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Negative and Positive Partisanship in the 2016 U.S. Presidential Elections

Abstract

Negative partisanship captures the notion that disdain for the opposing party is not necessarily accompanied by strong in-party attachments. Yet, lack of a theoretical framework as well as measurement issues have prevented researchers from utilizing this consequential concept. I address these concerns in several ways. First, I design and examine the measurement properties of a multi-item scale that gauges negative partisan identity. Second, I demonstrate that—while most Americans display aspects of both negative and positive partisan identity—the two are distinct constructs. Third, I compare the power of both types of partisan identity in predicting attitudes towards bipartisanship, political participation, and vote choice. I thereby demonstrate the distinctive effects of negative and positive partisan identity on a range of political behaviors. The results offer a more nuanced perspective on partisanship and its role in driving affective polarization.

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Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    This poll can be accessed here: https://www.people-press.org/2016/06/22/6-how-do-the-political-parties-make-you-feel/ (last access: 12/20/2018).

  2. 2.

    This poll can be accessed here: https://www.pewresearch.org/fact-tank/2018/03/29/why-do-people-belong-to-a-party-negative-views-of-the-opposing-party-are-a-major-factor/ (last access: 12/20/2018).

  3. 3.

    I thank an anonymous reviewer for pointing this out.

  4. 4.

    The standard three-point partisan strength measure categorizes respondents into partisan leaners, weak partisans, and strong partisans. While it correlates moderately with positive partisan identity, its predictive power is much weaker. To illustrate this point, I replicate the main analyses in this paper with the traditional strength measure.

  5. 5.

    In the following, I use the terms ‘negational identities’ and ‘negative identities’ interchangeably.

  6. 6.

    Zhong and colleagues flip the items of the identity subscale designed by Luhtanen and Crocker (1992) who also rely on Social Identity Theory to create their scale.

  7. 7.

    This poll can be accessed here: https://www.people-press.org/2016/08/18/1-voters-general-election-preferences/#more-negative-voting-than-in-08 (last access 03/14/2017).

  8. 8.

    ‘Political issue strength’ measures respondents’ attitude strength on ten salient issues including abortion, gun control, health care, same-sex marriage, and environmental regulations. The items scale well together (alpha = 0.78). Substituting ideology or ideological intensity for political issue strength does not change any of the results presented in this manuscript.

  9. 9.

    PPID and NPID correlate at 0.55 in the CCES sample.

  10. 10.

    For a scatterplot of the relationship between negative and positive partisan identity, see Fig. A1 in the Appendix.

  11. 11.

    The Chi-Square value of the two-factor model is significantly different from the value of the one-factor model [Chi-Square(df) = 1025.34(1), p < 0.001]. The fit of both models improves when co-variances between error terms are added but the two-factor model remains superior (see Table A4 in Appendix).

  12. 12.

    An exploratory factor analysis further supports these results (see Tables A5, A6, A7 in Appendix).

  13. 13.

    The corresponding table can be found in the Appendix (see Table A8) as well as analyses with the standard partisan strength item (see Table A9).

  14. 14.

    To address concerns regarding potential endogeneity between anti-bipartisanship and negative partisan identity, I replicate this analysis with two other dependent variables (see Table A10 in Appendix).

  15. 15.

    When predicting less partisan political engagement such as watching a presidential debate and interest in national news, positive partisanship remains a more powerful influence (see Table A11 in Appendix).

  16. 16.

    I replicate these findings with the CCES sample (see Table A12 in Appendix).

  17. 17.

    The coefficient for positive partisan identity is indeed statistically bigger than the coefficient for negative partisan identity (p < 0.01).

  18. 18.

    Similarly, positive partisanship is a predictor of turnout while negative partisanship does not exert any statistically significant effect (see Table A13 in Appendix).

  19. 19.

    I thank an anonymous reviewer for suggesting this additional analysis.

  20. 20.

    The corresponding table can be found in the Appendix (see Table A15 in Appendix).

  21. 21.

    The coefficients for negative and positive partisan identity are statistically indistinguishable from each other (p < 0.86).

  22. 22.

    Note that these results are relatively insensitive to the inclusion of feeling thermometer values for Trump and Clinton. The asymmetry between Republicans and Democrats remains (see Table A16 in Appendix).

  23. 23.

    I replicate these findings with a sample of 1788 undergraduate students. These analyses can be found in the Appendix (see Table A17).

  24. 24.

    The corresponding table can be found in the Appendix (Table A18).

  25. 25.

    ‘Ideological strength’ was constructed by folding the ideology self-placement measure at its mid-point.

  26. 26.

    The coefficients for negative and positive partisan identity are statistically indistinguishable from each other (p < 0.53).

  27. 27.

    These results also hold when controlling for individual preferences for divided government (see Table A19 in Appendix).

  28. 28.

    The negative partisan identity scale correlates with FT values at − 0.16 and with the negative vote item at 0.33. Figure A5 in the Appendix plots the relationship between the negative partisan identity scale and FT values for the out-party.

  29. 29.

    I replicate the main analyses in Figs. 1 and 2 with the shorter NPID scale (see Table A24 in Appendix).

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Acknowledgements

I wish to thank Jamie Carson, Joe Ura, Paul Kellstedt, Lilliana Mason, Joshua Robison, Eric Groenendyk, Patrick Kraft, Nicholas Nicoletti, a number of other colleagues, and four anonymous reviewers who provided thoughtful comments and helpful insight on the project. The survey data used in this paper as well as the replication file are available at the journal’s page on Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TDDMIB.

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Correspondence to Alexa Bankert.

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Bankert, A. Negative and Positive Partisanship in the 2016 U.S. Presidential Elections. Polit Behav (2020). https://doi.org/10.1007/s11109-020-09599-1

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Keywords

  • Partisanship
  • Identity
  • Negativity
  • Measurement
  • Voting
  • Bipartisanship