We apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. We show how the h-index can be used to express the broad impact of a scholar’s research output over time in more nuanced fashion than straight citation counts

Cronin, Blaise

Meho, Lokman I.

English

E-LIS

Published in the Journal of the American Society for Information Science and Technology, 57, no. 9 (July 2006): 1275-1278 Using the H-index to Rank Influential Information Scientists  Blaise Cronin and Lokman I. Meho  School of Library and Information Science, Indiana University Bloomington, IN 47401, USA {bcronin; meho}@indiana.edu    Abstract: We apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. We show how the h-index can be used to express the broad impact of a scholar’s research output over time in more nuanced fashion than straight citation counts.  Introduction  A new measure, the h-index, has been proposed to quantify the impact of individual scientists’ research output (Hirsch, 2005). It has generated considerable interest (e.g., Dumé, 2005) and attracted largely favorable comment (e.g., Ball, 2005). The measure is elegantly simple: “A scientist has index h if h of his/her Np papers have at least h citations each, and the other (Np - h) papers have fewer than h citations each” where Np is the number of papers published over n years (Hirsch, 2005, p. 1) (italics in original). That is to say, a scientist with an h-index of 30 has published 30 papers that have each attracted at least 30 citations. Of course, some of those 30 papers may have attracted considerably more than 30 citations, just as others of the author’s publications may have attracted considerably fewer than 30 citations. The value of h is likely to increase linearly with time. To state the obvious, a scientist’s h-index will never decrease, but may well increase as new papers are published, as ‘sleepers’ come to life and as the individual’s corpus incrementally attracts citations.   Hirsch provides a brief overview of the limitations of commonly used publication and citation measures before demonstrating the potential utility (and validity) of his h-index on a population of physicists and also a group of life scientists. The distinguishing feature of the h-index is that it captures the broad impact of a scientist’s work: “… Nobel prizes do not originate in one stroke of luck but in a body of scientific work” (Hirsch, 2005, p. 4). In other words, an author with very few high-impact papers or, alternatively, many low-impact papers will have a weak h-index. Crudely put, the h-index helps us distinguish between a one-hit wonder and an enduring performer. Hirsch estimates that a “successful scientist” will have an h-index of 20 after 20 years, an “outstanding scientist” one of 40 after 20 years, and a “truly unique” individual one of 60+ after 20 years (p. 3). However, he is quick to point out that values of h will be field dependent. As with all such reductionist measures of presumptive research quality, caution is required in both application and interpretation.   Approach and Methods  Here, for the first time, we apply the h-index to information science (IS) and compare rankings based on raw citation counts with those based on h-counts. In IS, as in many other fields, there is robust, ongoing debate on the pros and cons of evaluative bibliometrics and the associated techniques (e.g., Budd, 2000; Cronin & Overfelt, 1996; Meho & Spurgin, 2005). We identified 31 influential information science faculty from the U.S. (see Tables 1 and 2) based on our personal domain knowledge, augmented by a list of the 100+ most cited IS scholars in the Social Sciences Citation Index (SSCI), 1999-2005. We excluded highly-cited disciplinary outsiders (e.g., Jakob Nielsen, Wanda Orlikowski) and deceased grandees (e.g., Rob Kling, Gerald Salton). A majority of our names featured in the Budd (2000) and Meho and Spurgin (2005) studies of faculty productivity. Our sample, which is not strictly speaking a list of the most highly cited faculty in the field, includes a number of recently retired but active scholars (e.g., Marcia Bates, Howard White). Because a relatively high h-count depends to some extent on time-in-field, our list comprises mainly mid- to late-career researchers. Junior faculty will typically not have achieved the volume of output and level of citation impact necessary to generate a competitive h-index. For convenience, time-in-field is determined by the date of each faculty member’s earliest citation found in the ISI databases.   