










































 

1 
 

 
 
 
 

Peer Identification as Social Stratification: Comparing 
Media and Network Measures of Status in US 

Universities 
 

David Cavazos 
Tarleton State University 

 
William Ritchie 

James Madison University 
 

Steven Harper 
James Madison University 

 
 

This research investigates the extent to which peer group identification serves as a measure of 
university status. Examining Association of American Universities (AAU) member institutions reveals 
that university peer groups exhibit many of the same stratification qualities as social networks. 
Specifically, higher-status universities tend to have more reciprocal ties with one another while lower-
status universities, in addition to having more reciprocal ties with institutions of similar status, have 
more one-way ties with higher-status institutions. These findings are then used as a basis to illustrate 
how peer group networks can be used as an alternative measure of university status. Network-based 
measures are then compared with media rankings to illustrate a surprising disconnect between 
network-based status and media status. 
 
Keywords: social networks; peer identification; organizational status 
 

Introduction 
 External media reports of university prestige have been increasingly important to university 
programs. In management, business program rankings such as those developed by U.S. News, 
Business Week, and the Princeton Review have increased in their prominence. Such media rankings 
have become increasingly controversial as such measures are more widely used by the university 
administration. Empirical analysis of Business Week rankings, for instance, has found that such 
rankings are stable over time (Morgeson & Nahrgang, 2008). This is a particularly troubling trend 
since, as Morgeson and Nahrgang (2008) point out; it is highly unlikely that, given the over 500 
business programs, a program can be ranked in the top 30. Moreover, despite the slim chances of 
being highly ranked, most business schools participate in the ranking process (Corley & Gioia, 2000). 

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2023, Volume 13, pages 1-17 

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Such participation is particularly the case for highly ranked programs that want to maintain their 
rankings for fear of losing status and/or identity (Elsbach & Kramer, 1996). For lower-ranked 
programs, moving up in the rankings poses the possibility of increased visibility, higher quality 
applicants, improved recruiting (Gioia & Corley, 2002), and higher prestige job placement for 
graduates (Bedian et al., 2010).  
 Some scholars have been critical of ranking systems. Tayar (2017), for instance, argues that 
national rankings evaluating LGBT equity and inclusion programs are largely based on superficial 
changes, not substantive efforts toward increasing LGBT inclusion in organizations. These types of 
ranking systems legitimize only one version of acceptable practices that can be difficult for small 
organizations to implement and can encourage symbolic conformity to ranking principles rather than 
a true commitment to the practices espoused by the ranking organization. Gioia and Corley (2002) 
argue that media rankings, because of their superficiality, drive universities to pursue image 
management rather than more substantive endeavors. Zemsky (2008) finds that such rankings do not 
place the development of true indicators of university quality as a priority, despite what readers of 
such rankings are led to believe. Glick (2008) argues that such measures, particularly those of business 
schools, increase focus on measures of GPA, GMAT scores, and student/faculty rations without 
understanding their inherent dynamics. Moreover, some of the characteristics of these rankings, such 
as reputation and length of existence, are factors that are sticky at best and incapable of change at 
worst. As a result, university rankings remain mostly stable over time. Indeed, Morgeson & Nahrgang 
(2008) found that very few institutions drop out of the rankings.  
 Because of such criticisms; calls have been made for improvements in media rankings. Glick 
(2008), for instance, has called for the improved validity of the data used to develop media rankings. 
In this paper, we propose that an additional measure of status can be developed by examining 
university peer groups. Such an approach is rooted in empirical research in institutional theory and 
social networks that examines the stratification effect of social exchange. Such an approach to 
assessing status, we argue, can address some of the identified shortcomings of media-based measures, 
as well as those that draw on limited network measures (Sauder et al., 2012). 
 Peer groups can be used as a measure of organizational status because of the stratifying quality 
of social exchange. Building on the premise that economic activity is embedded in social relations 
(Granovetter, 1985), scholars have shown that network ties among organizations serve as transmitters 
of information, standing, and legitimacy (Owen-Smith & Powell, 2008). Network relations can also 
serve as status signals by serving as sources of distinction among categories of organizations (Davis & 
Greve, 1997; Benjamin & Podolny, 1999). Such status signals serve to create various positions of 
standing within the institutional context in which homogeneous organizations interact, also known as 
organizational fields. Regarding the importance of organizational fields, Sharkey and Bromley (2015) have 
shown how the number of firms in an industry that have a third-party rating increases the competitive 
and isomorphic pressures for unrated firms in the industry to conform to the guidelines reflected in 
those rankings. This is further supported by Tayar's work (2017) which supports the idea that 
institutional isomorphism acts as a powerful impetus for firms to conform to a ranking system that is 
adopted by multiple peer organizations. 
 Organizational fields are shaped by rules and conventions and are composed of various 
positions of standing. Such positions are created and reinforced by cognitive and social processes 
(Anand & Watson, 2004) that occur between organizations. Some positions within organizational 
fields are perceived as more attractive than others (Owen-Smith & Powell, 2008). Variation in position 
attractiveness is due to perceptions of legitimacy as well as status that stem from, among other things, 
organizational ties and affiliations (Podolny, 2001). Organizational ties between organizations can be 
based on reputation as well as status (Podolny, 2001). Such ties can reinforce or enhance organizational 
standing among other organizations (Owen-Smith & Powell, 2008). Accordingly, network ties among 



