Comparing Research Productivity Across Disciplines

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Comparing Research Productivity Across Disciplines and Career Stages Meghna Sabharwal

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School of Economic, Political and Policy Sciences , Program in Public Affairs, The University of Texas at Dallas , Richardson , USA Published online: 17 Apr 2013.

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Comparing Research Productivity Across Disciplines and Career Stages MEGHNA SABHARWAL School 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 as well as promotion. The current study thus examines and compares the research productivity of faculty members across disciplines and career stages. The research tests three life course theories (cumulative advantage, utility maximization theory and obsolescence theory) with data from eight disciplines (biology, computer sciences, mathematics/statistics, physical sciences, psychology, social sciences, engineering, and health fields). The data for this study are taken from the 2003 Survey of Doctorate Recipients. Unlike past studies, which solely use journal articles as a measure of research productivity, this study also takes into consideration publications in books and monographs. The study found that the majority of the research output (articles and books) produced in Health and Physical Sciences disciplines is by early and mid career faculty members, providing support for the obsolescence theory, which suggests that research performance declines as faculty members progress 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 of research productivity.

Introduction Disciplines are the lifeblood of higher education. Despite their pervasiveness, studies comparing research norms and practices across disciplines are modest at best. There is a need to contribute to the theoretical and empirical research that explores key distinctions and similarities between different disciplines, and offer implications for the practice of higher education research. This study is thus an effort to fill the gap that currently exists in the 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, Public Administration, The Social Science Journal among others. She has an edited book in print titled “Public Administration in South Asia: India, Bangladesh, and Pakistan.” Correspondence Address: Meghna Sabharwal, School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, GR 31, Richardson, TX 75080, USA. Tel.: 972-8836473; Fax: (972) 883–4939; Email: [email protected] The use of National Science Foundation (NSF) data does not imply NSF endorsement of the research methods or conclusions contained in this report.

© 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 and social sciences have been books and book chapters while the major means for disseminating research among natural scientists and mathematicians has been journal articles (Roe 1972; Bonzi 1992). However, this trend is changing within the humanities and social science disciplines (Kyvik 2003; Nederhof 2006; MLA 2007; McNay 2009). A report by the Modern Language Association of America (2007) highlighted the concerns of faculty members in the humanities about these definitions of productivity. The report found that tenure and promotion committees are placing increasing weight on articles published in scholarly research journals, a medium less utilized in the humanities profession. While journal articles are the key source of knowledge diffusion in science and engineering, books continue to play an important role in social sciences and humanities. Larivière et al. (2006: 1003) note that the proportion of references to journal articles in social science and humanities is lower than 50 per cent, the authors caution that “one should be careful in constructing performance measures on the sole basis of journal literature”. Thus this study utilizes both peer reviewed journal articles and books/monographs authored or co-authored as a measure of research productivity. Many social scientists publish books and monographs which are often left out in bibliometric studies that consider journal publications as a singular measurement of faculty performance (Bott and Hargens 1991; Archambault et al. 2006; Nederhof 2006). Faculty performance greatly impacts on decisions of annual faculty raises (McGregor 2008). While the natural sciences use grants and publications as a measure of salary increases, humanities are a little more complicated, with books being the traditional means of scholarly output, and articles recently gaining importance in decisions of promotion and tenure (P&T). Assigning weight to a book of similar scope and importance as an article can be challenging for P&T committees. Since productivity continues to be a major factor in decisions of salary raises, as well as tenure and promotion (Ramsden 1994; Bellas and Toutkoushain 1999; McGregor 2008), it is 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 solve problems that span disciplinary boundaries, an understanding of research norms that exist within disciplines is inevitable (Jenkins and Zetter 2002). In studies of research productivity, life course theories have been used in various contexts to explain the performance and trajectories of scientists. This study tests three life course theories (cumulative advantage, utility maximization theory and obsolescence theory) with data from eight disciplines (biology, computer sciences, mathematics/statistics, physical sciences, psychology, social sciences, engineering, and health fields) adding to the theoretical and empirical understanding of comparative higher education. The purpose of this article thus is to explore: (1) how does academic research productivity vary across disciplines? and (2) how are these variations related to the career stage of faculty members? Literature Review Disciplinary Differences in Productivity It was not until the 1960s and 1970s that Merton (1968) and other scholars (de Solla Price 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 academic research productivity within different disciplines. Productivity in the literature is generally 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 productivity as counting the number of articles published rather than resorting strictly to the economic definition that calculates ratio of outputs to inputs. Research productivity is measured in several ways. Citations, impact factor and h-index are among the more popular measurements used in recent bibliometric studies. However, studies continue to use self-reported publication counts (Stack 2004; Shin and Cummings 2010) as a measure of productivity; while not perfect it has been shown to correlate highly with actual publication counts (Alison and Stewart 1974). Journal articles in this study include peer reviewed publications, thus taking quality into account. The assumption is that each publication would have undergone the seal of approval through the expert judgment of peers. Most of the research on faculty productivity is focused on a single discipline, such as education (Smith et al. 2003; Mamiseishvili and Rosser 2010), music (Standley 1984; Brittin and Standley 1997; Reynolds and Hamann 2010), economics, marketing, and management (Long et al. 1998; Powers et al. 1998; Borokhovich et al. 2012), sociology (Axelson 1959; Keith and Babchuk 1998), political science (Morgan and Fitzgerald 1977; Robey 1979; De Maio and Kushner 1981; Hesli and Lee 2011), public administration (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; Bayer and Smart 1991; Stack 2004; Long et al. 2009). Only a handful of them compare research 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 and 1980s, there is renewed interest in this topic given the interdisciplinary nature of research and 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 disciplines using data from the 1972–1973 national survey of the American Council on Education (ACE). The authors concluded that there were significant differences between the publication productivity of physical/biological scientists and social scientists/humanists. Specifically, the publication rates for natural scientists exceeded social scientists and humanists by about 60 per cent. In another study, Fulton and Trow (1974) concluded that faculty members in biological sciences consistently report higher numbers of publications than scholars in the physical and social sciences. Similar results were found by Blackburn and colleagues (1978) when they used academic discipline as a control variable. They found that publication productivity among natural scientists was higher than for humanists. They argued, however, that differences in the nature of the products produced across disciplines would make direct comparisons of productivity difficult. 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 that faculty 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 disciplinary differences in productivity might not be indicative of the level of intellectual output necessary for respective fields but instead might reflect the number of resources available and the level of agreement in the disciplines (Merton 1968; Cole and Cole 1973; Wanner et 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 and Thelwall 2009; White et al. 2009) than natural scientists and engineers. Based on the above research, this study hypothesizes that:


