comparing research productivity across disciplines and career stages
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Comparing Research ProductivityAcross Disciplines and Career StagesMeghna Sabharwal aa School of Economic, Political and Policy Sciences , Program inPublic Affairs, The University of Texas at Dallas , Richardson , USAPublished online: 17 Apr 2013.
To cite this article: Meghna Sabharwal (2013) Comparing Research Productivity Across Disciplinesand Career Stages, Journal of Comparative Policy Analysis: Research and Practice, 15:2, 141-163,DOI: 10.1080/13876988.2013.785149
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Comparing Research Productivity AcrossDisciplines and Career Stages
MEGHNA SABHARWALSchool of Economic, Political and Policy Sciences, Program in Public Affairs, The University of Texas at Dallas,Richardson, USA
ABSTRACT In academia, productivity continues to be a major factor in decisions of salary raises aswell as promotion. The current study thus examines and compares the research productivity offaculty members across disciplines and career stages. The research tests three life course theories(cumulative advantage, utility maximization theory and obsolescence theory) with data from eightdisciplines (biology, computer sciences, mathematics/statistics, physical sciences, psychology, socialsciences, engineering, and health fields). The data for this study are taken from the 2003 Survey ofDoctorate Recipients. Unlike past studies, which solely use journal articles as a measure of researchproductivity, this study also takes into consideration publications in books and monographs. Thestudy found that the majority of the research output (articles and books) produced in Health andPhysical Sciences disciplines is by early and mid career faculty members, providing support for theobsolescence theory, which suggests that research performance declines as faculty members pro-gress in their careers. Further, aging shifts the output mix more towards books for social scientists,making them the most productive group when books or monographs are taken as a measure ofresearch productivity.
Introduction
Disciplines are the lifeblood of higher education. Despite their pervasiveness, studiescomparing research norms and practices across disciplines are modest at best. There is aneed to contribute to the theoretical and empirical research that explores key distinctionsand similarities between different disciplines, and offer implications for the practice ofhigher education research. This study is thus an effort to fill the gap that currently exists inthe area of comparative higher education. The two most important factors that impact on
Meghna Sabharwal is an Assistant Professor at the University of Texas at Dallas in the Public Affairs Program.Her research interests are focused on workforce policy as it relates to job satisfaction, productivity, and diversity.Her most recent work is published in Review of Public Personnel Administration, Research Policy, PublicAdministration, The Social Science Journal among others. She has an edited book in print titled “PublicAdministration in South Asia: India, Bangladesh, and Pakistan.”Correspondence Address: Meghna Sabharwal, School of Economic, Political and Policy Sciences, TheUniversity of Texas at Dallas, 800 West Campbell Road, GR 31, Richardson, TX 75080, USA. Tel.: 972-883-6473; Fax: (972) 883–4939; Email: [email protected] use of National Science Foundation (NSF) data does not imply NSF endorsement of the research methods orconclusions contained in this report.
Journal of Comparative Policy Analysis, 2013Vol. 15, No. 2, 141–163, http://dx.doi.org/10.1080/13876988.2013.785149
© 2013 The Editor, Journal of Comparative Policy Analysis: Research and Practice
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the academic success of a faculty member are productivity and visibility (Leahey et al.2008). Traditionally the avenues for publication for faculty members in humanities andsocial sciences have been books and book chapters while the major means for disseminat-ing research among natural scientists and mathematicians has been journal articles (Roe1972; Bonzi 1992). However, this trend is changing within the humanities and socialscience disciplines (Kyvik 2003; Nederhof 2006; MLA 2007; McNay 2009).
A report by the Modern Language Association of America (2007) highlighted theconcerns of faculty members in the humanities about these definitions of productivity.The report found that tenure and promotion committees are placing increasing weight onarticles published in scholarly research journals, a medium less utilized in the humanitiesprofession. While journal articles are the key source of knowledge diffusion in scienceand engineering, books continue to play an important role in social sciences and huma-nities. Larivière et al. (2006: 1003) note that the proportion of references to journal articlesin social science and humanities is lower than 50 per cent, the authors caution that “oneshould be careful in constructing performance measures on the sole basis of journalliterature”. Thus this study utilizes both peer reviewed journal articles and books/mono-graphs authored or co-authored as a measure of research productivity. Many socialscientists publish books and monographs which are often left out in bibliometric studiesthat consider journal publications as a singular measurement of faculty performance (Bottand Hargens 1991; Archambault et al. 2006; Nederhof 2006).
Faculty performance greatly impacts on decisions of annual faculty raises (McGregor2008). While the natural sciences use grants and publications as a measure of salaryincreases, humanities are a little more complicated, with books being the traditional meansof scholarly output, and articles recently gaining importance in decisions of promotionand tenure (P&T). Assigning weight to a book of similar scope and importance as anarticle can be challenging for P&T committees.
Since productivity continues to be a major factor in decisions of salary raises, as well astenure and promotion (Ramsden 1994; Bellas and Toutkoushain 1999; McGregor 2008), itis important to explore how productivity patterns differ across discipline and career stage.Further, as academics from different disciplines are coming together in an effort to solveproblems that span disciplinary boundaries, an understanding of research norms that existwithin disciplines is inevitable (Jenkins and Zetter 2002). In studies of research produc-tivity, life course theories have been used in various contexts to explain the performanceand trajectories of scientists. This study tests three life course theories (cumulativeadvantage, utility maximization theory and obsolescence theory) with data from eightdisciplines (biology, computer sciences, mathematics/statistics, physical sciences, psy-chology, social sciences, engineering, and health fields) adding to the theoretical andempirical understanding of comparative higher education.
The purpose of this article thus is to explore: (1) how does academic researchproductivity vary across disciplines? and (2) how are these variations related to the careerstage of faculty members?