 

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organizations may stem from similar status positions. As a result of the interplay between status 
processes and ties, organizational networks can shape possibilities of interaction as they pull 
organizations into positions within an organizational field (Owen-Smith & Powell, 2008). Such 
positions are defined by conceptions of legitimate action and social standing. As a result of the 
stratifying quality of networks, the basic composition of organization field structure may be 
determined by examining social ties among organizations within a particular field. To the degree that 
they are embedded in university peer groups, such ties can be used to examine groups of organizations 
(e.g., universities) to reveal status orderings. 
 This study has two primary purposes. Firstly, it aims to illustrate how university peer group 
identification can be used to examine organizational field stratification. Secondly, this research aims 
to develop a measure of university status based on peer group identification by applying network 
analysis methodology. We begin by developing hypotheses that aim to explain how network ties can 
serve as status signals. A review of data and methods follows. We explain our methodology which 
aims to examine how peer networks are based on conceptions of media ranking of universities. We 
then build on our data analysis by considering peer identification as an independent measure of 
university status.  
 

Theoretical Background and Hypotheses 
  The conception of peer identification as a source of university status is rooted in social 
network theory as well as institutional theory. Such work has used an organizational field level of 
analysis to examine the nature of interactions among organizations. Organizational fields consist of 
organizations that engage in common activities and are subject to similar social and political pressures. 
For example, the realm of higher education, and organizational fields may consist of business schools 
that are AACSB accredited, whereby the Deans of these organizations routinely interact at various 
accreditation events and share information related to the operational best practices.  
 Organizational fields have various positions of standing with some being perceived as more 
attractive than others (Owen-Smith & Powell, 2008). Such perceptions are forged by various social 
processes such as certification contests and tournament rituals. Examples of certification contests 
include tests of organizational performance such as automobile speed tests (Rao, 1994); while 
examples of tournament rituals include award ceremonies such as the Oscars or Grammy Awards 
Ceremonies (Anand & Watson, 2004). These processes reinforce field positions by defining legitimate 
action and organizational standing with those organizations earning the most “victories” being 
considered examples of legitimate behavior. Organizational networks within fields shape possibilities 
of interaction as they pull organizations into positions with the field. These positions will vary in terms 
of legitimacy and status depending on the ties that characterize such positions (Podolny, 2001). Status 
is generally regarded as shared social perceptions that people who belong to one social group are more 
esteemed and competent than those who belong to another social group (Webster & Foschi, 1988). 
Status is a relational component of social structure that is based on perceptions of various 
organizational outcomes applied to an organization by other organizations (Washington & Zajac, 
2005). An organization’s status may be conceptualized as an intangible resource, in that it contributes 
to performance differences, and it is rare, complex, and difficult to trade or imitate (Barney, 1991; 
Peteraf, 1993). Scholars have primarily relied on media measures and rankings of status (Bedian et al., 
2010; Wade et al., 2006; Washington & Zajac, 2005). Such external measures of status are indeed 
conferred upon organizations by external actors such as other organizations and media organizations. 
We argue that in addition to such measures, status is composed of behavioral factors such as peer 
identification.  
 Podolny and Phillips (1996) extended the notion of status as an indicator of quality to include 
organizational affiliation. They found support for the hypothesis that being affiliated with other high-



 

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status organizations improves organizational status. This finding leads to the conclusion that status is, 
in fact, partially related to the networks of a target firm. Moreover, in illustrating how ties with high-
status organizations can improve a focal organization's status, Podolny and Philips (1996) allude to 
the “leakiness” of status in which a high-status organization cannot engage in exchange relations with 
a low-status actor without possibly diluting status. Because of the leakiness of status, we hypothesize 
that reciprocal ties among high-status and low-status universities will be unlikely. For example, 
organizations of high status will not routinely reference low-status organizations. Instead, such ties 
will be one way stemming from lower status to higher status universities as lower status universities 
attempt to enhance their status by identifying higher status universities as peers. 
 
