PREDCITORS OF COVID-19 VACCINE INTENTION AMONG U.S ...

PREDCITORS OF COVID-19 VACCINE INTENTION AMONG U.S. UNDERGRADUATES

AND NON-STUDENT ADULTS

BY

SURYAA GUPTA

UNDERGRADUATE THESIS

Submitted to the Department of Psychology in the College of Liberal Arts and Sciences as part

of an undergraduate research program

University of Illinois at Urbana-Champaign, 2021

Urbana, Illinois

Faculty Advisor/mentor(s):

[DR. SEAN LAURENT, ASSISTANT PROFESSOR IN THE DEPARTMENT OF

PSYCHOLOGY]

[SHOKO WATANABE, DOCTORAL STUDENT IN THE DEPARTMENT OF

PSYCHOLOGY]

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Table of Contents

Abstract ................................................................................................................................. 3

Acknowledgements ................................................................................................................ 4

Introduction .......................................................................................................................... 5

Why Study Vaccine Hesitancy? ......................................................................................................6

Vaccine Hesitancy: Prior Research and Missing Elements ..............................................................7

The Present Research ....................................................................................................................9

Study 1: Undergraduate Sample .......................................................................................... 11

Methods ...................................................................................................................................... 11

Results and Discussion ................................................................................................................ 15

Study 2: Age-Diverse Panel of U.S. Adults ........................................................................... 21

Method ....................................................................................................................................... 21

Results and Discussion ................................................................................................................ 23

General Discussion.............................................................................................................. 27

References........................................................................................................................... 32

Study 1 Appendix................................................................................................................. 37

Participants (Full Sample, N = 346) ............................................................................................. 37

Measures .................................................................................................................................... 38

Results ........................................................................................................................................ 46

Study 1 Supplementary Variable Analysis (N = 308)................................................................... 51

Study 2 Appendix................................................................................................................. 56

Measures .................................................................................................................................... 56

Results ........................................................................................................................................ 63

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Abstract

As COVID-19 has disrupted societies across the world, scientists have been rapidly working to

develop a COVID-19 vaccine in order to achieve herd immunity and save lives. Understanding

the factors impacting whether people will receive this vaccine is therefore of utmost importance.

Past work on vaccine hesitancy—declining of vaccinations despite availability—has focused on

hypothetical, lower-threat or eradicated (e.g., influenza, polio) diseases in the US. The current

research takes a timelier approach, extending past work by examining predictors of COVID-19

vaccine intention during the ongoing pandemic. Study 1 (N=346 undergraduate college students)

was administered online as the pandemic escalated in the US (February-April 2020). Study 2

(preregistered; N=676) was administered to an age-diverse panel of US adults in July 2020. As

hypothesized, receiving flu shots and vaccine confidence were significant unique predictors of

COVID-19 vaccine intentions in both studies. Collective responsibility uniquely predicted

COVID-19 vaccine intention in Study 1 but not in Study 2, and constraint, self-community

overlap, perceived vaccine danger, and disease vulnerability uniquely predicted vaccine intention

in Study 2 but not Study 1. Vaccine knowledge, complacency, calculation, analytical thinking,

conspiracy belief, mistrust in science, and political ideology were not significant predictors in

regression models containing all predictors.

Keywords: COVID-19, Physical Health, Applied Social Science

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Acknowledgements

I would like to sincerely thank Dr. Sean Laurent and Shoko Watanabe for being incredible

mentors and for encouraging me to pursue my interests in medicine while exploring intersections

between social cognition and public health.

I would also like to acknowledge The Office of Undergraduate Research for supporting this

research through the Undergraduate Research Support Grant.

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Predictors of COVID-19 Vaccine Intention Among U.S. Undergraduates and Non-Student

Adults

Introduction

The rapid spread COVID-19 has resulted in a health crisis impacting people not only in

the United States, but across the world. On January 30, 2020, the Centers for Disease Control

and Prevention (CDC) confirmed the first case of U.S. person-to-person transmission of COVID-

19. On March 11, 2020, the World Health Organization (WHO) officially declared the outbreak

a pandemic, and as of March 27, 2020, 2,784,009 lives worldwide have been lost to COVID-19

(Worldometers, 2021).

Humans have suffered from respiratory viral infections throughout history, and one of the

worst outbreaks in recent history was the 1918 H1N1 influenza pandemic which was estimated

to have taken 50 million lives worldwide and approximately 675,000 lives in the United States

(CDC, 2019). Like influenza, COVID-19 is a respiratory disease that is transmitted via direct

contact, droplets, and fomites. However, unlike influenza, the number of secondary infections

generated by COVID-19 is much higher, with every person infected in turn infecting

(approximately) between 2 and 2.5 others (WHO, 2020). Thus, COVID-19 is much more

contagious than the flu. Importantly, despite the widespread recommendation of safety

precautions (e.g., physical distancing, frequent hand-washing), the lack of effective antiviral

treatment or vaccines for the majority of 2020 has increased the threat of further COVID-19

outbreaks. In addition to detrimental health impacts, the U.S. economy has also suffered due to

the pandemic. In March 2020, more than 22 million jobs were lost as the country began

implementing state-wide quarantine policies (Horsley, 2020), and the unemployment rate

continued to increase as prospects of a “normal” life seemed increasingly uncertain (Smith,

2020). A COVID-19 vaccine is therefore a promising solution for the extraordinary challenges

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involving public health and the national economy. However, an important question remains: Will

a large enough majority of Americans receive the vaccine to achieve the desired herd immunity?

