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An output perspective on theteaching–research nexus: an analysisfocusing on the United States highereducation systemHugo Horta a b , Vincent Dautel c & Francisco M. Veloso b ca Center for Innovation, Technology and Policy Research, InstitutoSuperior Técnico , Technical University of Lisbon , Avenida RoviscoPais 1049-001, Lisbon , Portugalb Department of Engineering and Public Policy , Carnegie MellonUniversity , Pittsburgh , PA , 15213 , USAc Enterprise Research Unit, Centre for Population, Povertyand Public Policy Studies , P.O. Box 48, L-4501 , Differdange ,Luxembourgd FCEE , Universidade Católica Portuguesa , 1649-023 , Lisbon ,PortugalPublished online: 18 Jul 2011.
To cite this article: Hugo Horta , Vincent Dautel & Francisco M. Veloso (2012) An outputperspective on the teaching–research nexus: an analysis focusing on the United States highereducation system, Studies in Higher Education, 37:2, 171-187, DOI: 10.1080/03075079.2010.503268
To link to this article: http://dx.doi.org/10.1080/03075079.2010.503268
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Studies in Higher Education
ISSN 0307-5079 print/ISSN 1470-174X online© 2012 Society for Research into Higher Education
http://www.tandfonline.com
An output perspective on the teaching–research nexus: an analysis focusing on the United States higher education system
Hugo Hortaa,b*, Vincent Dautelc and Francisco M. Velosob,d
aCenter for Innovation, Technology and Policy Research, Instituto Superior Técnico, Technical University of Lisbon, Avenida Rovisco Pais 1049-001, Lisbon, Portugal; bDepartment of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA; cEnterprise Research Unit, Centre for Population, Poverty and Public Policy Studies, P.O. Box 48, L-4501 Differdange, Luxembourg; dFCEE, Universidade Católica Portuguesa, 1649-023 Lisbon, PortugalTaylor and FrancisCSHE_A_503268.sgm10.1080/03075079.2010.503268Studies in Higher Education0307-5079 (print)/1470-174X (online)Article2011Society for Research into Higher Education0000000002011Dr [email protected]
This empirical study demonstrates that teaching and research can be leveragedsynergistically and contribute to research outputs. In particular, it is critical toconsider the nature of the learning environment associated with the teaching effort.First, by distinguishing between graduate and undergraduate education, theauthors conclude that involvement in graduate teaching can play a significant rolein driving research output. Moreover, both undergraduate and graduate studentscan provide a valuable contribution to the productivity of faculty members ifintegrated in research activities where teaching and research are combined withina learning perspective.
Keywords: teaching–research nexus; impact of graduate students on researchresults; impact of undergraduate students on research results; academic research
Introduction
The long-standing assumption that teaching and research overlap, in the sense thatengagement in one favours the other, is being questioned, debated and analysed(Simons and Elen 2007). Some literature emphasizes the mutual synergies and benefitsbetween teaching and research, whilst some asserts that teaching and research aremutually exclusive and competing activities (e.g. Austin 1996; Braxton 1996). Theformer perspective tends to be supported by qualitative studies whilst the latter issupported by quantitative studies (e.g. Olsen and Simmons 1996; Volkwein andCarbone 1994). The paradoxical results of these studies have led some authors to ques-tion the ability of quantitative-based analyses to capture the complex and often subtlerelationships between activities (e.g. Colbeck 1998; Neumann 1992, 1994). Thisdebate is becoming increasingly important because of the growing relevance placedby universities on the learning experience of students, while continuing to focus onthe production of academic research (Halse et al. 2007), a critical pillar for universitiesin today’s knowledge society (Horta 2009). This study aims to contribute to the debateby analysing how the relationship between teaching and research influences theproduction of academic outputs.
*Corresponding author. Email [email protected]; [email protected]
Vol. 37, No. 2, March 2012, 171–187
http://dx.doi.org/10.1080/03075079.2010.503268
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The study is based on a novel analytical, quantitative-based model that relies onthe notion that learning constitutes the vital link between research and teaching (Brewand Boud 1995; Elton 2001). Learning is used as the key concept linking the activitiesof teaching and research because these represent learning processes, and also becausefaculty are expert learners who guide the learning process of novice learners, thestudents (Brew 1999; Elton 2001). Through the use of this model, we re-examine avariety of results found in previous quantitative-based analyses, which identified teach-ing and research as independent or competing activities. We analyse the teaching–research relationship from a broad, encompassing and interlinked view, using a widerange of individual and organizational determinants that are known to condition theproduction of academic outputs. In order to achieve this, we: (1) introduce a method-ological innovation in the analytical model, (2) focus the analysis at the systemic level,and (3) observe how teaching graduate and undergraduate students in different learningenvironments affects the scientific productivity of the faculty.
The methodological innovation refers to the way the production of academicoutputs is analysed. Unlike previous empirical analyses, our analytical model consid-ers academic outputs to be produced simultaneously, not in isolation from one another.In other words, we consider that academic work is not performed in a sequence (i.e.first an article, then a book, then another output), but rather concurrently, as mostacademics tend to work on different outputs within the same temporal period (seeArreola 2000, 2007; Arreola, Theall, and Aleamoni 2003). Therefore, we assume inthe model two simultaneous but linked concurrencies: one between teaching andresearch and another between the various outputs being developed. We believe thisprovides a much more realistic model of the way research outputs are created and, assuch, a model that better fits the way academic work is performed (including howteaching-related activities affect the production of academic outputs). This approachallows an observation of how the engagement in teaching-related activities affects theoverall research effort of academics (and subsequent production), as it impacts theconcurrent production of the various research outputs.
