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on Sociology of Economics |
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Issue of 2013‒05‒19
seven papers chosen by Jonas Holmström Swedish School of Economics and Business Administration |
| By: | Chia-Lin Chang (National Chung Hsing University); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Kyoto University) |
| Abstract: | Experts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal. The robustness of the rankings is also analysed. |
| Keywords: | Expert scores, Journal quality, RAMs, Impact factor, IFI, C3PO, PI-BETA, STAR, Eigenfactor, Article Influence, h-index, harmonic mean, robustness |
| JEL: | C18 C81 C83 |
| Date: | 2013–02–18 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:2013029&r=sog |
| By: | Chia-Lin Chang (National Chung Hsing University Taichung, Taiwan); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam, Complutense University of Madrid, and Institute of Economic Research, Kyoto University;); Les Oxley |
| Abstract: | This paper examines the issue of coercive journal self citations and the practical usefulness of two recent journal performance metrics, namely the Eigenfactor score, which may be interpreted as measuring “Journal Influence”, and the Article Influence score, using the Thomson Reuters ISI Web of Science (hereafter ISI) data for 2009 for the 200 most highly cited journals in each of the Sciences and Social Sciences. The paper also compares the two new bibliometric measures with two existing ISI metrics, namely Total Citations and the 5-year Impact Factor (5YIF) (including journal self citations) of a journal. It is shown that the Sciences and Social Sciences are different in terms of the strength of the relationship of journal performance metrics, although the actual relationships are very similar. Moreover, the journal influence and article influence journal performance metrics are shown to be closely related empirically to the two existing ISI metrics, and hence add little in practical usefulness to what is already known, except for eliminating the pressure arising from coercive journal self citations. These empirical results are compared with existing results in the bibliometrics literature. |
| Keywords: | Journal performance metrics, Coercive journal self citations, Research assessment measures, Total citations, 5-year impact factor (5YIF), Eigenfactor, Journal influence, Article influence |
| JEL: | A12 |
| Date: | 2013–03–04 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:2013040&r=sog |
| By: | Chia-Lin Chang (National Chung Hsing University); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, and Kyoto University); Les Oxley (University of Waikato) |
| Abstract: | This paper examines the practical usefulness of two new journal performance metrics, namely the Eigenfactor score, which may be interpreted as measuring “Journal Influence”, and the Article Influence score, using the Thomson Reuters ISI Web of Science (hereafter ISI) data for 2009 for the 200 most highly cited journals in each of the Sciences and Social Sciences, and compares them with two existing ISI metrics, namely Total Citations and the 5-year Impact Factor (5YIF) of a journal (including journal self citations). It is shown that the Sciences and Social Sciences are different in terms of the strength of the relationship of journal performance metrics, although the actual relationships are very similar. Moreover, the journal influence and article influence journal performance metrics are shown to be closely related empirically to the two existing ISI metrics, and hence add little in practical usefulness to what is already known. These empirical results are compared with existing results in the literature. |
| Keywords: | Journal performance metrics, Research assessment measures, Total citations, 5-year impact factor (5YIF), Eigenfactor, Journal and Article influence |
| JEL: | A12 |
| Date: | 2013–01–04 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:2013002&r=sog |
| By: | Fooladi, Masood; Salehi, Hadi; Md Yunus, Melor; Farhadi, Maryam; Aghaei Chadegani, Arezoo; Farhadi, Hadi; Ale Ebrahim, Nader |
| Abstract: | Journal impact factor (IF) as a gauge of influence and impact of a particular journal comparing with other journals in the same area of research, reports the mean number of citations to the published articles in particular journal. Although, IF attracts more attention and being used more frequently than other measures, it has been subjected to criticisms, which overcome the advantages of IF. Critically, extensive use of IF may result in destroying editorial and researchers’ behaviour, which could compromise the quality of scientific articles. Therefore, it is the time of the timeliness and importance of a new invention of journal ranking techniques beyond the journal impact factor. |
| Keywords: | Impact factor (IF), Journal ranking, Criticism, Praise, SCOPUS, Web of science, Self-citation |
| JEL: | I0 I2 I21 I25 I29 O1 O10 P0 P00 Z00 Z13 |
| Date: | 2013–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:46899&r=sog |
| By: | Aghaei Chadegani, Arezoo; Salehi, Hadi; Md Yunus, Melor; Farhadi, Hadi; Fooladi, Masood; Farhadi, Maryam; Ale Ebrahim, Nader |
| Abstract: | Nowadays, the world’s scientific community has been publishing an enormous number of papers in different scientific fields. In such environment, it is essential to know which databases are equally efficient and objective for literature searches. It seems that two most extensive databases are Web of Science and Scopus. Besides searching the literature, these two databases used to rank journals in terms of their productivity and the total citations received to indicate the journals impact, prestige or influence. This article attempts to provide a comprehensive comparison of these databases to answer frequent questions which researchers ask, such as: How Web of Science and Scopus are different? In which aspects these two databases are similar? Or, if the researchers are forced to choose one of them, which one should they prefer? For answering these questions, these two databases will be compared based on their qualitative and quantitative characteristics. |
| Keywords: | web of science, scopus, database, citations, provenance, coverage, searching, citation tracking, impact factor, indexing, h-index, researcher profile, researcher ID |
| JEL: | I0 I2 I23 O1 O10 Z0 Z00 Z1 |
| Date: | 2013–02–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:46898&r=sog |
| By: | Yungron Li; Javier Ruiz-Castillo |
| Abstract: | This paper studies the role of extremely highly cited articles in two instances: the measurement of citation inequality, and mean citation rates. Using a dataset, acquired from Thomson Scientific, consisting of 4.4 million articles published in 1998-2003 in 22 broad fields with a five-year citation window, the main results are the following. Firstly, both within each of 22 broad fields and in the all-sciences case, citation inequality is strongly affected by the presence of a handful of extreme observations, particularly when it is measured by citation inequality indices that are very sensitive to citation differences in the upper tail of citation distributions. Secondly, the impact of extreme observations on citation averages is generally much smaller. The concluding Section includes some practical lessons for students of citation inequality and/or users of high-impact indicators |
| Date: | 2013–04 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:we1308&r=sog |
| By: | Liran Einav; Jonathan D. Levin |
| Abstract: | Many believe that “big data” will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics. |
| JEL: | C10 C18 C50 C80 |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:19035&r=sog |