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Measuring researcher independence using bibliometric data: A proposal for a new performance indicator.

ABSTRACT: Bibliometric indicators are increasingly used to evaluate individual scientists-as is exemplified by the popularity of the many other publication and citation-based indicators used in evaluation. These indicators, however, cover at best some of the quality dimensions relevant for assessing a researcher: productivity and impact. At the same time, research quality has more dimensions than productivity and impact alone. As current bibliometric indicators are not covering various important quality dimensions, we here contribute to developing better indicators for those quality dimensions not yet addressed. One of the quality dimensions lacking valid indicators is an individual researcher's independence. We propose indicators to measure different aspects of independence: two assessing whether a researcher has developed an own collaboration network and two others assessing the level of thematic independence. Taken together they form an independence indicator. We illustrate how these indicators distinguish between researchers that are equally productive and have a considerable impact. The independence indicator is a step forward in evaluating individual scholarly quality.

SUBMITTER: van den Besselaar P

PROVIDER: S-EPMC6436739 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Measuring researcher independence using bibliometric data: A proposal for a new performance indicator.

van den Besselaar Peter P Sandström Ulf U

PloS one 20190327 3

Bibliometric indicators are increasingly used to evaluate individual scientists-as is exemplified by the popularity of the many other publication and citation-based indicators used in evaluation. These indicators, however, cover at best some of the quality dimensions relevant for assessing a researcher: productivity and impact. At the same time, research quality has more dimensions than productivity and impact alone. As current bibliometric indicators are not covering various important quality d ...[more]

PMID: 30917110

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Project description:BACKGROUND: For health care performance indicators (PIs) to be reliable, data underlying the PIs are required to be complete, accurate, consistent and reproducible. Given the lack of regulation of the data-systems used in the Netherlands, and the self-report based indicator scores, one would expect heterogeneity with respect to the data collection and the ways indicators are computed. This might affect the reliability and plausibility of the nationally reported scores. METHODS: We aimed to investigate the extent to which local hospital data collection and indicator computation strategies differ and how this affects the plausibility of self-reported indicator scores, using survey results of 42 hospitals and data of the Dutch national quality database. RESULTS: The data collection and indicator computation strategies of the hospitals were substantially heterogenic. Moreover, the Hip and Knee replacement PI scores can be regarded as largely implausible, which was, to a great extent, related to a limited (computerized) data registry. In contrast, Breast Cancer PI scores were more plausible, despite the incomplete data registry and limited data access. This might be explained by the role of the regional cancer centers that collect most of the indicator data for the national cancer registry, in a standardized manner. Hospitals can use cancer registry indicator scores to report to the government, instead of their own locally collected indicator scores. CONCLUSIONS: Indicator developers, users and the scientific field need to focus more on the underlying (heterogenic) ways of data collection and conditional data infrastructures. Countries that have a liberal software market and are aiming to implement a self-report based performance indicator system to obtain health care transparency, should secure the accuracy and precision of the heath care data from which the PIs are calculated. Moreover, ongoing research and development of PIs and profound insight in the clinical practice of data registration is warranted.

Project description:OBJECTIVES:Effective researcher assessment is key to decisions about funding allocations, promotion and tenure. We aimed to identify what is known about methods for assessing researcher achievements, leading to a new composite assessment model. DESIGN:We systematically reviewed the literature via the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols framework. DATA SOURCES:All Web of Science databases (including Core Collection, MEDLINE and BIOSIS Citation Index) to the end of 2017. ELIGIBILITY CRITERIA: (1) English language, (2) published in the last 10 years (2007-2017), (3) full text was available and (4) the article discussed an approach to the assessment of an individual researcher's achievements. DATA EXTRACTION AND SYNTHESIS:Articles were allocated among four pairs of reviewers for screening, with each pair randomly assigned 5% of their allocation to review concurrently against inclusion criteria. Inter-rater reliability was assessed using Cohen's Kappa (ĸ). The ĸ statistic showed agreement ranging from moderate to almost perfect (0.4848-0.9039). Following screening, selected articles underwent full-text review and bias was assessed. RESULTS:Four hundred and seventy-eight articles were included in the final review. Established approaches developed prior to our inclusion period (eg, citations and outputs, h-index and journal impact factor) remained dominant in the literature and in practice. New bibliometric methods and models emerged in the last 10 years including: measures based on PageRank algorithms or 'altmetric' data, methods to apply peer judgement and techniques to assign values to publication quantity and quality. Each assessment method tended to prioritise certain aspects of achievement over others. CONCLUSIONS:All metrics and models focus on an element or elements at the expense of others. A new composite design, the Comprehensive Researcher Achievement Model (CRAM), is presented, which supersedes past anachronistic models. The CRAM is modifiable to a range of applications.

Dataset Information

Measuring researcher independence using bibliometric data: A proposal for a new performance indicator.

Publications

Measuring researcher independence using bibliometric data: A proposal for a new performance indicator.

Similar Datasets

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