H1. One-way peer identification will more likely stem from lower status to higher status Universities 
 
 The conception of status has been synthesized with research in organizational fields and social 
networks to explain how status contributes to field structure (Owen-Smith & Powell, 2008). Networks 
involve interconnected social relations that offer opportunities in terms of information sharing as well 
as setting standards of practice (Cattani et al., 2008; Brass et al., 2004). Such association is more likely 
among organizations of similar status (Podolny, 2001; Ring & Van de Ven, 1992). Moreover, network 
centrality, the possession of numerous reciprocal ties (i.e., mutual ties between low-status and high-
status organizations), has been conceptualized as a measure of organizational status whereby 
organizations with such ties are viewed within the organizational field as having higher status 
(Wasserman & Faust, 1994, Contractor et al., 2006). As a result of such association, distinct network 
clusters of organizations can form among organizations of similar status. Status variation in networks 
shapes stratification processes by creating distinct processes of association among actors of similar 
status (Owen-Smith & Powell, 2008). These network associations can become rule-like structures as 
practices by organizations can be driven by the actions of their peers (Galaskiewicz & Burt, 1991). 
Conceptions of legitimacy as well as the logic of practice can be transmitted within these networks to 
form distinct patterns of behavior among peer organizations (Davis & Greve, 1997). Accordingly, we 
hypothesize that universities of similar status and category will likely be characterized by reciprocal 
ties. 
 

H2. Reciprocal peer identification will occur among Universities of similar status. 
 

H3. Reciprocal peer identification will occur among Universities of similar categorical dimensions. 
 
AAU University Peer Identification 
 AAU was founded in 1900 by fourteen of the United States’ leading Ph.D. granting institutions 
at a conference held at the University of Chicago. The organization was created as a forum for 
discussion on matters relating to graduate study and with the ultimate goal of improving the reputation 
that the then-young American research universities. A lack of standards on what constituted the 
requirements for Ph.D. studies and degrees was weakening the international opinion of the American 
educational system. An additional concern was that the American system was unregulated and more 
decentralized than its European counterparts. The AAU is a merit-based invitation-only organization 
that initially consisted of eleven private and three public universities (Speicher, 1990). 
 Through the years, the AAU has expanded to sixty U.S. and two Canadian institutions. This 
total number is composed of thirty-six public and twenty-six private institutions. Of the fourteen 
original institutions, twelve are still members. The current sixty-two universities represent the nodes 
in the network created for this study. The list of university nodes in this network, their date of joining 
the AAU, and their public or private status are listed in Appendix A. 



 

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 Peer identification among universities is said to be driven, in part, by the need for developing 
organizational performance criteria. Accordingly, peer selection is based on university conceptions of 
resource needs, financial needs, and performance measurement (Hurley, 2002). Selection is, thus, 
shaped by determining both conceptions of one's institution as well as where the institution wants to 
be in terms of financial resources and performance. Research examining the nature of peer groups has 
developed four types of peer group types based on the above needs: competitor, peer, aspiration, and 
jurisdictional (Brinkman & Teeter, 1987). All, but jurisdictional, which is based on location, relies on 
the university conception of identification (who is like us?) and aspiration (who do we want to be?). 
 

Method 
 
Social Network Analysis 
 We apply social network analysis to illustrate how social ties among institutions in the form of 
peer identification can serve as a measure of status. Social network analysis has been used to study 
multiple relationships between multiple actors in many situations. At the heart of this analysis is the 
representation of data in a network form that can be illustrated by using network graphs (Wasserman 
& Faust, 1994). In network graphs, the actors in a network (e.g., people, places, or organizations) are 
considered nodes in the representation; most often as a circle with a title. The relationships between 
these nodes (e.g., friendship, or identified peers) are drawn as lines between the nodes, which are called 
links or edges. When appropriate, these lines will have arrowheads on one or both ends that represent 
the relationship link as either a one-way link or a reciprocal link. 
 Many statistical measures can be made on a social network graph. There are individual node 
measures, such as the number of links at that node (called degree for the total number of links, 
indegree for links into the node; or outdegree for links out of the node), the betweenness of a node 
(connecting people as in a broker arrangement), and closeness of a node to another node (via the 
shortest path of links). Additional measures exist when considering a dyadic pair of nodes or a triadic 
triple of nodes. The network as a whole can have measures of network density (the ratio of the actual 
number of links to the theoretical maximum number of links), network centralization (the extent to 
which some actors have higher betweenness than the other actors in the network, e.g., organizations 
with tall hierocracies are more central), network components (a maximal subset of mutually reachable 
actors), and network cliques (a subset of actors that are completely connected, there is a link between 
every pair of nodes in the subset). 
 Network representation is also shared with other physical networks such as roads connecting 
cities or the URL links connecting websites. Using the metrics above, it is possible to create a ranking 
system for the nodes that can indicate the prestige of each node as determined by the linkages between 
all the nodes in the system. A premier example of this ranking is the page rank algorithm used by 
Google to rank an individual web page's value for information of a given search term. This page rank 
algorithm is based upon eigenvector centrality, which uses an adjacent matrix of the network to 
determine the centrality value that corresponds to the prestige of the node. In this way, the prestige 
of each of the nodes is conserved in an iterative way to weigh the values of the incoming links to 
determine the final node prestige value. 
 
