Analysing the Experiences of NewSociology Department Postgraduate Students

Using Social Networkand Quantitative Analytic Techniques

MA Dissertation

Written By John Stevens

Supervisor Professor John Scott

Submitted 9 January, 1998

Abstract

This dissertation examines how an entering cohort of postgraduate students in the Sociology Department at the University of Essex made acquaintances among their fellow students as well as among staff. Three waves of questionnaires attracting slightly higher than 70% response rates were administered to students over the 1996-97 academic year. Asian and part-time students experienced greater problems integrating than other students, though the graduate weekend facilitated the expansion of support networks for most students. This dissertation situates the findings in the context of the current social networks literature.

Acknowledgements

I firstly thank the students who completed the questionnaires, as without their responses, this project would not of been possible. My gratitude also goes to Ms. Brenda Corti, a Sociology Department administrator, who provided valuable information for the development and interpretation of the questionnaires. I thank my supervisors, Professor John Scott and Professor Tony Coxon, for their input into this project. The person I have to thank the most is Kimberly Fisher for both assisting in press ganging students into returning questionnaires and for helping to transform this document into readable English.

Table of Contents

Abstract i

Acknowledgements ii

Table of Contents iii

Table of Tables vi

Table of Figures vii

1. Introduction 11.1 Introduction 11.2 The Aims of the Dissertation 1

1.2.1 Primary Aims on Acquaintances 21.2.2 Secondary Aims About Graduate Life in Sociology at Essex 2

1.3 The Structure of the Dissertation 2

2. The Social Networks Literature: A Focus on Student Acquaintanceship 52.1 Introduction 52.2 The Social Networks Literature 52.2.1 Early Social Network Analysis 62.2.2 Later Innovations 102.2.3 More Recent Work 162.3 Studies of Acquaintances Among Students 202.4 Summary 23

3. The Mechanics of Collecting the Data 253.1 Introduction 253.2 Data Collection Techniques 263.3 Questionnaire Design 26

3.3.1 Network Section 263.3.2 Demographics Section 293.3.3 Open-Ended Questions 30

3.4 Timing of the Questionnaire Waves 303.5 Population Selection 323.6 Demographics of the Respondents 33

3.6.1 Student Age 343.6.2 Student Sex 353.6.3 Student Ethnic Origin 363.6.4 Student by Location of Housing 373.6.5 Degree and Scheme of Study 38

3.7 Summary 39

4. Student Acquaintanceship Networks4.1 Introduction 414.2 Definitions and Assumptions 414.3 Wave 1: After One Month at Essex University 41

4.3.1 Wave 1 Network Graph With Numbers of Students and Acquaintances 43

4.3.2 Isolates and Outliers 464.3.3 Cliques 464.3.4 Gatekeepers 474.3.5 Summary 49

4.4 Wave 2: The End of the Autumn Term 494.4.1 Wave 2 Network Graph With Numbers of Students and

Acquaintances 494.4.2 Isolates and Outliers 524.4.3 Cliques 554.4.4 Gatekeepers 554.4.5 Summary 56

4.5 Wave 3: The Final Acquaintance Networks 584.5.1 The Final Network Graph with Numbers of Students and

Acquaintances 584.5.2 Isolates and Outliers 594.5.3 Cliques 604.5.4 Gatekeepers 604.5.5 The Graduate Conference 624.5.6 Summary 62

4.6 The Development of Student Acquaintance Networks 634.6.1 Raw Statistics from the Three Waves 634.6.2 Changes in the Acquaintance Groups 644.6.3 Variables That Affect Acquaintance Formation654.6.4 Changes in Isolation Over Time65

4.7 Recommendations for the Future 664.8 Summary on Acquaintance Networks 67

5. Being a Graduate Student of the Essex Sociology Department 675.1 Introduction 675.2 The Graduate Conference Weekend 67

5.2.1 Positive Feed Back 675.2.2 Negative Feed Back 695.2.3 Timing of the Graduate Conference 705.2.4 Summary on Graduate Conference 715.2.5 Possible Changes for the Future72

5.3 Students Before and After Sociology at Essex University 725.3.1 Where They Found Out About Sociology at Essex 735.3.2 Why Do Students Choose Sociology at Essex? 755.3.3 What Do Graduate Students Intend to do After Their Studies?755.3.4 Summary on Before and After Essex 77

5.3.5 Possible Changes for the Future78

5.4 Students’ Sources of Information About and Help With Academic Work 785.4.1 Who Helped With the Last Essay? 785.4.2 Staff Known by Students 795.4.3 Summary 83

5.5 Conclusions 83

6. Conclusions 856.1 Introduction 856.2 Summary of Methods 856.3 Answers to the Projects Aims 866.4 A Final Word 88

Bibliography 89

Appendix One: The Questionnaire 91

Table of Tables

Table 3.1 - Wording Of Network Columns By Questionnaire 27Table 3.2 - Data Collection Periods For Each Questionnaire 32Table 3.3 - Numbers of Returned Questionnaires by Wave 32Table 3.4 - Questionnaire Return Patterns 33Table 3.5 - The Crucial Matrix: Students by Nationality and Study Time 34Table 3.6 - Age Ranges of Incoming Postgraduates in 1996 34Table 3.7 - Age Ranges of Students 35Table 3.8 - Sex By Full and Part Time Study (With Column Percentages) 36Table 3.9 - Sex by Degree Sought (With Column Percentages) 36Table 3.10 - Students by Ethnic Origin 37Table 3.11 - Students by Type of Accommodation 37Table 3.12 - Students by Course of Study 38Table 3.13 - Students by Degree, Home/Overseas and Full/Part-Time Status 38Table 3.14 - Detailed MA Scheme Selection 39Table 3.15 - A Summary of the Demographic Data 40Table 4.1 - Summary of the Wave 1 Graph Data 43Table 4.2 - Residence by Mean Number of Students Known in Wave 1 46Table 4.3 - Standardised Betweeness Centrality Values for Wave 1 Gatekeepers 47Table 4.4 - Summary of the Wave 2 Graph Data 51Table 4.5 - Mean Students Know in Wave 2 by Sex 52Table 4.6 - Mean Students Known in Wave 2 by Location of Accommodation 52Table 4.7 - Size and Number of Cliques in Wave 2 55Table 4.8 - Standardised Betweeness Centrality Values for Wave 2 Gatekeepers 56Table 4.9 - Summary of the Wave 2 Graph Data 58Table 4.10 - Mean Number of Contacts by Sex in Wave 3 59Table 4.11 - Mean Number of Contacts by Residence in Wave 3 59Table 4.12 - Size and Number of Cliques in Wave 3 60Table 4.13 - Standardised Betweeness Centrality Values for Wave 3 Gatekeepers 60Table 4.14 - Summary of Acquaintances by Wave 63Table 4.15 - The Size and Number of Cliques by Wave 64Table 4.16 - Number of Isolates by Wave 66Table 5.1 - What Students Liked About the Graduate Conference Weekend

68Table 5.2 - What Students Did Not Like About the Conference 69Table 5.3 - Student Preferences for the Timing of the Graduate Weekend 71Table 5.4 - Source of Information About Sociology at Essex 73Table 5.5 - Why Students Chose to Study Sociology at Essex 75Table 5.6 - Students’ Intentions After They Complete Their Degree 76Table 5.7 - Post-Graduation Intentions by Home and Overseas Students 76Table 5.8 - Whom Students Turned to For Help With Essays 79Table 5.9 - The Number of Students Who Know Each Member of Staff 80

Table of Figures

Figure 3.1 - A Sample Network Question 27Figure 3.2 - A Sample Demographics Question 29Figure 4.1 - Wave 1 Network Graph 44Figure 4.2 - Number of Acquaintances Made by Students in Wave 1 45Figure 4.3 - Wave 1 Clique Graph 48Figure 4.4 - Wave 2 Network Graph 50Figure 4.5 - Number of Acquaintances Made by Students in Wave 2 51Figure 4.6 - Wave 2 Clique Graph 53Figure 4.7 - Wave 3 Network Graph 54Figure 4.8 - Number of Acquaintances Made by Students in Wave 3 58Figure 4.9 - Wave 3 Clique Graph 61Figure 5.1 - Hierarchical Clustering of Staff Known by Students 82

Chapter 1 : Introduction

1.1 Introduction

This dissertation investigates how a new cohort of postgraduate students

entering the Sociology Department at the University of Essex in the Autumn term of

1996 developed support networks with fellow students and with members of staff.

The cohort included qualifying year, masters, first-year Doctoral Programme and

first-year PhD students. This project was designed to apply social network analysis

and other quantitative methods to develop proposals to improve the quality of

students’ experiences in Sociology at Essex. I gathered student acquaintance data at

three points during the academic year. The first questionnaire also collected

demographic data; the second questionnaire asked for network information on staff;

and the third solicited student opinions of the graduate weekend. I found that students

made acquaintances quickly, and that early contacts often formed the basis for larger

social groups later in the year. Students generally found that the graduate weekend

improved the size and depth of their contacts with other students and with staff. Part-

time students made acquaintances more slowly than other students, and generally had

smaller fewer total contacts in the Department. Asian students readily made friends

with each other, but were also more slow than other groups to integrate within the

main core of graduate students.

1.2 The Aims of the Dissertation

A consultant neurologist whom I have come to respect advised me that “most

good research sets out with clear aims”. My initial aims centred around tracking the

early formation of acquaintance structures among postgraduate students, using a

simplified technique to that employed by Theodore Newcomb in the late 1950s.

Additionally, as I discussed my work in progress with members of the Sociology

Department, some staff requested that I include questions which would have more

direct policy implications, thus making my results of greater use to the Department. I

have since learned from conversations with several PhD students that many who had

thought that they had clear aims for their research at the beginning had found a need

to considerably modify their objectives during the research process. Likewise, I have

had to rework my own aims since initially writing them down. The primary and

secondary aims of the project are reported next.

1.2.1 Primary Aims on Acquaintances

Initially, I set out to investigate the development of student friendships by

considering the speed of friendship formation, changes to friendships circles over

time, the differences between friendship and acquaintance structures, the formation of

cliques, tracking isolated students, and identifying demographic characteristics

influencing friendship choices. As I examined the results of the first two

questionnaires, however, I found that students were not following the instructions to

make a delineation between students they knew and those whom they considered to

be friends. In consequence, I had to modify the project to look only at acquaintance

structures (I discuss this change in more detail in Chapter Three). In the end, my

primary aims included the following five questions:

1 How fast are acquaintances made?2 Do acquaintance circles change over time?3 How do cliques develop?4 Are there any isolates among the students, and if so, who are they?5 What demographic factors affect acquaintances?

1.2.2 Secondary Aims About Graduate Life in Sociology at Essex

The secondary aims centre around the experience of being a Sociology student

at Essex. These aims include the questions:

1 What are people’s feelings about the graduate weekend?Why did students choose Sociology at Essex?What do they intend to do after they graduate?What are students’ sources of help with academic work?

Answers to these questions facilitate suggestions for change in Departmental graduate

policies. The first and fourth secondary aims additionally supplement the primary

aims by giving insight into the depth of some acquaintance contacts.

1.3 The Structure of the Dissertation

Scholars began applying network approaches to the study of social

phenomenon in the mid 1920s. The second chapter briefly charts the evolution of

social network analysis, with a specific focus on techniques, theories, and findings

applied to the formation of acquaintance structures. Chapter Two also briefly reviews

the literature on student friendships. This study produced results similar to previous

findings: acquaintance structures form quickly, then gradually expand among

students; and ethnic divides emerge prominently, though in the case of Essex

Sociology, the divides are not so pronounced as those found among high school

students in the USA.

Chapter Three describes the mechanics of collecting the data for the project,

beginning with the data collection techniques. Discussion at this stage centres on the

questionnaire design, including the development of the demographic and network

sections, along with a look at my reasoning behind primarily choosing open-ended

questions. Chapter Three then explores the timing of the questionnaire waves, before

concluding with a description of the population studied.

Chapter Four addresses the main focus of this dissertation, the formation of

acquaintance networks. This chapter first offers a network graph, summary of

acquaintance numbers and structures, discussion of isolates (students who knew none

of their peers) and outliers (students who knew only one of their peers), analysis of

clique formation, and identification of gatekeepers linking the cliques for each

successive wave. Clique are defined by Freeman’s measure of betweeness centrality.

Chapter Four then looks at the data as a whole, highlighting the gradual expansions of

individual acquaintance circles, and the decreasing numbers of isolates and outliers.

This chapter found that part-time students make fewer contacts, and Asians integrated

differently than students from other ethnic groups. Finally, Chapter Four highlights

the significance of the graduate weekend for expanding student acquaintanceships.

Chapter Five reports on students’ general experiences of being Sociology

postgraduate students, primarily focusing on reactions to the graduate conference

weekend. Chapter Five additionally assesses students’ reasons for choosing to come

to Essex and their planned use of the degrees when they leave. This section highlights

possible courses for improving recruitment strategies for the Department. Finally,

Chapter Five examines sources of support for help with assignments. Home students

most often turned directly to members of staff, while overseas students relied on

friends and acquaintances, as well as the Resource Room.

The concluding chapter ties together the findings and policy recommendations

of this dissertation. In general, students had made broad-ranging contacts by the

Spring term. Nevertheless, the depth of acquaintance networks depended largely on

whether the student came from the UK or overseas and on whether the student studied

full-time or part-time.

Chapter 2 : The Social Networks Literature:A Focus on Student Acquaintanceship

2.1 Introduction

In a text which has gained acceptance as the near Bible of social network

analysis, Stanley Wasserman and Katherine Faust define a social network as “a finite

set or sets of actors and the relation or relations defined on them” (1994, p. 20). A

social network need not be formal, may span many forms of both formal and informal

groupings, and may allow the flexible entry and exit of members. Indeed, some

scholars applying network analysis have criticised structural-functional theorists for

concentrating excessively on formal organisations and giving insufficient attention to

informal and often transient networks (Boissevain 1974: pp. 5-13). Others have noted

that social networks are of key importance for gaining leadership positions in formal

organisations (Cartwright and Zander, 1968: 485-500). The study of social networks

arose from the application of mathematical network formulas to the examination of

the spatial arrangements of groups and the dimensions of interaction between people

in bounded settings. It has since developed into a field of social analysis in its own

right.

This chapter first briefly reviews changes in the social networks literature

from early texts to recent studies. I then highlight previous network studies of

students, and build on these reviews to draw general guidelines for the study of

graduate students at the University of Essex.

2.2 The Social Networks Literature

It is not my purpose to engage with the range of innovations in this literature.

