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.
Similar content being viewed by others
Notes
Replication data and code are publicly available on Political Behavior’s Dataverse page https://dataverse.harvard.edu/dataverse/polbehavior.
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.
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.
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.
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.
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.
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.
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.”
References
Abramowitz, Alan I. (2006). Comment on disconnected: The political class versus the people. In P. S. Nivola & D. W. Brady (Eds.), Red and blue nation? Characteristics and causes of America’s polarized politics (pp. 72–84). Washington, DC: Brookings Institution Press.
Abramowitz, A. I. (2010). The disappearing center: Engaged citizens, polarization, & American democracy. New Haven, CT: Yale University Press.
Abramowitz, A. I., & Saunders, K. (1998). Ideological realignment in the U.S. elections. Journal of Politics, 60(3), 634–652.
Abramowitz, A. I., & Saunders, K. (2005). Why can’t we all just get along? The reality of a polarized America. The Forum: A Journal of Applied Research in Contemporary Politics, 3(2), 1–22.
Abramowitz, A. I., & Saunders, K. (2008). Is polarization a myth? Journal of Politics, 70(2), 542–555.
Ahn, T. K., Huckfeldt, R., Mayer, A. K., & Ryan, J. B. (2013). Expertise and bias in political communication networks. American Journal of Political Science, 57(2), 357–373.
Ahn, T. K., Huckfeldt, R., & Ryan, J. B. (2010). Communication, influence, and informational asymmetries among voters. Political Psychology, 31(5), 763–787. Presented at the Conference on Social Dilemmas, sponsored by the Research Group for Experimental Social Science at Florida State University.
Ahn, T. K., Huckfeldt, R., & Ryan, J. B. (2014). Experts, activists, and democratic politics: Are electorates self-educating? Cambridge studies in public opinion and political psychology. Cambridge, MA: Cambridge University Press.
Ansolabehere, S., & Schaffner, B. Does survey mode still matter? Political Analysis (Forthcoming).
Asch, S. E. (1956). Studies of independence and conformity: A minority of one against a unanimous majority. Psychological Monographs, 70(9), 416.
Bafumi, J., & Shapiro, R. Y. (2009). A new partisan voter. Journal of Politics, 71(1), 1–24.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Barry, H., Child, L., & Bacon, M. (1959). Relation of child training to subsistence economy. American Anthropology, 61, 51–63.
Bennett, S. E., Flickinger, R. S., & Rhine, S. L. (2000). Political Talk over here, over there, over time. British Journal of Political Science, 30(1), 99–119.
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.
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon. com’s mechanical turk. Political Analysis, 20(3), 351–368.
Blanton, H., & Christie, C. (2003). Deviance regulation: A theory of identity and action. Review of General Psychology, 7, 115–149.
Blattman, C. (2015). Why I worry experimental social science is headed in the wrong direction. http://chrisblattman.com/2015/12/07/if-you-run-field-experiments-this-might-be-paper-that-will-make-it-harder-to-publish-your-work-in-a-few-years/.
Bond, R., & Smith, P. (1996). Culture and conformity: A meta-analysis of studies using asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119, 111–137.
Brewer, M. B., & Roccas, S. (2001). Individual, self, relational self, collective self. Psychology Press. Chapter Individual Values, Social Identity, and Optimal Distinctiveness, pp. 219–37.
Burnett, C. (2012). Artificial intelligence: Comparing survey responses for online and offline samples. In APSA 2012 annual meeting paper.
Campbell, A., Gurin, G., & Miller, W. E. (1954). The voter decides. Peterson: Row.
Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: Wiley.
Caro, F. G., Ho, T., McFadden, D., Gottlieb, A. S., Yee, Christine, Chan, Taizan, et al. (2012). Using the internet to administer more realistic vignette experiments. Social Science Computer Review, 30(2), 184–201.
Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910.
Cialdini, R. B., Wosinska, W., Barrett, D. W., Butner, J., & Gornik-Durose, M. (1999). Compliance with a request in two cultures: The differential influence of social proof and commitment/consistency on collectivists and individualists. Personality and Social Psychology Bulletin, 25, 1242–1253.
Cialdini, R. B. (2001). Influence: Science and practice (4th ed.). Newton, MA: Allyn & Bacon.
Cialdini, R. B., Alan Levy, C., Herman, P., & Evenbeck, S. (1973). Attitudinal politics: The strategy of moderation. Journal of Personality and Social Psychology, 25(1), 100–108.
Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Reviews Psychology, 55, 591–621.
Converse, P. E. (1964). The nature of belief systems in mass publics. Glencoe: Free Press.