Literature Review
Disciplinary Differences in Productivity
It was not until the 1960s and 1970s that Merton (1968) and other scholars (de SollaPrice 1963; Crane 1967; Cole 1970, 1979; Cole and Cole 1973; Allison and Stewart
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1974; Pfeffer et al. 1976; Long 1978; Reskin 1977, 1978) began studying academicresearch productivity within different disciplines. Productivity in the literature is gen-erally measured by the count of articles published in journals (Allison and Long 1987;Garfield and Welljams-Dorof 1992; Massy and Wilger 1995). Massy and Wilger (1995)found that a majority of the faculty members in four-year institutions define productivityas counting the number of articles published rather than resorting strictly to theeconomic definition that calculates ratio of outputs to inputs. Research productivity ismeasured in several ways. Citations, impact factor and h-index are among the morepopular measurements used in recent bibliometric studies. However, studies continue touse self-reported publication counts (Stack 2004; Shin and Cummings 2010) as ameasure of productivity; while not perfect it has been shown to correlate highly withactual publication counts (Alison and Stewart 1974). Journal articles in this studyinclude peer reviewed publications, thus taking quality into account. The assumptionis that each publication would have undergone the seal of approval through the expertjudgment of peers.
Most of the research on faculty productivity is focused on a single discipline, such aseducation (Smith et al. 2003; Mamiseishvili and Rosser 2010), music (Standley 1984;Brittin and Standley 1997; Reynolds and Hamann 2010), economics, marketing, andmanagement (Long et al. 1998; Powers et al. 1998; Borokhovich et al. 2012), sociology(Axelson 1959; Keith and Babchuk 1998), political science (Morgan and Fitzgerald1977; Robey 1979; De Maio and Kushner 1981; Hesli and Lee 2011), public adminis-tration (Morgan et al. 1981; Corley and Sabharwal 2010; Sabharwal 2013), psychology(Thomas 1980; Over 1982; Kranzler et al. 2011), and sciences (Bayer and Folger 1966;Zuckerman and Merton 1971, 1972; Reskin 1977, 1978; Long 1978; Fox 1983; Bayerand Smart 1991; Stack 2004; Long et al. 2009). Only a handful of them compareresearch productivity across disciplines (Fulton and Trow 1974; Wanner et al. 1981;Stack 2004). While a majority of research in this area was carried out in the 1970s and1980s, there is renewed interest in this topic given the interdisciplinary nature of researchand the ongoing pressure on universities to perform (Baldwin et al. 2005; Larivière et al.2006; Brew 2008; Shin and Cummings 2010; Stroebe 2010; Linton et al. 2011, 2012).
Wanner et al. (1981) compared the research productivity of faculty across disciplinesusing data from the 1972–1973 national survey of the American Council on Education(ACE). The authors concluded that there were significant differences between thepublication productivity of physical/biological scientists and social scientists/humanists.Specifically, the publication rates for natural scientists exceeded social scientists andhumanists by about 60 per cent. In another study, Fulton and Trow (1974) concludedthat faculty members in biological sciences consistently report higher numbers ofpublications than scholars in the physical and social sciences. Similar results werefound by Blackburn and colleagues (1978) when they used academic discipline as acontrol variable. They found that publication productivity among natural scientists washigher than for humanists. They argued, however, that differences in the nature of theproducts produced across disciplines would make direct comparisons of productivitydifficult.
A more recent study was undertaken by Stack (2004) utilizing the 1995 SDR dataset.His findings indicate that faculty in the biological sciences, physical sciences, and health/medical science fields all published more articles than the social science faculty, and thatfaculty in engineering and math fields had a level of research productivity that was not
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significantly different from the social sciences. Some scholars have argued that disciplin-ary differences in productivity might not be indicative of the level of intellectual outputnecessary for respective fields but instead might reflect the number of resources availableand the level of agreement in the disciplines (Merton 1968; Cole and Cole 1973; Wanneret al. 1981; Teodorescu 2000). Productivity also varies by the type of output produced.Book productivity has been shown to be higher among social scientists and humanists(Zuckerman and Merton 1971; Roe 1972; Wanner et al. 1981; Boyer 1990; Kousha andThelwall 2009; White et al. 2009) than natural scientists and engineers. Based on theabove research, this study hypothesizes that:
Hypothesis 1: Social scientists are most productive when books are taken as a measure ofproductivity and least productive when journal articles are the measure of researchproductivity.
Differences in Productivity by Life Age and Career Stage
Productivity rates not only differ by discipline, but also by age of faculty members (Pelzand Andrews 1966; Fulton and Trow 1974; Bayer and Dutton 1977; Blackburn et al.1978; Baldwin and Blackburn 1981; Palmer and Patton 1981; Kyvik 1990; Costas et al.2010). There are different ways of defining age when thinking about productivity. On theone hand, some scholars have focused on life age (i.e. the time from birth), while othersutilize career stage (i.e. time from receipt of doctoral degree) as a measure of productivity.Career stage, which is a measure of years of experience after the receipt of a doctoraldegree, is a better indicator of productivity than birth age, and is utilized in several studiesof faculty research productivity (Fulton and Trow 1974; Bayer and Dutton 1977; Baldwinand Blackburn 1981; Palmer and Patton 1981; Lynn et al. 1996). Recent studies by Costaset al. (2010), Lissoni et al. (2011), and Shin and Cummings (2010) report a negativeinfluence of age on research productivity, while Abramo et al. (2011) find the reverse to betrue. This issue remains contested. A variety of theories have been developed in responseto the impact of career stage on productivity: cumulative advantage, utility maximizing,and obsolescence (Kyvik 1990).
Briefly summarized, the cumulative advantage theory postulates that high levels ofproductivity early in the career lead to continued success – and greater levels of produc-tivity – throughout the career. Robert K. Merton in 1968 introduced the concept ofcumulative advantage in his seminal study on the Matthew effect, which is defined as“the accruing of greater increments of recognition for particular scientific contributions toscientists of considerable repute and the withholding of such recognition from scientistswho have not yet made their mark” (Merton 1968: 58). The cumulative advantageperspective thus argues that early success breeds future success as publications lead togrants, which lead to more time for research, which leads to even more publications. Theutility maximizing theory states that researchers have a peak level of productivity in theyears directly after receiving their doctorate degree. This theory follows a more traditionalpattern in academia, where productivity is an important factor in achieving tenure. Theutility maximization theory thus argues that once faculty members receive the utilitarianreward of tenure, they relax efforts at publication. The obsolescence theory states thatolder professionals do not stay up-to-date with cutting edge advances in their fields sotheir research eventually becomes obsolete over time. As expected, these theories havevarying degrees of support across different disciplines.