Two sets of data were gathered: one for the h-index and another for the total citation counts. For both measures, citation data were generated from the Arts & Humanities Citation Index (1980-2005), Science Citation Index (1974-2005) and the Social Sciences Citation Index (1972-2005) using Dialog. All the searches were done twice, first by a research assistant and subsequently by one of the authors (LM). No significant differences were found between the two sets of results. To generate h-counts, a list of all cited papers was created for each faculty  2member using the “EXPAND CR=…” function or command (CR stands for Cited Reference). Because cited items often had multiple entries (e.g., sometimes with and sometimes without the author’s middle initial), an effort was made to collate all entries that referred to the same cited item. In cases where a cited item did not provide enough information to determine where it belonged (due to citing or indexing errors), we relied on the author’s list of publications or on online database searching to verify the information. [It is interesting to note in passing that approximately two-thirds of the study sample had their bibliographies/curricula vitae available on the Web.]  For practical reasons, searches were limited to first authors. After searching for citations for each individual item, duplicate records were removed and the citation count was reported first with and then without self-citations. We included citations from all documents types: journal articles, conference papers, review articles, letters to editors, and so on. On average, it took roughly three hours of searching to generate the h-index for each individual author. Although we used the same searching method to generate the total citation counts, it took less than an hour on average to generate these data for each author. Hirsch and others have mentioned that generating h-indices using ISI's Web of Science is relatively straightforward. While this may be true, the scores generated from it are inaccurate. This is because Web of Science does not collate the different entries for the same cited item. Table 3 illustrates the different results achieved when comparing h-counts derived from Web of Science with those generated manually using the method employed in this study.  Results  Our 31 names are ranked according to four measures: (i) total number of citations, including self-citations; (ii) total number of citations, excluding self-citations; (iii) h-index, with self-citations included; (iv) h-index, with self-citations excluded (see Tables 1 and 2). The citation range is from 84 to 1,048. When self-citations are removed, the range contracts slightly, from 79 to 1,025. In both cases, Carbo and Belkin occupy the respective pole positions. Generally, the elimination of self-citations does not much influence the rank ordering, though both Cronin and Kantor drop two notches in the rankings, from 3 to 5 and 20 to 22, respectively. The h-index range is from 5 to 20 (from Carbo to Belkin, respectively), with citations included, and from 5 to19 with self-citations excluded. In the latter case, Belkin and Saracevic are tied on  319 while Carbo, Griffiths, and Schamber are tied at 5. The Spearman rank order correlation between citation ranks and h-index (with self-citations excluded) was 0.9, significant at the 0.01 level.  There are no tied ranks when citation counts are used, but bunching does occur with the h-index. For instance, eight individuals have an h-value of 11. The citation scores (self-citations excluded) for this octet range from 250 to 599. The year of the first item cited in the ISI databases for each author ranges from 1963 to 1991. Four are date stamped in the sixties, 13 each in the seventies and eighties. In only one case (Spink) does the earliest cited item date from the nineties. Irrespective of which of the four measures is used to generate rankings, five names are omni-present: Belkin, Saracevic, Bates, Borgman, and Cronin. These five authors had their first paper cited in either the 1960s or 1970s.   Discussion and Conclusions  The strong positive correlation between the h-index and citation counts suggests that the total number of citations (with or without self-citations) is, indeed, a reliable indicator of scholarly impact and influence. Nonetheless, the h-index provides some additional discriminatory power. By way of example, Schamber and Eisenberg have citation scores of 241 and 142, yet their h-indices are 5 and 8, respectively. In other words, although Schamber has accrued considerably more citations over approximately the same timeframe, more of Eisenberg’s publications have had a broader impact. To take another example, Smith and McCain have roughly comparable citation counts of 281 and 317, yet their h-indices are 7 and 12, respectively, despite the fact that McCain has had shorter time-in-field. A different example is provided by Spink, whose citation score (389)—though appreciably lower than either Cronin’s (782) or Marchionini’s (671)—has an h-index of 12, only one less than theirs. Additionally, her first cited paper (1991) is much more recent than either Cronin’s (1978) or Marchionini’s (1985). Spink may be a comparative newcomer, one who is moderately rather than heavily cited, but her publications are having a relatively high impact. A final illustration: despite considerable differences in citation yields and time-in-field, Fidel, McCain, Spink and White all have the same h-count.   