Another novelty of this article is the analysis of the teaching–research nexus for ahigher education system as a whole, instead of focusing on one or a few universities,schools or departments, which is the typical approach taken in previous empiricalwork (e.g. Olsen and Simmons 1996). The analysis focuses on the entire US highereducation system. This focus is explained by its level of maturity and stability, and itsrole as a system of reference for the rest of the world.
The third and final novelty this article introduces refers to the role of students inthe research productivity of academics. The impact of students is associated with theteaching role of the faculty and its effect on research production. This is accomplishednot only by analysing the impact of undergraduate and graduate students on facultyresearch productivity separately, but also by looking at the effect of the teachingenvironment in which students participate. This goes beyond the way in which theteaching role is incorporated in previous studies, which tends to perceive teachingsimply in terms of the number of credit classes, teaching hours or students taught.
Through the introduction of these novelties, we aim to address the criticism thatquantitative-based studies are often too narrowly focused, or are inappropriate to anal-yse the teaching–research nexus (see Colbeck 1998). However, the main objective ofthis article is to show empirically that the synergies between teaching and research inrelation to the production of research outputs can be much more intensive if teachingand student learning are considered more broadly.
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The teaching–research nexus discourse: arguments in favour and against
Universities have a variety of missions and goals directed towards a set of diverseinternal and external constituencies. Teaching and research are regarded as coremissions (Henkel 2000). They are also believed to be mutually reinforcing activities,and academics are expected to pursue them in an intertwined manner to contribute tothe quality of both (Braxton 1996; Coaldrake and Stedman 1999; Fairweather 1996).However, status among organizational and individual peers is usually enhanced byresearch visibility (Alpert 1985; Horta 2009). Research is perceived as an activity thatbrings higher returns in terms of prestige and reputation, when compared to teachingor any other activity (Boyer 1990). Thus, in spite of many institutional policies topromote the value of teaching, especially at undergraduate level, the value of generat-ing research outputs remains very high (Diamond 1999). This reputation disparityleads to some strain between the two activities. The declining percentage of facultymembers who believe that the primary means of attaining tenure and promotionshould be related to teaching activities underlines this strain (Finkelstein, Seal, andSchuster 1998).
The strain between research and teaching activities is not limited by reputationalscope. Prior results indicate that the relationship between the two activities is notnecessarily complementary. Marsh (1987) suggests that time, motivation and rewardsare antagonistic between teaching and research. He argues that the reward structuresand enthusiasm to undertake research and teaching activities are different, yet bothresearch and teaching require exclusive use of time and energy. Durning and Jenkins(2005) found similar results, particularly regarding reward structures, when analysingfour post-1992 universities in the UK. This rationale supports the argument thatquality teaching is done at higher education institutions where the faculty dedicatesless time to research, and quality research is done in universities that place low ornegligible emphasis on undergraduate education (Ramsden and Moses 1992). In thiscontext, Barnett (1992) refers to the creation of the graduate school, and the separationbetween graduate and undergraduate education at US universities, as a major boost totheir research output.
The transformation of the university into a ‘knowledge factory’ is believed to beleading to the integration of research and teaching at the graduate level only, espe-cially at the US doctoral universities (Ramsden and Moses 1992). This occurs becausethe socialization of graduate students – in particular those on doctoral courses – isbased on a mix of teaching and research activities (Freidson 1975; Golde and Dore2001). However, even at universities not mainly focused on academic research andgraduate education, departmental cultures and implementation strategies have drivenresearch and teaching apart (Colbeck 1998; Durning and Jenkins 2005). Universitieshave been influenced by national scientific and higher education policies, which,sometimes inadvertently, have helped to reward the two activities separately(Robertson 2007).
Although the mutually beneficial link between teaching and research has beenquestioned, the empirical research performed thus far presents conflicting findings. Indefending the teaching–research nexus complementarity, Volkwein and Carbone(1994) show that student development outcomes are greater in research-orienteddepartments than in teaching-oriented departments. However, the same authorsconclude that the student development outcomes would be the greatest in departmentsbalancing teaching and research activities. The study by Durning and Jenkins (2005)
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shows that the linkage between teaching and research is vital to ensure that studentsdevelop academic and professional skills, as well as a proper understanding of thecomplexity of knowledge. These results support Clark’s (1987) argument that educa-tional productivity is promoted and improved through the relationship betweenresearch and teaching.
These findings are contradicted by other studies. Bellas and Toutkoushian (1999),as well as Fox (1992), found a negative relationship between teaching load and researchoutput. Other results showed an uncorrelated relationship between research productiv-ity and teaching effectiveness (Hattie and Marsh 1996; Marsh and Hattie 2002), leadingthe authors to argue that these activities are independent constructs. Ramsden andMoses (1992) achieved similar results. With one resource variable, time allocation, thenegative relationship between teaching and research appears more evidently (Olsenand Simmons 1996). These authors’ findings also indicate that academic productivityis affected positively by time spent on research and negatively by time spent on teach-ing. The strain between teaching and research can be related to the scarcity of timethat does not allow academics to better complement teaching and research activities(Durning and Jenkins 2005; Fox 1992), especially since both activities are labourintensive (Trice 1992).