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For example, Kyvik (1990) in his study showed that faculty members tend to be moreproductive earlier in their careers if they work in a discipline where the knowledge baseis constantly changing and evolving. Yet Zuckerman and Merton (1972) showed that infields with well-developed paradigms career age might not be a predictor of productiv-ity. Kyvik (1990) utilized data from a 1982 survey of tenured faculty at four Norwegianuniversities and found that in disciplines such as physics, natural sciences and medicalsciences, which witness rapid scientific advances, it is often more difficult for seniorfaculty to catch up. Shin and Cummings (2010) reported similar findings in whichmedical and health science faculty members produced higher publications than huma-nities and social sciences. However, disciplines like social sciences and mathematicsand statistics that produce knowledge at a slower pace are likely to witness a cumulativeadvantage. Based on the above studies on career stage and discipline, this studyexpects:
Hypothesis 2: In rapidly advancing disciplines (computer science, health, physicalsciences), early career stage faculty members are utility maximizers. They are likely toproduce more journal articles than mid and late career stage faculty members.
Hypothesis 3: Late career stage faculty members in computer science, physical sciences,and health are obsolescent. They are likely to produce fewer journal articles than mid andlate career stage faculty members.
Hypothesis 4: Disciplines that do not witness rapid advancements (social sciences andmathematics and statistics) follow the cumulative advantage theory wherein researchproductivity increases with career age.
Additionally, publication rates can vary by the type of research products produced atvarious stages of an individual faculty’s career. Older cohorts are more likely to publishbooks/monographs across all disciplines when compared with early and mid cohortgroups (Blackburn et al. 1978; Bridgewater et al. 1982; Stroebe 2010). The number ofbooks published increased in later career stages, a phenomenon more pronounced in socialsciences than the sciences (Wanner et al. 1981). This study thus expects:
Hypothesis 5: Aging shifts the output mix more towards books for social scientists.
Additional Disciplinary Differences
There are a variety of institutional and career-level variables that impact on facultyproductivity. Studies have explored the correlations among factors like faculty rank,time spent on teaching and research, and productivity levels. Most of the studies in thepast have shown that younger faculty are more likely to spend their time teaching ascompared to doing research, a phenomenon that can impact on research productivity(Baldwin and Blackburn 1981; Smart 1990; Olsen et al. 1995; Hagedorn 2000). Baldwinand Blackburn (1981) used the career development theory as a framework to study theimpact of various career stages on research productivity. Career development theoryasserts that careers are not static and individuals experience different phases throughouttheir careers. These authors surveyed 106 male faculty members from 12 different liberal
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arts colleges in the Midwest and divided their careers into five different career stages. Theauthors found that faculty members spend more time teaching when they are in the earlystages of their career. The study thus expects:
Hypothesis 6: Early career faculty members across all disciplines spend the highestamount of time in teaching-related activities compared with other disciplines.
Additionally, given the results of multiple previous studies, this study expects to finddifferences in productivity by gender. Fox (2005) expands on the gender disadvantage,which she suggests is not a result of marital status or number of children, but productivityvaries by the type of marriage, occupation of spouse, and the ages of children. Inparticular, women with school-age children have lower productivity than women withpreschool children or no children. Similar findings were reported by Morrison et al. (2011)Link et al. (2008) report that women have lower research productivity than male membersowing to the amount of time they spend on service-related activities. With varying rates ofparticipation of women in different disciplines and their childbearing and childrearingresponsibilities (Fox 2005; Sonnert et al. 2007), this study expects:
Hypothesis 7: Women across all disciplines are likely to produce fewer journal articlesthan male faculty.
Data
Data for this study are taken from the 2003 Survey of Doctorate Recipients (SDR),which is a nationally representative dataset. This survey was funded by the NationalScience Foundation and the National Institutes of Health. The actual survey wasconducted by the National Opinion Research Center (NORC) at the University ofChicago. The data are collected from doctorate recipients with a degree from a USinstitution in a science, engineering, or health sciences field through June 30, 2002. Allthe participants were under 76 years of age as of October 1, 2003, which was taken asthe survey reference week. A total of 40,000 individuals with doctoral degrees wassampled in the 2003 survey. The original unweighted sample size was 29,915 and theweighted sample size was 685,296. The weighted variable1 is defined as the reciprocalof the probability of selection under the sample design and is further adjusted for non-response – the data thus used are representative of the population. The analysis in thisarticle focuses only on full-time academic scientists either tenured or on tenure track;hence respondents with non-academic jobs or employed as instructor, lecturer, oradjunct were filtered before beginning the analysis. This filtering process reduced theweighted sample size to 238,674.
Respondents were included from all disciplines who reported their highest degree inone of the following fields: (1) biological, agricultural, and environmental life sciences;(2) computer and information sciences; (3) engineering; (4) health; (5) mathematics andstatistics; (6) physical sciences; (7) psychology; or (8) social sciences. Social science alsoincludes individuals who reported receiving their degree in humanities, and is composedof a very small percentage of faculty members in the data. The entire data were dividedinto three categories based on the stage of faculty careers, with 34.5 per cent of the
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respondents in the late career stage category; 34.1 per cent in the mid career stage; and31.4 per cent of the respondents in the early career stage category. The career stages weredivided based on the experience, which was calculated by subtracting the year of thehighest degree from the year of the survey, thus leading to three main categories, withapproximately 33 percentile as late, middle, and early career stages. Since the degree yearvariable is not continuous the categories did not yield exact thirds. On average, earlycareer faculty members have 4.5 years of experience, mid career 14 years, and late careerfaculty members have 29 years of work experience.
The sample consists of 71.1 per cent male respondents and 28.9 per cent female.Biological sciences, agriculture, and environmental sciences composed of the highestpercentage of faculty with 29.9 per cent, followed by social sciences (18.3 per cent).Physical sciences came third with 15.5 per cent, psychology with 11.5 per centfollowed closely by engineering faculty (11.2 per cent). Mathematics and statisticsfaculty were in the sixth position with 6.5 per cent, health faculty comprising 5.1 percent, and lastly 2.0 per cent of the faculty belonged to computer and informationscience. The respondents held a variety of academic positions, including full professor(36.2 per cent), associate professor (22.7 per cent), assistant professor (20.8 per cent).More than half the faculty (51.3 per cent) were tenured and employed at a CarnegieResearch I or II institution. The sample was 78.7 per cent Caucasian faculty, 13.5 percent Asian, 3.3 per cent Hispanic, 3.7 per cent African Americans, and 0.8 per centfrom the remaining race/ethnicities. The median age of the faculty members in alldisciplines is 48 years.
