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Your Mind on Politics: Exploring a Theory of Identity Fusion during the 2016 Presidential Election
Grant Fergusson
Advised by Yarrow Dunham, Assistant Professor of Psychology, and
Antonia Misch, Postdoctoral Fellow in the Social Cognitive Development Lab
Submitted to the faculty of Cognitive Science in partial fulfillment of the requirements for the degree of Bachelors of Science
Yale University
April 21, 2017
2
Abstract Humans are predominantly social creatures. We form friend groups and families. We join
social networks and professional networks. We group into religions, nationalities, and political
parties. In extreme cases, we even abuse others (Zimbardo, 1973) and conform to false conclusions
(Asch, 1951) in order to maintain group membership. In other words, our group memberships
influence our behavior every day—even in ways that we do not perceive consciously (see Baumeister
& Leary, 1995; Haidt, 2001).
Much research has explored the influence that these groups have on our behavior; much less
has explored the mechanisms that determine changes in group membership. Identity fusion, defined
as a “visceral feeling of oneness with a group,” (see Swann et al., 2012) offers new insight into how
these group memberships form, change, and unravel. However, identity fusion has only been
studied in a few extreme cases (Besta, Gomez, & Vasquez, 2014; Swann et al., 2009; Vezzali et al.,
2016). To test the effect of identity fusion on group membership in less extreme contexts, the
present research analyzed the results of a national survey designed to test Americans’ political
identity fusion before and after the 2016 presidential election on November 8, 2016. Results suggest
that, compared to verbal measures of group identification, measures of identity fusion were more
sensitive to group-salient events and more predictive of prosocial giving. However, results differed
both between political groups and within groups over the course of the election cycle. These
findings suggest that identity fusion plays a more nuanced role in influencing group membership and
prosocial behavior than previously thought.
3
Contents
1 Introduction
1.1 Rational self-interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Social identity theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Identity fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Present study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Methods
2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Results
3.1 Identity fusion as a distinct measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Levels of identity fusion within political groups . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Impact of events on identity fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Effect of identity fusion on prosocial behavior . . . . . . . . . . . . . . . . . . . . . . . . 21
3.5 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Discussion
4.1 General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.3 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4
1 Introduction
On November 8, 2016, Donald Trump was elected President of the United States. His
election went against the predictions of top pollsters and news organizations alike (see Bialik &
Enten, 2016), causing both politicos and analysts to question the accuracy of predictive measures
meant to track voter behavior (see Osnos, 2016). Some have suggested that the methods used to
gather data are obsolete. For example, telephone polls are skewed against those with no publicly
listed phone number—or no phone at all—while online surveys tend to select for younger, more
left-leaning samples than the national average (see Lepore, 2015). Others have suggested that the
publication and coverage of national opinion polls by news media act as self-fulfilling prophecies,
amplifying both bandwagon and underdog effects in national elections (Gartner, 1976; McAllister &
Studlar, 1991; Rothschild & Malhotra, 2014; Simon, 1954).
This debate over sociopolitical behavior has wide-reaching implications for our daily lives.
What food we consume, what schools we attend, who we can marry, and even where we can live are
all results of policy decisions made by elected representatives or direct referenda (see Food Safety
Modernization Act, 2011; Elementary and Secondary Education Act, 1965; Defense of Marriage
Act, struck down 2013; Fair Housing Act, 1968). Understanding how citizens interact with their
political environments is integral to understanding how democratic societies operate; it can enable
politicians and advocates alike to more accurately improve the policies and institutions that
dominate our daily lives.
Modern research into the mechanisms of sociopolitical behavior primarily fall into two
camps: rational self-interest and motivated social cognition. However, neither camp has
comprehensively explained self-sacrificing behavior (e.g. voting against one’s self-interest—and
health, see Case & Deaton, 2015; Newkirk, 2016). This study seeks to advance a theory of identity
5
fusion in sociopolitical behavior in which self-sacrificing actions can be pursued in one’s self-
interest.
1.1 Rational self-interest
Classically, political behavior has been viewed through a lens of rational self-interest. As far
back as the seventeenth century, Thomas Hobbes viewed human beings as naturally mechanistic
creatures, acting always in their own self-interest (Hobbes & Gaskin, 1998; Mansbridge, 1990a).
Although Hobbes’ theory went against contemporary notions of humans as being motivated by civic
virtues like honor, honesty, and loyalty, the Hobbesian model inspired several sociopolitical theories
of the following century (Mansbridge, 1990a; Sears & Funk, 1991). Hobbes’ theory can even be seen
in the founding of the United States: the framers of the Constitution designed institutions that
would channel individual interests into the public good (Mansbridge, 1990b; Sears & Funk, 1991).
Starting in the 1950s, modern political theorists have applied the rational self-interest model
to the study of democratic politics. Using formal economic models, these theorists assume
rationality and self-interested behavior in their models of public and political life: voters behave
rationally in their self-interest, voting for the candidates and policies that appeal to their private
interests; appeals to public interest and civic virtue are only post-hoc rationalizations (Ansolabehere,
De Figueiredo, & Snyder, 2003; Frohlich, 1974; Gerber & Lupia, 1995; Mansbridge, 1990a; Mueller,
1979; Sears & Funk, 1991; but see also Haidt, 2012). More recently, these economic models have
expanded into the study of even apolitical human interaction. For example, social exchange theory
posits that all social change and stability are explained by self-interested motives during negotiations
and exchanges (Emerson, 1976; Homans, 1961; Thibaut & Kelley, 1959). Further, several studies of
moral and prosocial behavior have approached both experimental design and data analysis with an
6
assumption of self-interest—even when results can be plausibly explained by non-egoistic motives
(Campbell, 1975; Lynn & Oldenquist, 1986; Wallach & Wallach, 1983).