Sample and Data Collection 
 The sample used for this study consisted of the 62-member universities in the Association of 
American Universities (AAU). This sample of organizations was considered ideal for this study for 
many reasons. First, AAU organizations have been present in the marketplace for many years and 
have a relatively high level of resilience to external pressures, such as economic fluctuations, that might 
confound the study of organizational status. Second, these institutions had readily available data 



 

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related to the nature of the relationships between member organizations in the AAU. Third, these 
organizations are similar to other organizations in the marketplace in that they have an established 
culture, a diverse network of stakeholders, an international customer base (i.e., students), and unique 
mission statements.  

A network of universities was created by identifying those institutions which the universities 
in the sample considered as peers. These data were collected by doing an internet search using 
keywords that would deliver electronically available documents that give a listing of peer institutes as 
determined by the node of interest (i.e., the ego node). For example, the search term “peer institutes 
University of Illinois” produced a link to the University of Illinois Office for Planning and Budgeting’s 
“Data and Research: Links of Interest” page. This webpage contains a list of peer institutions for the 
University of Illinois, Chicago, Urbana-Champaign, and Springfield. This list of institutions was then 
used to create links between the ego node and other nodes included in the dataset. Links to peer 
institutes that were not current members of the AAU were not used in this network. 
 Finding a formal listing of peer institutions compiled on one webpage was rare and thus other 
university documents were used to obtain a list of peer institutions for the ego node. Many times these 
documents were found using the same search as described above, but the list of peers was listed in a 
written document available on the university's websites or implied from web pages. An example of 
the former is the use of minutes from meetings for various concerns (e.g., committee reports on 
diversity or educational issues) and the latter is a webpage of links to peer institution web-accessible 
databases. In many of these cases where there was not a formal online peer listing, there were several 
documents that listed a consistent set of peers; we were able to triangulate our data. Finally, in a few 
cases, one of the authors e-mailed or called the office of institutional studies, explained the study being 
conducted, and requested a list of institutional peers. This listing of peer institutions for all of the 
AAU universities was compiled over three months from July to September 2008. 
 In all cases, peer institutions that were identified that were not a member of the AAU were 
not used in creating the network for this study. For example, Rice University identifies James Madison 
University (JMU) as a peer, but since JMU is not a member of the AAU, a node for JMU was not 
included and the link from Rice University was dropped. In this way, the resulting network was a 
completely self-contained set of nodes and links. 
 
Measures 
Status 
The concept of status was measured using the US News and World Reports rankings of best colleges 
(www.colleges.usnews.rankingsandreviews.com/best-colleges). The media status of each AAU school 
was determined by assigning their US News ranking (e.g., Harvard is ranked number 1, and the 
University of California, Berkeley is ranked number 21). In the case of ties for our resulting ordinal 
list, the school with the highest network eigenvector centrality value was ranked higher (e.g., Princeton 
was tied for number 1 with Harvard, but was assigned a ranking value of 1.1 due to a lower centrality 
score). Eigenvector centrality gives a measure of the importance of a node in a network and is the 
basis of the page rank measure used by Google. In two cases (the University of Toronto and McGill 
University) AAU schools were not listed in the US News ranking. In these cases, the two schools were 
placed at the bottom of the media status list following the same ordering rules using network centrality. 
While using this popular press ranking is not our preferred measure of status (our recommendations 
will come later), this is a generally accepted university ranking and one that is available to universities, 
which may influence their choice of peer schools. 
 Using this resulting ordered status ranking, three levels of status were created: high, medium, 
and low. The top 16 schools were assigned a high status, the next 22 schools were assigned a medium 
status, and the final 24 were assigned low status. The cut points that determined the split of the 62 



