1. Introduction to Personal networks
Summer course “The Measurement of Personal Networks”
UAB 2011

2. The plan for this week Introduction to personal network analysis
Name generators
Name interpreters
Reliability and validity of network data
Mixed methods designs
Workshops with software for collecting, visualizing and analyzing personal network data:
EgoNet (José Luis Molina)
E-Net (Steve Borgatti)
Vennmaker (Markus Gamper)
(not specifically for personal networks) visone (Jürgen Lerner)

3. The plan for this morning
Introduction to personal networks.
A bit of history
Distinction between sociocentric, egocentric and personal networks
A definition of personal networks.
An overview of research in the area.
Types of personal network data
Designing a personal network study (goals, design, and sampling).

4. 1. Introduction to Personal Networks.

5. History and difference with sociocentric networks

6. A bit of History …
The “Manchester School”, led first by Max Gluckman and later by Clyde Mitchell, explored the personal networks of tribal people in the new cities of the Cooperbelt (but also in the India, Malta, Norway)
Faced with culture change, mobility and multiculturalism they used social networks as an alternative to Structural-Functionalist Theory in anthropology

7. Radcliffe Brown
A. R. Radcliffe Brown, a structural-functionalist, became disillusioned with the concept of culture and anthropological approaches using an institutional framework
As an alternative he suggested focusing on social relations, which, unlike culture, could be observed and measured directly
“But direct observation does reveal to us that these human beings are connected by a complex network of social relations. I use the term “social structure” to denote this network of actually existing relations.” (A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1-12.)

8. Two branches in the development of social network analysis
Sociology
Anthropology
Moreno
Radcliffe-Brown, Nadel
Coleman, H. White, Harary, Freeman, Rogers, Davis, Lorraine
Gluckman, Mitchell, Bott, Barnes, Kapferer, Epstein, Boissevain, Warner, Chappel
Granovetter, Burt, Wellman, Snijders, Frank, Doreian, Richards, Valente, Laumann, Kadushin, Bonacich, …
Romney, Bernard, Wolfe, Johnson, Schweizer, D. White
INSNA
For a complete review of the history of SNA read: Freeman, L. C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press.

9. Gossip network … (Epstein, 1957)
For instance …
Gossip network … (Epstein, 1957)

10. East York … (Wellman, 1984, 1999)

11. Two kinds of social network analysis
Personal (Egocentric) Network Analysis
Effects of social context on individual attitudes, behaviors …
Collect data from respondent (ego) about interactions with network members (alters) in all social settings.
Whole (Complete or Sociocentric) Network Analysis
Interaction within a socially or geographically bounded group
Collect data from group members about their ties to other group members in a selected social setting.
Chris, I deleted some nodes in the whole network of the right because SOME NODES are not captured by the sociocentric networks (for instance, family …).

12. Not a Simple Dichotomy
The world is one large (un-measurable) whole network
Personal and whole networks are part of a spectrum of social observations
Different objectives require different network “lenses”

13. Personal Networks: Unbounded Social Phenomena
Example: Predict depression among seniors using the cohesiveness of their personal network
Social or geographic space
Social influence crosses social domains
Network variables are treated as attributes of respondents
These are used to predict outcomes (or as outcomes)

14. Whole networks: Bounded Social Phenomena
Focus on social position within the space
Social or geographic space
Example: Predict depression among seniors using social position in a Retirement Home

15. Overlapping personal networks: Bounded and Unbounded Social Phenomena
Use overlapping networks as a proxy for whole network structure, and identify mutually shared peripheral alters
Social or geographic space
Example: Predict depression among seniors based on social position within a Retirement Home and contacts with alters outside the home

16. A note on the term “Egocentric”
Egocentric means “focused on Ego”.
You can do an egocentric analysis within a whole network
See much of Ron Burt’s work on structural holes
See the Ego Networks option in UCInet
Personal networks are egocentric networks within the whole network of the World (but not within a typical whole network).

17. Summary so far When to use whole networks
If the phenomenon of interest occurs within a socially or geographically bounded space.
If the members of the population are not independent and tend to interact.
When to use personal networks
If the phenomena of interest affects people irrespective of a particular bounded space.
If the members of the population are independent of one another.
When to use both
When the members of the population are not independent and tend to interact, but influences from outside the space may also be important.

