The Role of Social Motives in Affective Polarization

THE ROLE OF SOCIAL MOTIVES IN AFFECTIVE POLARIZATION

Alicia Shanti James

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF ARTS

May 2021

Committee:

Joshua Grubbs, Advisor

Catherine Stein

A. Shanti James

ABSTRACT

Joshua Grubbs, Advisor

The phenomenon wherein personal feelings towards others are defined by political party

identity, termed affective polarization (AP), has been shown to be on the rise since 1960 and is

thought to result in several forms of interpersonal conflict. While past research has explained AP

in terms of ideology, political sorting, and social identity theory (SIT), the present work aimed to

examine the potential for fundamental social motives (FSM) to further explain the potential

function of AP for the individual. This study used archival data from multiple timepoints to

model relationships among FSM, AP, ideological extremism (IE), and political/moral conflict

(PMC) both cross-sectionally and over a 6-month time-period (N = 1777, 1261 respectively).

Results showed IE to be a more robust predictor of AP than social motives. AP demonstrated no

significant connection to PMC, but IE and the Status motive predicted PMC cross-sectionally.

Exploratory analyses also demonstrated small but significant relationships between Status and

IE, and Status and PMC at a single time point. Implications and limitations of these findings are

discussed. Further research is necessary to understand the complex interplay between IE, AP,

and conflict, but the current results demonstrate that IE and AP may be more closely linked than

current literature implies.

Keywords: affective polarization, ideological extremism, fundamental social motives,

political and moral conflict. iv

For my father v

ACKNOWLEDGMENTS

Many people deserve recognition for their role in the completion of this document and

the degree it represents. Though specific individuals from the present stage of my life are

highlighted below, the reality is that every instructor from pre-K to the present has had a hand in

this accomplishment. Thank you for your encouragement, recommendations, and willingness to

listen.

Thanks first my advisor, Josh Grubbs, for his help throughout the process of developing and producing the thesis, and especially for tolerating the eccentricities of my writing. To my

committee members, Drs. Stein and Zickar: your flexibility, grace, and genuine interest in my

ideas (in your respective classes and for this degree requirement) have enriched the last three

years.

Next, to my friends scattered across the country who checked on and encouraged me,

including but not limited to Kyle and Lauren, my cheerleaders at large. Your belief in me

regularly exceeds my own, and your methods of support are often unconventional and always

perfect. To my friends here in town—the drinks, side trips, and commiseration kept me going

against the siren call of laying very still until I calcified.

Finally, I extend thanks to my parents. My choices are inseparable from the

circumstances that allowed me to make them, and thus, inseparable from you. Our disagreements

are immaterial in the end. Thank you for giving me every advantage. vi

TABLE OF CONTENTS Page

INTRODUCTION ...... 1

Affective Polarization ...... 2

Affective Polarization as a Social Problem ...... 3

Social Identity and Affective Polarization ...... 4

The Demographics of Polarization ...... 6

A Fundamental Motives Perspective ...... 8

The Present Study ...... 10

METHOD ...... 13

Participants and Procedures ...... 13

Measures ...... 14

Political Moral Conflict ...... 15

Affective Polarization ...... 15

Ideological Extremism ...... 16

Fundamental Social Motives...... 16

RESULTS ...... 18

Correlational Analyses ...... 18

Structural Equation Models ...... 18

DISCUSSION ...... 22

Hypotheses ...... 22

Exploratory Analyses ...... 23

Implications...... 24

Limitations and Future Research ...... 25 vii

Conclusion ...... 27

REFERENCES ...... 29

APPENDIX A: MEASURES ...... 35

APPENDIX B: TABLES ...... 37

APPENDIX C: FIGURES ...... 40

APPENDIX D: HSRB EXEMPTION LETTER ...... 43 1

INTRODUCTION

If an extraterrestrial being were to observe American news media today, they might assume that the identification of Democrat vs. Republican is the most meaningful distinction among human beings. A passing scroll through any social media platform will find that the identification of Republican/Conservative or Democrat/Liberal tends to sort people onto starkly opposite sides of issues like gun control, abortion, and climate change. These divides extend to affect as well. Partisan affect towards out-party individuals is also polarized in a way that actually exceeds differences in ideology; the negative personal feelings partisans have towards individuals of the opposite party cannot satisfactorily be accounted for by disparate positions on policy issues (Fiorina & Abrams, 2008). This phenomenon is now known as affective polarization (AP), which is generally thought of as an index of how political differences are reflected in emotional impressions (Iyengar et al., 2019). Importantly, the intensity of AP has only grown in the last five decades (Iyengar, Sood, & Lelkes, 2012, Doherty et al., 2016), and it seems to have rather severe adverse consequences for relationships and broader social functioning (Chen & Rohla, 2018; Conover, Gonçalves, Flammini, & Menczer, 2012).

Accordingly, it is important that we examine its possible motivational underpinnings.

Past psychological discussions of AP have utilized social identification theory (SIT) to address the reason for these attitudes (Greene, 2004; Iyengar et al., 2012), but to date, no research program has addressed AP in terms of fundamental motivational systems. Humans are inherently social creatures with innate needs for affiliation and belonging (Leary and Baumeister,

2000). Yet, groups of humans living together inevitably incur complications, as implied by the existence of governmental structures and political parties. Since political orientation is, in a sense, a summary of how one believes social living should function to maximize members’ gains 2 and safety, theory shaped by evolutionary thinking may lend insight to political attitudes. One such theory, that of Fundamental Social Motives (FSM), interprets social behavior in the context of key adaptive fitness goals, such as finding a mate, inserting oneself in a supportive community, achieving status in one’s social context, or avoiding diseases. These motives have been shown to influence attention, cognition, and behavior (Neel et al., 2016). The purpose of the present work is to apply the framework of fundamental social motives to current understandings of AP to explore the possible functions AP may serve for the individual.

Affective Polarization

The growth of AP within the last 50 years has outpaced ideological changes within parties (Webster & Abramowitz, 2017), suggesting a discrepancy between how people think about politics and how people feel about politics in American culture. Feeling thermometers, trait attribution and stereotyping measures, and social distance measures sketch the dimensions of AP, and these measures convey several marked changes in these attitudes. Although the trend of reporting colder, more disapproving feelings towards the out-party began around 1960, these feelings have intensified so much that they are currently the majority opinion among partisans

(Doherty, Kiley, & Jameson, 2016). That is, the majority of partisans feel cold and more disapproving of members of the other party. Additionally, stereotyping has proliferated considerably between parties; partisans demonstrate ingroup favorability and outgroup antagonism while simultaneously associating negative traits with the opposing party more often than one’s own (Iyengar, Sood, & Lelkes, 2012; Doherty, Kiley, & Jameson, 2016).