 

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universities into groupings of 16, 22, and 24 versus a more even number of schools in each status 
category were done with the distribution of the ranking among these schools. The eigenvector 
centrality measures the mean plus or minus one-half standard deviation to determine the number in 
the middle-status category (22 schools). These cutoff values were chosen to represent the schools that 
represent the middle of the status category and were chosen over a simple middle one-third count 
methodology, which would be appropriate for a uniform distribution. By using the distance from the 
mean of the distribution of eigenvector centrality measures, the resulting middle-status category has 
been adjusted for the actual distribution of values. 
School Type 
The type of school – public or private – was used to assign a categorical dimension to the 
universities.This classification was obtained from the AAU listing of member universities and is shown 
in Appendix A. The two Canadian universities were assigned to the public category (AAU places them 
in the Canadian category). 
  
One-way and Reciprocal Peer Links 
 To determine the one-way and reciprocal links for each university, a 62 by 62 matrix was 
created with a value of 1 placed in a cell when the university on the row indicated that that university 
in that column was a peer; all other cells received a value of 0. This created an asymmetric matrix 
representing all of the university-reported peer schools P. Transposing this matrix and doing an entry-
by-entry multiplication of the two matrices resulted in a new matrix that had a value of 1 for reciprocal 
ties and 0 in all other cells ( Ri,j = Pi,j x Pj,I). This resulting matrix could then be subtracted from the 
original matrix to obtain a matrix of only one-way links ( O = P – R ). To obtain counts of within-
status links, the sum of links in the appropriate square partition of the reciprocal tie matrix divided by 
two was used. For the links across status groups, off-diagonal rectangular partitions were used 
(partitions containing the high-to-medium, high-to-low, and medium-to-low links); the cell values 
were summed only in the upper diagonal of the reciprocal matrix R (since only half the matrix was 
used, no division by two was needed to obtain this count; the lower diagonal has symmetric and 
redundant entries). The resulting counts are shown in Table 1. A similar procedure was done after 
ordering the reciprocal link matrix by the public and private categories, which resulted in 63 reciprocal 
links between public universities, 32 reciprocal links between private universities, and 8 reciprocal links 
between public and private universities.  
 
Table 1 – Reciprocal Link Count using US News Ranking 

 
To determine the one-way links from lower to higher status schools, the one-way link matrix O was 
sorted by the US News modified ranking. Next, the one-way links in each column below the ego 

Status
High Medium Low

Hi
gh

26
13 1

St
at

us

M
ed

iu
m 19

22

Lo
w

22



 

8 
 

school were summed to create the count of one-way links from lower-status schools (∑ 𝑂𝑂𝑖𝑖,𝐸𝐸62
𝑖𝑖=𝐸𝐸+1 , 

where E is the row of the ego school). This value was subtracted from the total number of one-way 
links to a school (the sum of the entire column), thus obtaining the one-way links from higher schools. 
Using this procedure, there were a total of 91 one-way links from higher-status schools to lower-status 
schools and 422 one-way links from lower-status schools to higher-status schools.  
 
Sub-networks 
 The complete AAU network was parsed into two networks based on membership in the 
private and public university categories. This was done by deleting all of the public university nodes 
and their corresponding links into and out of these nodes to produce the AAU private university 
network. This resulted in a network with an edge density of 0.2754, with 26 nodes and 179 total links. 
Of these links, 32 were reciprocal links. A similar procedure was used to create the AAU public 
university network resulting in a network density of 0.3349 with 422 links of which 63 were reciprocal. 
The original AAU network had a density of 0.1901 with 719 total links with 206 being reciprocal. 
 

