Pigeonholing Partisans: Stereotypes of Party Supporters and Partisan Polarization

Abstract

What comes to mind when people think about rank-and-file party supporters? What stereotypes do people hold regarding ordinary partisans, and are these views politically consequential? We utilize open-ended survey items and structural topic modeling to document stereotypes about rank-and-file Democrats and Republicans. Many subjects report stereotypes consistent with the parties’ actual composition, but individual differences in political knowledge, interest, and partisan affiliation predict their specific content. Respondents varied in their tendency to characterize partisans in terms of group memberships, issue preferences, or individual traits, lending support to both ideological and identity-based conceptions of partisanship. Most importantly, we show that partisan stereotype content is politically significant: individuals who think of partisans in a predominantly trait-based manner—that is, in a way consistent with partisanship as a social identity—display dramatically higher levels of both affective and ideological polarization.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    By this, we mean based on increased dislike for the opposing party rather than explicit policy or ideological disagreements.

  2. 2.

    In addition, for many of the ideas our subjects report, it is difficult, if not impossible, to assess accuracy. For that reason, we do not directly explore accuracy.

  3. 3.

    The undergraduate sample was collected between March and April 2016, the MTurk sample was collected in April 2016, and the national sample was collected in early August 2016.

  4. 4.

    We use the “stm” package in R for all such analyses (Roberts et al. 2017). For guidance in using this package, we have relied heavily on the information provided by Roberts et al. (2014) and Roberts et al. (2016).

  5. 5.

    In our case, each participant’s responses are combined into a single document, and the STM then enables us to make sense of those responses taken as a whole.

  6. 6.

    We note that, because these calculations occur outside of the STM package (due to limitations on the analyses in STM), this procedure introduces additional uncertainty into the succeeding analyses. This suggests that caution should be taken when interpreting results on the edges of conventional levels of significance.

  7. 7.

    Before constructing the summary tables that appear below and in the Online Appendix, we first recoded the free responses to group together synonyms and words with highly similar or related meanings (e.g., “rich” and “wealthy” were combined into a single category). More details on this process can be found in the Online Appendix. While we used recoded data in the following Tables (1, A.2, A.3, A.4, A.5) for presentational purposes, all analyses after this section were conducted using raw, non-recoded data.

  8. 8.

    The inclusion of our convenience samples in addition to our nationally diverse sample jibes with prior work on stereotype content, which makes frequent use of student samples (Katz and Braly 1933; Devine and Elliot 1995; Madon et al. 2001), MTurk samples (Scherer et al. 2015), and other convenience samples (Graham et al. 2012). We also note that research on these kinds of convenience samples demonstrates the utility of both student (Druckman and Kam 2011) and MTurk samples (Mullinix et al. 2015; Levay et al. 2016) for making generalizable inferences. Furthermore, the STM procedures explicitly incorporate differences between samples when generating topics, allowing people from different samples to use the topics in different amounts. What this means in practical terms is, for example, if our national sample uses a particular topic frequently but the student sample does not (or vice versa), that topic will still appear in the final output.

  9. 9.

    While the appropriate number of topics cannot be objectively determined by the structural topic model, it provides a number of metrics that researchers may use to determine a sensible body of topics. See Roberts et al. (2014) and the Online Appendix of this paper for more details.

  10. 10.

    Instead of reporting the most frequent vocabulary terms found in each topic, we follow Roberts et al. (2014) in reporting words with the simplified frequency-exclusivity (FREX) scores. FREX words are summarized using the harmonic mean of the semantic coherence and exclusivity of a word within a given topic. Additionally, note that the words in these tables have been stemmed—that is, trailing characters (such as -ing or -ed) have been removed. This procedure is standard in any kind of textual analysis.

  11. 11.

    Here again we refer to FREX words.

  12. 12.

    We use strict topic categorization standards, erring on the side of labeling a topic ambiguous any time the appropriate placement seemed unclear. As a robustness check, we also noted whether each ambiguous topic “leaned” toward traits or groups/issues; if we group these topics with the corresponding “unambiguous” topics when performing the analyses that follow, our substantive results remain largely unchanged.

  13. 13.

    Note that frequency is not simply a function of the number of topics in each categorization. There was a great amount of variation in the usage of each topic, and a higher number of topics in one category (i.e., traits vs. issues and groups) does not necessarily imply a higher usage by subjects.

