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Putting Networks on Election Surveys

Putting Networks on Election Surveys. Nick Crossley. Background. Election surveys tend to individualise voters/respondents. Ignoring their dependencies and influence upon one another.

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Putting Networks on Election Surveys

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  1. Putting Networks on Election Surveys Nick Crossley

  2. Background • Election surveys tend to individualise voters/respondents. • Ignoring their dependencies and influence upon one another. • Voters aren’t isolated individuals. They are embedded in multiple relations and types of relations which shape their identities and actions. • A batch of studies have begun to indicate the importance of such influence on voting behaviour – whether an actor votes and, if so, how. • Political discussion networks.

  3. Analysing Networks Independent of social network analysis: • Resource/type generators. • Name generators Social network analysis • Ego-net analysis • Whole network analysis More amenable to random sampling and other big survey considerations. Greater consideration of network effects and properties

  4. Generator type questions: • Do you know (e.g. A plumber? A CEO? A Tory Voter?) + how well? Trust? Etc. • Who do you know? e.g. name 5 best friends (sources of support, people you discuss politics with ...etc.) and list their various relevant attributes. • This is dyadic, not really network data, so much less interesting from a network point of view. • But it poses no special problems for standard survey methods (perhaps beyond question construction).

  5. Ego-nets • Ask people who they know (or whatever). • Elicit alter attributes. • Elicit alter-alter ties (usually, as known to ego). • Still largely appropriate for big surveys. • Identifies different ‘net doms’ or circles. • Accesses some network properties – but only some.

  6. Whole network: • Identify a population; survey relevant relations between all members of that population + attributes. Student activists – not isolated individuals but forming a network!

  7. Some whole network measures • Density • Diameter. • Centralisation (multiple versions of) • Average geodesic. • Clustering co-efficient. • Cliques and cores. • Reciprocity • Transitivity. • Triad census. • Homophily • ERGM statistical models. • SIENA modelling of network/attribute change over time.

  8. Whole Networks Main advantages Main disadvantages Surveying a given network more thoroughly – access to a different level of analysis. Putting egos in a wider perspective. Wider range of measures available – more properties (and more structure) to look at. More chance of picking up e.g. cascades. Different sampling strategy to mainstream surveys. Generalisability? Can be difficult to get data. Typically only deals with one ‘net dom’ or social circle.

  9. Homophily NSMs Mainstream Trots

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