These findings are downplayed by authors who argue that nuances in the relation-ship between research and teaching are perceived only through qualitative analysis (e.g.Colbeck 1998). For example, studies based on students’ views tend to report benefitsfrom the teaching–research nexus (see Neumann 1994). In this sense, the contributionof graduate students to the production of scientific and other scholarly outputs is alsoacknowledged (e.g. Austin 2002). However, if the contribution from graduate studentsto research activities is recognized in the literature, the contribution of undergraduatesdoes not seem to have been taken into consideration (the literature on the subject isalso recent; see Bauer and Bennett 2003). Different learning perspectives (andassociated requirements), related to both graduate and undergraduate education, havealso not been considered in empirical analyses of the teaching–research nexus (Brew1999). These aspects are addressed in this article’s analysis of the teaching–researchrelationship in relation to the production of academic outputs.
Data and methods
The data
This study makes use of cross-sectional data from the ‘National Study of Postsecond-ary Faculty 03–04’ survey (NSOPF 04; see: http://nces.ed.gov/surveys/nsopf/index.asp), sponsored by the National Center for Education Statistics (NCES). Thedata set has a final sample population of slightly over 26,000 eligible faculty membersin Title IV (i.e. those entitled to receive student assistance from the government)degree-granting public and not-for-profit higher education institutions in the UnitedStates. We filtered the data set to focus on faculty involved in academic activities andintegrated into a specific organizational culture. First, we included only facultyemployed at a given university for at least two years. This period was chosen basedon the estimates of Long (1978), who concluded that an academic publication outputafter a brief period reflects the organizational publication structure of the institution inwhich the faculty is based. Second, we excluded faculty performing duties not relatedto academic activities (e.g. university administration and governance). After filteringthe database, nearly 16,000 faculty members remained in our sample.
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Variables
Dependent variables
The dependent variables used in this article correspond to four outputs reported byfaculty in the last two years (see Table 1). These are: (1) articles or creative workspublished in refereed journals; (2) articles or creative works published in non-refereedjournals; (3) reviews of books, articles or creative works and chapters in editedvolumes; and (4) textbooks, other books, monographs and client reports. Theseoutputs refer to the extent to which academic activities produce a body of researchresults. As such, productivity for all publications is defined ‘in terms of individualfaculty member outputs’ (Fairweather 2002, 32) by field.
Explanatory variables
The explanatory variables comprise the focus of the analysis and are the ones closelyrelated to learning processes. Four explanatory variables are used to assess how therelationship between teaching and research conditions the production of outputs. Thefirst of these variables refers to the presence of the teaching assistant, whose role isconsidered critical in supporting the research mission of US universities (Austin2002). The literature describes the dual role of the teaching assistant: (1) to releasetime – a scarce resource in academia – for the faculty to engage in activities other thanteaching, such as research; and (2) to contribute to the research effort of the faculty bycollaborating with them in research activities in the context of their graduate degree.
The second variable refers to the number of students in credit class, at graduateand undergraduate levels. This variable measures not only the effect of students in theclassroom, but also the potential of the classroom as a source of human resources tobe integrated in research activities. Thus, we included an explanatory variable thatallows for the possibility that the faculty member does not teach undergraduate and/or graduate credit classes (i.e. no undergraduate credit class; no graduate credit class).
The third variable is the percentage of time spent in research, other activities (i.e.administration, professional growth, service, and all other academic-related activitiesnot directly associated to teaching or research), and teaching in credit classes – grad-uate and undergraduate. The variable teaching in credit class represents the baseline.Time variables refer to the distribution of the faculty workload, which is a key explan-atory variable used in previous empirical analyses of the teaching–research nexus. Thescarcity of time seems to act as a barrier between teaching effectiveness and researchproductivity (Durning and Jenkins 2005; Marsh 1987). The fourth and final explana-tory variable aims to test the effect of having students engaged in research activities.This variable refers to the number of hours per week allocated to research activitieswith graduate/undergraduate students. In the literature review, teaching at graduateand undergraduate levels is seen as different because the two presuppose a differentabsorption of knowledge (Brew 1999), and each has different learning dynamics (seeConceição and Heitor 1999). In this framework, students at each of these levels arebelieved to condition the production of outputs differently. Therefore, their inclusionin the research effort is important for the teaching–research nexus analysis.
Control variables
The control variables used in this empirical study are used often in the literature onscientific productivity (e.g.: Bellas and Toutkoushian 1999; Bridgwater, Walsh, and
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Walkenbach 1982; Dundar and Lewis 1998; Fairweather 2002; Gonzalez-Brambilaand Veloso 2007; Keith et al. 2002; Marsh and Dillon 1980; Tien 2000; Xie andShauman 1998). These include workload, time spent per week on paid tasks outsidethe institution, contract length (9–10 months versus 11–12 months: empirical evidencesuggests that the length of the contracts affects academic productivity – see Marsh andDillon 1980), years in academia, tenure status, organization publication record (e.g.the average publication of articles in refereed journals by faculty members at a giveninstitution during the last two years), gender, age, nationality, maximum higher educa-tion degree obtained, institutional control (private versus public), publications peryear of career, and disciplinary area.
In terms of discipline, in the social and human sciences outputs such as books aremore important than many other forms of publications, whereas in the natural sciencesarticles assume a greater importance (Braxton and Hargens 1996). However, in allcases, an article published in a referred journal is always considered to be of a higherquality than an article published in a non-referred journal, since its content was peerreviewed and accepted by experts in the same disciplinary field of knowledge (Braxtonand Bayer 1986). Unfortunately, given the lack of data on which journals featured thepublications, we cannot assess the quality of the journal where the article waspublished, although we are aware of qualitative differences among them. The descrip-tive statistics of the output, explanatory and control variables are in Table 1.
