A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact

Costas, Rodrigo; Leeuwen, Thed N. van; Bordons, María

doi:10.1002/asi.21348

Cited by 131 publications

(157 citation statements)

References 76 publications

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“…Among the many alternatives, we classify all articles into 30 broad fields, namely,take as our starting point the partition of scientific activity into the 35 broad fields distinguished inintroduced by Tijssen et al (2010) except the following five that, which hasve been used in Buter & van Raan (2011), Hoekman, et al (2010), and Schneider & Costas, R. (2013 2. The accurate assignment of articles to individual authors is known to be plagued with formidable obstacles (Lindsey, 1980, andCostas et al, 2010). In this paper, we solve this problem using the algorithm recently generated by Caron & van Eck (2014).…”

Section: Ii1 Measurement Issuesmentioning

confidence: 99%

“…However, in the dataset used in this paper it is possible to take into account each author's citation impact. Therefore, in this Section, we define individual productivity as reveals that, as we know from previous research (Costas et al, 2010), the most prolific authors need not be those with the highest impact. Thus, the two concepts, although related, are best treated separately.…”

Section: Iv1 Characteristics Of Productivity Distributionsmentioning

confidence: 99%

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The skewness of scientific productivity

Ruiz‐Castillo

Costas

2014

Journal of Informetrics

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This paper exploits a unique 2003-2011 large dataset, indexed by Thomson & Reuters, consisting of 17.2 million disambiguated authors classified into 30 broad scientific fields, as well as the 48.2 million articles resulting from a multiplying strategy in which any article co-authored by two or more persons is wholly assigned as many times as necessary to each of them. The dataset is characterized by a large proportion of authors who have their oeuvre in several fields. We measure individual productivity in two ways that are uncorrelated: as the number of articles per person, and as the mean citation per article per person in the 2003-2011 period. We analyze the shape of the two types of individual productivity distributions in each field using size-and scale-independent indicators. For productivity inequality, we use the coefficient of variation. To assess the skewness of productivity distributions we use a robust index of skeweness, as well as the Characteristic Scores and Scales approach. For productivity inequality, we use the coefficient of variation. In each field, we study two samples: the entire population, and what we call "successful authors", namely, the subset of scientists whose productivity is above their field average. The main result is that, in spite of wide differences in production and citation practices across fields, the shape of field productivity distributions are very similar across fields. The parallelism of the results for the population as a whole and for the subset of successful authors when productivity is measured as mean citation per article per person, reveals the fractal nature of the skewness of scientific productivity in this case. These results are essentially maintained when any article co-authored by two or more persons is fractionally assigned to each of them.Acknowledgements. This is the third version of a Working Paper in this series with the same title whose second version appeared in May 2014. This paper was conceived while Ruiz-Castillo enjoyed the hospitality of the Centre for Science and Technology Studies, Leiden University, The Netherlands, during the 2013 spring term. Ruiz-Castillo also acknowledges financial help from the Spanish MEC through grant ECO2010-19596. The authors acknowledge fruitful conversations with Raquel Carrasco, as well as comments from the participants in a CWTS seminar. All remaining shortcomings are the sole responsibility of the authors.

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Section: Ii1 Measurement Issuesmentioning

confidence: 99%

Section: Iv1 Characteristics Of Productivity Distributionsmentioning

confidence: 99%

The skewness of scientific productivity

Ruiz‐Castillo

Costas

2014

Journal of Informetrics

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“…These methods are useful for showing the strong relationships among them, especially at journal level (Bollen, Van de Sompel, Hagberg, & Chute, 2009;Bornmann, Mutz, Hug, & Daniel, 2011;Costas, van Leeuwen, & Bordons, 2010;Leydesdorff, 2009;Yu & Lee, 2008), and the results could show the usability of centrality measures as a new bibliometric indicator (Leydesdorff, 2009).…”

Section: Background and Purposementioning

confidence: 99%

“…The studies that compare various indicators to find an effective indicator have increased (Bollen, Van de Sompel, Hagberg, & Chute, 2009;Bornmann, Mutz, Hug, & Daniel, 2011;Costas, van Leeuwen, & Bordons, 2010;Kim & Lee, 2010;Lee, 2011;Leydesdorff, 2009;Yu & Lee, 2008). The analysis methods are correlation analysis, factor analysis, or MDS.…”

Section: Previous Researchmentioning

confidence: 99%

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks

Yu¹

2012

Journal of the Korean Society for information Management

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The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

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“…Particularly, the determination of journal quality weights is an often discussed issue. The commonly used proxies for the quality of a journal-Journal Impact Factors (JIF) (Garfield 2006) and Normalized Journal Position (NJP) (Bornmann and Marx 2013;Costas et al 2010)-are based on a journal's citation frequency. However, this frequency can also be determined by non-scientific reasons (Bornmann and Daniel 2008;Judge et al 2007;Jones et al 1996) such as the reputation of a journal or its relationship to the authors and might create potential for manipulation or misuse (Archambault and Larivière 2009).…”

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confidence: 99%

Robustness of personal rankings: the Handelsblatt example

Lorenz

Löffler

2015

Bus Res

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In the last years, Handelsblatt has published several rankings of business economists from German, Swiss and Austrian research institutions based on their journal publication output. These rankings have a strong influence on the academic profession. We scrutinize the Handelsblatt methodology by examining the effect the rankings’ underlying algorithms and assumptions have on the scores and ranks of individual researchers. In doing so, we clarify how robust the result is with respect to these internal parameters. Since the parameters used by Handelsblatt are not scientifically substantiated but defined ad hoc, this question is of great importance. For each parameter variation, we provide several robustness measures for both the Handelsblatt life’s work ranking and the Handelsblatt recent research performance ranking. E.g., if one applies a weighting scheme that lays more emphasis on first tier journal publications such that the weight of a particular category is always double of the weight of the next lower category, rank correlations based on all researchers in both personal rankings exceed 80 %. However, if one solely considers the top 25 performing researchers rank correlations fall below 50 and 20 % of researchers even drop out of this top group. Further research as well as the discussion in the academic community should clarify whether these correlations verify the robustness of the ranking or manifest the opposite.

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A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact

Cited by 131 publications

References 76 publications

The skewness of scientific productivity

The skewness of scientific productivity

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks

Robustness of personal rankings: the Handelsblatt example

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