Findings
The descriptive statistics for the demographic, career, and productivity variables by eachof the eight disciplines are displayed in Table 1. The results demonstrate that male facultymembers dominate all disciplines except for health, where 62.4 per cent of the facultymembers are female. Over three-quarters of the faculty members across all disciplines aremarried. In examining the race/ethnicity of faculty in various disciplines, Asian facultymembers have the highest non-Caucasian representation across all disciplines exceptpsychology, where the percentage of African American faculty is highest. The medianage of faculty members ranges from low forties to early fifties, with the median age lowestamong computer and information sciences (43) faculty members and highest among socialscientists (51 years).
Over 40 per cent of faculty members across all disciplines are employed at a Carnegieresearch university. The median year of the highest degree received for all disciplinescombined is 1989 while the most recent doctoral graduates in computer and informationscience disciplines have a median year of graduation as 1995. Despite least years ofexperience (9.7 years), computer and information science faculty members are most likelyto be tenured (80 per cent). Engineering faculty members are compensated the most whencomparing the median salaries across the disciplines ($82,214), while psychology facultymembers are compensated the least ($71,435).
Results in Table 2 indicate that physical science (10.2) and biology (9) faculty membersproduce the highest number of journal articles over a five-year span from 1998 to 2003.The least number of articles produced on average by any discipline within five years issocial sciences (4.61). However, the lower number of articles published by social
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Tab
le1.
Means
fordemog
raph
icandcareer
variablesacross
disciplin
es
Variable
All
Biology
Com
puter
sciences
Mathan
dstats
Physical
sciences
Psycholog
ySocial
sciences
Engineering
Health
N23
8,67
471
,368
4,87
415
,458
36,921
27,480
43,623
26,668
12,281
Dem
ograph
icvariab
les
Male
71.1%
68.9%
82.3%
82.9%
85.4%
51.8%
67.4%
89.6%
37.6%
Married
80.3%
81.0%
83.3%
80.4%
82.4%
76.2%
78.4%
84.5%
75.7%
Caucasian
78.7%
77.5%
65.2%
78.2%
80.4%
85.4%
81.7%
69.4%
80.6%
Asian
13.5%
15.9%
30.1%
15.7%
14.1%
3.3%
8.3%
22.7%
9.4%
African
Am.
3.7%
2.6%
1.8%
2.5%
2.0%
6.0%
5.5%
3.9%
5.8%
Hispanic
3.3%
3.2%
2.9%
2.9%
2.8%
4.0%
3.6%
3.3%
3.1%
Other
ethn
icity
0.8%
0.8%
0.0%
0.6%
0.7%
1.3%
1.0%
0.7%
1.1%
Age
48.1
46.9
43.2
49.0
47.9
48.2
50.0
47.9
49.1
Individu
alcareer
variab
les
Yrs
experience
16.2
15.6
9.7
18.7
17.9
16.3
16.8
16.4
11.7
Degreeyear
1987
1987
1993
1984
1985
1987
1986
1987
1991
Tenured
51.3%
41.9%
51.3%
65.8%
50.0%
48.1%
63.6%
56.9%
43.1%
CarnegieRes.I/II
University
51.3%
59.1%
45.2%
44.0%
48.5%
43.8%
45.2%
59.8%
45.4%
Salary
77,568
77,163
80,055
75,579
77,284
71,435
76,814
88,281
75,418
Careerstag
eEarly
career
31.4%
32.8%
50.6%
25.8%
27.6%
31.7%
28.3%
31.2%
43.9%
Mid
career
34.1%
34.4%
41.8%
30.9%
33.5%
32.4%
33.4%
34.9%
40.1%
Latecareer
34.5%
32.8%
7.6%
43.3%
38.9%
35.8%
38.3%
33.9%
16.0%
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Tab
le2.
Means
forworkactiv
ityandprod
uctiv
ityvariablesacross
disciplin
es
Variable
All
Biology
Com
putersciences
Mathan
dstats
Physical
sciences
Psycholog
ySocialsciences
Eng.
Health
N23
8,67
471
,368
4,87
415
,458
36,921
27,480
43,623
26,668
12,281
Primaryworkactivity
R&D
41.8%
57.3%
37.4%
26.3%
42.4%
34.5%
28.3%
42.0%
35.2%
Teaching
38.7%
23.3%
48.2%
58.3%
39.3%
40.4%
54.0%
38.7%
40.0%
Managem
ent
12.1%
10.7%
8.8%
10.6%
11.6%
10.8%
14.0%
13.3%
17.9%
Com
puterapps
1.4%
0.6%
4.8%
2.2%
3.4%
0.1%
1.0%
2.2%
0.8%
Other
activ
ity5.9%
8.0%
0.9%
2.6%
3.3%
14.2%
2.6%
3.7%
6.1%
Produ
ctivity
variab
les(199
8–20
03)
Articles
7.9
9.3
5.1
5.9
10.2
7.1
4.6
8.2
8.2
Boo
ks0.7
0.7
0.4
0.4
0.6
0.6
0.8
0.6
0.8
Produ
ctivity
variab
le(200
2–20
03)
Receivedfederalgrant
48.7%
63.6%
51.2%
33.4%
60.0%
36.6%
23.3%
59.0%
40.8%
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scientists is compensated by publishing on average the highest number of books duringthe five-year period (0.84). The next section highlights the differences by career stages.
Characteristics of Early Career Stage Faculty Members Across Disciplines
Results in Table 3 indicate that early career stage faculty members constitute 31.4 percent of the data, and are individuals with 4.5 years of experience on average from thetime they graduated to the time of the survey (2003). Among early career health facultymembers the difference between female and male faculty is almost double (33.1 per centmale and 66.9 per cent female). The greatest difference is seen in computer andinformation science and engineering disciplines where male faculty members outnumberfemales by more than four times. This result is not surprising given that several studieshave indicated that male faculty members, especially in science and engineering dis-ciplines, far outnumber female faculty (Menges and Exum 1983; Sonnert and Holton1995; Wolfinger et al. 2008). Early career faculty members in mathematics and statistics,social sciences, and health report spending the most time on teaching-related activitiesthan faculty across mid and late career stages, thus partially confirming hypothesis 6which expected early career faculty across all disciplines to report spending the bulk oftheir time in teaching-related activities. All these values are reported as significant usinga Chi-square test at p < 0.001.
