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Introduction to Personal networks

Introduction to Personal networks

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Introduction to Personal networks

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  1. Introductionto Personal networks Summercourse “TheMeasurement of Personal Networks” UAB 2011

  2. 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)

  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. Introductionto Personal Networks.

  5. History and differencewithsociocentricnetworks

  6. 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

  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. 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.

  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 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)

  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 outsidethe 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 relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…). • “Ego”: the focal individual • “Alter”: a networkmember

  20. Personal network • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…). • “Ego”: aninformant • “Alter”: a networkmember

  21. Personal network • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts (family, work, neighbourhood, associations, religiouscommunity, school, online community…). • “Ego”: aninformant • “Alter”: a networkmember

  22. Personal network • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts. • “Ego”: aninformant • “Alter”: a networkmember

  23. Boundaryspecification ± 1500 ± 500 Personal network± 150 Active orclosenetwork± 50 Sympathygroup± 15 Support clique ± 5 ego RobinDunbar: Hierarchically inclusive levels of acquaintanceship

  24. “Dunbar’snumber” http://www.cabdyn.ox.ac.uk/complexity_PDFs/CABDyN%20Seminars%202007_2008/CABDyN%20Seminar%20Slides%20RIMDunbar.pdf

  25. 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

  26. 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

  27. Distribution of c in thepopulation(McCormick, doctoral thesis, 2011)

  28. Back tothedefinition • The set of social relationshipssurroundingan individual, whichstemfromdifferentcontexts. • “Ego”: the focal individual • “Alter”: a networkmember

  29. 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

  30. Thisisonlythestrength of ties, but… • Personal networkstendtodisplayhighclustering, particularly in relationto roles / settings of meeting • Contents, frequencyof relationships, …

  31. Anoverview of research

  32. 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

  33. (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

  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 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)

  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 thevariation of personal networks (45 alters)

  37. 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

  38. (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

  39. (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.

  40. 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 • …

  41. 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”)

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

  43. (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

  44. 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

  45. (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), …

  46. 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

  47. 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

  48. Evolution of obesity in theFraminghamHeartStudyChristakis & Fowler, 2007 Christakis & Fowler (2007), New EnglandJournal of Medicine, 357, 370-379

  49. (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.

  50. (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.