Identifying the domains of ideological similarities and differences in attitudes. Emily Kubin & Mark J. Brandt. Comprehensive Results in Social Psychology, Volume 4, 2020 - Issue 1. Pages 53-77. May 4 2020. https://doi.org/10.1080/23743603.2020.1756242
Rolf Degen's take: https://twitter.com/DegenRolf/status/1311634367517732866
ABSTRACT: Liberals and conservatives disagree, but are there some domains where we are more or less likely to observe ideological differences? To map the types of attitudes where we may be more or less likely to observe ideological differences, we draw on two approaches, the elective affinities approach, which suggests individual differences explains differences between liberals and conservatives, and the divergent content approach, which posits the key distinction between ideologues are their value orientations. The goal of the current research was to explore when and why liberals and conservatives disagree. We tested whether ideological differences are more likely to emerge in attitudes characterized by threat, complexity, morality, political ideology, religious ideology, or harm (as compared to objects not characterized by these domains) using both explicit and implicit measures of 190 attitude objects. While all domains predicted ideological differences, the political domain was the only significant predictor of ideological differences when controlling for the other domains. This study provides insight into which attitudes we are most and least likely to find ideological differences.
KEYWORDS: Ideological differences, attitudes, ideology, threat, complexity
Discussion
We have two key findings. First, we find support for both the elective affinities and divergent content approaches; topics associated with threat, complexity, morality, politics, religion, and harm are also characterized by greater ideological disagreement than topics not associated with these domains. Second, we found that the political domain was the strongest predictor of ideological disagreement.
The current research attempted to map the types of attitudes where we are most and least likely to observe ideological differences. We used two approaches, the elective affinities approach, and the divergent content approach to explore which domains characterize attitudes when there is disagreement between liberals and conservatives. The elective affinities approach, which suggests people prefer views that match their dispositions (Hirsh et al., 2010; Jost et al., 2009), posits that attitude objects characterized by threat (i.e., threat hypothesis) or complexity (i.e., complexity hypothesis) are more likely to be associated with liberal-conservative differences, compared to attitude objects not associated with threat or complexity. The divergent content approach, which suggests that they key difference between groups are underlying values (Haidt & Graham, 2007), posited that attitude objects characterized by morality (i.e., moral hypothesis), politics (i.e., political hypothesis), religion (i.e., religion hypothesis), or harm (i.e., harm hypothesis) are more likely to be associated with liberal-conservative disagreement, compared to attitude objects not associated with morality, politics, religion, or harm.
We tested these hypotheses by estimating ideological differences on implicit (IAT) and explicit (preference and evaluation) measures of attitudes and analyzed the extent to which attitude objects characterized by the proposed domains are more likely to be associated with ideological disagreement. When focusing on each domain individually, we found support for both the elective affinities and divergent content approaches. Results suggested attitudes associated with threat, complexity, morality, politics, religion, and harm were also attitude objects liberals and conservatives tended to disagree on. This was the case for both reaction time (IAT) and self-report (preference or evaluations) measures and for both joint (IAT or preference) and individual judgment (evaluation) contexts. Further, these findings were consistent when controlling for attitude object category, in nearly all cases was not affected by domain rater ideology, and was consistent across robust regression analyses.
Analyses also indicated that the political domain was the most robust predictor of ideological disagreement. When controlling for the other domains, the political domain was the only domain that still predicted the size of ideological differences – suggesting that ideological differences are substantially reduced outside of the political realm. For example, below the midpoint of political ratings the maximum ideological difference is never larger than a small effect (in r, maximum difference on IAT = .20, preference = .22, evaluations = .18) and the means are quite small (in r, mean difference on IAT = .02, preference = .03, evaluations = .03). 10 This is most consistent with the political hypothesis from the divergent content approach (e.g., Brandt & Crawford, in press; Graham et al., 2011).
Primacy of politics?
There are multiple ways to interpret the primacy of politics result. First, this result might suggest that the results supporting the elective affinities approach, as well as the harm, religion, and morality hypotheses of the divergent content approach are not good evidence because domains such as threat and complexity, as well as morality and religion are conflated with political differences. This is consistent with arguments that suggest that links between political ideology, personality, and motivations may be due to content overlap rather than personality or motivational differences per se (Malka et al., 2017). This possibility is represented in the causal structure in Figure S1 (in supplemental materials). 11 All of the domains have the possibility of directly causing ideological differences, but due to shared variance with politics, the political variable is the only significant predictor of ideological differences.
A second possibility is that factors like threat and complexity are the very topics that humans are likely to make into political, moral, or religious issues. When times are threatening or particularly complex, turning issues into political, moral, or religious issues may give people a sense of certainty or a method for interpreting the world that they otherwise would not have. If this is the case, then politics may act more like a mediator of the effects of threat and complexity. This possibility is represented in the causal structure in Figure S2 (in supplemental materials). Notably, in exploratory analyses where we excluded politics as a predictor, threat and complexity were still not significant predictors. Instead either no domain was a significant predictor, or morality was a significant predictor. This hints that morality may also be more proximal than threat and complexity.