I have the narrow focus of considering the establishment of networks of acquaintance

among graduate students at the University of Essex. Consequently, I will review the

origins of the application of mathematical network approaches to the social sciences,

then concentrate on the development of network analysis as it has been used to study

acquaintanceship.

2.2.1 Early Social Network Analysis

The application of network approaches to social phenomenon did not attract

interest until the early part of this century. Austrian government officials observed

members of Austrian-Italian communities transplanted from the Tyrol border to an

estate near Vienna between 1915 and 1918 to see who assumed leadership roles and

who remained inactive in emerging social structures in the amalgamated community

(Moreno 1934). The psychologist J. L. Moreno conducted the first systematic studies

of acquaintance structures using network analysis in the 1920s.

Moreno postulated that the human brain had developed a level of complexity

to enable humans to devise social structures with the power to partially shape the

psychology of individual people. These structures, in turn, constituted more than the

sum of the individual members. People create these structures by networking with

each other. The network serves the purpose of efficiently transmitting information

between people, then providing the framework from which formalised structures can

later develop (p. 261). Moreno contended that “the mechanism of psychological

expansion which drives individuals, groups, and currents towards further and further

differentiation produces its own controls”: a process of differentiation, which

encourages individuals to rebel against social structures; and a process of

transmission, or social networking, by which people construct social structures (p.

266). He described “the alternating rhythm” of differentiation and networking as “the

law of social gravitation” (p. 266).

Moreno had four objectives in using mathematically-based network methods

(which he described as sociometric methods). First, he wished to begin identifying

the “sociogenetic laws” which he believed governed the processes of networking and

which operated in parallel with biogenetic laws. Second, he sought to study the

development of networking abilities among children. Finally, he hoped to contribute

to the creation of “therapeutic procedures” for social networks, which he called

“assignment therapy” (pp. 298-304). Moreno suggested that anti-social behaviour

represented not only mental problems within the individual offender but also a

breakdown of communication between an offender and people in his social networks.

He thus suggested that treatment for anti-social behaviour would have to consider

both individual-based causes of undesirable actions as well as ways of healing

relations between the offender and family members, neighbours, public officials and

potential work colleagues (pp. 298-304).

Finally, Moreno sought to counter assertions of innate human hierarchies

which eugenicists in his day argued should be assumed in the construction of social

policy. Moreno contended that eugenics offered nothing more than a dream which

was as likely to produce disaster as to produce utopia (pp. 365-369). In particular, he

rejected the assignment of hierarchically ordered value to any genetically

distinguishable human groups, arguing that:

the notion of the unfit, at least for a large number of those who are now considered in this category, becomes relative, as there are uncovered numerous groups of varying eugenic value. Some groups among those today classified as unfit for propagation may be found unfit when in relation to certain groups, but fit in relation to other groups, just as we have found in respect to populations that some groups which foster disintegration and decline in certain communities aid in the fruitful development of others (p. 369).

Moreno did see benefit to sociometric engineering, but only to the end of arranging

groups to allow the maximum potential for successful networking.

The deterministic elements of Moreno’s work are no longer widely assumed,

and his purpose for developing a network-based therapeutic approach to dealing with

deviance is no longer a research aim, however, many of his basic techniques remain

common in network analysis. Moreno placed large groups of babies in playrooms for

multiple hour periods over several weeks, and observed which babies made contact

with which others. He also followed entire classes of primary school children through

multiple years of schooling, and asked members of individual classrooms to nominate

the name of the child they would first, then second, then third most like to sit next to

them during lessons, then interviewing them about their choices.

He later studied networks among girls living in the New York State Training

School for Girls in Hudson (which aided young women in difficulties), observing the

girls interacting with each other, asking the girls to nominate the girl they would first,

second, third, and so forth, most like to sleep in the bed next to them, and conducting

depth interviews with the girls to ask them the reasons for their choices. Moreno then

counted the number of words each girl used to describe the others whom she

discussed, and argued that higher numbers of words reflected stronger feelings of

liking or disliking. He also surveyed household members in a New York community,

asking them to nominate the households they would first, second, and third most like

to live next door. Moreno collected some demographic details, and assessed his

results controlling for sex, race, employment status, migration/citizenship status,

marital status, and (indirectly) social class. He then mapped his findings, producing

output not at all dissimilar to plots generated by contemporary computer packages,

like UCINet and Krackplot, though in some respects more impressive as he produced

his figures by hand.

Moreno stressed the importance of surveying complete populations, and

providing instruments which enabled each member of a population to nominate any

other member. He demonstrated that collecting network information on a population

allowed the mapping of the social geography of a community - identifying dyads,

triads, and larger groups, as well as highlighting isolated members (p. 256), but

stressed that accurate measurement required the testing of the same population at

regular intervals (p. 57). Additionally, he demonstrated that the people he observed

partially defined their own individuality in relation to their (physical, attitudinal, and

status) proximity to other group members in a range of social contexts (p. 80). Social

networks, he observed, could catalyse chain reactions (members of a network directly

or indirectly contemplate a range of courses of action; once one member chooses and

engages in a particular action, other members of that network follow suit) (pp. 258-

60). Finally, Moreno observed that networks perform a balancing function within a

larger population (p. 76).

Moreno’s basic methods proved highly influential in the 1950s, though some

authors, notably Homans, expanded on the sophistication of their application. George

Homans, a sociologist who believed his profession to be a science of human

interaction which lacked general theories and laws, as could be found in sciences like

chemistry and physics, set out to locate laws of human interactions (1951). Homans

cited Moreno as one of the significant thinkers pointing toward the means of finding

such laws (pp. 40-43). In accordance, Homans expanded upon Moreno’s methods.

Homans focused on dynamics between cliques, particularly when cliques were

arranged in a social hierarchy, and examined the position of people who served as

gatekeepers, or who provided other kinds of links between cliques. In studying social

groups within a workplace, Homans found that people holding jobs of intermediate

social status within a workplace can serve as links between the high prestige worker

cliques and the lower prestige worker cliques, while also constituting a clique in and

of themselves (pp. 146-147). These people are partially outsiders and partially

insiders to both the high and low prestige cliques.

2.2.2 Later Innovations

Theodore Newcomb also identified networks as a key feature of human

thinking, but rather than adopting a psychological or grand theory approach, he

examined networks as a part of the system of meaning on which humans rely to

interact with the world. Newcomb sought to expand understanding of how people

orient themselves to other people and to events and objects in their environment

(1961, p. 4). He identified three key dimensions of orientation: attraction (orientation

toward other people); attitude (orientation to objects and events); and perceived

orientation of others (what one person perceives others’ attitudes toward objects or

events to be).

Newcomb and his PhD students expanded on Moreno’s procedures to study

university students (1961). Newcomb and his research team purchased a house with

19 sleeping places, two in the basement for live-in research assistants, and 17 for

students. To avoid the hassles associated with first year students enjoying life outside

parental constraints, Newcomb collected two successive samples of 17 transfer

students to MIT in 1954 and 1955, who were offered free accommodation in the

house in exchange for devoting four to five hours per week to the networks study.

Each week, the students received a pile of cards baring the names and subject

numbers of the other students. They were asked to remove their own card, and sort

the remaining cards into piles of those they preferred, those they did not prefer, and

those about whom they did not have a solid opinion; then to rank-order each of the

three piles from most liked to most disliked. The research team interviewed students

about their relations with other house members, and organised guest speakers to

provoke the students to discuss controversial topics. At regular intervals, participants

completed questionnaires about general attitudes. Newcomb and his team compared

the correlations between the participants actual responses to the free sorting and

questionnaire tests, as well as comparing how each participant suspected other

participants answered these tests. Participants were occasionally asked for their

opinions of the members of the research team.

Newcomb and his team not only wished to study a complete group (defined by

residence in a house), they also wished to observe the formation of acquaintances

among a homogenous sample which had not previously met and whose members

would be selected in such a way as to minimise the possibility that sample members

would feel that they had been selected because of particular characteristics. These

researchers chose to select only white men who were US citizens, and to balance

religious and age groups (between traditionally-aged 19-20-year-olds and military

veterans in their mid-twenties) so that no sample members would be isolated on these

fronts. The students were studying a variety of arts, humanities, science, and

engineering courses, and the sample was selected from men who had no previous

contact (determined by a pre-qualifying questionnaire sent to all transfer students in

each respective year).

Newcomb and his fellow researchers placed great emphasis on ethics,

reporting that they kept a promise that “no observations of any kind would ever be

recorded by staff members except in full view and with the subject’s full knowledge”

(1961, p. 29), by answering those questions which did not bias test answers during the

study, and by offering a “no-holds-barred” question and answer session for interested

participants following the completion of the data collection.

The research team found that acquaintance networks form quickly after people

meet, and that the degree to which one person feels bonds with another person is

related to the extent to which the first person perceives that the second person shares

similar views on issues which the first person holds to be of great importance (pp. 68-

70). These bonds stabilised once house members were no longer finding out

significant amounts of information about each other (1961, p. 207).

They also found evidence that the participants used networks to minimise

strain within the house (1961, p. 70). As one might expect, the ability of each sample

member to predict the attitudes of other sample members accurately increased over

time (1961, p. 121). Nevertheless, while participants held consistent attitudes on

general survey questions, all respondents did demonstrate some tempering of their

opinions to promote balance within the house (1961, p. 121). Students with non-

authoritarian personalities tended to be more sensitive to balance and harmony in the

house than students with authoritarian personalities (1961, p. 143). While Newcomb

and his colleagues did not assert that their findings were either inevitable in the

formation of friendships nor universal, they did identify what they regarded to be a

“theoretical capstone” that friendship involves a process of interaction in which

people locate themselves in a group by continually assessing their orientations in

relation to what they perceive to be the orientations of the people with whom they feel

the closest bonds (1961, pp. 260-1). As a consequence, they concluded, stable

friendship would be associated with a clearly defined set of overlapping shared

orientations between two or more people (1961, p. 261).

Stanley Schachter approached Newcomb’s conclusion that networks tend to

promote balance from a different angle (1968). Schachter created experimental social

groups based around an interest (including movies and radio), and had selected

experimenters pose a question of a moral dilemma to the group. Schachter found that

experimenters who assume a position in line with the modal view, or who appeared to

allow themselves to be persuaded to move toward the modal view scored well on a

scale of acceptance in the group, while experimenters who assumed a position highly

deviant from the modal view tended to score as disliked (1968). Schachter concluded

that a newly formed network may be more accepting of those members willing to

promote or at least contribute to group balance (1968). Schachter and Newcomb

imply a definition of balance akin to the conception widely adopted at this period of

network analysis, that balance means positive agreement rather than negative

agreement or agreement to disagree (Scott, 1991: 12).

Jeremy Boissevain, among other social anthropologists, applied social network

analysis to compare interactions in a range of cultures, particularly comparing African

and European communities (Boissevain and Mitchell 1973). Boissevain argued that

examination of social networks provided more useful data than the study of people’s

positions in formal organisations. Informal, flexible and changing social networks, he

argued, enabled people to manipulate transactions either for their own benefit or for

the benefit of other people, a cause or a belief (p. 25). Transactions through networks,

he demonstrated using multiple examples, can occur more regularly than formally

limited behaviour within more clearly defined and purpose-orientated structures (pp.

24-28). Scholars focusing on formal organisations, he suggests, can mistakenly see

people as passive actors manipulated by formal structures, and miss out on the

everyday exercises of will in networks which feature prominently in people’s every

day lives (p. 25).

Boissevain observed that a person did not necessarily need to have regular

contact with another adult to have a strong social tie, particularly in the case of ties to

family. Barry Wellman reached a similar conclusion studying networks in a Canadian

urban community, though he also found that telephone lines facilitated the

maintenance of strong network ties over long distances (1979). Boissevain thus

suggested that the strength or weaknesses of any person’s social networks could be

assessed by five criteria: their size (number of acquaintances), the number of

dimensions of contact with each acquaintance, density (the mean number of channels

of contact a person has with each acquaintance), centrality (the extent to which a

person is acquainted to other members of a network, and clustering (the number of

sub-networks in which a person is a member) (pp. 35-45). Nevertheless, John Scott

(1991) cautions on the overemphasis of density as a measure, as density calculations

can simply express the number of connections between a particular point and other

points in a system as a proportion of the total possible connections; but it cannot

reveal the intensity of those connections. Scott notes that a loving connection, for

example, would be much more intense that an acquaintance connection (1991, p. 78).

Boissevain argued that acquaintance itself is a desirable level of interaction to

maintain with as many people as possible. Acquaintances may not only prove useful

at some point as sources of help or information in and of themselves, but

acquaintances may also serve as gateways to other people who may be similarly

useful. The more people you know, the more likely they are to know how do deal

with an event or to know someone else who knows how to deal with that event (pp.

26-28). Moreover, the more dimensions of interaction one has with acquaintances,

the more useful their acquaintance potentially becomes (pp. 28-37). M. Granovetter

reached a similar conclusion about the value of acquaintance for its own sake in

examining how people found jobs (1974). Granovetter found that people in the US

were more likely to find a job through informal contact with acquaintances than

through formal recruitment or advertising processes or through contact with close

friends and family (1974: 54).

One important dimension of network analysis, Boissevain argued, is the

examination of cliques. He distinguishes between sociometric cliques (people who

express mutual preferences for each other on a test and who cluster together in

subsequent analysis) and a network clique, comprised of people who feel a sense of

identity as a group and who share common interests and affection, but who do not

organise themselves formally as a group with a purpose to achieve anything (pp. 174-

181). Cliques, he suggests, facilitate the efficient occasional use of resources

available in the network, and permit more flexible entry and exit of members than

formal organisations.

Many scholars expanded upon the concept of centrality in the late 1970s.

Maureen Hallinan modified Boissevain’s definition, looking at centrality as a measure

of the number of close contacts a person X, who is a close friend of person A, has

with other close contacts of person A (1978/79). L. C. Freeman (summarised in Scott

1991 and Wasserman and Faust, 1994, pp. 178-191) made considerable innovations

on the concept of centrality in the late 1970s. Freeman noted that the measure of

crude centrality (simply the number of lines emanating into or out of any particular

point) did not allow for the easy comparison of centrality between sets of different

sizes. To improve comparability, he refined the formula to look at centrality as a

proportion of the in or out connections for any given point out of the total possible

connections which could be made (Scott, 1991, p.85-87). Freeman also highlighted

the significance of global centrality, points which have the shortest distances in a

network system to the largest number of other points (Scott, 1991, p. 88). Identifying

these points would indicate which points (or people) were most widely connected, and

who consequently would be best placed to transfer certain kinds of information

around the network. Additionally, Freeman examined a form of centrality he

described as betweeness, that is, points located between and connected to cliques

(mathematically defining the position of potential bridges and gatekeepers) (Scott,

1991, pp. 89-90). Finally, Freeman distinguished between centrality of points in

relation to other points, and centrality of points in relation to the map of the entire

network, which John Scott labels centralisation.