Information science is a small field (certainly by comparison with either physics or the life sciences) and it is not surprising to find relatively modest citation yields and correspondingly  4modest h-indices. The median h-index (self-citations excluded) for our sample was 11, the highest 19, and the lowest five. These numbers are considerably lower than those generated by Hirsch (2005, p. 4) for either physics (median = 46) or the life sciences (median = 157), which should make it abundantly clear that cross-field comparisons are inappropriate. It would be inappropriate to stratify our sample the way Hirsch did, as successful, outstanding, or truly unique. As Hirsch (2005, p. 4) has already noted, “more research in understanding similarities and differences of h-index distributions in different fields of science” is needed. Others may wish to extend and refine the approach we have piloted here by, for example, enlarging the sample to accommodate both more junior and senior IS scholars or by including more detailed analysis of authors’ publication data.  Acknowledgment  We are most grateful to Sara Franks for assistance with online database searching and to both Yvonne Rogers and Debora Shaw for helpful comments.  5References Ball, P. (2005). Index aims for fair ranking of scientists. Available online at: http://www.nature.com/nature/journal/v436/n7053/full/436900a.html Budd, J. M. (2000). Scholarly productivity of U.S. LIS faculty: An update. Library Quarterly, 70(2), 230-245. Cronin, B. & Overfelt, K. (1996). Postscript on program rankings. Journal of the American Society for Information Science, 47(2), 173-176. Dumé, B. (2005). Number theory. Available online at: http://physicsweb.org/articles/news/9/8/9/1 Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Available online at: http://xxx.arxiv.org/abs/physics/0508025Meho, L. I. & Spurgin, K. M. (2005). Ranking the research productivity of LIS faculty and schools: An evaluation of data sources and research methods. Journal of the American Society for Information Science and Technology, 56(12), 1314-1331.   6TABLE 1 Faculty Members Ranked by Number of Citations  Total number of citations Name Including self-citations Excluding self-citations Year first item was cited  Total Rank Total Rank  Belkin, Nicholas J. 1,048 1 1,025 1 1974 Saracevic, Tefko 1,013 2 972 2 1963 Bates, Marcia J. 830 5 795 3 1971 Borgman, Christine L. 839 4 786 4 1975 Cronin, Blaise 875 3 782 5 1978 Marchionini, Gary 706 6 671 6 1985 Tenopir, Carol 655 7 599 7 1979 Buckland, Michael K.  618 9 571 8 1967 Hernon, Peter 627 8 527 9 1968 McClure, Charles R. 583 10 508 10 1974 Kuhlthau, Carol C. 480 11 463 11 1983 White, Howard D. 475 12 441 12 1974 Fidel, Raya 450 14 427 13 1981 Spink, Amanda 458 13 389 14 1991 Dillon, Andrew 373 15 352 15 1987 McCain, Katherine W. 349 16 317 16 1981 Budd, John M. 324 17 302 17 1981 Smith, Linda C. 287 19 281 18 1974 Koenig, Michael E.D. 294 18 254 19 1971 Larson, Ray R. 263 21 250 20 1981 Van House, Nancy 255 22 243 21 1983 Kantor, Paul B. 284 20 242 22 1972 Schamber, Linda 250 23 241 23 1987 Soergel, Dagobert 198 25 194 24 1967 Case, Donald O. 210 24 192 25 1981 Griffiths, José-Marie 157 27 152 26 1975 Eisenberg, Mike 148 28 142 27 1986 Losee, Robert M. 166 26 136 28 1986 Liddy, Elizabeth D. 128 29 116 29 1987 Wildemuth, Barbara M. 121 30 108 30 1977 Carbo, Toni 84 31 79 31 1975  7TABLE 2 Faculty Members Ranked by H-Index  H-Index Name Including self-citations Excluding self-citations Year first item was cited  H-Index Rank H-Index Rank  Belkin, Nicholas J. 20 1 19 1T 1974 Saracevic, Tefko 19 2 19 1T 1963 Bates, Marcia J. 18 3T 17 3 1971 Borgman, Christine L. 18 3T 15 4 1975 Cronin, Blaise 14 5T 13  5T 1978 Marchionini, Gary 14 5T 13 5T 1985 Fidel, Raya 13 11T 12  7T 1981 McCain, Katherine W. 14 5T 12 7T 1981 Spink, Amanda 14 5T 12 7T 1991 White, Howard D. 14 5T 12  7T 1974 Buckland, Michael K. 12 13T 11  11T 1967 Budd, John M. 11 17T 11  11T 1981 Dillon, Andrew 12 13T 11  11T 1987 Hernon, Peter 14 5T 11  11T 1968 Kuhlthau, Carol C. 12 13T 11  11T 1983 Larson, Ray R. 11 17T 11  11T 1981 McClure, Charles R. 13 11T 11  11T 1974 Tenopir, Carol 12 13T 11  11T 1979 Kantor, Paul B. 10 19T 9 19T 1972 Koenig, Michael E.D. 9 21T 9 19T 1971 Van House, Nancy 9 21T 9 19T 1983 Case, Donald O. 9 21T 8  22T 1981 Eisenberg, Mike 8 24 8  22T 1986 Losee, Robert M. 10 19T 7  24T 1986 Smith, Linda C. 7 25T 7 24T 1974 Soergel, Dagobert 7 25T 7  24T 1967 Wildemuth, Barbara M. 7 25T 7 24T 1977 Liddy, Elizabeth D. 6 28T 6 28 1987 Carbo, Toni  5  29T 5  29T 1975 Griffiths, José-Marie 5 29T 5  29T 1975 Schamber, Linda 5 29T 5 29T 1987  8TABLE 3 Comparison of H-indices* Derived from Web of Science and Dialog Searches  Name Web of Science Dialog Searching Belkin 17 20 Bates 16 18 Borgman 14 18 Saracevic 13 19 Cronin 12 14 Budd 11 11 Kantor 8 10 Losee 8 10 Soergel 5 7 *Includes self-citations.       9