Table 1. Summary statistics (weighted).
Variable MeanStandard deviation Min Max
Output variablesArticles, refereed journals 1.57 3.48 0 30Articles, non-refereed journals 1.05 2.97 0 30Book reviews and chapters 0.64 1.77 0 20Books, textbooks, reports 0.48 1.63 0 20
Explanatory variablesTeaching Assistant 0.22 0.41 0 1No undergraduate students in credit class 0.32 0.47 0 1Number of undergraduate students in credit class 55.82 59.04 0 1250No graduate students in credit class 0.76 0.42 0 1Number of graduate students in credit class 13.08 42.34 0 1250Average working hours per week 0.33 0.12 0 1Hours outside university doing paid tasks 0.46 0.50 0 1Number of hours per week allocated to research
activities with undergraduate students0.96 3.12 0 50
Number of hours per week allocated to research activities with undergraduates squared
10.65 76.80 0 2500
Number of hours per week allocated to research activities with graduate students
0.73 2.94 0 50
Number of hours per week allocated to research activities with graduate students squared
9.16 80.73 0 2500
Percentage of time performing research 0.12 0.18 0 1
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Method
In accordance with the aim of the article, we construct an empirical model, where it isassumed that the function that determines the four outputs is given by:
Where i identifies the faculty, and Oj represents the different outputs, Xij are explan-atory variables, Zij control variables, and eij the unobserved effects or measurementerrors. The dependent variables (outputs) represented in this article are count variablesdefined as random non-negative integer variables. The usual way to deal with the
O f X Z ej ij ij ij= −( , , ) with j = 1 4
Table 1. (Continued).
Variable MeanStandard deviation Min Max
Percentage of time performing other activities 0.14 0.19 0 1
Control variablesHas 9/10 months contract length 0.49 0.50 0 1Years in academia 16.28 10.90 2 62Organization publication records – articles in
refereed journals0.07 0.11 0 3.33
Organization publication records – articles in non-refereed journals
0.05 0.09 0 1.44
Organization publication records – book reviews and chapters
0.03 0.06 0 1.38
Organization publication records – books, textbooks, reports
0.02 0.04 0 1.22
Publications per career year 2.13 5.00 0 125Gender (male) 0.62 0.49 0 1Nationality (non US national) 0.15 0.36 0 1Highest degree attained – master 0.35 0.48 0 1Highest degree attained – first professional 0.06 0.24 0 1Highest degree attained – bachelor 0.07 0.26 0 1Tenured 0.40 0.49 0 1Tenure track 0.12 0.32 0 1Engineering 0.05 0.21 0 1Health Sciences 0.10 0.31 0 1Humanities 0.23 0.42 0 1Natural Sciences 0.21 0.41 0 1Social Sciences 0.26 0.44 0 1Carnegie institutional classification: doctoral
universities0.36 0.48 0 1
Carnegie institutional classification: Baccalaureate colleges
0.08 0.27 0 1
Carnegie institutional classification: Associate colleges
0.34 0.47 0 1
Institutional control (public) 0.69 0.46 0 1
Note: The analysis uses sampling weights supplied by the NCES for the NSOPF:04 survey.
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nature of our dependent variables is to consider the simple Poisson regression model,expressed as:
Such a model supposes the equality between the conditional mean and the conditionalvariance:
However, in our case, this condition is not met for each of our four scientific outputs,i.e. as frequently with a cross-section model an over-dispersion is observed (the condi-tional variance exceeds the conditional mean). The failure to include individualspecific effects is one explanation for this unobserved heterogeneity. Indeed, we donot have a variable relating to the faculty ability and, as shown by Merton (1968), onlya few academics are likely to produce numerous articles in the observed period,whereas many produce very few. Positive contagion is a second explanation (Long1997). Contagion means that, for individuals with a given set of explanatory variables,the probability that an event occurs (e.g. to be published) changes as events occur. Inour case, success in publishing may offer additional resources for research, and, as aconsequence, is likely to increase the rate of future publishing.
This over-dispersion would lead to an overestimation of the significance of theindependent variables due to the downward biased nature of the standard errors (seeLong 1997). In order to circumvent this, we first add to the model a variable relatingto the intensity of publications during the career (Wi) as a proxy of faculty’s ability.That is, we add to the model the number of previous publications in the four types,divided by the length in years of the career. However, since over-dispersion is stillhigh, we use a negative binomial model that adds to the Poisson specification arandom error term, assuming the unobserved heterogeneity and, thus, allowing theconditional variance to exceed the conditional mean. Therefore, the model isexpressed as:
Where the expected value of Yi is the same as the Poisson distribution, but the conditionalvariance differs (we assume the so-called Negbin II parameterisation of the negative
binomial model, which leads to the following conditional variance: Moreover, since the outputs are produced simultaneously, one might expect that theerror terms of the four stacked equations are to some extent correlated:
Indeed, the faculty achievement in one output is likely to be associated with successin other outputs, even after having controlled for explanatory factors. This still mightbe due to missing factors related to researcher ability. As a consequence, the standarderrors of the independent variables have to be adjusted. In order to solve that issue, weemploy a seemingly unrelated estimation technique (see Weesie 1999), that combinesthe (co)variance matrices of the separate regressions so as to estimate simultaneous
µ β βij ij ij ij ij ijE Y X Z X Z= = +( , ) exp( )
E Y X Z V Y X Zij ij ij ij ij ij ij( , ) ( , )= = µ
˜ ( , , ) exp( )µ ε β β βij ij ij ij i j ij ij iE Y X Z W X Z W= = + + +
V Yij ij j ij( ) ˜ ˜ .= +µ α µ 2
Cov e eij ik im( , ) = ≠ = −λ for all j k andm 1 4
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sandwich/robust (co)variance structures. This correction enables an unbiased estimationof whether the independent variables affect differentially the four dependent variables.