Characteristics of Mid Career Stage Faculty Members Across Disciplines
Mid career stage faculty members constitute 34.1 per cent of the data, and are individualswith 14.1 years of experience on average from the time they graduated to the time of thesurvey (2003). The trends across gender seen among early career faculty are almost allcarried through to the mid career stage faculty members. The gender gap narrowed amongpsychologists in their mid career stages (45 per cent male) as compared with the early careerstage faculty members (38 per cent male). This occurrred across all disciplines, but biologyfaculty members report spending 10 per cent or more of their time in teaching-relatedactivities as compared with R&D. All reported values are significant at p < 0.001 using theChi-square test.
Characteristics of Late Career Stage Faculty Members Across Disciplines
Late career stage faculty members constitute 34.5 per cent of the data, and are indivi-duals with 29 years of experience on average from the time they graduated to the time ofthe survey (2003). As faculty members progress in career age the gender gap across alldisciplines widens. Psychology and health disciplines have high percentages of femalefaculty in both the early career (62 per cent and 67 per cent) and mid career stages (55per cent and 69 per cent). This is most likely a result of the growth in the share ofwomen in recent cohorts of PhDs. While late career stages continue to be dominated bymales, the rise of women in these positions is a matter of time. Future studies can trackthese trends. Results in Table 3 suggest that late career stage faculty members across alldisciplines report spending the highest percentage of their time in teaching relatedactivities.
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le3.
Produ
ctivity
ofearly,
mid,andlate
career
stagefaculty
across
disciplin
es
Variable
All
Biology
Com
puter
sciences
Mathan
dstats
Physical
sciences
Psycholog
ySocial
sciences
Engineering
Health
Early
career
stag
eArticles
6.1
6.3
4.5
5.2
8.6
5.4
3.9
6.4
7.5
Boo
ks0.4
0.3
0.3
0.1
0.2
0.5
0.6
0.4
0.7
Primaryworkactiv
ity–
teaching
34.7%
17.5%
50.5%
61.8%
33.7%
37.4%
56.3%
27.7%
41.5%
Receivedfederalgrants
50.6%
67.1%
46.7%
35.6%
61.1%
38.6%
23.2%
61.3%
37.8%
Mid
career
stag
eArticles
8.7
10.3
6.1
7.1
11.2
7.0
5.6
8.9
8.3
Boo
ks0.9
0.9
0.4
0.6
1.2
0.6
0.9
0.8
0.9
Primaryworkactiv
ity–
teaching
38.8%
23.1%
43.3%
54.8%
43.0%
38.3%
53.4%
43.0%
39.3%
Receivedfederalgrants
52.0%
65.6%
60.7%
35.1%
66.1%
35.7%
27.5%
62.3%
43.8%
Latecareer
stag
eArticles
8.6
11.3
3.5
5.3
10.5
8.7
4.2
9.2
9.4
Boo
ks0.7
0.8
0.7
0.5
0.5
0.8
1.0
0.5
0.9
Primaryworkactiv
ity–
teaching
42.3%
29.4%
59.5%
58.7%
40.1%
45.0%
53.0%
44.6%
37.7%
Receivedfederalgrants
43.7%
58.1%
29.9%
30.8%
53.9%
35.7%
19.8%
53.5%
41.8%
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Tab
le4.
OLSregression
forrelatio
nshipbetweenprod
uctiv
ityandcareer
ageacross
disciplin
e
Independentva
riab
les
Biology
Com
p.Sci.
Mathan
dstats
Physical
sciences
Psych.
Socialsciences
Engin.
Health
Dem
ograph
ics
Male
1.12
***
−0.53
*0.85
***
1.46
***
0.91
***
0.28
***
0.60
**1.40
***
Married
0.26
*−1.04
***
1.63
***
−0.16
0.04
0.89
***
0.00
41.65
***
USbo
rn−0.17
−1.11**
*0.03
0.34
−2.13
***
−0.80
***
−3.57
***
−2.43
***
Race/ethn
icity
(cau
casian
isthereferencegrou
p)Asian
0.01
1.82
***
2.83
***
−0.77
**−0.66
*0.11
−0.78
***
−3.80
***
African
Am.
−2.72
***
−1.55
*−0.86
*−2.57
***
−1.48
***
−0.98
***
−0.65
−3.79
***
Hispanic
0.55
*−0.52
0.61
−0.96
*−0.34
0.02
0.19
−3.59
***
Other
ethn
icity
5.02
***
n/a
−1.65
*−1.10
−0.70
1.71
***
−0.64
5.98
***
Careervariab
les
CarnegieRes.I/II
1.17
***
0.11
2.21
***
3.75
***
1.91
***
0.93
***
3.79
***
1.95
***
Tenured
3.48
***
4.07
***
1.35
***
4.64
***
1.75
***
1.38
***
3.52
***
1.70
***
Salary
6E-05*
**2E
-05*
**3E
-05*
**9E
-04*
**5E
-05*
**2E
-05*
**5E
-05*
**7E
-05*
**Rec’d
federalgrants
3.47
***
1.46
***
4.56
***
4.94
***
3.64
***
2.06
***
3.64
***
4.56
***
Primaryworkactivity
(researchisthereferencegrou
p)Teaching
−4.70
***
−3.37
***
−2.40
***
−5.56
***
−4.60
***
−2.84
***
−3.35
***
−3.47
***
Managem
ent
−3.47
***
−3.87
***
−4.19
***
−7.88
***
−6.78
***
−3.77
***
−5.53
***
−5.42
***
Com
puter
−5.20
***
−3.74
***
−7.21
***
−6.39
***
−10
.75*
**−5.12
***
−3.71
***
1.10
Other
−4.13
***
−4.66
***
−0.61
−9.00
***
−6.01
***
−1.07
***
−4.55
***
−4.68
***
Careerstag
e(latecareer
isthereferencegrou
p)Early
career
−0.14
3.24
***
2.02
***
4.33
***
−0.11
0.99
***
0.23
2.50
***
Mid
career
1.12
***
1.54
***
2.38
***
3.15
***
0.32
**1.59
***
0.37
*1.64
***
Con
stant
0.56
*1.76
*−1.53
***
−4.28
***
5.22
***
2.69
***
0.94
*1.33
*AdjustedR-squ
ared
0.22
0.22
0.21
0.26
0.31
0.18
0.20
0.25
Fvalue
1,18
8.44
***
87.48*
**23
2.38
***
751.49
***
729.99
***
551.36
***
402.18
***
242.24
***
Notes:*p
<0.05
,**
p<0.01
,**
*p<0.00
1.