Using the H-index to Rank Influential Information Scientists

E-LIS repository

Published in the Journal of the American Society for Information Science and Technology, 57, no. 9 (July 2006): 1275-1278 
Using the H-index to Rank Influential Information Scientists 
 
Blaise Cronin and Lokman I. Meho 
 
School of Library and Information Science, Indiana University Bloomington, IN 47401, USA 
{bcronin; meho}@indiana.edu 
 
 
 
Abstract: We apply a new bibliometric measure, the h-index (Hirsch, 2005), to 
the literature of information science. Faculty rankings based on raw citation 
counts are compared with those based on h-counts. There is a strong positive 
correlation between the two sets of rankings. We show how the h-index can be 
used to express the broad impact of a scholar’s research output over time in more 
nuanced fashion than straight citation counts. 
 
Introduction 
 A new measure, the h-index, has been proposed to quantify the impact of individual 
scientists’ research output (Hirsch, 2005). It has generated considerable interest (e.g., Dumé, 
2005) and attracted largely favorable comment (e.g., Ball, 2005). The measure is elegantly 
simple: “A scientist has index h if h of his/her Np papers have at least h citations each, and the 
other (Np - h) papers have fewer than h citations each” where Np is the number of papers 
published over n years (Hirsch, 2005, p. 1) (italics in original). That is to say, a scientist with an 
h-index of 30 has published 30 papers that have each attracted at least 30 citations. Of course, 
some of those 30 papers may have attracted considerably more than 30 citations, just as others of 
the author’s publications may have attracted considerably fewer than 30 citations. The value of h 
is likely to increase linearly with time. To state the obvious, a scientist’s h-index will never 
decrease, but may well increase as new papers are published, as ‘sleepers’ come to life and as the 
individual’s corpus incrementally attracts citations.  
 Hirsch provides a brief overview of the limitations of commonly used publication and 
citation measures before demonstrating the potential utility (and validity) of his h-index on a 
population of physicists and also a group of life scientists. The distinguishing feature of the h-
index is that it captures the broad impact of a scientist’s work: “… Nobel prizes do not originate 
in one stroke of luck but in a body of scientific work” (Hirsch, 2005, p. 4). In other words, an 
author with very few high-impact papers or, alternatively, many low-impact papers will have a 
weak h-index. Crudely put, the h-index helps us distinguish between a one-hit wonder and an 
enduring performer. Hirsch estimates that a “successful scientist” will have an h-index of 20 
after 20 years, an “outstanding scientist” one of 40 after 20 years, and a “truly unique” individual 
one of 60+ after 20 years (p. 3). However, he is quick to point out that values of h will be field 
dependent. As with all such reductionist measures of presumptive research quality, caution is 
required in both application and interpretation.  
 
