Webinar Notes: Social Network Analysis

Webinar hosted by Canadian Foundation for Healthcare Improvement (CFHI)

    • social network analysis:
      • allows us to visualize and measure relationships in a network (e.g., how information is passed within the network)
      • can help you identify people who have connections to groups you need to work with/influence
      • can map out who is engaged in a project and who has not yet been engaged
      • can map out how information is passed through a network for your project
Relationships between "top 50" UK PR twitterers

Source: Posted on Flickr by Porter Novelli Global  with a Creative Commons licence.

  • definitions:
    • node: an individual in the network
    • edge: connection between two individuals
    • ego-net: the network of an individual
    • centralized: closely connected network
    • de-centralized: loosely connected network
    • nominations survey: respondents identify each other
    • roster survey: possibly nominees are identified for respondents
    • degree: # of connections between individuals
    • density: concentration of relationships
    • attributes: characteristics of an individual in the network which are of interest (e.g., location, professional role, gender, etc.)
  • theory behind SNA
    • relationships are important (vs. just individual’s attributes)
    • networks are powerful influences on behaviour (e.g., a teen who is in a network with smokers is more likely to become a smoker)
  • you do a nominations survey (you give a roster of all the people in the organization to respondents and have them list, e.g., “who do you go to for advice” or “who do you collaborate with”)and then can make a matrix showing who is connected to whom
  • note that one person might name someone but that person might not name the first person
  • can take the matrix and turn it into a graph of the network
  • can calculate measures (e.g., outdegree: how many times a person is named by others, indoor: how many times a person was named by other people)
    • individual  measures- centrality: (degree, closeness, betweenness, eigenvector), persona network destiny, constraint, homophily (on any characteristics) – how similar are your friends on a given characteristic (e.g., girls friends with girls, boys friends with boys), structural equivalence, group membership, clique membership
    • network measures : size, density, transitivity, clustering, average path length
  • he uses UCINet (software program – can get 3 month free trial online), but there are many others
  • in the graph, can identify highly connected people and recruit them as change agents for your project; can identify tight groups (e.g., if they are resisters, they will likely influence each other as a group)
  • can also map out team structure – e.g., is your team tightly knit? are all team members equally connected to each other or are some isolated? how well is your team reaching out to others in the organization (e.g., will help you diffuse your idea out to the rest of the org)
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