Author Impact Metrics in Communication Sciences and Disorder Research Purpose The purpose was to examine author-level impact metrics for faculty in the communication sciences and disorder research field across a variety of databases. Method Author-level impact metrics were collected for faculty from 257 accredited universities in the United States and Canada. Three databases (i.e., Google Scholar, ResearchGate, ... Research Article
Open Access
Research Article  |   October 16, 2017
Author Impact Metrics in Communication Sciences and Disorder Research
 
Author Affiliations & Notes
  • Andrew Stuart
    East Carolina University, Greenville, NC
  • Sarah P. Faucette
    East Carolina University, Greenville, NC
  • William Joseph Thomas
    East Carolina University, Greenville, NC
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Andrew Stuart: stuarta@ecu.edu
  • Editor: Frederick (Erick) Gallun
    Editor: Frederick (Erick) Gallun×
  • Associate Editor: Jennifer Lentz
    Associate Editor: Jennifer Lentz×
Article Information
Professional Issues & Training / Hearing / Research Articles
Research Article   |   October 16, 2017
Author Impact Metrics in Communication Sciences and Disorder Research
Journal of Speech, Language, and Hearing Research, October 2017, Vol. 60, 2704-2724. doi:10.1044/2017_JSLHR-H-16-0458
History: Received December 19, 2016 , Revised February 20, 2017 , Accepted March 13, 2017
 
Journal of Speech, Language, and Hearing Research, October 2017, Vol. 60, 2704-2724. doi:10.1044/2017_JSLHR-H-16-0458
History: Received December 19, 2016; Revised February 20, 2017; Accepted March 13, 2017

Purpose The purpose was to examine author-level impact metrics for faculty in the communication sciences and disorder research field across a variety of databases.

Method Author-level impact metrics were collected for faculty from 257 accredited universities in the United States and Canada. Three databases (i.e., Google Scholar, ResearchGate, and Scopus) were utilized.

Results Faculty expertise was in audiology (24.4%; n = 490) and speech-language pathology (75.6%; n = 1,520). Women comprised 68.1% of faculty, and men comprised 31.9% of faculty. The percentage of faculty in the field of communication sciences and disorders identified in each database was 10.5% (n = 212), 44.0% (n = 885), and 84.4% (n = 1,696) for Google Scholar, ResearchGate, and Scopus, respectively. In general, author-level impact metrics were positively skewed. Metric values increased significantly with increasing academic rank (p < .05), were greater for men versus women (p < .05), and were greater for those in audiology versus speech-language pathology (p < .05). There were statistically significant positive correlations between all author-level metrics (p < .01).

Conclusions These author-level metrics may serve as a benchmark for scholarly production of those in the field of communication sciences and disorders and may assist with professional identity management, tenure and promotion review, grant applications, and employment.

