AC 2010-1047: ANALYSIS OF ASEE-ELD CONFERENCE PROCEEDINGS:2000-2009

David Hubbard, Texas A&M UniversityDavid E. Hubbard is an Assistant Professor and Science & Engineering Librarian at the SterlingC. Evans Library, Texas A&M University, College Station, TX. He received his B.A. inchemistry from the University of Missouri-St. Louis in 1988 and M.A in library science from theUniversity of Missouri-Columbia in 2003.

© American Society for Engineering Education, 2010

Page 15.177.1

Analysis of ASEE-ELD Conference Proceedings: 2000-2009

Abstract

This study examines the papers and posters from the annual American Society for

Engineering Education – Engineering Libraries Division (ASEE-ELD) programs over the

last ten years. The bibliometric analysis provides an overview of authorship and content, as

well as insights into the Division and what it values. It also gives context that is useful to

both newer and longtime ASEE-ELD members. Unlike many other studies of single

publications, contributions to annual ASEE-ELD programs were not systematically

indexed and tangible artifacts do not exist for all contributions. Primary sources were

consulted to identify the contributors and their contributions; however, this was often

limited to just bibliographic information. The advent of “publish-to-present” for all papers

and posters in 2009 will provide systematic archiving in the future, but is of limited use for

the period studied. Considering the move to “publish-to-present” and a decade that brought

significant change to the profession, it seems appropriate to reflect upon the past decade

through such an analysis. The contributors were summarized both qualitatively and

quantitatively in terms of authorship, co-authorship, and institutional/organizations

affiliations. Since full-length papers do not exist for all contributions prior to 2009, content

analysis is based on titles of the papers and posters. The titles were analyzed using both a

standard classification scheme and textual analysis software to identify topics and

keywords/phrases, respectively. These topics and keywords/phrases were further analyzed

for patterns and trends. The analysis not only presents a snapshot of where our profession

and Division has been, but potentially identifies future directions.

Introduction

Bibliographic analysis of a single publication can provide valuable insights into a publication, as

well as the interests and values of an organization if associated with a particular professional

society. Single publication studies are performed for a variety of reasons, though most often to

study the characteristics, trends, impact, and authorship within a specific publication or

discipline. The objective of this study is to examine authorship and content of the American

Society for Engineering Education – Engineering Libraries Division (ASEE-ELD) papers and

posters from 2000 to 2009 in order to obtain a snapshot of the state of the publication and

identify trends.

The idea of a single bibliometric publication study is not new. To date, there have been over 180

single journal bibliometric studies and the field has matured to the point that there are at least

two review articles summarizing these studies. Tiew1 reviewed the literature from 1969 to 1997

and identified 102 single publication studies covering library and information science, medicine,

science and technology, and the arts, social sciences, and humanities. He categorized the 102

studies into four general categories: bibliometric studies on single journals (40 studies), citation

analysis of single journals (45 studies), content analysis of single journals (11 studies), and other

aspects of bibliometric study on single journals (6 studies). These studies were reviewed and

discussed in terms of the four categories and the aforementioned disciplines. Anyi, Zainab, and

Anuar 2

examined the literature from 1998 to 2008, essentially continuing the work of Tiew.

Page 15.177.2

They identified 82 single publication studies covering: arts, humanities, and social sciences (12

studies), medical and health sciences (16 studies), science and technology (25 studies), library

and information science (21 studies). It should be noted that the 182 studies do not correspond to

182 unique titles, since some publications were revisited several times over the years.

Based on the analysis of the 82 studies, Tiew1 identified seven general bibliometric measures for

single publication studies: article productivity, author characteristics, author’s productivity, co-

authorship pattern, content analysis, citation analysis, and characteristics of the editorial board.

Within each of the major categories there were one or more specific measures totaling over 40

individual measures (or methodologies). Appendix A lists all the measures identified, though it is

important to note that not all of these measures are explored in every study and some studies

only focused on a specific aspect (or used a specific methodology).

ASEE-ELD was organized in 1967 though there was no requirement for authors to submit full-

length papers for ASEE-ELD sessions prior to 2009; however, some authors voluntarily

submitted full-length papers to the annual proceedings. Since 2009, all authors are required to

submit full-length, peer reviewed papers for all ASEE paper and poster presentations. Pre-

conference, panel discussion, and distinguished speaker sessions are exempted from this

publication requirement. So unlike many other studies of single publications, contributions to

annual ASEE-ELD programs were not systematically indexed and no tangible artifacts (e.g.,

papers or abstracts) exist for all contributions. In the case of ASEE-ELD papers and posters, the

tangle artifact is often limited to just bibliographic information (title, author, and affiliation). The

lack of papers and indexing limits the types of bibliometric analyses that can be performed.