Furthermore, this antagonism seems to be increasing: Polls in 1960 found that partisans of the time did not feel strongly against the idea of their child marrying someone from the opposite party, while double the percentage of people in both parties were upset by the same scenario in a 3

2010 survey (Iyengar, Sood, & Lelkes, 2012). In experimental situations, people demand higher compensation just to have a conversation with an affiliate of the opposing party (Settle &

Carlson, 2019). And, in a direct assessment of AP, people report that the opposite party makes them feel fear, anger, and frustration. Many even endorse that part of the reason they affiliate with their own party is to stand against the harmful policies of the opposition (Doherty, Kiley, &

Jameson, 2016). In short, AP is at historically high levels, which is very concerning given its social effects.

Affective Polarization as a Social Problem

AP is more than just a political phenomenon; it impacts numerous aspects of social functioning. Studies have found that partisans on each political side perceive their opponents to be more extreme than they really are (Blatz & Mercier, 2018). This suggests that the antagonism may be at least partially due to differences that, though imagined, seem to have real effects. Self- segregation along political lines is now the norm, especially in the context of social media, where echo chambers reinforce polarized thinking and antagonism permeates any bipartisan discourse that does occur (Williams, McMurray, Kurz, & Lambert, 2015). Such mutual insulation arguably serves to reinforce polarization by reducing opportunities to challenge or refute opinions about the out-party. This point is particularly concerning, given that most people find their opinions tempered when they consider out-party members individually (Iyengar, Sood, & Lelkes, 2012).

The social consequences of AP are multifaceted. Unsurprisingly, AP has been implicated in conflict among families (Chen & Rohla, 2018), coworkers (Conover, Gonçalves, Flammini, &

Menczer, 2012), and increasingly, strangers on the internet (Johnson and Roberto, 2018). On a larger scale, polarization is thought to be restricting the ability of legislative bodies to compromise, thus leading to more extreme policies (Barber & McCarty, 2015). Finally, some 4

researchers highlight the potential interference of AP with democracy itself, as people, especially

those that are more politically moderate, become more frustrated with their government due to

executive or legislative gridlock, yet also are understandably reluctant to talk about or identify

with politics in such an uncivil climate (Layman, Carsey, & Horowitz, 2006).

In short, in the U.S., AP is an apparently increasing phenomenon that has clear effects on

the well-being of social groups and potentially society at large. Unsurprisingly, a great deal of

research has attempted to explain what might be driving AP. Several lines of empirical and

theoretical inquiry have sought to address the affective dimension of polarization, with both

theories and social changes being addressed in explanation of these trends. Below, I elaborate on

a few of these explanations, address their value and shortcomings, and introduce a novel

explanation—fundamental social motives—that may add new dimensions to our current

understanding of AP.

Social Identity and Affective Polarization

Social Identity Theory (SIT) is the basis for one of the primary mechanisms proposed to

explain AP. While ideology may explain one’s choice of a political party (Barber & McCarty,

2015), such sorting alone seems to exert only a mild influence on affect (Iyengar, Sood, &

Lelkes, 2012), and there is evidence that the two have begun to vary inversely in recent years

(Iyengar et al., 2019). This means that ideological differences alone do not adequately translate to polarization, and AP is more than the outcome of an ideological divide.

The introduction of SIT considers that AP is perhaps a result of the dynamic that emerges between competing groups more broadly. SIT has been used to explain antagonism toward outgroup members in many different contexts, such as race and religion (Kelly, 1988). SIT operates on the premise that political party memberships are psychologically meaningful to 5 people—they value and identify with a group to which they belong, which leads to perceptual and cognitive distortions that favor this group and feed self-concept, often beyond the true differences (Greene, 1999; Greene, 2004). In this framework, AP is initiated by the base ideological differences that sort people into opposite sides, but is extended by group dynamics

(Iyengar, Sood, & Lelkes, 2012).

SIT explains that affective biases are a function of affiliation salience: People who identify more strongly with their party should display more robust biases in the form of feeling thermometer ratings, stereotyped beliefs about party supporters, and feelings about interparty marriage (Iyengar, Sood, & Lelkes, 2012). However, support for these predictions is mixed. On the one hand, individuals who are more active within their party tend to exhibit more biased opinions than those who are not activists, but polarization is also increasing among non-activists and among independents (Iyengar, Sood, & Lelkes, 2012; Iyengar and Westwood, 2015). This suggests that social identification may explain polarization for some people (i.e., those who are already sorted into parties) but does not convincingly represent the cause of polarization more broadly. Furthermore, national data show that in-party bias is stable, while out-party bias is increasing over time (Iyengar, Sood, & Lelkes, 2012; Doherty, Kiley, & Jameson, 2016).

According to the logic of SIT, positive associations for one’s own group are propped up and/or caused by perceiving the outgroup to have hyperbolic negative qualities. Thus, there is no functional reason for negative bias to increase independently at the rate seen in the last fifty years.

Overall, the current literature on AP in terms of SIT answers some questions while prompting others. Group identification may explain polarization for sorted individuals but cannot speak to the polarized views of those who do not claim either side and does not include a 6 satisfactory mechanism for the historical increase of outgroup bias. Of course, here we must acknowledge that the foundation of political party identity is less enduring than those of racial and gender identity to which SIT has previously been applied. Democrats in the southern USA were slavery apologists following the civil war whereas current democrats are known for endorsing much more egalitarian politics and civil rights more generally. However, this is itself a reason that a trait-based framework addressing social motives might be a useful complementary tool.

Finally, the function of political polarization for individual needs is not explained within the framework of SIT. That is, it remains to be known what, if any, desirable outcomes individuals might be gaining from maintaining polarized views with seemingly undesirable consequences. The question of the motivational systems underlying group identification processes and the resulting biases is outside the scope of SIT.

The Demographics of Polarization

Central to the study of polarization is the question of whether identity or demographic characteristics may have some role in driving or influencing polarization. In modeling the effects of modern media on polarization, at least one study has demonstrated trends in polarization to be increasing more among older adults (65+) than among younger, presumably more internet and social media-savvy counterparts. Specifically, this research found that, from 1996 to 2016, the increase in polarization among older adults was almost double that of those aged 18-39, and have suggested that preferred route of political media consumption (i.e. by 24-hr cable news) may explain this distinction (Boxell, Gentzkow, & Shapiro, 2017).

Beyond age as a potential factor in polarization, there is also evidence that locale or geography may also influence it (Scala & Johnson, 2017). The 2016 presidential election 7 highlighted, perhaps more starkly than ever before, the nuances of American region (broadly, urban vs. rural) in polarization. This research contends that regional distinction exists on a continuum, and that, among other things, migration of people to and from these broad geographical regions may align with the trends in polarization. In theory, this would increase the heterogeneity of values and beliefs in an area and opportunities for disagreement and conflict

(Scala & Johnson, 2017). Even so, it is not clear how much of polarization is accounted for by geographical and residential variables alone.