18. Definition of personal networks

19. Personal network
The set of social relationships surrounding an individual, which stem from different contexts (family, work, neighbourhood, associations, religious community, school, online community…).
“Ego”: the focal individual
“Alter”: a network member

20. Personal network
The set of social relationships surrounding an individual, which stem from different contexts (family, work, neighbourhood, associations, religious community, school, online community…).
“Ego”: an informant
“Alter”: a network member

21. Personal network
The set of social relationships surrounding an individual, which stem from different contexts (family, work, neighbourhood, associations, religious community, school, online community…).
“Ego”: an informant
“Alter”: a network member

22. Personal network
The set of social relationships surrounding an individual, which stem from different contexts.
“Ego”: an informant
“Alter”: a network member

23. Boundary specification
± 1500
± 500
Personal network ± 150
Active or close network ± 50
Sympathy group ± 15
Support clique ± 5
ego
Personal networks seem to be segregated into a series of expanding circles centered on the respondent, with the circles corresponding to points at which the level of intimacy dropped off precipitously.
Support clique, or circle of five closest friends: all those individuals from whom one would seek advice, support or help
Sympathy group (about 15 individuals): those with whom an individual contacts at least monthly
Close network (50): people with whom ego feels he has a personal relationship, and ego makes a conscious effort to keep in contact: alters whom ego has contacted in the last two years.
Personal social network (150 tends to be the cognitive limit): those individuals with whom we can have a personalized relationship, whom we know as individuals on the basis of either personal contacts or secondary contacts through close contacts of our more intimate social network. These individuals seem to be characterized by reciprocity and obligation
Supernetwork (500)
Language community / linguistic tribe (1500)
Robin Dunbar: Hierarchically inclusive levels of acquaintanceship

24. “Dunbar’s number”
the number of people one knows and keeps social contact with, and it does not include the number of people known personally with a ceased social relationship, nor people just generally known with lack a persistent social relationship, a number which might be much higher and likely depends on long-term memory size
Hominoids. Strong correlation among neocortex ratio and mean group size (.80) even when controlled for body mass index
(a 95% confidence interval of 100 to 230).
Cognitive constraints on sociality.

25. How many people does a person know?
Year-long observation of two individuals (Boissevain, 1973):
Contact diaries: Pool & Kochen´s (1978) 1 person experiment:
Gurevitch (1961) 18 persons:
Fu (2007): 54 persons, 3 months:
Telephone Book Studies (Freeman & Thompson, 1989): Estimate based on the number of names informants recognize from a random sample of surnames from a telephone book, extrapolated to match the total number of names in the phonebook.
Reversed Small World experiment (e.g., Killworth & Bernard, 1978/79): Informants are asked who is the most appropriate first intermediary to send a package to each of 1267 (originally) or 500 persons in the world, each equipped with a location and an occupation. Extrapolation based on distribution.
Known population method (Bernard et al., 1991): Estimate based on the size of the population, the size of known subpopulations, and the number of persons one knows in each of the subpopulations. Various studies.
Summation method (McCarty et al., 2001): Sum of respondents’ estimates of the number of people they know in each of 16 relationship categories
Extrapolation on the basis of the relation between neocortex size and average group size of primates (Dunbar, 1993) ±
2025
250
±290
611
150
290 – with a median of 230
► Christmas cards (Hill & Dunbar, 2003)
125

26. How many people does a person know?
Year-long observation of two individuals (Boissevain, 1973):
Contact diaries: Pool & Kochen´s (1978) 1 person experiment:
Gurevitch (1961) 18 persons:
Fu (2007): 54 persons, 3 months:
Telephone Book Studies (Freeman & Thompson, 1989): Estimate based on the number of names informants recognize from a random sample of surnames from a telephone book, extrapolated to match the total number of names in the phonebook.
Revised by Killworth et al. at
Reversed Small World experiment (e.g., Killworth & Bernard, 1978/79): Informants are asked who is the most appropriate first intermediary to send a package to each of 1267 (originally) or 500 persons in the world, each equipped with a location and an occupation. Extrapolation based on distribution.
Known population method (Bernard et al., 1991): Estimate based on the size of the population, the size of known subpopulations, and the number of persons one knows in each of the subpopulations. Various studies.
Revised by McCormick et al. (2010) at
Summation method (McCarty et al., 2001): Sum of respondents’ estimates of the number of people they know in each of 16 relationship categories
Extrapolation on the basis of the relation between neocortex size and average group size of primates (Dunbar, 1993) ±
2025
250
±290
611
150
► Christmas cards (Hill & Dunbar, 2003)
125

