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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

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introduction to personal networks

Introductionto Personal networks

Summercourse “TheMeasurement of Personal Networks”

UAB 2011

the plan for this week
The plan forthisweek
  • Introductionto personal networkanalysis
  • Namegenerators
  • Nameinterpreters
  • Reliability and validity of network data
  • Mixedmethodsdesigns
  • Workshopswith software forcollecting, visualizing and analyzing personal network data:
    • EgoNet (José Luis Molina)
    • E-Net (Steve Borgatti)
    • Vennmaker (MarkusGamper)
    • (notspecificallyfor personal networks) visone (Jürgen Lerner)
the plan for this morning
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).
a bit of history
A bit of History …
  • The “Manchester School”, led first by Max Gluckman and later by Clyde Mitchell, explored the personal networksof 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
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
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.

for instance
For instance …

Gossip network …(Epstein, 1957)

two kinds of social network analysis
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.
not a simple dichotomy
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”
slide13

Personal Networks: Unbounded Social Phenomena

Example: Predict depressionamong 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)
slide14

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

slide15

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 outsidethe home

a note on the term egocentric
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
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.
personal network
Personal network
  • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…).
    • “Ego”: the focal individual
    • “Alter”: a networkmember
personal network1
Personal network
  • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…).
    • “Ego”: aninformant
    • “Alter”: a networkmember
personal network2
Personal network
  • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…).
    • “Ego”: aninformant
    • “Alter”: a networkmember
personal network3
Personal network
  • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts.
    • “Ego”: aninformant
    • “Alter”: a networkmember
boundary specification
Boundaryspecification

± 1500

± 500

Personal network± 150

Active orclosenetwork± 50

Sympathygroup± 15

Support clique ± 5

ego

RobinDunbar: Hierarchically inclusive levels of acquaintanceship

dunbar s number
“Dunbar’snumber”

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

how many people does a person know
Howmanypeopledoes a personknow?
  • Year-longobservation of twoindividuals (Boissevain, 1973):
  • Contactdiaries: Pool & Kochen´s (1978) 1 personexperiment:

Gurevitch (1961) 18 persons:

Fu (2007): 54 persons, 3 months:

    • TelephoneBookStudies(Freeman & Thompson, 1989): Estimate based on the number ofnamesinformantsrecognizefrom a randomsample of surnamesfrom a telephonebook, extrapolatedto match the total number of names in the phonebook.
      • Reversed Small Worldexperiment(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 know1
Howmanypeopledoes a personknow?
  • Year-longobservation of twoindividuals (Boissevain, 1973):
  • Contactdiaries: Pool & Kochen´s (1978) 1 personexperiment:

Gurevitch (1961) 18 persons:

Fu (2007): 54 persons, 3 months:

    • TelephoneBookStudies(Freeman & Thompson, 1989): Estimate based on the number ofnamesinformantsrecognizefrom a randomsample of surnamesfrom a telephonebook, extrapolatedto match the total number of names in the phonebook.
      • Revised by Killworth et al. at
      • Reversed Small Worldexperiment(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

back to the definition
Back tothedefinition
  • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts.
    • “Ego”: the focal individual
    • “Alter”: a networkmember
definition affects the network characteristics e g
Definitionaffectsthenetworkcharacteristics, e.g.

Coreties

Peripheralties

  • Relativelyfew
  • Kin-centered(± 50% *, **, ***)
  • Highhomogeneity
  • Multistranded
  • Lowspatialdispersion

(67% at max 1 hr **)

  • Highdensity

(.57 for 3 ties*; .44 for 18.5 ties**, butsee .33 for 5 ties***)

  • Relativelystableover time
  • More numerous
  • Lowproportion of kin(± 20% #)
  • Lowhomogeneity
  • Specialized
  • Spatially more dispersed

(40% #)

  • Lowerdensity

(± .25 for 41,5 peripheralties#)

  • Unstableover time

* Marsden, 1987

** Fischer, 1982

*** Wellman, 1979

#These are very rough estimatesbasedonproportionsreportedbyMcCarty (1992) fornetworks of 60 ties, takingintoaccountvaluesfor 18.5 coretiesbasedon Fischer

this is only the strength of ties but
Thisisonlythestrength of ties, but…
  • Personal networkstendtodisplayhighclustering, particularly in relationto roles / settings of meeting
  • Contents, frequencyof relationships, …
an overview of research into personal networks
Anoverview of researchinto personal networks
  • Interest in patterns and processes of socialization and social integration
  • Predicting interindividual variationinthesepatterns
  • Theinfluence of personal networkson individual outcomes
    • Network-basedinterventions
  • Use of personal networks as a meansforothergoals - studyinghard-to-reachpopulations
  • Methoddevelopment

predictors

networks

outcomes

1 description of patterns and processes of socialization and social integration
(1) Description of patterns and processes of socialization and social integration
  • The personal networkrepresentsthesocial contextof an individual, theintermediatelevelbetweenthe individual and society, anessentialmechanismbymeans of whichthe individual isconnectedtothelargerworld
  • Shows thecurrentpattern of sociability, althoughitis in part a product of thepast
  • Personal networks are usedtounderstandtheorganization of informal relationships in society at large
examples
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 sociallyisolatednowthantheyweretwentyyearsago?

