Download - Introduction to Personal networks Summer course “The Measurement of Personal Networks” UAB 2011

Introduction to Personal networks

Summer course “The Measurement of Personal Networks”

UAB 2011

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)

The plan for this morning

1. 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.

2. Types of personal network data3. Designing a personal network study

(goals, design, and sampling).

1. Introduction to Personal Networks.

History and difference with sociocentric networks

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

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.)

Two branches in the development of social network analysis

Moreno

INSNA

Radcliffe-Brown, Nadel

Gluckman, Mitchell, Bott, Barnes, Kapferer, Epstein, Boissevain, Warner, Chappel

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.

Romney, Bernard, Wolfe, Johnson, Schweizer, D. White

Coleman, H. White, Harary, Freeman, Rogers, Davis, Lorraine

Granovetter, Burt, Wellman, Snijders, Frank, Doreian, Richards, Valente, Laumann, Kadushin, Bonacich, …

Sociology Anthropology

For instance …

Gossip network …

(Epstein, 1957)

Besa

=

Red externa oextendidad

Mrs.Mutw ale

Mónica

Ponde

Phiri

Nicholas

Misma tribu o grupo lingüístico

Dirección del chisme

Misma escuela Misma iglesia

Vecinos

East York … (Wellman, 1984,

1999) FamiliaInmediata

Extendida

Vecinos

Amigos Compañeros de trabajo

Lazos íntimosactivos

Lazos no íntimosactivos

Personade East

York

Two kinds of social network analysisPersonal (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.

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”

Personal Networks: Unbounded Social Phenomena

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)

Example: Predict depression among seniors using the cohesiveness of their personal network


Whole networks: Bounded Social Phenomena

Example: Predict depression among seniors using social position in a Retirement Home

Focus on social position within the space


Overlapping personal networks: Bounded and Unbounded Social Phenomena

Example: Predict depression among seniors based on social position within a Retirement Home and contacts with alters outside the home

Use overlapping networks as a proxy for whole network structure, and identify mutually shared peripheral alters

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).

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.

Definition of personal networks

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


individual, which stem from different contexts (family, work, neighbourhood, associations, religious community, school, online community…). “Ego”: an informant “Alter”: a network member


individual, which stem from different contexts. “Ego”: an informant “Alter”: a network member

Robin Dunbar: Hierarchically inclusive levels of acquaintanceship

± 500

± 1500

ego Support clique ± 5

Sympathy group ± 15

Active or close network ± 50

Personal network ± 150

Boundary specification

“Dunbar’s number”

http://www.cabdyn.ox.ac.uk/complexity_PDFs/CABDyN%20Seminars%202007_2008/CABDyN%20Seminar%20Slides%20RIMDunbar.pdf

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 20-30 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)

± 1750 500-1500 2130 227 5520

2025

250

±290

611

±290

150

► Christmas cards (Hill & Dunbar, 2003) 125

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 20-30 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)

± 1750 500-1500 2130 227 5520

2025

250

±290

611

±290

150

► Christmas cards (Hill & Dunbar, 2003) 125

Distribution of c in the population (McCormick, doctoral thesis, 2011)

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

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

#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

* Marsden, 1987** Fischer, 1982*** Wellman, 1979

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, …

An overview of research

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

networks

predictors

outcomes

(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

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)

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

Example of the variation of personal networks (45 alters)

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

(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

(3a) Social supportBarrera, 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.

Social support studiese.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 …

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”)

Network structure and its effects

Illustration: Sune Lehmann, Complexity and Social Networks Blog: http://www.iq.harvard.edu/blog/netgov/

(3b) Social capitalBourdieu, 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

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

(3c) Diffusion and contagionValente, 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), …

Diffusion Structural models:

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

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”

http://www.edge.org/3rd_culture/christakis08/christakis08_index.html 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

http://www.edge.org/3rd_culture/christakis08/christakis08_index.html

Evolution of obesity in the Framingham Heart StudyChristakis & Fowler, 2007

Christakis & Fowler (2007), New England Journal of Medicine, 357, 370-379

(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: http://www.civ.utoronto.ca/sect/traeng/ilute/processus2005/PaperSession/ Paper18_Axhausen_ActivitySpacesBiographies_CD_.pdf.