2.2.3 More Recent Work

By the late 1970s and early 1980s, scholars in the US sought to use network

studies to challenge the popular belief (promoted by Chicago School sociologists like

Wirth and Park, as well as others) that urban environments promote the breakdown and

destruction of community values (Wellman, 1979; Fischer 1982). Claude Fischer

interviewed people in a stratified random sample of residential communities in Northern

California (stratified to include a range of very urban to marginally urban environments),

asking them to indicate if they knew people they could count on in a range of

circumstances, to identify their relationship to those people, to list organisations of

which they were members, and to answer opinion, general happiness and psychological

instruments. He found that people living in very urban areas developed different types

of networks to people in marginally urban areas, but that both groups maintained strong

networks (pp. 251-261). He did find differences across type of setting by age and sex

(with older men and young mothers least well connected), as well as by income (with

people of higher incomes having stronger and more reliable networks than people on

low incomes), and by education (with people with higher levels of education have larger

and more diverse networks (pp. 251-261). To gain a sample representative of US

communities, Fischer opted to not select communities around universities (or other

institutions, like military bases), communities with higher proportions of non-English

speakers (which would minimise contact with Native American and Hispanic

communities), and predominantly black communities (with the latter two exclusions

limiting the applicability of the study to white majority communities in the US).

While race surfaces only as a secondary concern in control variable lists in much

research before the 1980s (and, indeed, is entirely avoided in Newcomb’s study of a

university student house and Fischer’s studies of residential communities), scholars in

the United States have given recent attention to the formation (or non-formation) of

inter-racial friendships (Hallinan and Williams, 1989, p. 68). This research develops in

the context of government officials seeking academic validation of public policies to

redress the high profile history of racial tensions in that country. Hallinan and Williams

studies a large sample of high school students, and asked the students to nominate their

three best friends at school. The authors note serious limitations with this approach, as

well-connected students would have to leave out many close friends under such

constraints, and as the results gave the likely misleading impression that small schools

have closer friendship ties than large schools (p. 71). These concerns aside, Hallinan

and Williams found confirming evidence that ties between students who nominate each

other are more likely to be stronger than instances where one party’s nomination is not

reciprocated (p. 77). These authors also found that formal policies of racial integration

backed by solid institutional support were related to increased reporting of inter-racial

friendships. These authors also added an innovation in the study of dyads. In addition to

examining mutual and asymmetrical dyads, they also drew a random sample of dyads

which could have occurred, but which were not reported, for comparison against dyads

which were recorded (p. 70).

More importantly, network analysis has gained status as a legitimate field of

social inquiry in its own right in the last few decades. Whereas scholars developing the

technique in the 1950s and 1960s regularly noted that they rejected structural

functionalism and sought to contribute toward a new theoretical paradigm, scholars by

the late 1980s proudly proclaimed the utility of examining the structures of interaction

and relations (Wellman and Berkowitz, 1988). Barry Wellman and S. D. Berkowitz go

so far as to proclaim that:

Although the structural analysis presented in this book fits comfortably into this extended structuralist family, it is not simply an extension of other forms of structuralism. It is distinguished from them by its focus on concrete social relations among specific social actors. Indeed, its emphasis on exchange puts it closer to input-output economics and quantum physics than to Lćvi-Straussian structuralism (1988, p. 5).

While the comparison to quantum physics may be something of an overstatement,

network analysis has developed into the examination of the structures of recurring

relational ties which link actors in a social system. The focus on the relations between

members of a system distinguishes this approach from other varieties of social

investigation (Wasserman and Faust 1994: 4). Stanley Wasserman and Katherine Faust

explain that:

In social network analysis the observed attributes of social actors (such as race or ethnicity of people, or size or productivity of collective bodies such as corporations or nation-states) are understood in terms of patterns or structures of ties among the units. Relational ties among actors are primary and attributes of actors are secondary (1994, p. 8).

Once one decides to focus on the network - systems of relations between actors (how

dyads, pairs and subgroups form and disintegrate within a social system, and how these

structures interact within the system), then assumptions of modelling of individual

behaviour, or the use of random samples to generalise to broader populations are no

longer relevant (Wasserman and Faust 1994, p. 21). Network analysis instead entails

measurement of other features, as the degree of interconnectedness among members,

presence or absence and interaction of cliques, centrality of dyads, triads or individuals

in the system, among others. Most contemporary network analysis tends to focus on one

mode (a single set or system of actors) or two modes (two sets or systems of actors, or

one set or system of actors and one set of events in which the actors participate), though

some research has assessed more complex sets of interacting systems (Wasserman and

Faust, 1994, p. 29).

I conclude this overview with a brief note on sampling with network analysis. G.

Kalton (1983), among a number of authors, have observed that traditional social research

methods of sampling do not fit comfortably with network analysis, which does not make

the probability assumptions made when one draws a random sample A random sample

of a population may well not include central triads, key gatekeepers, or many examples

of a significant kind of relationship, and the subsequent picture the sample would create

of the population could be misleading. In consequence, Scott observes, “sampling may

result in unreliable data” (1991: 62). The network literature offers a number of solutions

for dealing with situations in which a network lacks clear or easily located boundaries

(Wasserman and Faust, 1994). In the case of the Essex Graduate School of Sociology,

though some initial complications arose in defining the network members (discussed in

Chapter Three), the membership of the network could be determined. By the third

questionnaire, I had reasonable confidence of contacting the entire population. In many

respects, the Essex Sociology Department, which has a unique mix of staff and an

exceptionally high proportion of overseas students compared with many other

universities in the UK, in unique. As the suggestions for change are developed to aid the

Essex Sociology Department in future recruitment, it was not necessary to either sample

to population or to attempt to draw correlations between the Essex population and other

student populations. In this respect, this study is in keeping with a large section of

network research, which focuses on relations within a whole population (Scott 1991: 60-

62).

2.3 Studies of Acquaintances Among Students

Studies of students have tended to involve variations of nomination data

collection techniques, in which researchers either asked students to name key friends

or acquaintances, or provided a list of other students in a class or school and asked

respondents to tick off those students whom they considered to be friends

(Wasserman and Faust, 1994, p. 46). Some studies, notably Newcomb’s work with

the university student house, have also asked students to rank their choices by order of

preference. For reasons to be explained in the next chapter, a simple, non-ranked

roster approach was adopted for this study.

Moreno found that girls in Kindergarten were more likely to nominate boys

than boys were to nominate girls. From the first through seventh grades, children

tended to nominate other children of the same sex, but from grade eight, boys became

more likely to nominate girls (pp. 50-55). As children progressed through the grades,

fewer children became isolated, while the number of pairs increased. By the older

grades, children were also more likely to cluster in triads and larger structures

(Moreno, 1934 p. 60). Hallinan reached similar conclusions studying primary school

children in the 1970s. She found the mutual best-friend dyads tended to be more

stable among sixth-graders than fourth-graders (1978/79).

When students had the opportunity to nominate as many students as they

chose, however, Moreno found that the children made limited numbers of selections.

Among the primary school children, Moreno found that “intimate acquaintances”

tended to develop outside the classroom in non-educationally related settings (p. 58).

Further, he noticed that teachers did not make very accurate predictions about which

children would gain the most and fewest nominations, which he interprets to mean

that teachers and students do not share the same criteria for forming social bonds, and

that they have only a marginal understanding each other’s criteria (pp. 50-58).

Hallinan observed that friendships among primary children tended to be transitory,

but mutually agreed friendships lasted longer than asymmetrical friendships (where

one child nominates another, but the second child does not return the nomination),

which tended not to develop into mutually agreed friendships (1978/79: 208).

While Moreno found that networks form early in life, Newcomb and his

fellow researchers found that networks of acquaintance form early on after people

first meet each other. While pairs and triad which formed in the first week tended not

to be stable, pairs and triads formed from the second week did tend to be stable

throughout the academic year. Students generally nominated the same choices as the

two people they most liked from the second week to the end of the study period,

indicating that triads and parings form quickly (1961, pp. 62-4). The number of pairs

and triad increased over each study year (1961, p. 166). Respondents regularly

assumed the other two members of a stable triad of which they were a member liked

each other, which was often but not always the case (1968, p. 549). Unpopular men

living in the student house made more erratic choices of attraction than the other

participants, and the students whom they nominated as most preferred tended not to

reciprocate the preference (1968, p. 549).

Newcomb’s research group found inconsistent evidence that demographic

features had any effect on acquaintance networks (perhaps because they chose a

homogenous sample). Even so, they did find that their sample members tended to

forms pairs and triads with other sample members who responded similarly to

questions on general attitude questionnaires after the first few weeks (a finding made

interesting by the absence of significant change in answers to these questionnaires

over the testing period) (1961, pp. 95-6). The men showed no particular allegiance to

other men housed on the same floor (the house had two floor for sample member

accommodation), and roommates were also not necessarily more likely to be more

friendly with each other than men who did not share rooms (1961, pp. 208-220).

Each sample group did quickly form a set of stereotypes for classifying themselves

and each other, and “nearly all House members must have been more or less agreed

on the basis for categorising each other” (1961, p. 250). Many of these stereotypes,

like “corn-fed” and “Eastern-sophisticate” had a regional association (1961, p. 235).

When looking at friendship levels between high school students of different

ethnic backgrounds in the US, Hallinan and Williams found evidence of cross-racial

friendships, as well as finding that policies of integration which received institutional

support increased cross-racial contact. Nevertheless, US students reported inter-racial

friendships in strikingly low frequencies in the mid-1980s (1989: 76). The studies of

students thus give consistent evidence that demographic characteristics of students

have some bearing on acquaintance and friendship structures, though these studies

also indicate that demographics alone do not explain the entirety of the levels of

contact between students.

2.4 Summary

Some questions about network approaches remain. Wasserman and Faust note

that little work has been done to establish the validity and reliability of network

approaches (1994, pp. 56-59). While some studies have found that people do not

always accurately recall when, how frequently, or for how long they engaged in

interaction with other people, more recent work has also found that accurate recording

of every interaction is not necessary to construct maps of social geography or to

assess the general nature of ties between to people or subsets of people in a group

(1994, p. 57). In addition, some network studies involve the use of direct observation

or diaries to collect data on total sets of interactions.

A question not asked by the present literature is the effect which network

studies may have on the member of a network. It may be possibly that the

presentation of a network questionnaire prompts some people to evaluate, and perhaps

to change their membership in a particular social network. One might also be wary of

the interpretation of proximity questions. Members of a close dyad may not nominate

each other on a question like “who would you most like to sleep in the bed next to

you” if both know that one’s snoring disturbs the other’s sleep. Finally, there may be

uncomfortable situations arising if network members compare notes after data

collection concludes. It may be interesting to find out how a central person who did

not nominate an outlier in a network responds to a question from that outlier along the

lines of “I selected you. You selected me, didn’t you?”. Nevertheless, network

approaches have proven useful, not only for facilitating analysis of the behaviour of

people and organisations, but also for the development of mathematical procedures

(such as writing programmes to address constraint satisfaction).

This literature review highlights the value of identifying systems of

acquaintance among graduate students. The more connections a student has, the more

easily they would be able to access information, such as finding out which members

of staff to work with or to avoid, what parts of a country they have not yet seen they

might like to visit on holiday, how to resolve an immigration or financial aid

difficulty, and so forth. In the graduate school setting, where students have to devote

time to completing in-depth study while living on modest incomes before (hopefully)

moving into a more productive and better paid stage of their lives, having many close

connections can be a disadvantage. To maintain those ties, one would have to take

away time and energy from study. Keeping up a system of acquaintances, however,

requires less effort and time, and potentially yields significant rewards in providing

ground to test ideas.

Locating the pattern of acquaintances among graduate students also provides

useful information for the Sociology Department at Essex. Such an investigation can

reveal who gets left out of the networks, and thus may fall into a structural position

which does not promote the attainment of maximum benefit from the Essex

experience (or, alternatively, which students do not desire to be approached at more

than a purely academic level). More importantly, as many studies indicate that

acquaintance structures form early, this study may give insight into how the

Department may improve the first few weeks of new graduate students’ experiences

with the University.

Chapter 3 : The Mechanics of Collecting the Data

3.1 Introduction

This chapter initially describes the data collection techniques and

questionnaire design. The questionnaires included both demographic and network

sections, and this chapter explains my reasoning behind including some open-ended

and some tick-box questions. I then consider the timing of the questionnaire waves.

More than 60% of students completed questionnaires in each wave. I discuss the

rationale for assessing one complete student cohort, and conclude by summarising the

demographic characteristics of the respondents. Four key clusterings emerged among

the students, defined largely by were students fit into the two by two matrix of

overseas and home students across full- and part-time periods of study.

3.2 Data Collection Technique

The data for the project was collected using three self-completed

questionnaires sent to the entire cohort of post-graduate students who entered the

Sociology Department during the 1996-97 academic year. Similarly to Newcomb

(1961), I pledged to maintain total confidentiality with replies to questionnaires. This

pledge has meant that I am not able to discuss some features of the final network

graphs, as any such discussion would reveal the identity of certain individuals.

Nonetheless, by guaranteeing not to reveal identities, I was both able to cajole more

people to respond, and will save some students with continuing association with the

Department to hassle of addressing uncomfortable questions that might arise from this

research.

3.3 Questionnaire Design

Three questionnaires served as the primary data collection tools of this project.

Each included a core section gathering network data plus a supplementary section.

The first questionnaire collected demographic information on the respondents; the

second ascertained where or to whom respondents turned to for help with essays, and

the third solicited views of the graduate weekend as well as respondents’ future plans

for the use of their degrees. The three questionnaires are displayed in Appendix One.

3.3.1 Network Sections

The core network question, a sample of which is illustrated in Figure 3.1, was

designed to collect data about student friendships which will be analysed later using

social network techniques. The question excerpt shown in Figure 3.1 appeared in the

first questionnaire, and measured when students who recognised each other by the

first week of the Autumn term had met. The two tick columns of the network

question were adjusted (with the full text appearing in Table 3.1) for the subsequent

two questionnaires to distinguish between those people respondents simply knew and

those people whom respondents considered to be friends. The second questionnaire,

administered mid-way through the Autumn term, contained an additional network

question ascertaining which members of staff students knew from their courses and

which staff they had met outside of teaching.

Tick-off rosters have been commonly used in network studies (Wasserman

and Faust 1994). Unlike some key studies of students (such as Moreno, 1934;

Newcomb, 1961; and Hallinan and Williams, 1989), this study did not ask students to

rank order their acquaintances. I avoided this procedure primarily because of the

ethos of the Sociology Department, which prides itself on promoting inclusive

practices and well as feminist and queer theories which question the morality of some

forms of social ranking. I felt that in this context, some students would find a ranking

question offensive, and decided that higher response rates were preferable to more

detailed network data.