Among the many demographic factors that might be related to polarization, the most compelling topic to consider might be race and racial attitudes. Insofar as polarization may be explained in part by increased political sorting (Fiorina & Abrams, 2008; Lelkes, 2018), some have highlighted that race may be an important factor, especially as the racial composition in this country grows increasingly diverse and, presumably, politically participative. Though causality is obscured, this group did find that diversity had increased doubly in countries where polarization is rising compared to countries where polarization is decreasing (Boxell et al.,

2020). While the detectability of this connection (i.e., rising diversity and rising polarization) on the individual level is questionable, this finding suggests that large scale changes in the composition of the populace may trickle down to individual views. There is likely some feedback between social identity and the political climate that feeds polarized views. For example, the politicization of the Black Lives Matter movement (BLM) as a “liberal” phenomenon, would logically be assumed to prompt adjusted political sorting and partisan animosity. The notion of racial resentment, which has risen among Republicans and decreased among Democrats, captures one potential mechanism by which this sorting and polarization might occur (Kimball, Vorst, Green, Coffeey, & Cohen, 2014). 8

In summary, there are a few ways in which personal characteristics and identity may influence polarization. There is some evidence and reasoning to support that age, region, and identity politics, particularly on race, are relevant to this conversation. However, the interplay of identities/characteristics with each other, as well as with one’s political sorting, is likely more dynamic than our current methods measure (i.e. differentiating polarization by distinct identity

“categories”).

A Fundamental Motives Perspective

So far, I have established that SIT only partially describes AP and that demographic factors, though relevant to, are not explanatory of AP. Thus, there remains a need for deeper understanding of the factors driving AP. In this vein, ideas that directly assess human motivational systems should be addressed. The Fundamental Social Motives (FSM) framework describes individual social behavior as being guided by central adaptive goals: protecting oneself from bodily harm, avoiding disease, finding and keeping a mate, caring for offspring and family, attaining status, and being affiliated with a community (Griskevicius & Kenrick, 2013). Political party affiliation in the American cultural context naturally converges with such motivations, as the American party system reflects different approaches to managing social challenges (Jones,

2019). Calling oneself Democrat or Republican is akin to a concise summary of how one believes social living should function and how best to minimize and maximize the risk and gain that come with participating in a society. Thus, the FSM framework may help to clarify what drives the antagonism between these groups, which, on the surface, seems itself to stem from competing opinions about social living.

The primary goal of behavior is usually thought to be survival and reproduction, but modern humanity has added layers of complexity. In order to initiate and maintain that goal, 9

other building blocks are needed. FSM describe not just the goal of finding a sexual partner and

procreating, but everything that leads up to and proceeds from that point. Though there are

additional nuances and variation on these themes among people, we may reasonably expect that

social motives aim to minimize risk and maximize opportunity with respect to at least one of

these goals in any given social experience.

Studies using the FSM framework can take two approaches: experimental (state-based)

and cross-sectional (trait-based). Experimental activation of motives by prompted recollection or cinematic primes has been shown to influence attention, perception, and memory according to motive-specific features. For example, when people are prompted to empathize with a film character in a life-threatening situation, they demonstrate greater perceptual acuity for facial features. This is taken to mean that activated self-protection motives correspond behaviorally to heighten awareness of (and thus, ability to respond to) potential threat (Kenrick et al., 2010;

Maner et al., 2012). Likewise, mate-seeking motives activated by recollection of one’s attraction

to a potential partner predict enhanced attention to and memory for attractive potential partners

in real time (Maner et al., 2012) and predicts male willingness to spend money on modern-day

plumage of cars and electronics (Kenrick et al., 2010). This attentional effect is adjusted by

cultural stereotypes about race and gender, resulting in social categorization effects: self-

protection priming led to enhanced memory for Black faces (Maner et al., 2012) and angry male

faces (Kenrick et al., 2010).

In the trait-based approach, measuring motives in a cross-sectional fashion captures a

snapshot of a person’s motives when they are not acutely activated by stimuli, thereby getting a

baseline assessment of their social goals (Neel et al., 2016). As we might expect, this approach

reflects that age, gender, and personality connote different experiences and thus, different social 10

priorities (Neel et al., 2016). For example, affiliation and dimensions of mate-seeking decline with age, consistent with the idea that with age comes greater stability in existing relationships and less interest in seeking new ones (Neel et al., 2016). Additionally, these snapshot measures of FSM have cogent personality and behavioral correlates: i.e. self-protection is correlated with

neuroticism and carrying items to defend oneself; affiliation strongly relates to extraversion,

agreeableness, and participation in communal and pro-social activities; status seeking is linked to

work achievement and participation in performance art (Neel et al., 2016). Importantly, FSM and

personality are distinct enough from each other to make unique behavioral predictions (Neel et

al., 2016).

In summary, FSM have demonstrated ecological validity, perceptual, attentional, and

cognitive correlates, and behavioral outcomes in experimental and cross-sectional studies. A

cross-sectional inventory of FSM is easy to obtain and can indicate the defining motives of an

individual, suggesting what social goals are most prominent in their current place and role in life.

As AP is a social phenomenon, understanding the trait motives of the people who endorse

polarization may lend insight to its function for the individual. Recent findings have defined a

form of moral discourse called moral grandstanding as a function of status seeking motives

(Grubbs et al., 2019), and connected it to political polarization (Grubbs et al., 2020). However,

no research has directly applied the FSM framework to political polarization or affect. In doing

so, we may determine a different set of outcomes for this behavior and further clarify its

function.

The Present Study

Humans are a deeply involved social species. We form relationships, families,

communities, religions, companies, and complex governments, all defined by unique power 11

dynamics and benefits. Logically, there must be some adaptive value to these complex social

strata, or they would not be sustained. Given that demographics and social identity seem to

describe AP only partially, theories that incorporate human motivational systems should be

included in the ongoing discussion. The goal of the present study is to understand AP in terms of

motives underlying social behavior. Given that AP seems to have caustic consequences for

relationships and civil engagement (Chen & Rohla, 2018; Layman, Carsey, & Horowitz, 2006),

social motives may help clarify how AP could serve some desirable function for the individual.

Specifically, this study will seek to confirm AP’s connection to conflict and determine the

contribution of FSM that seem most likely to play a role in the realm of politics.