27. Distribution of c in the population (McCormick, doctoral thesis, 2011)
Right skewed, average is higher than central tendency

28. Back to the definition
The set of social relationships surrounding an individual, which stem from different contexts.
“Ego”: the focal individual
“Alter”: a network member

29. Definition affects the network characteristics, e.g.
Core ties
Peripheral ties
Relatively few
Kin-centered (± 50% *, **, ***)
High homogeneity
Multistranded
Low spatial dispersion
(67% at max 1 hr **)
High density
(.57 for 3 ties*; .44 for 18.5 ties**, but see .33 for 5 ties***)
Relatively stable over time
More numerous
Low proportion of kin (± 20% #)
Low homogeneity
Specialized
Spatially more dispersed
(40% #)
Lower density
(± .25 for 41,5 peripheral ties #)
Unstable over time
Over a period of 10 years, only 25% of close ties persist (Wellman et al., 1997; Suitor & Keeton, 1997).
* Marsden, 1987
** Fischer, 1982
*** Wellman, 1979
#These are very rough estimates based on proportions reported by McCarty (1992) for networks of 60 ties, taking into account values for 18.5 core ties based on Fischer

30. This is only the strength of ties, but…
Personal networks tend to display high clustering, particularly in relation to roles / settings of meeting
Contents, frequency of relationships, …

31. An overview of research

32. An overview of research into personal networks
Interest in patterns and processes of socialization and social integration
Predicting interindividual variation in these patterns
The influence of personal networks on individual outcomes
Network-based interventions
Use of personal networks as a means for other goals - studying hard-to- reach populations
Method development
predictors
networks
outcomes

33. (1) Description of patterns and processes of socialization and social integration
The personal network represents the social context of an individual, the intermediate level between the individual and society, an essential mechanism by means of which the individual is connected to the larger world
Shows the current pattern of sociability, although it is in part a product of the past
Personal networks are used to understand the organization of informal relationships in society at large
The whole of the personal network as it appears today is composed of these ties of
varying duration. Some stem from childhood and have persisted through many periods of
life; others last only a few years and remain ‘typical’ of a period which has ended but is
still recalled in practices more or less tinged with nostalgia; still, others are contemporary
with current preoccupations and activities.
To some extent, then the network ‘testifies’ to the personal history. It points to the
different strata of relationships, with their varied origins and durations, and these in turn
reveal different time frames within its components.

34. Examples
What effects does the far-reaching social systemic division of labor have on the organization and content of primary relationships?
(“The Community Question”, Wellman, 1979)
Are Americans more socially isolated now than they were twenty years ago?
(McPherson, Smith-Lovin, Brashears)
What are the processes of socialization and social integration as young persons enter adulthood?
(Bidart et al.)
To what extent do immigrants and natives mix in society?
(Molina, McCarty, Lubbers / Molina, Lozares, Lubbers)
Is online sociability differently structured than offline sociability? Is it replacing offline sociability?
(Wellman and associates)
Is it a small world (local clustering and short path lengths)?
(Milgram, Watts & Strogatz)
e.g., personal broadcasting, asynchronicity of relationships
A description of sociability itself – sometimes in relation to characteristics, other times not
Most often chains were not completed
Starters were so not random (more sociable, higher educated etc)
There were ethnic and social class barriers that made path lengths longer
Revitalized by Watts & sTROGATZ
Small world experiment first conducted by Stanley Milgram
Asked a (not so random) sample of respondents in a town in Nebraska to send a package to a Mr. G., a stockbroker in Boston, Massachusetts.
If they didn´t know Mr. G. directly, they were asked to think of a friend or relative they knew personally who was more likely to know the target and send it to that person.
Completed chains were on average steps in the US
Revitalized by Watts & Strogatz, who presented a mathematical model that produces networks with small-world properties (short path lengths and local clustering) and replicated the experiment using messages.