Figure 3.1 - A Sample Network QuestionThe next section asks you which of your fellow students that you have met

during the first few days of the course or new before the course started. Please tick the boxes beside each name which are relevant.Name Students

known by you before

this course

Students you first met at Essex

this year

Name Students known by you before

this course

Students you first met at Essex

this year

AL-RUMAIHI madiha

[ ] [ ] COOK Esther [ ] [ ]

ABDUL HAMID Ahmad Shukri

[ ] [ ] COULTATE Nicholas

[ ] [ ]

ABDULRAHMAN A

[ ] [ ] CUMMING Jon

[ ] [ ]

Table 3.1 - Wording Of Network Columns By QuestionnaireQuestionnaires Questions

1 Students known by you before this courseStudents you first met at Essex this year

2 & 3 Students who are known by youStudents you consider your friends

I had several problems with the network sections. First, due to the flexibility

of entry into and exit from the Sociology MA schemes, I had difficulty acquiring a

final list of who had and who had not enrolled. While I provided space for the

addition of names not included on the list, I faced the prospect of irritating people

whose names I accidentally omitted. The list of names posed other problems as well.

A respondent might not know the surname of friends, and might know some people

by face or voice, but not by name, and, in consequence, return an incomplete

questionnaire. Some students (mainly Asians) use names with friends which bare

little or no resemblance to their formal names on their registration forms, posing

recognition challenges for respondents and wording problems for me.

From the initial stages of the project, I considered using pictures of the

students to aid the recognition. I ultimately rejected this option, in part because the

Department only collects pictures from those postgraduates who teach classes, and in

part because some (especially older) students oppose giving their picture to the

Department - let alone letting a photograph appear in a questionnaire which is given

to all the other students. Clearly, ethical considerations would not allow me to

photograph people without their knowledge, and the use of pictures acquired in such a

manner would potentially seriously aggravate some potential respondents. If I had

pictures of some students but not all, then I would risk distorting the numbers who

reported knowing the people who had not supplied a picture.

To address the surname problem, I reordered the network question in the

second and third questionnaires, listing students alphabetically by their first names,

and also by substituting the name people preferred others to call them for their

registration first names in those cases in which respondents made me aware that they

did not use their registration name. Ultimately, however, I concluded that all new

postgraduates would be in similar positions with regard to learning names. This

method would slightly distort the results in favour of those people known by those

students with the best ability to recall names, but then I can partially account for this

bias in examining the results, as I can note and discuss separately those cases of

respondents who report knowing the most people. As it happens, those students who

remembered the most names also occupied the position of gatekeeper between distinct

cliques in Wave 2. Alternative strategies relying on cold recall, such as asking

respondents to write down names of students whom they know in a blank space,

would have posed greater recognition problems. Other possible approaches, such as

introducing myself to students individually, then asking them to point out whom they

knew in a room of other postgraduates, would have proved impractical, in part

because other commitments and illness would prevent some people from attending a

collective meeting of postgraduates, and in part because such an approach would have

required me to have a good working foreknowledge of people’s identities, which was

not possible under the circumstances. As a result, the format I adopted proved to be a

workable, if not ideal, data collecting mechanism.

3.3.2 Demographics Section

Figure 3.2 provides a shortened example question from the demographics

section from the first questionnaire. I based this section on the demographic and

background questions in a questionnaire created by Dr. Tom McManus for Project

Sigma (a study of the sexual behaviour of gay men in the UK) and published in GAY

Times. I have adapted the questions to gain information which I considered might

have an effect on the formation of acquaintance cliques.

By employing tick box options for answers, I was able to easily code and enter

responses into a computer package, and also to help the respondent by reducing the

effort required in completion. Production of a question like this requires effort in the

selection of the options for the tick boxes. The researcher must both ensure that the

options will not insult or upset the respondents, and, secondly, make the list as

complete as possible to reduce the number of “other” options which are required for

completion.

Figure 3.2 - A Sample Demographics QuestionWhich of the following best describes you ethnic origin[ ] Asian [ ] African / Caribbean [ ] White European[ ] East Asian [ ] Middle Eastern [ ] White other[ ] Hispanic/Latin American Other .........................

3.3.3 Open-Ended Questions

I used a small number open-ended questions in all three questionnaires to

gather data on subjects in which I did not feel I could fully pre-guess the main

categories of answers, such as why students chose to study at Essex University. In

some cases, I wished to obtain personal perceptions and goals, and as I would be

equally interested in unique as well as frequent answers, I designed these questions to

enable respondents to characterise their feelings in their own words.

3.4 Timing of the Questionnaire Waves

The timing of the distribution of each questionnaire was concluded after

discussions with Professor John Scott. Previous research has found that in group

situations where people from differing backgrounds temporarily join together for a

specific purpose, the majorities of friendships are made early (Newcomb, 1961;

Newcomb, 1968; Hallinan and Williams, 1989). The first week when a new group of

students officially begins study at university provides a classic example of an

environment where this phenomena might occur. For this reason, the first

questionnaire was presented at the Graduate Induction Conference held on campus in

the first week of term (thus enabling me to follow Newcomb’s procedures of tracking

students interactions from the beginning). I hoped that the enthusiasm and excitement

of the first week and the regularity with which students have to fill in forms might

facilitate a good response rate at that time. In the end, while the response rate to the

first questionnaire was relatively reasonable (Fischer 1982), fewer people answered

the first than the subsequent questionnaires.1 Even so, the conference setting in which

the first questionnaire was introduced contained an atmosphere of responsibility (as 1 In part, the gradually improving response rate resulted from my own increasing knowledge of the students. I found that people felt a greater sense of responsibility to turn in the forms once they knew the person who would analyse them. Also, I later found that some part time students deliberately did not hand in the first questionnaire because they felt self-conscious about not knowing anybody, and did not wish to admit to this fact, even in confidential research. As these students made acquaintances, they became more willing to complete questionnaires.

lecturers introduced courses at this time so that students could make final selections

for their study) which would not have been present had I administered the

questionnaire at a different time during the first two weeks. Consequently, the timing

of this questionnaire may well have positively impacted the response rate. The

second questionnaire was presented in Week Four of the Autumn term, again to

determine who had met whom early on.

For the past five years, the Sociology Department at the University of Essex

has held a graduate conference at differing venues in East Anglia. This conference,

which is predominantly for first year postgraduate students, has the declared aims of

furthering academic thought and also providing a setting for students to meet each

other, form friendships, and establish informal support networks with other students.

The last aim of the conference inspired me to implement the final friendship

questionnaire at the end of the conference weekend. I handed out questionnaires on

the coach taking most conference participants home. To catch others who drove

themselves to that conference or who did not attend, I placed additional copies in the

pigeon holes of people I did not encounter on the coach.

In all cases, I found that I improved my response rate by putting a second copy

of questionnaires along with a pleading letter in the pigeonholes of people who had

not responded after the first week of administration. I opted to allow one month to

catch stragglers from each wave. Table 3.2 displays these collection periods. My

wife and I also tried handing out questionnaires to non-respondents who attended the

MA core course and the core course in research methods. I made an exception to the

use of cut off points with the demographics section of the first questionnaire. I

collected this information from some people well after Week Four, and, in a few

cases, I needed to consult the departmental administrator, Brenda Corti, to fill in gaps

in this section of the data.

Table 3.2 - Data Collection Periods For Each QuestionnaireStart Finish

Questionnaire 1 Week 1 Week 4Questionnaire 2 Week 4 Week 7Questionnaire 3 Week 11 Week 14

3.5 Population Selected

I agree with Moreno (1934), Scott (1991) and Wasserman and Faust (1994)

that whole population assessments provide the best vantage from which to observe

social structures in a population. In the end, the easily tracked total of 61

postgraduate students entered the Department of Sociology in 1996, I attempted to

contact all of them. The questionnaires attracted response rates of 62.3%, 70.5% and

77.0%, respectively with 88.5% of students completing at least on questionnaire.

Table 3.3 gives the numbers of returned questionnaires by wave. Most respondents

missed out at least one questionnaire, and, as Table 3.4 shows, every possible

permutation for questionnaire response and non-response occurred. More hopefully,

the modal pattern of questionnaire completion was the completion of all three

questionnaires. As I later took a decision to assume that acquaintance relationships

were reciprocated (this decision is further discussed in Section 4.2), and as I also

found that acquaintance dyads remained in tact once formed, the over 88% total

response rate provided a reasonably clear picture of the postgraduate student

interactions.

Table 3.3 - Numbers of Returned Questionnaires by WaveQuestionnaire Number 1 2 3

Number Completed 38 43 47** Includes 4 on which respondents only completed the section on graduate weekend.

Table 3.4 - Questionnaire Return PatternsQuestionnaire 1 Questionnaire 2 Questionnaire 3 Number of

Respondentsx 9

x 3x 5

x x 4x x 5

x x 10x x x 17

Total 54

The main problem I had during data collection was nonresponse by students. I

can offer three general observations about this problem. First, students who initially

felt isolated were often reluctant to admit this on a questionnaire. Second, older and

part-time students were less likely to respond to any questionnaires - a reason

associated with the first, as older and part-time students also proved less likely to live

on campus or in student housing areas, and spent less time on campus than full-time

students. As a result, the part-time students had fewer opportunities to mix with other

students and staff. Third, people of certain nationalities, mainly British students and

students from many East and South East Asian countries, responded better than

others, particularly those from Islamic countries.

3.6 Demographics of the Respondents

This section reports on the demographics of the incoming postgraduates who

responded to the first questionnaire and of students who only completed subsequent

questionnaires but for whom I obtained reliable demographic data from Brenda Corti

or from friends of the student.2 Throughout this section, the category PhD in tables

should be read to mean PhD and Doctoral Program, with the only exception being the

tables were courses undertaken are analysed.

2 In those cases in which I obtained data from friends, I specifically declined to collect data on ages when friends did not know a respondent’s age for certain.

38 of the responding postgraduates study full-time, while 20 study part-time.

Half (n=29), are home students, while the other half (n=29) come from other

countries. Much to my surprise, there is a very high correlation within this

demographic data between whether a student is full-time or part-time and whether

they are a home or overseas student. Where a student sits within this four square

matrix shown in Table 3.5 is a very good predictor of the student’s demographic

details, as will be demonstrated subsequently.

Table 3.5 - The Crucial Matrix: Students by Nationality and Study TimeFull Time Part Time

UK Students 13 16Overseas Students 25 4

3.6.1 Students by Age

The incoming postgraduates in 1996 spanned an age range from 20 to over 55,

with a mode age range between 25 and 29. Nearly one quarter of postgraduates

started at a later than traditional age, reflecting a significant intake of mature students.

Nevertheless, this data includes 26 missing cases, all of whom are full-time, overseas

students, resulting from my decision not to ask students to guess the ages of other

students.

Table 3.6 - Age Ranges of Incoming Postgraduates in 1996Age Range No of Students %* of Postgraduates Cumulative %

20 to 24 9 25.7% 25.7%25 to 29 12 34.3% 60.0%30 to 34 6 17.1% 77.1%35 to 39 3 8.6% 85.7%40 to 44 1 2.9% 88.6%45 to 49 1 2.9% 91.5%50 to 54 1 2.9% 94.4%Over 55 2 5.6% 100%

*Valid % excluding the missing cases

Clear age patterns emerge between full- and part-time students, as well as

between home and overseas students. Full-time students tended to be younger, having

an age range between 20 and 39 and a modal age category of 25-29. Part-time

students tended to start study later and had a far wider age range, from 25 to over 55.

(Nevertheless, the variable age did not figure in any statistically significant

relationships when I examined the mean numbers of acquaintances, in contrast with

the full-time/part-time variable, which figured in many highly significant

relationships). The modal age category for part-timers is 30-34. Table 3.7 displays

that a similar pattern emerges between home and overseas students. Among overseas

students, ages lie between 20 and 39, with a modal category of 25-29. With home

students, ages lie between 20 and over 55, though the modal category is also 25-29.

Overall 77% of the students are aged between 20 and 34 with modes of 20-24 for

qualifying year student, 25-29 for MS candidates, and 30-34 for PhD students

respectively.

Table 3.7 - Age Ranges of StudentsFull Time Part Time Overseas UK

20 – 24 9 7 225 – 29 10 2 8 430 – 34 3 3 4 235 – 39 2 1 2 140 – 44 1 145 – 49 1 150 – 54 1 1over 55 2 3

3.6.2 Students by Sex

As there was no question about the respondent’s gender or sex on the

questionnaires, this data was collected from Departmental administrative staff. While

women outnumber men by a 2:1 margin, the postgraduates are more sex balanced

than the undergraduates, where women outnumber men by nearly 10:1. While, as

Table 3.8 indicates, the proportion of women to men was relatively balanced among

full-time students, women part-timers outnumbered men by a ration of 3:1. Among

the degree candidates, women outnumbered men by 3:2 margins, and all qualifying

year students were women.

Women students also were more likely to be older. While slightly under half

of the female students (47.4%) started a postgraduate degree in their 20s, 75% of men

had not yet reached their thirties. While women spanned the age categories, 79% of

women were in their twenties or thirties. The age group Over 55 included one man

and one woman.

Table 3.8 - Sex By Full and Part Time Study (With Column Percentages)Sex Full Time Part Time Totals

Male 16 (42.1%) 5 (33.3%) 21Female 22 (57.9%) 15 (66.7%) 37Totals 38 20 58

Table 3.9 - Sex by Degree Sought (With Column Percentages)Sex Qualifying Year MA and MPhil PhD Totals

Male 0 ( 0%) 14 (39%) 7 (39%) 21Female 4 (100%) 22 (61%) 11 (61%) 37Totals 4 36 18 58

3.6.3 Students by Ethnic Origin

Table 3.10 shows the ethnic origin of students in the Department. As I

inadvertently missed out the word South from South East Asian in questionnaire one,

the Asian and East Asian categories have been grouped together as one variable. I

also have grouped the two people who selected the option “Other” into the Asian

category. One of these people recorded “Korean” as his ethnic origin, while the

second used the description of Mongolian extraction. White, followed by Asian

students account for the majority of students in the Department. Curiously, the ethnic

composition of the staff, while not quite meeting the same ratio of white to Asian

members, parallels this aspect of the ethnic composition of the postgraduate students,

and some of the white lecturing staff also speak Asian languages.