Despite the apparent limitations, various aspects of at least five measures outlined in Appendix A

were examined in this study. The five measures examined in this paper focus on article

productivity, author characteristics, author’s productivity, co-authorship pattern, and content

analysis. Only one of the measures, citation analysis, was not possible due to the lack of citation

indexing. Another measure, characteristics of the editorial board, was not explored since the

publication does not have an editorial board per se. The descriptions of these measures are

provided in Appendix A and will not be reproduced in this section; however, content analysis

requires further comment and is discussed below. The other measures employed will be

discussed in more detail within the context of the methodology.

There are numerous definitions for content analysis, but one of the classic definitions is “…a

research technique for the objective, systematic, and quantitative description of the manifest

content of communication.”3 Allen and Reser

4 found that the meaning of content analysis varies

considerably within library and information science (LIS) literature as does its application.

Approaches to content analysis in the LIS literature are almost as numerous as the individuals

that use the methodology. According to Allen and Reser, there are two basic types of content

analysis: classification analysis and elemental analysis.4 The former uses methods to assign

documents to various subject groups based on an exhaustive and mutually exclusive subject or

classification scheme, whereas the latter identifies words and word frequencies of documents.

Anyi, Zainab, and Anuar2 identified 19 different measures or methodologies used for content

analysis among the 82 bibliometric studies examined (See Appendix A). A number of the

Page 15.177.3

measures cannot be used in this study for the aforementioned reasons (i.e., lack of a full-length

papers and indexing). Since the only information available for each paper and poster is

bibliographic, one cannot determine such measures as “types of models, theories and framework

used” or the “number of pages per article”. Some of the methods that can be used on the ASEE-

ELD papers and posters are “subject area of articles” and “article title analysis” based on paper

and poster titles.

Several classification schemes have been developed and used for content analysis within the LIS

literature (e.g., Atkins5; Feehan, Gragg, Havener, and Kester

6; Nour

7; and Pertiz

8), however, the

scheme developed by Jarvelin and Vakkari9 has gained widespread acceptance for its systematic

approach to subject analysis compared to the more theoretical focus of other studies. The

classification scheme of Jarvelin and Vakkari9 is outlined in Table 5 and used in this study.

Textual analysis is a form of content analysis that examines a text and its words for meaning.

Identification of the words and their frequency is the most basic form of textual analysis – it is

what Allen and Reser4 would refer to as elemental analysis. There is a wide array of software

available for performing textual analysis, both free and commercial. Several websites list

commercial and freely available software for textual analysis (e.g., www.textanalysis.info,

www.kdnuggets.com, and http://www.content-analysis.de/). In addition to standard textual

analysis software, Wordles10

are increasing being used for textual analysis to better visualize data

as word clouds and to obtain word frequencies. For example, Harris, Lecroq, Kucherov and

Lonardi11

used Wordle to analyze the titles of conference proceedings for the Symposium of

Combinatorial Pattern Matching. This study will also employ Wordle to perform article title

analysis, as well as use freely available textual analysis software called Textalyser.12

Using bibliometric measures and content analysis methodologies outlined above, this paper

will provide a snapshot of the ASEE-ELD Annual Conference proceedings and identify

trends.

Methodology

The analysis was limited to papers and posters presented at ASEE-ELD annual conferences from

2000 to 2009. The data were obtained from the annual conference program guides, except for the

2001 poster session that was omitted from the original program guide and obtained from the

ASEE-ELD archives. Only papers and posters that included authors in the index of conference

program guide were included, so almost all pre-conference programming, panel discussions, and

forums were omitted from this study. These were omitted for several reasons, but mainly because

they often lacked specific authors and individual titles beyond the session title. Due to the

similarity of paper and poster presentations at ASEE-ELD sessions and to simplify the language

throughout this paper, papers and posters will hereafter be referred to as papers unless specified

otherwise.

The authors, paper titles, affiliations, year, and other bibliographic information were entered into

an Excel spreadsheet based on the annual ASEE conference program guides. The data were then

sorted and analyzed in several ways to explore authorship and content.

