resource, efforts, infrastructure, education, personality skills, network, organization, curiosity, communication skills, and contextual and institutional factors.…”

Section: A Summary View and A Pragmatic Perspectivementioning

confidence: 99%

A Framework for the Assessment of Research and Its Impacts

Daraio

2017

Journal of Data and Information Science

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This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and “unit-free” property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as “quality.” Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three “enabling” conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided.

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Research on research evaluation

Cited by 40 publications

References 95 publications

Rankings and university performance: A conditional multidimensional approach

Rankings and university performance: A conditional multidimensional approach

The Influence of Peer Review on the Research Assessment Exercise

A Framework for the Assessment of Research and Its Impacts

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