Table 3.10 - Students by Ethnic Origin

Ethnic Origin Number of StudentsAsian / East Asian 18

Latin American / Hispanic 1African / Caribbean 4

Middle Eastern 1White European 30

White Other 4

3.6.4 Students by Location of Housing

I added the category living in London to the original five categories (listed

first in Table 3.11) in questionnaire one after I observed that all of the people who

selected the “other” option were living in London. Though Essex regulations require

students to live within 20 miles of the University, some exceptions are clearly taken

for three full-time an two part-time postgraduates. The three most popular housing

options include living locally in their own home (the modal case), living on campus,

and renting in Wivenhoe.

Table 3.11 - Students by Type of AccommodationLocation of Housing Total Full Time Part Time Overseas UK On Campus (University) 15 15 13 2Off Campus (University) 3 3 2 1Wivenhoe (Rented) 13 8 5 4 9Colchester (Rented) 6 6 6Own Home 16 3 13 16London 5 3 2 4 1Totals 58 38 20 29 29

With housing, the two main predictive variables are whether the students are

home or overseas and whether they are part-time or full-time. As part-time students

are effectively blocked from being in University-owned accommodation, all live off

campus. Home students, who both have greater opportunities and more personal

reasons to invest in property in this country, are more likely to live off campus in their

own homes.

3.6.5 Degrees and Schemes of Study

Postgraduates most regularly elected to study for PhDs or MAs in straight

sociology, as Table 3.12 (presenting the number and percentage of students on each

course) reflects. The MA by dissertation and the MA schemes in psychoanalytical

studies, culture, and economic development had no students in the 1996/1997

academic year. Table 3.13 indicates that home students most regularly opted for part-

time rather than full-time MA schemes (2:1), though this ratio precisely reverses

among home PhD students. Overseas students preferred full-time study for all

courses. All qualifying year students come from South East Asia and study full-time.

Table 3.12 - Students by Course of StudyCourse of Study Number of

Students%

PhD 18 31.0%Doctoral Program 3 5.2%

MPhil 1 1.7%MA Community Mental Health 4 6.9%MA Social & Cultural History 4 6.9%MA Gender, Culture & Society 3 5.2%

MA Development 2 3.4%MA Sociology & Health Studies 2 3.4%

MS Sociology Government of Japan 1 1.7%MA Pacific Rim Studies 2 3.4%

MA Sociological Research Methods 3 5.2%MA Sociology 11 19.1%

Qualifying Year 4 6.9%

Table 3.13 - Students by Degree, Home/Overseas and Full/Part-Time StatusUK Students Overseas Students

Full-Time Part-Time Full-Time Part-TimeQualifying Year 4

Masters 7 13 11 2PhD 6 3 10 2

Selection of the different masters courses differs across the full-time/part-time

and overseas/home matrix as well. More than half the full-time students working

towards the MA in sociology degree are younger students, and often from overseas.

Overseas students also display a tendency to follow courses based around their home

culture. Half of the part-time students are following what could be described as

practical MA subjects, health and research methods. This could be explained as part-

time students are older and more likely to already have regular employment.

Table 3.14 - Detailed MA Scheme SelectionFull Time Part Time

Overseas 7 MA Sociology2 MA Pacific Rim Studies1 MA Soc\Gov of Japan

1 MA Health

Home 3 MA Sociology2 MA Gender Culture & Society2 MA Development1 MA Research Methods

1 MA Sociology1 MA Gender Culture & Society4 MA Community Mental Health2 MA Research Methods1 MA Health4 MA Cultural History

3.8 Summary

To summarise, data was collected using three self-completed questionnaires

administered at three points when students would be likely to be forming

acquaintances. Each questionnaire contained a network section as well as questions

gathering data on other subjects. The overall response rate of students completing at

least one questionnaire was 88.5%, and over 60% responded to each individual

questionnaire.

Several trends emerged among the demographic data. Women outnumbered

men in most categories, and constituted all qualifying year students. Age trends also

emerge in what people study and whether they study full- or part-time, though the

best predictor of a student’s demographic characteristics is where they fit into the

matrix of home and overseas and full- and part-time students. Table 3.15 summarises

these general characteristics.

Table 3.15 - A Summary of the Demographic DataFull Time Part Time

Overseas Qualifying year Asia womenMA sociology (60%), and PhDYoungerLive in university accommodationMost overseas men

Live in London (50%)Older

Home MA Sociology (50%) and PhDYoungerRent off of campusMost home men

Mainly MA (81%)Study health or methods MAsOlderLive in own home (65%)Mainly white women

Chapter 4 : Student Acquaintanceship Networks

4.1 Introduction

This chapter reports the findings from the main focus of this dissertation: how

students make a support network of acquaintances during their first six months in the

Sociology Department at Essex University. After providing definitions for techniques

used and setting out the assumptions made during this analysis, this chapter analyses

the three Waves of acquaintanceship data collected in the first, third and sixth months

respectively. In these sections, I include the network graph, numbers of students,

numbers of acquaintance pairs, and measures of centrality, as well as noting isolates

and gatekeepers within the department, and identifying the attributes of the cliques

formed within the body of graduate students.

This chapter then turns to discussion of the features of acquaintance cliques

which stayed constant, and those which changed over time. The chapter then

examines the influence of other variables on acquaintance formation. Asian students

integrated differently than whites, and full-time students generally developed wider

acquaintance networks than part-timers. The graduate weekend partially levelled the

origin and period of study differences. The chapter concludes with my

recommendations for facilitating the earlier formation of acquaintance networks,

especially for students who are more likely to become isolated.

4.2 Definitions and Assumptions

Variations of the concept of gatekeeper appear in the network literature. In

this dissertation, I am defining a gatekeeper as a student (A) who is positioned in the

association graph between two other students (B and C). A is thus positioned to act as

a connecting gateway or gatekeeper between B and C. Unless B and C become

acquainted, both could gain information about the other via A. I am more interested

in students situated so that they might perform this task for a great many students.

Students in such a position will have both a high point centrality in the graph and a

high flow of students who pass through them to reach other students by the shortest

path (an approach developed by Freeman, 1979). Freeman’s measure of flow

centrality, which the UCInet network analysis package I am using calculates, will thus

be used to identify gatekeepers. I am defining isolates as students who had no

acquaintances, and outliers as those who identified only one acquaintance.

There are assumptions that I have made during this analysis which I feel

should be detailed up front rather than hidden in the text. First, I am assuming that if

student A knows student B, then B knows A. Many network studies of students have

found that one student’s feelings of friendship (or animosity) toward another may not

be reciprocated (Newcomb, 1961; 1968; Hallinan and Wilson 1989). While I initially

asked students to distinguish between other students they simply knew and those that

they considered to be friends, a majority of respondents completed only the “know”

line or otherwise confused the columns (such as marking someone as a friend but not

as someone they knew). By the end, I was only left with data on who knew whom. In

the absence of data on how students evaluated each other, the assumption of

reciprocity of acquaintanceship can be more readily made. Leaving memory

problems aside, the odds that an event which has enabled two students to meet each

other will foster mutual recognition between A and B is reasonably high, even if such

events do not result in reciprocated acquaintanceship in absolutely every case. The

assumption of reciprocity helps to mitigate against the effects of missing data from

students who did not complete questionnaires. Even so, the collection of data which

only indicates who knows whom rather than what each person thinks of the others

means that the majority of the following graph analysis will be structural rather than

substantive.

It also should be noted that missing cases may have skewed the results,

especially in Waves 1 and 2. The cliques to which people who responded belong will

surface in the analysis, while some cliques of non-respondents may be missed out.

Some potential error has to expected and taken into account when reading the results.

4.3 Wave 1: After One Month at Essex University

In the first wave, as would be expected, students are setting about making

acquaintances at their new university. There are several students who knew each

other before starting their course who obviously had a head start in this process. In

this wave, the nationality of the student proved to be a key variable influencing who

makes whose acquaintance, and how many acquaintanceships are made. It should be

noted that there were 12 students on the first questionnaire who never started their

course, which accounts for some of the non-response difficulties with the first wave

data.

4.3.1 Wave 1 Network Graph With Numbers of Students and Acquaintances

The graph of student links for Wave 1 (Figure 4.1) is given on the following

page. There are several groupings in the graph which are noticeable to the eye, and

which will be explored in the clique subsection later. Table 4.1 below highlights basic

details about the graph.

Table 4.1 - Summary of the Wave 1 Graph DataNumber of names on the questionnaire 83Number of completed questionnaires 32

Number of nodes in the graph 61Number of isolates 10

Number of links \ acquaintanceships 252 (Mean 3.0)

Figure 4.2 shows how the number of acquaintances are spread amongst the

students. One MA student (No. 59) met 15 people, more than anyone else. The

numbers declined steadily from this maximum. This student will be highlighted again

later, as No. 59 became a major player in the student body.

Figure 4.2 - Number of Acquaintances Made by Students in Wave 1

Range = 0 - 15 and Standard devation = 3.323

Few variables have a significant correlation with the number of acquaintances

which students made. Across the complete group of respondents in Wave 1, neither

sex (men gained a mean of 4.3 acquaintances compared with women’s mean of 4.2),

nationality (home students made an average of 3.8 acquaintances, compared with the

overseas mean of 4.8), nor age had statistically significant effects on the number of

acquaintances a student made. The largest difference occurred between the mean

number of contacts made by full-time students (4.8) and part-time students (3.2).

Part-time females, on average made more contacts (3.7) than part-time males (1.0);

and overseas part-timers made more contacts (5.0) than home part-timers (2.9). In

both cases, however, the number of respondents is too small to allow for analysis of

statistical significance. Table 4.2 shows the only significant finding: the location in

which the student lived had an effect on the number of acquaintances they made.

Table 4.2 - Residence by Mean Number of Students Known in Wave 1

Residence Mean Number of Students KnownUniversity-Owned On Campus 6.8 (sd 3.4)University-Owned Off Campus 3.7 (sd 3.8)

Rented Colchester 4.4 (sd 2.8)Rented Wivenhoe 4.8 (sd 3.4)

Own Home 2.7 (sd 2.6)London 2.7 (sd 3.1)

4.3.2 Isolates and Outliers

There are ten students who have no links with any other student, and therefore

could be described as isolates. Five of the isolated students are full-time. Three of

these five are Asian, and the other two live in London. All five part-time isolated

students are studying for masters degrees and live in their own homes. Curiously,

three students came from within the department and knew each other, but by Wave 1,

they had not made the acquaintance of any of the new intake of graduate students.

These three could be also described as an isolated group away from the main body of

graduate students.

The outliers in the data were students with only one acquaintance. There are

14 outliers in the first wave, and eight of these gave personal details. Seven out of

eight were female (though the six who did not give details were all male, giving an

equal sex split in the Wave 1 outliers); five of the eight are white; and five of the eight

live in their own home. I feel that every effort should be made in future to assist

students in the groups identified above to integrate more quickly into the Department.

4.3.3 Cliques

In keeping with the findings of Newcomb (1961), this data reveals that

students are clustering into acquaintance cliques early - by the first month. The

largest example of a clique includes two partially inter-locking groups of four Asian

students.

There are 18 triads in the Wave 1 data, which are shown plotted in Figure 4.3,

and two cliques of four students. Figure 4.3 shows an isolated group which is made

up of mainly home PhD students. The rest of the students are grouped into three sets.

One set contains Asian students, including the two inter-locking cliques of four

described in the previous paragraph. The second set includes white MA students, and

the final set includes the other overseas students, as well as a few Asians and British

minorities. The students that connect these three sub-groups together (59, 51, 58, 47)

can be described tenuously as gatekeepers, and the next section turns to discussion of

the students who fulfil this role.

4.3.4 Gatekeepers

To identify gatekeepers in the data, I am using Freeman’s geodesic betweeness

centrality (as described in Freeman 1979). This measure creates values for each node

(or point representing a particular respondent) on the graph which represents the

number of paths that have to pass through this point when every student is connected

to every other student by the shortest path. These values can also be standardised by

dividing by the number of nodes in the graph to enable comparison between graphs

measuring similar relationships. Table 4.3 contains the results from applying

Freeman’s betweeness centrality to the plot in Figure 4.1 with standardised scores

above an arbitrarily selected value of 5.00.

Table 4.3 - Standardised Betweeness Centrality Values for Wave 1 GatekeepersPerson Identifier Value Standardised Values

59 818 12.3351 485 7.3158 405 6.1147 344 5.19

Mean = 75.46(1.14) with a Range = 0(0) to 818.99(12.33) and Standard deviation = 131.88(1.98)

All four of these students are very gregarious. The first, a white MA student,

and the second, a white PhD student, act as a bridge between British and European

MA and PhD students to the clique of Asian students. The third and forth gatekeepers

are Asian, one of whom is a gatekeeper between white and Asian students, and the

other who is a central figure in the Asian clique.

4.3.5 Summary

By this wave students, were starting to get to know each other and find their

feet in the Department. Some Asian students effectively formed networks quickly,

while others had problems and were initially isolated. Some part-time students and

students living in London also experienced isolation. Most students had made

acquaintances and established their place in the Department early in the term.

4.4 Wave 2: The End of the Autumn Term

Student acquaintance structures in this wave still largely reflect clustering of

students by nationality; however, some interaction between clusters emerges among

people following the same schemes of study. A few gregarious home and overseas

students had made a large number of new acquaintances. By this wave, full-time

students are establishing more contacts than part-time students.

4.4.1 Wave 2 Network Graph with Numbers of Students and Acquaintances

Figure 4.4 displays the graph of student links for Wave 2. Table 4.4

summarises the graph’s basic details. Figure 4.5 shows that while the spread of

acquaintances is more even by this wave, the number of acquaintances decreases

steadily from the most connected MA student (No. 59), who knows 17 people. Three

overseas students, Nos. 46, 56 and 48, have 15, 14 and 13 friends respectively and

are becoming central players in the graduate student population.

Table 4.4 - Summary of the Wave 2 Graph DataNumber of names on the questionnaire 70Number of completed questionnaires 41

Number of nodes in the graph 65Number of isolates 5

Number of links \ acquaintances 371 (Mean 5.3)

Figure 4.5 - Number of Acquaintances Made by Students in Wave 2

Range = 0 - 17 and Standard devation = 3.965

Whether a student studies full-time or part-time has become a significant

determinant of the number of acquaintances they are likely to have. Full-time

students, on average, have 7.2 contacts, while part-time students know an average of

3.4 students. This pattern surfaces across all ties of students, but is particularly

pronounced among overseas students. Full-time overseas students know a mean of

7.5 people, compared with the mean of 2.6 people known by part-time overseas

students. Overseas students generally continue to have made more contacts than

home students, with overseas students in Wave 2 knowing a mean of 7.0

acquaintances, and home students knowing 5.0 other people on average. Tables 4.5

and 4.6 highlight the distributions by sex and the location of accommodation. The

differences of means by sex are not statistically significant, but the differences of

means by location of housing are highly significant. The independent sample two-

tailed T-Test for equivalence comparing mean acquaintances made by students living

on campus compared with the mean of all other students is significant at the level of

p<.001. The independent sample two-tailed T-Test for equivalence comparing mean

acquaintances made by students living on campus, in university accommodation, or

renting in Wivenhoe, compared with the mean of all other students is significant at the

level of p<.004. Other differences in this table have lower levels of significance, but

are unlikely to have happened by chance.

