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Political Chameleons: An Exploration of Conformity in Political Discussions

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Abstract

Individuals do not always express their private political opinions in front of others who disagree. Neither political scientists nor psychologists have been able to firmly establish why this behavior occurs. Previous research has explored, at length, social influence on political attitudes and persuasion. However, the concept of conformity does not involve attitude change or persuasion; it more accurately involves self-censoring to match a socially desirable norm. In an effort to improve our understanding of this behavior, we conduct two experiments to investigate perceptions and behavioral responses to contentious political interactions. Study 1 asked participants to predict how a hypothetical character would respond to a variety of political interactions among coworkers. In Study 2, participants discussed political issues with confederates who were scripted to disagree with them. The studies reveal that individuals are uncomfortable around political interactions in which they hold an opinion counter to the group. Participants both expected a hypothetical character to conform in Study 1 and actually conformed themselves in the lab session in Study 2.

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Notes

  1. Replication data and code are publicly available on Political Behavior’s Dataverse page https://dataverse.harvard.edu/dataverse/polbehavior.

  2. In developing our experiments, we pilot tested a variety of political stressor stimuli. We asked individuals in a participant pool (\(n=280\)) at a large, western public university to identify which of a series of political situations would cause them to be anxious. See the "Appendix" section for full details of the pilot studies.

  3. We furthermore do not expect everyone in a political minority to conform. We expect that there are individual differences such as partisan attachment, political interest, conflict avoidance, and social anxiety that contribute to an individual’s susceptibility to political conformity. We explore these individual differences in future work.

  4. The differences in covariates shown in Table 5 related to political engagement are likely due to the fact that there was a gubernatorial election during the fall semester while the study was being fielded.

  5. As detailed later, we measure conformity in two ways: potential and pure conformity. Pure conformity requires data from the posttest; 17 participants did not complete the posttest, so they are not included in the pure conformity analyses. Results for potential conformity hold with and without these 17 participants, but for statistical power purposes, we include them in the analyses for potential conformity. As shown in Table 10 in the "Appendix" section, participants who did not complete the posttest did not meaningfully differ from those who did complete the posttest, at least based on the observable data we have available.

  6. Previous versions of this manuscript reported the pure conformity results as being significant at the .05 level, but upon preparing the replication data and code in accordance with Political Behavior’s data availability and replication policy, we discovered a coding error. The findings presented in the paper reflect the results based on the corrected code.

  7. The data used in this paper were primarily gathered for the purposes of conducting a survey experiment. We recognize the challenges of drawing inferences using Mechanical Turk for survey-based analysis, and consider these results preliminary.

  8. The wording read: “People often find that there are many things about politics that bring stress to their lives, such as negative campaigning, contentious disagreements between their friends or neighbors, or the words or actions of politicians. Being as specific as possible, please list up to three things relating to politics that add stress to your life.”

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Acknowledgments

The authors thank the William & Mary Omnibus Project for facilitating participant recruitment and the Social Networks and Political Psychology (SNaPP) Lab for providing both the infrastructure and research assistant team that made this study possible. The authors are also grateful for support from the National Science Foundation (grant SES-1423788), as well as the Charles Center at William & Mary for providing honors fellowship funding for Study 2. Finally, the authors thank the anonymous reviewers whose helpful comments greatly improved this paper.

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Correspondence to Taylor N. Carlson.

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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. This article does not contain any studies with animals performed by any of the authors.

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

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Appendices

Appendix

See Fig. 7, Tables 5, 6, 7, 8, 9, and 10.

Fig. 7
figure 7

Distribution of Study 1 dependent variables. a shows the distribution of responses to the question “What is the likelihood that Sally feels uncomfortable answering this question?” b shows the distribution of responses to the question “What is the likelihood that Sally expresses her true opinion to the group?”

Table 5 Distribution of key demographic variables (Study 1)
Table 6 Mean levels of discomfort and social consequences, by reported conformity (Study 1)
Table 7 Study 2 script
Table 8 Study 2 balance table—all participants
Table 9 Study 2 balance table for those who completed posttest
Table 10 Study 2 Summary Statistics

Pilot Data

Pilot Study 1: Stressful Dimensions of the Political Sphere

In the fall of 2010, as part of a set of studies run on a sample of 280 undergraduates at a large public university in the West, subjects were asked about their anticipated emotional response to a set of 13 diverse stimuli consisting of a variety of political situations that people could encounter in their political environment, especially the environment of a competitive or salient election. The goal of this pilot study was to characterize the political environment, differentiating what aspects of the routine encounters a person has are likely to provoke emotion, and whether different emotions are provoked by different scenarios. Respondents were presented with these instructions:

How do you feel about politics? Place a 0 in the corresponding cell in the table below if the political situation does not elicit the stated emotion. If the situation does elicit that emotion, place a number in the cell that corresponds to the strength of your emotional reaction, from 1 (weak) to 5 (strong). A political situation may evoke more than one emotion.

