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Chapter1. Social Net Work Data
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  1. Section2 Social Network Data Chapter1. Social Net Work Data 温玥 12011211790 2013 Autumn Special Topics in Marketing

  2. Outline • Difference between conventional and network data • Glossary • Populations, samples, and boundaries • Modality and levels of analysis • Sampling ties • Multiple relations • Scales of measurement • A note on statistics and social network data • Supplemental reading 2013 Autumn Special Topics in Marketing

  3. Difference between conventional and network data describes the attributes of "ego" • "Conventional" sociological data • Rectangular array of measurements • Rows: cases/subjects, or observations • Columns: attributes, or variables, or measures • "Network" data • Square array of measurements • Rows: cases/subjects, or observations → in whom they choose • Columns: cases/subjects, or observations too → being chosen by others • Defined by • actors(nodes) • relations (ties) actor is embedded/nested within network 2013 Autumn Special Topics in Marketing

  4. GLOSSARY • Node\Actor: • An object that may or may not be connected to other objects in a network. • Tie\Relation: • A connection between two nodes that can be either one-way (directed) or two-way (bilateral). • Ego: • The person whose behavior is being analyzed. • Alter: • A person connected to the ego who may influence the behavior of the ego. 2013 Autumn Special Topics in Marketing

  5. Populations, samples, and boundaries • Sample • Actors cannot be sampled independently • Usually conduct a census (not always, see sampling ties) • Population • The elements of the population to be studied are defined by falling within some boundary • Boundary • By the actors themselves • Existing institutionalized social network • E.g. members of a classroom, organization, • By a priori "demographic" or "ecological"population • Researcher’s suspected social network • E.g. people who have gross family incomes over $1,000,000 per year 2013 Autumn Special Topics in Marketing

  6. Modality and levels of analysis • Individual people nested within networks • Individual people • Social entities • Multi-modal data structures • First Mode: Students/Teacher • Second Mode: Classes • Third Mode: Schools • Macro-meso-micro levels of analysis • Individual, group, organization, community, institution, society, global • Advantages • How the individual is embedded within a structure • How the structure emerges from the micro-relations between individual parts 2013 Autumn Special Topics in Marketing

  7. Sampling ties • Full network methods • Taking a census of ties in a population of actors • +: very powerful descriptions and analyses of social structures • -: very expensive and difficult to collect • Application: small group • Non full network methods • Tracking down the full networks beginning with focal nodes • +: less costly, easier generalization to larger population • -: "isolates“ are not located, not complete 2013 Autumn Special Topics in Marketing

  8. Snowball methods • Until no new actors are identified or the new actors being named are very marginal to the group • Application: elite network (∵natural start point as initial node) • Ego-centered approach with alter connections • Begin with a selection of focal nodes (egos), and identify the nodes to which they are connected. Then, we determine which of the nodes identified in the first stage are connected to one another. • Micro-network data sets -- samplings of local areas of larger networks • Ego-centric methods (ego only) • Begin with a selection of focal nodes (egos), and collecting information on the connections among the actors connected to each focal ego • Focus on the individual 2013 Autumn Special Topics in Marketing

  9. Multiple relations • Multiple kinds of ties that connect actors in a network • How select relations to be examined? • Systems theory • Material things are "conserved" in the sense that they can only be located at one node of the network at a time. • Informational things, to the systems theorist, are "non-conserved" in the sense that they can be in more than one place at the same time. • Methodologies for working with multi-relational data • network correlation, • multidimensional scaling and clustering, and role algebras 2013 Autumn Special Topics in Marketing

  10. Scales of Measurement • Binary measures of relations: • Ties being absent (coded zero), • Ties being present (coded one) • Multiple-category nominal measures of relations: • Select the category that describes your relationship with them the best • Create a series of binary measures • “Binarize“ the ties: simply code whether a tie exists for a dyad, or not • Grouped ordinal measures of relations: • Rate the ties as A, B, C.. • "strength" of ties • “Binarize“ the ties by choosing cut point or treated the ties as interval 2013 Autumn Special Topics in Marketing

  11. Full-rank ordinal measures of relations: • Rank order from strongest to weakest • “Full rank order scale" reflects differences in degree of intensity, but not necessarily equal differences • Treated the ties as interval, dichotomize the data or form a scale • Interval measures of relations: • Scale reflects differences in degree of intensity also with equal differences • Refined level analysisor even reduce to binary level 2013 Autumn Special Topics in Marketing

  12. A note on statistics and social network data • More "mathematical" sociology than of "statistical analysis“ • not probability samples • not independent actors • Statistics are used in • describe characteristics of individual observations • assessing the degree of similarity among actors • assessing the reproducibility or likelihood of the pattern that we have described • simulation 2013 Autumn Special Topics in Marketing

  13. Supplemental reading • Marsden, P. V. (1990). "Network data and measurement." Annual review of sociology: 435-463. • Carrington, P. J., et al. (2005). Models and methods in social network analysis, Cambridge university press. • Butts, C. T. (2007). "Carrington, P.J., Scott, J., Wasserman, S., 2005. Models and Methods in Social Network Analysis. Cambridge: Cambridge University Press." Social Networks 29(4): 603-608. • Christakis, N. A. and J. H. Fowler (2007). "The spread of obesity in a large social network over 32 years." New England journal of medicine 357(4): 370-379. 2013 Autumn Special Topics in Marketing