  14. 14.

    Another way to consider this point is to look at the correlation between the trait topics and the issues/groups topics. If subjects used only one or the other, we would expect a correlation near to -1. However, what we observe in our data is a correlation of -0.07 (p=0.02) using our strict topic groupings, 0.22 (p=0.000) using broader groupings.

  15. 15.

    In these models, we include sample fixed effects (that is, dummy variables for each data collection) in order to account for baseline differences between our samples. The OLS results presented here are also robust to alternative specifications (most notably, beta regression), but we prefer OLS owing to its simplicity of interpretation and general familiarity. Results from alternative models are available from the authors upon request.

  16. 16.

    In Fig. 1, we only include traits, issues/groups, and “don’t know” topics as dependent variables, omitting results for ambiguous topics. This is due to the fact that we did not have prior expectations about the ambiguous topics - a parallel figure containing these results can be found in Fig. A.1 of the Online Appendix. We also include parallel analyses predicting use of each individual topic within the STM package, but here we have little theoretical guidance on what to expect. These can be found in Table A.7, which reports when various respondent characteristics are related to specific topics. In Table A.6 and Table A.7, all independent and dependent variables are rescaled from 0-1 for ease of interpretation.

  17. 17.

    In addition to the model in Table A.6 depicting the relationships between these characteristics and use of different broad categories of topics, Table A.7 in the Online Appendix summarizes significant correlations with the use of specific topics.

  18. 18.

    In the interest of graphical clarity, we do not report coefficients for the ambiguous topics in Fig. 2. These (largely non-significant) results can be found in the Online Appendix.

  19. 19.

    In each case, we use the absolute value of the difference between these items.

  20. 20.

    We note that the proportion of ambiguous topics also relates to stronger respondent ideology, as well as greater perceived ideological distance between party supporters and between congresspersons. If we use a less stringent standard for designating predominantly trait-based and group/issue-based topics, the association with ambiguous topics disappears—but the consistent effect of using trait-dominant topics on all of our outcomes remains.

  21. 21.

    We obtain similar results when including subjects from our student sample. Unfortunately, we lack these outcome measures for the Mechanical Turk sample.

  22. 22.

    The possibility remains that these results are an artifact of the particular way we chose to categorize our topics. Given the apparent convergence of partisanship with other identities (see Mason 2016), it may be appropriate to put trait- and group-based topics together, with issue-based topics standing alone. We explored an alternative grouping of stereotype topics along these lines. In that case, the use of trait/group topics positively relates to all the forms of ideological and affective polarization described previously. The use of issue topics positively correlates with only one of these variables: perceptions of ideological differences in Congress. Thus, however we choose to place stereotypes based on social groups—with political issues or with traits—the relationship between trait-based stereotyping and various forms of polarization holds.

  23. 23.

    To conduct this robustness check, we recoded the individual words provided by subjects as either traits, issues/groups, or other/ambiguous, and used the proportion of each as regressors. For this recoding procedure, we relied on lists of political issues, social groups associated with the parties, and personality traits generated by subjects themselves in another portion of our data collection (not reported here) as our recoding dictionaries. In brief, we obtain results that accord with those shown above: only traits consistently exerted a statistically significant impact on these measures of polarization (with the sole exception being perceived ideological distance between Democrats and Republicans in Congress); in each case, the effect was large and positive.

  24. 24.

    We also examined the possibility that an interaction between stereotype content and valence relates to our polarization variables. However, interacting the use of trait-based topics with outparty valence reveals little evidence of such a relationship; the positive correlation between trait content and polarization remains constant across all our dependent variables, except for the extremity of respondents’ self-rated ideology. In that case, as outparty valence increases, the relationship between trait content and more extreme ideology diminishes as outparty valence becomes more positive. We find no evidence of an interaction between the use of group/issue topics and outparty valence.

References

  1. Abramowitz, A. I., & Saunders, K. L. (2006). Exploring the bases of partisanship in the American electorate: Social identity vs. ideology. Political Research Quarterly, 59(2), 175–187.

    Article  Google Scholar 

  2. Abramowitz, A. I., & Saunders, K. L. (2008). Is polarization a myth? Journal of Politics, 70(2), 542–555.

    Article  Google Scholar 

  3. Achen, C. H., & Bartels, L. M. (2016). Democracy for realists: Why elections do not produce responsive government. Princeton: Princeton University Press.