Prior work hints that two specific FSM should be of interest in AP. Given the meta-social context of political behavior, it makes sense to focus on motives that are most directly defined by and achieved in groups: affiliation and social status. Support for these motives in AP is logically calculable, if not overtly present in the existing literature. Firstly, if building one’s identity in group membership is a driving factor in intergroup attitudes, as posited in SIT (Greene, 2004), the Affiliation motive can be expected to reflect this (i.e. affiliation tends to correspond to group membership and need to belong, see Neel et al., 2016). The findings of Grubbs et al. suggest that being morally opinionated about issues in public discourse corresponds with status seeking motives (2019; 2020). As AP often includes opinions about the morality of the other side (see

Doherty, Kiley, & Jameson, 2016), Grubbs’s finding suggests some status-earning function of polarized behavior (Grubbs et al., 2020). It is possible that expressing such views earns respect or admiration among likeminded others, thereby reinforcing polarization. Finally, past work relating demographic characteristics to polarization suggests that polarization likely differs by age, racial/ethnic diversity, and regional characteristics (Boxell, Gentzkow, & Shapiro, 2017; 12

Kimball, Vorst, Green, Coffeey, & Cohen, 2014; Scala & Johnson, 2017). Though the interplay of the mechanisms behind this variation are likely beyond the scope of this project, replicating the connection between age and polarization may further clarify any potential role of social motives. This study will test the above ideas using politically diverse, representative data.

Based on the current AP literature, the following are hypothesized:

1. AP will be correlated with the frequency of political and moral conflict.

Theoretical and experimental work on AP suggest that it has consequences for

interactions in digital and physical contexts (Chen & Rohla, 2018; Conover et al.,

2012; Johnson & Roberto, 2018; Settle & Carlson, 2019). This hypothesis will

assess the validity of the AP measure by including the conception of AP as a

social problem.

2. Status seeking and affiliation motives will predict AP over and above ideological

polarization. Status seeking is theoretically driven by power dynamics and/or the

desire to be perceived as competent and wise. This drive correlates with

performative and ambitious behavior (Neel et al., 2016), both of which have been

connected to polarized interactions (e.g. civil discourse in Grubbs et al., 2019).

METHOD

This study was the product of secondary data analysis. The sampling method and relevant measures of the original study are reviewed below.

Participants and Procedure

Participants were those recruited as a part of a larger, longitudinal study that began in

August of 2019 (Grubbs, Tosi, & Warmke, 2019). Full information regarding this larger project is available via the Open Science Framework at https://osf.io/zbg3d/. This original study used

YouGov opinion polling to collect a nationally representative sample of U.S. adults. YouGov is an international research data and analytics company that samples nationally representative demographic compositions for accurate opinion polling, research, and marketing. Prior work has demonstrated that YouGov’s sampling methods and proprietary weighting formulae provide more accurate representative data than most other public opinion polling companies, including those that rely on probability sampling methods (i.e., PEW; see Rivers, 2016). Thus, this method was appropriate for both the original and the present study questions.

At baseline, the sample consisted of 2,519 adults matched to U.S. nationally representative norms for age, gender, race, education, and Census Region as of the 2017

American Community Survey (Mage= 48.5, SD = 17.8; 51.4% women; 38.7% Democrat, 27.2%

Republican, 25.4% Independent, 3.6% other, and 5.1% not sure; 64.1% White, 12.0% Black,

15.7% Hispanic, 3.3% Asian, 0.9% Native American, 2.5% Mixed, 1.5% other, 0.2% Middle

Eastern). One week after completing baselines measures, participants were asked to respond to additional measures with the goal of retaining 65% of the original sample. This retention target was based on budgetary constraints rather than a-priori estimates of needed sample size. Of the initial 2,516 responses, 1,777 were retained (retention rate = 70.6%). Only those who had 14

completed the Fundamental Social Motives measure were included in the analytic sample,

leaving a final sample of 1,777 (final inclusion rate = 100%). Multivariate analysis of variance

revealed no systematic differences between those who completed the follow-up and those who did not (Wilk’s λ = .998; F(4, 2743) = 1.153; p = .330).

Six months after the short-term follow-up, 1,624 of the original 2,519 participants accepted a request to complete another wave of follow-up measures (retention rate= 64%). Of this group, 1261 individuals ultimately completed all measures of interest between both follow- up waves. In the final sample of valid data, 54% identified as female with a mean age of 51. 8 years (SD= 16.5), and the majority identified as White (64.3%), followed by Hispanic (14.7%),

Black (12.5%), with smaller percentages of individuals reporting Asian, Mixed, Other/Not specified, Native American, and Middle Eastern racial-ethnic backgrounds. Once again, about a third of the sample had graduated from high school (31.5%), followed by these who had taken some college coursework (19.3%) and those with four-year degrees (19.3%). Those with two- year degrees made up 11.9% of the population, followed by those with post-graduate education

(10.3%) and those who did not complete high school (7.7%). The political demographics of this sample mirrored the first: the plurality identified as Democrats (39.2%), followed by

Republicans (26.5%) and Independents (26.2%). Markedly fewer individuals identified with other parties (4.1%) or were unsure of their identification (4.1%). Demographics of the baseline,

1-week follow-up, and six-month follow-up sample, as well as demographics for the final sample with complete data, is displayed in Table 1 of the appendix.

Measures

Table 2 shows means, standard deviations, ranges, and obtained Cronbach’s Alpha values for all measures. Below, I describe included measures. 15

Political and Moral Conflict

I assessed political and moral conflict using a scale developed for a study of political discourse and personality (Grubbs et al., 2019). This scale assessed the frequency of conflict over political or moral subject matter in participants’ daily life in the past year. These ten items include such incidences as “lost friends because of my political/moral beliefs”, “gotten into fights on social media because of the political/moral beliefs of others”, “severed ties with a friend over moral/political differences”, etc. Responses were recorded on a scale of 1 (never/not at all) to 4 (several times) and were averaged for all items. This scale was first used in a multi- study investigation of moral grandstanding, where it was used with both college and nationally representative samples.

Affective Polarization

In keeping with the literature, affective polarization was measured using a feeling

“thermometer,” wherein lower numbers reflect metaphorically cold feelings and higher numbers reflect feelings of warmth towards the item in question (Iyengar et al., 2019). Participants rated the “temperature” of their feelings towards Democrats and Republicans. As with previous use of such measures (see Garrett, Long, & Jeong, 2019; Iyengar & Westwood, 2015), a gap index was derived from these two ratings that reflected the absolute magnitude of the temperature distance between participants’ self-reported feelings towards Democrats and Republicans. The difference between temperature ratings was derived and then squared. The square root of this squared difference serves as an absolute, linear value gap index of partisan affect that reflects the absolute value of the difference between a respondent’s feelings for Democrats and Republicans.