Determining research impact is important for numerous reasons, including professional identity management, tenure review, promotion review, grant application, and employment. The impact of scholarly research can be examined on several levels. Journal-level metrics demonstrate a rank of a particular journal within its particular discipline, and they are used as an indirect means to evaluate the potential impact of particular articles. Primary journal-level metrics include the impact factor, Scimago journal rank, and source normalized impact per paper. Additional journal-level metrics are available; for example, Eigenfactor scores, Article Influence metrics, and Google Scholar Citation metrics.
Instead of showcasing only the journal-level metric, researchers may additionally examine article-level metrics (e.g., Altmetric or Plum Analytics) to examine article use information. These alternative metrics, or altmetrics, may include indices on how one's scholarly products are viewed/downloaded, any social media attention (e.g., Twitter, Google+, or Facebook), news coverage, dialogues on scholarly blogs, and/or usage by online reference managers (e.g., CiteULike or Mendeley).
What if one is interested in enumerating the collective impact or relevance of an individual's research output? The impact of the work of a scientist can also be estimated by author impact metrics. Measures of a scientific author's influence are called bibliometrics. Such quantification can be used for evaluation and comparison purposes. Author-level impact metrics are essential in assessing an individual's reputation and the impact of their career (Petersen, Wang, & Stanley, 2010; Petersen et al., 2014). These bibliometrics can be used for university faculty recruitment and advancement (Hirsch, 2005), awarding of fellowships (Bornmann & Daniel, 2006), providing grant funding (Council of Canadian Academies Expert Panel on Science Performance and Research Funding, 2012), predicting future achievement (Hirsch, 2007), and comparing scientific impact across disciplinary boundaries (Council of Canadian Academies Expert Panel on Science Performance and Research Funding, 2012; Kaur, Radicchi, & Menczer, 2013; Pan & Fortunato, 2014). Author impact has conventionally been measured by a simple count of an author's publications or publication citations. Over the past decade, there have been a proliferation of mathematical equations and scholarly impact metrics that can be used to quantify author impact.
The most widely adopted author impact metric is the h index developed by Hirsch (2005, 2007) . The h index is an indexed number that is based on the number of citations and number of published articles. For a given index h, the author impact metric is defined as the number of published articles with a citation number ≥ h. The value reflects an author's number of publications and the number of citations per publication. For example, an author with an h index of 10 has at least 10 publications that have each received at least 10 citations. Numerous variations of the h index and other author-level indices have since emerged (Bornmann & Daniel, 2009; Bornmann, Mutz, & Daniel, 2008). For example, the hm index modifies the h index to account for manuscripts with multiple authors (Schreiber, 2008). The hf index (Radicchi, Fortunato, & Castellano, 2008) is a generalized h index that generates an unbiased index for citations across disciplines and years. Kaur et al. (2013)  proposed the hs index as a normalized h index that allows comparisons of author impact across scientific disciplines. The i10-Index, introduced by Google Scholar, represents the number of publications an author has with at least 10 citations from other authors. Another is the g index proposed by Egghe (2006) . It represents the global performance of a set of publications where the g index represents the highest number g of publications that together receive at least g2 citations.
Author impact metrics can be easily gleaned from a number of databases (e.g., Thomson Institute for Scientific Information [ISI] Web of Science 1 , Google Scholar, Scopus, and ResearchGate). Also, one can use subject-area databases and journal publisher resources to count citations. Examples of some subject-area databases are EBSCOhost databases (e.g., CINAHL and MLA Bibliography), ProQuest databases (e.g., ABI/INFORM and Earth Science Collection), and Medline (via PubMed or Ovid). Publisher platforms include Cambridge University Press's Cambridge Journals (https://www.cambridge.org/core/what-we-publish/journals), Elsevier's ScienceDirect (http://www.sciencedirect.com), Springer (http://www.springer.com/us/), and JSTOR (http://www.jstor.org).
Author-level impact metrics have been examined in a number of medical fields, including pediatric anesthesiology (O'Leary & Crawford, 2010), radiology (Chow, Ha, & Filippi, 2015), emergency medicine (DeLuca et al., 2013), laboratory medicine (Escobar, Nydegger, Risch, & Risch, 2012), urology (Kutikov et al., 2012), and neurology (Tinazzi et al., 2014). To date, however, there has been no report of author-level impact metrics in the field of communication sciences and disorders. The purpose of this study was to address this deficiency and undertake a comprehensive analysis of author-level impact metrics in the field of communication sciences and disorders. Three databases (i.e., Google Scholar, ResearchGate, and Scopus) were utilized.
Google Scholar (https://scholar.google.com), which was launched in 2004, is a freely available web search engine. Google Scholar indexes journal manuscripts, conference papers, theses/dissertations, books, preprints, abstracts, technical reports, patents, etc., across disciplines. Google Scholar Citations is a service that Google Scholar provides to authors as a means to keep track of their article citations. “Author profiles” are created by first creating a Google account. Once created and signed in, an author then completes a “Citations sign-up form”, confirming the spelling of their first and last names, and affiliation(s), etc. Google Scholar will then perform a search of articles with the author's name. The author confirms the articles are theirs, and they are added to their author profile. Google Scholar updates the profiles periodically, and new articles are added when identified. On occasion, Google Scholar may add an article by someone else, and the author must remove the errant entry. An author must approve their profile to be made public to be viewed; otherwise, it is not accessible.
ResearchGate (https://www.researchgate.net) is a social network service site, launched in 2008, for scientists. Membership is free to individuals that have an institutional email address or a published researcher authenticated by the site. Individuals who wish to use the site must create an account. Once an account is created, members can view other accounts, post questions, and communicate with other members. It has been reported that ResearchGate had 11 million members in 2016 (ResearchGate, n.d.). Most users are in the fields of medicine and biology; however, it also draws from a large community of scientists in engineering, computer sciences, chemistry, and agriculture (Gruzd, 2012).
Scopus (https://www.elsevier.com/solutions/scopus) is an abstract and citation database of peer-reviewed scientific journals, books, and conference proceedings. Scopus is owned by the publisher Elsevier and is available online with a subscription (since 2004). Scopus includes over 21,500 titles from more than 5,000 international publishers worldwide. Author searches can be conducted in Scopus by entering an author's Open Researcher and Contributor ID (ORCID) or the author's last name, initials or first name, and affiliation. Scopus uses an author identification algorithm that matches an author name on the basis of affiliation, address, subject area, source title, dates of publication, citations, and coauthors and assigns a single identifier number to each author. On occasion, Scopus will have multiple author profiles for the same person, and they should be merged.
The specific goals of this study were fourfold. The first was to collect and disseminate author-level impact metrics in the field of communication sciences and disorders. As noted above, there have been no previous reports of such bibliometrics. The data are of importance for university faculty recruitment and advancement, predicting future achievement, and comparing author impact across disciplinary boundaries. Second, it was of interest to examine author-level impact metrics within the field of communication sciences and disorders. That is, do an author's academic rank, gender, and/or field of study affect impact? Third, it was of interest to examine the association of author-level impact metrics in the field of communication sciences and disorders across databases. Last, it was of interest to examine scholarship longevity across faculty careers. That is, whereas author-level impact metrics can be assessed at a specific point in time, one may also inquire about metrics averaged across one's career. In particular, author-level impact metrics (e.g., publications, citations, h index, etc.) from the three databases (i.e., Google Scholar, ResearchGate, and Scopus) were examined as a function of faculty gender, academic rank, and area of expertise (i.e., audiology or speech-language pathology).
Method
Participants
Author-level impact metrics were collected for faculty from accredited universities in the United States and Canada. The lists of accredited programs were gathered from the Council on Academic Accreditation in Audiology and Speech-Language Pathology of the American Speech-Language-Hearing Association (2015)  and the Council for Accreditation of Canadian University Programs in Audiology and Speech-Language Pathology (n.d.) . All academic faculty listed in each program at each institution were included. 2  
Procedures
Beginning in February 2015 and ending in September 2015, demographic and personal data were collected from accredited universities program websites. 3   Information included institution location (i.e., state/province), gender, area of expertise (i.e., audiology or speech-language pathology), terminal degree, year of terminal degree, and academic rank.