Results
Our analysis (see Table 2) starts by focusing on the explanatory variables used byprevious empirical analyses to mediate the relationship between teaching and researchactivities. The objective is to determine if the same results are obtained using ourmodel. The results suggest that, when analysing the relationship between teaching andresearch, taking teaching in the conventional format (i.e. considering the number ofcredit students in class), the synergies between the two scholarly activities are weak.The overwhelming non-significant character of these variables underlines the weaklinkages between teaching credit classes and the production of research outputs. Thenon-existence of credit classes (at the undergraduate level), or the teaching of graduateor undergraduate students in credit classes does not impact the production of outputs.The only exception is the positive impact of having undergraduate credit classstudents for the production of reviews and book chapters. These results confirm theanalysis and conclusions drawn by authors such as Hattie and Marsh (1996).
When the analysis turns to the role of time, a variable also used in previous studies,we note that teaching and research assume a mutually exclusive, competing character.Our analysis confirms Olsen and Simmons’s (1996) conclusions that scientificproductivity is influenced positively by time spent in research activities, and nega-tively by time spent in teaching activities for all scientific outputs. For an increasedstandard deviation of 0.18 in the percentage of time spent in research (in relation totime spent on teaching), the faculty member production of articles in refereed journalsincreases by 39%, reviews and book chapters by 21%, books, textbooks and reportsby 8%, and articles in non-refereed journals by 7%. Such results present teaching asan academic activity which may be detrimental to the production of research outputs.This is reinforced by the time spent on other activities being more beneficial to theproduction of some outputs than time spent teaching. This is valid for the productionof articles in non-refereed journals (increase of 7%) and reviews and chapters(increase of 11%).
So far, results show that when considering teaching as only performed in creditclass (both at graduate and undergraduate level), and time dedicated to teaching versustime dedicated to research or other activities, the impact of teaching on researchresults is either weak or disadvantageous. This changes when the analysis explores thenew explanatory variables introduced in our analytical model. A critical result of ouranalysis is that, as suggested by some authors (such as Austin 2002; Grunig 1997), thepresence of graduate students is important to guarantee a better scholarly performanceof departments. Results show that an academic who does not teach graduate creditclasses has a reduced productivity of 20% in the production of reviews, book chapters,books and reports, 18% in articles published in refereed journals and 16% in articlespublished in non-refereed journals. This shows, as expected, that graduate studentsrepresent a critical resource for the research activities of the faculty.
This result is reinforced when graduate students are available to assume the roleof teaching assistants. The analysis shows that the teaching assistant affects positivelythe production of only one output: the production of articles in refereed journals(increasing the faculty member production of articles in refereed journals by 26% ina period of two years). The presence of the teaching assistant reduces the strain in
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Tabl
e 2.
See
min
gly
unre
late
d es
tim
atio
n of
neg
ativ
e bi
nom
ial
regr
essi
ons
anal
yses
and
cha
nge
in e
xpec
ted
coun
t fo
r fo
ur s
cien
tifi
c ou
tput
s.
Art
icle
s, r
efer
eed
jour
nal
Art
icle
s, n
on re
fere
ed
jour
nal
Rev
iew
s; b
ook
chap
ters
Boo
ks;
repo
rts
bfc
bfc
bfc
bfc
Org
aniz
atio
n pu
blic
atio
n re
cord
s1.
64**
*1.
192.
91**
*1.
295.
42**
*1.
3710
.44*
**1.
48(0
.11)
(0.1
1)(0
.11)
(0.1
1)T
each
ing
Ass
ista
nt0.
23**
*1.
26−0
.03
0.97
0.01
1.02
0.05
1.05
(0.4
1)(0
.41)
(0.4
1)(0
.41)
No
unde
rgra
duat
e st
uden
ts i
n cr
edit
cla
ss0.
031.
03−0
.07
0.94
−0.0
70.
93−0
.07
0.93
(0.3
8)(0
.38)
(0.3
8)(0
.38)
Num
ber
of u
nder
grad
uate
stu
dent
s in
cre
dit
clas
s0.
000.
980.
001.
020.
00**
*1.
100.
001.
02(5
9.04
)(5
9.04
)(5
9.04
)(5
9.04
)N
o gr
adua
te s
tude
nts
in c
redi
t cl
ass
−0.2
0***
0.82
−0.1
7***
0.84
−0.2
3***
0.80
−0.2
2***
0.80
(0.4
5)(0
.45)
(0.4
5)(0
.45)
Num
ber
of g
radu
ate
stud
ents
in
cred
it c
lass
0.00
1.02
0.00
0.99
0.00
1.01
0.00
0.97
(42.
34)
(42.
34)
(42.
34)
(42.
