We often argue that "the objectivity of science derives from the … norms of the professional community of scientists" (NRC, 2002); however, little empirical research has characterized such behaviors. In this study, we are conducting a social network analysis on research interactions that span across research specializations within the same discipline. The first part of the survey requests each member to identify colleagues with whom they interact, the frequency of interaction, and the value they place on those interactions. The second part of the survey captures salient indicators of organizational climate. We calculate network measures and aggregate the climate indicators to the disciplinary level to analyze the relationship between the two dimensions.
We undertook this study to develop an empirical understanding of a commonly held belief in academia: that the objectivity of research derives from the professional research community (NRC, 2002) and that it requires vigorous yet civil debate about methods, inferential strength, and underlying epistemological assumptions (Scheurich & Young, 1997; Yanow & Ybema, 2009). It is this value that underlies the rationale for academic freedom and the integrity of the academic peer review process. Despite the nearly universal acceptance of the importance of this assumption, however, there is almost no empirical research into the frequency, configuration, and depth of such interactions across research specializations in public research.
In an effort to strengthen the base of knowledge that surrounds our own communities of practice, we designed a three-stage, mixed-methods project to conceptualize “interaction” across research specialization, to assess the different configurations of interaction networks in research intensive departments, and to characterize the meaning of those interactions and roles that different members play.
There is little empirical research which specifically looks at the interaction of researchers within the same general discipline but across research or methodological focus. Some researchers have investigated co-authorship networks (e.g., Ding, 2011; Velden, Haque, & Lagoze, 2010), but research collaborations are likely to bring together researchers that share similar foci or methodological approaches and would fail to capture professional interactions that span significant intellectual distance. Ethnographic and qualitative studies, on the other hand, have provided case examples of departmental culture and explored collaboration within learning communities and interdisciplinary teams (Becher & Trowler, 2001; Denicolo, 2004; Holley, 2009; Jedele, 2010; Snyder & Draheim, 2002). Some of these studies provide evidence that supports the benefits of interaction across specializations, but others characterize cultural divisions between subdisciplines, substantial power hierarchies based on seniority, and differential experiences for women and racial minorities. Further, while these studies provide case examples, they fail to provide information on how pervasive these findings are or how much they vary across disciplines and academic units.
The purpose of our broader set of studies is to build an empirical research base surrounding professional research interactions that cut across the faculty member’s specific specialization. This study employs social network analysis [SNA] to characterize the frequency and depth of such interactions within the disciplinary unit1 and the variation observed across different units and different fields;2 we also employ inferential statistics on survey data to complement the SNA and relate network characteristics to indicators of organizational climate.
The research questions are:
Social network theory and analysis is a set of theories and methodological approaches to understand how the interconnected social relationships within an organization encourage or inhibit individual processes and organizational outcomes. In contrast to other prevalent approaches in education research, social network theory focuses on the organizational level, investigating how the configuration of social relationships relate to aggregate outcomes by mapping the relationships between individuals (Borgatti & Ofem, 2010; Kilduff & Brass, 2010). SNA emphasizes that individual actions are interdependent. Information and resources are transmitted through interpersonal relationships, and the patterns of relationships can both constrain or empower the members of the organization (Wasserman & Faust, 1994). Further, SNA has been identified as being capable of measuring social relationships as numerical data while simultaneously allowing researchers to also consider the construction, reproduction, and variability within complex social systems (Edwards, 2010).
This study focused on research-intensive disciplinary units at a large public university listed in the Carnegie Classification as having very high research activity3. We used the presence of a doctoral program as an indicator of a disciplinary focus on research, leading to an inclusion of over 60 units in the initial sample for selection.
In prior work, we designed an exploratory grounded theory study to conceptualize the dimensions of interaction that are valued by academic faculty (Cavera, Crouse, & Joyce, 2015). We operationalized these dimensions into an online questionnaire with two sections.
The first section captured information for the SNA. We took a sociocentric approach, the strength of which allows us to map the network structure of the entire disciplinary unit. We asked each tenured or tenure-track faculty member to indicate the following of all other faculty within their disciplinary unit with whom they interact with any significant frequency:
In the second segment of the questionnaire, we captured the perceived professional climate of the unit. To understand differences between perceptions of self and of the broader faculty, some items are repeated in the first-person context (e.g., “I don't interact with faculty because I feel my research needs to remain private until accepted for publication”) and of the faculty at large (e.g., “I believe other faculty don't interact because they feel research needs to...”). The dimensions we relate to interaction are:
Given the complexity of the social network items, the number of disciplinary units, faculty cross-classification into multiple units, and the need to maximize response rates, we designed a custom online survey engine that leveraged modern web interfaces and a lightweight database backend. The identities of respondents, colleagues they identified in their network, and their departmental affiliation were all coded to ensure confidentiality. We visited faculty meetings to encourage participation and reached out personally to faculty non-responders to encourage additional responses.