Table 4.5 - Mean Students Know in Wave 2 by SexAll Students Part-Time Only Overseas

StudentsFemale 5.4 3.5 6.1Male 7.0 3.3 8.0

Table 4.6 - Mean Students Known in Wave 2 by Location of AccommodationAll Students Part-Time Only Overseas

StudentsUniversity owned

On Campus8.9 0.0 9.0

University owned Off Campus

7.0 0.0 5.5

Rented Wivenhoe 7.2 0.0 7.2Rented Colchester 5.3 2.6 4.0

Own home 3.7 3.9 0London 4.4 2.0 3.0

4.4.2 Isolates and Outliers

By this wave, nine students remain outliers, and of these, five had completed

the questionnaire. Five of the outliers are part-time, and five are male. Five students

had no recorded links with other students in the Department. One of these isolated

students started late, thus having less time to meet people and also facing the

challenge of breaking into structures which had already formed. Three of the four

other isolated students three were part-time MA and MPhil candidates, while the forth

was an older PhD student. As can be seen by this Wave, the Asian students have

become more integrated into the student body, while some part-time students still

remain isolated.

4.4.3 Cliques

Again, consistent with the findings of Newcomb (1961), (Moreno, 1934), and

(Hallinan, 1978/79) this study found that the number and size of cliques had increased

by Wave 2, as Table 4.7 displays. The largest of the cliques again is made up of

Asian students, being a superset of the previous two cliques of four plus an additional

member. The plot of the cliques of five and above reveals three distinct groups. One

of the smaller groups shown in Figure 4.6 includes white, home, female masters

students, and the other smaller group contains PhD students of varying ethnic origins.

The largest group is made up of 12 Asian students and one home student, No. 58, who

has been highlighted previously as both having a large number of contacts and being

central to the main student population.

Table 4.7 - Size and Number of Cliques in Wave 2Size of cliques 3 4 5 6

Number of cliques 45 28 11 1

4.4.4 Gatekeepers

To my surprise, this wave produced a larger number of gatekeepers than Wave

1. Two factors may account for this finding. First, by this wave the cliques of

students were becoming stronger, while the links between the groups stayed roughly

the same. Consequently, a greater number of paths go through the gatekeepers to

reach other cliques. Second, by this wave the students who did not attend were

dropped from the roster, reducing the matrix by over 20 cases, thus, the number of

students reaching the standardised centrality value of five has increased to eight (with

the exact values gained by each appearing in Table 4.8). Five of the gatekeepers act

as bridges between the Asian student community and the rest of the students. Student

77 linked females across many backgrounds, and student 25 links a groups of students

who are all white and predominantly female to the graduate population at large.

The most interesting student is No. 34, who is a white PhD student who knows

only two other students, Nos. 42 and 51. These two students, in turn, have large

groups of mutually exclusive contacts, with the exception of student 34. The only

link between these two groups of students is via No. 34. The first person student 34

knows is a white PhD student and who has contact with mainly white British and

European PhD students, while the second student is Asian and knows exclusively

Asian students (apart from 34). Student 34 thus is positioned to connect the two

predominant ethnic groups, even though this person has only two personal contacts.

Table 4.8 - Standardised Betweeness Centrality Values for Wave 2 GatekeepersPerson Identifier Value Standardised Values

15 623 13.282 519 11.0748 452 9.6525 437 9.3234 328 7.0177 312 6.6557 296 6.3259 259 5.53

Mean = 91.20(1.94) with a Range = 0(0) to 623(13.28) and Standard deviation = 133.36(2.84)

4.4.5 Summary

In this wave, part-time students had just under half as many friends as full-

time students. Isolated students were mainly part-timers, though the number of

isolated students had dropped to under half the total in the previous wave. Also, the

Asian students had formed a highly interconnected group within the graduate

population. Several gregarious students were making associations with members of

multiple major clusters. To summarise, the students in the Department were making

more acquaintances, and contact dyads and triads were clustering into larger groups.

4.5 Wave 3: The Final Acquaintance Networks

As would be intuitively expected, by Wave 3 the student population has grown

very interconnected, with students making more friends and acquaintances as time

passes, and fewer students experiencing isolation. Students reported that the graduate

conference, held immediately before this wave, had a major effect on the formation of

new acquaintanceships. There are still some noticeable structures and groups within

the graph, and the trend highlighted in the previous section for part-timers to have

fewer contacts than full-timers is further exacerbated.

4.5.1 The Final Network Graph with Numbers of Students and Acquaintances

Figure 4.7 displays the final network graph of student links, and Table 4.9

summarises the basic details about the graph. Figure 4.8 shows that the number of

acquaintances has both greatly increased since Wave 2 as well as spreading more

evenly amongst the students than in the previous two waves.

Table 4.9 - Summary of the Wave 3 Graph DataNumber of names on the questionnaire 67Number of completed questionnaires 37Number of nodes in the graph 64Number of isolates 3Number of links \ acquaintances 796 (Mean 11.8)

Figure 4.8 - Number of Acquaintances Made by Students in Wave 3

Range = 0 - 33 and Standard deviation = 8.619

As with Wave 2, the most significant indicator of the number of acquaintances

that a student had made was whether the student was full-time or part-time. By this

Wave, the part-times continue to have a mean number of acquaintances (8.0) half the

size of the mean full-time acquaintance circles (16.7). Table 4.10 shows that part-

time females made fewer contacts than part-time males. Part-time students who live

in their own home made fewer contacts than part-time students who live in London,

and, as Table 4.11 displays, considerably fewer acquaintances than those part-timers

who rent accommodation in Colchester. As with the previous waves, overseas

students on average had made more contacts (16.4) than home students (11.5).

Table 4.10 - Mean Number of Contacts by Sex in Wave 3All Students Part-Time Only Overseas Only

Male 15.2 8.8 15.9Female 13.2 4.0 17.0

Table 4.11 - Mean Number of Contacts by Residence in Wave 3All Students Part-Time Only Overseas Only

University Owned On Campus

18.9 0.0 17..6

University Owned Off Campus

10.6 0.0 9.5

Rented Wivenhoe 18.4 0.0 18.4Rented Colchester 15.4 11.6 22.0

Own Home 7.6 4.7 0.0London 12.8 7.7 9.5

4.5.2 Isolates and Outliers

By this final wave, there were only three students that were isolated within the

student community. One student started late, dropped out quickly, and made few

visits to the campus. I know through my own contacts that a second student of

Middle Eastern origin had friends in the Department, but this person’s friends either

were not among the new graduate student cohort or were only among graduate

students who declined to participate in my study. The third isolate is an older, part-

time MA student who had been recorded as an isolate since Waves 1. Similarly, the

number of outliers in this Wave reduced to three, but I have data on only one of these

students. She is a full-time, white PhD student.

4.5.3 Cliques

By the time this wave was completed, the graduate student cohort was

becoming very interconnected, and, therefore, the analysis of the cliques had become

less revealing. Table 4.12 below shows the number of cliques found in the data for

Wave 3. The clique with nine members contains Asian students. Cliques with seven

and eight members cross the divides which surfaced in previous waves. Of more

interest in this wave are the students who are not included rather than those who are

included in these groups. Those excluded are mainly part-timers who, on average,

had fewer acquaintances.

Table 4.12 - Size and Number of Cliques in Wave 3Size of cliques 3 4 5 6 7 8 9

Number of cliques 38 22 43 71 20 6 1

4.5.4 Gatekeepers

Wave 3 contained only half the number of gatekeepers (4) which appeared in

Wave 2. One of these four students is white, and the other three are Asian. All were

positioned to perform the role of gatekeeper between Asians and other students.

Asians remained the less well-integrated compared to white students, though it has to

be noted the integration of students had come a long way since Wave 1.

Table 4.13 - Standardised Betweeness Centrality Values for Wave 3 GatekeepersPerson Identifier Value Standardised Values

48 549 12.8159 356 8.312 339 7.9247 284 6.64

Mean = 63.37(1.48) with a Range = 0(0) to 549.53(12.81) and Standard deviation = 98.14(2.29)

4.5.5 The Graduate Conference

I have found that, as many in the Department had hoped, the graduate

weekend is a time when graduate students meet and make friends. The 31 students

who completed the Wave 3 questionnaire, administered on the coach on the way

home from the graduate conference, reported making a mean of 7.90 new

acquaintances. The mean for overseas students was higher, 8.61, compared with 7.38

for home students. Asian students met a mean of 12.25 new contacts. It is possible

that these finding are not as dramatic as they may at first seem. The questionnaire did

require that students could put a face to a name. It may be that students were better

able to match faces of acquaintances to names by the end of the graduate weekend.

At the very least, however, these data do indicate that the quality of acquaintances did

improve after this event. To summarise, even if half the reported number of new

acquaintances made at the graduate conference were actually “new”, the conference is

well worth holding from a networking perspective. This is especially true for

overseas students. The impacts of the graduate conference will be investigated further

in the next chapter.

4.5.6 Summary

By this final wave, the graduate students had become more interconnected.

Fewer students were isolated, though part-time students had fewer than half as many

contacts as full-time students. When looking at acquaintance groups, the Asian

students are again the most interconnected group, but now the separate groups of

friends are more integrated together. The graduate conference is reported to have

helped a great deal in facilitating the creation of new acquaintances and in extending

support networks, though the length of time spent at Essex must also be considered a

major factor in this finding.

4.6 The Development of Students Friendships Networks

This next section is intended to bring together the findings from the previous

sections, both to look at the findings as one unit and to examine how acquaintances

develop over time.

4.6.1 Raw Statistics from the Three Waves

As would be expected, the number of acquaintances, and, therefore, the

average number of acquaintances per person, increased from Wave 1 to Wave 3. The

largest increase occurred between Waves 2 and 3. This rise partly reflects increasing

familiarity with names and faces, helped by the graduate weekend, but also reflects

the integration of some isolates (discussed further in Section 4.6.4). Table 4.14

summarises the increase in contact between students. Another less major trend to be

noted is that there is very little change in the average number of acquaintances per

person between Wave 1 and Wave 2, which could possibly indicate that, as Newcomb

found, the friends made in the first few weeks are the friends you keep throughout a

university course. The approximate answer to the question posed in the primary aims,

how fast are acquaintances made, is one acquaintance per academic week. Next we

look at how these individual acquaintance dyads group together to form cliques in the

data.

Table 4.14 - Summary of Acquaintances by WaveWave Number of People

(Excluding Isolates)Number of

AcquaintancesMean Acquaintances

Per Person1 60 252 4.22 65 371 5.73 64 796 12.4

4.6.2 Changes in the Acquaintance Groups

As time goes by, students form a greater numbers of acquaintances. Table

4.15 summarises the total number and size of cliques by wave. It should be noted that

overlapping cliques of smaller values were removed when calculating these figures,

accounting for the apparent anomaly in the figures for Wave 3.

Table 4.15 - The Size and Number of Cliques by WaveSize of Cliques

3 4 5 6 7 8 9Wave 1 18 2Wave 2 85 40 12 1Wave 3 38 33 43 71 20 6 1

The largest clique in each wave has always been made up of Asian students.

In Wave 1, this group was very much isolated from other groups of students, though it

should be noted that Asian students make acquaintances very quickly within their

ethnic group. In the first two waves, gatekeepers were central features in the network

graphs as a whole, being the people who had the most contacts. The five students

who performed this role were mainly very out-going and friendly home students. In

the second wave, other cliques of students formed mainly on scheme lines. After the

graduate conference, students reported meeting on average 7.5 students whom they

had not previously met. By the third wave, the students had formed a connected

group with a 30% interconnection rate, though three students remained isolated and

one person knew only one other person.

This section answers two questions from the primary aims: how do cliques

develop; and do acquaintance circles change over time? Cliques initially formed

along ethnic lines. By Wave 2, acquaintances also grouped on course and scheme

lines, and became more widespread by the final wave. Second, once an acquaintance

dyad forms, it stays formed, and later joins with other dyads and groups over time.

4.6.3 Variables That Affect Acquaintance Formation

In answer to the question from the primary aims: what demographic factors

affect acquaintances, I found that ethnicity, scheme, and whether a student is full-time

or part-time had significant influences. While the age and the gender of students had

very little impact, ethnicity proved to be the most significant factor in the first few

weeks, while scheme and period of study rose to greater prominence at the second

wave. The graduate weekend also had a pronounced effect on networking, with

students making an average 7.5 new acquaintances during this event. Part-time

students, who tended to live in their own homes, had fewer than half the contacts

made by full-time students - even by Wave 3. Students who live in London also

tended to have fewer acquaintances than students who live more locally, particularly

those renting accommodation (either through the university or privately).

Nevertheless, I suspect that I have only just scratched the surface of the many things

that affect student acquaintances in the Department. The next section turns to the fate

of the isolates in the student population.

4.6.4 Changes in Isolation Over Time

The number of isolates in the student population reduced with each successive

wave, though, as Table 4.16 shows, some students did not integrate. In every wave,

part-time students were over-represented among the isolates. In the first wave, Asians

and students based in London also were more likely not to know anyone, though most

of these people had made connections by Wave 2. This section thus answers one of

the primary aims: are there any isolates among the students, and if so, who are they?

I feel that more effort could be made to reach out to potentially isolated students by

organising meetings or events in the first week of term, and particularly encouraging

part-time and Asian students to attend so that they can meet each other as well as

other students.

Table 4.16 - Number of Isolates by WaveWave Number Number of Isolates

1 102 53 3

4.7 Recommendations for the Future

Although there is no one single recommendation that I feel will cause new

graduate students to integrate either faster or more completely, there are several

actions the department might take to facilitate networking. First, it could encourage

students to bond sooner by moving the graduate weekend to the end of the Christmas

term. Every effort should be made to encourage attendance by students, especially by

those in groups identified as having significantly fewer acquaintances than the

majority of students. Some changes could be made to broaden the appeal of this event

to a wider audience.