34)
Ave
rage
wor
king
hou
rs p
er w
eek
1.21
***
1.15
1.23
***
1.16
1.08
***
1.14
1.38
***
1.18
(0.1
2)(0
.12)
(0.1
2)(0
.12)
Hou
rs o
utsi
de u
nive
rsit
y do
ing
paid
tas
ks0.
11**
*1.
120.
15**
*1.
160.
20**
*1.
230.
25**
*1.
29(0
.50)
(0.5
0)(0
.50)
(0.5
0)N
umbe
r of
hou
rs p
er w
eek
allo
cate
d to
res
earc
h 0.
011.
010.
02**
1.03
0.06
***
1.06
0.05
***
1.05
acti
viti
es w
ith
unde
rgra
duat
e st
uden
ts(3
.07)
(3.0
7)(3
.07)
(3.0
7)N
umbe
r of
hou
rs p
er w
eek
allo
cate
d to
res
earc
h −0
.00
1.00
−0.0
01.
00−0
.00*
*0.
99−0
.00*
**0.
99ac
tivi
ties
wit
h un
derg
radu
ate
stud
ents
squ
ared
(76.
12)
(76.
12)
(76.
12)
(76.
12)
Num
ber
of h
ours
per
wee
k al
loca
ted
to r
esea
rch
0.02
***
1.03
0.01
1.01
0.00
1.00
0.01
1.01
acti
viti
es w
ith
grad
uate
stu
dent
s(2
.83)
(2.8
3)(2
.83)
(2.8
3)N
umbe
r of
hou
rs p
er w
eek
allo
cate
d to
res
earc
h −0
.00*
**1.
00−0
.00*
1.00
−0.0
01.
00−0
.00
1.00
acti
viti
es w
ith
grad
uate
stu
dent
s sq
uare
d(7
7.65
)(7
7.65
)(7
7.65
)(7
7.65
)
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Tabl
e 2.
(Con
tinu
ed).
Art
icle
s, r
efer
eed
jour
nal
Art
icle
s, n
on re
fere
ed
jour
nal
Rev
iew
s; b
ook
chap
ters
Boo
ks;
repo
rts
bfc
bfc
bfc
bfc
Has
9/1
0 m
onth
s co
ntra
ct l
engt
h0.
09*
1.09
−0.0
70.
930.
13**
1.14
0.05
1.05
(0.5
0)(0
.50)
(0.5
0)(0
.50)
Per
cent
age
of t
ime
perf
orm
ing
rese
arch
1.84
***
1.39
0.38
***
1.07
1.05
***
1.21
0.45
***
1.08
(0.1
8)(0
.18)
(0.1
8)(0
.18)
Per
cent
age
of t
ime
perf
orm
ing
othe
r ac
tivi
ties
0.13
1.03
0.38
***
1.07
0.58
***
1.11
0.05
1.01
(0.1
9)(0
.19)
(0.1
9)(0
.19)
Yea
rs i
n ac
adem
ia0.
01**
*1.
130.
02**
*1.
260.
02**
*1.
240.
02**
*1.
29(1
0.90
)(1
0.90
)(1
0.90
)(1
0.90
)P
ubli
cati
ons
per
care
er y
ear
0.13
***
1.92
0.22
***
2.99
0.13
***
1.88
0.16
***
2.20
(4.9
9)(4
.99)
(4.9
9)(4
.99)
Gen
der
(mal
e)0.
07*
1.08
0.05
1.05
0.04
1.04
0.05
1.05
(0.4
9)(0
.49)
(0.4
9)(0
.49)
Nat
iona
lity
(no
n U
S n
atio
nal)
0.22
***
1.25
0.10
1.10
0.21
***
1.23
−0.1
7**
0.85
(0.3
6)(0
.36)
(0.3
6)(0
.36)
Hig
hest
deg
ree
atta
ined
– m
aste
r−0
.70*
**0.
50−0
.13*
*0.
88−0
.41*
**0.
66−0
.41*
**0.
66(0
.48)
(0.4
8)(0
.48)
(0.4
8)H
ighe
st d
egre
e at
tain
ed –
fir
st p
rofe
ssio
nal
−0.1
10.
90−0
.13
0.88
0.00
0.99
0.04
1.04
(0.2
4)(0
.24)
(0.2
4)(0
.24)
Hig
hest
deg
ree
atta
ined
– b
ache
lor
−0.7
9***
0.45
−0.4
7***
0.63
−0.6
7**
0.51
−0.0
70.
93(0
.26)
(0.2
6)(0
.26)
(0.2
6)T
enur
ed0.
24**
*1.
28−0
.05
0.95
0.24
***
1.27
−0.1
8**
0.84
(0.4
9)(0
.49)
(0.4
9)(0
.49)
Ten
ure
trac
k0.
25**
*1.
28−0
.10
0.90
0.33
***
1.40
−0.3
0***
0.74
(0.3
2)(0
.32)
(0.3
2)(0
.32)
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Tabl
e 2.
(Con
tinu
ed).
Art
icle
s, r
efer
eed
jour
nal
Art
icle
s, n
on re
fere
ed
jour
nal
Rev
iew
s; b
ook
chap
ters
Boo
ks;
repo
rts
bfc
bfc
bfc
bfc
Eng
inee
ring
−0.0
20.
98−0
.27*
*0.
76−0
.81*
**0.
45−0
.02
0.98
(0.2
1)(0
.21)
(0.2
1)(0
.21)
Hea
lth
Sci
ence
s0.
25**
*1.
29−0
.44*
**0.
640.