35. Personal networks are unique
Like snowflakes, no two personal networks are exactly alike (courtesy to José Luis )
Social networks may share attributes, but the combinations of attributes are different

36. Example of the variation of personal networks (45 alters)

37. 2. Explaining variations in personal networks
Do the size, composition, structure, stability of networks vary with individual characteristics?
Demographic characteristics, education, personality, life events, …
At the relationship level, do alter characteristics affect the contents of the relationship with ego, the stability of the relationship, or the existence of the ties with other alters?
E.g., Louch (2002)
Comparison across societies
E.g., Fischer-Fischer/Shavit-Grossetti-Bastani; Burt-Ruan-Völker

38. (3) The influence of personal networks on individual and social outcomes
Influences on: mental and physical health, job mobility, migration decisions, risky behavior, geographical mobility…
Four areas of research into the influence of personal networks:
A. Social support
B. Social capital
C. Diffusion and contagion
D. Network geographies

39. (3a) Social support Barrera, Vaux, Berkman, Uchino… / Fischer, Antonucci…
Three elements (Vaux, 1988; Barrera, 1986):
Sources of social support (social integration)
Types of social support received (enacted social support)
Appraisal of social support (perceived social support)
Personal networks are sources of social support
Instrumental, emotional, informational support, social companionship
Theories of social support and mental and physical health:
Social support helps individuals to cope with major life stressors and the challenges of daily life, thereby protecting them from the negative consequences of stress on health
Social support maintains well-being in the absence of stress.
Social support becomes part of an adaptive personality profile throughout a person’s life.
These three elements are connected by a complex dynamic of exchange and communication processes between an individual and his/her social environment
Size, composition (homogeneity, tie strength, geographical distance) and structure (density) of the network are important
Specialization among network members

40. Social support studies e.g., Ertel, Glymour & Berkman, 2009
Host of studies showing direct and/or buffering effects of social support on mental and physical health
Explanatory pathways, e.g., for cardiovascular diseases:
Perceived social support has direct relations with cardiovascular reactivity to stressors, neuroendocrine and immune systems
Strong and supportive alters promote health behaviors (sleep, diet, exercise,…)
Provision of informational and tangible resources …
Especially of perceived support and social integration

41. The strength of weak ties / structural holes… Granovetter / Burt
Research focused mainly on the beneficial effects of strong ties
Granovetter (1973) showed that weak ties can be instrumentally strong too …
The search for a job (Granovetter, 1974)
The search for a place to live (Freeman & Sunshine, 1976)
The search for an abortionist (Lee, 1969)
Burt (1992): It is not the quality of any particular tie but rather the way different parts of networks are bridged (“structural holes”)
extended and reformulated the “weak ties” argument by emphasizing that what is of central importance is not

42. Network structure and its effects
Illustration: Sune Lehmann, Complexity and Social Networks Blog:

43. (3b) Social capital Bourdieu, Coleman, Wellman, Lin, Burt, Marsden, Flap…
People have human capital (abilities), economic capital (material resources) and social capital
Social capital refers to the resources embedded in one’s network which are accessed and/or mobilized through one’s ties in purposive actions
Returns can be instrumental (wealth, power, reputation) or expressive (health and life satisfaction)
Focus on embedded resources (e.g., wealth and power of alters) and network locations (e.g., structural holes).
Social capital exists at the individual level and at the community level
So social capital brings together theories on social support, social cohesion and integration

44. Social capital and job prestige (Lin, 1999)
A host of studies show that access and mobilization of embedded resources significantly enhances the attainment of jobs with higher prestige of the job, controlling for education, father´s status, etc.
They also showed that:
The strength of tie is negatively related with contact status
Father´s status is positively related with contact status

45. (3c) Diffusion and contagion Valente, Coleman, Costenbader, …
Social networks are the infrastructures for social influence.
Diffusion: Ideas and practices spread through networks. As friends do something, you are more likely to do something.
Contagion is the special case of diffusion in which only contact is required for adoption/spread.
Both sociocentric and egocentric network data are used for studying such models
Applied to innovations (Coleman, Valente), diseases and health conditions (e.g., Klovdahl, the Christakis & Fowler studies), mobilization to protest (Opp & Gern, 1993; Araya Dujisin, 2009), …
Farmers in Brazilian villages, doctors in a certain area, …