Approach and Methods 
 Here, for the first time, we apply the h-index to information science (IS) and compare 
rankings based on raw citation counts with those based on h-counts. In IS, as in many other 
fields, there is robust, ongoing debate on the pros and cons of evaluative bibliometrics and the 
associated techniques (e.g., Budd, 2000; Cronin & Overfelt, 1996; Meho & Spurgin, 2005). We 
identified 31 influential information science faculty from the U.S. (see Tables 1 and 2) based on 
our personal domain knowledge, augmented by a list of the 100+ most cited IS scholars in the 
Social Sciences Citation Index (SSCI), 1999-2005. We excluded highly-cited disciplinary 
outsiders (e.g., Jakob Nielsen, Wanda Orlikowski) and deceased grandees (e.g., Rob Kling, 
Gerald Salton). A majority of our names featured in the Budd (2000) and Meho and Spurgin 
(2005) studies of faculty productivity. Our sample, which is not strictly speaking a list of the 
most highly cited faculty in the field, includes a number of recently retired but active scholars 
(e.g., Marcia Bates, Howard White). Because a relatively high h-count depends to some extent 
on time-in-field, our list comprises mainly mid- to late-career researchers. Junior faculty will 
typically not have achieved the volume of output and level of citation impact necessary to 
generate a competitive h-index. For convenience, time-in-field is determined by the date of each 
faculty member’s earliest citation found in the ISI databases.  
 Two sets of data were gathered: one for the h-index and another for the total citation 
counts. For both measures, citation data were generated from the Arts & Humanities Citation 
Index (1980-2005), Science Citation Index (1974-2005) and the Social Sciences Citation Index 
(1972-2005) using Dialog. All the searches were done twice, first by a research assistant and 
subsequently by one of the authors (LM). No significant differences were found between the two 
sets of results. To generate h-counts, a list of all cited papers was created for each faculty 
 2
member using the “EXPAND CR=…” function or command (CR stands for Cited Reference). 
Because cited items often had multiple entries (e.g., sometimes with and sometimes without the 
author’s middle initial), an effort was made to collate all entries that referred to the same cited 
item. In cases where a cited item did not provide enough information to determine where it 
belonged (due to citing or indexing errors), we relied on the author’s list of publications or on 
online database searching to verify the information. [It is interesting to note in passing that 
approximately two-thirds of the study sample had their bibliographies/curricula vitae available 
on the Web.] 
 For practical reasons, searches were limited to first authors. After searching for citations 
for each individual item, duplicate records were removed and the citation count was reported 
first with and then without self-citations. We included citations from all documents types: journal 
articles, conference papers, review articles, letters to editors, and so on. On average, it took 
roughly three hours of searching to generate the h-index for each individual author. Although we 
used the same searching method to generate the total citation counts, it took less than an hour on 
average to generate these data for each author. Hirsch and others have mentioned that generating 
h-indices using ISI's Web of Science is relatively straightforward. While this may be true, the 
scores generated from it are inaccurate. This is because Web of Science does not collate the 
different entries for the same cited item. Table 3 illustrates the different results achieved when 
comparing h-counts derived from Web of Science with those generated manually using the 
method employed in this study. 
 
Results 
 Our 31 names are ranked according to four measures: (i) total number of citations, 
including self-citations; (ii) total number of citations, excluding self-citations; (iii) h-index, with 
self-citations included; (iv) h-index, with self-citations excluded (see Tables 1 and 2). The 
citation range is from 84 to 1,048. When self-citations are removed, the range contracts slightly, 
from 79 to 1,025. In both cases, Carbo and Belkin occupy the respective pole positions. 
Generally, the elimination of self-citations does not much influence the rank ordering, though 
both Cronin and Kantor drop two notches in the rankings, from 3 to 5 and 20 to 22, respectively. 
The h-index range is from 5 to 20 (from Carbo to Belkin, respectively), with citations included, 
and from 5 to19 with self-citations excluded. In the latter case, Belkin and Saracevic are tied on 
 3
19 while Carbo, Griffiths, and Schamber are tied at 5. The Spearman rank order correlation 
between citation ranks and h-index (with self-citations excluded) was 0.9, significant at the 0.01 
level. 
 There are no tied ranks when citation counts are used, but bunching does occur with the 
h-index. For instance, eight individuals have an h-value of 11. The citation scores (self-citations 
excluded) for this octet range from 250 to 599. The year of the first item cited in the ISI 
databases for each author ranges from 1963 to 1991. Four are date stamped in the sixties, 13 each 
in the seventies and eighties. In only one case (Spink) does the earliest cited item date from the 
nineties. Irrespective of which of the four measures is used to generate rankings, five names are 
omni-present: Belkin, Saracevic, Bates, Borgman, and Cronin. These five authors had their first 
paper cited in either the 1960s or 1970s.  
 
