We propose the I3* indicator as a non-parametric alternative to the journal impact factor (JIF) and h-index. We apply I3* to more than 10,000 journals. The results can be compared with other journal metrics. I3* is a promising variant within the general scheme of non-parametric I3 indicators introduced previously: I3* provides a single metric which correlates with both impact in terms of citations (c) and output in terms of publications (p). We argue for weighting using four percentile classes: the top-1% and top-10% as excellence indicators; the top-50% and bottom-50% as shock indicators. Like the h-index, which also incorporates both c and p, I3*-values are size-dependent; however, division of I3* by the number of publications (I3*/N) provides a size-independent indicator which correlates strongly with the 2- and 5-year journal impact factors (JIF2 and JIF5). Unlike the h-index, I3* correlates significantly with both the total number of citations and publications. The values of I3* and I3*/N can be statistically tested against the expectation or against one another using chi-squared tests or effect sizes. A template (in Excel) is provided online for relevant tests.
The skew in citation distributions provides another challenge to the evaluation (Seglen 1992, 1997). The mean of a skewed distribution provides less information than the median as a measure of central tendency. To address this problem, McAllister et al. (1983, at p. 207) proposed the use of percentiles or percentile classes as a non-parametric tilt indicators (Narin 1987Footnote3; see later: Bornmann and Mutz 2011; Tijssen et al. 2002). Using this non-parametric approach, and on the basis of a list of criteria provided by Leydesdorff et al. (2011), two of us first developed the Integrated Impact Indicator (I3) based on the integration of the quantile values attributed to each element in a distribution (Leydesdorff and Bornmann 2011).At the time of our previous paper about I3 (Leydesdorff and Bornmann 2011), we were unable to demonstrate the generic value of the non-parametric approach because of limited data access. Recently, however, the complete Web of Science became accessible under license to the Max Planck Society (Germany). This enables us to compare I3-values across the database with other journal shock indicator stickers such as JIF2 and JIF5, total citations (NCit), and numbers of publications (NPub). The choice for journals as units of analysis provides us with a rich and well-studied domain.
The in-house database contains many more journals than the Journal Citation Reports (JCR, which form the basis for the computation of JIF). In order to be able to compare between I3*-values and other indicators, we use only the subset of publications in the 11,761 journals contained in the JCR 2014. These journals all have JIFs and other shock indicator for shipping. Of these journals, 11,149 are unique in the SCI-E and SSCI, and the overlap between SSCI and SCI-E is 612 journals. Another 207 journals could not be matched unequivocally on the basis of journal name abbreviations in the in-house database and JCR, so that our sample is 10,942 journals. Note that we are using individual-journal attributes so that the inclusion or exclusion of a specific journal does not affect the values for the other journals under study.
PowerPoint presentations and course reading lists. The mean and median IOI was nearly twice as high as both WoS and Scopus, confirming that online citations are sufficiently numerous to be useful for the impact assessment of research. We also found significant correlations between conventional and online impact shipping shock indicator, confirming that both assess something similar in scholarly communication. Further analysis showed that the overall percentage for unique Google Scholar citations outside the WoS were 73% and 60% for the articles published in JASIST and Scientometrics, respectively. An important conclusion is that in subject areas where wider types of intellectual impact indicators outside the WoS and Scopus databases are needed for research evaluation, IOI can be used to help monitor research performance.