Page 15.177.4

The authorship was first examined quantitatively by determining the overall number of papers

and posters, as well as the average number of authors per paper and poster for 2000 to 2009.

Authorship was also explored on an individual basis by determining the number of times each

author presented a paper. A similar approach was used to examine affiliation and the number of

times each institution/organization was represented. While authorship was credited each time an

author’s name appeared as an author or co-author, affiliation was handled a little differently. For

affiliation, the institution was only credited once for each paper; otherwise a paper written by

five authors from the same institution would be counted five times. On the other hand, each of

the five authors would each be counted once for their contribution. Co-authorship was examined

in terms of both the number of co-authors and the types of co-authorship (or collaboration). The

various types of co-authorships were plotted and linear regression used to identify any significant

trends over time.

The papers were categorized based on a standard classification scheme developed by Jarvelin

and Vakkari 9, which has been widely used, adopted, and cited for this purpose. The 30 classes

(or topics) are listed in Table 5. One major limitation with this particular analysis is that the

content analysis is based solely on the titles of the papers since full-length papers generally do

not exist.

Textual analysis of the paper titles was performed using Textalyser for single and two-word

phrase frequencies. This was accomplished by cutting and pasting all the titles of the ASEE-ELD

papers from the spreadsheet into a Word document and then ultimately the Textalyser software.

The most frequently used words and phrases were then identified. In a similar manner, Wordle

was used to provide a better visualization of the word frequencies.

Results and Discussion

There were 258 papers and posters presented at the ASEE-ELD sessions during ASEE Annual

Conferences from 2000-2009. Of the 258, there were 170 (66%) papers and 88 (34%) posters.

Figure 1 shows the annual number of papers and posters presented at each annual conference

from 2000-2009.

The authorship was examined in a number of ways. Figure 2 presents the number of authors per

publication. The majority of the publications (72.5%) were single authored. The number of

authors per paper averaged 1.50 and ranged from 1 to 9 authors.

Figure 3 and Figure 4 show plots of the average number of authors per paper and poster over the

10-year period, respectively. The average number of authors was found to increase from 1 to 2

authors per paper and poster over the last 10 years and the trend is statistically significant.

There were 258 individual authors or co-authors responsible for the 258 papers from 2000-2009.

Considering that there are over 600 colleges and universities with ABET-accredited engineering

programs in the United States13

, each with at least one engineering librarian, the number of

ASEE-ELD papers contributed compared to the total number of engineering librarians is

relatively small or from a select group. Furthermore, some of the authors and co-authors of the Page 15.177.5

258 papers are not engineering librarians, but instruction librarians, engineering faculty, vendors,

etc.

Figure 1. Number of Papers and Posters, 2000-2009

0

5

10

15

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year

Nu

mb

er o

f P

ap

er/P

ost

er r

Paper Poster

Figure 2. Number of Authors per Publication, 2000-2009

0

50

100

150

200

1 2 3 4 5+

Number of Authors

Nu

mb

er o

f P

ap

ers s

ss

Page 15.177.6

Figure 3. Average Number of Authors per Paper, 2000-2009

r = 0.8767, p = 0.0009

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2000 2002 2004 2006 2008 2010

Year

Nu

mb

er o

f A

uth

ors

s

Figure 4. Average Number of Authors per Poster, 2000-2009

r = 0.8217, p = 0.0035

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2000 2002 2004 2006 2008 2010

Year

Nu

mb

er o

f A

uth

ors

s

Table 1 summarizes the number of times an individual authored or co-authored a paper for an

ASEE-ELD session. The productivity of individual authors loosely follows Lotka’s Law, which

predicts that the number of authors contributing a particular number of papers is inversely

proportional to the number of papers contributed. For comparison, percentages of authors

contributing a certain number of papers as predicted by Lotka’s Law are also presented in

Table 1.

Page 15.177.7

Table 1. Number of Individuals Authoring or Co-Authoring

a Specified Number of Papers, 2000-2009

Number of

Papers

Number of

Individuals Lotka’s Prediction (%)

1 184 (73%) 60

2 42 (15%) 15

3 15 (5%) 7

4 8 (3%) 4

5 8 (3%) 2.4

6 0 (0%) 1.7

7 1 (0.4%) 1.2

There were 127 institutions/organizations associated with the 258 papers. Table 2 presents the

number of individual institutions/organizations associated with each of the 258 papers and the

institutions/organizations associated with 5 or more papers are presented in Table 3.