061.
060.
031.
03(0
.31)
(0.3
1)(0
.31)
(0.3
1)H
uman
itie
s0.
33**
*1.
39−0
.11
0.90
0.62
***
1.86
−0.1
50.
86(0
.42)
(0.4
2)(0
.42)
(0.4
2)N
atur
al S
cien
ces
0.13
**1.
13−0
.71*
**0.
49−0
.33*
**0.
72−0
.23*
*0.
80(0
.41)
(0.4
1)(0
.41)
(0.4
1)S
ocia
l S
cien
ces
0.09
1.10
−0.1
3*0.
880.
061.
060.
20**
1.22
(0.4
4)(0
.44)
(0.4
4)(0
.44)
Inst
itut
iona
l co
ntro
l (p
ubli
c)0.
021.
02−0
.03
0.97
−0.1
00.
910.
091.
09(0
.46)
(0.4
6)(0
.46)
(0.4
6)C
arne
gie
inst
itut
iona
l cl
assi
fica
tion
: do
ctor
al
0.20
***
1.22
−0.1
3**
0.87
0.19
***
1.21
−0.1
30.
88un
iver
siti
es(0
.48)
(0.4
8)(0
.48)
(0.4
8)C
arne
gie
inst
itut
iona
l cl
assi
fica
tion
: −0
.12
0.89
−0.2
7***
0.77
−0.1
5*0.
86−0
.26*
0.77
Bac
cala
urea
te c
olle
ges
(0.2
7)(0
.27)
(0.2
7)(0
.27)
Car
negi
e in
stit
utio
nal
clas
sifi
cati
on:
Ass
ocia
te
−0.5
9***
0.56
−0.3
3***
0.72
−0.2
0**
0.82
−0.1
3***
0.88
coll
eges
(0.4
8)(0
.48)
(0.4
8)(0
.48)
Not
es:
b =
raw
coe
ffici
ent;
fc
= c
hang
e in
exp
ecte
d co
unt
(for
bin
ary
vari
able
s: f
acto
r ch
ange
in
expe
cted
cou
nt f
or u
nit
incr
ease
in
X;
for
met
ric
vari
able
s: c
hang
e in
expe
cted
cou
nt f
or s
tand
ard
devi
atio
n in
crea
se i
n X
); T
he a
naly
sis
uses
sam
plin
g w
eigh
ts s
uppl
ied
by t
he N
CE
S f
or t
he N
SO
PF
:04
surv
ey.
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teaching undergraduate credit classes, which is an activity that does not seem tobenefit the production of most research outputs. In other words, the teaching assistantrepresents a human resource that compensates for the time spent teaching creditclasses, which is suggested by many authors as the main reason for the lack of comple-mentarity between teaching and research (e.g. Fox 1992; Marsh 1987). The role of theteaching assistant, and the importance of teaching graduate credit classes, shows thatthe benefits from teaching credit classes to graduate students does not derive from thenumber of students in class (the variable is not significant), but from other lessobvious or direct contributions.
The relationship between teaching and research becomes even more synergeticwhen students are integrated in activities that are based on a learning approach whereteaching and research activities converge. As shown in Table 2, time spent performingresearch with graduate students impacts mostly the production of articles in refereedjournals. An increased standard variation of 1.07 hours per week performing researchwith graduate students leads the production of articles in refereed journals to increaseby 3%. However, the analysis also shows that positive synergies between teaching andresearch which impact the production of research outputs also occur with the partici-pation of undergraduate students in research activities. Integrating undergraduatestudents in research activities leads the productivity of the faculty to increase in termsof articles in non-refereed journals, writing of chapters and reviews, and books andreports. Interestingly, the results show a specialization trend with regard to the inte-gration of graduate and undergraduate students in research activities. Graduatestudents contribute mainly to increase the production of articles published in refereedjournals, whilst undergraduate students contribute to an increase in productivity in allother outputs. A possible explanation for this is the greater significance of publishingarticles in referred journals for the sake of a future career in academia for graduatestudents (especially at the doctoral level), rather than focusing on the production ofother types of scientific outputs (see Golde and Dore 2001).
The results of the control variables are in conformity with previous results foundin the literature (e.g. not holding a doctoral degree lowers the ability to produce scien-tific outputs), but still offer interesting results. In particular, articles in refereed jour-nals and, to a lesser extent, reviews and writing book chapters, are clearly importantpublication outputs to tenured faculty. The similar production of outputs by tenuredfaculty and faculty on tenure track suggests that the tenure track system is playing itsrole as a means of guaranteeing prolific production along the academic career inspecific outputs. Also, men produce 8% more articles in refereed journals thanwomen, although no gender differences are perceived in the other outputs. Finally, theimportance that non-US born nationals have for the US science is confirmed. Non-USnationals produce 25% more articles in refereed journals and 23% more reviews andchapters than US nationals, but 15% less books, textbooks and reports.
Discussion and conclusion
This article shows that synergies between teaching and research are not as weak asprevious quantitative analyses have shown. The findings of this article stress threemain ideas, emphasizing methodological and policy implications for future studiesand institutional strategies.
