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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

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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.

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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

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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

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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

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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

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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;

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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).

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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.

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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

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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

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(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

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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.

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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).

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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.

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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

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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.

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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.

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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.

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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.

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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

References

Abelson R. P., Aronson, E., McGuire, W. J., Newcomb, T. M., Rosenberg, M. J., and Tannenbaum,

P. H. (Eds.) (19680. Theories of Cognitive Consistency: A Sourcebook, Chicago: Rand-McNally.

Abrams, D. (1982). How does identity influence behavior? Paper presented at the Annual

Conference of the Social Psychology Section of the British Psychological Society,

Edinburgh.

Abrams, D. (1983). The impact of evaluative context on intergroup behavior. Paper presented at the

Annual Conference of the Social Psychology Section of the British Psychological Society,

Sheffield.

Aldrich, J. H. (1995). Why parties?: The origins and transformation of political parties in America. University

of Chicago Press.

Amir, O., & Rand, D. G. (2012). Economic games on the internet: The effect of $1 stakes. Plos one

7(2), e31461.

Ansolabehere, S., De Figueiredo, J. M., & Snyder, J. M. (2003). Why is there so little money in

politics? Journal of Economic Perspectives, 17(1), 105-130.

Arceneaux, K. (2012). Cognitive biases and the strength of political arguments. American Journal of

Political Science 56: 271–285.

Asch, S. E. (1948). The doctrine of suggestion, prestige, and imitation in social psychology.

Psychological Review, 55, 250–276.

Asch, S. E. (1952). Social psychology. Englewood Cliffs, NJ: Prentice Hall.

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments

as a fundamental human motivation. Psychological Bulletin, 117, 497–529.

Berinsky, A. J., Huber, G. A., and Lenz, G. S. (2012). Evaluating online labor markets for

experimental research: Amazon.com's mechanical turk. Political Analysis 20:351-368.

31

Besta, T., Gomez, A., Vazquez, A. (2014). Readiness to deny group's wrongdoing and willingness to

fight for its members: the role of Poles' identity fusion with the country and religious group.

Current Issues in Personality Psychology, 2(1): 49-55.

Bettencourt, B. A., & Hume, D. (1999). The cognitive contents of social group identity: Values,

emotions, and relationships. Journal of Social Psychology, 29, 113–121.

Bialik, C., & Enten, H. (2016). The polls missed Trump. We asked pollsters why. FiveThiryEight.

November 9. Retrived from https://fivethirtyeight.com/features/the-polls-missed-trump-

we-asked-pollsters-why/.

Borg, J. S., Lieberman, D., & Kiehl, K. A. (2008). Infection, incest, and inequity: Investigating the

neural correlates of disgust and morality. Journal of Cognitive Neuroscience, 20, 1529-1546.

Brooks, A. C. (2006). Who really cares: The surprising truth about compassionate conservatism. New York,

NY: Basic Books.

Brooks, A. C., & Lewis, G. B. (2001). Giving, volunteering, and mistrusting government. Journal of

Policy Analysis and Management 20(4), 765-769.

Campbell, D. T. (1975). On the conflicts between biological and social evolution and between

psychology and moral tradition. American Psychologist, 30, 1103-1126.

Caprara, G. V., & Steca, P. (2005). Self-efficacy beliefs as determinants of prosocial behavior

conducive to life satisfaction across ages. Journal of Social and Clinical Psychology, 24(2), 191-217.

Caprara, G.V., & Steca, P. (2007). Prosocial agency: The contribution of values and self-efficacy

beliefs to prosocial behavior across ages. Journal of Social and Clinical Psychology, 26(2), 218-239.

Case, A. & Deaton, A. (2015). Rising morbidity and mortality in midlife among white non-Hispanic

Americans in the 21st century. Proceedings of the National Academy of Sciences, 112(49), 15078-

15083.

32

Cohen, G. L. (2003). Party over policy: The dominating impact of group influence on political

beliefs. Journal of Personality and Social Psychology, 85(5), 808-822.

Conover, P. J., & Feldman, S. (1984). Group identification, values, and the nature of political beliefs.

American Politics Quarterly, 12, 151–175.

Defense of Marriage Act (DOMA). (1996, struck down 2013). Pub.L. 104-199, 110 Stat. 2419. 1

U.S.C. § 7 & 28 U.S.C. § 1738C.

Elementary and Secondary Education Act (ESEA). (1965). Pub. L. 89-10, 79 Stat. 27, 20 U.S.C. ch.

70.

Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2, 335-362.