Certain groups of students have problems bonding than other students. Part-

time MA students, especially those on health-related courses, experience the greatest

problems. To help them, it might be advisable to condense the days in which

graduate courses occur to, ideally, one day. The day the health students come into the

Department ideally should be the same day as the current disputes course or the

research methods core course, which might facilitate closer ties as well as injecting

some realism into the full-time students. The department also could actively

encourage part-time students to participate in special meetings during the introductory

conference, to introduce them to each other if no one else. A greater emphasis could

be placed on encouraging the students to attend the graduate conference, though the

previous theoretical bias of the conference did have some negative effects.

The other groups more likely to experience isolation or outlier status are part-

time research students, research students living in London, and Middle Eastern

research students. Currently, there is no reason for these people to frequent the

Department or to meet other people. Possibile solutions might be to strictly enforce

attendance at Departmental seminars for graduates, and to synchronise these seminars

with the day of the MA current disputes class.

4.8 Summary on Acquaintance Networks

The postgraduate student cohort which started in Autumn 1996 had integrated

nearly completely by the early part of the Spring term. Most students in the

Department seemed to make individual acquaintances quickly. Formation of larger

groups took longer. Though there were some notable exceptions, students integrated

a lot better than I had expected.

Chapter 5 : Being a Graduate Student of the Essex Sociology Department

5.1 Introduction

This chapter reports on the opinions students have formed of their experience

of being a graduate student of the Sociology Department at Essex University. These

opinions were solicited on the questionnaire handed out at the Graduate Conference

Weekend, around the subject of students before and after their entry in Sociology at

Essex. The questionnaire asked for information about recruitment to the department,

people’s research and study aspirations, intentions following the completion of their

studies, and students’ sources of information and help with academic work while in

the department.

5.2 The Graduate Conference Weekend

58 students and 12 members of staff attended the 1996/97 graduate conference

weekend. Thirty-seven attending students completed questionnaires, and of these, 35

indicated that they had found the weekend worthwhile. In the previous chapter, I

investigated the friendships made during the weekend. This section examines and

summarises student comments on the conference; including what they liked and

disliked, their suggestions about the future timing of this event; and a summary of

suggestions on how future conferences might be improved.

5.2.1 Positive Feed Back

Students reported enjoying the conference for a variety of reasons. Around

one-sixth (14.2%) most appreciated the formal academic discussions. One person

commented that these sessions “clarified several theoretical issues with which I was

unsure”. The majority of students preferred either “informal discussions during

coffee breaks” (40%) or “socialising with other students” (37.1%). Other people

(8.5% ) found the conference environment appealing. One wrote that the “weekend

away (in a) different setting led to different institutionalised dynamics”; while another

noted that “it was a ‘safe’ environment to express ideas, opinions etc.” Table 5.1

details the number of students selecting each option in the “things they liked”

category.

Table 5.1 - What Students Liked About the Graduate Conference WeekendWhat Respondents Liked Number of RespondentsFormal Academic Sessions 5

Informal Discussions Between Sessions 14Socialising With Other Students 13

The Facilities of the Conference Centre 3

Table 5.1, however, does not show the full picture. First, part-time students,

who comprised 34% of the responding attendees (12 part-time to 23 full-time

students) were more likely to highlight academic reasons for liking the conference,

such as reporting that it provided “a chance to focus on my studies”. Two-thirds

(66.7%) of part-time students gave such answers, compared with less than half

(43.5%) of the full-time students. The proportions reversed among students who

reported that they got most from socialising at the weekend: one third (33%) of part

time student selected this option, compared with 56% of full time students. Ten out

of 21 white European respondents indicated that they got the most out of socialising

during the weekend, while people from other groups were more likely to give other

answers.

To summarise, the majority of students, particularly full-timers and white

Europeans, preferred either the “informal discussions during coffee breaks” or the

“socialising”, with part-timers disproportionately getting more from the academic side

of conference.

5.2.2 Negative Feed Back

Only 23 of the 37 respondents reported disliking some aspect of the weekend

(Table 5.2 offers a breakdown of these reasons). The most striking comments in this

section arose from nine students who reported feeling put off, threatened or confused

by the academic work, with an additional eight students indicating that they did not

have time in their schedules for the additional work expected by the conference. The

nine students who felt threatened splits into two sub-groups. Five are UK nationals

doing part-time masters degrees. Four of these five are aged between 35 and 39 years

old. This group represents 38.4% of the part-time students who attended the

conference. These five people offered more text in the dislike section than on other

areas of the questionnaire. They expressed dislike of the “language and power”,

“sociological language”, and atmosphere being “too theoretical”. One lamented that

“the sociology-speak did not give a voice to other disputes etc.”; while a second

dismissed the “rather woolly discussions not covered in the set readings”. A third

disliked “the lectures in the big room”.

The other four students who disliked or were confused by the conference

where all East Asian, and three were completing the qualifying year. These people

felt “intimidated by the sociological words”, with one noting that “it was difficult for

me to speak”. Only four students on the qualifying year attended the conference.

Table 5.2 - What Students Did Not Like About the ConferenceWhat Respondents Disliked Number of Respondents

Disliked/Confused by the Work 9Too Much Work (for Academic

Reasons)8

Too Much Work (for Social Reasons) 2The Location of the Conference 1

Socialising Needs Improving 3

Ten people complained of too “much reading as preparation for conference”,

generally “too much work” and “not enough time to sleep”. Over half (57.1%) of the

full-time students made such remarks, in contrast to only 22.0% of the part-time

students. Perhaps a correlation exists between the work ethic or ability to budget

work time of people who have a job to earn their living and those who have the luxury

of full-time study.

Four full-time students commented on location and social program at the

conference. One would have preferred “no disco”. Another suggested that “I would

like the entertainment program to be improved upon in future”, and a third regretted

“having to stay outside of Danbury”. The fourth, an Asian student with whom I have

to agree, felt that the atmosphere in some communal gatherings was marred by the

fact that “most of them smoked”.

To summarise, both part-time and qualifying year students had problems with

the language and theoretical content of the conference (though part-timers also were

more likely to appreciate the generally academic-oriented atmosphere). Full-time

students wanted less work, and some students wanted an improvement in the social

events.

5.2.3 Timing of the Graduate Conference

Table 5.3 below displays students’ preferences on the timing of the

conference. Over half (51.4%) of the responding students felt that the conference

should be held at the same time next year. Reasons for this preference included a

feeling that the “first term is very hectic anyway; I think it would be difficult to fit a

conference in”; that a gathering in the first term is “too soon”; and the spring timing

“brightens up a dark month”. This feeling varies depending on whether a student

studies full- or part-time. Nearly three quarters (69.2%) of part-time students prefer to

have the conference at the same time next year, in contrast with 40.9% of full-time

students. While two people reported having complex schedules and needing the time

to plan for the conference in advance, most of those happy with the conference timing

offered only generally affirmative notes, such as “OK as is”.

Table 5.3 - Student Preferences for the Timing of the Graduate WeekendMonth Number of Students Selecting the OptionOctober 6

Later Autumn 9Spring ( Stay the Same ) 18

Easter 1Summer 1

Not everyone felt happy with the timing, however. Two people professed to

have wished for a later event. The respondent who selected the Easter break was part-

time student who asked for “any time that is not to near deadlines”. The person

selecting the summer option pointed out that there are “not many classes in the

summer term. This time there is an essay deadline and normal class readings”.

A significant 42.8% of respondents would have preferred an Autumn event,

noting that “I would make some friends” more quickly; that “I wish I had known

people earlier”; and that “spring term is a little too late”. Indeed, 70.2% of Asian

students selected an earlier option, perhaps reflecting that they have a harder time than

European students in fitting into the Essex Community upon arrival. To summarise

most students, particularly part-timers, are happy with the timing as it was; while

Asian students would prefer an earlier gathering to expand their social contacts more

quickly.

5.2.4 Summary on Graduate Conference

One secondary aim of this report is to answer to the Question - What are

people’s feelings about the graduate weekend. The questionnaires indicate that most

of students found the gathering generally enjoyable, and some, particularly part-time

students, reported feeling intellectually stimulated. Part-time students and qualifying

year Asian students, however, also felt intimidated by the level of jargon and intensity

of the theory in the conference readings. Nevertheless, part-time students had an

easier time fitting the readings into their schedules than the full-time students. Full-

time students want less work and improved social events.

A majority got more from informal discussions and socialising. While white

European students particularly enjoyed socialising, Asian students expressed a wish

for the conference to have been held earlier so that they would have been able to

enjoy expanded social contacts earlier in their Essex experiences.

5.2.5 Possible Changes for the Future

The extent to which changes may be required depends on what the Department

aims to gain from next year’s conference. If the weekend is for students to make

friends, then it would be more successfully held at the end of the first term.

Alternatively, some earlier, less intensive social events might be introduced to help

Asian students broaden their social contacts. If the conference is primarily to enhance

academic discussion, more effort should be made to help qualifying year and older

part-time students feel competently included. If the weekend is predominately for

full-time home students, then the work lode should be reduced and the social element

of the weekend expanded.

5.3 Students Before and After Sociology at Essex University

This section, included after discussion with members of the department,

covers three subjects. First, where did students first find out about the Sociology

Department at Essex University? Second, why did they chose this department over

the competing sociology departments? Finally, what do the students intend to do after

they complete their postgraduate degrees?

5.3.1 Where They Found Out About Sociology at Essex

Students first heard about the Essex Sociology Department from a far wider

range of sources than I had expected. 50% of Asian students found out about Essex

from publications, compared with 31.3% of UK nationals. One quarter of home

students reported that their supervisors or other members of staff had recommended

Essex. Nevertheless, students reporting to have heard of Essex from the other sources

shared no major demographics. Table 5.4 displays the number of respondents who

learned about Essex Sociology from each source. After further investigation,

however, I found that some responses, such as one person’s reference to “a West

Suffolk College and Prospectus”, actually referred to how students found out about

the undergraduate degree scheme, which they had completed before transferring to the

MA scheme. This means that the entries in the table are slightly misleading for home

students, and might explain the why no demographic correlations were found.

Table 5.4 - Source of Information About Sociology at EssexSource of Information Number of Respondents

Publications 8Recommendation by Previous Institution 8

Recommendation by a Friend 4Recommendation by a Previous Student 6

Reputation 3Was an Undergraduate at Essex 2

Students who found out about Essex by reading published sources consulted a

number of publications, though few Asian students indicated from where they had

obtained a university prospectus. One student listed the “University Postgraduate

Handbook” as the primary source of prior information, while another reported reading

unspecified publications in the “University Library in the British Council Library in

Bangkok”. A Cypriot student reported acquiring information “from the British

Council in Nicosia” - a recruiting route I had not expected.

A student working on an MA in Community and Mental Health opted for

Essex after reading “advice” in Community Care magazine. By the word advice, I

suspect that the student may have meant an editorial article, not a advertisement for

students. Other students studying on the same specialist part-time degree scheme

heard of Essex through a “work colleague”, through completing “my student nurse

training partly here”, and, in two other cases, because the local mental health authority

will pay if they study at Essex.

Word of mouth and interest in particular people also proved important pulling

factors for Sociology at Essex. 25% of UK nationals reported receiving

recommendations from previous supervisors or former institutions. Two of these

students reported hearing endorsements from the “Development Studies Department

at UEA”. One student learned of Essex from a an Open University text book written

by Paul Thompson which had been required reading on a syllabus in a previous

course. Another stated that “Dr. Woodiwiss tempted me when he was visiting my

university in Hong Kong”.

To summarise, students gather information from a variety of locations. Asian

students were more likely to read about Essex, while home students were more likely

to hear personal recommendations. Students on health-related MA schemes learned

of Essex through the health-oriented press as well as the encouragement of local

health authorities. It might be worth ensuring that all British Consulates and

Embassies are on the mailing list for recruiting overseas students.

5.3.2 Why Do Students Choose Sociology at Essex?

Once students had decided to study sociology and had heard of the department

at Essex, reputation proved to be the major factor in swaying the decision to choose

Essex over other institutions. Table 5.5 shows that proximity to home and course

availability also proved important factors for some students choosing Essex.

Table 5.5 - Why Students Chose to Study Sociology at EssexWhy Chose Essex Number of Respondents

Reputation 19Proximity to Home 7Course Availability 7

Other 1

With one exception, students selecting Essex because of its reputation signed

up for a full-time course. Six of the seven students who identified proximity to home

as the major reason for choosing Essex, including the two who learned of Essex when

reviewing courses for which their local mental health authority would pay, studied

part-time. Unsurprisingly, all of the part-time students are British nationals. Several

non-British residents also included a second reason for preferring Essex on their

questionnaires. The most frequent of these was being “close to London”, followed by

“the quality of the department and the range of courses it offers”.

5.3.3 What Do Graduate Students Intend to do After Their Studies?

Table 5.6 displays student’s intentions following the completion of their

degrees. People with indeterminate plans constitute the modal category, followed by

those who will continue with their studies on another course. Nearly half, however,

are or intend to be working after they complete their degree.

More interesting results appear in Table 5.7, which compares responses of

home and overseas MA students. While most overseas students do not presently hold

jobs, they have more focused ambitions, with only 22.2% (compared with 46.1% of

home students) unsure of what they will do next. Comments such as “finish my

contract with my sponsor and do a PhD” and “I am going back to my country, but I

am going to continue my study” were common from overseas students. The higher

personal commitment required to move to a new country as well as the higher tuition

paid by overseas students provides ample incentive to have more clear intentions.

Table 5.6 - Students’ Intentions After They Complete Their DegreeWhat They Will Do Afterwards Number of Respondents

Continue Their Studies 9Unsure/Don’t Know 10

Get a Job 5Research Position 5Already Working 4

Table 5.7 - Post-Graduation Intentions by Home and Overseas StudentsWhat Do Afterwards UK Students Overseas StudentsContinue Their Studies 2 ( 15.4% ) 4 ( 44.4% )Unsure / Don’t Know 6 ( 46.2% ) 2 ( 22.2% )

Get a Job 2 ( 15.4% ) 1 (11.1% )Research Position 1 ( 7.7% ) 2 ( 22.2% )Already Working 2 ( 15.4% ) 0 ( 0% )

Some of the uncertainty for UK students arises from the difficulty of securing

funding to continue to study for a PhD. Such comments as “PhD - I should be so

F**king lucky!” attest to the emotion felt by would-be continuing postgraduates. One

person plans to “use it to try and get development work overseas”. One of the two

employed MA students (who both work in the health field) expressed confidence to

be able to use the MA training “in my work - which I am doing already. It’s helpful”.

The other felt less certainty, noting that the significance of the degree is “not clear”,

but “may help my CV”.

Two of the four qualifying year students did no yet have future plans

formulated, while the other two intended to continue with their studies, but curiously,

not at Essex. One wrote that “I am going back to my country, but I am going to

continue my study”, while the other indicated that “I am going to another university in

England for an MA”. These rather surprising responses may indicate that qualifying

training and overseas student support at Essex is particularly strong, or that the

department at Essex could do more to capitalise on its reputation when recruiting

qualifying year students.