46. Diffusion Structural models: Threshold models (Valente):
Structural aspects of the network influence adoption (size of the network, weak ties, brokerage…)
Threshold models (Valente):
Individuals engage in a new behavior based on their network exposure (the number of others in the network who already engage in that behavior)
Individuals have different thresholds for adoption
Especially in cross-sectional personal network studies causal modeling is problematic
Difficult to disentangle selection and influence

47. Example: Framingham Heart Study Christakis & Fowler
Christakis “the illness of the person dying affects the health status of other individuals in the family (…) a kind of non- biological transmission of disease”
Does obesity, happiness, smoking cessation, alcohol consumption, loneliness,… spread through personal relationships?
FHS:
Longitudinal study over 30 years (measurements each 5 years) among residents in the town Framingham, Massachusetts
Respondents were asked to identify their spouse, relatives, “close friends,” place of residence (neighbours), and place of work (coworkers)
As alters were often also participants in the Framingham Heart Study, personal networks could be combined to study the community

48. Evolution of obesity in the Framingham Heart Study Christakis & Fowler, 2007
Christakis & Fowler (2007), New England Journal of Medicine, 357,

49. (3d) Activity spaces
The spatial dispersion and structure of personal networks can be regarded as indicators of individual “activity spaces” that influence individual mobility patterns (e.g., Axhausen et al.; also Carrasco et al.)
The illustration is from: Paper18_Axhausen_ActivitySpacesBiographies_CD_.pdf.

50. (4) Hidden and hard-to-count populations
Design of network-based sampling methods
Snowball sampling:
A small number of initial participants is selected (“seeds”) from the target population
Who provide researchers with information on their network connections, who subsequently form the pool from which new participants are selected
Who are asked — and typically provided financial incentive—to recruit their contacts in the population directly
Current sample members (inform about their networks to) recruit the next wave of sample members, and so on, continuing until the desired sample size is reached.
Commercial sex workers, Injection drug users, Men who have sex with men, illegal immigrants, homeless, Jazz musicians, aging artists, professional and semi-professional storytellers

51. Probability sampling: Incomplete coverage
Snowball sampling: No valid inferences

52. (4) Hidden / hard-to-reach populations
Respondent driven sampling (Heckathorn, 1997, 2002, 2007) combines network-based (snowball) sampling with a mathematical model of the recruitment process that weights the sample to compensate for non-random recruitment patterns
RDS generates approximately unbiased population estimates, but the variance of RDS estimates are higher than in random samples

53. (4) Hidden / hard-to-reach populations
Some key issues and assumptions in respondent-driven sampling
Seeds should be selected carefully and should comprise key subpopulations
Respondents know one another as members of the target population, and respondents know sufficient others (3-5) to keep the recruitment process going.
Respondents are linked by a network composed of a single component, or if they are not, seeds represent the different components.
Sampling occurs with replacement.
Respondents can accurately report on the number of personal contacts they have within the target population – to be used to adjust for selection bias.
Peer recruitment is a random selection from the recruiter’s network.
Each respondent recruits a fixed and small number of peers (e.g., 3).
Researchers should keep track of whom recruited whom – to be used to generate relative inclusion probabilities
Long recruitment chains (sometimes up to 20) allow for deeper penetration into the target population networks and help to ensure that the sample meets several theoretical assumptions indicating representativeness
Large personal networks, be persuasive
Tax cheaters

54. Networks with 1st and 2nd degree alters
Sometimes it may be interesting to do these chain referral designs, not for good estimates, but for better understanding the networks

55. (4) Size of hidden populations (Killworth et al
(4) Size of hidden populations (Killworth et al., 1998; McCormick, Salganik & Zheng, 2010)
Design of network-based methods (scale-up methods) to estimate the size of hidden, hard-to-count populations (e.g., at risk for HIV)
It assumes that everyone’s network in a society reflects the distribution of subpopulations in that society
So, if you know:
(a) the size of the total population within which the subpopulation is embedded;
(b) the number of people whom informants know in a subpopulation;
(c) the total number of people whom informants know (the personal network size)
you can estimate:
(d) the size of the subpopulation (= a × b / c)
Development of methods to estimate the personal network size.
The known population method
The summation method