Crabtree, C., Fariss, C. J., & Kern, H. L. (2015). Truth replaced by silence: A field experiment on private censorship in Russia. Available at SSRN 2708274.
Crutchfield, R. S. (1955). Conformity and character. The American Psychologist, 10(5), 191–198.
Dalton, R. J. (2008). Citizenship norms and the expansion of political participation. Political Studies, 56, 76–98.
Downs, A. (1957). An economic theory of democracy. Harper and Row.
Druckman, J., & Nelson, K. (2003). Framing and deliberation: How citizens’ conversations limit elite influence. American Journal of Political Science, 47(4), 729–745.
Dryzek, J. S. (1994). Discursive democracy: Politics, policy, and political science. Cambridge, MA: Cambridge University Press.
Festinger, L. (1950). Informal social communication. Psychological Review, 57(5), 271–282.
Gerber, A. S., & Green, D. P. (2000). The effects of canvassing, telephone calls, and direct mail on voter turnout: A field experiment. American Political Science Review, 94(3), 653–663.
Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. New York, NY: WW Norton.
Gerber, A. S., Green, D. P., & Larimer, C. W. (2008). Social pressure and voter turnout: Evidence from a large-scale field experiment. American Political Science Review, 102(1), 33–48.
Gerber, A. S., Huber, G. A., Doherty, D., & Dowling, C. M. (2012). Disagreement and the avoidance of political discussion: Aggregate relationships and differences across personality traits. American Journal of Political Science, 56(4), 849–874.
Giuseffi, K. E., Smith, K. B., & Hibbing, J. R. (2013). Social anxiousness and political participation. Paper presented at the 2013 Annual Meeting of the American Political Science Association, Chicago, IL.
Gosling, S. D., Rentfrow, P. J., & Swann, W. B, Jr. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37, 504–528.
Green, D. P., Palmquist, B., & Schickler, E. (2002). Partisan hearts and minds. New Haven, CT: Yale University Press.
Greene, S. (2002). The social-psychological measurement of partisanship. Political Behavior, 24(3), 171–197.
Haidt, J. (2014). Your personality makes your politics. Time Magazine. http://science.time.com/2014/01/09/your-personality-makes-your-politics/.
Haidt, J., & Wilson, C. (2014). Can TIME predict your politics? See how your preferences in dogs, Internet browsers, and 10 other items predict your partisan leanings. TIME Magazine. http://time.com/510/can-time-predict-your-politics/.
Haidt, J., & Hetherington, M. J. (2012). Look how far we’ve come apart. Campaign Stops, September 17. http://campaignstops.blogs.nytimes.com/2012/09/17/look-how-far-weve-come-apart/
Hayes, A. F. (2007). Exploring the forms of self-censorship: On the sprial of silence and the use of opinion expression avoidance strategies. Journal of Communication, 57, 785–802.
Hayes, A. F., Glynn, C. J., & Shanahan, J. (2005). Willingness to self-censor: A construct and measurement tool for public opinion research. International Journal of Public Opinion Research, 17(3), 298–323.
Hetherington, M. J., & Weiler, J. D. (2009). Authoritarianism and polarization in american politics. Cambridge, MA: Cambridge University Press.
Hibbing, J. R., & Theiss-Morse, E. (2002). Stealth democracy: Americans’ beliefs about how government should work. Cambridge, MA: Cambridge University Press.
Hibbing, J. R., Ritchie, M., & Anderson, M. R. (2010). Personality and political discussion. Political Behavior, 33(4), 601–624.
Hlavac, M. (2015). Stargazer: Well-formatted regression and summary statistics tables. R package version 5.2 http://CRAN.R-project.org/package=stargazer.
Hofstede, G. (1980). Cultures consequences: International differences in work-related values. Beverly Hills, CA: Sage.
Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political disagreement: The survival of diverse opinions within communication networks. Cambridge, MA: Cambridge University Press.
Huckfeldt, R. R., & Sprague, J. (1995). Citizens, politics, and social communication: Information and influence in an election campaign. Cambridge, MA: Cambridge University Press.
Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.
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.
Karpowitz, C. F., & Mendelberg, T. (2007). Groups and deliberation. Swiss Political Science Review, 13(4), 645–662.
Karpowitz, C. F., Mendelberg, T., & Shaker, L. (2012). Gender inequality in deliberative participation. American Political Science Review, 106(3), 533–547.
Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. New York: Free Press.
Khan, R., Misra, K., & Singh, V. (2013). Ideology and brand consumption. Psychological Science, 24(3), 326–333.
Kim, H. S., & Markus, H. R. (1999). Deviance or uniqueness, harmony or conformity? A cultural analysis. Journal of Personality and Social Psychology, 77, 785–800.