1.2 Social identity theory
Despite this historical skew toward rational self-interest, much psychological research has
emphasized irrational and subconscious motivations for social behavior. For example, research into
embodied cognition has found that physical disgust cues—from a dirty chair to a pungent odor to a
bitter taste—can affect the direction and severity of moral and political judgments (Borg, Lieberman,
& Kiehl, 2008; Eskine, Kacinik, & Prinz, 2011; Haidt, 2003; Rozin, Haidt, & McCauley, 2009;
Schnall, Benton, & Harvey, 2008a; Schnall et al., 2008b). Behaviorism, which highlights the role of
reflexive habits and subconscious, operant conditioning in decision-making, has an impressive body
of research going back to the late nineteenth century (Thorndike, 1927; Skinner, 1938; Sears &
Funk, 1991; see also System 1 versus System 2 in Kahneman, 2011). And a recent neuroimaging
study by Kaplan, Gimbel, & Harris (2016) found neural correlates to support the broader notion
that emotions play a role in political belief-change resistance.
Social identity theory, first proposed by Tafjel and Turner in the 1970s to describe the
portion of one’s self-concept derived in relation to perceived memberships in social groups, has
shown particular promise in explaining patterns of sociopolitical behavior (Tafjel, 1974; Tajfel &
Turner, 1979; Turner & Oakes, 1986). Under this theory, one’s relevant social groups can serve both
informational and motivational roles. For example, social groups are a reliable reference point for
defining one’s personal values (Bettencourt & Hume, 1999; Cohen, 2003; Conover & Feldman,
1984; Heaven, 1999; Hogg & Abrams, 1988; Newcomb, 1943; Sherif, Sherif, & Nebergall, 1965;
Turner 1991); they frame how members evaluate factual information (Asch, 1948; Asch, 1952;
Cohen, 2003; Ichheiser, 1970; Robinson, Keltner, Ward, & Ross, 1995); and they transmit
7
sociopolitical meaning to individual members through assumed expertise and group consensus
(Baumeister & Leary, 1995; Cohen, 2003; Haidt, 2001).
Ultimately, though, social identity theory is a relational model. Individuals are motivated to
conform to their relevant social groups in order to improve their individual self-esteem (Lemyre &
Smith, 1985; Oakes & Turner, 1980; Tajfel & Turner, 1979; but see Abrams, 1982; Abrams, 1983;
Vickers, Abrams, & Hogg, 1988) or the coherence of their beliefs (Markus & Zajonc, 1985; Tajfel,
1969; see balance theory, Abelson et al., 1968). By presenting a relational model, social identity
theorists are able to advance their research without contesting models of rational self-interest.
Rather, social interactions and group memberships are maintained in the pursuit of individual
interests such as self-esteem, status, and belief coherence. Sociopolitical behavior like voting,
volunteering, and donating can occur without full knowledge of every politician’s platform or policy
in question, as long as individuals see a benefit in maintaining group memberships and perceive their
beliefs to align with those of their relevant social groups.
1.3 Identity fusion
Because social identity theory dictates that social group membership serves to advance one’s
individual interests, individuals whose groups are threatened by negative events face a dilemma:
either break from the group to preserve a positive social self-concept or double-down to advance
the group in the face of social threats (Tajfel & Turner, 1979). Either option can result in protecting
one’s social self-concept. However, such a theory of prosocial behavior fails to adequately explain
extreme behaviors, like martyrdom and murder, which tend to involve far greater costs than benefits
for the individual (Swann et al., 2010a; Swann et al., 2012).
While certain instances of extreme, prosocial behavior can be explained by an individual’s
failure to correctly weight social cues and calculate an action’s costs and benefits (Cohen, 2003;
8
Epley & Dunning, 2000; Nisbett & Wilson, 1977), the frequency with which individuals pursue
costly, prosocial actions suggests that both rational self-interest and social identity models are
imperfect tools to characterize sociopolitical behavior. In order to explain these apparent
inconsistencies, Swann et al. (2009) proposed the notion of identity fusion, defined as the visceral
sense of oneness with a group. Under a theory of identity fusion, individuals fused to a group view
their personal and relevant social self-concepts as functionally equivalent; breaking from a fused
group following a social threat is no longer possible without completely restructuring one’s self-
concept (Swann et al., 2012). In effect, challenges to relevant social groups will lead fused individuals
to bolster their relevant self-concepts in an attempt to band together against a threat, even when
doing so will incur large individual costs (see Swann & Hill, 1982; Swann et al., 1992; Swann & Read,
1981, Study 2). Further, identity fusion predicts that fused individuals will use their personal and
social self-concepts as mutual support structures to not only resist challenges to either, but spark
increased prosocial activity.
Central to the theory of identity fusion is the understanding that a fused individual’s social
self-concept is not characterized relationally, like in social identity theory, but as functionally
equivalent to one’s personal self-concept. Fused individuals do not maintain membership in a group
because of how that group treats them or what role that group serves in validating the coherence of
their beliefs (see self-verification theory, Swann, Chang-Schneider, & Angulo, 2008). Rather, they
view group membership as an intrinsic part of their personal self-concept. Fused individuals are
therefore far more likely than non-fused individuals to act in extreme, prosocial ways because they
weight the interests of the group as equal to their own self-interests.
In order to measure identity fusion, Swann et al. (2009) adopted a pictorial measure (Fig. 1)
first developed to measure attachment in close relationships (Aron, Aron, & Smollan, 1992) and
group identification (Coats, Smith, Claypool, & Banner, 2000).
9
Fig. 1. The identity fusion scale used by Swann et al. (2009). Participants were asked to indicate which picture best represented their relationship with the group. The scale includes a normally distributed range of
overlap: 0%, 25%, 50%, 75%, and 100%.