    Google Scholar 

  4. Ahler, D., & Sood, G. (Forthcoming). The parties in our heads: Misperceptions about party composition and their consequences. Journal of Politics.

  5. Allport, G. W. (1954). The nature of prejudice. Reading, MA: Addison-Wesley.

    Google Scholar 

  6. Ashmore, R. D., & Del Boca, F. K. (1981). Conceptual approaches to stereotypes and stereotyping. In D. L. Hamilton (Ed.), Cognitive processes in stereotyping and intergroup behavior (pp. 1–35). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  7. Bastian, B., & Haslam, N. (2006). Psychological essentialism and stereotype endorsement. Journal of Experimental Social Psychology, 42(2), 228–235.

    Article  Google Scholar 

  8. Bauer, P. C., Barberá, P., Ackermann, K., & Venetz, A. (2017). Is the left-right scale a valid measure of ideology? Individual-level variation in associations with “left” and “right” and left-right self-placement. Political Behavior, 39(3), 553–583.

    Article  Google Scholar 

  9. Baumer, D. C., & Gold, H. J. (1995). Party images and the American electorate. American Politics Research, 23(1), 33–61.

    Article  Google Scholar 

  10. Baumer, D. C., & Gold, H. J. (2007). Party images and partisan resurgence. The Social Science Journal, 44(3), 465–479.

    Article  Google Scholar 

  11. Berelson, B. R., Lazarsfeld, P. F., & McPhee, W. N. (1954). Voting: A study of opinion formation in a presidential campaign. Chicago, IL: University of Chicago Press.

    Google Scholar 

  12. Biernat, M. (2003). Toward a broader view of social stereotyping. American Psychologist, 58(12), 1019–1027.

    Article  Google Scholar 

  13. Bordalo, P., Coffman, K., Gennaioli, N., & Shleifer, A. (2016). Stereotypes. Quarterly Journal of Economics, 141(4), 1753–1794.

    Article  Google Scholar 

  14. Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: Wiley.

    Google Scholar 

  15. Chambers, J. R., Baron, R. S., & Inman, M. L. (2006). Misperceptions in intergroup conflict: Disagreeing about what we disagree about. Psychological Science, 17(1), 38–45.

    Article  Google Scholar 

  16. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56(1), 5–18.

    Article  Google Scholar 

  17. Devine, P. G., & Elliot, A. J. (1995). Are racial stereotypes really fading? The Princeton trilogy revisited. Personality and Social Psychology Bulletin, 21(11), 1139–1150.

    Article  Google Scholar 

  18. Druckman, J. N., & Kam, C. D. (2011). Students as experimental participants: A defense of the ‘narrow data base’. In J. N. Druckman, D. P. Green, J. H. Kuklinski, & A. Lupia (Eds.), Cambridge handbook of experimental political science (pp. 41–56). New York: Cambridge University Press.

    Google Scholar 

  19. Druckman, J. N., Peterson, E., & Slothuus, R. (2013). How elite partisan polarization affects public opinion formation. American Political Science Review, 107(1), 57–79.

    Article  Google Scholar 

  20. Eagly, A. H., & Mladinic, A. (1989). Gender stereotypes and attitudes toward women and men. Personality and Social Psychology Bulletin, 15(4), 545–558.

    Article  Google Scholar 

  21. Glick, P., Diebold, J., Bailey-Werner, B., & Zhu, L. (1997). The two faces of Adam: Ambivalent sexism and polarized attitudes toward women. Personality and Social Psychology Bulletin, 23(12), 1323–1334.

    Article  Google Scholar 

  22. Goggin, S. N., & Theodoridis, A. G. (2017). Disputed ownership: Parties, issues, and traits in the minds of voters. Political Behavior, 39(3), 675–702.

    Article  Google Scholar 

  23. Graham, J., Nosek, B. A., & Haidt, J. (2012). The moral stereotypes of liberals and conservatives: exaggeration of differences across the political spectrum. PLoS ONE, 7(12), e50092.

    Article  Google Scholar 

  24. Green, D., Palmquist, B., & Schickler, E. (2002). Partisan hearts and minds. New Haven: Yale University Press.

    Google Scholar 

  25. Greene, S. (1999). Understanding party identification: A social identity approach. Political Psychology, 20(2), 393–403.