( ) 2 � 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 16

Greater absolute values indicate more extremity in these feelings, regardless of the side at

which they are directed. Thus, this index is a streamlined assessment of overall affective

polarization.

Ideological Extremism

Ideological extremism was measured with slider question that asked participants to report

how liberal/conservative they considered their political views to be, with -100 being very liberal,

0 being perfectly moderate, and 100 being very conservative. The score was coded akin to AP

such that an absolute value index of extremism was derived by squaring and then taking the

square root of the value reported to reflect ideology using this slider.

( ) 2 Higher numbers reflect more extreme� 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 views,𝑉𝑉 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉regardless of the party with which they are

associated. The result is a streamlined assessment of ideological extremism.

Fundamental Social Motives

This 66-item scale measures motives on the following dimensions: Status Seeking,

Affiliation, Mate Seeking, Mate Retention, Kin Care, Disease Avoidance, and Self Protection

(Neel et al., 2016). Based on the results of other work involving social motives and discourse

(Grubbs et al., 2019), the subscales of Affiliation and Status were the only subscales included in the proposed project. All items comprised response options ranging from 1 (strongly disagree) to

5 (strongly agree). Affiliation contains three subscales: Group, Exclusion Concern, and

Independence. Only Group was included in this study. Affiliation (Group) measures the desire to

be in groups and is separate from but related to extraversion, agreeableness, and need to belong.

Items include “Being part of a group is important to me” and “When I’m in a group, I do things

to help the group stay together”. Status measures the desire to cultivate authority for oneself or 17 garner the respect of others, usually by means of prestige or dominance, and has been correlated with extraversion and need to belong. items include “It’s important to me that other people look up to me”, and “I do things to ensure that I don’t lose the status I have”.

RESULTS

Correlational Analyses

Table 3 shows correlations among demographic variables (age, gender, education level)

and status, affiliation, affective polarization, and ideological extremism. Age bore moderate

relationships to status motives, AP, and ideological extremism (r = -.26, .26, .23 respectively, p <

.010). Gender showed a small relationship to status (r = -.11, p < .010), and education bore small relationships to status and affiliation (r = .12 and .13 respectively, p < .010). Status motives showed small to moderate relationships to age and all other variables of interest (r = -.07 to .356,

p < .010), while affiliation related only to status (r = .36, p < .001). Finally, affective

polarization and ideological extremism showed small to moderate relationships to all other

variables (r = -.07 to .49, p < .010) except affiliation motives.

Table 4 shows correlations between social motives, polarization, and extremism at

baseline and follow-up time points. Affiliation motives bore no significant relationship to either

AP or IE, while Status demonstrated small negative relationships to polarization at both time

points (r = -.07 to -.09, p < .010), but only related to baseline, and not follow-up, extremism (r =

-.07, p < .010). Initial AP and IE metrics both correlated highly with follow-up reports of their

respective levels (r = .74 to .77, p < .010).

Structural Equation Models

To model the relationship of status and affiliation to affective polarization and ideological

extremism, I conducted structural equation models using the lavaan package (Rosseel, 2012) for

R statistical software (RC Team, 2015). The hypothesized model regressed affective polarization

(via the gap index derived from observed feeling ratings) on the latent variables of Status and

Affiliation (defined by observed items of their respective subscales), both cross-sectionally and 19 over time. The Robust Diagonally Weighted Least Squares method (WLSM) of parameter estimation was employed. WLSM does not assume normality and homoscedasticity in residuals and is more appropriate for ordinal data than is Maximum Likelihood estimation (Flora and

Curran, 2004; Mîndrilã, 2010).

The first step of the cross-sectional model was initially tested as proposed, wherein I defined the latent variables of Affiliation and Status by their respective items and regressed polarization on these and the observed variable of IE within the first wave of data (see figure 1).

This model returned marginally acceptable fit indices suggesting unaddressed sources of variation. In other words, the items as they were modeled did not satisfactorily account for the variance in the outcome variable (Robust χ2[75] = 705.79 (p <.001), Robust CFI = .952, Robust

TLI = .941, Robust RMSEA = .055, SRMR = .055). Status bore a small, inverse relationship to

AP while Affiliation was unrelated to AP, and IE demonstrated a strong positive relationship to

Affective Polarization. This model accounted for a moderate amount of the variance in AP (R2=

.233).

Given concerns about fit, I employed modification indices on an exploratory basis to determine an alternative model that would demonstrate better fit given the present data. Though the use of modification indices signifies a shift to the exploratory realm, such action is within reason given the nature of this project and the incipient status of the research program to which it belongs. The purpose of understanding relationships among ideology, affective polarization, and social consequences of the latter remains the primary goal, and some exploratory analyses are necessary to serve that goal. Modification indices demonstrated that the model would better represent the data if an item from the FSM Affiliation scale was removed (“Working in a group is usually more trouble than it’s worth”), as its residual seemed to covary with many other 20

elements in the model. Accordingly, I conducted the same model as described above with the

sole modification of removing this item. This resulted in substantially better fit

2 (χ dif [14] = 576.22, p < .001).

The ultimate model structure for cross sectional relationships is pictured in figure 2.

Status and Affiliation were defined by their items and allowed to covary, as described in the first iteration. The residual variances of all individual items were constrained to be equal to each other, and IE, AP and PMC were standardized according to their respective scales to equalize scale differences across these variables (i.e. z-scores were used for analyses). Ideological extremism and affective polarization were allowed to covary, and both these and Political/Moral

Conflict were then regressed on status and affiliation. Finally, Political/Moral Conflict was regressed on IE and AP. These changes returned acceptable fit statistics: Robust χ2[79] = 661.73

(p <.001), Robust CFI = .959, Robust TLI = .953, Robust RMSEA = .048, SRMR = .052. Status and affiliation both significantly predicted IE, while only status predicted AP. Status, Affiliation, and IE were all predictors of PMC but differed in valence. Standardized path estimates (i.e., beta values) can be viewed in figure 2. For outcomes IE, AP, and PMC, the overall variances explained by the model were R2 = .014,.004, and .076, respectively.

A similar structure was used to model these relationships over time, as pictured in figure

3. In addition to the steps described above, the longitudinal model regressed six-month follow-up

measures of IE, AP, and PMC on their respective initial measures, as well as on the status and

affiliation motives assessed at the first time point. Overall model fit returned good statistics:

Robust χ2[106] = 511.095 (p <.001), Robust CFI = .975, Robust TLI = .967, Robust RMSEA =

.041, SRMR = .045). Initial measures of IE, AP, and PMC all significantly predicted their

follow-up measures. Additionally, AP1 predicted IE2, and likewise, initial IE predicted AP2. 21

Affiliation was the only variable besides baseline PMC to predict follow-up PMC. Based on the longitudinal model, the variances explained for initial IE AP, and PMC were as follows: R2 =

.011, < .001, .527, respectively. Follow-up measures of IE, AP, and PMC returned R2 = .569,

.598, and .527 respectively.