The measure adopted by Swann et al. (2009) was modified from an earlier scale by Schubert
and Otten (2002) that correlated with verbal measures of group identification perceived similarity of
beliefs to those of one’s group (Smith & Henry, 1996; Tropp & Wright, 2001). This modified scale
(Fig. 1) was tested in five preliminary studies, the results of which suggested that identity fusion was
a unique state of group membership, categorically distinct but correlated to measures of group
identification (Swann et al. 2009). Further, results from this scale have proven to be highly predictive
of extreme pro-group behavior, including one’s willingness to fight and die for a group and one’s
willingness to self-sacrifice to save other group members (Gomez et al., 2011a; Gomez et al., 2011b;
Besta, Gomez, & Vasquez, 2014; Swann et al., 2010b; Swann et al. 2012; Swann et al., 2014).
Despite promising findings, research into identity fusion is relatively new. As a result, the
body of research supporting these findings comes from a limited number of cases (child victims of
two earthquakes in Italy, Vezzali et al. 2016; Spanish nationalism, Swann et al., 2009; and polish
nationalism, Besta, Gómez, & Vasquez, 2014), often with limited sample sizes. Further, the pictorial
measure developed by Swann et al. (2009) is imprecise, allowing participants to select from only five
options along a 100-point scale. As a result, the applicability of identity fusion to the broader study
of sociopolitical and interpersonal behavior has yet to be shown.
10
In an attempt to improve the precision of identity fusion metrics, Jiménez et al. (2015)
developed a Dynamic Identity Fusion Index (DIFI; see Appendix C) for use in modern, online
questionnaires. The main strength of the DIFI is its sliding scale. Rather than select from five
distinct options, participants are able to drag the personal and group circles into the position that
most accurately represents their relationship with the relevant group. Further, the DIFI has
demonstrated higher predictive fidelity and more reliability than the original pictorial measure
(Jiménez et al., 2015). Despite this increased precision and predictive merit, however, measures of
identity fusion have yet to be applied to a broader range of social settings. Therefore, the
generalizability of earlier findings is still debated.
1.4 Present study
The present study seeks to test the applicability of identity fusion to a broader range of social
contexts. Specifically, it examines whether identity fusion may be predictive of in-group donations,
volunteering, and voting during the American presidential election of 2016. In so doing, this study
had three goals. First, the study tests the validity of identity fusion measures on a broader
sociopolitical context. Although modern American politics are highly polarized, this polarization
impacts partisan elites more than the average American (Fiorina & Abrams, 2008; Prior, 2013). Still,
around 90% of Americans report a party preference (Pew Research Center, 2016). And while
American political parties maintain large group memberships, American elections are candidate-
centered (see Aldrich, 1995). Therefore, the 2016 presidential election, with broad participation and
two relevant group affiliations (party membership and candidate support), proved to be a prime
context through which to test the generalizability of identity fusion.
Second, the study examined how aggregate trends of identity fusion may change over time as
a result of positive or negative events that impact relevant social groups. (For more information on
11
this study’s cross-sectional design, see Sections 2.1, 2.2, and 4.2.) This line of research seeks to test
the finding by Vezzali et al. (2016) that a major negative event may increase rather than decrease
identity fusion in highly fused individuals. The main event to be tested in this study was the election
outcome on November 8, 2016, but daily opinion polls were also tracked for ten weeks prior to the
election in order to examine the effects of any smaller events.
Finally, the present study explores whether identity fusion predicts less extreme prosocial
behavior than the life-or-death results of previous research (Gomez et al., 2011a; Gomez et al.,
2011b; Besta, Gomez, & Vasquez, 2014; Swann et al., 2010b; Swann et al. 2012; Swann et al., 2014).
Namely, this research examines whether identity fusion is predictive of in-group giving,
volunteering, and voting.
Accordingly, the present study tested four hypotheses: (1) identity fusion as measured by the
DIFI (Jiménez et al., 2015) will be distinct from verbal measures of group identification in the
American political setting, as posited by Swann et al. (2009). (2) Given the polarized nature of
American politics and the individual costs of donating, volunteering, and voting, fusion among self-
reported members of political groups will skew high. (3) Assuming that fusion within political
groups is skewed high, events that negatively impact a relevant political group will lead to increased
levels of identity fusion within that group. (4) Fused individuals will exhibit higher rates and
amounts of giving, higher rates of volunteering, and higher rates of in-group voting than non-fused
individuals.
2 Method
2.1 Subjects
3072 subjects were recruited from Amazon Mechanical Turk (MTurk) to participate in one
of ten trials of a cross-sectional survey occurring from October 12, 2016 to December 5, 2016
12
(approval rating ≥ 95%). Each trial involved a distinct pool of survey respondents (N = 300). After
removing cross-party voters from analysis (N = 373), 2699 subjects remained, ranging from 18 years
to 87 years (Nfemale = 1407, Nmale = 1216, Nother = 76; Mage = 36.83 years, SDage = 12.18 years; Nrepublican
= 967, Ndemocrat = 1732). The MTurk population has been shown to be representative of the
American population for a variety of relevant variables, such as race, education, income, age, and
religion (Berinsky et al., 2012; see also Arceneaux, 2012; Huber et al., 2012). The subjects in this
study generally corroborate this finding, although their mean education level (M = college degree)
was higher and mean income (M = $35,000/year) lower than the most recent national average
(Meducation = some college, Mincome = $56,516/year; United States Census Bureau, 2015a; United States
Census Bureau, 2015b).
2.2 Design
Political group affiliation was measured using three metrics: self-reported identification, the
Dynamic Identity Fusion Index (DIFI; Jiménez et al., 2015), and an in-group dictator game (see
Kahneman, Knetsch, & Thaler, 1986; Forsythe et al., 1994). These metrics were manipulated across
political groups (both political party and candidate support) and time (5 trials before the 2016
presidential election and 5 trials after) using a cross-sectional design. Analysis involved survey
responses and presidential polling data (Appendix E).