Table 2. Number of Institutions/Organizations Associated with

a Specified Number of Papers, 2000-2009

Number of

Papers

Number of

Institutions/Organizations

13 1

12 2

11 0

10 0

9 1

8 0

7 5

6 3

5 4

4 5

3 16

2 23

1 67

Table 4 describes the relationships and summarizes the frequency of the six different types of co-

authorships identified in the ASEE-ELD papers. Of the 258 papers, 71 papers were authored by

2 or more authors. The nature of these collaborations was explored based on the relationship of

the co-authors.

Page 15.177.8

Table 3. Institutions/Organizations Associated with 5 or More Papers, 2000-2009

Rank Institution/Organization Number of

Papers

1 Purdue University 13

2 Massachusetts Institute of Technology 12

North Carolina State University 12

3 Pennsylvania State University 9

4 Drexel University 7

University of Arizona 7

University of California - Berkeley 7

University of Illinois at Urbana-Champaign 7

5 University of Minnesota - Twin Cities 7

Colorado School of Mines 6

Cornell University 6

University of Wisconsin - Madison 6

6 Bucknell University 5

Northern Illinois University 5

University of Washington 5

Virginia Polytechnic Institute and State University 5

Table 4. Type and Frequencies of Co-Authorship

Type of Co-authorship Description Number of

Papers

Librarian + Faculty Co-authorship involving at least one librarian

and faculty member outside of the library.

21

Librarian + Librarian

(Same Institution)

Co-authorship involving two or more librarians

at the same institution.

32

Librarian + Librarian

(Different Institution)

Co-authorship involving two or more librarians

at different institutions.

10

Librarian + Vendor Co-authorship involving at least one librarian

and one vendor.

5

Librarian + Other Co-authorship involving at least one librarian

and one other individual (e.g., students and/or

staff outside the library).

2

Other Co-authorship that does meet any of the other

five criteria (e.g., two engineering faculty).

1

Page 15.177.9

All six types of co-authorship were plotted for the 10-year period and linear regression was used

to explore trends; however, only one proved to be statistically significant. The one type of co-

authorship that that showed a statistically significant increase over time is the number of Library

+ Faculty co-authorships (Figure 5), which represented 8.1% of all 258 papers and 30% of the 71

co-authored papers.

Figure 5. Librarian + Faculty Co-Authorships, 2000-2009

r = 0.8657, p = 0.0012

0

1

2

3

4

5

6

2000 2002 2004 2006 2008 2010

Year

Nu

mb

er o

f P

ap

ers

sss

Table 5 summarizes the classification of the 258 papers based on the Jarvelin & Vakkari9

classification scheme. The four largest topics were user education, information/ reference

services, other types of databases (i.e., non-bibliographic), and collections. These four categories

represent over 50% of the topics covered within in the 258 papers. All but 4 of the 30 topics from

the Jarvelin and Vakkari9 classification scheme were represented. The four topics that were not

represented were: methodology (i.e., study of research methods), analysis of LIS (i.e., empirical

and theoretical methods used), automation study, and cataloging.

Considering that the Jarvelin and Vakkari9 study was conducted almost 20 years ago and used 37

core journals covering a range of LIS topics, one must be cautious about comparing results and

drawing inferences between the two studies. If one does compare what Jarvelin and Vakkari9

referred to as “Professional” LIS articles to the ASEE-ELD papers in this study, many more

ASEE-ELD papers were found under the major categories of Library and Information Service

Activities (59.3% versus 35.2%) and Information Seeking (10.9% versus 2.3%). The majority of

the former was primarily due to a large number of papers focusing on user education and

information/reference services. Information Seeking was buoyed by a number of user studies.

One area that was noticeably lower was Information Storage and Retrieval despite a number of

papers on various databases and search interfaces. It appears that the absence of cataloging and

classification might have led to a lower percentage in this category compared to LIS literature in

general. The underrepresented topics may represent areas for future papers, though there may be

an inherent preference for the areas currently represented by the ASEE-ELD membership.