Using R, Perl, and Python, our analysis calculates network measures, generates sociograms, and organizes the measures for easy comparison between disciplinary units. To ensure that identities cannot be inferred in analsyis, the specific discipline is coded as a general field identifier, such as Social Sciences Unit A or Humanities Unit C. For each disciplinary unit, we calculate:
From the professional climate survey, we will aggregate responses to assess views of the (a) general professional climate within the disciplinary unit, (b) perceived effect of hierarchy on number of factors, and (c) perceived effect of the built environment. We will perform regression analysis on these indicators based on network centralization, density, and an aggregate measure of density-tie strengths to determine how network characteristics generally relate to the perception of the interaction climate (and vice-versa). We will also replicate these analyses using HLM with disciplinary units nested within super-disciplinary fields to determine if there are systematic differences due to the field classification. Finally, we will conduct a cluster analysis using dimensions of professional climate, average degree centrality, and network density to identify organizationally similar disciplinary units.
We had to make several decisions to bound the study in order to make it feasible. We chose to focus on a single research university with the hypothesis that university-level effects would be minor in comparison to the variation between individual disciplinary units. This may not be the case, especially in comparison to private universities that have substantially more resources available.
Organizational literature highlights the importance of administrative staff in the functioning of businesses, and our prior work has revealed that academic leaders attach significant professional value to post-doctoral students, lab managers, and administrative staff as mediators for faculty interaction; however, including all individuals in the department would lengthen the network questionnaire to the point in which we would expect few to take the time to participate, and so the impact of non-faculty on faculty interaction is left for future work.
Finally, interactions are not, in reality, bounded by a formal disciplinary unit or the university campus, and collaborations are common between researchers with similar specializations throughout the academy; our prior work has also revealed that these relationships may not be limited by specialization. While we do not capture relationships extending outside of the research site, we feel that these relationships provide a particularly interesting area of future research. Since there are a growing number of organizations focused on specific methodological approaches and research sub-disciplines, we wonder if increased extra-departmental connections moderate interaction with local colleagues that have different foci: in effect, is the modern day ease of communication with like-minded but distant colleagues leading to a decrease in interactions with neighbors with whom you might disagree? If so, this will lead to intriguing future research.
For the SNA in the overall study, we expect to understand the variation in frequency, density, and interconnection of research faculty with other researchers outside of their research specialization. Because respondents indicate whether their colleagues have a similar research focus, we will be able to determine how frequent interactions across specialization and/or methodology compare to those within the same focus. Our data will allow for inferences into the stability of interactions across units. We will also determine whether disciplinary units with high interaction indicators result from a small number of individuals that are highly connected or if they are more likely to be the result of broadly distributed patterns of interactions.
This study seeks to provide an empirical base regarding faculty interactions across research specializations, an area with little extant research. While it will provide information about the frequency and configuration of network interactions, it does not allow for inferences regarding the quality or character of those interactions. In future work, we hope that colleagues or other researchers will use these findings to conduct ethnographic work to understand what behaviors actually occur in high-interaction units, what effects they have on community members, and what administrative and leadership practices appear to encourage or inhibit them.
Similar studies should be replicated at other universities, both public and private, that vary in size and research intensity. Additional studies should include support staff, research technicians, and doctoral students to determine how they moderate or mediate researcher interaction. Finally, once a base of understanding is more established, more advocative studies should look at organizational dynamics to determine what university policies, faculty expectations, or leadership approaches may encourage higher levels of interaction. Since the value and integrity of public research rests on the dissemination of different viewpoints and open, professional discourse and critique, it is critical that we further understand the meaning of interactions and ways to further encourage them.
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1 We define a disciplinary unit as the largest formal organizational unit for a conceptual discipline, such as physics, economics, or education. Different institutions will refer to a disciplinary unit in different ways (e.g., "department", "school", "college") and multiple identifiers may even be used in the same institution. Some formal organizational units related to super-disciplinary units that relate more closely to a field (see footnote #2).
2 We operationalize field as a super-disciplinary group of conceptually similar disciplines, such as physical and life sciences or the humanities. In prior work, we identified five fields to accommodate our units of study in an a priori manner. We plan to conduct post hoc analyses of the data to see if perhaps there are different field arrangements.
3 Formerly R-1 research university