56. (5) Method development
Methods for measuring aspects of personal networks and for collecting personal network data
Software development such as Egonet, E-net, Vennmaker, …
Paper-and-pencil methods (e.g., Hogan et al., 2007)
Estimates for social capital and network size
Methods for the statistical analysis of personal network data, e.g.,
Triad counts (Kalish & Robins, 2006)
Duo-centered studies (Coromina et al., 2008)
Joint analysis of dynamics in network structure and network composition (Lubbers et al, in preparation)

57. An overview of research into personal networks
Interest in patterns and processes of socialization and social integration
Predicting interindividual variation in these patterns
The influence of personal networks on individual outcomes
Network-based interventions
Use of personal networks as a means for other goals - studying hard-to- reach populations
Method development

58. Summary
For the measurement of personal networks, it is important to operationally define what constitutes a social relationship, based on the research question
In the case of influence, it is important to specify the mechanism through which personal networks are expected to influence individual outcomes
Usually based on some form of transmission from node to node (resources, information, affection, ideas, control, sanctions, disease…).
Define under which conditions these transmissions take place (structural and relational constraints and opportunities)
Aggregation of these transmissions
(Total amount of resources? A minimum amount? Thresholds? Probability that one tie has a certain resoure? The absence of negative relations?)

59. Types of personal network data

60. Personal network data We collect data on relationships…
The relationships that an informant has with others
The relationships that the others have among them
…in order to aggregate them to variables that characterize the networks
Number of the informant’s relationships – Size
Contents of the informant’s relationships – Composition
Existence of relationships among the others – Structure

61. Personal network data
Some questions are analyzed at the relationship level, e.g. ,
Are kin relationships more supportive than acquired relationships?
Do stronger ties persist better over time?
Others at the network level, e.g.,
Do people with larger support networks cope better with [some sort of life event] than people with smaller support networks?
Are geographically dispersed networks less dense?

62. Types of aggregate personal network data
Composition: Variables that summarize the attributes of alters and of ego-alter relationships in a network.
Average age of alters.
Proportion of alters who are women.
Proportion of alters who provide emotional support.
Structure: Metrics that summarize the structure of alter-alter relationships, e.g.
Density.
Number of components.
Betweenness centralization.
Composition and Structure: Variables that capture both.
Sobriety of most between alters.
E-I index.

63. Ego-alter relationships and network composition Alter summary file
Name
Closeness
Relation
Sex
Age
Race
Where Live
Year_Met
Joydip_K 5 14 1 25
1994
Shikha_K 4 12 34
2001
Candice_A 2 24 3
1990
Brian_N 23
Barbara_A 42
1991
Matthew_A 20
Kavita_G 22
Ketki_G 54
Kiran_G
Kristin_K
1986
Keith_K 26
1995
Gail_C 33
1992
Allison_C 19
Vicki_K
2002
Neha_G .

64. Personal network composition variables
Characteristics of alters and ego-alter relationships
Aggregate variables of network composition, e.g.:
Sex
Age
Occupation
Strength of tie
Duration of tie
Place of residence of alter
Proportion of women
Average age of network alters
Range of occupations (Highest – lowest score)
Proportion of strong ties
Average duration of ties
Geographical dispersion

65. Percent of alters from host country
36 Percent Host Country
44 Percent Host Country
Percent from host country captures composition
Does not capture structure

66. Alter-alter relationships and network structure Alter adjacency matrix
Joydip_K
Shikha_K
Candice_A
Brian_N
Barbara_A
Matthew_A
Kavita_G
Ketki_G . 1

67. Personal network structure variables
Aggregate variables of network structure:
Alter positions
in structure
Degree centrality
Closeness centrality
Betweenness centrality
Average degree centrality (density)
Average closeness centrality
Average betweenness centrality
Core/periphery
Number of components
Number of isolates

68. Components Components: 1 Components: 10
Components captures separately maintained groups (network structure)
It does not capture type of groups (network composition)

69. Average Betweenness Centrality
SD 26.5
Average Betweenness 14.6
SD 40.5
Betweenness centrality captures bridging between groups
It does not capture the types of groups that are bridged