2.3 Procedure
Each subject participated in a ten-minute survey tracking political identity and group
membership (see Appendix A & B). The survey, altered slightly for pre- and post-election
conditions, included 7 sections: validation; preference selection; campaign support; political party
support; a dictator game; event evaluation; and demographic questions. Trials of 300 participants ran
13
every six days from October 12 to December 5, 2016, for a total of ten trials: five pre-election and
five post-election.
After indicating which major party and major party candidate they preferred, subjects
indicated their strength of support of identification along a 7-point Likert scale (1 = very weakly; 7 =
very strongly). Subjects were also asked whether they had donated or volunteered for their party
and/or campaign of choice, as well as which candidate they had voted for in the primary, if any.
To track identity fusion, subjects were twice shown a screen with two circles: one large circle
indicating their chosen group (“Group Circle”—campaign supporters in the first instance; political
party in the second) and one small circle indicating their personal identity (“Me Circle”). Subjects
were then asked to indicate their relationship with the group by dragging the Me Circle horizontally
to a position that best captures their relationship with the Group Circle. Distance and overlap were
tracked for each instance (Jiménez et al., 2015; see Appendix C).
The dictator game paired each subject with another subject who shared his or her preferred
presidential candidate and the party of that candidate. Subjects were informed that they would
choose how many of 40 cents to keep or transfer to the other subject along 5 cent intervals. Subjects
were then asked to explain their choice. After each trial, transferred bonuses were randomly assigned
to participants in that trial who fit the necessary criteria (Democrat/Clinton supporter or
Republican/Trump supporter).
Subjects automatically received 70 cents for completing the survey. Following bonus
distribution, subjects were then sent an extra amount equal to the amount of money they chose to
keep during the dictator game, as well as any randomly assigned bonus given to them by another
respondent or respondents. Bonuses were received no later than 48 hours after completion of the
survey.
14
3 Results
3.1 Identity fusion as a distinct measure
A Pearson correlation matrix revealed a moderate positive relationship between both DIFI
measures and their corresponding verbal measures for both party identification and candidate
support (Fig. 2). The imperfect correlation between DIFI and verbal measures suggest that identity
fusion does exhibit some distinct characteristics in the American sociopolitical context, confirming
the study’s first hypothesis. Further, the two DIFI measures, distance and overlap, were highly
correlated. Given the high correlation between DIFI measures, the present analysis will consider the
two measures as functionally equivalent and only report on the distance measure, which included a
wider range of responses (see Appendix C).
Fig. 2. Correlation table reporting all Pearson coefficients (ρ) for fusion and identification measures. Overlap is measured as the overlapping area of
both circles (see Appendix C).
15
3.2 Levels of identity fusion within political groups
Because the present study had a cross-sectional rather than longitudinal design, shifts in
identity fusion were analyzed as aggregate changes to the membership of political groups over time.
Further, in order to analyze the effects of group-salient events on identity fusion, it is first necessary
to determine whether political group members are generally fused to their groups. If political groups
do not generally have fused memberships, then analyses that presuppose identity fusion among
group members will not offer any meaningful results.
Histograms of candidate and party fusion among all participants revealed marginal to
moderate levels of identity fusion across party lines (Figs. 3.1 & 3.2). Democrats tended to exhibit
more party fusion (M = 43.99) than candidate fusion (M = 34.97), while Republicans tended to
exhibit marginally more candidate fusion (M =43.40) than party fusion (M = 41.15). This result
suggests that members of political groups do tend be fused with their groups during national
elections. However, levels of identity fusion within this sample tended to be lower, on average, than
those of groups studied in prior research. Therefore, prosocial behavior within political groups may
be less influenced by identity fusion—and less impacted by group-salient events—than prior
research indicates.
Fig. 3.1. Histogram of candidate fusion responses by preferred candidate.
16
3.3 Impact of events on identity fusion
Of particular interest to this study was the impact of election results on identity fusion.
Because the American presidential election involves two competing political groups, the election
result would act as a major positive event for the winning political group and as a major negative
event for the losing positive political group. Whether or not aggregate levels of identity fusion
changed because of these events—and if those effects persisted over time—will impact the
resilience and predictive value of identity fusion in the American political setting. In this study, two
election results were analyzed: (1) the general election on November 8, 2016; and (2) the party
primaries during the spring of 2016.
A multiple regression was conducted to analyze the impact of the general election result on
prosocial giving, as well as the interaction between identity fusion and the general election result
(F(3,2652) = 34.87, p < 0.001, adjusted R2 = 0.037). Results suggest that there was more prosocial,
in-group giving following the election (r = 2.866, p < 0.001). However, the influence of identity
fusion on giving was significantly reduced following the election as well (r = -0.021, p = 0.001; Fig
4). This may be an effect of group salience. Prior research suggests that prosocial, in-group behavior
Fig. 3.2. Histogram of party fusion responses by favored party
17
varies as a result of group salience in one’s social self-concept; behavior toward highly salient in-
groups tends to be more tribalistic (Espinoza & Garza, 1985; Reicher, 1984). Therefore, it is possible
that one’s political identity and threats to one’s political groups become more salient during national
elections, and that this group salience mediates the relationship between identity fusion and
prosocial behavior. Despite the decreased influence of identity fusion, prosocial giving still increased
following the election. Such a result suggests that factors other than identity fusion may mediate
how group-salient events influence prosocial behavior (see Section 4.3).