DISCUSSION

The purpose of this project was to examine any possible relationships among social motives, polarization, and relational conflict. These relationships are important as they may speak to the possible function of polarization for individuals, and thus, have implications for means of attenuating the negative consequences of polarization. Below I elaborate on my efforts to clarify these relationships in terms of my proposed hypotheses as well as exploratory analyses.

Hypotheses

Primarily, I hypothesized that AP would correlate with the frequency of political and moral conflict. Correlations between these variables failed to indicate any meaningful relationship between the two, both at a single time point and at follow up. Baseline AP correlated significantly with follow-up PMC, but this relationship was quite small in magnitude and thus, it is unlikely that any meaningful interpretation of this can be provided.

My second central hypothesis was that status and affiliation motives would predict affective polarization beyond ideological polarization, which is a separate but related construct that is defined by self-identification with a party rather than emotions toward party members. I tested the hypothesized relationships using structural equation modeling. Results ran largely counter to my hypotheses. Overall, it seems that ideological extremism, at least when measured as a left vs. right spectrum, plays a greater role in AP than do social motives. Both the cross- sectional and longitudinal models indicate that the relationship between IE and AP is stronger than those of fundamental motives to AP. Specifically, status seeking was the only motive that bore any significant relationship to AP, and an inverse relationship at that; the valence of this relationship runs counter to my hypothesis that status motives drive AP. Ultimately, all 23

relationships between fundamental social motives and AP were too small to warrant explanatory

speculation, both at a single time point and over time.

Exploratory Analyses

Generally speaking, my analyses adhered to the proposed hypotheses. However, as SEM

offers many tools to finely tune models with respect to data, I utilized these functions to serve the

overall purpose of understanding connections among ideological and affective polarization,

conflict, and motives. Since IE bore a more robust connection to AP than motives, it was worth

investigating whether motives might predict IE, thereby connecting with AP indirectly. As with

AP, status bore an inverse relationship to IE that did not extend beyond the cross-sectional analyses. This was unsurprising in the context of this study; all outcome variables were best predicted by their respective initial measures. However, past work has found consistent support for a connection between grandstanding, which is theorized to be underlaid by status motives, and both IE and AP. It is unclear why general status striving might not demonstrate the same connections. This may be a consequence of the time points-- overall changes in polarization and conflict over the course of six months were minimal, which suggests that IE and AP are relatively stable attitudes.

The average reported conflict was minimal, suggesting that the events surveyed by this measure occur infrequently for most people. The fact that status predicted conflict at the first time was consonant with previous findings using this measure; status striving in the form of moral grandstanding demonstrated a robust connection to conflict (Grubbs et al., 2019). The lack of connection between PMC and polarization was unforeseen, given the literature on the consequences of polarization. Potential explanations range from measurement error to political selectiveness in curating one’s social circles; past findings have ratified the connection between 24 status-seeking and conflict, as well as status-seeking and polarization (Grubbs et al., 2019;

Grubbs et al., 2020).

Implications

The central predictors of this project, Status motives and Affiliation motives, bore minimal relationships to the central outcome, AP. This suggests that the way these motives play out in interpersonal relationships may not apply to politicized social behavior as directly as posited. However, the relationships of FSM to other outcomes of interest (AP, IE, PMC), as well as the relationships among these outcomes over time, still allow for valuable insights about the mechanisms of polarization in society.

The connection between status seeking and PMC held true, replicating prior work on this area. At the same time, it is interesting to consider where AP might fit into this relationship between status and PMC. Specifically, the lack of connection between AP and conflict was troubling. The means of these variables suggest that most respondents do not experience a substantial level of either, which makes it difficult to imagine how these connections might manifest in everyday life. However, finding that status positively predicts PMC but negatively relates to AP and IE disrupts the expectation that conflict is the natural result of AP. This finding may reflect that status-seekers tend to incur more conflict in general, with the political nature of this conflict being incidental and unrelated to political attitudes.

These inversions may be somewhat explained by the distinct prestige and dominance pathways to status (Cheng et al., 2013). Prestige-driven status seeking is achieved by cultivating the respect of others (i.e. by presenting oneself as wise or moral), while Dominance connotes the use of force and fear to bend others to one’s will (Cheng et al., 2013). Moral grandstanding behavior (MG), which is framed by the status motive, is defined by Dominance/Prestige 25 pathways as well. In a recent study connecting polarization and MG, Grubbs et al. (2020) that parsing dominance from prestige status strivings yielded differential effects of these two pathways on the relationship between MG and IE. Specifically, they found robust, repeated relationships between prestige-MG and IE, while also finding small inverse correlations between

IE and dominance-IE. A similar effect could be at play in the data for this project, wherein parsing dominance from prestige might yield different relationships to IE.

Lastly, the fact that IE and AP held so closely together was intriguing given the way that ideological polarization is treated separately from AP in the literature (i.e. Iyengar, Sood, &

Lelkes, 2012). Some have posited that party sorting can be both a cause and consequence of AP

(Lelkes, 2018). If this is so, finding IE and AP hang so closely together may also speak to the motives and/or desires of those driving sorting (i.e. to reflect their extreme views by sorting or to adjust their views once sorted to agree with statements of elites; see Levendusky, 2009). The best predictors of short-term changes in polarization, extremism, and conflict were their own baseline measures. This suggests that these are not volatile; they likely hold steady over time or change at a slower rate than what I was able to observe with these timepoints. Overall, though, the low amount of variance in IE and AP explained by the cross-sectional model suggests that factors outside the model (and thus, the scope and aims of this project) are likely better predictors of these variables.

Limitations and Future Research

The first, most obvious limitation of this study is its reliance on self-report data. While the advance of internet survey services has made it possible for researchers to conduct larger, studies more efficiently than ever before, impression management and social desirability may still temper responses, especially on certain topics. Organizational psychologists discuss this in 26 their arguments for field research: Domains such as religion, sex, money, and politics are riddled with issues that have the potential to embarrass or incite conflict, as well as have some consequence for the group(s) which participants will be representing (King, Hebl, Morgan, &

Ahmad, 2013). Thus, any questions with some social value would likely be susceptible to some social desirability, such as socially expected behaviors or decorum (Van de Mortel, 2008).