70. Designing a personal network study
Goals, design, sampling

71. Make sure you need a network study!
Personal network data are time-consuming and difficult to collect with high respondent burden
Sometime network concepts can be represented with proxy questions
Example: “Do most of your friends smoke?”
By doing a network study you assume that the detailed data will explain some unique portion of variance not accounted for by proxies…
It is difficult for proxy questions to capture structural properties of networks

72. Sometimes the way we think and talk about who we know does not accurately reflect the social context

73. FAMILY
WORK
FRIENDS

74. Research methods to capture personal networks
Observation (Boissevain)…
Surveys
Contact diaries (Fu, Lonkila, …)
Experiments (Killworth & Bernard)
Extraction of data from SNS (Boyd, Hogan, …) / mobile phones (Lonkila)

75. Steps to a personal network survey
Part of any survey
1. Identify a population.
2. Select a survey mode.
3. Select a sample of respondents.
4. Ask questions about respondent.
Unique to personal network survey
4. Elicit a list of network members (“name generator”).
5. Ask questions about each network member.
6. Ask respondent to evaluate alter-alter ties.
7. Discover with the informant new insights about her personal network (through visualization + qualitative interview).

76. 1. Selecting a Population
Choose wisely, define properly – this largely will determine your modes of data collection and the sampling frame you will use to select respondents.
Certain populations tend to cluster spatially, or have lists available, while others do not
Race and ethnicity may seem like good clustering parameters, but are increasingly difficult to define.

77. 2. Modes of Survey Research
Face-to-face, telephone, mail, and Web (listed here in order of decreasing cost)
The majority of costs (minus incentives) are not incurred in actually interviewing the respondent, but in finding available and willing respondents
Depending on the population, there may be no convenient or practical sample frame for making telephone, mail, or contact

78. 3. Sample Frames
This can be thought of as a list representing, as closely as possible, all of the people in the population you wish to study.
The combination of population definition and survey mode suggests the sample frames available.
Sample frames may be census tracts, lists of addresses, membership rosters, or individuals who respond to an advertisement.

79. Prevalence vs. Relations
Estimate the prevalence of a personal network characteristic in a population
Sampling should be as random and representative as possible.
Sample size should be selected to achieve an acceptable margin of error.
Example: Sample 400 personal networks to estimate the proportion of supportive alters with a five percent margin of error.
Analyze the relation between a personal network characteristic and some outcome variable
Sampling should maximize the range of values across variables to achieve statistical power.
Example: Sample 200 personal networks of depressed and 200 of not depressed seniors to test whether the number of isolates is associated with depression.

80. 4. Questions about Ego
These are the dependent (outcome) variables you will predict using network data, or the independent (explanatory) variables you will use to explain network data and for controls
Outcome variables: Depression, Smoking, Income, …
Explanatory variables: Number of moves in lifetime, Hobbies, …
Controls: Age, Sex, …
Be aware that it is common to find relationships between personal network variables and outcomes that disappear when control variables are introduced

81. Steps to a personal network survey
Part of any survey
1. Identify a population.
2. Select a survey mode.
3. Select a sample of respondents.
4. Ask questions about respondent.
Unique to personal network survey
4. Elicit a list of network members (“name generator”).
5. Ask questions about each network member.
6. Ask respondent to evaluate alter-alter ties.
7. Discover with the informant new insights about her personal network (through visualization + qualitative interview).

82. Steps to a personal network survey
Part of any survey
1. Identify a population.
2. Select a survey mode.
3. Select a sample of respondents.
4. Ask questions about respondent.
Unique to personal network survey
4. Elicit a list of network members (“name generator”). TOMORROW
5. Ask questions about each network member.
6. Ask respondent to evaluate alter-alter ties.
7. Discover with the informant new insights about her personal network (through visualization + qualitative interview).

83. Steps to a personal network survey
Part of any survey
1. Identify a population.
2. Select a survey mode.
3. Select a sample of respondents.
4. Ask questions about respondent.
Unique to personal network survey
4. Elicit a list of network members (“name generator”).
5. Ask questions about each network member.
6. Ask respondent to evaluate alter-alter ties.
7. Discover with the informant new insights about her personal network (through visualization + qualitative interview).
WEDNESDAY