To examine the effect of smaller events on identity fusion, national opinion polls were
analyzed alongside fusion data (see Appendix D). Accordingly, shifts in national polling numbers
were correlated with changes in identity fusion and in-group giving (Fig. 5). As the projected
chances of one candidate winning increased, supporters of that candidate became less fused while
supporters of that candidate’s opponent became more fused. However, Clinton supporters were
more sensitive to polling shifts than Trump supporters. Further, in-group giving correlated
Fig. 4. The effects of fusion on in-group giving before and after the general election. The gray underlay represents
the 95% confidence interval for each subgroup.
18
marginally with the perceived closeness of the presidential race via polling (Fig. 6). This effect was
similar across party lines, challenging prior findings that identity fusion increases in fused members
when faced with a social threat like lower polling numbers. Such a finding suggests that, at least for
minor social threats, the relationship between identity fusion and prosocial behavior in the American
political setting may not be direct, but rather moderated by other factors like the perceived efficacy
of prosocial actions (see Caprara & Steca, 2005/2007), limitations to the underdog effect (see
Goldschmied, 2005), or differences in how and what polling information was presented to each
political group (see Iyengar, 1990).
An analysis of primary versus general election support found that the effects of events on
levels of identity fusion persisted over time. All respondents were asked to select their preferred
candidates during the party primaries. Several respondents preferred non-winning candidates during
the party primaries (Democrats: NClinton = 565, NSanders = 1166; Republicans: NTrump = 515, NOther =
452). Respondents who preferred Bernie Sanders in the primaries were significantly less fused with
Clinton in the general election (t(1690) = -4.914, p < 0.001). Similar results occurred for respondents
Fig. 5. Correlations between fusion and in-group giving for Clinton supporters (left) and Trump supporters
(right). For definitions of variable differences, see Appendix E.
19
who preferred a Republican candidate other than Trump in the primaries (t(937) = -4.862, p <
0.001). These results persisted over the course of the general election (Fig. 6).
There were also several primary effects of time on measures of candidate support (t(2697) =
3.125, p = 0.002), candidate fusion (t(2697) = 3.111, p = 0.002), party identification (t(2697) = 2.253,
p = 0.024), party fusion (t(2697) = 3.741, p < 0.001), and in-group giving (t(2697) = 3.849, p <
0.001). All of these effects were small in size but positive in direction, suggesting that both party and
candidate groups became slightly more salient over the course of the study (Figs. 7.1 & 7.2).
Fig. 6. Candidate fusion over time (left: Democrats; right: Republicans) by primary candidate support.
Fig. 5. Predictions of Clinton’s chances of winning the election (%), based on national opinion polls, influenced
in-group giving.
20
Further, this effect was stronger for Trump supporters than Clinton supporters on measures
of candidate support (t(2696) = 1.833, p = 0.067) and candidate fusion (distance: t(2696) = 3.925, p
< 0.001; overlap: t(2696) = 4.332, p < 0.001). On the other hand, this effect was marginally stronger
for Democrats on measures of party identification (t(2693) = -1.743, p = 0.081). There was no
significant difference between Republicans and Democrats on measures of party fusion (p = 0.121).
In-group giving via the dictator game also increased over time (t(2654) = 3.849, p < 0.001).
Fig. 7.1. Candidate support (left) and party identification (right) over time.
Fig. 7.2. Left: candidate fusion (distance) over time. Right: party fusion (distance) over time.
21
3.4 Effect of identity fusion on prosocial behavior
Next, a multiple linear regression analysis was conducted to examine the independent
effects of candidate fusion, party fusion, candidate support, and party identification on prosocial, in-
group giving (F(4,2648) = 21.26, p < 0.001, adjusted R2 = 0.0297). Of these variables, only party
fusion (r = 0.017, p = 0.024) and candidate fusion (r = 0.014, p = 0.063) were significantly predictive
of increased prosocial giving (Fig. 8). Similar regressions were also conducted for two other
measures of prosocial behavior, measured as the aggregate of past volunteering and donation
behavior: previous candidate support (F(4,2691) = 21.99, p < 0.001, adjusted R2 = 0.0302) and
previous party support (F(4,2691) = 34.62, p < 0.001, adjusted R2 = 0.0475). As anticipated, past
volunteering and donation behavior toward a candidate’s campaign was significantly predicted by
both candidate fusion (r = 0.001, p = 0.001) and self-reported candidate support (r = 0.021, p =
0.001). However, past volunteering and donation behavior toward one’s political party was only
significantly predicted by self-reported party identification (r = 0.053, p < 0.001). These results
suggest that identity fusion is predictive of prosocial behavior. However, this relationship may be
Fig. 8. The effect of identity fusion on in-group giving via dictator game, sorted by preferred candidate
22
moderated by the perceived concreteness of the behavior’s recipient; a political party is less conrete
than a political candidate’s campaign, and a campaign is less concrete than another survey
respondent. In addition, the effect of identity fusion on prosocial giving was marginally stronger in
Clinton supporters than Trump supporters (r = 0.023, p = 0.018).
3.5 Summary of results
Fused individuals were far more likely than non-fused individuals to exhibit prosocial in-
group behavior in both political party and candidate supporter groups. This effect was true for
previous support via donations and volunteerism as well as present in-group giving via an
anonymized dictator game. Further, both group identification and identity fusion increased
marginally over the course of the general election, suggesting that both party and candidate groups
became more salient to respondents over the course of the election. However, identity fusion
became less predictive of in-group giving after the election on November 8, 2016; group salience
may therefore play a role in translating identity fusion into prosocial in-group behavior.
Although the results of the general election did not provide clear evidence to support or
refute the claim that major positive and negative events that affect a relevant social group would lead
to a shift in identity fusion, examinations of polling data suggest that respondents were sensitive to
small threats to their relevant social groups, increasing identity fusion when the general election was
perceived to be closer. However, data of respondents’ primary candidate support suggest that when
respondents preferred a different candidate than the one their party selected during the primaries,
their fusion with the winning candidate remained persistently lower than their candidate-supporting
peers throughout the general election. The results of this study were statistically significant, but often
small in effect size.