Discussion and Conclusions 
 The strong positive correlation between the h-index and citation counts suggests that the 
total number of citations (with or without self-citations) is, indeed, a reliable indicator of 
scholarly impact and influence. Nonetheless, the h-index provides some additional 
discriminatory power. By way of example, Schamber and Eisenberg have citation scores of 241 
and 142, yet their h-indices are 5 and 8, respectively. In other words, although Schamber has 
accrued considerably more citations over approximately the same timeframe, more of 
Eisenberg’s publications have had a broader impact. To take another example, Smith and 
McCain have roughly comparable citation counts of 281 and 317, yet their h-indices are 7 and 
12, respectively, despite the fact that McCain has had shorter time-in-field. A different example 
is provided by Spink, whose citation score (389)—though appreciably lower than either Cronin’s 
(782) or Marchionini’s (671)—has an h-index of 12, only one less than theirs. Additionally, her 
first cited paper (1991) is much more recent than either Cronin’s (1978) or Marchionini’s (1985). 
Spink may be a comparative newcomer, one who is moderately rather than heavily cited, but her 
publications are having a relatively high impact. A final illustration: despite considerable 
differences in citation yields and time-in-field, Fidel, McCain, Spink and White all have the 
same h-count.  
 Information science is a small field (certainly by comparison with either physics or the 
life sciences) and it is not surprising to find relatively modest citation yields and correspondingly 
 4
modest h-indices. The median h-index (self-citations excluded) for our sample was 11, the 
highest 19, and the lowest five. These numbers are considerably lower than those generated by 
Hirsch (2005, p. 4) for either physics (median = 46) or the life sciences (median = 157), which 
should make it abundantly clear that cross-field comparisons are inappropriate. It would be 
inappropriate to stratify our sample the way Hirsch did, as successful, outstanding, or truly 
unique. As Hirsch (2005, p. 4) has already noted, “more research in understanding similarities 
and differences of h-index distributions in different fields of science” is needed. Others may wish 
to extend and refine the approach we have piloted here by, for example, enlarging the sample to 
accommodate both more junior and senior IS scholars or by including more detailed analysis of 
authors’ publication data. 
 
Acknowledgment 
 We are most grateful to Sara Franks for assistance with online database searching and to 
both Yvonne Rogers and Debora Shaw for helpful comments. 
 5
References 
Ball, P. (2005). Index aims for fair ranking of scientists. Available online at: 
http://www.nature.com/nature/journal/v436/n7053/full/436900a.html 
Budd, J. M. (2000). Scholarly productivity of U.S. LIS faculty: An update. Library Quarterly, 
70(2), 230-245. 
Cronin, B. & Overfelt, K. (1996). Postscript on program rankings. Journal of the American 
Society for Information Science, 47(2), 173-176. 
Dumé, B. (2005). Number theory. Available online at: 
http://physicsweb.org/articles/news/9/8/9/1 
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Available 
online at: http://xxx.arxiv.org/abs/physics/0508025
Meho, L. I. & Spurgin, K. M. (2005). Ranking the research productivity of LIS faculty and 
schools: An evaluation of data sources and research methods. Journal of the American 
Society for Information Science and Technology, 56(12), 1314-1331. 
 