Results 
 A Chi-squared test of independence was performed on the resulting link counts. This test 
assumed that in a random matrix of links that there should be an equal probability of forming a link 
or not forming a link. The resulting matrix has expected values of reciprocal links between one status 
class and another or within a status class that is proportional to the total possible reciprocal links. For 
the three levels of the status model with 16 nodes in high, 22 nodes in medium, and 24 nodes in low 
status, there are 596 possible within status reciprocal ties and 1264 possible between status reciprocal 
ties. For the one-way links from higher to lower status or from lower to higher status, the expected 
value for the entire sample would be 1891 from low to high and 1891 from high to low. This follows 
since the determination is relative to the ego node; for the entire network, there is an equal chance 
from high to low as from low to high. The expected values of links within and between the categories 
of public and private were also determined by the total possible links: there are 924 links within the 
category and 936 ties between categories. 
 Hypothesis H1 proposes that one-way peer identification will more likely stem from lower-
status Universities to higher-status Universities. To verify hypothesis H1 we found that there were 91 
one-way links from a higher to lower status and 422 one-way links from a lower to a higher status. 
The test resulted in Chi-Squared = 213.569, p< 0.001, and thus supported hypothesis H1. 
 Hypothesis H2 proposes that Reciprocal peer identification will occur among Universities of similar 
status. To verify hypothesis H2 we found that there were 67 within status reciprocal links and 36 
across status reciprocal links. The expected number of ties was 34.4 within status reciprocal links and 
68.8 across status links. This test resulted in Chi-Squared = 47.267, p< 0.001, and this supported 
hypothesis H2. Hypothesis H3 proposes that Reciprocal peer identification will occur among 
Universities of similar categorical dimensions. To verify hypothesis H3 we found there were 95 within 
classification reciprocal links and 8 across classification reciprocal links. The expected number of ties 
was 52 within classification reciprocal links and 51 across classification reciprocal links. The test 
resulted in Chi-Squared = 71.755, p <0.001, and thus supported hypothesis H3. Comparison of the 
sub-network densities was done using an expected equal value and the observed value using a Chi-
Squared test of independence and the results were not significant (Chi-Squared = 0.410, p = 0.522). 
Additionally, a categorical variable was created that was equal to 1 when there was a greater than 1.0 
graduate-to-undergraduate student ratio and zero when the ratio was equal to or below 1.0. This 
resulted in within category reciprocal link count of 84 and a between-category count of 19. The 
expected values based on possible reciprocal links for the 15 schools above one and the 47 schools 



 

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below or equal to one are 64.6 between and 38.4 across. The test resulted in Chi-Squared = 15.628, p 
<0.001, and thus supported hypothesis H3. 
 
A Network-based Measure of Status 
 This research has examined how affiliation through peer groups can form a basis for field-
level stratification. An important extension of this research involves developing the notion of network 
affiliation into a behavioral measure of status. Status is generally seen as something that does not 
belong to organizations; rather it is determined by external audiences. Washington and Zajac (2005), 
for instance, describe status as something that is “conferred” upon an organization. The social 
processes by which status can be earned by organizations is certainly an area for future research. For 
instance, status is not necessarily earned when organizations take actions that meet legitimacy criteria. 
At the same time, particular organizations that meet legitimacy criteria can be considered high status 
(Anand & Watson, 2004).  
 The measurement of status is an additional area that merits further consideration. Scholars 
have relied on various external rankings to measure both individual status (Bedeian et al., 2010) as well 
as organizational status (Washington & Zajac, 2005). While such methods have their merits, a 
behavioral approach may be a more dynamic, complementary measure of status (Sauder et al., 2012). 
We refer to a behavioral approach as one which relies on examining the behavior of the actors whose 
status is being assessed as opposed to applying external reports of actor status. This also allows for 
the assessment of power dynamics among and between peer groups, and groups with differing status 
structures. Hence, examining the institutions which a given set of universities identify as peers is a 
behavioral approach since it involves examining the behavior of university peer identification. Indeed, 
it is not what you look like on paper that matters, it is how your peers see you. To illustrate this point, 
this research has used a peer set of research Ph.D. universities as determined by membership in the 
AAU, and measured and supported the idea that the behavior of creating organizational peer groups 
can be consistent with other, external, measures of university status. We propose to take this to the 
next step and use measures indicating links of who each university considers their peers as the basis 
for creating a new and different status ranking. 
 The process of conferring status upon organizations is illustrated by media rankings of 
universities. The Business Week rankings of business schools, for instance, rely on an assessment of 
institutions from corporations and recruiters. Such measures, however, are problematic because of 
their extreme stability (Morgeson & Nahrgang, 2008). Given the stratification quality of networks; as 
well as the network-like qualities found among university peer groups, we make the case that university 
peer group identification can serve as an alternative measure of university status. University peer 
groups are based on internal university identification as well as aspiration. Thus, rather than an external 
measure of perceptions of quality, peer group identification is based on internal assessments of who 
(what) a university is and where it wants to be. Using the same original network of the 62 by 62 matrix, 
we calculated the eigenvector centrality measure for the directed link network using NETDRAW 
(Borgatti, 2002). This resulted in the ranking of status that is shown in Table 2 in which the University 
of Michigan displaces Harvard University as the highest-status school. Using this measure of status, 
unlike the US News ranking that places private universities at the top of the list (the highest-ranked 
public university was the University of California, Berkeley at number 21), this new ranking has more 
balance between public and private schools. The resulting network diagram with the eigenvector 
centrality represented as size is shown in Figure 1. The resulting network-based rankings illustrate a 
surprising disconnect between network-based status and media status.  
 