Hypothesis 1: Social scientists are most productive when books are taken as a measure of productivity and least productive when journal articles are the measure of research productivity. Differences in Productivity by Life Age and Career Stage Productivity rates not only differ by discipline, but also by age of faculty members (Pelz and 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 the one hand, some scholars have focused on life age (i.e. the time from birth), while others utilize 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 doctoral degree, is a better indicator of productivity than birth age, and is utilized in several studies of faculty research productivity (Fulton and Trow 1974; Bayer and Dutton 1977; Baldwin and Blackburn 1981; Palmer and Patton 1981; Lynn et al. 1996). Recent studies by Costas et al. (2010), Lissoni et al. (2011), and Shin and Cummings (2010) report a negative influence of age on research productivity, while Abramo et al. (2011) find the reverse to be true. This issue remains contested. A variety of theories have been developed in response to 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 of productivity early in the career lead to continued success – and greater levels of productivity – throughout the career. Robert K. Merton in 1968 introduced the concept of cumulative advantage in his seminal study on the Matthew effect, which is defined as “the accruing of greater increments of recognition for particular scientific contributions to scientists of considerable repute and the withholding of such recognition from scientists who have not yet made their mark” (Merton 1968: 58). The cumulative advantage perspective thus argues that early success breeds future success as publications lead to grants, which lead to more time for research, which leads to even more publications. The utility maximizing theory states that researchers have a peak level of productivity in the years directly after receiving their doctorate degree. This theory follows a more traditional pattern in academia, where productivity is an important factor in achieving tenure. The utility maximization theory thus argues that once faculty members receive the utilitarian reward of tenure, they relax efforts at publication. The obsolescence theory states that older professionals do not stay up-to-date with cutting edge advances in their fields so their research eventually becomes obsolete over time. As expected, these theories have varying degrees of support across different disciplines.



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For example, Kyvik (1990) in his study showed that faculty members tend to be more productive earlier in their careers if they work in a discipline where the knowledge base is constantly changing and evolving. Yet Zuckerman and Merton (1972) showed that in fields with well-developed paradigms career age might not be a predictor of productivity. Kyvik (1990) utilized data from a 1982 survey of tenured faculty at four Norwegian universities and found that in disciplines such as physics, natural sciences and medical sciences, which witness rapid scientific advances, it is often more difficult for senior faculty to catch up. Shin and Cummings (2010) reported similar findings in which medical and health science faculty members produced higher publications than humanities and social sciences. However, disciplines like social sciences and mathematics and statistics that produce knowledge at a slower pace are likely to witness a cumulative advantage. Based on the above studies on career stage and discipline, this study expects: Hypothesis 2: In rapidly advancing disciplines (computer science, health, physical sciences), early career stage faculty members are utility maximizers. They are likely to produce 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 and late career stage faculty members. Hypothesis 4: Disciplines that do not witness rapid advancements (social sciences and mathematics and statistics) follow the cumulative advantage theory wherein research productivity increases with career age. Additionally, publication rates can vary by the type of research products produced at various stages of an individual faculty’s career. Older cohorts are more likely to publish books/monographs across all disciplines when compared with early and mid cohort groups (Blackburn et al. 1978; Bridgewater et al. 1982; Stroebe 2010). The number of books published increased in later career stages, a phenomenon more pronounced in social sciences 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 faculty productivity. Studies have explored the correlations among factors like faculty rank, time spent on teaching and research, and productivity levels. Most of the studies in the past have shown that younger faculty are more likely to spend their time teaching as compared to doing research, a phenomenon that can impact on research productivity (Baldwin and Blackburn 1981; Smart 1990; Olsen et al. 1995; Hagedorn 2000). Baldwin and Blackburn (1981) used the career development theory as a framework to study the impact of various career stages on research productivity. Career development theory asserts that careers are not static and individuals experience different phases throughout their 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. The authors found that faculty members spend more time teaching when they are in the early stages of their career. The study thus expects:


Hypothesis 6: Early career faculty members across all disciplines spend the highest amount of time in teaching-related activities compared with other disciplines. Additionally, given the results of multiple previous studies, this study expects to find differences 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 productivity varies by the type of marriage, occupation of spouse, and the ages of children. In particular, women with school-age children have lower productivity than women with preschool 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 members owing to the amount of time they spend on service-related activities. With varying rates of participation of women in different disciplines and their childbearing and childrearing responsibilities (Fox 2005; Sonnert et al. 2007), this study expects: Hypothesis 7: Women across all disciplines are likely to produce fewer journal articles than 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 National Science Foundation and the National Institutes of Health. The actual survey was conducted by the National Opinion Research Center (NORC) at the University of Chicago. The data are collected from doctorate recipients with a degree from a US institution in a science, engineering, or health sciences field through June 30, 2002. All the participants were under 76 years of age as of October 1, 2003, which was taken as the survey reference week. A total of 40,000 individuals with doctoral degrees was sampled in the 2003 survey. The original unweighted sample size was 29,915 and the weighted sample size was 685,296. The weighted variable1 is defined as the reciprocal of the probability of selection under the sample design and is further adjusted for nonresponse – the data thus used are representative of the population. The analysis in this article focuses only on full-time academic scientists either tenured or on tenure track; hence respondents with non-academic jobs or employed as instructor, lecturer, or adjunct were filtered before beginning the analysis. This filtering process reduced the weighted sample size to 238,674. Respondents were included from all disciplines who reported their highest degree in one of the following fields: (1) biological, agricultural, and environmental life sciences; (2) computer and information sciences; (3) engineering; (4) health; (5) mathematics and statistics; (6) physical sciences; (7) psychology; or (8) social sciences. Social science also includes individuals who reported receiving their degree in humanities, and is composed of a very small percentage of faculty members in the data. The entire data were divided into 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; and 31.4 per cent of the respondents in the early career stage category. The career stages were divided based on the experience, which was calculated by subtracting the year of the highest degree from the year of the survey, thus leading to three main categories, with approximately 33 percentile as late, middle, and early career stages. Since the degree year variable is not continuous the categories did not yield exact thirds. On average, early career faculty members have 4.5 years of experience, mid career 14 years, and late career faculty 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 highest percentage 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 cent followed closely by engineering faculty (11.2 per cent). Mathematics and statistics faculty were in the sixth position with 6.5 per cent, health faculty comprising 5.1 per cent, and lastly 2.0 per cent of the faculty belonged to computer and information science. 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 Carnegie Research I or II institution. The sample was 78.7 per cent Caucasian faculty, 13.5 per cent Asian, 3.3 per cent Hispanic, 3.7 per cent African Americans, and 0.8 per cent from the remaining race/ethnicities. The median age of the faculty members in all disciplines is 48 years. Findings The descriptive statistics for the demographic, career, and productivity variables by each of the eight disciplines are displayed in Table 1. The results demonstrate that male faculty members dominate all disciplines except for health, where 62.4 per cent of the faculty members are female. Over three-quarters of the faculty members across all disciplines are married. In examining the race/ethnicity of faculty in various disciplines, Asian faculty members have the highest non-Caucasian representation across all disciplines except psychology, where the percentage of African American faculty is highest. The median age of faculty members ranges from low forties to early fifties, with the median age lowest among computer and information sciences (43) faculty members and highest among social scientists (51 years). Over 40 per cent of faculty members across all disciplines are employed at a Carnegie research university. The median year of the highest degree received for all disciplines combined is 1989 while the most recent doctoral graduates in computer and information science disciplines have a median year of graduation as 1995. Despite least years of experience (9.7 years), computer and information science faculty members are most likely to be tenured (80 per cent). Engineering faculty members are compensated the most when comparing the median salaries across the disciplines ($82,214), while psychology faculty members are compensated the least ($71,435). Results in Table 2 indicate that physical science (10.2) and biology (9) faculty members produce 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 is social sciences (4.61). However, the lower number of articles published by social

N Demographic variables Male Married Caucasian Asian African Am. Hispanic Other ethnicity Age Individual career variables Yrs experience Degree year Tenured Carnegie Res.I/II University Salary Career stage Early career Mid career Late career