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Research Productivity Across Disciplines
Multivariate analyses were conducted to explore the relationship between productivity andcareer stage across all disciplines. The results of the regressions are presented in Table 4.The dependent variable used in this model is journal article productivity, which is a self-reported measure of the number of articles produced over a span of five years from 1998to 2003. Several explanatory variables were used in the model including demographicvariables such as gender, marital status, and race/ethnicity, along with employmentvariables of Carnegie classification of the employer, tenure, grant information, salary,and primary work activity, and career stage variables.
Holding demographic and employment factors constant, early career stage faculty aremore productive than late career stage faculty in the following disciplines: computer andinformation sciences, health, and physical sciences, giving support to hypothesis 2 whichstates that early career stage faculty members in rapidly advancing disciplines (computerscience, health, physical sciences) are utility maximizers. They are likely to produce morejournal articles than mid and late career stage faculty members. The findings also supporthypothesis 3, in which late career stage faculty members in computer science, physicalscience, and health produce fewer journal articles than mid and late career stage facultymembers, thus giving support to the obsolescence theory.
Disciplines that do not witness rapid advancements (social sciences and mathematics andstatistics) follow the cumulative advantage theory wherein research productivity increaseswith career age, confirming hypothesis 4. Biologists and engineers build their researchrecord with time – they are most productive in their mid career years following the Matheweffect. Mid career faculty members at many research institutions have sabbatical and otherresearch leaves that affords them an opportunity to work on their research.
Additionally, the results suggest that male faculty members across all disciplines exceptcomputer science publish significantly more articles in comparison with female facultyover a span of five years. Interestingly, female faculty members in computer and informa-tion sciences are slightly more productive than their male peers, a finding that contradictsprevious studies in engineering (Cole and Zuckerman 1984; Long and Fox 1995; Sonnert1995; Xie and Shauman 1998; Stack 2004). The greatest significant difference in thenumber of articles published by gender was observed in biology, physical sciences, andhealth, where male faculty members produced approximately one full article more thantheir female counterparts over a span of five years. The results partially confirm hypoth-esis 7, which states that women in all disciplines are likely to produce fewer journalarticles than their male peers.
Asian faculty members in computer and information sciences and mathematics andstatistics produced approximately two more articles than Caucasian faculty members inthese disciplines. In contrast, Asian faculty members in the health disciplines producedclose to four fewer articles than Caucasian faculty members. In computer science,psychology, social sciences, engineering, and health disciplines US-born faculty are lessproductive as compared with foreign-born faculty members. Foreign-born engineeringfaculty members produced approximately four more articles during the five-year periodthan US-born faculty members. These findings mirror previous studies that have foundforeign-born faculty in science and engineering to have higher research productivity ascompared with their US-born counterparts (Levin and Stephan 1999; Corley andSabharwal 2007; Sabharwal 2011).
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Not surprisingly, across all eight disciplines, faculty that spend more time teaching arelikely to produce three to five fewer articles as compared with faculty who report spending10 per cent or more of their time on research. A similar trend followed when facultyreported spending more time in administrative and computer-related applications ascompared with research. Faculty with federal research grants have more articles thanfaculty without any grants, a finding that holds across all disciplines. In addition, salary ispositively correlated with number of journal articles across the disciplinary range. Theregression models across most of the disciplines explained over 20 per cent of thevariance in the article productivity of faculty members.
Additional regression models were run, one using books as the dependent variable andthe other using journal articles and books for the overall data. The results are presented inTables 5 and 6. In social sciences when journal articles are taken as a measure ofproductivity, early and mid career faculty members are more productive than late careerfaculty members. However, when books are the measure of productivity, late careerfaculty members are most productive, confirming hypothesis 5, which stated that agingshifts the output mix more towards books for social scientists. A similar pattern emergesin computer and information sciences and psychology. In biology, mathematics andstatistics, and engineering, mid career faculty members are more likely to producebooks when compared with late career faculty members. However, among physicalsciences and health faculty members both early and mid career faculty members producemore books when compared with late career faculty members. The results suggest that themajority of the research output (articles and books) produced in health and physicalsciences disciplines is by early and mid career faculty members, providing support forthe obsolescence theory, which suggests that research performance declines as facultymembers progress in their careers.
Further, results in Table 6 suggest that across all eight disciplines, physical scientistsproduce the highest number of articles, and health and social science faculty membersproduce the most books. The results partially confirm hypothesis 1, which states thatsocial scientists are most productive when books are taken as a measure of productivityand least productive when journal articles are the measure of research productivity. Indisciplines like health and physical sciences there are often several authors on onepublication, thus increasing their productivity (Kyvik 2003). While there is an upwardtrend in co-authorship, social sciences and humanities continue to be dominated by sole-authored publications – this is often a requirement for promotion and tenure in thesedisciplines (Corley and Sabharwal 2010).
Discussion and Conclusions
An aggregate understanding of faculty productivity patterns across disciplines andcareer stages can serve as an important policy guide for administrators and departmentheads in evaluating faculty work. Faculty productivity is an important predictor ofquality in institutions of higher education. The amount of research produced by facultymembers can lead to improved visibility of departments, which in turn dictates therankings of the schools (Fairweather 2002). In addition, research performance has adirect impact on rewards, tenure and promotion decisions (Blackburn and Lawrence1995; Fairweather and Rhoads 1995; Tierney and Bensimon 1996; Bland et al. 2006;Costas et al. 2010).
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Tab
le5.
OLSregression
forrelatio
nshipbetweenbo
okprod
uctiv
ityandcareer
ageacross
disciplin
e
Independentva
riab
les
Biology
Com
p.sci.
Mathan
dstats
Physical
sciences
Psych.
Socialsciences
Engin.