 



 

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Table 2 – Status and Eigenvector Network Centrality 
 

University Short Name Eigenvector Centrality US NewsRank 

Michigan 3.74 27 

UCB 3.152 21 

Yale 3.147 3 

Harvard 2.955 1 

Stanford 2.944 4 

UIUC 2.762 35 

Wisconsin 2.747 39 

Cornell 2.447 15 

UW 2.325 42 

Princeton 2.111 1 

Penn 2.053 4 

UCLA 2.009 24 

Columbia 2.004 8 

Texas 1.953 47 

UNC 1.753 28 

Minnesota 1.727 61 

Chicago 1.433 8 

Brown 1.429 16 

MIT 1.429 4 

Duke 1.408 8 

OSU 1.263 53 

Penn State 1.058 47 

UCSD 1.052 35 

JHU 1.045 14 

UVA 1.004 24 

Purdue 0.976 61 

Indiana 0.935 71 

MSU 0.923 71 

Iowa 0.898 71 

Wash U 0.768 12 

USC 0.749 26 

CalTech 0.708 4 

Arizona 0.698 102 

NYU 0.668 32 

UCD 0.668 42 

Vanderbilt 0.639 17 



 

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Oregon 0.627 115 

Texas A&M 0.587 61 

NW 0.582 12 

Florida 0.572 47 

Maryland 0.462 53 

Buffalo 0.458 121 

Rutgers 0.44 66 

Colorado 0.437 77 

UCSB 0.392 42 

Emory 0.381 17 

Rochester 0.37 35 

UCI 0.347 46 

Toronto 0.33 NR 

Pittsburgh 0.326 56 

Nebraska 0.299 41 

Kansas 0.294 96 

Missouri 0.275 102 

Rice 0.194 17 

Case 0.172 41 

CMU 0.152 22 

Iowa SU 0.144 88 

Stonybrook 0.134 96 

Tulane 0.093 50 

Syracuse 0.051 58 

Brandeis 0.033 31 

McGill 0 NR 
 



 

12 
 

Figure 1 – Network of Peer AAU Universities 

 

 
 

Color represents Public (Gray) or Private (Black). Thicker links indicate reciprocal ties. Node size indicates status as determined by directional eigenvector 
centrality.University names are abbreviated, but full names are listed in Appendix A 



 

13 
 

Two pairs of counts for testing hypothesis H1 and hypothesis H2 (retesting hypothesis H3 was not 
needed since the status was not used, rather public or private classification, which did not change) 
were recalculated. This yielded 119 higher-to-lower one-way ties and 394 lower-to-higher one-way ties 
(Chi-Squared = 147.417, p< 0.001). This also yielded 64 within status and 39 across status reciprocal 
links (Chi-Squared = 39.027, p< 0.001). These results are consistent with hypotheses H1 and H2 
above. Additionally, the results, while illustrating some consistency with the results obtained with US 
News ranking, show several differences in ranking. This determination of status using the behaviorally 
created links does not completely contradict the determination of status by measures collected by a 
third party. At the same time, the resulting ranking is distinct enough to merit further consideration 
of both media measures of status as well as network-derived approaches. For instance, scholars have 
devised specific measures of media reputation (Deephouse, 2000). The disparity between media 
measures and network measures illustrated in this research merit further exploration regarding 
possible differences between media measures of status and behavioral measures such as organizational 
affiliation. 
 

Discussion 
 This paper’s purpose was to examine how closely university actions of deciding on who and 
who not to call their peers fit within one highly-visible ranking of status (US News College Ranking) 
and one categorical classification (public and private). After determining the validity of the links within 
these two systems, we extend suggestions that a more direct way of determining status can be by social 
network analysis (Sauder et al., 2012) using the universities’ behaviors as the raw source of linkage 
between these universities. In doing so we have further research in a potentially rich area of exploration 
regarding how organizational status can be assessed. 
 One of the primary conclusions of this project is the finding that network affiliations can serve 
as viable measures of organizational status. Findings support this assertion by illustrating how within-
field network affiliations result in similar orderings as those developed by external actors. University 
status is a valuable measure that can help determine future student enrollment, research funding, and 
charitable donations. However, much of the determination of this status has been transferred to the 
third-party popular press, instead of by the members of the network themselves. The proposed 
measure, a score based on network centrality, assigns each organization a relative position of status. 
The proposed measure additionally allows one to assess the relative distance among ranked 
organizations. This has been actualized by Jacquelyn Elias with an online display tool on the 
chronicle.com website (Elias, 2022). It must be noted at the time of this writing that this online tool 
is not complete, some universities have not submitted a peer list. Methods using network analysis can 
be used to augment the current commercial rankings. 
 This study is not without limitations. The set of 62 AAU schools, for instance, can be extended 
to include a larger sample of universities. Moreover, the results of this project, while illustrating 
significant variability in centrality scores among the field of AAU universities, does so for a sample of 
organizations that, by their membership in AAU, may be considered a higher status group of 
universities than non-AAU institutions. The nature of peer identification, while many times indicating 
aspiration, may additionally reflect other factors such as benchmarking. Hence, additional measures 
of affiliation can be considered in future work. In addition, multiple years, and possible sources, of 
university rankings may be applied and compared with corresponding peer identification to give 
provide a more temporal-oriented study. Finally, additional network methodologies can be applied to 
gain further insights. Future research can explore the temporal qualities of network measures of status. 
Media rankings, such as those devised by Business Week, are stable over time. While we suspect that 
our measure is likely to exhibit variation over time, future research can explore the extent of such 
variation. Additional research can apply this conception of status to either a broader sample of 