23
4 Discussion
4.1 General discussion
The results of this study suggest that identity fusion distinctly predicts prosocial behavior in
a broader context. Consistent with previous research on ways of measuring identity fusion (Swann et
al., 2009; Jiménez et al., 2016), the DIFI not only exhibited distinct characteristics not found in
verbal measures of group identification, but also offered more granularity than earlier pictorial
measures. This supports the first hypothesis proposed in this study.
Extending prior research on the behavioral analogs of identity fusion, results indicate that
identity fusion is not only predictive of extreme prosocial behavior like killing and self-sacrifice, but
also low-commitment behaviors like the giving of small amounts of money and volunteering one’s
time (Gomez et al., 2011a; Gomez et al., 2011b; Besta, Gomez, & Vasquez, 2014; Swann et al.,
2010b; Swann et al. 2012; Swann et al., 2014). While earlier research using the five-point pictorial
measure assumed a dichotomous vision of identity fusion in which fused individuals are highly
committed and non-fused individuals are not, the present study’s results using the DIFI suggest that
identity fusion exists as a spectrum. The degree to which someone is willing to sacrifice their time
and resources for prosocial reasons may vary widely, with highly fused individuals willing to behave
in extreme ways while marginally or moderately fused individuals may only sacrifice a small to
moderate amount of their time and resources to their relevant social groups.
The relationship between identity fusion and prosocial behavior may also be mediated by
other factors, such as social group salience (see Espinoza & Garza, 1985; Reicher, 1984) and political
ideology (see Brooks, 2006; Brooks & Lewis, 2001; but also Vaidyanathan, Hill, & Smith, 2011). The
existence of mediating factors would help to explain the decreased effect of identity fusion on
prosocial giving after the general election, as well as the disparities in prosocial giving between the
political groups throughout the study.
24
The results of this study are inconclusive in explaining how events influence identity fusion.
Prior research indicated that threats to relevant social groups would correspond to an increase in
fusion among group members (Vezzali et al., 2016). Evidence surrounding public opinion polls
within the present study suggest a correlation between identity fusion and shifts in perceived group
strength. Namely, both Trump and Clinton supporters showed slightly higher levels of identity
fusion and in-group giving when the general election was perceived to be closer, suggesting that
fusion may increase for (1) members of relatively strong and stable social groups when those groups
are threatened, and (2) members of “underdog” social groups when those groups gain social
standing. These findings support a model similar to social identity theory, whereby individuals
become more or less fused with groups as a way of maximizing the strength and standing of their
social self-concepts (Lemyre & Smith, 1985; Oakes & Turner, 1980; Tajfel & Turner, 1979).
However, the present study’s finding that Trump supporters tended to decrease, rather than
increase, prosocial giving when Trump’s chances of winning decreased runs contrary to prior
findings. Accordingly, the relationship between identity fusion and prosocial behavior may be
mediated by other factors like the perceived efficacy of prosocial behavior (see Caprara & Steca,
2005/2007) or the accessibility of group-relevant information to different groups (see Iyengar,
1990).
Examination of the general election and party primary results supports a mediated account
of identity fusion as well. Although the correlation between identity fusion and in-group giving
decreased following the general election outcome, there were no significant primary effects of the
election outcome on measures identity fusion. This suggests that the election result, hypothesized to
be a large positive or negative event for fused individuals, did not influence identity fusion. An
alternative hypothesis suggests that the salience of relevant social groups—that is, how prominent
the groups tested were in respondents’ social self-concepts—decreased following the election,
25
reducing the centrality of fused groups in individuals’ self-concepts without changing their identity
fusion (see Espinoza & Garza, 1985; Reicher, 1984).
Analysis of respondents’ primary candidate preferences further suggests that events may
impact the identity fusion of members in more varied ways than initially suggested by Vezzali et al.
(2016). In the present study, respondents who preferred a losing candidate during their party’s
primary had persistently lower levels of identity fusion with the winning candidate throughout the
general election. In other words, individuals who believed their political party chose the wrong
candidate in the 2016 primaries were resistant to later attempts by their political party to increase
identity fusion with the winning candidate. This result suggests that events may have lasting effects
on identity fusion, but that those effects are not always positive. One possible explanation for these
conflicting results is that earlier research involved a negative event that affected all in-group
members equally (Vezzali et al., 2016), while this study’s primary results involved a negative event
that resulted from a schism within an in-group, affecting certain members positively and others
negatively.
4.2 Limitations
Although the present study expanded the scope of identity fusion research into a broader
context, the American presidential election of 2016 did, in retrospect, elicit unusually passionate
responses in the American electorate, including appeals to previously unrepresented populist
ideologies (see Oliver & Rahn, 2016). The present body of identity fusion research is not sufficiently
substantive to discount the possibility that the 2016 election was an exceptional case of sociopolitical
behavior and tribalism. Therefore, despite this study’s large sample size, its results must be viewed
within its unusual context when attempting to generalize to the broader study of identity fusion.
26
Further, this study began on October 12, 2016, long after the beginning of the 2016
presidential election. As a result, the present study was unable to analyze the impact of several major
events—including the presidential primaries and several media controversies—in real-time. It is
possible that these events may have influenced identity fusion in different ways than those analyzed
within this study. In addition, Swann et al. (2009/2014) posits that identity fusion is a mechanism by
which fused group members band together against a social threat. Because presidential elections
occur four years apart, the conditions following the presidential election on November 8, 2016, may
not have provided an immediate outlet for threatened group members to pursue, thus impeding
their ability to double-down against the social threat of an opposing political group. If this
motivational impediment did, in fact, influence levels of identity fusion after the election, then the
election results may not be an accurate characterization of how negative events influence identity
fusion.
