Data Collection and Management

Survey Design

  • Network Generator – question or prompt which generates a list of alters related to a specific relationship or connection
    • Connect, interact, communicate, influence
  • Name interpreters – questions designed to collect information regarding the alters listed above
    • Gender, age, frequency of contact, perception of activity/support
  • Alter interrelater – questions designed to determine connections between alters
    • Does Tom know Bob?
    • Details structural holes


  • Can use all three – Generator, Interpreter, Interrelater
  • Names are not important in this setting
  • Collect information on alters from the ego perception
  • Alter Limits – Some surveys have limited the number of alters an ego can nominate

Egocentric Example

Whole Network

  • May only use name generator as all other elements are reported by the others in the network
  • Roster based - supplies a roster of names from the bounded network
    • Can be helpful to match names
    • May be difficult with large networks or not possible if you do not have all of the names
  • Free recall - the ego supplies names from memory
    • Larger networks or networks in which you do not know all members
    • May be difficult to match names (Bob/Robert)
  • Both come with a level of bias – roster may lead to over reporting, free recall may lead to under reporting

Whole Network Example

Survey Administration

  • Researcher administered – industry standard
  • Online surveys – can be difficult based on software
  • Nomination limit concerns
  • Roster / recall concerns

Software Available (collection)

Other types of collection

  • Observational networks
    • Observational techniques similar to SOPARC, SOPLAY, SOFIT can be modified to track target individuals and the interactions between them and others
    • Multiple timepoints are needed to detail multiple connections
  • Natural Networks
    • Networks already in place in which you would like to determine impact of their structure
    • Example: Parks or crossings connected by trails / paths
    • Nodes – parks and attributes of the park
    • Ties / Edges – trails which connect them
    • May determine important trails to maintain or parks which are important midpoints in trail networks
  • Two-Mode Networks
    • Nodes are not connected to each other but are connected through a second type of node (mode)
    • Example: people using a park – person -> park -> person
    • This can then tell the parks of PA resources people may have “shared”
  • Cognitive Mapping
    • Type of whole network interviewing based on perception
    • Every member asked to map all connections between every person in the network
    • Perceptions of all individuals are overlapped and condensed to develop a final network
    • Example: adolescents connections at after-school program
  • Public Records
    • Social media accounts – scraping data from accounts
    • Email records
    • Public announcements
    • Organization member lists

Data Files

  • Relational data: connects one node to another
    • Edgelist: easiest form – A-B, B-C, A-D
    • Matrix: all members are listed on X and Y axis, 1 is placed in each cell which a connection is present a 0 is placed if there is no connection
  • Attribute table – file containing all ego information
    • Demographics, outcome variables, etc.