Page 15.177.10

Table 5. Distribution of Topics in ASEE - ELD Papers and Professional LIS Articles

ASEE - ELD Papers Jarvelin and Vakkari

Topics n % n %

The profession 15 5.8 16 4.2

Library history 2 0.8 5 1.3

Publishing 4 1.6 23 6.0

Education in LIS 2 0.8 10 2.6

Methodology 0 0.0 – 0.0

Analysis of LIS 0 0.0 2 0.5

Library and information service activities 153 59.3 135 35.2

Circulation or interlibrary loans 3 6

Collections 21 30

Information/reference services 26 9

User education 63 6

Library buildings and facilities 9 1

Administration or planning 2 31

Automation study 0 14

Other L&I service activities 11 1

Several interconnected activities 18 37

Information storage and retrieval 33 12.8 96 25.0

Cataloguing 0 17

Classification and indexing 1 21

Information retrieval 4 19

Bibliographic databases/bibliographies 6 24

Other types of databases 22 15

Information seeking 28 10.9 9 2.3

Dissemination of information 2 3

Use/users of channels/sources of info 16 –

Use of L&I services 1 –

Information seeking behavior 5 –

Use of information 3 –

Information management, IRM 1 6

Scientific & professional communication 13 5.0 6 1.6

Scientific/professional publishing 9 4

Citation pattern & structures 1 –

Other aspects 3 2

Other aspects of LIS 8 3.1 82 21.4

Totals 258 100.0 384 100.0

Page 15.177.11

The results of the textual analysis on the titles are presented in Table 6 and Table 7. Textalyser

found 831 unique words among the titles of the ASEE-ELD papers, but the results presented in

Table 6 and their frequencies are limited to the twelve most frequent words. This was done to

create a manageable list with a logical break in the data. The Textalyser analysis was hampered

in a number of ways, including the inability to omit stop words from 2-word phrase frequencies.

For example, “of engineering” and “in the” were the second and third most common 2-word

phrases, respectively. To make more sense of the results, these types of 2-word phrases were

omitted from list of the five highest ranked two-word phrases list in Table 7.

Table 6. Words and Frequencies Appearing in Titles of ASEE-ELD Papers

Rank Word Frequency Percent of Total

Words

1 engineering 111 5.9

2 information 59 3.2

3 library 58 3.1

4 literacy 26 1.4

5 students 25 1.3

6 digital 19 1.0

7 new 17 0.9

collection 17 0.9

8 faculty 16 0.9

9 science 15 0.8

10 using 14 0.7

university 14 0.7

Table 7. Two-Word Phrases and Frequencies Appearing in

Titles of ASEE-ELD Papers

Rank Two-Word Phrases Frequency

1 information literacy 26

2 engineering students 15

3 engineering library 7

4 digital library 6

library instruction 6

collection development 6

5 first year 5

engineering design 5

case study 5

More sophisticated textual analysis software might be able to eliminate these stopwords from

phrase lists, as well as create categorical hierarchies of subjects. However, this was beyond the

functionality of Textalyser. The results of this textual analysis point to an emphasis on

information literacy/instruction/education, engineering students, faculty, collection development,

Page 15.177.12

reference, information, libraries, and various digital aspects. It might be just as interesting to

consider what words or phrases (i.e., topics) are absent from the lists.

The results of the Wordle analysis are presented in Figure 6. To ensure the figure was legible, the

analysis was limited to the 50 most frequent words from the ASEE-ELD paper titles. The results

provide greater visual impact compared to the list presented in Table 6 but is essentially same

information using different software. While neither of the textual analyses is particularly

insightful, both support many of the results found during the classification analysis.

Figure 6. Wordle of the Fifty Most Frequent Words Appearing in Titles of ASEE-ELD Papers

Conclusion

This study provided a snapshot of the ASEE-ELD conference papers and posters from 2000 to

2009. The analysis of these conference proceedings posed several unique challenges compared to

other single publication bibliometric studies. This was primarily due to the lack full-length

papers and indexing. This limited the types of bibliometric and content analyses that could be

performed. Despite these limitations, several methodologies were successfully applied and

significant trends identified.

One of the most significant trends is the increase in co-authorship. Co-authorship doubled over

the 10-year period. More interesting was the increase in co-authorship with faculty outside the

library, which also showed a statistically significant increase. While not surprising, authorship

did loosely follow Lotka’s Law in that the proportion of individual authors making a contribution

decreased according to an inverse power law.

The classification scheme for the content analysis was subjective, but did point to the emphasis

on information literacy, information/reference services, collections and novel databases. While

textual analysis was limited in terms of results, it did provide further support for the

classification analysis based on word and phrase frequencies of the paper titles. A future study

might apply a more rigorous textual analysis methodology (and software) to obtain topic

hierarchies and to perform more sophisticated analyses.