It is also possible that the dictator game used in the present study, which asked respondents
to divide 40 cents, may not have used a sufficient monetary sum to induce feelings of self-sacrifice in
respondents. If respondents did not feel as if they were giving something up for the benefit of an in-
group member, then the dictator game would not have accurately measured truly prosocial behavior.
Although prior research suggests that MTurk participants of trust games like the dictator game tend
to perform similarly regardless of the stake size, the possibility remains that larger stake sizes would
more directly target feelings of self-sacrifice and prosociality (Amir & Rand, 2012).
More generally, results from the dictator game used in the present study may have been
mediated by factors other than identity fusion. Research by Schulz, Fischbacher, Thöni, and Utikal
(2014) found that players of dictator games were more generous when under high cognitive load.
This finding was corroborated by Rand and Kraft-Todd (2014), who found that intuitive decisions
tended to be more prosocial. Given the potential for heightened cognitive load when evaluating
27
group-salient events—and one’s social self-concept more generally—it is likely that the results of the
dictator game may have been artificially bolstered for participants facing social threats. Current
research into identity fusion is not yet robust enough to determine whether the influences of
cognitive load on prosocial decision-making are distinct from, related to, or central to the
mechanisms behind identity fusion.
4.3 Future directions
The tools used to measure identity fusion in this study are recent additions to the literature
(Jiménez et al., 2016). Therefore, many questions still remain regarding its applicability to different
fields. Of particular note is the difference between the two DIFI measures, distance and overlap.
Although the two measures include different scales—distance is linear and includes individuals with
no overlap, while overlap is exponential but only includes individuals that report some fusion—little
research has teased out the predictive differences between the two measures. The present study did
not find a meaningful difference between distance and overlap, but results may vary for studies
requiring increased precision. Future research could examine these two measures in detail to
determine their relative strengths and weaknesses.
One main goal of the present research was to expand the literature on identity fusion to
include the study of identity fusion over time. If identity fusion was resilient and stable over time,
then it could be highly predictive of future prosocial behavior. Alternatively, if fusion was
consistently sensitive to outside influences, then controlling for these influences would enable
experimenters to predict how identity fusion and its correlated behaviors would change over time.
Given this study’s timescale and the inconclusive results regarding the effect of events, future
28
research may need to include a far longer timescale to conclusively analyze the effect of events and
time on identity fusion.
Additionally, the present study’s use of political groups introduces a new line of research into
how identity fusion changes during internal group conflicts and schisms. While the present study did
not set out to test for the influence of internal conflicts on identity fusion, prior research has shown
that social group membership often involves a homogenization of member values, morals, and goals
(Bettencourt & Hume, 1991; Cohen, 2003; Conover & Feldman 1984; Heaven, 1999; Hogg &
Abrams, 1988; Newcomb, 1943; Sherif, Sherif, & Nebergall, 1965; Turner, 1991). However, political
coalitions can include several distinct subgroups, all of which may hold independent sets of values
and priorities. Further, findings from the present study suggest that intra-group conflicts like those
during the political primaries may lead to persistent shifts in levels of identity fusion. Future research
could examine the impact of intragroup disputes and schisms on identity fusion.
4.4 Concluding remarks
The introduction of identity fusion into the literature of sociopolitical behavior has sparked
new debates into how people interact with others. And, at a time when several global powers, stoked
on by fears of terrorist activity, are becoming increasingly tribal and populist, understanding the
impact of social groups on individual behavior is more important than ever. This study expanded the
emerging body of research surrounding identity fusion to the broader sociopolitical context of
modern American politics. The results demonstrate a clear effect of identity fusion on even low-
commitment, prosocial behavior. However, there is still much work to be done to understand how
identity fusion changes over time. This line of research will be important in the future study of
29
prosocial behavior as the literature expands from a model of rational self-interest toward a theory of
integrated social self-concepts and self-sacrificing behavior.
Acknowledgements
I am eternally grateful to my advisors, Professor Yarrow Dunham and Postdoctoral Fellow
Antonia Misch, for their guidance, patience, and mentorship through this year-long project. Without
their support, this research would not have been possible. I would also like to thank the Cognitive
Science department, who supported me through my strange foray into political cognition, and the
2016 presidential election, which proved to be a better test case for this study than anyone could
have imagined. Finally, I am forever indebted to my family and friends for their boundless love and
support throughout this process.
30
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Appendix A – Pre-Election Survey
1. Initial Validation
a. Please enter your Amazon Mechanical Turk WorkerID.
b. Please copy this handwritten text into the blow below: “Speak in rhythms now you’re
three / Watch your new years evening wash / Alvin raw tangled in your kite.”
2. Preference Selection
a. Do you prefer the Democratic or the Republican Party?
b. Which candidate do you prefer in the general election?
3. Campaign Support Metrics
a. How strongly do you support your candidate? (1-7)
b. Which candidate did you prefer in the primary election?
c. Did you vote for this candidate in the primary election?
d. Have you ever donated to your preferred candidate’s campaign?
e. Have you ever volunteered for your preferred candidate’s campaign?
f. Dynamic Identity Fusion Index: Candidate Supporters (see Appendix C)
4. Political Party Support Metrics
a. How strongly do you identify with your preferred political party? (1-7)
b. Are you registered to vote?
i. If yes, which party are you registered for?
ii. If no, do you plan to register?
c. Have you ever donated to your preferred political party?
d. Have you ever volunteered for your preferred political party?
e. Dynamic Identity Fusion Index: Political Party (see Appendix C)
5. Dictator Game
a. Random assignment (see Appendix D)
b. Transfer checks
i. What transfer maximizes the other person’s bonus? (0-40 cents)
ii. What transfer maximizes your bonus? (0-40 cents)
iii. What transfer results in both of you earning the same bonus? (0-40 cents)
c. Please choose how many cents you will transfer to the other person. (0-40 cents)
d. Please describe how you made your decision.