    Article  Google Scholar 

  26. Greene, S. (2004). Social identity theory and party identification. Social Science Quarterly, 85(1), 136–153.

    Article  Google Scholar 

  27. Hamilton, D. L., & Sherman, S. J. (1996). Perceiving persons and groups. Psychological Review, 103(2), 336–355.

    Article  Google Scholar 

  28. Hetherington, M. J. (2001). Resurgent mass partisanship: The role of elite polarization. American Political Science Review, 95(3), 619–631.

    Article  Google Scholar 

  29. Hogg, M. A. (1992). The social psychology of group cohesiveness: From attraction to social identity. New York: New York University Press.

    Google Scholar 

  30. Hogg, M. A., & Abrams, D. (1988). Social identifications: A social psychology of intergroup relationships and group processes. New York: Routledge.

    Google Scholar 

  31. Huddy, L., Mason, L., & Aarøe, L. (2015). Expressive partisanship: Campaign involvement, political emotion, and partisan identity. American Political Science Review, 109(1), 1–17.

    Article  Google Scholar 

  32. Iyengar, S. (1996). Framing responsibility for political issues. Annals of the American Academy of Political and Social Science, 546(1), 59–70.

    Article  Google Scholar 

  33. Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

    Article  Google Scholar 

  34. Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707.

    Article  Google Scholar 

  35. Josefson, J. (2000). An exploration of the stability of partisan stereotypes in the United States. Party Politics, 6(3), 285–304.

    Article  Google Scholar 

  36. Katz, D., & Braly, K. W. (1933). Racial stereotypes of 100 college students. Journal of Abnormal and Social Psychology, 28(3), 280–290.

    Article  Google Scholar 

  37. Key, V. O. (1964). Parties, politics, and pressure groups (5th ed.). New York: Crowell.

    Google Scholar 

  38. Kinder, D. R., & Kalmoe, N. (2017). Neither liberal nor conservative: Ideological innocence in the American public. Chicago: University of Chicago Press.

    Google Scholar 

  39. Levay, K. E., Freese, J., & Druckman, J. N. (2016). The demographic and political composition of mechanical turk samples. SAGE Open, 6(1), 1–17.

    Article  Google Scholar 

  40. Levy, S. R., Plaks, J. E., Hong, Y.-y., Chiu, C.-y., & Dweck, C. S. (2001). Static versus dynamic theories and the perception of groups: Different routes to different destinations. Personality and Social Psychology Review, 5(2), 156–168.

    Article  Google Scholar 

  41. Levy, S. R., Stroessner, S. J., & Dweck, C. S. (1998). Stereotype formation and endorsement: The role of implicit theories. Journal of Personality and Social Psychology, 74(6), 1421–1436.

    Article  Google Scholar 

  42. Lippmann, W. (1922). Public opinion. New York: Harcourt, Brace, and Company.

    Google Scholar 

  43. Lodge, M., & Hamill, R. (1986). A partisan schema for political information processing. American Political Science Review, 80(2), 505–519.

    Article  Google Scholar 

  44. Macrae, C. Neil, & Bodenhausen, G. V. (2000). Social cognition: Thinking categorically about others. Annual Review of Psychology, 51, 93–120.

    Article  Google Scholar 

  45. Madon, S., Guyll, M., Aboufadel, K., Montiel, E., Smith, A., Palumbo, P., et al. (2001). Ethnic and national stereotypes: The Princeton trilogy revisited and revised. Personality and Social Psychology Bulletin, 27(8), 996–1010.

    Article  Google Scholar 

  46. Mason, L. (2013). The rise of uncivil agreement: Issue versus behavioral polarization in the American electorate. American Behavioral Scientist, 57(1), 140–159.

    Article  Google Scholar 

  47. Mason, L. (2015). ‘I disrespectfully agree’: The differential effects of partisan sorting on social and issue polarization. American Journal of Political Science, 59(1), 128–145.

    Article  Google Scholar 

  48. Mason, L. (2016). A cross-cutting calm: How social sorting drives affective polarization. Public Opinion Quarterly, 80(S1), 351–377.

    Article  Google Scholar 

  49. Mullinix, K. J., Leeper, T. J., Druckman, J. N., & Freese, J. (2015). The generalizability of survey experiments. Journal of Experimental Political Science, 2(2), 109–138.