Epley, N., & Dunning, D. (2000). Feeling “holier than thou”: Are self-serving assessments produced

by errors in self- or social prediction? Journal of Personality and Social Psychology, 79, 861–875.

Eskine, K. J., Kacinik, N. A., & Prinz, J. J. (2011). A bad taste in the mouth: Gustatory disgust

influences moral judgment. Psychological Science, 22(3), 295-299.

Espinoza, J. A., & Garza, R. T. (1985). Social group salience and interethnic cooperation. Journal of

Experimental Social Psychology, 21(4), 380-392.

Fair Housing Act (1968). 42 U.S.C. 3601-3619.

Fiorina, M. P., & Abrams, S. J. (2008). Political polarization in the American public. Annu. Rev. Polit.

Sci., 11, 563-588.

Food Safety Modernization Act (FSMA). (2011). 21 U.S.C., 301 U.S.C. et seq.

Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining

experiments. Games and Economic Behavior, 6, 347-369.

Frohlich, N. (1974). Self-interest or altruism, what difference? Journal of Conflict Resolution, 18, 55-73.

Gartner, M. (1976). Endogenous bandwagon and underdog effects in a rational choice model. Public

Choice, 25(1), 83-89.

33

Gerber, E. R. & Lupia, A. (1995). Campaign competition and policy responsiveness in direct

legislative elections. Political Behavior, 17(3), 287-304.

Gómez, Á., Brooks, M.L., Buhrmester, M.D., Vázquez, A., Jetten, J. & Swann, W. B. (2011a). On

the nature of identity fusion: Insights into the construct and a new measure. Journal of

Personality and Social Psychology, 100, 918-933.

Gómez, Á., Morales, J. F., Hart, S., Vázquez, A. & Swann, W. B. (2011b). Rejected and excluded

forevermore, but even more devoted: Irrevocable ostracism intensifies loyalty to the group

among identity fused persons. Personality and Social Psychology Bulletin, 37, 1574-1586.

Haidt, J. (2003). The moral emotions. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.),

Handbook of affective sciences (pp. 852-870). Oxford: Oxford University Press.

Haidt, J. (2012). The Righteous Mind: Why Good People Are Divided by Politics and Religion. New York:

Pantheon Books.

Heaven, P. C. L. (1999). Group identities and human values. Journal of Social Psychology, 139, 190–195.

Hobbes, T., & Gaskin, J. C. A. (1998). Leviathan (pp. 64, 72, 79, 89). Oxford: Oxford University

Press.

Hogg, M., & Abrams, D. (1988). Social identifications: A social psychology of intergroup relations and group

processes. London: Routledge.

Homans, G.C. (1961). Social Behavior: Its elementary forms. New York: Harcourt, Brace & World.

Huber, G. A., Hill, S. J., and Lenz, G. S. (2012). Sources of bias in retrospective decision making:

Experimental evidence on voters’ limitations in controlling incumbents. American Political

Science Review 106: 720–741.

Ichheiser, G. (1970). Appearances and realities. San Francisco: Jossey-Bass.

Iyengar, S. (1990). The accessibility bias in politics: Television news and public opinion. International

Journal of Public Opinion Research, 2(1), 1-15.

34

Jiménez, J., Gómez, Á, Buhrmester, M. D., Vázquez, A., Whitehouse, H., & Swann, W. B. (2016).

The dynamic identity fusion index: A new continuous measure of identity fusion for web-

based questionnaires. Social Science Computer Review, 34(2), 215-228.

Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1986). Fairness and the assumptions of economics.

Journal of Business, 59(4), S285-S300.

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Kaplan, J. T., Gimbel, S. I., & Harris, S. (2016). Neural correlates of maintaining one's political

beliefs in the face of counterevidence. Scientific Reports, 6.

Lemyre, L. & Smith, P. M. (1985). Intergroup discrimination and self-esteem in the minimal group

paradigm. Journal of Personality and Social Psychology, 29, 660-670.

Lepore, J. (2015). Politics and the new machine: What the turn from polls to data science means for

democracy. The New Yorker. November 16. Retrieved from

http://www.newyorker.com/magazine/2015/11/16/politics-and-the-new-machine.

Lynn, M. & Oldenquist, A. (1986). Egoistic and nonegoistic motives in social dilemmas. American

Psychologist, 41, 529-534.

Mansbridge, J. J. (1990a). The rise and fall of self-interest in the explanation of political life. In J. J.

Mansbridge (Ed.), Beyond self-interest. Chicago: University of Chicago Press.