Variable

15.6 1987 41.9% 59.1%

77,163

32.8% 34.4% 32.8%

16.2 1987 51.3% 51.3%

77,568

31.4% 34.1% 34.5%

50.6% 41.8% 7.6%

80,055

9.7 1993 51.3% 45.2%

82.3% 83.3% 65.2% 30.1% 1.8% 2.9% 0.0% 43.2

68.9% 81.0% 77.5% 15.9% 2.6% 3.2% 0.8% 46.9

71.1% 80.3% 78.7% 13.5% 3.7% 3.3% 0.8% 48.1

Computer sciences 4,874

Biology

238,674 71,368

All

25.8% 30.9% 43.3%

75,579

18.7 1984 65.8% 44.0%

82.9% 80.4% 78.2% 15.7% 2.5% 2.9% 0.6% 49.0

15,458

Math and stats

Table 1. Means for demographic and career variables across disciplines

27.6% 33.5% 38.9%

77,284

17.9 1985 50.0% 48.5%

85.4% 82.4% 80.4% 14.1% 2.0% 2.8% 0.7% 47.9

36,921

Physical sciences

31.7% 32.4% 35.8%

71,435

16.3 1987 48.1% 43.8%

51.8% 76.2% 85.4% 3.3% 6.0% 4.0% 1.3% 48.2

27,480

Psychology


28.3% 33.4% 38.3%

76,814

16.8 1986 63.6% 45.2%

67.4% 78.4% 81.7% 8.3% 5.5% 3.6% 1.0% 50.0

43,623

Social sciences

31.2% 34.9% 33.9%

88,281

16.4 1987 56.9% 59.8%

89.6% 84.5% 69.4% 22.7% 3.9% 3.3% 0.7% 47.9

26,668

43.9% 40.1% 16.0%

75,418

11.7 1991 43.1% 45.4%

37.6% 75.7% 80.6% 9.4% 5.8% 3.1% 1.1% 49.1

12,281

Engineering Health

148 M. Sabharwal

All 15,458 26.3% 58.3% 10.6% 2.2% 2.6% 5.9 0.4 33.4%

4,874 37.4% 48.2% 8.8% 4.8% 0.9% 5.1 0.4 51.2%

60.0%

10.2 0.6

42.4% 39.3% 11.6% 3.4% 3.3%

36,921

36.6%

7.1 0.6

34.5% 40.4% 10.8% 0.1% 14.2%

27,480

23.3%

4.6 0.8

28.3% 54.0% 14.0% 1.0% 2.6%

43,623

Biology Computer sciences Math and stats Physical sciences Psychology Social sciences

N 238,674 71,368 Primary work activity R&D 41.8% 57.3% Teaching 38.7% 23.3% Management 12.1% 10.7% Computer apps 1.4% 0.6% Other activity 5.9% 8.0% Productivity variables (1998–2003) Articles 7.9 9.3 Books 0.7 0.7 Productivity variable (2002–2003) Received federal grant 48.7% 63.6%

Variable

Table 2. Means for work activity and productivity variables across disciplines


Health

59.0%

8.2 0.6

42.0% 38.7% 13.3% 2.2% 3.7%

40.8%

8.2 0.8

35.2% 40.0% 17.9% 0.8% 6.1%

26,668 12,281

Eng.

Comparing Research Productivity Across Disciplines and Career Stages 149

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scientists is compensated by publishing on average the highest number of books during the 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 per cent of the data, and are individuals with 4.5 years of experience on average from the time they graduated to the time of the survey (2003). Among early career health faculty members the difference between female and male faculty is almost double (33.1 per cent male and 66.9 per cent female). The greatest difference is seen in computer and information science and engineering disciplines where male faculty members outnumber females by more than four times. This result is not surprising given that several studies have indicated that male faculty members, especially in science and engineering disciplines, far outnumber female faculty (Menges and Exum 1983; Sonnert and Holton 1995; Wolfinger et al. 2008). Early career faculty members in mathematics and statistics, social sciences, and health report spending the most time on teaching-related activities than faculty across mid and late career stages, thus partially confirming hypothesis 6 which expected early career faculty across all disciplines to report spending the bulk of their time in teaching-related activities. All these values are reported as significant using a 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 individuals with 14.1 years of experience on average from the time they graduated to the time of the survey (2003). The trends across gender seen among early career faculty are almost all carried through to the mid career stage faculty members. The gender gap narrowed among psychologists in their mid career stages (45 per cent male) as compared with the early career stage faculty members (38 per cent male). This occurrred across all disciplines, but biology faculty members report spending 10 per cent or more of their time in teaching-related activities as compared with R&D. All reported values are significant at p < 0.001 using the Chi-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 individuals with 29 years of experience on average from the time they graduated to the time of the survey (2003). As faculty members progress in career age the gender gap across all disciplines widens. Psychology and health disciplines have high percentages of female faculty in both the early career (62 per cent and 67 per cent) and mid career stages (55 per cent and 69 per cent). This is most likely a result of the growth in the share of women in recent cohorts of PhDs. While late career stages continue to be dominated by males, the rise of women in these positions is a matter of time. Future studies can track these trends. Results in Table 3 suggest that late career stage faculty members across all disciplines report spending the highest percentage of their time in teaching related activities.