Among the PhD students, roughly even numbers of home and overseas

students reported that they did not yet have plans (apart from taking time to “bask in

it” once they have their doctorate); that they would get a job (mainly in teaching,

lecturing, writing, and/or research); or that they would gain a research position. Two

are already working, and one plans to continue with further studies.

To summarise, overseas MA students are more focused on what they intend to

do in the future than home MA students. PhD students often have ambiguous plans,

though, as they have three more years of academic work, this level of uncertainty is

not surprising. Qualifying year students either have undetermined ambitions or plan

to continue studies elsewhere.

5.3.4 Summary on Before and After Essex

If I repeated this study in the future, I would ask for more specific details in

the recruitment section. Nevertheless, the data I gathered do allow me to offer

preliminary answers to the questions I set out as goals for this research. In answer to

the: Question - Why did they choose Sociology at Essex?, I found that students

primarily chose this Sociology Department because of its reputation, and, in the case

of home part-time students, because of the proximity to home and work. In answer to

the: Question - What will they do after Essex, I found that overseas MA students are

more focused, while home MA and PhD students have less clear objectives.

5.3.5 Possible Changes for the Future

Improving recruitment will require specific targeting of different subgroups.

Potential health-related MA candidates might be reached both through the trade press

and through enhanced contact with the local health authorities. To increase the

number of overseas postgraduate students, it might be worth mailing information

about Sociology at Essex to British Consulates and Embassies, particularly in

countries where Essex has an established reputation. Also, a reassessment of the

recruitment of qualifying year students may prove of value.

The department already offers lectures in some courses, like the methods core

course, about publishing and academic employment. These activities could be

assessed together for their overall impact. Consultation with the Careers Centre or

employment agencies might offer ideas for expanding the range of information on

employment options made available to students in the Department. Faculty might

also consider tailoring some courses more specifically with an eye to how that

particular training might improve student’s employment prospects.

5.4 Students’ Sources of Information About and Help With Academic Work

To answer this secondary aim, I investigated two areas. First, I asked

questions to ascertain whom the students turned to for help with their last essay.

Second, I asked which staff the students knew and in what capacity they knew them.

I designed these questions to ascertain the level of support networks which the

Department provided or which informally developed and were used by students.

5.4.1 Who Helped With the Last Essay?

Over half of the students who responded to the question said that they had

turned to staff for help with their last essay. The staff members consulted included:

Mary James (4), Jane Hindley (3), Tony Coxon (3), Tony Woodiwiss (2), Ted Benton

(2), Mike Roper (2), and two who simply said “staff”. Table 5.8 indicates that

students who did not seek advice from staff made relatively equal use of teaching

assistants, the Resource Room, other students, or no one at all. Students who

consulted the Resource Room or other students generally reported requesting

assistance with proof-reading, and six of these seven people came from other

countries. One part-time student recorded the response “haven’t enough opportunity

to discuss any work with anyone”.

Table 5.8 - Whom Students Turned to For Help With EssaysWho Students Turned To For Help Number of Responses

Staff 18 ( 54.5% )Teaching Assistant / PhD Students 4 ( 12.1% )

Resource Room 4 ( 12.1% )Another Student 3 ( 9.1% )

None 4 ( 12.1% )

To summarise, members of staff are the most common source to which

students turn for help with essays. The most senior and the most junior staff were

more likely to be consulted. Overseas students made greater use of the Resource

Room, mainly for proof reading.

5.4.2 Staff Known by Students

Although I acknowledge that there is only a loose relationship between the

members of staff that students know and the amount of support provided by these

staff members with academic work, students may well find it easier to approach

people they know. To find out about which staff were known by the students, I asked

two questions: who taught them; and which members of staff did they know.

Apparently, several of the 39 respondents misunderstood the questions, as many did

not mark that they knew staff members with whom they were taking courses. While

this may be possible, I will proceed from the assumption that students who study with

a particular member of staff have some idea of who that staff member is, and hence I

condensed the variables into a single “know them or don’t know them” variable.

Table 5.9 - The Number of Students Who Know Each Member of StaffStaff Name No of Students Staff Name No of StudentsJohn Scott 31 Nigel South 11

Andrew Canessa 25 Lydia Morris 10Ken Plummer 25 Paul Thompson 9

Tony Woodiwiss 25 David Lockwood 9Helen Hannick 23 Maggy Lee 8Tony Coxon 22 Mary James 7

Kimberly Fisher 21 Joan Busfield 7Ted Benton 20 Carlo Ruzza 6Rob Stones 20 Oriel Sullivan 5

Colin Samson 19 Charlie Davidson 5Miriam Glucksmann 19 Gill Green 4

Ian Craib 18 Sue Aylott 4Catherine Hall 18 Diane Streeting 3Hiroko Tanaka 17 Gorge K 3John Stevens 17 Alison Scott 2Sean Nixon 14 Ray Pahl 2Mike Roper 14 Leonore Davidoff 2Mary Girling 12 Michael Harloe 2

Dennis Marsden 11 David Rose 1

Table 5.9 summarises the popularity of each member of staff. The staff most

known by students, not surprisingly, are the staff who teach the core courses and the

MA options. The least known are those staff who have a low profile among graduate

students as a consequence of being on sabbatical, being a pro-vice chancellor,

performing research in one of the centres, or only teaching undergraduates.

Surprisingly few people reported knowing two most regularly present

members of staff, the departmental secretaries Sue Aylott and Diane Streeting, but

then, some long-serving members of staff don’t know their names either. In contrast,

other non-academic staff, including Helen Hannick, Mary Girling and myself, have a

higher profile. Helen’s work in the Resource Room, which has proved particularly

important to the overseas students, accounts for her high level of recognition.

Students often have to see Mary to make appointments with Tony Woodiwiss and to

get copy account numbers, two essential needs for many students. As I collected the

data for this study, I expected that many students would know something about me.

The most embarrassing mistake I made with the project was missing out Brenda Corti

from the staff list which I provided to students. I don’t know how I did made such a

mistake.

To further assist in the analysis of which staff students knew, I performed a

hierarchical clustering analysis, the results of which are shown in a vertical

dendrogram in Figure 5.1. The further to the left the lines connecting two members of

staff are, the more closely connected they are in the responses of students who know

them.

This dendrogram shows the two departmental secretaries as being grouped

together at the initial stage with the most similar grouping of students who know

them. Thus, while most students do not report knowing the secretaries, those who

know one of them also know the other. The next most similar are the staff members

whom few students know. Next most closely grouped are people associated with the

same core courses, such as Tony Coxon and Kimberly Fisher, or John Scott and Tony

Woodiwiss.3 The latter two likely are also similarly known as one is the Director of

Graduate Studies and the other the current Head of Department, and thus,

theoretically at least, should be known by all students.4

3 Rob Stones is likely not clustered with Tony Woodiwiss and John Scott as he had not yet started his section of the core course when I administered my questionnaire.4 Andrew Canessa also had a low profile, however, as this survey was administered before he and Ted Benton took over as Directors of Graduate Studies while John Scott was on sabbatical, his profile likely increased toward the end of the spring term.

Figure 5.1 - Hierarchical Clustering of Staff Known by Students

By Wave 3 (administered after the graduate conference) students had met an

average of 1.09 members of staff for the first time. Overseas students met more

members of staff at the graduate conference (1.46) than home students (0.83). Female

students met more staff (1.22) compared to males (0.77), and full-time students met

an average of 1.21 new members of staff, compared to part-time students, who met

0.91 new staff members. None of these results is statistically significant, though the

small numbers involved do not facilitate ruling out many results as unlikely to have

happened by chance. Even so, the difference between the full-time and part-time

means is interesting. Part-time students spend less time at the university than full-

time students, and thus have less opportunity to meet staff. Even so, overseas

students, who are more likely to be full-time, also appear least likely to make

connections with staff inside the normal departmental activities.

In summary, most students report knowing the staff who teach the MA core

and optional courses. Students knowledge of staff was grouped by courses which

they taught, as well as by their administrative roles. The notable exceptions were the

students lack of knowledge of the names of the secretarial staff. Had the sample been

larger, the results may also have shown that overseas students make fewer

acquaintances with staff than home students.

5.4.3 Summary

In answer to the: Question - What are Students’ Sources of Information About

and Help With Academic Work, I found that students generally consult with members

of staff. Overseas students also depend on each other and the Resource Room. Staff

with the highest profiles teach postgraduates or perform an administrative functions of

importance to postgraduates. If I repeated this study, I would change the

acquaintance with staff questions to gather more detailed information.

5.5 Conclusions

The three dimensions of this chapter reveal that overseas students, particularly

Asian students, have different experiences of Sociology at Essex than British students.

Overseas students made greater use of the Resource Room, tended to have clearer

career objectives for their degrees, learned of Essex from publications rather than

personal recommendations, and desired either an earlier graduate conference or more

organised opportunities to establish friendship networks earlier in their course of

study at Essex. This research also highlights the need to give more consideration to

the position of qualifying year students in the department. Clear differences also

emerge between full-time and part-time students. Part-timers have more difficulty

with sociological jargon, seem to fit extra work for events like the Graduate

Conference Weekend more easily into their schedules, attend Essex because of

geographic convenience, and have a more clear idea of how their studies will relate to

their employment. The Department might offer more direct advise to all students on

how they might usefully translate their degree knowledge into employment. Students

primarily choose Essex over other institutions because of its reputation. The

Department can effectively capitalise on its reputation with more targeted recruitment

strategies.

Chapter 6 : Conclusions

6.1 Introduction

This final chapter draws the conclusions from the dissertation together, firstly

by summarising the methods, and secondly by assessing answers to the projects aims.

Generally, the 1996-97 cohort of postgraduate students integrated successfully.

Asians tended to form a highly interconnected sub-community which did not quickly

integrate with the rest of the Department. Part-time students, research students, and

Middle Eastern students were more likely to remain on the fringes than other groups

of postgraduates. Specific targeting of these people at (at least partly) social events

held earlier in the year could help facilitate closer connections among more

postgraduate students.

6.2 Summary of Methods

This dissertation did not contribute to the study of social networks per se,

though it does demonstrate that these techniques can be employed with practical

policy implications in mind. In this respect, this document does contribute an

advance to the present networks literature, which focuses on using network techniques

solely to gain information and to test academic theories.

While using self-completed, tick-roster questionnaires had some drawbacks,

and while the limitation of this analysis to discussion of who knew whom rather than

of how each student evaluated the others may not have been ideal, this approach did

efficiently collect information from over 80% of the studied cohort. Assessing

contact between students has value in its own right. If, as Granovetter (1974) found, a

casual acquaintance is more likely to give fruitful leads to finding employment than a

close acquaintance, and if, as Boissevain demonstrated (1974), merely knowing

people increases the chance of either knowing someone who knows how to address a

problem, or knowing someone who knows someone who can help with a problem,

then the study of acquaintance structures among postgraduate students has intrinsic

value. Besides, one cannot make friends with people one does not know.

Several trends emerged among the demographics data. Women outnumbered

men in most categories, and constituted all qualifying year students. Part-time

students were more likely to be older and currently employed. The best predictor of

students’ demographic characteristics, however, was their location in the matrix of

home and overseas by full- and part-time students.

It should be noted that the reason I did not make more use of clustering or

employ multi-dimensional scaling techniques, such as smallest space analysis, is that

such approaches in their basic form would not have added significantly to the

interpretation of the figures. Had I had the luxuries of time, research assistants, and a

higher word length for my dissertation, I could have made use of more complex

techniques, but none of these factors was on my side. I have noticed, however, that

the graph plotting package, Krackplot, could be enhanced by using smallest space

analysis as a method of positioning the points. The random and annealing options

presently available tend to produce a visual mess which has to be sorted out by the

user after the plotting completes.

6.3 Answers to the Projects Aims

The studied postgraduate students made acquaintances from the first week of

their studies, then generally gained one additional contact per week. Students initially

congregated to other students of the same ethnic background, but cliques expanded to

include other students from the same courses and the same period of study by Wave 2.

At the middle of the Autumn term, part-timers, especially those owning homes, living

in London, and/or studying on a health-related MA, had substantially fewer contacts

than the rest of their peers, and Asian students had clustered into a well-connected

subgroup with few contacts with the rest of the student population. Curiously, in

Wave 2, one student with only two personal acquaintances was positioned to link the

Asian and white student cliques. By Wave 3, only some part-time and Middle Eastern

students had not developed at least a loose mesh of peer contacts. The graduate

weekend had a major effect on both the number and quality of acquaintances.

The general increase in the number and size of acquaintances found here is

consistent with other research. My findings with regard to race, however, need to be

interpreted separately. The research in the United States has concentrated on troubled

race relations in that country (Hallinan and Williams, 1989). Britain can by no means

claim to have avoided similar general race-based problems. The Essex Sociology

Department, however, is a different context. This Department has a comparatively high

mix of Asian staff members, as well as having many white members of staff who speak

Asian languages. Asian students, like other overseas students, more regularly consulted

the resource room for help with essays. It is not surprising that people who had to deal

with study in their second, third, or nth language would turn first to people in a similar

position when entering the University. After the graduate weekend, students from many

ethnic backgrounds had made each others’ acquaintance.

Not surprisingly, most students found the graduate weekend generally

enjoyable, and some, particularly part-time students, reported feeling intellectually

stimulated. Part-time students and qualifying year Asian students, however, also felt

intimidated by the level of jargon and intensity of the theory in the conference

readings. Nevertheless, part-time students had an easier time fitting the readings into

their schedules than the full time students. Full-time students wanted less work and

improved social events. A majority of students got more from informal discussions

and socialising than from formal sessions. While white European students wanted an

improved social programme, Asian students expressed a wish for the conference to be

held earlier so that they could have expanded their social contacts sooner.

Students mostly reported knowing the staff who teach the MA courses or held

graduate administrative roles. Though most students consulted with members of staff

for help with assignments, overseas students also depend on each other and the

Resource Room for some levels of assistance.

Most students chose Essex because of the reputation of the Department, and,

in the case of home part-time students, because of the proximity of Essex to home and

work. Overseas MA students generally have the most clear career objectives.

6.4 A Few Final Words

The following three comments sum up the project. First, the Department

should pay more attention to part-time students, especially as increasing numbers of

students are choosing to study part-time while also working. Second, an increased

emphasis on offering (or prodding attendance at) events which facilitate networking

early on in the first term would improve the quality of the Essex experience for many

students. Third, network analysis provides a useful strategy for assessing some

dimensions of services provided by an academic department.

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