Network Mapping. The practical purpose for the use network mapping is to assist
schools in building effective collegial ties. The formation of the guiding coalition and
action teams with diverse membership is intended to improve the strength of a school’s
professional community by altering the nature of collegial relations among faculty
within a school. We intend to collect network data at four treatment and four control
sites at four time points. The first time point will reveal existing collegial ties and will be
useful for SLMS facilitators. Since three treatment schools have implemented the
intervention for one year, network data at these school sites will reveal whether
collegial ties that were more diverse than expected resulted after one year of
implementing the original SLMS model. Since one treatment school will have
implemented the program for only a brief period by the time network data are first
collected, collegial ties represented in this schools maps will approximate preintervention collegial ties. We will first compare the first three treatment and new
treatment schools’ network maps for differences and similarities, providing a more
detailed picture of the nature of collegial ties in each school. Secondly, we will compare
treatment and control schools’ network maps for differences and similarities, providing
a more detailed picture of the nature of collegial ties fostered by the SLMS intervention.
We will subsequently provide faculty members at each school with a map of their social
network. Social networks can be mapped to identify regions where ties are absent
defining structural holes (Burt, 1992) or positions of actors relative to others in their
networks. SLMS facilitators will encourage change agents to identify leverage points for
modifying network flows by either strategically locating resources in the network (e.g.,
teaching the members of one subgroup about a new science unit) or by creating venues
for new ties (e.g., creating a new workshop that brings together members of different
units). For example, while faculty members may know the members of their particular
action teams, they may not know which teams all the members of their department
serve. If they need help with, say, a technology issue, the network map will help them
to identify which members of their department may be of assistance.
While providing this network information will be helpful for schools in their own
development of stronger collegial ties, these data will also be helpful for program
developers in their attempts to improve the effectiveness of the program. The first
round of network data will be used by MRSH to identify and target areas of weak ties.
Future professional development sessions will be modified to incorporate activities that
focus on strengthening these ties. For example, an “icebreaker” activity may be
introduced in the beginning of the next professional development session specifically
aimed at raising action team members’ awareness of specific skills their team members
possess. Alternately, a facilitator may introduce a “homework” assignment asking
faculty to seek assistance from one person outside their normal circle of colleagues
before the next professional development session. While these two examples are
specific and may not be introduced, they represent the types of activities that MRSH will
employ to strengthen collegial as a part of their facilitated sessions. The second round
of network data will reveal the extent to which network ties in fact increased between
time points.
As an example of a network map, Frank and Zhao (2005) graphically represented the
informal structure of school actor collegial ties using Frank’s (Frank, 1995, 1996)
KliqueFinder algorithm for identifying cohesive subgroups based on the pattern of ties
among actors, and then for embedding the subgroup boundaries in a crystallized
sociogram as shown in Figure 1 below. We refer to the sociogram as crystallized
because it reveals an isomorphic nesting of structures. Within each subgroup there is a
structure of people and ties, and between subgroups there is a structure of subgroup
boundaries and bridging ties. Thus the boundaries differentiate regions of dense and
sparse concentrations of collegial ties. Ultimately, the technique reorganizes the data to
generate a more accessible, clearer image of the social structure that is consistent with
characterizations of systemic social structures.
In Figure 1 on the right side of this page, each number represents a school actor. The
text following the number indicates
the grade in which the teacher teaches
35
(e.g., G3 indicates grade 3, MG
indicates multiple grades, and GX
E
indicates unknown grade). The
25
subgroup boundaries are represented
B
by circles around subsets of actors.
The lines connecting pairs of school
15
actors indicate that at least one school
actor listed the other as a close
5
colleague (solid lines within subgroups,
C
dotted lines between subgroups).