(McPherson, Smith-Lovin, Brashears)

  • What are theprocesses of socialization and social integration as youngpersonsenteradulthood?

(Bidart et al.)

  • Towhatextent do immigrants and nativesmixin society?

(Molina, McCarty, Lubbers / Molina, Lozares, Lubbers)

  • Isonline sociabilitydifferentlystructuredthan offline sociability? Isitreplacing offline sociability?

(Wellman and associates)

  • Isit a smallworld(local clustering and short pathlengths)?

(Milgram, Watts & Strogatz)

personal networks are unique
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

2 explaining variations in personal networks
2. Explainingvariations in personal networks
  • Do thesize, composition, structure, stability of networksvarywithindividual characteristics?
    • Demographiccharacteristics, education, personality, lifeevents, …
  • At therelationshiplevel, do alter characteristicsaffectthecontents of therelationshipwith ego, thestability of therelationship, ortheexistence of thetieswithotheralters?
    • E.g., Louch (2002)
  • Comparisonacrosssocieties
    • E.g., Fischer-Fischer/Shavit-Grossetti-Bastani; Burt-Ruan-Völker
3 the influence of personal networks on individual and social outcomes
(3) Theinfluence of personal networkson individual and social outcomes
  • Influenceson: mental and physicalhealth, jobmobility, migrationdecisions, riskybehavior, geographicalmobility…
  • Fourareas of researchintotheinfluence of personal networks:
    • A. Social support
    • B. Social capital
    • C. Diffusion and contagion
    • D. Network geographies
3a social support barrera vaux berkman uchino fischer antonucci
(3a) Social supportBarrera, Vaux, Berkman, Uchino… / Fischer, Antonucci…
  • Threeelements (Vaux, 1988; Barrera, 1986):
    • Sourcesof social support (social integration)
    • Types of social supportreceived (enacted social support)
    • Appraisalof social support (perceived social support)
  • Personal networks are sources of social support
        • Instrumental, emotional, informationalsupport, social companionship
  • Theories of social support and mental and physicalhealth:
    • Social supporthelpsindividualsto cope withmajorlifestressors and thechallenges of dailylife, therebyprotectingthemfromthenegativeconsequences of stress onhealth
    • 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 studies e g ertel glymour berkman 2009
Social supportstudiese.g., Ertel, Glymour & Berkman, 2009
  • Host of studiesshowingdirect and/orbufferingeffects of social supporton mental and physicalhealth
  • Explanatorypathways, e.g., for cardiovascular diseases:
    • Perceived social support has directrelationswith cardiovascular reactivitytostressors, neuroendocrine and immunesystems
    • Strong and supportivealterspromotehealthbehaviors (sleep, diet, exercise,…)
    • Provision of informational and tangible resources
the strength of weak ties structural holes granovetter burt
Thestrength of weakties / structuralholes…Granovetter / Burt
  • Researchfocusedmainlyonthe beneficial effects of strongties
  • Granovetter (1973) showedthatweakties can beinstrumentallystrongtoo …
    • Thesearchfor a job (Granovetter, 1974)
    • Thesearchfor a place tolive(Freeman & Sunshine, 1976)
    • Thesearchforanabortionist (Lee, 1969)
  • Burt (1992): It is not the quality of any particular tie but rather the way different parts of networks are bridged (“structuralholes”)
network structure and its effects
Network structure and itseffects

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

3b social capital bourdieu coleman wellman lin burt marsden flap
(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
Social capital and jobprestige (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 contagion valente coleman costenbader
(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
  • Appliedtoinnovations (Coleman, Valente), diseases and healthconditions (e.g., Klovdahl, theChristakis & Fowlerstudies), mobilizationtoprotest(Opp & Gern, 1993; Araya Dujisin, 2009), …
diffusion
Diffusion
  • Structuralmodels:
    • Structuralaspects of thenetworkinfluenceadoption (size of thenetwork, weakties, brokerage…)
  • Thresholdmodels (Valente):
    • Individualsengage in a new behaviorbasedontheirnetworkexposure (thenumber of others in thenetworkwhoalreadyengage in thatbehavior)
    • Individualshavedifferentthresholdsforadoption
example framingham heart study christakis fowler
Example: FraminghamHeartStudyChristakis & 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
  • Doesobesity, happiness, smoking cessation, alcohol consumption, loneliness,… spread through personal relationships?
  • FHS:
    • Longitudinal studyover 30 years (measurementseach 5 years) amongresidents in thetownFramingham, Massachusetts
    • Respondentswereaskedtoidentifytheirspouse, relatives, “close friends,” place of residence (neighbours), and place of work (coworkers)
    • As alterswereoftenalsoparticipants in theFraminghamHeartStudy, personal networkscouldbecombinedtostudythecommunity
evolution of obesity in the framingham heart study christakis fowler 2007
Evolution of obesity in theFraminghamHeartStudyChristakis & Fowler, 2007

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

3d activity spaces
(3d) Activityspaces
  • Thespatialdispersion and structure of personal networks can beregarded as indicators of individual “activityspaces” thatinfluence individual mobilitypatterns (e.g., Axhausen et al.; also Carrasco et al.)

