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From Respondents to Networks: Bridging Between Individuals, Discussants, and the Network in the Study of Political Discussion

Abstract

Much of our understanding of social influence in individual political behavior stems from representative surveys asking respondents to identify characteristics of a small number of people they talk to most frequently. By focusing only on these few close contacts, we have implicitly assumed that less-intimate associates and features of network structure hold little influence over others’ attitudes and behavior. We test these assumptions with a survey that attempted to interview all students at a small university during a highly-salient municipal election. By focusing on a small, well-defined community, we are able to explore the relationship between individuals, their close associates, and also less-immediate associates. We are also able to explore features of network structure unobtainable in representative samples. We demonstrate that these less-immediate associates and network features have the potential to exert important influence that conventional survey approaches would miss.

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Notes

  1. 1.

    The study was approved by the College of William & Mary IRB on Human Subjects, PHSC-2010-03-04-6495-rbrapo.

  2. 2.

    In total, 1833 of the 5836 undergraduates provided at least one friend’s full name in the name generator, yielding an AAPOR non-probability internet panel participation rate of 31% (The American Association for Public Opinion Research 2015, 40).

  3. 3.

    We rely on a database produced by the Williamsburg Voter Registrar including all registered voters, their date of registration, date of birth, address, and voting history. We used first and last names to find all potential matches between individuals in our student sample and those registered to vote.  In each case, we used both the date of birth as well as the address to help confirm that a matched name corresponded to a student. According to Ansolabehere and Hersh (2010) summary measure, Virginia maintains some of the highest-quality registration records in the US.

  4. 4.

    Indegree provides a better measure of global centrality than either of its variants: outdegree and total degree. In this case, outdegree is equal to the number of people an individual names as friends. Outdegree is thus limited only to survey respondents and provides little variation (as described above, 78% of students responding to the name generator identified the maximum five friends). Total degree is also problematic because it equals the sum of indegree and outdegree. For non-respondents, this sum is always the same as indegree, but for respondents, it is an average of 4.6 units greater than their indegree. Thus, total degree conflates popularity and survey participation.

  5. 5.

    Alternatively, the local clustering coefficient would provide a measure of the uniqueness or redundancy of friends. We rely instead on the two-step neighborhood density because it captures uniqueness or redundancy of not only friends (Zone 1), but also second-order friends (Zone 2).

  6. 6.

    All tables in this manuscript were originally typeset using the texreg package in R (Leifeld 2013).

  7. 7.

    This indicator variable is constructed from an item asking, “Have you ever attended an off-campus party where the police issued a citation to you or someone else at that party for violating the noise ordinance?”.

  8. 8.

    This and all other reported predictions and confidence intervals come from simulations from the posterior (Gelman and Hill 2007), setting other covariates to their medians.

  9. 9.

    These means are equal to the number of friends in the zone who state that they have been to a party cited by police for violating the noise ordinance, divided by the number of friends in the zone.

  10. 10.

    The other controls are the respondent’s interest in national politics (“In general how interested are you in national politics?”), family economic status (“How would you describe your family's economic status?”), and indicators of race and gender.

  11. 11.

    Zone 1’s mean turnout is equal to the number of validated voters in the respondent’s Zone 1 network, divided by the total number of friends in Zone 1. An analogous Zone 2 measure is introduced in Table 4, Model 3.

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Correspondence to Matthew T. Pietryka.

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Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

Additional information

We thank Dennis Langley and Jessica Parsons for research assistance and Quintin Beazer and Scott McClurg for helpful comments. Our data and replication files can be downloaded from the Political Behavior Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GUAY8P.

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Pietryka, M.T., Reilly, J.L., Maliniak, D.M. et al. From Respondents to Networks: Bridging Between Individuals, Discussants, and the Network in the Study of Political Discussion. Polit Behav 40, 711–735 (2018). https://doi.org/10.1007/s11109-017-9419-3

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Keywords

  • Political discussion
  • Social networks
  • Egocentric networks
  • Name-generator survey batteries
  • Attitudes
  • Political behavior