Google Scholar, ResearchGate, and Scopus databases were utilized to gather author-level impact metrics. Identified programs were examined in a random order. With Google Scholar, author “user profiles” were first identified. Google Scholar automatically calculates and displays the individual's metrics once the author has been verified. The following six metrics were collected from public Google Scholar user profiles: total number of citations, number of citations in last 5 years, h index (i.e., total and in last 5 years), and i10-Index (i.e., total and in last 5 years). Three metrics were gathered from ResearchGate: number of publications, number of citations, and ResearchGate (RG) score. RG score is a number “calculated by ResearchGate using an algorithm that is not fully disclosed but which is based on contributions to members' ResearchGate profiles, interactions with other members, and reputation among other members” (Thelwall & Kousha, 2015, p. 880). That is, RG scores integrate both bibliometrics and altmetrics, where researcher publications, questions asked and answered, and number of followers are considered. Therefore, a researcher with X articles and Z citations with zero question and answer activity will have a lower RG score than a researcher with the same article and citation numbers who has question and answer activity. Author searches conducted in Scopus collected the following metrics: number of documents (i.e., total and in last 5 years), number of coauthors, number of citations (i.e., total and in last 5 years), number of citations excluding self-citations (i.e., total and in last 5 years), most cited (i.e., highest citation for a single publication), h index (i.e., total and in last 5 years), and h index excluding self-citations (i.e., total and in last 5 years).
Results
Institutions and Faculty
In total, we identified 257 accredited programs (see the Appendix). There were 246 programs in the United States and 11 in Canada. In the United States, programs were located in the following U.S. Census Bureau regions: South (35.8%; n = 88), Midwest (26.8%; n = 66), Northeast (22.4%; n = 55), and West (15.0%; n = 37). Of the 257 programs, 1.2% (n = 3), 71.6% (n = 184), and 27.2% (n = 70) offered training in audiology only, speech-language pathology only, and both audiology and speech-language pathology, respectively. With respect to 2015 Carnegie Classification of Institutions of Higher Education, programs in the United States were housed in research/doctoral universities (54.5%), master's colleges and universities (41.9%), and special focus institutions (3.6%; see Table 1).
Table 1. Number and percentage of U.S. institutions as a function of Carnegie classification.
Number and percentage of U.S. institutions as a function of Carnegie classification.×
Carnegie classification n %
Research universities (very high research activity) 53 20.6
Research universities (high research activity) 56 21.8
Doctoral/research universities 25 9.7
Master's colleges and universities (larger programs) 87 33.9
Master's colleges and universities (medium programs) 14 5.4
Master's colleges and universities (smaller programs) 2 .8
Special focus institutions (medical schools and medical centers) 6 2.3
Special focus institutions (other health professions schools) 2 .8
Baccalaureate colleges (arts & sciences) 1 .4
Table 1. Number and percentage of U.S. institutions as a function of Carnegie classification.
Number and percentage of U.S. institutions as a function of Carnegie classification.×
Carnegie classification n %
Research universities (very high research activity) 53 20.6
Research universities (high research activity) 56 21.8
Doctoral/research universities 25 9.7
Master's colleges and universities (larger programs) 87 33.9
Master's colleges and universities (medium programs) 14 5.4
Master's colleges and universities (smaller programs) 2 .8
Special focus institutions (medical schools and medical centers) 6 2.3
Special focus institutions (other health professions schools) 2 .8
Baccalaureate colleges (arts & sciences) 1 .4
×
Academic faculty totaled 2,010 individuals. Faculty expertise was in audiology (24.4%; n = 490) and speech-language pathology (75.6%; n = 1,520). Women comprised 68.1% (n = 1,368) of faculty, and men comprised 31.9% of faculty (n = 642). Terminal degrees held by faculty were overwhelmingly doctoral degrees (87.9%), followed by master's (5.5%), doctor of audiology (2.7%), and doctor of education (2.4%) degrees. Other degrees (e.g., doctor of speech-language pathology, doctor of communication sciences and disorders, medical doctor, master of education, and education specialist) totaled 1.5%. Rank was evenly distributed, with 35.1% (n = 705) assistant, 33.8% (n = 679) associate, and 31.1% (n = 626) full professorship. The average number of faculty per program was 7.9 (SD = 4.2). Gender, terminal degree, and rank, as a function of faculty expertise, are shown in Table 2. Box plots illustrating number of years since the terminal degree was completed, as a function of area of expertise and academic rank, are shown in Figure 1.
Table 2. Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.
Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.×
Parameter Area of expertise
Audiology
Speech-language pathology
n % n %
Gender
 Female 263 53.7 1,106 72.7
 Male 227 46.3 415 27.3
Terminal degree
 Ph.D. 420 85.9 1,346 88.5
 Au.D. 55 11.2
 Master's 6 1.2 104 6.8
 Other 8 1.6 71 4.7
Rank
 Full 170 34.7 456 31.1
 Associate 186 38.0 493 33.8
 Assistant 134 27.3 571 35.1
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.×
Table 2. Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.
Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.×
Parameter Area of expertise
Audiology
Speech-language pathology
n % n %
Gender
 Female 263 53.7 1,106 72.7
 Male 227 46.3 415 27.3
Terminal degree
 Ph.D. 420 85.9 1,346 88.5
 Au.D. 55 11.2
 Master's 6 1.2 104 6.8
 Other 8 1.6 71 4.7
Rank
 Full 170 34.7 456 31.1
 Associate 186 38.0 493 33.8
 Assistant 134 27.3 571 35.1
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.×
×
Figure 1.

Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 1.

Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Online Presence
The percentage of faculty in the field of communication sciences and disorders identified in each database was 10.5% (n = 212), 44.0% (n = 885), and 84.4% (n = 1,696), for Google Scholar, ResearchGate, and Scopus, respectively. Of all faculty members, 53.7% (n = 1,080) were not located in either Google Scholar or ResearchGate databases, which require active participation by the faculty. Of the remaining faculty members, 2.2% (n = 45) were found only in Google Scholar, 35.7% (n = 718) were found only in ResearchGate, and 8.3% (n = 167) were found in both Google Scholar and ResearchGate. Absolute count and percentage of faculty identified in each database as a function of rank are displayed in Table 3. There was no significant difference between the proportions of faculty absent as a function of rank for Google Scholar (χ2 = 1.87, df = 2, p = .39), but there was for ResearchGate (χ2 = 11.82, df = 2, p = .003) and Scopus (χ2 = 78.46, df = 2, p < .001). Assistant professors had a higher proportion of absences in ResearchGate and Scopus. With regard to the proportion of faculty present by rank, the opposite was evidenced. There was a significant difference between the proportions of faculty present as a function of rank for Google Scholar (χ2 = 10.68, df = 2, p = .005), but there was not for ResearchGate (χ2 = 3.38, df = 2, p = .18) and Scopus (χ2 = 4.30, df = 2, p = .12). Assistant professors had a higher proportion of presence in Google Scholar.
Table 3. Absolute count and percentage of faculty identified in each database as a function of rank.
Absolute count and percentage of faculty identified in each database as a function of rank.×
Database Rank
Total
Full Associate Assistant
Google Scholar
 Absent count 572 613 613 1,798
  Absent (%) 31.8 34.1 34.1 100.0
  Rank (%) 91.4 90.3 87.0 89.5
  Total (%) 28.5 30.5 30.5 89.5
 Present count 54 66 92 212
  Present (%) 25.5 31.1 43.4 100.0
  Rank (%) 8.6 9.7 13.0 10.5
  Total (%) 2.7 3.3 4.6 10.5
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
ResearchGate
 Absent count 338 359 428 1,125
  Absent (%) 30.0 31.9 38.0 100.0
  Rank (%) 54.0 52.9 60.7 56.0
  Total (%) 16.8 17.9 21.3 56.0
 Present count 288 320 277 885
  Present (%) 32.5 36.2 31.3 100.0
  Rank (%) 46.0 47.1 39.3 44.0
  Total (%) 14.3 15.9 13.8 44.0
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Scopus
 Absent count 55 82 177 314
  Absent (%) 17.5 26.1 56.4 100.0
  Rank (%) 8.8 12.1 25.1 15.6
  Total (%) 2.7 4.1 8.8 15.6
 Present count 571 597 528 1,696
  Present (%) 33.7 35.2 31.1 100.0
  Rank (%) 91.2 87.9 74.9 84.4
  Total (%) 28.4 29.7 26.3 84.4
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Table 3. Absolute count and percentage of faculty identified in each database as a function of rank.
Absolute count and percentage of faculty identified in each database as a function of rank.×
Database Rank
Total
Full Associate Assistant
Google Scholar
 Absent count 572 613 613 1,798
  Absent (%) 31.8 34.1 34.1 100.0
  Rank (%) 91.4 90.3 87.0 89.5
  Total (%) 28.5 30.5 30.5 89.5
 Present count 54 66 92 212
  Present (%) 25.5 31.1 43.4 100.0
  Rank (%) 8.6 9.7 13.0 10.5
  Total (%) 2.7 3.3 4.6 10.5
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
ResearchGate
 Absent count 338 359 428 1,125
  Absent (%) 30.0 31.9 38.0 100.0
  Rank (%) 54.0 52.9 60.7 56.0
  Total (%) 16.8 17.9 21.3 56.0
 Present count 288 320 277 885
  Present (%) 32.5 36.2 31.3 100.0
  Rank (%) 46.0 47.1 39.3 44.0
  Total (%) 14.3 15.9 13.8 44.0
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Scopus
 Absent count 55 82 177 314
  Absent (%) 17.5 26.1 56.4 100.0
  Rank (%) 8.8 12.1 25.1 15.6
  Total (%) 2.7 4.1 8.8 15.6
 Present count 571 597 528 1,696
  Present (%) 33.7 35.2 31.1 100.0
  Rank (%) 91.2 87.9 74.9 84.4
  Total (%) 28.4 29.7 26.3 84.4
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
×
Author-Level Impact Metrics
Google Scholar
The distribution of all Google Scholar indices did not differ significantly between the two categories of area of expertise (Mann–Whitney p > .05). All median values of Google Scholar indices were greater for men versus women. The differences were statistically significant for four of the measures: h index and i10-Index, both current and in the last 5 years (Mann–Whitney p < .05). The distribution of all Google Scholar indices differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, index values increased. Box plots of Google Scholar total number of citations, h index, and i10-Index, both current and in the last 5 years, as a function of academic rank, are presented in Figure 2. The five number summaries of the Google Scholar box plot values, collapsed across area of expertise, are presented in Table 4.
Figure 2.

Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 2.

Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 4. Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.
Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.×
Quartiles Rank
Assistant Associate Full
Citations
 Minimum 2 1 143
 25% 98 181 995
 50% 274 387 2,068
 75% 552 810 3,618
 Maximum 3,800 2,667 22,360
Citations in last 5 years
 Minimum 2 1 74
 25% 90 142 531
 50% 234 229 1,063
 75% 414 452 1,613
 Maximum 3,246 1,925 9,531
h index
 Minimum 1 1 5
 25% 4 7 16
 50% 9 9 24
 75% 11 15 33
 Maximum 33 29 67
h index in last 5 years
 Minimum 1 1 5
 25% 4 6 12
 50% 8 8 18
 75% 11 12 22
 Maximum 17 26 47
i10-Index
 Minimum 0 0 5
 25% 3 5 21
 50% 8 9 36
 75% 12 18 61
 Maximum 55 60 146
i10-Index in last 5 years
 Minimum 0 0 4
 25% 3 5 14
 50% 7 8 27
 75% 11 14 42
 Maximum 54 58 101
Table 4. Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.
Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.×
Quartiles Rank
Assistant Associate Full
Citations
 Minimum 2 1 143
 25% 98 181 995
 50% 274 387 2,068
 75% 552 810 3,618
 Maximum 3,800 2,667 22,360
Citations in last 5 years
 Minimum 2 1 74
 25% 90 142 531
 50% 234 229 1,063
 75% 414 452 1,613
 Maximum 3,246 1,925 9,531
h index
 Minimum 1 1 5
 25% 4 7 16
 50% 9 9 24
 75% 11 15 33
 Maximum 33 29 67
h index in last 5 years
 Minimum 1 1 5
 25% 4 6 12
 50% 8 8 18
 75% 11 12 22
 Maximum 17 26 47
i10-Index
 Minimum 0 0 5
 25% 3 5 21
 50% 8 9 36
 75% 12 18 61
 Maximum 55 60 146
i10-Index in last 5 years
 Minimum 0 0 4
 25% 3 5 14
 50% 7 8 27
 75% 11 14 42
 Maximum 54 58 101
×
ResearchGate
The distribution of all ResearchGate indices differed significantly between the two categories of area of expertise (Mann–Whitney p < .001). All median values were greater for those in audiology. Also, all median values of ResearchGate indices were greater for men versus women (Mann–Whitney p < .001). The distribution of all ResearchGate indices also differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, index values increased. Box plots of RG score, number of publications, and number of citations, as a function of area of expertise and academic rank, are presented in Figure 3. The five number summaries of the ResearchGate box plot values, as a function area of expertise and rank, are presented in Table 5.
Figure 3.

Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 3.

Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 5. Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.
Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
RG score
 Minimum 2.8 0 0 0 0 0
 25% 10.5 5.8 15.7 8.9 17.0 14.3
 50% 16.4 10.8 19.7 15.3 26.9 22.9
 75% 21.8 17.4 24.5 21.5 31.1 29.2
 Maximum 31.5 38.5 38.0 36.8 42.2 41.2
Number of publications
 Minimum 1 0 0 0 0 0
 25% 7 5 15 8 21 15
 50% 14 9 23 16 47 38
 75% 22 16 33 27 68 64
 Maximum 49 116 116 122 261 170
Number of citations
 Minimum 0 0 0 0 0 0
 25% 14 8 75 34 109 96
 50% 103 32 159 116 419 336
 75% 276 134 345 250 1226 907
 Maximum 732 2,614 3,993 5,080 12,506 8,410
Note. SLP = speech-language pathology; RG = ResearchGate.
Note. SLP = speech-language pathology; RG = ResearchGate.×
Table 5. Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.
Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
RG score
 Minimum 2.8 0 0 0 0 0
 25% 10.5 5.8 15.7 8.9 17.0 14.3
 50% 16.4 10.8 19.7 15.3 26.9 22.9
 75% 21.8 17.4 24.5 21.5 31.1 29.2
 Maximum 31.5 38.5 38.0 36.8 42.2 41.2
Number of publications
 Minimum 1 0 0 0 0 0
 25% 7 5 15 8 21 15
 50% 14 9 23 16 47 38
 75% 22 16 33 27 68 64
 Maximum 49 116 116 122 261 170
Number of citations
 Minimum 0 0 0 0 0 0
 25% 14 8 75 34 109 96
 50% 103 32 159 116 419 336
 75% 276 134 345 250 1226 907
 Maximum 732 2,614 3,993 5,080 12,506 8,410
Note. SLP = speech-language pathology; RG = ResearchGate.
Note. SLP = speech-language pathology; RG = ResearchGate.×
×
Scopus
The distribution of all number of documents in Scopus differed significantly between the two categories of area of expertise (Mann–Whitney p < .001). All median values for number of documents were greater for those in audiology. Also, all median values for number of documents were greater for men versus women (Mann–Whitney p < .001). The distribution of Scopus number of documents also differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, the number of documents increased. Box plots of Scopus total number of documents and those in the last 5 years, as a function of area of expertise and academic rank, are presented in Figure 4. The five number summaries of the Scopus number of documents box plot values, as a function area of expertise and rank, are presented in Table 6.
Figure 4.

Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 4.

Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 6. Five number summaries for Scopus number of documents as a function of area of expertise and rank.
Five number summaries for Scopus number of documents as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Number of documents
 Minimum 1 1 1 1 1
 25% 4 2 6 3 11 7
 50% 10 5 12 8 32 21
 75% 20 11 25 16 58 47
 Maximum 53 105 113 88 292 214
Number of documents in last 5 years
 Minimum 0 0 0 0 0 0
 25% 2 1 1 0 1 0
 50% 6 3 5 3 5 4
 75% 12 7 10 7 14 12
 Maximum 45 51 106 39 120 54
Number of coauthors
 Minimum 0 0 0 0 0 0
 25% 6 3 8 5 11 9
 50% 15 8 17 11 42 24
 75% 27 18 33 24 76 58
 Maximum 99 150 355 331 150 200
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 6. Five number summaries for Scopus number of documents as a function of area of expertise and rank.
Five number summaries for Scopus number of documents as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Number of documents
 Minimum 1 1 1 1 1
 25% 4 2 6 3 11 7
 50% 10 5 12 8 32 21
 75% 20 11 25 16 58 47
 Maximum 53 105 113 88 292 214
Number of documents in last 5 years
 Minimum 0 0 0 0 0 0
 25% 2 1 1 0 1 0
 50% 6 3 5 3 5 4
 75% 12 7 10 7 14 12
 Maximum 45 51 106 39 120 54
Number of coauthors
 Minimum 0 0 0 0 0 0
 25% 6 3 8 5 11 9
 50% 15 8 17 11 42 24
 75% 27 18 33 24 76 58
 Maximum 99 150 355 331 150 200
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
The distribution of number of coauthors in Scopus differed significantly between the two categories of area of expertise (Mann–Whitney p < .001). All median values for number of coauthors were greater for those in audiology and were greater for men versus women (Mann–Whitney p < .001). The distribution of Scopus number of coauthors also differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, the number of coauthors increased. Box plots of Scopus total number of coauthors as a function of area of expertise and academic rank are also presented in Figure 4. The five number summaries of the Scopus number of coauthors box plot values, as a function area of expertise and rank, are also presented in Table 6.
The distribution of all Scopus citation indices differed significantly between the two categories of area of expertise (Mann–Whitney p < .002) and across gender (Mann–Whitney p < .002). All median values for citation indices were greater for those in audiology and greater for men. The distribution of all Scopus citations differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, the citation indices increased. Box plots of Scopus citation indices (i.e., number of citations and those in last 5 years, citations with no self-citations and those in the last 5 years, and most cited document), as a function of area of expertise and academic rank, are presented in Figure 5. The five number summaries of the Scopus citation box plot values, as a function of area of expertise and rank, are presented in Table 7.
Figure 5.

Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 5.

Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 7. Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Citations
 Minimum 0 0 0 0 0 0
 25% 12 5 30 19 78 64
 50% 70 29 124 70 408 296
 75% 227 110 319 213 1,102 786
 Maximum 2,101 2,962 4,049 5,176 10,370 8,517
Citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 19 8 10 5 18 10
 75% 58 29 36 20 62 44
 Maximum 617 831 1,173 1,652 1,977 1,014
No self-citations
 Minimum 0 0 0 0 0 0
 25% 10 5 28 15 74 56
 50% 63 26 112 59 351 271
 75% 208 101 306 187 1,021 716
 Maximum 1,771 2,869 3,492 5,082 9,733 7,969
No self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 17 8 9 4 13 8
 75% 51 25 30 18 56 36
 Maximum 460 777 747 1,622 1,197 621
Most cited
 Minimum 0 0 0 0 0 0
 25% 7 4 16 11 26 23
 50% 28 14 35 28 70 52
 75% 61 39 83 58 163 115
 Maximum 394 997 511 797 2,487 780
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 7. Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Citations
 Minimum 0 0 0 0 0 0
 25% 12 5 30 19 78 64
 50% 70 29 124 70 408 296
 75% 227 110 319 213 1,102 786
 Maximum 2,101 2,962 4,049 5,176 10,370 8,517
Citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 19 8 10 5 18 10
 75% 58 29 36 20 62 44
 Maximum 617 831 1,173 1,652 1,977 1,014
No self-citations
 Minimum 0 0 0 0 0 0
 25% 10 5 28 15 74 56
 50% 63 26 112 59 351 271
 75% 208 101 306 187 1,021 716
 Maximum 1,771 2,869 3,492 5,082 9,733 7,969
No self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 17 8 9 4 13 8
 75% 51 25 30 18 56 36
 Maximum 460 777 747 1,622 1,197 621
Most cited
 Minimum 0 0 0 0 0 0
 25% 7 4 16 11 26 23
 50% 28 14 35 28 70 52
 75% 61 39 83 58 163 115
 Maximum 394 997 511 797 2,487 780
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
The distribution of all Scopus h index values differed significantly between the two categories of area of expertise (Mann–Whitney p < .001) and across gender (Mann–Whitney p < .001). All median values for h indices were greater for those in audiology and greater for men. The distribution of all Scopus h indices differed significantly across academic rank (Kruskal–Wallis p < .001). With increasing rank, the h indices increased. Box plots of Scopus h index and h index with no self-citations and those in the last 5 years, as a function of area of expertise and academic rank, are presented in Figure 6. The five number summaries of the Scopus h indices box plot values, as a function area of expertise and rank, are presented in Table 8.
Figure 6.

Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 6.

Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 8. Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
h index
 Minimum 0 0 0 0 0 0
 25% 2 1 2 2 4 4
 50% 4 2 5 4 10 9
 75% 9 5 9 7 18 16
 Maximum 22 26 37 28 59 53
h index in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 2 1 2 1 2 2
 75% 4 3 4 3 4 4
 Maximum 15 17 22 14 25 19
h index no self-citations
 Minimum 0 0 0 0 0 0
 25% 1 1 2 2 4 4
 50% 4 2 5 4 10 8
 75% 8 5 9 7 17 15
 Maximum 20 43 36 27 50 51
h index no self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 2 1 2 1 2 1
 75% 4 3 3 2 4 3
 Maximum 13 15 14 13 18 12
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 8. Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
h index
 Minimum 0 0 0 0 0 0
 25% 2 1 2 2 4 4
 50% 4 2 5 4 10 9
 75% 9 5 9 7 18 16
 Maximum 22 26 37 28 59 53
h index in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 2 1 2 1 2 2
 75% 4 3 4 3 4 4
 Maximum 15 17 22 14 25 19
h index no self-citations
 Minimum 0 0 0 0 0 0
 25% 1 1 2 2 4 4
 50% 4 2 5 4 10 8
 75% 8 5 9 7 17 15
 Maximum 20 43 36 27 50 51
h index no self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 2 1 2 1 2 1
 75% 4 3 3 2 4 3
 Maximum 13 15 14 13 18 12
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
Association of Google Scholar, ResearchGate, and Scopus Indices
Spearman rank-order correlation coefficients (rs) were determined to examine the association between all 21 author-level metrics. There were statistically significant positive correlations between all author-level metrics (p < .01). Correlation coefficients ranged from .44 to .99, with a mean of .77.
Assessing Scholarship Longevity
It was of interest to examine scholarship longevity across faculty careers. Because the Scopus database identified the overwhelming majority of faculty in the field of communication sciences and disorders, examination was restricted to that database. Three indices were generated for this analysis. A Scopus average documents/year was calculated. This was calculated by dividing the total number of Scopus documents by the number of years since the terminal degree was completed. Also, a Scopus average citations/year was calculated by dividing the total number of Scopus citations by the number of years since the terminal degree was completed. A final value, Scopus “m,” was calculated by dividing the h index by the number of years of activity (i.e., years since the terminal degree was completed). For example, a researcher with 20 years of activity with an h index of 20 has an m value of 1. Hirsch (2005)  described this as the slope of the h index and suggested that it is “a useful yardstick to compare scientists of different seniority” (p. 16570). Hirsch noted that the m parameter is impractical if a scientist does not maintain productivity, whereas the h index remains a practical measure of collective accomplishment over time, even if the scientist has stopped publishing.
As with previous Scopus indices, these three indices differed significantly between the two categories of area of expertise (Mann–Whitney p < .02) and across gender (Mann–Whitney p < .003). All median values for were greater for those in audiology and greater for men. Box plots of Scopus average documents/year, average citations/year, and m, as a function of area of expertise, are presented in Figure 7. The five number summaries of these Scopus indices, as a function area of expertise, are presented in Table 9.
Figure 7.

Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 7.

Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 9. Five number summaries for box plots of average Scopus indices as a function of area of expertise.
Five number summaries for box plots of average Scopus indices as a function of area of expertise.×
Quartiles Area of expertise
Audiology Speech-language pathology
Document average/year
 Minimum 0.0 0.0
 25% 0.4 0.3
 50% 1.1 0.8
 75% 2.0 1.6
 Maximum 28.2 16.0
Citations average/year
 Minimum 0.0 0.0
 25% 2.1 1.4
 50% 11.0 6.0
 75% 29.7 18.8
 Maximum 700.3 329.1
m (h index/years since terminal degree)
 Minimum 0.0 0.0
 25% 0.2 0.1
 50% 0.4 0.3
 75% 0.7 0.7
 Maximum 7.3 4.0
Table 9. Five number summaries for box plots of average Scopus indices as a function of area of expertise.
Five number summaries for box plots of average Scopus indices as a function of area of expertise.×
Quartiles Area of expertise
Audiology Speech-language pathology
Document average/year
 Minimum 0.0 0.0
 25% 0.4 0.3
 50% 1.1 0.8
 75% 2.0 1.6
 Maximum 28.2 16.0
Citations average/year
 Minimum 0.0 0.0
 25% 2.1 1.4
 50% 11.0 6.0
 75% 29.7 18.8
 Maximum 700.3 329.1
m (h index/years since terminal degree)
 Minimum 0.0 0.0
 25% 0.2 0.1
 50% 0.4 0.3
 75% 0.7 0.7
 Maximum 7.3 4.0
×
Discussion
These are the first reported author-level impact metrics in the field of communication sciences and disorders. Over 2,000 faculty members were surveyed from 257 accredited audiology and speech-language pathology programs in the United States and Canada. The majority of faculty members were housed in research/doctoral universities and master's colleges and universities. Approximately three quarters had an expertise in speech-language pathology. Academic rank was generally equally spread out. Although the majority of faculty members were women (68%), the distribution was widely different across area of expertise. Men comprised 46.3% of faculty in audiology and 27.3% of faculty in speech-language pathology. The faculty representation was not equal across the three databases. The majority of faculty members were identified in Scopus. This is not unexpected considering it represents those who have some peer-reviewed scholarship, and voluntary membership is not required. Faculty were identified the least in Google Scholar, where membership is required, and members must make their profiles public to be viewed. Interestingly, academic rank was not equally represented across databases. Assistant professors had a higher proportion of presence in Google Scholar and a higher proportion of absences in ResearchGate and Scopus. One could speculate that assistant professors may be more likely to be concerned with career advancement (i.e., tenure and promotion). As such, they would be more concerned with following author-level metrics on Google Scholar and less likely to be engaged with social networking in ResearchGate. On the contrary, older faculty may be less “tech savvy” and may not be familiar with Google Scholar profiles. It is not unexpected that assistant professors are less represented on Scopus because they may not have published yet, being early in their career. There may be some bias in the metrics found in Google Scholar and ResearchGate, where membership is voluntary. It may be that faculty members who are more productive are more likely to sign up and make their profile public. Less productive faculty members may shy away from these databases. In consequence, author metrics may be inflated in Google Scholar and ResearchGate.
There were statistically significant associations among author-level impact metrics in the three databases. This was not unexpected. Although the three databases differ in the means by which they collected data, some measures reflected the same author-level impact metric. The number of citations was an index in all three databases. Document counts were found in both ResearchGate and Scopus. The h index was found in both Google Scholar and Scopus. The Google Scholar i10-Index is a similar publication/citation metric to the h index. The RG score and impact points are global metrics that consider publication and citation metrics. Previous reports have also noted a significant correlation among author-level metrics (e.g., h index) across citation databases (Meho & Rogers, 2008; Sanderson, 2008). Google Scholar citations have been reported to correlate well with traditional bibliometric data citation sources (Harzing & van der Wal, 2008). Citation counts have been reported, however, to be different across databases in some fields (e.g., Arora & Eden, 2011; Bakkalbasi, Bauer, Glover, & Wang, 2006; Kulkarni, Aziz, Shams, & Busse, 2009). ResearchGate publication counts have also been demonstrated to positively correlate with traditional bibliometric data document sources (Thelwall & Kousha, 2015; Yu, Wu, Alhalabi, Kao, & Wu, 2016).
Six general trends were evidenced across all author-level impact metrics. First, most of the indices were positively skewed. Second, not surprisingly, the examination of Google Scholar and Scopus indices (i.e., number of citations, documents, h index, and i10-Index) in the last 5 years revealed values that are lower relative to an author's full career indices. Third, removing self-citations lowered Scopus indices (i.e., number of citations and h index). Fourth, there was a significant effect of academic rank. With increasing rank, all index values increased. Considering that academic promotion comes with increased scholarly productivity over time, this would be expected. In addition, with increasing rank across one's career, the number of collaborations would increase, and hence the number of coauthors would likely increase. Fifth, all median values for author-level metrics were greater for men versus women, with the exception of Google Scholar's total number of citations and citations in the last 5 years. Last, the distribution of all author metric indices, with the exception of Google Scholar metrics, differed significantly between the two categories of area of expertise. All values were greater for those in audiology versus speech-language pathology. The reason for the exception with Google Scholar may be the small representation of faculty in the field of communication sciences and disorders in that database (i.e., only 10.5%).
Removing self-citations in Scopus lowered total citations by 8.8%, citations in the last 5 years by 16.7%, h index by 7.0%, and h index in the last 5 years by 10.5%, collapsed across rank and area of expertise. These values are comparable to those in clinical medicine, computer science, and engineering (Dehghani, Basirian, & Ganjoo, 2011). High rates of self-citation, as much as 36%, have been reported in the field of communication sciences and disorders (e.g., Aksnes, 2003). Fowler and Aksnes (2007)  argued that even a small amount of self-citation affects total citations across a career. They reported that each self-citation yields an additional 3.65 citations from others after 10 years. Taken cumulatively, “it means an additional 40% of total citations may be generated indirectly by self-citations. Adding these effects together, self-citation may therefore account directly or indirectly for more than half of all citations after 10 years” (Fowler and Aksnes, 2007, p. 434). They concluded that this is important to consider when evaluating an average researcher who has a relatively few number of publications and citations, because “a few self-citations could easily tip the balance for funding and promotion decisions” (Fowler and Aksnes, 2007, p. 434).
The gender effect seen on author-level impact metrics is consistent with previously reported gender effects in scholarly productivity. For example, a gender effect has been observed with h index in the fields of psychology (Geraci, Balsis, & Busch, 2015; Nosek et al., 2010) and ecology/evolutionary biology (Kelly & Jennions, 2006; Symonds, Gemmell, Braisher, Gorringe, & Elgar, 2006). In addition, men have been shown to publish more manuscripts than women across scientific disciplines (Cole & Zuckerman, 1984; Kelly & Jennions, 2006; Sax, Serra Hagedorn, Arredondo, & Dicrisi, 2002; Symonds et al., 2006; Xie & Shauman, 1998). The cause of the difference is difficult to identify and has been deemed the “productivity puzzle” (Cole & Zuckerman, 1984; Xie & Shauman, 1998). Several social factors have been suggested. For example, it has been argued that women bear a disproportionate amount of domestic responsibility that affects scholarship (Cole & Zuckerman, 1984; Kelly & Jennions, 2006; Symonds et al., 2006). Women are particularly more affected than men, in terms of publication productivity, if they have young (i.e., less than 10 years of age) children (Hunter, 2010; Kyvik, 1990; Kyvik & Teigen 1996). Knapp (2005)  suggested that women are more greatly burdened with additional nonresearch obligations as a result of their scarcity and the appeal of having a balance of the genders on administrative committees. There is evidence that at least at the level of associate professor, the larger service responsibility imposed on women affects research productivity and career advancement (Misra, Hicke Lundquist, Holmes, & Agiomavritis, 2011; Xie & Shauman, 1998). Differences in research funding have been also attributed differences in scholarly production. Duch et al. (2012)  examined 437,787 science, technology, engineering, and mathematics publications authored by 4,292 faculty members in U.S. research universities. They found that lower publication rates by women were significantly correlated with the amount of research resources typically needed. Last, the lack of professional networks involving women has been identified as a possible detriment to scholarly productivity (Durbin, 2011; Kyvik & Teigen, 1996; Villanueva-Felez, 2015). Indeed, women had fewer coauthors than men (see Figure 4 and Table 6), a pattern that may have contributed to fewer research collaborations and hence scholarly production.
The fact that significant differences were found across all author-level impact metrics in ResearchGate and Scopus across area of expertise is puzzling, considering approximately three quarters of faculty work in speech-language pathology. However, considering the effect of gender discussed above, one cannot discount the interpretation that the disparity seen across area of expertise may just reflect the gender disparity across audiology and speech-language pathology faculty membership. From Table 2, one can see that the majority of faculty in audiology (53.7%) and speech-language pathology (72.7%) are female researchers. To examine this possible confounding factor, we reanalyzed the ResearchGate and Scopus author-level impact metrics as a function of area of expertise excluding female faculty. On all Scopus indices and two of four ResearchGate indices (i.e., number of publications and citations), there was no statistically significant effect of area of expertise (Mann–Whitney p > .05). This strongly suggests that differences in author-level metrics across audiology and speech-language pathology faculty membership are related to gender. Another contributing effect to differences across area of expertise is the significant difference in number of coauthors. Recall, faculty in audiology had a higher coauthor count. Having more coauthors can lead to more collaborations and a concomitant increase in scholarly production. Also, it has been demonstrated that that there is a positive association between the number of coauthors and coauthor citation (Costas, Van Leeuwen, & Bordons, 2010; Davarpanah & Amel, 2009; Dehghani et al., 2011).
An obvious question is: How can one use these author-level metrics in the field of communication sciences and disorders? Comparing some metrics across disciplines is not recommended (Hirsch, 2005). For example, h index values are determined “by the average number of references in a paper in the field, the average number of papers produced by each scientist in the field, and the size (number of scientists) of the field” (Hirsch, 2005, p. 16571), and so one would expect varying values across disciplines. In fact, varying h index values are generally higher in the sciences, followed by engineering, health sciences, business, and humanities (e.g., Batista, Campiteli, Kinouchi, & Martinez, 2006; Harzing & Alakangas, 2016; Jarvey, Usher, & McElroy, 2012; Podlubny, 2005). Nonetheless, comparisons across disciplines are necessary, for example, with grant funding (Council of Canadian Academies Expert Panel on Science Performance and Research Funding, 2012). In these cases, the use of normalized indices (e.g., whereby raw numbers of citations are divided by a discipline-dependent factor) is suggested to control discrepancies across scientific domains (e.g., Kaur et al., 2013; Li, Radicchi, Castellano, & Ruiz-Castillo, 2013; Lundberg, 2007; Radicchi & Castellano, 2012) and allow comparisons of author impact across scientific disciplines.