That being said, there is little evidence that other methods of psychological data collection are particularly good at cutting through noise. Rather, it is more accurate to say that we exchange one form of noise for another in preferring physiological or neurological data to self-report. Indeed, self-report findings have been corroborated by neuroscience data (i.e. Yaseen et al., 2016), and interpretations of psychophysiology data are still inconsistent (i.e. Zaehringer et al., 2020). Regardless of the comparative viability of self-report to other data, it is easy enough for future studies to include an indicator of social desirability or impression management (i.e. those described in Larson, 2019).

The measures themselves may also represent a limitation in this study. As with most survey research, there was little room for nuance in responding to Likert scale questions. The way in which the questions are asked may not have connected with the participants’ perceptions, and thus, results may not fully represent the experiences of interest in this study. A conversation with some of these respondents could reveal a different estimation of their affective polarization than they reported. Conjoined to this is the issue of accuracy-- especially in the case of ideology, people’s estimation of their place on the political spectrum may not be accurate with respect to party platforms. The two central elements of this study, IE and AP, were constructed from slider questions that necessarily reduce complex phenomena to sliding scales. A collection of issue or domain-based left/right slider questions may create a more accurate estimation of peoples’ 27

ideological extremism. Similarly, affective polarization was estimated with only one comparative

question based on a “temperature” slider.

The literature on AP includes many other ways to address these attitudes, such as

querying traits of the opposite vs. one’s own political side, social distance questions, and even

experiments that allow us to observe peoples’ interactions with opposite others. As it stands, this

study measured a microcosm of what might constitute AP. Future study should integrate diverse

approaches to measuring AP and/or mixed methods (i.e. field research, focus groups, qualitative

analysis) to better approximate reality.

Conclusion

Affective polarization has been on the rise since 1960 (Iyengar, Sood, and Lelkes, 2012,

Doherty et al., 2016). It has been linked to negative social consequences (Chen & Rohla, 2018;

Conover, Gonçalves, Flammini, & Menczer, 2012) and is projected to have large-scale effects on the functioning of democracy (Layman, Carsey, & Horowitz, 2006). However, to date, no studies have sought to directly ascertain social motives behind this collection of antagonistic attitudes and behavior. This study sought to build on tangentially related work (Grubbs et al., 2019;

Grubbs et al., 2020) by investigating the potential role of status and affiliation motives in predicting polarization and political/moral conflict over time via structural equation modeling.

My results demonstrated that motives play a negligible role in AP compared to ideological extremism, though status significantly predicts conflict at a single time point. Over time, ideological extremism demonstrated the most robust connection to AP, and the model itself

explained little of the variance in the variables of interest. In short, the results of this study

indicate that an evolutionary conceptualization of social motives may not adequately describe the

motivations behind polarization. However, research on motives behind AP should continue, as it 28 is increasingly relevant in the American political scene and addressing motives is likely key to intervention. Future work may benefit from incorporating mixed methods (i.e. focus groups and surveys), more extensive measurement of AP and conflict, and some attempt to mitigate the influence of social desirability on responses about political feelings.

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APPENDIX A: MEASURES Political/Moral Conflict Scale

Please report the frequency with which you have experienced the following things in the past

three months. 1= never/not at all, 2=one time 3=a few times, 4=several times.

1. I have lost friends because of my political/moral beliefs

2. ...gotten into debates on social media because of my political/moral beliefs

3. ...gotten into fights on social media because of my political/moral beliefs

4. ...grown distant from a friend because of my political/moral beliefs

5. ...experienced conflict in my home because of moral/political issues

6. ...felt as if moral/political issues were interfering with my relationships

7. ...gotten into fights on social media because of the political or moral beliefs of others

8. ...tried to convince someone that their political or moral beliefs were wrong

9. ...severed ties with a friend over moral/political differences

10. ...ended a relationship with a family member over moral/political issues

Affective Polarization: Feeling Thermometer Gap

Please rate your feelings towards Democrats (Republicans) on the sliding scale below. 0= very

cold or completely negative, 100=very warm or completely positive.

||||||||||||||||||||||||||||| 0 50 100

Ideological Extremism: Left/Right Spectrum

Please rate your political views from “left” to “right” on the sliding scale below. −100= liberal or

progressive), +100= conservative or traditional.

||||||||||||||||||||||||||||| -100 0 +100 36

Fundamental Social Motives Inventory

We are interested in whether the following statements are true of you at this point in your life.

Please answer how well the questions apply to you in general now, not whether these have been true of you in the past or may be true in the future. For each question, think about the extent to which you agree or disagree with the statement. 1= strongly disagree, 2= disagree, 3= neither agree nor disagree, 4= agree, 5= strongly agree.

Status

1. It’s important to me that other people look up to me.

2. I want to be in a position of leadership.

3. It’s important to me that others respect my rank or position.

4. I do things to ensure that I don’t lose the status I have.

5. I do not like being at the bottom of a hierarchy.

6. I do not worry very much about losing status.

Affiliation

1. Being part of a group is important to me.

2. I enjoy working with a group to accomplish a goal.

3. I like being part of a team.

4. Working in a group is usually more trouble than it’s worth

5. When I’m in a group, I do things to help the group stay together.

6. Getting along with the people around me is a high priority.

APPENDIX B: TABLES Table 1. Demographics of Participants at Baseline and Follow-Up Baselinea 1 Week Follow-Up b 6 Month Follow-Upc Complete Data d Gender Female 1295 (51.4%) 862 (48.5%) 871 (53.7%) 580 (46%) Male 1224 (48.6%) 915 (51.5%) 753 (46.3%) 681 (54%) Age (mean/SD) 48.5 (SD=17.6) 48.9 (SD=17.7) 52.08 (SD=16.3) 51.83 (SD=16.45) Race Ethnicity White 1614 (64.1%) 1116 (62.8%) 1072 (66.0%) 811 (64.3%) Hispanic 395 (15.7%) 291 (16.4%) 223 (13.7%) 185 (14.7%) Black 302 (12.0%) 220 (12.4%) 198 (12.2%) 158 (12.5%) Asian 82 (3.3%) 63 (3.6%) 51 (3.1%) 44 (3.5%) Mixed 62 (2.5%) 40 (2.3%) 44 (2.7%) 33 (2.6%) Other 37 (1.5%) 26 (1.5%) 22 (1.4%) 19 (1.5%) Native American 22 (0.9%) 19 1.1%) 10 (0.6%) 10 (0.8%) Middle Eastern 4 (0.2%) 1 (0.1%) 3 (0.2%) 1 (0.1%) Education No HS 217 (8.6%) 152 (8.6%) 110 (6.8%) 97 (7.7%) High school graduate 786 (31.2%) 558 (31.4%) 512 (31.5%) 397 (31.5%) Some college 515 (20.4%) 354 (19.9%) 322 (19.8%) 244 (19.3%) 2-year 274 (10.9%) 203 (11.4%) 187 (11.5%) 150 (11.9%) 4-year 463 (18.4%) 327 (18.4%) 319 (19.7%) 243 (19.3%) Post-graduate 265 (10.5%) 182 (10.2%) 174 (10.7%) 130 (10.3%) Political Party Democrat 975 (38.7%) 690 (38.8%) 636 (39.2%) 495 (39.2%) Republican 686 (27.2%) 500 (28.2%) 431(26.5%) 340 (26.9%) Independent 640 (25.4%) 456 (25.7%) 425 (26.2%) 330 (26.2%) Other 90 (3.6%) 58 (3.2%) 66 (4.1%) 46 (3.6%) Not sure 128 (5.1%) 73 (4.1%) 66 (4.1%) 51 (4.0%) Note: a N = 2519; b N= 1777; c N= 1624 d N= 1261 38

