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Partisanship and Preference Formation: Competing Motivations, Elite Polarization, and Issue Importance

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Abstract

An enduring and increasingly acute concern—in an age of polarized parties—is that people’s partisan attachments distort preference formation at the expense of relevant information. For example, research suggests that a Democrat may support a policy proposed by Democrats, but oppose the same policy if proposed by Republicans. However, a related body of literature suggests that how people respond to information and form preferences is distorted by their prior issue attitudes. In neither instance is information even-handedly evaluated, rather, it is interpreted in light of partisanship or existing issue opinions. Both effects are well documented in isolation, but in most political scenarios individuals consider both partisanship and prior opinions—yet, these dynamics may or may not pull toward the same preference. Using nationally representative experiments focused on tax and education policies, I introduce and test a theory that isolates when: partisanship dominates preference formation, partisanship and issue opinions reinforce or offset each other, and issue attitudes trump partisanship. The findings make clear that the public does not blindly follow party elites. Depending on elite positions, the level of partisan polarization, and personal importance of issues, the public can be attentive to information and shirk the influence of party elites. The results have broad implications for political parties and citizen competence in contemporary democratic politics.

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Notes

  1. People also possess “accuracy” motivations—a drive to make the “correct” decision (Kunda 1990).

  2. This process is related to but distinct from heuristic cue-taking (Petersen et al. 2013).

  3. Polarization may also heighten perceptions of threat that prompt in-group solidarity and negative out-group prejudices (Huddy et al. 2005). Partisans are also more likely to exert effort to defend beliefs when the opposing party is viewed as competitive (Matthews 2013).

  4. This is distinct from dual-process models of reasoning (Petty and Cacioppo 1986; Petty and Wegener 1999). Dual-process models such as the ELM suggest that attitude change occurs through “central” and “peripheral” routes. Central-route attitude change are based on “effortful information processing aimed at scrutinizing and uncovering the central merits of the issue,” while the peripheral route relies on less cognitive processes such as heuristics (Petty and Wegener 1999). The theory of intersecting motivations outlined here differs in at least a couple ways. Issue-motivated reasoning often leads people to engage in effortful information processing, but this is done so as to defend one’s prior attitudes, not to uncover the merits of the issue as in central processing. Further, partisan motivated reasoning need not be seen as heuristic processing (Petersen et al. 2013), since party cues activate motivational processes that require effortful strategies such as counter arguing incongruent messages.

  5. Leeper and Slothuus (2014) posit, “motivated reasoning will look differently for individuals depending on what issues are at stake and how intensely they need to defend their prior attitudes or identities” (28). Carsey and Layman (2006) also suggest that the relationship between issue positions and partisanship is moderated by issue importance. That the outcome of conflicting motivations is contingent on the salience of motivations is akin to Converse’s (1964) discussion of “centrality.” That is, if the policy attitude is in conflict with the party position but the attitude is less central, the policy attitude will be updated to reflect the party position.

  6. As discussed below, I avoid these assumptions with an alternative experimental design that measures people’s priors in a follow-up study.

  7. Research Now implemented the survey via the Internet with a representative sample of the U.S. adult population. Percent Democrat (36.88), percent Republican (31.51), percent liberal (33.98), percent conservative (36.66), median age category (35–50), percent female (56.06), percent white (84.54), and median income category ($30,000–69,999). The sample employs an opt-in panel that acquires potential respondents from various partners (e.g., commercial sponsors). They invite individuals to join the survey panel and financially compensate them for participation. For each survey, ResearchNow (2015, p. 2) employs “a proprietary method of exclusively inviting pre-validated individuals, or individuals who share known characteristics…” That is, they employ an algorithm based on response rates to ensure a sample that is representative of the U.S. population, based on U.S. census figures. The company also implements a wide range of data quality validation checks, including checking for logical answers, consistent reporting, minimal non-response, realistic response times, etc. (for general discussion of these types of samples, see, e.g., Callegaro et al. 2014).