 6
TABLE 1 
Faculty Members Ranked by Number of Citations 
 
Total number of citations Name 
Including self-citations Excluding self-citations 
Year first item 
was cited 
 Total Rank Total Rank  
Belkin, Nicholas J. 1,048 1 1,025 1 1974 
Saracevic, Tefko 1,013 2 972 2 1963 
Bates, Marcia J. 830 5 795 3 1971 
Borgman, Christine L. 839 4 786 4 1975 
Cronin, Blaise 875 3 782 5 1978 
Marchionini, Gary 706 6 671 6 1985 
Tenopir, Carol 655 7 599 7 1979 
Buckland, Michael K.  618 9 571 8 1967 
Hernon, Peter 627 8 527 9 1968 
McClure, Charles R. 583 10 508 10 1974 
Kuhlthau, Carol C. 480 11 463 11 1983 
White, Howard D. 475 12 441 12 1974 
Fidel, Raya 450 14 427 13 1981 
Spink, Amanda 458 13 389 14 1991 
Dillon, Andrew 373 15 352 15 1987 
McCain, Katherine W. 349 16 317 16 1981 
Budd, John M. 324 17 302 17 1981 
Smith, Linda C. 287 19 281 18 1974 
Koenig, Michael E.D. 294 18 254 19 1971 
Larson, Ray R. 263 21 250 20 1981 
Van House, Nancy 255 22 243 21 1983 
Kantor, Paul B. 284 20 242 22 1972 
Schamber, Linda 250 23 241 23 1987 
Soergel, Dagobert 198 25 194 24 1967 
Case, Donald O. 210 24 192 25 1981 
Griffiths, José-Marie 157 27 152 26 1975 
Eisenberg, Mike 148 28 142 27 1986 
Losee, Robert M. 166 26 136 28 1986 
Liddy, Elizabeth D. 128 29 116 29 1987 
Wildemuth, Barbara M. 121 30 108 30 1977 
Carbo, Toni 84 31 79 31 1975 
 7
TABLE 2 
Faculty Members Ranked by H-Index 
 
H-Index Name 
Including self-citations Excluding self-citations 
Year first item 
was cited 
 H-Index Rank H-Index Rank  
Belkin, Nicholas J. 20 1 19 1T 1974 
Saracevic, Tefko 19 2 19 1T 1963 
Bates, Marcia J. 18 3T 17 3 1971 
Borgman, Christine L. 18 3T 15 4 1975 
Cronin, Blaise 14 5T 13  5T 1978 
Marchionini, Gary 14 5T 13 5T 1985 
Fidel, Raya 13 11T 12  7T 1981 
McCain, Katherine W. 14 5T 12 7T 1981 
Spink, Amanda 14 5T 12 7T 1991 
White, Howard D. 14 5T 12  7T 1974 
Buckland, Michael K. 12 13T 11  11T 1967 
Budd, John M. 11 17T 11  11T 1981 
Dillon, Andrew 12 13T 11  11T 1987 
Hernon, Peter 14 5T 11  11T 1968 
Kuhlthau, Carol C. 12 13T 11  11T 1983 
Larson, Ray R. 11 17T 11  11T 1981 
McClure, Charles R. 13 11T 11  11T 1974 
Tenopir, Carol 12 13T 11  11T 1979 
Kantor, Paul B. 10 19T 9 19T 1972 
Koenig, Michael E.D. 9 21T 9 19T 1971 
Van House, Nancy 9 21T 9 19T 1983 
Case, Donald O. 9 21T 8  22T 1981 
Eisenberg, Mike 8 24 8  22T 1986 
Losee, Robert M. 10 19T 7  24T 1986 
Smith, Linda C. 7 25T 7 24T 1974 
Soergel, Dagobert 7 25T 7  24T 1967 
Wildemuth, Barbara M. 7 25T 7 24T 1977 
Liddy, Elizabeth D. 6 28T 6 28 1987 
Carbo, Toni  5  29T 5  29T 1975 
Griffiths, José-Marie 5 29T 5  29T 1975 
Schamber, Linda 5 29T 5 29T 1987 
 8
TABLE 3 
Comparison of H-indices* Derived from Web of Science and Dialog Searches 
 
Name Web of Science Dialog Searching 
Belkin 17 20 
Bates 16 18 
Borgman 14 18 
Saracevic 13 19 
Cronin 12 14 
Budd 11 11 
Kantor 8 10 
Losee 8 10 
Soergel 5 7 
*Includes self-citations. 
 
 
 
 
 
 9


We apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. We show how the h-index can be used to express the broad impact of a scholarâ s research output over time in more nuanced fashion than straight citation counts

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Using the H-index to Rank Influential Information Scientists

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