Health
Dem
ograph
ics
Male
0.14
***
–0.23*
**–0.04(N
S)
0.29
***
0.21
***
0.04
*0.11
(NS)
0.31
***
Married
0.12
***
0.10
**0.14
**0.15
*–0.25*
**0.29
***
–0.53*
**0.22
***
USbo
rn0.02
(NS)
0.07
*0.29
***
–0.08(N
S)
–0.05(NS)
0.17
***
–0.33*
**0.35
***
Race/ethn
icity
(cau
casian
isthereferencegrou
p)Asian
0.06
*0.06
*0.04
(NS)
1.36
***
–0.02(N
S)
0.02
(NS)
–0.16*
*0.47
***
African
Am.
–0.14*
*–0.28*
**–0.12(N
S)
0.24
(NS)
–0.16*
*0.19
***
2.01
***
–0.18(N
S)
Hispanic
0.13
**–0.34*
**0.03
(NS)
–0.25(N
S)
–0.10(N
S)
0.26
***
–0.17(N
S)
0.10
(NS)
Other
ethn
icity
0.55
***
n/a
0.51
**2.37
***
0.18
(NS)
1.43
***
0.21
(NS)
2.28
***
Careervariab
les
CarnegieRes.I/II
0.03
*–0.01(NS)
0.20
***
0.43
***
0.11**
*0.16
***
0.30
***
0.09
**Tenured
0.20
***
0.13
***
0.34
***
0.76
***
–0.04(NS)
0.16
***
0.32
***
0.35
***
Salary
6E-06*
**–1E-06*
**1E
-06*
*7E
-06*
**2E
-06*
**1E
-06*
**3E
-06*
**2E
-06*
**Rec’d
federalgrants
0.09
***
0.22
***
0.23
***
–0.68*
**0.12
***
0.18
***
0.26
***
0.35
***
Primaryworkactivity
(researchisthereferencegrou
p)Teaching
–0.42*
**–0.14*
**–0.28*
**–0.24*
*–0.34*
**–0.35*
**–0.35*
**–0.19*
**Managem
ent
–0.12*
**–0.18*
**–0.61*
**–0.43*
**–0.40*
**–0.53*
**–0.58*
**–0.07(N
S)
Com
puter
–0.51*
**–0.40*
**–0.41*
**–0.71*
**–1.18*
**–0.59*
**–0.65*
**1.08
***
Other
–0.28*
**–0.19(N
S)
–0.14(N
S)
–0.59*
**–0.56*
**–0.20*
**–0.56*
**–0.52*
**Careerstag
e(latecareer
isthereferencegrou
p)Early
career
–0.02(N
S)
–0.40*
**0.02
(NS)
0.41
***
–0.25*
**–0.29*
**0.07
(NS)
0.19
**Mid
career
0.25
***
–0.39*
**0.26
***
1.09
***
–0.18*
**–0.08*
**0.34
***
0.27
***
Con
stant
–0.11*
*0.86
***
–0.20*
–0.90*
**0.93
***
0.45
***
0.36
**–0.42*
**AdjustedR-squ
ared
0.06
0.09
0.04
0.03
0.04
0.05
0.04
0.07
Fvalue
248.59
***
31.33*
**36
.30*
**67
.04*
**60
.74*
**14
2.90
***
70.41*
**60
.78*
**
Notes:*p
<0.05
,**
p<0.01
,**
*p<0.00
1;NS=Not
sign
ificant.
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The variances noted among the different disciplines present several interesting andnotable conclusions. The lower number of articles produced by social scientists is in part areflection of the nature of the discipline (longer publication time, lengthier articles, fewergrants, and the difficulty of obtaining data) (Becher 1994). The differences in productivitycan also be attributed to co-authorship rates. Lack of data on co-authorship is a limitationof the SDR dataset. While co-authorship has been the norm in several science andengineering disciplines, social science is catching up. The latest study by Corley andSabharwal (2010) found that the publications in the field of public administration andpolicy are transforming from being “lone wolves” to co-authored works, which can raisethe article count. For example, the total number of papers produced can stay exactly thesame say in physics, but if the average number of co-authors increases, the number ofpapers by any single physicist can double or more. However, it is hard to get consensus on
Table 6. OLS regression – productivity, discipline, and career stages
Independent variables Journal articles (unstd. coefficients) Books (unstd. coefficients)
Discipline (biological sciences as the reference group)Computer sciences –3.81*** –0.33***Mathematics and statistics –1.55*** –0.20***Physical sciences 1.48*** –0.01 (NS)Psychology 0.51*** 0.12***Social sciences –2.32*** 0.23***Engineering –1.71*** –0.19***Health 1.21*** 0.29***DemographicsMale 0.91*** 0.14***Married 0.38*** 0.06***US born –0.99*** –0.02***Race/ethnicity (caucasian is the reference group)Asian –0.22** 0.22***African Am. –1.75*** 0.22***Hispanic –0.05(NS) 0.03 (NS)Other ethnicity 1.66*** 0.99***Career variablesCarnegie Res. I/II 1.93*** 0.15***Tenured 2.90*** 0.30***Salary 5E-05*** 4E-06***Rec’d federal grants 3.82*** 0.04***Primary work activity (research is the reference group)Teaching –3.88*** –0.32***Management –5.22*** –0.38***ComputerApps –5.24*** –0.52***Other –4.50*** –0.39***Career stage (late career is the reference group)Early career 1.06 0.01 (NS)Mid career 1.41 0.27***Constant 1.10*** –0.03 (NS)Adjusted R-squared 0.23 0.02F value 2,922.52*** 228.52***
Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.
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co-authorship patterns across disciplines. In some disciplines sole authorship continues tobe important when promotion and tenure decisions are being considered, despite a rise ininterdisciplinarity. It is thus important to take the nature and type of work into considera-tion when committees make promotion decisions. Future research should take co-author-ship and disciplinary requirements into consideration when assessing researchproductivity.
Early career social scientists also spend the majority of their time teaching; while ourdata do not allow us to determine causal relationships, it is safe to note that the time spentteaching is negatively related to research productivity (Baldwin and Blackburn 1981;Smart 1990; Olsen et al. 1995; Hagedorn 2000; Durning and Jenkins 2005). Whileteaching remains an important element while assessing tenure and promotion, the bulkof the decision in research universities is contingent upon the scholarly contributionsmade by these faculty members to their discipline. Department heads should be protectiveof the time of early career faculty members and provide them with opportunities forformal and informal mentoring.