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

4 hidden and hard to count populations
(4) Hidden and hard-to-countpopulations
  • Design of network-basedsamplingmethods
  • Snowballsampling:
    • 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
    • Currentsample members (inform about their networks to) recruit the next wave of sample members, and so on, continuing until the desiredsamplesizeisreached.
4 hidden hard to reach populations
(4) Hidden / hard-to-reachpopulations
  • 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 generatesapproximatelyunbiased population estimates, but the variance of RDS estimates are higher than in random samples
4 hidden hard to reach populations1
(4) Hidden / hard-to-reachpopulations
  • 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 withinthe target population – tobeusedtoadjustforselectionbias.
    • 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
4 size of hidden populations killworth et al 1998 mccormick salganik zheng 2010
(4) Size of hiddenpopulations(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 methodstoestimatethe personal networksize.
    • Theknownpopulationmethod
    • Thesummationmethod
5 method development
(5) Methoddevelopment
  • Methodsformeasuringaspects of personal networks and forcollecting personal network data
    • Software developmentsuch as Egonet, E-net, Vennmaker, …
    • Paper-and-pencilmethods (e.g., Hogan et al., 2007)
    • Estimatesfor social capital and networksize
  • Methodsforthestatisticalanalysis of personal network data, e.g.,
    • Triad counts (Kalish & Robins, 2006)
    • Duo-centeredstudies (Coromina et al., 2008)
    • Jointanalysis of dynamics in networkstructure and networkcomposition (Lubbers et al, in preparation)
an overview of research into personal networks1
Anoverview of researchinto personal networks
  • Interest in patterns and processes of socialization and social integration
  • Predicting interindividual variationinthesepatterns
  • Theinfluence of personal networkson individual outcomes
    • Network-basedinterventions
  • Use of personal networks as a meansforothergoals - studyinghard-to-reachpopulations
  • Methoddevelopment
summary
Summary
  • Forthemeasurement of personal networks, itisimportanttooperationally define whatconstitutes a social relationship, basedontheresearchquestion
  • In the case of influence, itisimportanttospecifythemechanismthroughwhich personal networks are expectedtoinfluence individual outcomes
    • Usuallybasedonsomeform of transmissionfromnodetonode (resources, information, affection, ideas, control, sanctions, disease…).
    • Define underwhichconditionsthesetransmissionstake place (structural and relationalconstraints and opportunities)
    • Aggregationof thesetransmissions
personal network data
Personal network data
  • Wecollect data onrelationships…
    • Therelationshipsthataninformant has withothers
    • Therelationshipsthattheothershaveamongthem
  • …in ordertoaggregatethemto variables thatcharacterizethenetworks
    • Number of theinformant’srelationships – Size
    • Contents of theinformant’srelationships – Composition
    • Existence of relationshipsamongtheothers – Structure
personal network data1
Personal network data
  • Somequestions are analyzed at therelationshiplevel, e.g. ,
    • Are kinrelationships more supportivethanacquiredrelationships?
    • Do strongertiespersistbetterover time?
  • Others at thenetworklevel, e.g.,
    • Do peoplewithlargersupportnetworks cope betterwith [somesort of lifeevent] thanpeoplewithsmallersupportnetworks?
    • Are geographicallydispersednetworksless dense?
types of aggregate personal network data
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.
personal network composition variables
Personal network composition variables

Characteristics of alters and ego-alter relationships

Aggregate variables of networkcomposition, 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
percent of alters from host country
Percent of alters from host country

36 Percent Host Country

44 Percent Host Country

  • Percent from host country captures composition
  • Does not capture structure
personal network structure variables
Personal network structure variables

Alter positions

in structure

Aggregate variables of networkstructure:

  • Degreecentrality
  • Closenesscentrality
  • Betweennesscentrality
  • Average degree centrality (density)
  • Average closeness centrality
  • Average betweenness centrality
  • Core/periphery
  • Number of components
  • Number of isolates
components
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 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

Designing a personal networkstudy

Goals, design, sampling

make sure you need a network study
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
slide72
Sometimes the way we think and talk about who we know does not accurately reflect the social context
slide73

FAMILY

WORK

FRIENDS

research methods to capture personal networks
Researchmethodsto capture personal networks
  • Observation (Boissevain)…
  • Surveys
  • Contactdiaries (Fu, Lonkila, …)
  • Experiments (Killworth & Bernard)
  • Extraction of data from SNS (Boyd, Hogan, …) / mobilephones (Lonkila)
slide75

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).

slide76

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.
2 modes of survey research
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 email contact
slide78

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.
slide79

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.
4 questions about ego
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
slide81

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).

slide82

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).

slide83

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