  8. See Supporting Information for power analyses.

  9. This ensures the first issue is uncontaminated. Randomizing issue order allows for spillover effects.

  10. Certainly, participants come into the experiment with pre-existing levels of personal issue importance. Yet, due to random assignment, similar proportions of high/low issue importance individuals should be found in each experimental condition. However, I am primarily concerned with average treatment effects, and, as such, I simply need the manipulation to alter personal issue importance on average for most participants—which was confirmed by pre-tests. An alternative approach would be to explore whether preference formation is moderated by people’s existing personal issue importance (i.e., observational survey data analysis), but here I opt to experimentally manipulate issue importance in an effort to wield greater causal leverage.

  11. Data and code for replication are available on the Political Behavior Dataverse webpage. Because hypotheses are directional in nature, one-tailed p values and 90 % confidence intervals are reported except when otherwise noted. As evident by p values, most results remain significant if two-tailed tests are employed. I also reference results from ANOVA tests with Bonferroni corrections to account for multiple group comparisons.

  12. ANOVA results for multiple group comparison of when motivations pull in same directions: Democrats tax issue F(4, 542) = 168.09, p < 0.001. Bonferroni multiple comparison test reveals significant differences (p < 0.001) between all groups except conditions 3 and 4. Democrats education issue F(4, 528) = 98.20, (p < 0.001). Bonferroni test reveals significant differences (p < 0.01) between all groups except conditions 3 and 4. Republicans tax issue F(4, 439) = 58.28, (p < 0.001). Bonferroni test reveals significant differences (p < 0.02) between all group except 3 and 4, 3 and 5, and 5 and 6. Republicans education issue F(4, 426) = 27.59, (p < 0.001). Bonferroni test reveals significant differences (p < 0.02) between all groups except 3 and 4, 3 and 5 (p < 0.065), 4 and 6, and 5 and 6.

  13. When analyses of both issues are broken down by political knowledge (see Supporting Information), there are few differences between low, moderate, and high knowledge respondents. The one exception is high knowledge Democrats in the education issue. These individuals reveal little movement across conditions on the education issue, and may, in part, be driving the null effect of the reversed party endorsement.

  14. Because hypothesis is not directional, two-tailed tests of significance are employed here.

  15. ANOVA results for multiple group comparison of when motivations compete: Republicans tax issue F(4, 463) = 78.11, p < 0.001. Bonferroni multiple comparison test reveals significant differences (p < 0.001) between all groups except conditions 7 and 9, and 1 and 10 (expected). Republicans education issue F(4, 445) = 47.95, p < 0.001. Bonferroni test reveals significant differences (p < 0.01) between all groups except conditions 1 and 10 (expected). Democrats tax issue F(4, 497) = 14.71, p < 0.001. Bonferroni test reveals significant differences (p < 0.01) between all groups except 1 and 10 (expected), 1 and 9, 7 and 9, 7 and 10, and 9 and 10. Democrats education issue F(4, 483) = 4.27, p < 0.003. Bonferroni test reveals significant differences only between groups 8 and 9 (p < 0.001).

  16. People were also asked, post-stimuli, about the importance of their partisan identification to determine if the polarization and issue importance prompts shape the salience of partisanship as intended. The results are shown in the Supporting Information and reveal two patterns consistent with expectations. First, the high polarization prompts increase the importance of party identification. Second, the high issue importance cues which seems to encourage respondents to focus on substantive information, and in doing so, reduces the importance of partisanship.

  17. Due to the small sample size and the preponderance of Democratic respondents, Republicans do not afford sufficient statistical power for analysis. Focusing only on Democrats who initially oppose the proposal (i.e. take the traditional party position) I mitigate assumptions in the previous experimental design about people’s prior attitudes.

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Acknowledgments

The author thanks Jamie Druckman, Yanna Krupnikov, Georgia Kernell, D.J. Flynn, Thomas Leeper, and the Northwestern University Political Parties Working Group for their feedback on earlier drafts of this paper.

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  1. Department of Government and Justice Studies, Appalachian State University, Anne Belk Hall, Boone, NC, 28608, USA

    Kevin J. Mullinix

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Fig. 5
figure 5

Bars show mean effectiveness rating of the argument opposed to the tax proposal. Lines represent confidence intervals

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Mullinix, K.J. Partisanship and Preference Formation: Competing Motivations, Elite Polarization, and Issue Importance. Polit Behav 38, 383–411 (2016). https://doi.org/10.1007/s11109-015-9318-4

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