The distinction between these two possibilities is theoretically important. The first possibility, represented in Figure S1, would suggest that the elective affinities approach, at least for our research questions, is not viable. The findings that seem to support it are merely due to the confounds between threat, complexity, and politics. The same conclusion could also be drawn from the moral, religious, and harm-related versions of the divergent content approach. However, the second possibility, represented in Figure S2, would suggest that the elective affinities approach, at least for our research questions, is viable. These findings show the disagreements over the political domain is the strongest predictor of ideological disagreement; however, the other domains are potentially still casually potent as precursors to the political domain. Unfortunately, it is not possible to tease apart these possibilities with the current data as the data are cross-sectional. Teasing apart these two possibilities is a necessary task for future research. Ideally, tests might include tracking ideological differences in large numbers across a great diversity of attitudes over time to study changes and stability in ideological differences.
Strength, limitations, and future directions
These findings are just one-step in mapping which attitudes we are most and least likely to anticipate ideological differences. We studied the 190 attitude objects from the AIID study. However, we expect that our findings will likely generalize to other attitudes, especially in the American context. We also would expect a similar pattern of results in other countries with polarized political systems (cf. Pew Research Center, 2017; Vachudova, 2019; Wendler, 2014). We are less certain that these results would replicate in political systems with less polarization and where political differences are, presumably, less important.
In contrast to many studies of ideological differences that focus on differences in one particular attitude, the attitudes in the AIID study cover many topics. These topics range from abstract principles (e.g., realism vs. idealism) to people (e.g., celebrities such as Denzel Washington vs. Tom Cruise) and regions of the world (e.g., Japan vs. the United States). The diversity of attitude objects should make our findings more comparable to the large swath of attitudes in the everyday world. Thus, these findings improve our ability to predict the locations of ideological differences and similarities in untested fields. One challenge with using a large number of attitudes is that not every attitude conforms to the model. As one example, some of the attitudes that scored highly in the political domain nonetheless had low levels of ideological differences. One reason for this is that there were attitudes that were political (e.g., preferences for Bill Clinton vs. Hillary Clinton; evaluations of politicians), but which did not map onto differences between liberals and conservatives. This suggests that more precise predictions can be made by considering the political dimension the attitude maps on to.
Despite the large sample of people and attitudes, and replication across multiple measures, this study also had several limitations. The domains used were based on previously discussed perspectives; however, other domains that we did not include may also play a key role. For example, we did not test the domain of disgust, but attitude objects associated with disgust may be associated with ideological differences, as previous research highlights ideological differences in what is viewed as disgusting (Elad-Strenger et al., 2019; Inbar et al., 2012). Moreover, as previously mentioned, the findings are cross-sectional. Although we discussed the results in causal terms for illustrative purposes, the current data is consistent with a number of different causal models. One way to test this will be to examine if and how ideological differences emerge as an attitude is imbued with different properties. Things that were once not moralized, politicized, threatening, etc., can become linked to our moral or political sensibilities, or be viewed as highly threatening. For example, at one point attitudes about the NFL may have not seemed overly political, however once NFL athletes began kneeling during the national anthem, and President Trump began commenting about these actions (Klein, 2018), the league may have become more politicized. We would expect ideological differences in opinions on the NFL to track this politicization.
Finally, this study is primarily based on self-reports and survey methodology. In the AIID study participants reported which attitude objects they prefer and the extent to which they positively evaluate attitude objects. In Rating Samples 1 and 2, collected many years after the original AIID study, participants self-reported the extent to which they think each domain could explain other people’s disagreement about the attitude objects. Thus, Rating Samples 1 and 2 focus on measuring what people think could cause others to disagree about attitude objects, rather than measuring what actually causes people to disagree about attitude objects. This may limit the validity of the study as participants in Rating Samples 1 and 2 may not be aware of the true factors that drive disagreement over attitude objects.
Furthermore, while implicit associations are assessed, and similar results are found across results, some of our findings are based on self-reports. Tracking ideological disagreement in terms of preference and evaluation can only occur in contexts where individuals are able and willing to report their true political attitudes – meaning further exploration into mapping ideological differences using these self-report methodologies relies on participants willingness to disclose their attitudes.
Behavioral manipulations could also be included in future work, such as having participants actually choose between helping one group (e.g., gay people) vs. another (e.g., straight people). This would help map attitudes where ideological disagreement is (or is not) present to aid in our understanding of the behavioral consequences of differences between liberals and conservatives. However, the consistency in results between the reaction time and self-report measures gives us some confidence that these findings are robust to measurement type.
The current research attempted to map where we are most and least likely to see ideological disagreement. In this paper, we have two key findings. First, we find support for both the elective affinities and divergent content approaches; topics associated with threat, complexity, morality, politics, religion, and harm are also characterized by greater ideological disagreement than topics not associated with these domains. Second, we found that the political domain was the strongest predictor of ideological disagreement. These findings provide evidence for a systematic explanation for when and why liberals and conservatives disagree, and can aid in predicting whether future events will be heavily contested or will be similarly perceived by liberals and conservatives.
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