Klofstad, C. A., McDermott, R., & Hatemi, P. K. (2012). The dating preferences of liberals and conservatives. Political Behavior, 120.
Lasswell, H. D. (1936). Politics: who gets what, when, how. Peter Smith .
Lasswell, H. D. (1941). Democracy through public opinion. George Banta Publishing Company.
Latane, B. (1996). Dynamic social impact: The creation of culture by communication. Journal of Communication, 46, 13–25.
Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1968). The people’s choice: How the voter makes up his mind in a presidential campaign. New York: Columbia University Press.
Levendusky, M. (2009). The partisan sort: How liberals became democrats and conservatives became republicans. Chicago: University of Chicago Press.
Levitan, L. C., & Verhulst, B. (2015). Conformity in groups: The effects of others views on expressed attitudes and attitude change. Political Behavior.
Levitan, L., & Visser, P. (2009). Social network composition and attitude strength: Exploring the dynamics within newly formed social networks. Journal of Experimental Social Psychology, 45, 1057–1067.
Levy, M., & Dubinsky, A. J. (1983). Identifying and addressing retail salespeople’s ethical problems: A method and application. Journal of Retailing, 59(1), 46–66.
Lupia, A., & McCubbins, M. D. (1998). The democratic dilemma: Can citizens learn what they need to know?. Cambridge, MA: Cambridge University Press.
Mason, L. (2013). The rise of uncivil agreement: Issue versus behavioral polarization in the American electorate. American Behavioral Scientist, 57(1), 140–159.
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.
Mondak, J. J. (2012). Personality and the foundations of political behavior. Cambridge, MA: Cambridge University Press.
Mutz, D. C. (1998). Impersonal influence: How perceptions of mass collectives affect political attitudes. Cambridge, MA: Cambridge University Press.
Mutz, D. C. (2006). Hearing the other side: Deliberative versus participatory democracy. Cambridge, MA: Cambridge University Press.
Mutz, D. C., & Mondak, J. J. (1998). The workplace as a context for cross-cutting political discourse. Journal of Politics, 68(1), 140–155.
Noelle-Neumann, E. (1993). The spiral of silence: Public opinion-our social skin. Chicago: University of Chicago Press.
Nowak, A., & Vallacher, R.R. (2001). Societal transition: Toward a dynamical model of social change. The Practice of Social Influence in Multiple Cultures, 151–71.
Pool, G. J., Wod, W., & Leck, K. (1998). The self-esteem motive in social influence: Agreement with valued majorities and disagreement with derogated minorities. Journal of Personality and Social Psychology, 75, 967–975.
Putnam, R. D. (2001). Bowling alone: The collapse and revival of american community. Touchstone Books by Simon and Schuster.
Ryan, J. B. (2010). The effects of network expertise and biases on vote choice. Political Communication, 27, 44–58.
Ryan, J. B. (2011). Social networks as a shortcut to correct voting. American Journal of Political Science, 55(4), 752–765.
Schoemaker, P. J. H. (1993). Multiple Scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14(3), 193–213.
Sears, D. O. (1986). College sophomores in the laboratory: Influences of a narrow data base on social psychology’s view of human nature. Journal of Personality and Social Psychology, 51(3), 515–530.
Settle, J. E., Bond, R., & Levitt, J. (2011). The social origins of adult political behavior. American Politics Research, 39(2), 239–263.
Sinclair, B. (2012). The social citizen: Peer networks and political behavior. Chicago: University of Chicago Press.
Suhay, E. (2015). Explaining group influence: The role of identity and emotion in political conformity and polarization. Political Behavior, 37, 221–251.
Triandis, H. C. (1990). Cross-cultural studies of individualism and collectivism. In J. J. Berman (Ed.) Nebraska symposium on motivation (Vol. 37, pp. 41–133).
Ulbig, S. G., & Funk, C. (1999). Conflict avoidance and political participation. Political Behavior, 21(3), 265–282.
Weber, J. (1992). Scenarios in business ethics research: Review, critical assessment, and recommendations. Business Ethics Quarterly, 2(2), 137–160.
Wilson, G. D. (1973). Conservatism and art preferences. Journal of Personality and Social Psychology, 25(2), 286–288.
Young, A. (2016). Channelling fisher: Randomization tests and the statistical insignificance of seemingly significant experimental results. Working Paper as of February 2016.
Zuckerman, A. S. (2005). The social logic of politics: Personal networks as contexts for political behavior. Temple University Press.
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.
Author information
Authors and Affiliations
Corresponding author
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. This article does not contain any studies with animals performed by any of the authors.
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.
Appendices
Appendix
See Fig. 7, Tables 5, 6, 7, 8, 9, and 10.
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).
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).
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11109-016-9335-y