Further work could compare these results to other LIS publications in the field of science and

engineering librarianship.

Page 15.177.13

References

1. Tiew, S. 1997. Single journal bibliometric studies: A review. Malaysian Journal of Library & Information

Science 2 (2): 93-114.

2. Anyi, K., A. Zainab, and N. Anuar. 2009. Bibliometric studies on single journals: A review. Malaysian Journal

of Library & Information Science 14 (1): 17-55.

3. Berelson, B. 1971. Content Analysis in Communication Research. New York: Hafner, 18.

4. Allen, B., and D. Reser. 1990. Content analysis in library and information science research. Library &

Information Science Research 12 (3): 251–262.

5. Atkins, S. 1988. Subject trends in library and information science research, 1975-1984. Library Trends 36 (4):

633-658.

6. Feehan, P., W Gragg, W. Havener, and D. Kester. 1987. Library and information science research: An analysis

of the 1984 journal literature. Library & Information Science Research 9 (3): 173-185.

7. Nour, M. 1985. A quantitative analysis of the research articles published in core library journals of 1980. Library

& Information Science Research 7 (3): 261-273.

8. Peritz, B. 1980. The methods of library science research: Some results from a bibliometric survey. Library

Research 2 (1): 251-264.

9. Jarvelin, K., P. Vakkari. 1990. Content analysis of research articles in library and information science. Library &

Information Science Research 12 (4): 395-421.

10. Feinberg, J. 2009. Wordle. http://www.wordle.net/ [accessed October 1, 2009].

11. Harris, E., T. Lecroq, G. Kucherov, and S. Lonardi. 2009. CPM’s 20th anniversary: A statistical retrospective. In

20th Annual Symposium on Combinatorial Pattern Matching, edited by G. Kucherov and E. Ukkonen, 1-11.

Berlin: Springer.

12. Textalyser.net. 2004. Textalyser. http://textalyser.net/ [accessed October 1, 2009].

13. ABET. 2010. ABET. http://www.abet.org/ [accessed March 2, 2010].

Page 15.177.14

Appendix A. Bibliometric Measures Identified by Tiew1

1. Article productivity

≠ Number of articles published by issues,

volumes and years

2. Author characteristics

≠ Authors’ gender, profession, rank,

academic title

≠ Authors’ geographical affiliations by

institutional names and types of

institutions (academic, professionals)

≠ Authors’ location by region, or country

3. Author’s productivity

≠ Rank list of core and active authors

≠ Authorship productivity pattern may be

tested with Lotka’s Law of authorship

distribution

4. Co-authorship pattern

≠ Types of co-authored works

≠ Degree of collaboration

≠ Local and foreign collaboration activities

among authors by country and

institution

≠ Internationalization status of the journal

5. Content analysis

≠ Subject areas of articles, keyword

analysis, keyword co-occurrence

network

≠ Article title analysis, number of words,

punctuation usage, word frequency and

preposition usage

≠ Number of pages per article

≠ Journal circulation

≠ Journal frequency

≠ Types of research methodology used

≠ Types of models, theories and

framework used

≠ Analysis of acknowledgement

≠ Analysis of funding received

≠ Analysis of article appendices

≠ Analysis of article abstracts

≠ Acceptance rate

≠ Analysis of indexation and abstraction

information

≠ Language of publication

6. Citation analysis

≠ Number and distribution of citations per

article, volumes and years

≠ Authorship pattern of citations

≠ Author co-citation analysis network

≠ Most cited author

≠ Types of literature cited

≠ Age of cited literature

≠ Cited literature’s half-life

≠ Rank list of core journals using

Bradford’s Law

≠ Extent and growth of web citations

≠ Journal citation identity, analysis of

references in articles from the journal

≠ Journal citation image, analysis of

citations to the journal

≠ Journal influence and diffusion in other

subject areas

≠ Geographical location and language

distribution of cited literature

≠ Journal self-citation

≠ Author self-citations

≠ Journal performance, quality and prestige

as measured by journal impact factor,

prestige index, trajectory index,

immediacy index, journal attraction

power, journal consumption power and

discipline contribution score

7. Characteristics of the editorial board

≠ List and geographical distribution of

editorial board members

≠ List and geographical distribution of

reviewers

≠ Editorials and reviewer’s gender,

profession, qualification, academic

rank, publication productivity prior and

post appointment

≠ Editorial policy

Page 15.177.15