Early career stage Articles Books Primary work activity – teaching Received federal grants Mid career stage Articles Books Primary work activity – teaching Received federal grants Late career stage Articles Books Primary work activity – teaching Received federal grants

Variable

46.7% 6.1 0.4 43.3% 60.7% 3.5 0.7 59.5% 29.9%

50.6% 67.1%

8.7 10.3 0.9 0.9 38.8% 23.1%

52.0% 65.6%

8.6 11.3 0.7 0.8 42.3% 29.4%

43.7% 58.1%

Computer sciences 4.5 0.3 50.5%

Biology

6.1 6.3 0.4 0.3 34.7% 17.5%

All

30.8%

5.3 0.5 58.7%

35.1%

7.1 0.6 54.8%

35.6%

5.2 0.1 61.8%

Math and stats

Table 3. Productivity of early, mid, and late career stage faculty across disciplines

53.9%

10.5 0.5 40.1%

66.1%

11.2 1.2 43.0%

61.1%

8.6 0.2 33.7%

Physical sciences

35.7%

8.7 0.8 45.0%

35.7%

7.0 0.6 38.3%

38.6%

5.4 0.5 37.4%

Psychology


19.8%

4.2 1.0 53.0%

27.5%

5.6 0.9 53.4%

23.2%

3.9 0.6 56.3%

Social sciences

53.5%

9.2 0.5 44.6%

62.3%

8.9 0.8 43.0%

61.3%

6.4 0.4 27.7%

41.8%

9.4 0.9 37.7%

43.8%

8.3 0.9 39.3%

37.8%

7.5 0.7 41.5%

Engineering Health


Biology

Comp. Sci.

Notes: *p < 0.05, ** p < 0.01, *** p < 0.001.

Demographics Male 1.12*** −0.53* Married 0.26* −1.04*** US born −0.17 −1.11*** Race/ethnicity (caucasian is the reference group) Asian 0.01 1.82*** African Am. −2.72*** −1.55* Hispanic 0.55* −0.52 Other ethnicity 5.02*** n/a Career variables Carnegie Res. I/II 1.17*** 0.11 Tenured 3.48*** 4.07*** Salary 6E-05*** 2E-05*** Rec’d federal grants 3.47*** 1.46*** Primary work activity (research is the reference group) Teaching −4.70*** −3.37*** Management −3.47*** −3.87*** Computer −5.20*** −3.74*** Other −4.13*** −4.66*** Career stage (late career is the reference group) Early career −0.14 3.24*** Mid career 1.12*** 1.54*** Constant 0.56* 1.76* Adjusted R-squared 0.22 0.22 F value 1,188.44*** 87.48***

Independent variables

−0.66* −1.48*** −0.34 −0.70

−0.77** −2.57*** −0.96* −1.10 3.75*** 4.64*** 9E-04*** 4.94*** −5.56*** −7.88*** −6.39*** −9.00*** 4.33*** 3.15*** −4.28*** 0.26 751.49***

2.83*** −0.86* 0.61 −1.65* 2.21*** 1.35*** 3E-05*** 4.56*** −2.40*** −4.19*** −7.21*** −0.61 2.02*** 2.38*** −1.53*** 0.21 232.38***

−2.84*** −3.77*** −5.12*** −1.07*** 0.99*** 1.59*** 2.69*** 0.18 551.36***

−0.11 0.32** 5.22*** 0.31 729.99***

0.93*** 1.38*** 2E-05*** 2.06***

0.11 −0.98*** 0.02 1.71***

0.28*** 0.89*** −0.80***

Social sciences

−4.60*** −6.78*** −10.75*** −6.01***

1.91*** 1.75*** 5E-05*** 3.64***

0.91*** 0.04 −2.13***

Psych.

1.46*** −0.16 0.34

Physical sciences

0.85*** 1.63*** 0.03

Math and stats

Table 4. OLS regression for relationship between productivity and career age across discipline


0.23 0.37* 0.94* 0.20 402.18***

−3.35*** −5.53*** −3.71*** −4.55***

3.79*** 3.52*** 5E-05*** 3.64***

−0.78*** −0.65 0.19 −0.64

0.60** 0.004 −3.57***

Engin.