The following stimuli were placed in a table with four other columns labeled “Anxious”, “Angry”, “Enthusiastic”, and “Don’t Know.”

  • Living in a community where most of your neighbors affiliate with a political party you don’t support

  • Seeing bumper stickers or yard signs in your neighborhood for candidates or parties you don’t support

  • Talking with your neighbors or friends about politics when you agree on most things

  • Talking with your neighbors or friends about politics when you disagree on most things

  • Being the only person in your group of friends who supports a candidate, a party, or a political issue

  • Reading a poll predicting the opposition’s candidate is likely to win an important race

  • Seeing political protests in some other city depicted on TV

  • Seeing live political protests in your area

  • Watching a political debate on television

  • Receiving a political email forward with which you disagree

  • Receiving a political email forward with which you agree

  • Reading a friend’s post in your Facebook news feed that expresses political views with which you disagree

  • Reading a friend’s post in your Facebook news feed that expresses political views with which you agree

The order of the stimuli was randomized across respondents (Fig. 8).

Fig. 8
figure 8

Proportion of participants reporting that they would experience anxiety (marked >3) on each item in the pilot study

Pilot Study 2: Free Response Answers about Political Stress

To explore our hypotheses, we took advantage of Amazon’s Mechanical Turk platform. Mechanical Turk is an online environment where individuals can hire others to accomplish tasks in return for monetary compensation (see Berinsky et al. 2012 for a more complete discussion). These tasks can be completed by anyone with access to Mechanical Turk, in other words, anyone with a computer and an internet connection. Some political scientists have voiced concerns about using the Internet population for research given characteristics unique to its members. For example, Ansolabehere and Schaffner (0000) argue that the Internet population is somewhat more knowledgeable than the off-line population. However, some evidence suggests this may result from respondents supplementing what they know by using Google or other Internet sources Burnett (2012) (see also footnote 23 in Berinsky et al. 2012). Likewise, while disproportionate numbers of groups such as the disabled, elderly, poor, and minorities remain off-line, increasing Internet penetration has made this coverage bias critique less consequential (Ansolabehere and Schaffner 0000).

Recently, political scientists have begun using Mechanical Turk to recruit subjects for computer-based experiments. Berinsky et al. (2012) examined the validity of experiments using the Mechanical Turk platform, finding that it often provides more representative samples than the typical student and convenience samples drawn for experimental research. Moreover, they determined that threats to validity including heterogeneous treatment effects, subject attentiveness, and the prevalence of habitual survey takers offer only minor issues in practice. Perhaps most conclusively, they replicate findings from existing experimental research in the social sciences. The findings (Berinsky et al. 2012) present suggest that drawing subjects from the Internet population provides comparable results to taking subjects from a university’s undergraduate population.Footnote 7

We gathered our data using a survey programmed in Qualtrics. A link to the survey was placed in the Mechanical Turk environment with the task title “Survey of Personal Behavior and Personality.” It was available in the two weeks preceding election day in 2012, from October 20 to November 6. The completion rate was 92 %, and we have complete responses for 1,834 respondents for most analyses. The survey included batteries to evaluate a respondent’s social anxiety level using the SIAS scale, personality (using the Ten Item Personality Scale (TIPI) Gosling et al. 2003), and standard survey questions for demographics, political interest, information seeking, and political behavior. Specific question wording can be found in the appendix. Approximately halfway through the survey, a question was included to verify that subjects were reading the instructions and not simply answering questions randomly.

The free response answers were embedded into an experiment at the end of the survey. Participants were randomly assigned to one of four groups: a control that skipped the treatment, one asked to write about three things in her daily life that cause stress, one asked to write three things about politics that cause stress, and one asked to name three things that brighten life. Participants’ answers were then displayed on the screen and the subjects were asked to confirm their responses. Although we do not analyze the results of the experiment in this paper, we do use the free-response answers generated by subjects in the “political stress” condition.Footnote 8

In total, 440 respondents were in the “political stress” condition, generating a total of 1320 free response answers. We coded these responses during the spring of 2013. Research assistants familiar with the project designed a coding scheme and trained three students completely unfamiliar with the project in the actual implementation of the scheme. Responses were first coded into three broad categories of stressors: the process of politics, policy issues, and political participation. Over 96 % of all answers were coded into one of these three categories, and the rates of agreement between the coders exceeded 80 % at the category level. Responses were then further coded into sub-categories and topics. Intercoder agreement at the sub-category level ranged from 75–80 % and agreement at the topic level ranged from 70–75 %. More detailed information about the validity of the coding process is available from the authors upon request. The table below shows the results for the 21.97 % of responses that were coded into the “participation” category (Table 11, 12, 13, and 14).

Table 11 Data from the second pilot study
Table 12 Model control variables
Table 13 Study 2 regression models: potential conformity
Table 14 Study 2 regression models: pure conformity

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Carlson, T.N., Settle, J.E. Political Chameleons: An Exploration of Conformity in Political Discussions. Polit Behav 38, 817–859 (2016). https://doi.org/10.1007/s11109-016-9335-y

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