Mansbridge, J. J. (1990b). Self-interest in political life. Political Theory, 18, 132-153.

Markus, H. & Zajonc, R. B. (1985). The cognitive perspective in social psychology. In Lindzey, G. &

Aronson, E. (Eds.), The Handbook of Social Psychology, 3(1), Reading: Addison-Wesley, pp. 137-

229.

McAllister, I., & Studlar, D. T. (1991). Bandwagon underdog, or projection? Opinion polls and

electoral choice in Britain, 1979-1987. The Journal of Politic, 53(3), 720-741.

Mueller, D.C. (1979). Public Choice. New York: Cambridge University Press.

35

Newcomb, T. M. (1943). Personality and social change. New York: Dryden Press.

Newkirk, V.R. (2016). Simply repealing Obamacare will hurt the white working class. The Atlantic.

November 22. Retrieved from

https://www.theatlantic.com/politics/archive/2016/11/trump-healthcare-plan-working-

class-whites/508325/.

Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental

processes. Psychological Review, 8, 231–259.

Oakes, P. J. & Turner, J. (1980). Social categorization and intergroup behavior: Does minimal

intergroup discrimination make social identity more positive. European Journal of Social

Psychology, 10, 295-301.

Oliver, J. E., & Rahn, W. M. (2016). Rise of the Trumpenvolk: Populism in the 2016 election. The

ANNALS of the American Academy of Political and Social Science, 667(1), 189-206.

Osnos, E. (2016). Why political pundits are becoming more wrong. The New Yorker. February 23.

Retrieved from http://www.newyorker.com/news/daily-comment/why-political-pundits-

are-becoming-more-wrong.

Pew Research Center. (2016). The parties on the eve of the 2016 election: Two coalitions, moving

further apart. September 13. Retrieved from http://www.people-

press.org/2016/09/13/the-parties-on-the-eve-of-the-2016-election-two-coalitions-moving-

further-apart/.

Prior, M. (2013). Media and political polarization. Annual Review of Political Science, 16, 101-127.

Rand, D. G., & Kraft-Todd, G. T. (2014). Reflection does not undermine self-interested

prosociality. Front. Behav. Neurosci., 8(300).

36

Reicher, S. D. (1984). Social influence in the crowd: Attitudinal and behavioural effects of de-

individuation in conditions of high and low group salience. British Journal of Social Psychology,

23(4), 341-350.

Robinson, R. J., Keltner, D., Ward, A., & Ross, L. (1995). Actual versus assumed differences in

construal: “Naive realism” in intergroup perception and conflict. Journal of Personality and

Social Psychology, 68, 404–417.

Rothschild, D. & Malhotra, N. (2014). Are public opinion polls self-fulfilling prophecies? Research

and Politics. July-September 2014: 1-10.

Rozin, P., Haidt, J. & McCauley, C. (2009). Disgust: The body and soul in the 21st century. In B.O.

Olatunji & D. McKay (Eds.), Disgust and its disorders: Theory, assessment, and treatment implications

(pp. 9-29). Washington, DC: American Psychological Association.

Schnall, S., Benton, J., & Harvey S. (2008a). With a clean conscience: Cleanliness reduces the severity

of moral judgments. Psychological Science, 19, 1219-1222.

Schnall, S., Haidt, J., Clore, G. L., & Jordan, A. H. (2008b). Disgust as embodied moral judgment.

Personality and social psychology bulletin 34(8), 1096-1109.

Schubert, T., & Otten, S. (2002). Overlap of self, ingroup, and outgroup: Pictorial measures of slef-

categorization. Self & Identity, 1, 535-576.

Schulz, J. F., Fischbacher, U., Thöni, C., & Utikal, V. (2014). Affect and fairness: Dictator games

under cognitive load. Journal of Economic Psychology, 41, 77-87.

Sears, D. O., & Funk, C. L. (1991). The role of self-interest in social and political attitudes. In

Advances in Experimental Social Psychology, 24, 1-91.

Sherif, C. W., Sherif, M., & Nebergall, R. E. (1965). Attitude and attitude change: The social judgment

involvement approach. Philadelphia: W. B. Saunders.

37

Simon, H. A. (1954). Bandwagon and underdog effects and the possibility of election predictions.

Public Opinion Quarterly, 18(3), 245-253.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. BF Skinner Foundation.

Smith, E. R., & Henry, S. (1996). An in-group becomes part of the self: Response time evidence.

Personality and Social Psychology Bulletin, 22, 635-642.