2.50*** 1.64*** 1.33* 0.25 242.24***

−3.47*** −5.42*** 1.10 −4.68***

1.95*** 1.70*** 7E-05*** 4.56***

−3.80*** −3.79*** −3.59*** 5.98***

1.40*** 1.65*** −2.43***

Health

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Research Productivity Across Disciplines Multivariate analyses were conducted to explore the relationship between productivity and career 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 selfreported measure of the number of articles produced over a span of five years from 1998 to 2003. Several explanatory variables were used in the model including demographic variables such as gender, marital status, and race/ethnicity, along with employment variables 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 are more productive than late career stage faculty in the following disciplines: computer and information sciences, health, and physical sciences, giving support to hypothesis 2 which states that early career stage faculty members in rapidly advancing disciplines (computer science, health, physical sciences) are utility maximizers. They are likely to produce more journal articles than mid and late career stage faculty members. The findings also support hypothesis 3, in which late career stage faculty members in computer science, physical science, and health produce fewer journal articles than mid and late career stage faculty members, thus giving support to the obsolescence theory. Disciplines that do not witness rapid advancements (social sciences and mathematics and statistics) follow the cumulative advantage theory wherein research productivity increases with career age, confirming hypothesis 4. Biologists and engineers build their research record with time – they are most productive in their mid career years following the Mathew effect. Mid career faculty members at many research institutions have sabbatical and other research leaves that affords them an opportunity to work on their research. Additionally, the results suggest that male faculty members across all disciplines except computer science publish significantly more articles in comparison with female faculty over a span of five years. Interestingly, female faculty members in computer and information sciences are slightly more productive than their male peers, a finding that contradicts previous studies in engineering (Cole and Zuckerman 1984; Long and Fox 1995; Sonnert 1995; Xie and Shauman 1998; Stack 2004). The greatest significant difference in the number of articles published by gender was observed in biology, physical sciences, and health, where male faculty members produced approximately one full article more than their female counterparts over a span of five years. The results partially confirm hypothesis 7, which states that women in all disciplines are likely to produce fewer journal articles than their male peers. Asian faculty members in computer and information sciences and mathematics and statistics produced approximately two more articles than Caucasian faculty members in these disciplines. In contrast, Asian faculty members in the health disciplines produced close to four fewer articles than Caucasian faculty members. In computer science, psychology, social sciences, engineering, and health disciplines US-born faculty are less productive as compared with foreign-born faculty members. Foreign-born engineering faculty members produced approximately four more articles during the five-year period than US-born faculty members. These findings mirror previous studies that have found foreign-born faculty in science and engineering to have higher research productivity as compared with their US-born counterparts (Levin and Stephan 1999; Corley and Sabharwal 2007; Sabharwal 2011).


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Not surprisingly, across all eight disciplines, faculty that spend more time teaching are likely to produce three to five fewer articles as compared with faculty who report spending 10 per cent or more of their time on research. A similar trend followed when faculty reported spending more time in administrative and computer-related applications as compared with research. Faculty with federal research grants have more articles than faculty without any grants, a finding that holds across all disciplines. In addition, salary is positively correlated with number of journal articles across the disciplinary range. The regression models across most of the disciplines explained over 20 per cent of the variance in the article productivity of faculty members. Additional regression models were run, one using books as the dependent variable and the other using journal articles and books for the overall data. The results are presented in Tables 5 and 6. In social sciences when journal articles are taken as a measure of productivity, early and mid career faculty members are more productive than late career faculty members. However, when books are the measure of productivity, late career faculty members are most productive, confirming hypothesis 5, which stated that aging shifts the output mix more towards books for social scientists. A similar pattern emerges in computer and information sciences and psychology. In biology, mathematics and statistics, and engineering, mid career faculty members are more likely to produce books when compared with late career faculty members. However, among physical sciences and health faculty members both early and mid career faculty members produce more books when compared with late career faculty members. The results suggest that the majority of the research output (articles and books) produced in health and physical sciences disciplines is by early and mid career faculty members, providing support for the obsolescence theory, which suggests that research performance declines as faculty members progress in their careers. Further, results in Table 6 suggest that across all eight disciplines, physical scientists produce the highest number of articles, and health and social science faculty members produce the most books. The results partially confirm hypothesis 1, which states that social scientists are most productive when books are taken as a measure of productivity and least productive when journal articles are the measure of research productivity. In disciplines like health and physical sciences there are often several authors on one publication, thus increasing their productivity (Kyvik 2003). While there is an upward trend in co-authorship, social sciences and humanities continue to be dominated by soleauthored publications – this is often a requirement for promotion and tenure in these disciplines (Corley and Sabharwal 2010). Discussion and Conclusions An aggregate understanding of faculty productivity patterns across disciplines and career stages can serve as an important policy guide for administrators and department heads in evaluating faculty work. Faculty productivity is an important predictor of quality in institutions of higher education. The amount of research produced by faculty members can lead to improved visibility of departments, which in turn dictates the rankings of the schools (Fairweather 2002). In addition, research performance has a direct impact on rewards, tenure and promotion decisions (Blackburn and Lawrence 1995; Fairweather and Rhoads 1995; Tierney and Bensimon 1996; Bland et al. 2006; Costas et al. 2010).