Table 2. Descriptive Statistics for Fundamental Social Motives, Affective Polarization, and Ideological Extremism Mean (SD) Cronbach’s α Observed Range Baselinea FSM: Status 3.90 (1.05) .75 1.00-7.00 FSM: Affiliation 4.59 (1.01) .79 1.00-7.00 Affective Polarization 47.98 (32.38) - .00-100.00 Ideological Extremism 56.31 (35.49) - .00-100.00 Political/Moral Conflict 1.72 (.71) .89 1.00-4.00 Follow-upb Affective Polarizationb 53.10 (32.33) - .00-100.00 Ideological Extremismb 56.12 (35.74) - .00-100.00 Political/Moral Conflictc 1.56 (.63) .87 1.00-4.00 Note: a N = 2519; b N = 1624, c N = 1635

Table 3. Correlations Between Demographics, Motives, and Polarization at Baseline 1. 2. 3. 4. 5. 6. 7. 8. 1. Agea - .035 .020 -.264** .044 .257** .230** -.180** 2. Genderb .035 - .029 -.107** -.012 .016 -.023 -.139** 3. Education level .020 .029 - .130** .128** .015 .036 .049** 4. FSM: Statusc -.264** -.107** .130** - .356** -.086** -.070** .186** 5. FSM: Affiliationc .044 -.012 .128** .356** - .006 .022 -.009 6. Affective Polarizationd .257** .016 .015 -.086** .006 - .489** .033 7. Ideological Extremisme .230** -.023 .036 -.070** .022 .489** - .112** 8. Political/Moral Conflicta -.180** -.139** .049** .186** -.009 .033 .112** - Note: a N= 2519; b Male = 1.00, Female = 2.00; c N= 1777; d N= 2513; e N= 2518; ** p ≤ 0.01

Table 4. Correlations Between Social Motives, Ideological Extremism, and Affective Polarization at Baseline and Follow-Up Time Points 1. 2. 3. 4. 5. 6. 7. 8. 1. FSM: Affiliationa - .356** .006 .006 .022 .019 -.009 .067* 2. FSM: Statusa .356** - -.086** -.075** -.070** -.055 .186** .215** 3. Affective Polarization Baselineb .006 -.086** - .765** .489** .450** .033 .055* 4. Affective Polarization Follow-upc .006 -.075** .765** - .478** .508** .075** .041 5. Ideological Extremism Baselined .022 -.070** .489** .478** - .739** .112** .119** 6. Ideological Extremism Follow-upe .019 -.055 .450** .508** .739** - .128** .118** 7. Political/Moral Conflict Baseline -.009 .186** .033 .075** .112** .128** - .695** 8. Political/Moral Conflict Follow-up .067* .215** .055* .041 .119** .118** .695** - Note: a N= 1777; b N = 2513; c N = 1624; d N = 2518; e N = 1622; * p < 0.05 ** p < 0.01

APPENDIX C: FIGURES Figure 1.

Structural Equation Model Predicting Affective Polarization. Robust Fit Indices: CFI = .952, TLI = .941, RMSEA = .055, SRMR = .055

Note. Factor loadings were omitted from this diagram for clarity but can be viewed on table 5. Directional arrows represent standardized path estimates; Bi-directional arrows represent standardized covariances. Solid lines indicate statistically significant beta values; dotted lines indicate non-significant values for theorized paths. AP= Affective Polarization; IE= Ideological Extremism. CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. * indicates p<.05; ** indicates p<.001 41

Figure 2.

Structural Equation Model Predicting Affective Polarization, Ideological Extremism, and Political/Moral Conflict at a Single Time Point. Robust Fit Indices: CFI = .959, TLI = .953, RMSEA = .048, SRMR = .052

Note. Directional arrows represent standardized path estimates. Bi-directional arrows represent standardized covariances. Solid lines indicate paths with statistically significant beta values; dotted lines indicate non-significant values for theorized paths. AP= Affective Polarization; IE= Ideological Extremism; PMC= Political/Moral Conflict. CFI = Comparative Fit Index; TLI = Tucker- Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. *indicates p<.05; ** indicates p<.001 42

Figure 3.

Structural Equation Model Predicting Affective Polarization, Ideological Extremism, and Political/Moral Conflict Over 6 Months. Robust Fit Indices: CFI = .975, TLI = .967, RMSEA = .041, SRMR = .045

Note. Directional arrows represent standardized path estimates. Bi-directional arrows represent standardized covariances. Solid lines indicate paths with statistically significant beta values; dotted lines indicate non-significant values for theorized paths. AP= Affective Polarization; IE= Ideological Extremism; PMC= Political/Moral Conflict. CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual.*indicates p<.05; ** indicates p<.001 43 APPENDIX D: HSRB EXEMPTION LETTER

DATE: April 16, 2020

TO: Alicia James FROM: Bowling Green State University Institutional Review Board

PROJECT TITLE: [1577752-1] Affective Polarization and Social Motives SUBMISSION TYPE: New Project

ACTION: DETERMINATION OF EXEMPT STATUS DECISION DATE: April 14, 2020

REVIEW CATEGORY: Exemption category #4

Thank you for your submission of New Project materials for this project. The Bowling Green State University Institutional Review Board has determined this project is exempt from IRB review according to federal regulations AND that the proposed research has met the principles outlined in the Belmont Report. You may now begin the research activities.

Note that changes cannot be made to exempt research because of the possibility that proposed changes may change the research in such a way that it no longer meets the criteria for exemption. If you want to make changes to this project, contact the Office of Research Compliance for guidance.

We will retain a copy of this correspondence within our records.

If you have any questions, please contact the Office of Research Compliance at 419-372-7716 or [email protected]. Please include your project title and reference number in all correspondence with this committee.

This letter has been electronically signed in accordance with all applicable regulations, and a copy is retained within Bowling Green State University Institutional Review Board's records.