Swann, W. B., Chang-Schneider, C., & Angulo, S. (2008). Self-verification in relationships as an

adaptive process. In J. Wood, A. Tesser, & J. Holmes (Eds.), The Self and Social Relationships.

Ann Arbor, MI: Psychology Press.

Swann, W. B., Gómez, Á., Buhrmester, M. D., López-Rodríguez, L., Jiménez, J., & Vázquez, A.

(2014). Contemplating the ultimate sacrifice: Identity fusion channels pro-group affect,

cognition, and moral decision making. Journal of personality and social psychology, 106(5), 713.

Swann, W. B., Gómez, Á., Dovidio, J. F., Hart, S., & Jetten, Jolanda. (2010a). Dying and killing for

one’s group: Identity fusion moderates responses to intergroup versions of the trolley

problem. Psychological Science, 21(8), 1176-1183.

Swann, W. B., Gómez, A., Huici, C., Morales, J. F. & Hixon, J. G. (2010b). Identity fusion and self-

sacrifice: Arousal as a catalyst of pro-group fighting, dying, and helping behavior. Journal of

Personality and Social Psychology, 99, 824-841.

Swann, W. B., Gómez, Á., Seyle, D. C., Morales, J. F., & Huici, C. (2009). Identity fusion: The

interplay of personal and social identities in extreme group behavior. Journal of Personality and

Social Psychology, 96(5), 995-1011.

Swann, W. B., & Hill, C. A. (1982). When our identities are mistaken: Reaffirming self-conceptions

through social interaction. Journal of Personality and Social Psychology, 43, 59-66.

Swann, W. B., Jetten, J., Gómez, Á., Whitehouse, H. & Bastian, B. (2012). When group membership

gets personal: A theory of identity fusion. Psychological Review, 119, 441-456.

38

Swann, W. B., & Read, S. J. (1981). Self-verification processes: How we sustain our self-conceptions.

Journal of Experimental Social Psychology, 17, 351-372.

Swann, W. B., Wenzlaff, R. M., & Tafarodi, R. W. (1992). Depression and the search for negative

evaluations: More evidence of the role of self-verification strivings. Journal of Abnormal

Psychology, 101, 314-371.

Tajfel, H. (1969). Cognitive aspects of prejudice. Journal of Social Issues, 25, 79-97.

Tafjel, H. (1974). Social identity and intergroup behavior. Inforation (International Social Science Council),

13(2), 65-93.

Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. The social psychology of

intergroup relations, 33(47), 74.

Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups. New York: Wiley.

Thorndike, E. L. (1927). The law of effect. The American Journal of Psychology, 39(1/4), 212-222.

Tropp, L. R., & Wright, S. C. (2001). Ingroup identification as the inclusion of ingroup in the self.

Personality and Social Psychology Bulletin, 27, 585-600.

Turner, J. & Oakes, P. (1986). The significance of the social identity concept for social psychology

with reference to individualism, interactionism and social influence. British Journal of Social

Psychology, 25(3), 237-252.

Turner, J. (1991). Social influence. Bristol, England: Open University Press.

United States Census Bureau. (2015a). Income and Earnings Summary Measures by Selected Characteristics:

2014 and 2015 [Data file]. Retrieved from

https://www.census.gov/data/tables/2016/demo/income-poverty/p60-256.html.

United States Census Bureau. (2015b). Years of School Completed by People 25 Years and Over, by Age and

Sex: Selected Years 1940 to 2015 [Data file]. Retrieved from

39

https://www.census.gov/data/tables/time-series/demo/educational-attainment/cps-

historical-time-series.html.

Vaidyanathan, B., Hill, J. P., & Smith, C. (2011). Religion and charitable financial giving to religious

and secular causes: Does political ideology matter?. Journal for the Scientific Study of Religion,

50(3), 450-469.

Vezzali, L., Drury, J., Versari, A., & Cadamuro, A. (2016). Sharing distress increases helping and

contact intentions via social identification and inclusion of the other in the self: Children’s

prosocial behavior after an earthquake. Group Processes & Intergroup Relations, 19(3), 314-327.

Vickers, E., Abrams, D., & Hogg, M. A. (1988). The influence of social norms on discrimination in

the minimal group paradigm. Unpublished manuscript, University of Dundee, Scotland.

Wallach, M.A., & Wallach, L. (1983). Psychology's sanction of selfishness: The error of egoism in theory and

therapy. San Francisco, CA: Freeman.

Zimbardo, P.G. (1973). On the ethics of intervention in human psychological research: With special

reference to the Stanford prison experiment. Cognition, 2(2), 243-256.

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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.

47

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.