(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.

Figure by Thomas Valente

59 indexpatients

aaaaaa aaaaa307 (306 )

aaaaa31 alters enrolled

31 alters, not previously named,

enrolled

62 1st degree alters enrolled

aaaaaa aaaaa357 (339 )

aaaaa aaaaa297alters

(287 )

19 indexpatients

(14 )

17 enrolledalters

(17 )

aaaaaaaaaa24 named by indexes

21( individuals)

First Degree Alters

Second Degree Alters

(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

(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

Networks with 1st and 2nd degree alters

Graphs by Thomas Valente

(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

(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)

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

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

Types of personal network data

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

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?

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.

Types of aggregate personal network data

Ego-alter relationships and network compositionAlter summary file

Name Closeness Relation Sex Age Race Where Live Year_Met

Joydip_K 5 14 1 25 1 1 1994

Shikha_K 4 12 0 34 1 1 2001

Candice_A 5 2 0 24 3 2 1990

Brian_N 2 3 1 23 3 2 2001

Barbara_A 3 3 0 42 3 1 1991

Matthew_A 2 3 1 20 3 2 1991

Kavita_G 2 3 0 22 1 3 1991

Ketki_G 3 3 0 54 1 1 1991

Kiran_G 1 3 1 23 1 1 1991

Kristin_K 4 2 0 24 3 1 1986

Keith_K 2 3 1 26 3 1 1995

Gail_C 4 3 0 33 3 1 1992

Allison_C 3 3 0 19 3 1 1992

Vicki_K 1 3 0 34 3 1 2002

Neha_G 4 2 0 24 1 2 1990

. . . . . . . .

. . . . . . . .

. . . . . . . .

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

Personal network composition variables

Percent of alters from host country

36 Percent Host Country 44 Percent Host Country

•Percent from host country captures composition •Does not capture structure

Alter-alter relationships and network structureAlter adjacency matrix

Joydip_K Shikha_K Candice_A Brian_N Barbara_A Matthew_A Kavita_G Ketki_G . . .

Joydip_K 1 1 1 1 0 0 0 0 . . .

Shikha_K 1 1 0 0 0 0 0 0 . . .

Candice_A 1 0 1 1 1 1 1 1 . . .

Brian_N 1 0 1 1 1 1 1 1 . . .

Barbara_A 0 0 1 1 1 1 0 0 . . .

Matthew_A 0 0 1 1 1 1 1 1 . . .

Kavita_G 0 0 1 1 0 1 1 1 . . .

Ketki_G 0 0 1 1 0 1 1 1 . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

Alter positions in structure

Aggregate variables of network 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

Personal network structure variables

Components

Components: 1 Components: 10

•Components captures separately maintained groups (network structure)•It does not capture type of groups (network composition)

Average Betweenness Centrality

Average Betweenness 12.7

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

Designing a personal network study

Goals, design, sampling

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

Make sure you need a network study!

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

f r iendspeople f r om t he wor kplace

M y f amily and me

Close f r iends

dist ant f amilydiver se acquaint ances

N eighbor s

acquaint ances f r om t hewor kplace

Neighbors

Friends & acquaintances from the workplace

Hairdresser Former job

Friends here from Bosnia

Family in Serbia

Husband family Friends

FAMILY

WORK

FRIENDS

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)

Part of any survey1. Identify a population.2. Select a survey mode.3. Select a sample of respondents.4. Ask questions about respondent.

Unique to personal network survey4. 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).

Steps to a personal network survey

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.

1. Selecting a Population

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 email contact

2. Modes of Survey Research

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.

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.

Prevalence vs. Relations

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

4. Questions about Ego


Unique to personal network survey4. 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




Unique to personal network survey4. Elicit a list of network members (“name generator”).

TOMORROW5. Ask questions about each network member.6. Ask respondent to evaluate alter-alter ties.7. Discover with the informant new insights about her




Unique to personal network survey4. 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


WEDNESDAY


Download - Introduction to Personal networks Summer course “The Measurement of Personal Networks” UAB 2011

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