The higher rate of productivity among physical and health scientists can be linked in partto the time spent on research activities and the availability of grants and industrial funding.In fact, receiving a National Institutes of Health (NIH) or a National Science Foundation(NSF) grant is a precursor to promotion and tenure at many US research universities. Grantsoften lead to publishable research (Ali et al. 2010; Jacob and Lefgren 2011), furtherenhancing the research productivity of faculty in these disciplines.
Another notable finding is the difference in productivity at various career stages. Indisciplines such as computer and information sciences, health, and physical scienceswhere the paradigms are constantly changing and new knowledge is rapidly created wefind that the early and mid career stage faculty members are more productive than thelate career stage faculty members. There can be several possible explanations for thisfinding – while one of them finds support in the obsolescence theory which suggests thatresearch performance declines as one progresses in his/her career, there are otherjustifications. The findings are contrary to past studies which report that full professorspublish more than associate and assistant professors (Abramo et al. 2011), the argumentbeing that senior faculty members are well established and possess the knowledge,skills, and networks to advance research (Cole and Cole 1973; Bozeman et al. 2001;Abramo et al. 2011). On the other hand, major discoveries and scientific breakthroughswere made by early career scientists. The demand to publish in early career stages offaculty members in the US is ever increasing, contributing to their rise in publications(Stroebe 2010). Further, post-doctoral fellowships in physical sciences and healthdisciplines are very common, giving them a head start on publications. Additionally,late career faculty members are often heavily involved with administration, mentoring,chairing PhD committees, consultancy and other activities that do not readily lendthemselves to research publications. In fact, these are time-consuming and often taketime away from research and publication (Costas et al. 2010). The reasons why earlycareer faculty members in physical sciences, computer and information sciences, andhealth produce more journal articles when compared with mid and late career stagefaculty members is a topic that needs further investigation.
The findings have implications for faculty members competing for prestigious grants bythe NIH and NSF which are increasingly being awarded to senior faculty.
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In 1980, the largest share of grants from the National Institutes of Health (NIH) wentto scientists in their late 30s. By 2006 the curve had been shifted sharply to the right,with the highest proportion of grants going to scientists in their late 40s. In 2007, themost recent year available, there were more grants to 70-year-old researchers thanthere were to researchers under the age of 30. (Lehrer 2010: 2)
Historically, most noted discoveries have been made by scientists in their twenties andthirties; however, increasingly the enterprise of science is becoming older – a concernexpressed by Lehrer (2010) in his article in the Wall Street Journal. The author argues thatinnovation and creativity, which normally peak in early career stages, are hampered by thechanging funding patterns among agencies. On the contrary, research productivity inengineering, biological sciences, and psychology disciplines follow Merton’s theory ofcumulative advantage, in which faculty build a reputation over time, which is why midcareer stage faculty produce more than early career faculty. Interestingly, the advantagedoes not hold up for late career faculty across all disciplines, after controlling for personal,institutional, and career factors. Late career stage faculty members are disadvantaged as aresult of changing productivity output – they produce more books over time.
Additionally, across all disciplines except computer and information sciences the studyfound male faculty members are more productive when compared with female faculty.The cumulative advantage theory also serves as a framework to explain the gender gap inproductivity witnessed among women faculty across majority of the disciplines. Overtime, women faculty members are disadvantaged due to the negative “kicks” (Cole andSinger 1991; Hamil-Luker 2005) they experience in early and mid career stages. Care-giving responsibilities, childbearing, ages of children, and greater time spent on teachingand administration are all activities that take time away from research resulting in anaggregate disadvantage accumulated by female faculty members over time further low-ering their research productivity (Stack 2004). Future studies can investigate the effects ofgender on various career stages of faculty members across disciplines.
The study is not without limitations. While the data used in this study includes USacademics, a large amount of bibliometric research is carried in UK and other Europeancountries, in fact the leading journal in bibliometric research – Scientometrics is based inBudapest, Hungary. Thus most of the literature cited is from non-US publications.However, recent studies show that in Asian countries, especially South Korea, Taiwan,Japan, and China, the US model for tenure and promotion is adopted and widelypromoted (Tien 2007; Shin and Cummings 2010), providing external validity to thestudy. Further, while interdisciplinarity is the new mantra among researchers and admin-istrators, disciplinary silos are still very active. Many departments continue to rewardpublications in disciplinary journals during tenure and promotion. Funding agencies oftentreat interdisciplinary research as nonconforming and deviant (Brew 2008). These data arenot conducive to making any conclusions about the interdisciplinary nature of facultyresearch. However, comparing research products across career stages and disciplines helpsbuild an understanding of research norms that exist within disciplines (Jenkins andZetter 2002).
Additionally, the current study does not include a measure of quality of publications,which though absent in the current dataset has been shown to correlate highly with thetotal number of articles published (Cole and Zuckerman 1984; Duffy et al. 2008). Facultymembers who are prolific publishers also heavily impact on the research in the field by
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being cited by other authors. To bring out interesting nuances, future research could assessboth the quality and quantity of research across various career stages and disciplines.
These results should be interpreted with caution as productivity could be a result ofseveral individual factors that are not influenced by type of discipline or degree-grantinginstitution. Fox (1983) described a variety of individual psychological factors that couldimpact on researcher productivity. Factors such as IQ, level of independence, self-suffi-ciency, and cognitive structure may affect productivity at a much deeper level than age orinstitution. Given the nature of scholarly work and the socialization of scholars, Fox(1983) cautions that these factors may be present in the majority of doctorate recipients.Most of the studies on productivity have not measured these individual-level character-istics. Given the difficulty of collecting such information, past research has insteadfocused on tangible variables (as the ones used in the current study). However, in thefuture analyzing institutional and academic goals, environment, and personal character-istics that go beyond demographics can help predict more variability in research produc-tivity of faculty across disciplines and career stage.
Note
1. Weighting was done to reduce nonresponse bias in the survey estimates. “The first step of the weightingprocess calculated a base weight for all cases selected into the 2003 SDR sample. The base weight accountsfor the sample design, and it is defined as the reciprocal of the probability of selection under the sampledesign. In the next step, an adjustment for nonresponse was performed on completed cases to account for thesample cases that did not complete the survey” (NSF, 2006: 154). For more details refer to: http://www.nsf.gov/statistics/nsf06320/appa.htm#weights
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