-5
There is a clear alignment of grade and
D
subgroup boundaries embedded in the
sociogram in Figure 1. Subgroup A
-15
consists of mostly third grade teachers
and subgroup B consists of mostly
second grade teachers, and there is a
-25
dense concentration of collegial ties in
each subgroup. But the subgroup
-35
structure also characterizes those
faculty, administrators, and staff who
A
do not neatly fit into the categories of
-45
the formal organization. For example,
-25
-15
-5
5
15
ID/Grade Level; - within subgroups, ... between subgroups.
subgroup C contains the physical
Within Subgrop Scale Expanded by a Factor of 9
education teacher, a special education teacher, the principal, and two teachers who do
not have extensive ties with others in their grades. The members of subgroup C are not
25
unaffiliated, as they are members of a cross-grade subgroup within which roughly 50
percent of the possible collegial ties are realized. This is enough to distinguish the
density of ties within subgroup C from all of the between subgroup densities (which
range from 0 to 29 percent).
One can then use representations such as in Figure 1 to modify flows through the social
system. For example, note that there are no close collegial relationships between the
teachers in subgroups A (mostly grade 3) and B (mostly grade 2) in the example above.
This could create coordination problems for the school. In response, action teams could
strategically be organized to facilitate cross group conversations that we expect to lead
to close collegial ties across subgroups. Thus the maps could help develop local capacity
to modify their social structures to improve coordination.
While such maps could be extraordinarily useful in exploring the SLMS intervention, the
challenge of providing them is that, the more detailed the information they provide, the
more difficult it is to protect respondent confidentiality (Breiger, 2005; Kadushin, 2005).
For example, in Figure 1, there is only one grade 2 teacher not in B. If this teacher is
able to identify herself, then she could ascertain which others did and did not nominate
her, compromising confidentiality. And if confidentiality cannot be protected, then
subjects might not be willing to participate, or consent forms will be violated.
The solution we will take draws on work developed with Penuel et al. (2009). In this
approach, we will report to schools the membership of each subgroup (e.g. teachers 22,
25, 26 and 58 are members of subgroup E), and then provide a map of relationships
between subgroups, such as in Figure 2. This figure uses the same social geography as
Figure 1, but ties are shown only between subgroups, with the thickness of the line
representing the density of the ties.
Combined with information about the membership of each subgroup, a figure such as 2
could be enormously informative to school leaders. For example, it would reveal the
relative lack of collegial ties between subgroups A and B. This information could then be
used to inform the composition of action teams.
Critically, this value of the information in Figure 2 below is provided without
compromising the confidentiality of individual responses (note that we will not provide
a figure such as in Figure 1). Knowing a teacher’s subgroup does not indicate all of her
responses, only a preference for her to have ties with subgroup members (Frank, 1995).
Similarly, knowing the extent of ties between subgroups does not reveal the specific ties
of any one subgroup member.
Of course, we anticipate several challenges in conducting the network research. First,
we will have to obtain network data, including who is close colleagues with whom, as
well as who collaborates teaching practices with whom. Second, we will have to
educate participants about how to interpret the social network figures, including
information about subgroup membership, and ties within versus between subgroups.
Third, we will have to work to protect confidentiality where small numbers in certain
groups could reveal specific responses. This might be done by intentionally adding a
small amount of noise to the data without altering the overall representation.
E
B
C
D
A
Figure 2: between Subgroup Ties only in Westville
Alternative figures
All data shown, compromised confidentiality – actual responses shown
Only groups shown, preserves confidentiality (can even include names of people)
One
One can even include names within the subgroups without compromising confidentiality
Breiger, R.L., “Ethical Dilemmas in Social Network Research: Introduction to Special
Issue.” Social Networks 27 / 2 (2005): 89 – 93. Read it online.
http://www.u.arizona.edu/~breiger/2005BreigerIntroEthics.pdf
 (All issues of social networks available via science direct)
Frank, K. 1996. “Mapping interactions within and between cohesive subgroups.” Social
Networks 18: 93-119.
Frank. K.A. 1995. “Identifying Cohesive Subgroups.” Social Networks (17): 27-56.
Frank, K. A. and Zhao, Y. 2005. “Subgroups as a Meso-Level Entity in the Social
Organization of Schools.” Chapter 10, pages 279-318. Social Organization of Schools.
Book honoring Charles Bidwell’s retirement, edited by Larry Hedges and Barbara
Schneider. New York: Sage publications.
Kadushin, Charles “Who benefits from network analysis: ethics of social network
research” Social Networks 27 / 2 (2005): Pages 139-153. chapter 11 of Understanding
Social Networks