The first main idea is that any analysis of the teaching–research nexus requirestaking into account connections between the two activities beyond the conventional
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concepts of teaching and research. Our results demonstrate that, if teaching is onlyunderstood as ‘teaching in credit classes’, the concept of teaching is too narrow. Anyunderstanding of the teaching–research nexus analysed under such a framework isincomplete. With such a perspective, a weak or almost non-existent relationshipbetween teaching and research is found. This is consistent with previous quantitative-based studies. But we show that a proper understanding of the teaching–researchnexus requires a broader perspective, that can encompass a multitude of learningprocesses that are part of the process of knowledge construction (Brew and Boud1995; Elton 2001). Therefore, in the same way that a broad conception of research isneeded to have an impact on education (Elton 2001), a broad conception of teachingis needed to uncover its impact in research outputs.
The use of time as an explanatory variable in the teaching–research nexus alsorequires caution. Time is definitely a scarce commodity in academia; it is limited andneeds to be well organized (Fox 1992). Prior results highlighted the competitivenature of research and teaching activities, because time variables were conceived asisolated and at odds from the start (time in teaching versus time in research). If timeis to be used as a critical variable in empirical analyses of the teaching–researchnexus, it should not be seen as having only a ‘competitive nature’. In reality theseactivities often overlap, even when one activity seems to be clearly defined. For exam-ple, teaching students in graduate classes, although it can be considered mainly ateaching activity, has also a component of research; ideas are being discussed andstudents often propose novel approaches. Faculty members can research ideasdiscussed in class and even pursue them in collaboration with students, leading to apublication (see Robertson 2007). Our analysis supports this assessment, showing thatthe number of graduate credit class students does not impact faculty outputs, butresearch outputs decrease when faculty do not teach graduate credit classes. It is notthe number of graduate credit class students that matters (or the teaching time spentthere), but rather the dynamics that occur within those classes that contribute to bothteaching and research activities.
In this sense, teaching and research should not be understood solely as simplisticconcepts. Instead the concepts should be perceived in a complex form (e.g. Griffiths2004; Robertson 2007). When these concepts (as well as the concept of time dedicatedto these activities) are perceived as such, the linkages between research and teachingare enhanced, and can be captured through quantitative analyses. The broad analyticalapproach pursued by this study lends support to this idea, answering Colbeck’s (1998)criticisms and Neumann’s (1992, 1994) questions on whether it is possible to demon-strate the existence of a complementary relationship between teaching and researchactivities through quantitative research methods. The answer appears to be positive.
The second main idea of this article is that the distinction between undergraduateand graduate education is important to understand better how teaching at differenteducational levels might affect faculty output. Teaching undergraduate and graduatestudents is different. Yet, the literature refers to a greater integration between teachingand research at the graduate level only because of the greater orientation of educationat this level towards research activities (Durning and Jenkins 2005). Our resultsprovide some qualification to this perspective, suggesting that such a sharp distinctionmay be unwarranted. In particular, a specialization trend is found, whereby graduatestudents tend to support faculty production of articles in refereed journals, whilstundergraduate students tend to impact positively on the publication of other outputs.This can be explained by the importance of publishing articles in refereed journals for
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both graduate students and faculty, but other explanations might exist. For example,the preparation and publication of articles in refereed journals involves a level ofknowledge and effort that some other outputs may not require. Also, undergraduatestudents’ level of engagement in research-oriented activities is expected to be less thanthat of graduate students (e.g. data mining, interviewing), which makes them goodcontributors to articles in non-refereed journals, reviews, chapters or reports.
The third idea, and probably the most important of them all, is that academic activ-ities have to move beyond the conventional teaching formats to leverage the linkagebetween teaching and research. The results show that the contribution of students tothe research output of faculty occurs when the curriculum is designed to includeinquiry-based processes, instead of simply focusing on knowledge acquisitionprocesses (more fit for credit classes). Inquiry-based curricula require students tobecome involved in faculty research activities, minimizing the division of rolesbetween faculty and students, and reinforcing the synergy between teaching andresearch as part of the same learning process (Griffiths 2004). The integration of under-graduate and graduate students in research-oriented activities helps to intertwine teach-ing and research activities, resulting in an increased production of scientific outputsby the faculty. These results meet Brew and Boud’s (1995) argument that teaching andresearch are integrated when they become two aspects of the same activity (i.e. learn-ing). In this sense, our research agrees with Barnett, when he states that ‘what isrequired is not that students become masters of bodies of thought, but that they areenabled to begin to experience the space and the challenge of open, critical enquiry’(1997, 110). Recent qualitative studies demonstrate that academics are increasinglyaware of this (see Robertson 2007).
The effort to better link teaching and research activities into an integrated learningprocess requires changing teaching practices, which entails an organizational effort byuniversities, schools, departments and their staff. The main teaching structure contin-ues to be based on credit classes and, in this context, it is not surprising that the impactof teaching on the production of research outputs continues to be perceived asnegative or almost non-existent in many empirical analyses. These analyses reflect themajor organizational practices currently being used (teaching in credit classes), ratherthan the less often used organizational practices (teaching by integrating students inresearch-oriented activities), which could help significantly in the promotion of agreater teaching–research nexus.
To conclude, it is important to note that this study is focused on the US higher educa-tion system. As such, generalizing results to other higher education systems has to beconsidered with care. However, there are two reasons that lead us to argue that similarresults could be potentially attained elsewhere. The first reason is that the relevant vari-ables used in the analytical model are found in higher education systems throughoutthe world (except for the one related to the annual term length of the academic contract).Second, although the Carnegie classification of higher education institutions is a featureof the US higher education system, it represents a rather loose clustering of universitiesand colleges according to main institutional missions and academic outputs. In thiscontext, similar clusters could be constructed for other higher education systems.
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