Biology

Comp. sci. 0.29*** 0.15* –0.08 (NS) 1.36*** 0.24 (NS) –0.25 (NS) 2.37*** 0.43*** 0.76*** 7E-06*** –0.68*** –0.24** –0.43*** –0.71*** –0.59*** 0.41*** 1.09*** –0.90*** 0.03 67.04***

0.04 (NS) –0.12 (NS) 0.03 (NS) 0.51** 0.20*** 0.34*** 1E-06** 0.23*** –0.28*** –0.61*** –0.41*** –0.14 (NS) 0.02 (NS) 0.26*** –0.20* 0.04 36.30***

Physical sciences

–0.04 (NS) 0.14** 0.29***

Math and stats

Notes: *p < 0.05, ** p < 0.01, *** p < 0.001; NS = Not significant.

Demographics Male 0.14*** –0.23*** Married 0.12*** 0.10** US born 0.02 (NS) 0.07* Race/ethnicity (caucasian is the reference group) Asian 0.06* 0.06* African Am. –0.14** –0.28*** Hispanic 0.13** –0.34*** Other ethnicity 0.55*** n/a Career variables Carnegie Res. I/II 0.03* –0.01(NS) Tenured 0.20*** 0.13*** Salary 6E-06*** –1E-06*** Rec’d federal grants 0.09*** 0.22*** Primary work activity (research is the reference group) Teaching –0.42*** –0.14*** Management –0.12*** –0.18*** Computer –0.51*** –0.40*** Other –0.28*** –0.19 (NS) Career stage (late career is the reference group) Early career –0.02 (NS) –0.40*** Mid career 0.25*** –0.39*** Constant –0.11** 0.86*** Adjusted R-squared 0.06 0.09 F value 248.59*** 31.33***


–0.35*** –0.53*** –0.59*** –0.20*** –0.29*** –0.08*** 0.45*** 0.05 142.90***

–0.34*** –0.40*** –1.18*** –0.56*** –0.25*** –0.18*** 0.93*** 0.04 60.74***

0.16*** 0.16*** 1E-06*** 0.18***

0.02 (NS) 0.19*** 0.26*** 1.43***

–0.02 (NS) –0.16** –0.10 (NS) 0.18 (NS) 0.11*** –0.04(NS) 2E-06*** 0.12***

0.04* 0.29*** 0.17***

Social sciences

0.21*** –0.25*** –0.05(NS)

Psych.

Table 5. OLS regression for relationship between book productivity and career age across discipline


0.07 (NS) 0.34*** 0.36** 0.04 70.41***

–0.35*** –0.58*** –0.65*** –0.56***

0.30*** 0.32*** 3E-06*** 0.26***

–0.16** 2.01*** –0.17 (NS) 0.21 (NS)

0.11 (NS) –0.53*** –0.33***

Engin.

0.19** 0.27*** –0.42*** 0.07 60.78***

–0.19*** –0.07 (NS) 1.08*** –0.52***

0.09** 0.35*** 2E-06*** 0.35***

0.47*** –0.18 (NS) 0.10 (NS) 2.28***

0.31*** 0.22*** 0.35***

Health


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Table 6. OLS regression – productivity, discipline, and career stages



Journal articles (unstd. coefficients)

Discipline (biological sciences as the reference group) Computer sciences –3.81*** Mathematics and statistics –1.55*** Physical sciences 1.48*** Psychology 0.51*** Social sciences –2.32*** Engineering –1.71*** Health 1.21*** Demographics Male 0.91*** Married 0.38*** US born –0.99*** Race/ethnicity (caucasian is the reference group) Asian –0.22** African Am. –1.75*** Hispanic –0.05(NS) Other ethnicity 1.66*** Career variables Carnegie Res. I/II 1.93*** Tenured 2.90*** Salary 5E-05*** Rec’d federal grants 3.82*** Primary work activity (research is the reference group) Teaching –3.88*** Management –5.22*** ComputerApps –5.24*** Other –4.50*** Career stage (late career is the reference group) Early career 1.06 Mid career 1.41 Constant 1.10*** Adjusted R-squared 0.23 F value 2,922.52***

Books (unstd. coefficients) –0.33*** –0.20*** –0.01 (NS) 0.12*** 0.23*** –0.19*** 0.29*** 0.14*** 0.06*** –0.02*** 0.22*** 0.22*** 0.03 (NS) 0.99*** 0.15*** 0.30*** 4E-06*** 0.04*** –0.32*** –0.38*** –0.52*** –0.39*** 0.01 (NS) 0.27*** –0.03 (NS) 0.02 228.52***

Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.

The variances noted among the different disciplines present several interesting and notable conclusions. The lower number of articles produced by social scientists is in part a reflection of the nature of the discipline (longer publication time, lengthier articles, fewer grants, and the difficulty of obtaining data) (Becher 1994). The differences in productivity can also be attributed to co-authorship rates. Lack of data on co-authorship is a limitation of the SDR dataset. While co-authorship has been the norm in several science and engineering disciplines, social science is catching up. The latest study by Corley and Sabharwal (2010) found that the publications in the field of public administration and policy are transforming from being “lone wolves” to co-authored works, which can raise the article count. For example, the total number of papers produced can stay exactly the same say in physics, but if the average number of co-authors increases, the number of papers by any single physicist can double or more. However, it is hard to get consensus on