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6. Event Evaluation (randomized)
a. How would you feel if Donald Trump won the general election? (1 = very unhappy,
7 = very happy)
b. How would you feel if Hillary Clinton won the general election? (1 = very unhappy,
7 = very happy)
7. Demographics
a. Have you already voted in the general election, either by early voting or absentee
ballot?
b. Gender
c. Age
d. Highest level of education completed
e. Income
f. Are you a United States citizen?
g. In about how many surveys/studies have you participated on MTurk before?
h. To what extent have you previously participated in other studies like this one (i.e.
that involve the dividing up of money)?
i. To what extent did you believe that the other person was real when making your
decision?
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Appendix B – Post-Election Survey
1. Initial Validation
a. Please enter your Amazon Mechanical Turk WorkerID.
b. Please copy this handwritten text into the blow below: “Speak in rhythms now you’re
three / Watch your new years evening wash / Alvin raw tangled in your kite.”
2. Preference Selection
a. Do you prefer the Democratic or the Republican Party?
b. Which candidate did you prefer in the general election?
3. Campaign Support Metrics
a. How strongly did you support your candidate? (1-7)
b. Which candidate did you prefer in the primary election?
c. Did you vote for this candidate in the primary election?
d. Have you ever donated to your preferred candidate’s campaign?
e. Have you ever volunteered for your preferred candidate’s campaign?
f. Dynamic Identity Fusion Index: Candidate Supporters (see Appendix C)
4. Political Party Support Metrics
a. How strongly do you identify with your preferred political party? (1-7)
b. Are you registered to vote?
i. If yes, which party are you registered for?
c. Have you ever donated to your preferred political party?
d. Have you ever volunteered for your preferred political party?
e. Dynamic Identity Fusion Index: Political Party (see Appendix C)
5. Dictator Game
a. Random assignment (see Appendix D)
b. Transfer checks
i. What transfer maximizes the other person’s bonus? (0-40 cents)
ii. What transfer maximizes your bonus? (0-40 cents)
iii. What transfer results in both of you earning the same bonus? (0-40 cents)
c. Please choose how many cents you will transfer to the other person. (0-40 cents)
d. Please describe how you made your decision.
6. Event Evaluation
43
a. How do you feel now that your candidate won/lost the general election? (1 = very
unhappy, 7 = very happy)
b. Did you vote in the general election?
i. If yes, which candidate did you vote for?
c. In your opinion how do other supporters of your preferred candidate feel now that
your candidate won/lost the general election? (1 = very unhappy, 7 = very happy)
7. Demographics
a. Gender
b. Age
c. Highest level of education completed
d. Income
e. Are you a United States citizen?
f. In about how many surveys/studies have you participated on MTurk before?
g. To what extent have you previously participated in other studies like this one (i.e.
that involve the dividing up of money)?
h. To what extent did you believe that the other person was real when making your
decision?
44
Appendix C – Dynamic Identity Fusion Index (DIFI)
Fig. 9. The image above depicts the DIFI tool, as developed by Jiménez et al. (2015). This screen was presented to respondents who selected Hillary Clinton as their preferred candidate
in the general election.
Respondents are able to freely move the “Me” circle along the horizontal axis until the
distance and overlap between their “Me” circle and the group in question fits their desired
relationship. The groups measured were candidate supporters (Trump or Clinton supporters) and
political parties (Republican or Democratic parties). The DIFI records both distance (the distance
between the centers of the two circles) and overlap (the percent of intersection between the areas of
the two circles) (Jiménez et al., 2015). Distance is computed directly from the participants’
movements. With the radius of the small circle, r = 50 pixels (see Fig. 9), the overlapping area is
computed indirectly using the following formula:
𝑂𝑣𝑒𝑟𝑙𝑎𝑝 = 100𝑎
𝜋𝑟2
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Fig. 10. The formula for computing the area of overlap between two circles of different sizes, as described
in Jiménez et al. (2016).
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Appendix D – Dictator Game: Random Assignment
Fig. 11. The screen shown to a respondent who selected Hillary Clinton as their preferred candidate. The order between party and candidate was randomized. To control for in-group behavior, the party of the randomly assigned partner always corresponded to the party of candidate. Bonuses were randomly spread to respondents that fit the in-
party demographics (i.e. those who preferred the same party as their preferred candidate) following each trial.
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Appendix E – Presidential Polling Data
Date Trial Aggregate (%) 538 (%) 538 Chance of Winning (%)
10/12 1 45.3 40.4 49.4 42.7 86.9 13.1
10/18 2 45.1 41.0 49.8 42.8 87.4 12.6
10/24 3 45.2 41.1 49.5 43.2 86.3 13.7
10/30 4 45.9 42.7 49.4 44.2 78.5 21.4
11/05 5 46.1 43.4 48.4 45.5 64.7 35.3
Fig. 12. The polling data used for pre-election polling analysis. The aggregate polling includes all polls currently listed in the Huffington Post’s polling database, used by most news agencies as the standard for daily polling aggregates. The
percentages in blue (left) represent the percentage favoring Hillary Clinton; percentages in red (right) represent the percentage favoring Donald Trump. Excluded from this study were any polls that weren’t updated more frequently
than every twelve days (two trials). The aggregate polling numbers (ClintonUW and TrumpUW) are unweighted after reporting, 538’s aggregate polling (ClintonW and TrumpW) is weighted by 538, and 538’s Chance of
Winning metric (ClintonWin) is both weighted and applied to Electoral College votes via state district demographics.