 

14 
 

institutions such as all accredited universities, or specific kinds of schools, such as AACSB-accredited 
business schools. Future research can additionally apply the network-based measures of status to other 
organizational fields such as investment banks and/or government agencies. Research has investigated 
affiliation among firms in terms of social identity (Rao et al., 2000) as well as the degree to which 
affiliation facilitates joint ventures (Lu, Ma; 2009). Future research can, thus, examine how firm 
affiliation in conjunction with behavior serves as a measure of status. Other factors that can be 
examined can include the role of university leadership turnover in peer identification as well as the 
role that proximity/geography play in peer identification. 
 
 

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17 
 

Appendix A 
  

List of Current Institution Members of the American Association of Universities 
 
Brandeis University (1985, Private) 
Brown University (1933, Private) 
California Institute of Technology (1934, 
Private) 
Carnegie Mellon University (1982, Private) 
Case Western Reserve University (1969, Private) 
Columbia University (1900, Private) 
Cornell University (1900, Private) 
Duke University (1938, Private) 
Emory University (1995, Private) 
Harvard University (1900, Private) 
Indiana University (1909, Public) 
Iowa State University (1958, Public) 
The Johns Hopkins University (1900, Private) 
Massachusetts Institute of Technology (1934, 
Private) 
McGill University (1926, Canadian) 
Michigan State University (1964, Public) 
New York University (1950, Private) 
Northwestern University (1917, Private) 
The Ohio State University (1916, Public) 
The Pennsylvania State University (1958, 
Public) 
Princeton University (1900, Private) 
Purdue University (1958, Public) 
Rice University (1985, Private) 
Rutgers, The State University of New Jersey 
(1989, Public) 
Stanford University (1900, Private) 
Stonybrook University-State University of New 
York (2001, Public) 
Syracuse University (1966, Private) 
Texas A&M University (2001, Public) 
Tulane University (1958, Private) 
The University of Arizona (1985, Public) 
University at Buffalo, The State University of 
New York (1989, Public) 
University of California, Berkeley (1900, Public) 
University of California, Davis (1996, Public) 
University of California, Irvine (1996, Public) 

University of California, Los Angeles (1974, 
Public) 
University of California, San Diego (1982, 
Public) 
University of California, Santa Barbara (1995, 
Public) 
The University of Chicago (1900, Private) 
University of Colorado at Boulder (1966, 
Public) 
University of Florida (1985, Public) 
University of Illinois at Urbana-Champaign 
(1908, Public) 
University of Iowa (1909, Public) 
The University of Kansas (1909, Public) 
University of Maryland at College Park (1969, 
Public) 
University of Michigan (1900, Public) 
University of Minnesota, Twin Cities (1908, 
Public) 
University of Missouri-Columbia (1908, Public) 
University of Nebraska-Lincoln (1909, Public) 
The University of North Carolina at Chapel Hill 
(1922, Public) 
University of Oregon (1969, Public) 
University of Pennsylvania (1900, Private) 
University of Pittsburgh (1974, Public) 
University of Rochester (1941, Private) 
University of Southern California (1969, 
Private) 
The University of Texas at Austin (1929, Public) 
University of Toronto (1926, Canadian) 
University of Virginia (1904, Public) 
University of Washington (1950, Public) 
The University of Wisconsin-Madison (1900, 
Public) 
Vanderbilt University (1950, Private) 
Washington University in St. Louis (1923, 
Private) 
Yale University (1900, Private)

 


	References