    Article  Google Scholar 

  50. Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289–316.

    Article  Google Scholar 

  51. Niemi, R. G., & Jennings, M. K. (1991). Issues and inheritance in the formation of party identification. American Journal of Political Science, 35(4), 970–988.

    Article  Google Scholar 

  52. Rahn, W. M. (1993). The role of partisan stereotypes in information processing about political candidates. American Journal of Political Science, 37(2), 472–496.

    Article  Google Scholar 

  53. Rahn, W. M., & Cramer, K. J. (1996). Activation and application of political party stereotypes: The role of television. Political Communication, 13(2), 195–212.

    Article  Google Scholar 

  54. Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A model of text for experimentation in the social sciences. Journal of the American Statistical Association, 111(515), 988–1003.

    Article  Google Scholar 

  55. Roberts, M. E., Stewart, B. M., & Tingley, D. (2017). stm: R package for Structural Topic Models, version 1.2.1. http://www.structuraltopicmodel.com.

  56. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., et al. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082.

    Article  Google Scholar 

  57. Scherer, A. M., Windschitl, P. D., & Graham, J. (2015). An ideological house of mirrors: Political stereotypes as exaggerations of motivated social cognition differences. Social Psychological and Personality Science, 6(2), 201–209.

    Article  Google Scholar 

  58. Sherman, J. W., Kruschke, J. K., Sherman, S. J., Percy, E. J., Petrocelli, J. V., & Conrey, F. R. (2009). Attentional processes in stereotype formation: A common model for category accentuation and illusory correlation. Journal of Personality and Social Psychology, 96(2), 305–323.

    Article  Google Scholar 

  59. Sniderman, P. M., & Stiglitz, E. H. (2012). The reputational premium: A theory of party identification and policy reasoning. Princeton: Princeton University Press.

    Google Scholar 

  60. Stangor, C., & Lange, J. E. (1994). Mental representations of social groups: Advances in understanding stereotypes and stereotyping. Advances in Experimental Social Psychology, 26, 357–416.

    Google Scholar 

  61. Tajfel, H. (1981). Human groups and social categories: Studies in social psychology. New York: Cambridge University Press.

    Google Scholar 

  62. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin (Ed.), The social psychology of intergroup relations (pp. 33–47). New York: Cambridge University Press.

    Google Scholar 

  63. Theodoridis, A.G. (2017). Me, myself, and (I), (D), or (R)? Partisanship and political cognition through the lens of implicit identity. Journal of Politics.

  64. Westfall, J., Van Boven, L., Chambers, J. R., & Judd, C. M. (2015). Perceiving political polarization in America: Party identity strength and attitude extremity exacerbate the perceived partisan divide. Perspectives on Psychological Science, 10(2), 145–158.

    Article  Google Scholar 

  65. Yzerbyt, V., Rocher, S., Schadron, G. (1997). Stereotypes as explanations: A subjective essentialistic view of group perception. In R. Spears, P. J. Oakes, N. Ellemers, & S. A. Haslam (Eds.), The social psychology of stereotyping and group life (pp. 20–50). Cambridge: Blackwell.

Download references

Acknowledgements

We are grateful to Jamie Druckman, Doug Ahler, participants in the Druckman political science research lab, participants in the Thursday group at Brigham Young University, three anonymous reviewers, and discussants at MPSA and WPSA for insightful feedback and suggestions. We also thank Brandon Stewart and Matthew Lacombe for their helpful methodological advice. All errors are our own. Financial support for this research came from the Political Science Department at Northwestern University. This research was approved by the Institutional Review Board at Northwestern University. The authors contributed equally to this work. Data and replication code for the analyses presented in this paper can be accessed at https://doi.org/10.7910/DVN/U23L09.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Richard M. Shafranek.

Ethics declarations

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.

Informed consent

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 56 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rothschild, J.E., Howat, A.J., Shafranek, R.M. et al. Pigeonholing Partisans: Stereotypes of Party Supporters and Partisan Polarization. Polit Behav 41, 423–443 (2019). https://doi.org/10.1007/s11109-018-9457-5

Download citation

Keywords

  • Partisanship
  • Political parties
  • Partisan polarization
  • Social identity
  • Stereotypes