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co-authorship patterns across disciplines. In some disciplines sole authorship continues to be important when promotion and tenure decisions are being considered, despite a rise in interdisciplinarity. It is thus important to take the nature and type of work into consideration when committees make promotion decisions. Future research should take co-authorship and disciplinary requirements into consideration when assessing research productivity. Early career social scientists also spend the majority of their time teaching; while our data do not allow us to determine causal relationships, it is safe to note that the time spent teaching is negatively related to research productivity (Baldwin and Blackburn 1981; Smart 1990; Olsen et al. 1995; Hagedorn 2000; Durning and Jenkins 2005). While teaching remains an important element while assessing tenure and promotion, the bulk of the decision in research universities is contingent upon the scholarly contributions made by these faculty members to their discipline. Department heads should be protective of the time of early career faculty members and provide them with opportunities for formal and informal mentoring. The higher rate of productivity among physical and health scientists can be linked in part to 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. Grants often lead to publishable research (Ali et al. 2010; Jacob and Lefgren 2011), further enhancing the research productivity of faculty in these disciplines. Another notable finding is the difference in productivity at various career stages. In disciplines such as computer and information sciences, health, and physical sciences where the paradigms are constantly changing and new knowledge is rapidly created we find that the early and mid career stage faculty members are more productive than the late career stage faculty members. There can be several possible explanations for this finding – while one of them finds support in the obsolescence theory which suggests that research performance declines as one progresses in his/her career, there are other justifications. The findings are contrary to past studies which report that full professors publish more than associate and assistant professors (Abramo et al. 2011), the argument being 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 breakthroughs were made by early career scientists. The demand to publish in early career stages of faculty members in the US is ever increasing, contributing to their rise in publications (Stroebe 2010). Further, post-doctoral fellowships in physical sciences and health disciplines 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 lend themselves to research publications. In fact, these are time-consuming and often take time away from research and publication (Costas et al. 2010). The reasons why early career faculty members in physical sciences, computer and information sciences, and health produce more journal articles when compared with mid and late career stage faculty members is a topic that needs further investigation. The findings have implications for faculty members competing for prestigious grants by the 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) went to 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, the most recent year available, there were more grants to 70-year-old researchers than there were to researchers under the age of 30. (Lehrer 2010: 2) Historically, most noted discoveries have been made by scientists in their twenties and thirties; however, increasingly the enterprise of science is becoming older – a concern expressed by Lehrer (2010) in his article in the Wall Street Journal. The author argues that innovation and creativity, which normally peak in early career stages, are hampered by the changing funding patterns among agencies. On the contrary, research productivity in engineering, biological sciences, and psychology disciplines follow Merton’s theory of cumulative advantage, in which faculty build a reputation over time, which is why mid career stage faculty produce more than early career faculty. Interestingly, the advantage does 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 a result of changing productivity output – they produce more books over time. Additionally, across all disciplines except computer and information sciences the study found 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 in productivity witnessed among women faculty across majority of the disciplines. Over time, women faculty members are disadvantaged due to the negative “kicks” (Cole and Singer 1991; Hamil-Luker 2005) they experience in early and mid career stages. Caregiving responsibilities, childbearing, ages of children, and greater time spent on teaching and administration are all activities that take time away from research resulting in an aggregate disadvantage accumulated by female faculty members over time further lowering their research productivity (Stack 2004). Future studies can investigate the effects of gender on various career stages of faculty members across disciplines. The study is not without limitations. While the data used in this study includes US academics, a large amount of bibliometric research is carried in UK and other European countries, in fact the leading journal in bibliometric research – Scientometrics is based in Budapest, 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 widely promoted (Tien 2007; Shin and Cummings 2010), providing external validity to the study. Further, while interdisciplinarity is the new mantra among researchers and administrators, disciplinary silos are still very active. Many departments continue to reward publications in disciplinary journals during tenure and promotion. Funding agencies often treat interdisciplinary research as nonconforming and deviant (Brew 2008). These data are not conducive to making any conclusions about the interdisciplinary nature of faculty research. However, comparing research products across career stages and disciplines helps build an understanding of research norms that exist within disciplines (Jenkins and Zetter 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 the total number of articles published (Cole and Zuckerman 1984; Duffy et al. 2008). Faculty members 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 assess both 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 of several individual factors that are not influenced by type of discipline or degree-granting institution. Fox (1983) described a variety of individual psychological factors that could impact on researcher productivity. Factors such as IQ, level of independence, self-sufficiency, and cognitive structure may affect productivity at a much deeper level than age or institution. 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 characteristics. Given the difficulty of collecting such information, past research has instead focused on tangible variables (as the ones used in the current study). However, in the future analyzing institutional and academic goals, environment, and personal characteristics that go beyond demographics can help predict more variability in research productivity 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 weighting process calculated a base weight for all cases selected into the 2003 SDR sample. The base weight accounts for the sample design, and it is defined as the reciprocal of the probability of selection under the sample design. In the next step, an adjustment for nonresponse was performed on completed cases to account for the sample 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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