Hirsch (2005)  suggested that the h index could be used for advancement evaluation for tenure and promotion. The m parameter can also be used to identify successful, outstanding, and truly unique individuals in a field. The values found in Tables 8 and 9 could be used in the field of communication sciences and disorders by assigning quartile values to certain benchmarks. For example, an h index above the 25%, 50%, and 75% quartiles could be benchmarks for promotion to associate professor, awarding tenure, and promotion to full professor, respectively. Likewise, an m above the 25% quartile could be the standard for tenure, and an m above the 75% quartile could be the standard for promotion to full professor. Hirsch (2007)  also suggested that the h index is superior in predicting future scientific achievement as compared with other indices such as total citation count, citations per document, and total document count. Hence, administrators could use these indices (e.g., h index) for decisions for tenure and promotion and when hiring midlevel and senior faculty. It should be cautioned, however, that bibliometrics alone do not capture the full range of scholarly activity (Hirsch, 2005; Jarvey et al., 2012). For example, the true quality of the research output may be misrepresented by the citation count. Consider Martin Fleischmann and Stanley Pons's (1989)  ill-fated report of “cold fusion” in the Journal of Electroanalytical Chemistry and Interfacial Electrochemistry and the resulting positive and negative citations in the following months (Anonymous, 1990). In addition, teaching ability is not considered. Last, other means of assessing faculty should be used. Aksnes and Taxt (2004), for example, suggested bibliometrics and expert peer review are stronger when used in combination.
We agree with the notion that “science is a gift economy; value is defined as the degree to which one's ideas have freely contributed to knowledge and impacted the thinking of others” (Bollen, Van de Sompel, Hagberg, & Chute, 2009, p. 1). As such, it is our opinion that examining quantitative measures of scientific impact at the author level in the field of communication sciences and disorders is justified. The motivation may vary depending whether one is an individual faculty member examining one's own or someone else's scholarship; an administrator making a decision regarding hiring or tenure and/or promotion; or a granting agency making decisions about grant application funding. We suggest that an open database (e.g., Scopus), where voluntary membership is not required, be utilized. In our opinion, the following metrics are of the most value: number of documents (i.e., total and in last 5 years), number of citations excluding self-citations (i.e., total and in last 5 years), and h index excluding self-citations (i.e., total and in last 5 years). Longevity metrics (i.e., Scopus average documents/year, Scopus average citations/year, and Scopus m) are also recommended for evaluation for tenure and/or promotion, post-tenure review, and grant funding. For any examination of an individual faculty member's impact, we suggest some consideration of a faculty member's research workload and their institution's Carnegie classification.
In conclusion, this is the first report of author-level metrics in the field of communication sciences and disorders. Over 2,000 faculty members were surveyed from 257 accredited audiology and speech-language pathology programs in the United States and Canada. The overwhelming majority of faculty was represented in Scopus, followed by ResearchGate and Google Scholar databases. In general, author-level impact metrics were positively skewed; metric values increased significantly with increasing academic rank; author-level metrics were greater for men versus women; and values were greater for those in audiology versus speech-language pathology. Self-citation inflated total citations and h index values by approximately 10%. These author-level metrics may serve as a benchmark for scholarly production of academic research faculty in the field of communication sciences and disorders. The data can assist faculty with professional identity management, tenure review, promotion review, grant applications, and future employment opportunities.
Acknowledgments
This research was presented in part at the 2015 American Speech-Language-Hearing Association Annual Convention, Denver, CO, and the 2016 Annual Convention of the North Carolina Speech, Hearing, and Language Association, Raleigh, NC. Amber F. P. Jackson and Melissa N. Work assisted with data collection. This work is dedicated to a friend and colleague of great impact—Joseph Kalinowski.
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Radicchi, F., & Castellano, C. (2012). Testing the fairness of citation indicators for comparison across scientific domains: The case of fractional citation counts. Journal of Informetrics, 6, 121–130. [Article]
Radicchi, F., & Castellano, C. (2012). Testing the fairness of citation indicators for comparison across scientific domains: The case of fractional citation counts. Journal of Informetrics, 6, 121–130. [Article] ×
Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences of the United States of America, 105, 17268–17272.
Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences of the United States of America, 105, 17268–17272.×
ResearchGate. (n.d.). About us . Retrieved from: https://www.researchgate.net/about
ResearchGate. (n.d.). About us . Retrieved from: https://www.researchgate.net/about ×
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Sanderson, M. (2008). Revisiting h measured on US LIS and IR academics. Journal of the American Society for Information Science and Technology, 59, 317–330. [Article] ×
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Schreiber, M. (2008). To share the fame in a fair way, h m modifies h for multi-authored manuscripts. New Journal of Physics, 10, 040201. [Article] ×
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Appendix
Institutions Included in the Survey
United States
 A.T. Still University
 Abilene Christian University
 Adelphi University
 Alabama Agricultural and Mechanical University
 Andrews University
 Appalachian State University
 Arizona State University
 Arkansas State University
 Armstrong State University
 Auburn University
 Ball State University
 Baylor University
 Bloomsburg University of Pennsylvania
 Boston University
 Bowling Green State University
 Brigham Young University
 Brooklyn College
 Buffalo State College
 California State University–Chico
 California State University–East Bay
 California State University–Fresno
 California State University–Fullerton
 California State University–Long Beach
 California State University–Los Angeles
 California State University–Northridge
 California State University, Sacramento: Sacramento State
 California University of Pennsylvania
 Calvin College
 Case Western Reserve University
 Central Michigan University
 Chapman University
 Clarion University of Pennsylvania
 Cleveland State University
 College of Saint Rose
 Duquesne University
 East Carolina University
 East Stroudsburg University
 East Tennessee State University
 Eastern Illinois University
 Eastern Kentucky University
 Eastern Michigan University
 Eastern Washington University
 Edinboro University
 Emerson College
 Florida Atlantic University
 Florida International University
 Florida State University
 Fontbonne University
 Fort Hays State University
 Gallaudet University
 George Washington University
 Georgia State University
 Governors State University
 Hampton University
 Harding University
 Hofstra University
 Howard University
 Hunter College
 Idaho State University
 Illinois State University
 Indiana State University
 Indiana University–Bloomington
 Indiana University of Pennsylvania
 Ithaca College
 Jackson State University
 James Madison University
 Kansas State University
 Kean University
 Kent State University
 La Salle University
 Lamar University
 Lehman College
 Loma Linda University
 Long Island University–Brooklyn
 Long Island University–Post
 Longwood University
 Louisiana State University
 Louisiana Tech University
 Loyola University Maryland
 Louisiana State University Health–New Orleans
 Louisiana State University Health–Shreveport
 Marquette University
 Marshall University
 Marywood University
 Mercy College
 Massachusetts General Hospital (MGH) Institute of Health Professions
 Miami University
 Michigan State University
 Minnesota State University–Mankato
 Minnesota State University–Moorhead
 Minot State University
 Misericordia University
 Missouri State University
 Molloy College
 Montclair State University
 Murray State University
 Nazareth College
 New Mexico State University
 New York Medical College
 New York University
 North Carolina Central University
 Northeastern State University
 Northeastern University
 Northern Arizona University
 Northern Illinois University
 Northwestern University
 Nova Southeastern University
 The Ohio State University
 Ohio University
 Oklahoma State University
 Old Dominion University
 Our Lady of the Lake University
 Pennsylvania State University
 Portland State University
 Purdue University
 Queens College
 Radford University
 Rockhurst University
 Rush University
 Saint Louis University
 Saint Xavier University
 Salus University
 San Diego State University
 San Francisco State University
 San Jose State University
 Seton Hall University
 South Carolina State University
 Southeast Missouri State University
 Southeastern Louisiana University
 Southern Connecticut State University
 Southern Illinois University–Carbondale
 Southern Illinois University–Edwardsville
 Southern University and Agricultural and Mechanical College
 St. Ambrose University
 St. Cloud State University
 St. John's University
 State University of New York at Buffalo
 State University of New York at Fredonia
 State University of New York at New Paltz
 State University of New York at Plattsburgh
 Stephen F. Austin State University
 Syracuse University
 Teachers College, Columbia University
 Temple University
 Tennessee State University
 Texas A&M University–Kingsville
 Texas Christian University
 Texas State University
 Texas Tech University Health Sciences Center
 Texas Woman's University
 Touro College
 Towson University
 Truman State University
 University of Akron
 University of Alabama
 University of Arizona
 University of Arkansas
 University of Arkansas at Little Rock
 University of Central Arkansas
 University of Central Florida
 University of Central Missouri
 University of Central Oklahoma
 University of Cincinnati
 University of Colorado Boulder
 University of Connecticut
 University of Florida
 University of Georgia
 University of Hawaii at Manoa
 University of Houston
 University of Illinois–Urbana-Champaign
 University of Iowa
 University of Kansas
 University of Kentucky
 University of Louisiana at Lafayette
 University of Louisiana at Monroe
 University of Louisville
 University of Maine
 University of Maryland
 University of Massachusetts Amherst
 University of Memphis
 University of Minnesota–Duluth
 University of Minnesota–Twin Cities
 University of Mississippi
 University of Missouri
 University of Montana
 University of Montevallo
 University of Nebraska–Lincoln
 University of Nebraska at Kearney
 University of Nebraska Omaha
 University of Nevada–Reno
 University of New Hampshire
 University of New Mexico
 University of North Carolina at Chapel Hill
 University of North Carolina at Greensboro
 University of North Dakota
 University of North Texas
 University of Northern Colorado
 University of Northern Iowa
 University of Oklahoma
 University of Oregon
 University of Pittsburgh
 University of Redlands
 University of Rhode Island
 University of South Alabama
 University of South Carolina
 University of South Dakota
 University of South Florida
 University of Southern Mississippi
 University of Tennessee–Knoxville
 University of Texas at Austin
 University of Texas at Dallas
 University of Texas at El Paso
 University of Texas–Pan American
 University of the District of Columbia
 University of the Pacific
 University of Toledo
 University of Tulsa
 University of Utah
 University of Vermont
 University of Virginia
 University of Washington
 University of West Georgia
 University of Wisconsin–Eau Claire
 University of Wisconsin–Madison
 University of Wisconsin–Milwaukee
 University of Wisconsin–River Falls
 University of Wisconsin–Stevens Point
 University of Wisconsin–Whitewater
 University of Wyoming
 Utah State University
 Valdosta State University
 Vanderbilt University
 Washington State University
 Washington University in St. Louis
 Wayne State University
 West Chester University
 West Texas A&M University
 West Virginia University
 Western Carolina University
 Western Illinois University
 Western Kentucky University
 Western Michigan University
 Western Washington University
 Wichita State University
 William Paterson University
 Worcester State University
Canada
 Dalhousie University
 McGill University
 Université Laurentienne
 Université de Montréal
 Université du Québec à Trois–Rivières
 Université Laval
 University of Alberta
 University of British Columbia
 University of Ottawa
 University of Toronto
 University of Western Ontario
Footnotes
1 As of 2017, Web of Science is now maintained by Clarivate Analytics.
As of 2017, Web of Science is now maintained by Clarivate Analytics.×
2 Emeritus/retired faculty members were excluded.
Emeritus/retired faculty members were excluded.×
3 If complete information was not available on program websites, additional Internet searches were undertaken (e.g., Google Scholar, ProQuest Dissertations & Theses Global, and ResearchGate) in an effort to glean desired demographics.
If complete information was not available on program websites, additional Internet searches were undertaken (e.g., Google Scholar, ProQuest Dissertations & Theses Global, and ResearchGate) in an effort to glean desired demographics.×
Figure 1.

Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 1.

Box plots of years since terminal degree as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 2.

Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 2.

Box plots of Google Scholar indices as a function of area of expertise and rank. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 3.

Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 3.

Box plots of ResearchGate indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 4.

Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 4.

Box plots of Scopus number of document indices and number of coauthors as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 5.

Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 5.

Box plots of Scopus number of citation indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 6.

Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 6.

Box plots of Scopus h indices as a function of area of expertise and rank, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Figure 7.

Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

 Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).
Figure 7.

Box plots of Scopus average documents/year, average citations/year, and m (i.e., slope of h index/years since terminal degree) as a function of area of expertise, where SLP is speech-language pathology. The top, bottom, and line through the middle of the box denote the 75th, 25th, and 50th percentile (median), respectively. Circles denote outliers (i.e., cases with values between 1.5 and 3 times the interquartile range). Asterisks denote extreme outliers (i.e., cases with values greater than 3 times the interquartile range).

×
Table 1. Number and percentage of U.S. institutions as a function of Carnegie classification.
Number and percentage of U.S. institutions as a function of Carnegie classification.×
Carnegie classification n %
Research universities (very high research activity) 53 20.6
Research universities (high research activity) 56 21.8
Doctoral/research universities 25 9.7
Master's colleges and universities (larger programs) 87 33.9
Master's colleges and universities (medium programs) 14 5.4
Master's colleges and universities (smaller programs) 2 .8
Special focus institutions (medical schools and medical centers) 6 2.3
Special focus institutions (other health professions schools) 2 .8
Baccalaureate colleges (arts & sciences) 1 .4
Table 1. Number and percentage of U.S. institutions as a function of Carnegie classification.
Number and percentage of U.S. institutions as a function of Carnegie classification.×
Carnegie classification n %
Research universities (very high research activity) 53 20.6
Research universities (high research activity) 56 21.8
Doctoral/research universities 25 9.7
Master's colleges and universities (larger programs) 87 33.9
Master's colleges and universities (medium programs) 14 5.4
Master's colleges and universities (smaller programs) 2 .8
Special focus institutions (medical schools and medical centers) 6 2.3
Special focus institutions (other health professions schools) 2 .8
Baccalaureate colleges (arts & sciences) 1 .4
×
Table 2. Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.
Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.×
Parameter Area of expertise
Audiology
Speech-language pathology
n % n %
Gender
 Female 263 53.7 1,106 72.7
 Male 227 46.3 415 27.3
Terminal degree
 Ph.D. 420 85.9 1,346 88.5
 Au.D. 55 11.2
 Master's 6 1.2 104 6.8
 Other 8 1.6 71 4.7
Rank
 Full 170 34.7 456 31.1
 Associate 186 38.0 493 33.8
 Assistant 134 27.3 571 35.1
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.×
Table 2. Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.
Number and percentage of faculty as a function of expertise, gender, terminal degree, and academic rank.×
Parameter Area of expertise
Audiology
Speech-language pathology
n % n %
Gender
 Female 263 53.7 1,106 72.7
 Male 227 46.3 415 27.3
Terminal degree
 Ph.D. 420 85.9 1,346 88.5
 Au.D. 55 11.2
 Master's 6 1.2 104 6.8
 Other 8 1.6 71 4.7
Rank
 Full 170 34.7 456 31.1
 Associate 186 38.0 493 33.8
 Assistant 134 27.3 571 35.1
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.
Note. Ph.D. = doctor of philosophy; Au.D. = doctor of audiology.×
×
Table 3. Absolute count and percentage of faculty identified in each database as a function of rank.
Absolute count and percentage of faculty identified in each database as a function of rank.×
Database Rank
Total
Full Associate Assistant
Google Scholar
 Absent count 572 613 613 1,798
  Absent (%) 31.8 34.1 34.1 100.0
  Rank (%) 91.4 90.3 87.0 89.5
  Total (%) 28.5 30.5 30.5 89.5
 Present count 54 66 92 212
  Present (%) 25.5 31.1 43.4 100.0
  Rank (%) 8.6 9.7 13.0 10.5
  Total (%) 2.7 3.3 4.6 10.5
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
ResearchGate
 Absent count 338 359 428 1,125
  Absent (%) 30.0 31.9 38.0 100.0
  Rank (%) 54.0 52.9 60.7 56.0
  Total (%) 16.8 17.9 21.3 56.0
 Present count 288 320 277 885
  Present (%) 32.5 36.2 31.3 100.0
  Rank (%) 46.0 47.1 39.3 44.0
  Total (%) 14.3 15.9 13.8 44.0
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Scopus
 Absent count 55 82 177 314
  Absent (%) 17.5 26.1 56.4 100.0
  Rank (%) 8.8 12.1 25.1 15.6
  Total (%) 2.7 4.1 8.8 15.6
 Present count 571 597 528 1,696
  Present (%) 33.7 35.2 31.1 100.0
  Rank (%) 91.2 87.9 74.9 84.4
  Total (%) 28.4 29.7 26.3 84.4
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Table 3. Absolute count and percentage of faculty identified in each database as a function of rank.
Absolute count and percentage of faculty identified in each database as a function of rank.×
Database Rank
Total
Full Associate Assistant
Google Scholar
 Absent count 572 613 613 1,798
  Absent (%) 31.8 34.1 34.1 100.0
  Rank (%) 91.4 90.3 87.0 89.5
  Total (%) 28.5 30.5 30.5 89.5
 Present count 54 66 92 212
  Present (%) 25.5 31.1 43.4 100.0
  Rank (%) 8.6 9.7 13.0 10.5
  Total (%) 2.7 3.3 4.6 10.5
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
ResearchGate
 Absent count 338 359 428 1,125
  Absent (%) 30.0 31.9 38.0 100.0
  Rank (%) 54.0 52.9 60.7 56.0
  Total (%) 16.8 17.9 21.3 56.0
 Present count 288 320 277 885
  Present (%) 32.5 36.2 31.3 100.0
  Rank (%) 46.0 47.1 39.3 44.0
  Total (%) 14.3 15.9 13.8 44.0
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
Scopus
 Absent count 55 82 177 314
  Absent (%) 17.5 26.1 56.4 100.0
  Rank (%) 8.8 12.1 25.1 15.6
  Total (%) 2.7 4.1 8.8 15.6
 Present count 571 597 528 1,696
  Present (%) 33.7 35.2 31.1 100.0
  Rank (%) 91.2 87.9 74.9 84.4
  Total (%) 28.4 29.7 26.3 84.4
 Total count 626 679 705 2,010
  Total (%) 31.1 33.8 35.1 100.0
  Rank (%) 100.0 100.0 100.0 100.0
×
Table 4. Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.
Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.×
Quartiles Rank
Assistant Associate Full
Citations
 Minimum 2 1 143
 25% 98 181 995
 50% 274 387 2,068
 75% 552 810 3,618
 Maximum 3,800 2,667 22,360
Citations in last 5 years
 Minimum 2 1 74
 25% 90 142 531
 50% 234 229 1,063
 75% 414 452 1,613
 Maximum 3,246 1,925 9,531
h index
 Minimum 1 1 5
 25% 4 7 16
 50% 9 9 24
 75% 11 15 33
 Maximum 33 29 67
h index in last 5 years
 Minimum 1 1 5
 25% 4 6 12
 50% 8 8 18
 75% 11 12 22
 Maximum 17 26 47
i10-Index
 Minimum 0 0 5
 25% 3 5 21
 50% 8 9 36
 75% 12 18 61
 Maximum 55 60 146
i10-Index in last 5 years
 Minimum 0 0 4
 25% 3 5 14
 50% 7 8 27
 75% 11 14 42
 Maximum 54 58 101
Table 4. Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.
Five number summaries for box plots of Google Scholar indices as a function of area of rank collapsed across area of expertise.×
Quartiles Rank
Assistant Associate Full
Citations
 Minimum 2 1 143
 25% 98 181 995
 50% 274 387 2,068
 75% 552 810 3,618
 Maximum 3,800 2,667 22,360
Citations in last 5 years
 Minimum 2 1 74
 25% 90 142 531
 50% 234 229 1,063
 75% 414 452 1,613
 Maximum 3,246 1,925 9,531
h index
 Minimum 1 1 5
 25% 4 7 16
 50% 9 9 24
 75% 11 15 33
 Maximum 33 29 67
h index in last 5 years
 Minimum 1 1 5
 25% 4 6 12
 50% 8 8 18
 75% 11 12 22
 Maximum 17 26 47
i10-Index
 Minimum 0 0 5
 25% 3 5 21
 50% 8 9 36
 75% 12 18 61
 Maximum 55 60 146
i10-Index in last 5 years
 Minimum 0 0 4
 25% 3 5 14
 50% 7 8 27
 75% 11 14 42
 Maximum 54 58 101
×
Table 5. Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.
Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
RG score
 Minimum 2.8 0 0 0 0 0
 25% 10.5 5.8 15.7 8.9 17.0 14.3
 50% 16.4 10.8 19.7 15.3 26.9 22.9
 75% 21.8 17.4 24.5 21.5 31.1 29.2
 Maximum 31.5 38.5 38.0 36.8 42.2 41.2
Number of publications
 Minimum 1 0 0 0 0 0
 25% 7 5 15 8 21 15
 50% 14 9 23 16 47 38
 75% 22 16 33 27 68 64
 Maximum 49 116 116 122 261 170
Number of citations
 Minimum 0 0 0 0 0 0
 25% 14 8 75 34 109 96
 50% 103 32 159 116 419 336
 75% 276 134 345 250 1226 907
 Maximum 732 2,614 3,993 5,080 12,506 8,410
Note. SLP = speech-language pathology; RG = ResearchGate.
Note. SLP = speech-language pathology; RG = ResearchGate.×
Table 5. Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.
Five number summaries for box plots of ResearchGate indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
RG score
 Minimum 2.8 0 0 0 0 0
 25% 10.5 5.8 15.7 8.9 17.0 14.3
 50% 16.4 10.8 19.7 15.3 26.9 22.9
 75% 21.8 17.4 24.5 21.5 31.1 29.2
 Maximum 31.5 38.5 38.0 36.8 42.2 41.2
Number of publications
 Minimum 1 0 0 0 0 0
 25% 7 5 15 8 21 15
 50% 14 9 23 16 47 38
 75% 22 16 33 27 68 64
 Maximum 49 116 116 122 261 170
Number of citations
 Minimum 0 0 0 0 0 0
 25% 14 8 75 34 109 96
 50% 103 32 159 116 419 336
 75% 276 134 345 250 1226 907
 Maximum 732 2,614 3,993 5,080 12,506 8,410
Note. SLP = speech-language pathology; RG = ResearchGate.
Note. SLP = speech-language pathology; RG = ResearchGate.×
×
Table 6. Five number summaries for Scopus number of documents as a function of area of expertise and rank.
Five number summaries for Scopus number of documents as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Number of documents
 Minimum 1 1 1 1 1
 25% 4 2 6 3 11 7
 50% 10 5 12 8 32 21
 75% 20 11 25 16 58 47
 Maximum 53 105 113 88 292 214
Number of documents in last 5 years
 Minimum 0 0 0 0 0 0
 25% 2 1 1 0 1 0
 50% 6 3 5 3 5 4
 75% 12 7 10 7 14 12
 Maximum 45 51 106 39 120 54
Number of coauthors
 Minimum 0 0 0 0 0 0
 25% 6 3 8 5 11 9
 50% 15 8 17 11 42 24
 75% 27 18 33 24 76 58
 Maximum 99 150 355 331 150 200
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 6. Five number summaries for Scopus number of documents as a function of area of expertise and rank.
Five number summaries for Scopus number of documents as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Number of documents
 Minimum 1 1 1 1 1
 25% 4 2 6 3 11 7
 50% 10 5 12 8 32 21
 75% 20 11 25 16 58 47
 Maximum 53 105 113 88 292 214
Number of documents in last 5 years
 Minimum 0 0 0 0 0 0
 25% 2 1 1 0 1 0
 50% 6 3 5 3 5 4
 75% 12 7 10 7 14 12
 Maximum 45 51 106 39 120 54
Number of coauthors
 Minimum 0 0 0 0 0 0
 25% 6 3 8 5 11 9
 50% 15 8 17 11 42 24
 75% 27 18 33 24 76 58
 Maximum 99 150 355 331 150 200
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
Table 7. Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Citations
 Minimum 0 0 0 0 0 0
 25% 12 5 30 19 78 64
 50% 70 29 124 70 408 296
 75% 227 110 319 213 1,102 786
 Maximum 2,101 2,962 4,049 5,176 10,370 8,517
Citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 19 8 10 5 18 10
 75% 58 29 36 20 62 44
 Maximum 617 831 1,173 1,652 1,977 1,014
No self-citations
 Minimum 0 0 0 0 0 0
 25% 10 5 28 15 74 56
 50% 63 26 112 59 351 271
 75% 208 101 306 187 1,021 716
 Maximum 1,771 2,869 3,492 5,082 9,733 7,969
No self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 17 8 9 4 13 8
 75% 51 25 30 18 56 36
 Maximum 460 777 747 1,622 1,197 621
Most cited
 Minimum 0 0 0 0 0 0
 25% 7 4 16 11 26 23
 50% 28 14 35 28 70 52
 75% 61 39 83 58 163 115
 Maximum 394 997 511 797 2,487 780
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 7. Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus citation indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
Citations
 Minimum 0 0 0 0 0 0
 25% 12 5 30 19 78 64
 50% 70 29 124 70 408 296
 75% 227 110 319 213 1,102 786
 Maximum 2,101 2,962 4,049 5,176 10,370 8,517
Citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 19 8 10 5 18 10
 75% 58 29 36 20 62 44
 Maximum 617 831 1,173 1,652 1,977 1,014
No self-citations
 Minimum 0 0 0 0 0 0
 25% 10 5 28 15 74 56
 50% 63 26 112 59 351 271
 75% 208 101 306 187 1,021 716
 Maximum 1,771 2,869 3,492 5,082 9,733 7,969
No self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 17 8 9 4 13 8
 75% 51 25 30 18 56 36
 Maximum 460 777 747 1,622 1,197 621
Most cited
 Minimum 0 0 0 0 0 0
 25% 7 4 16 11 26 23
 50% 28 14 35 28 70 52
 75% 61 39 83 58 163 115
 Maximum 394 997 511 797 2,487 780
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
Table 8. Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
h index
 Minimum 0 0 0 0 0 0
 25% 2 1 2 2 4 4
 50% 4 2 5 4 10 9
 75% 9 5 9 7 18 16
 Maximum 22 26 37 28 59 53
h index in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 2 1 2 1 2 2
 75% 4 3 4 3 4 4
 Maximum 15 17 22 14 25 19
h index no self-citations
 Minimum 0 0 0 0 0 0
 25% 1 1 2 2 4 4
 50% 4 2 5 4 10 8
 75% 8 5 9 7 17 15
 Maximum 20 43 36 27 50 51
h index no self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 2 1 2 1 2 1
 75% 4 3 3 2 4 3
 Maximum 13 15 14 13 18 12
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
Table 8. Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.
Five number summaries for box plots of Scopus h indices as a function of area of expertise and rank.×
Quartiles Rank
Assistant
Associate
Full
Audiology SLP Audiology SLP Audiology SLP
h index
 Minimum 0 0 0 0 0 0
 25% 2 1 2 2 4 4
 50% 4 2 5 4 10 9
 75% 9 5 9 7 18 16
 Maximum 22 26 37 28 59 53
h index in last 5 years
 Minimum 0 0 0 0 0 0
 25% 1 1 0 0 0 0
 50% 2 1 2 1 2 2
 75% 4 3 4 3 4 4
 Maximum 15 17 22 14 25 19
h index no self-citations
 Minimum 0 0 0 0 0 0
 25% 1 1 2 2 4 4
 50% 4 2 5 4 10 8
 75% 8 5 9 7 17 15
 Maximum 20 43 36 27 50 51
h index no self-citations in last 5 years
 Minimum 0 0 0 0 0 0
 25% 0 1 0 0 0 0
 50% 2 1 2 1 2 1
 75% 4 3 3 2 4 3
 Maximum 13 15 14 13 18 12
Note. SLP = speech-language pathology.
Note. SLP = speech-language pathology.×
×
Table 9. Five number summaries for box plots of average Scopus indices as a function of area of expertise.
Five number summaries for box plots of average Scopus indices as a function of area of expertise.×
Quartiles Area of expertise
Audiology Speech-language pathology
Document average/year
 Minimum 0.0 0.0
 25% 0.4 0.3
 50% 1.1 0.8
 75% 2.0 1.6
 Maximum 28.2 16.0
Citations average/year
 Minimum 0.0 0.0
 25% 2.1 1.4
 50% 11.0 6.0
 75% 29.7 18.8
 Maximum 700.3 329.1
m (h index/years since terminal degree)
 Minimum 0.0 0.0
 25% 0.2 0.1
 50% 0.4 0.3
 75% 0.7 0.7
 Maximum 7.3 4.0
Table 9. Five number summaries for box plots of average Scopus indices as a function of area of expertise.
Five number summaries for box plots of average Scopus indices as a function of area of expertise.×
Quartiles Area of expertise
Audiology Speech-language pathology
Document average/year
 Minimum 0.0 0.0
 25% 0.4 0.3
 50% 1.1 0.8
 75% 2.0 1.6
 Maximum 28.2 16.0
Citations average/year
 Minimum 0.0 0.0
 25% 2.1 1.4
 50% 11.0 6.0
 75% 29.7 18.8
 Maximum 700.3 329.1
m (h index/years since terminal degree)
 Minimum 0.0 0.0
 25% 0.2 0.1
 50% 0.4 0.3
 75% 0.7 0.7
 Maximum 7.3 4.0
×