By examining what psychological factors are associated with COVID-19 vaccine intention, this

research provides valuable insights into how to decrease vaccine hesitancy and thus increase our

ability to fight the ongoing pandemic or those that might emerge in the future.

Why Study Vaccine Hesitancy?

Vaccine hesitancy is characterized by delay in acceptance or refusal of vaccination

despite availability of vaccination services (MacDonald et al., 2015). To demonstrate the

potentially negative impacts of vaccine hesitancy, consider how the U.S. has managed measles—

a highly contagious viral disease that, prior to vaccine availability, infected 3 to 4 million

Americans each year (CDC, 2018). As a result of the MMR (measles, mumps, and rubella)

vaccine implementation, which is 97% effective with two doses, measles was declared

eliminated from the U.S. in 2000 (CDC, 2018). In 2019, however, measles outbreaks resurfaced,

with the majority of the 1,282 new cases among people who were not vaccinated (CDC, 2020).

Explaining this, measles is still common in many parts of the world, and travelers who become

infected abroad can germinate outbreaks when traveling to the U.S. However, this germination

will only occur if there are communities of unvaccinated people who are susceptible to the

disease. Thus, even though safe and effective vaccines reliably prevent wide transmission of

communicable diseases, the strategy fails if people refuse vaccination.

Exacerbating the problem and raising further concerns about the current public health

crisis (Olive et al., 2018; World Health Organization, 2017) is an increase in nonmedical

exemptions from school immunization requirements as well as persisting controversies and

myths regarding vaccination, caused in part caused by Wakefield et al.’s (1998) retracted article

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about MMR and autism. These are the challenges faced by public health experts right now, and

recent data suggest that the crisis in the United States may continue. For example, in a September

2020 survey conducted by the Pew Research Center, approximately half of Americans indicated

that they would not get vaccinated against COVID-19, decreasing the likelihood that the U.S.

will reach herd immunity (Tyson, Johnson, & Funk, 2020).

Generally, healthcare decisions impact only those making the decisions. However, in the

context of viral infections, individual vaccination behaviors impact the collective. Herd

immunity, or community immunity, only occurs when a sufficient proportion of a population is

immune to an infectious disease through vaccination and/or prior illness, which makes its spread

between people less likely (CDC, 2016). With herd immunity, even those who are unable to

receive the vaccination (e.g., people with serious allergies or weakened immune systems) are

offered some protection since the disease has less opportunity to spread. Yet, herd immunity

cannot be established when vaccination is met with resistance, and at present, opinions about

COVID-19 vaccinations are sharply divided (Tyson, Johnson, & Funk, 2020). This suggests that

greater understanding of what predicts vaccine hesitancy is urgently needed, in order to target

these predictors and increase people’s willingness to vaccinate.

Vaccine Hesitancy: Prior Research and Missing Elements

Past work on vaccine hesitancy has focused on a number of variables that influence

vaccine intentions and behavior, identifying reasons that are both external and internal to those

people who might be able to otherwise receive a vaccine. For instance, prior studies have

identified contextual reasons such as financial costs and cultural norms, as well as mistrust of

vaccines and potential side effects of vaccination (Larson et al, 2014). Similarly, some studies

have investigated parents’ intentions to vaccinate their children as a function of media statements

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and experimental manipulations (e.g., Biano et al., 2019). These approaches have strengths, such

as taking advantage of controlled lab environments and using game models that inform strategic

vaccine-related decisions (e.g., Böhm et al, 2016), or understanding broad country-level and

regional differences in vaccination behavior (e.g., Bianco et al., 2019; Jung & Albarracin, 2020).

With the emergence of COVID-19, work on vaccine hesitancy has expanded, including work

aimed at understanding the extent to which COVID-19 is perceived as a threat (Calvillo et al.,

2020) and recently published work on the relevance of predictors of preventative behavior during

COVID-19 (Clark et al., 2020; Jung & Albarracin, 2020).

In addition, along with the rise of COVID-19 has been an increase in belief of conspiracy

theories, which are associated with an increased lack of trust in the medical community, a

decreased sense of collective responsibility, and reduced containment-related behaviors (Imhoff

& Lamberty, 2020). Additionally, links between analytical thinking and conspiracy theory have

shown that those who are less likely to engage in critical thinking are more likely to believe that

the pandemic is a hoax, which decreases willingness to participate in containment efforts

(Stanley et al., 2020).

Although this work has advanced our understanding, extant research before the COVID-

19 pandemic primarily focused on vaccine intentions or behaviors for diseases that are

hypothetical (e.g., Betsch & Sachse, 2013; Jolley & Douglas, 2017; Haase, 2015), low-threat

(e.g., seasonal flu; Betsch & Sachse, 2013; Larson et al., 2014), or that have been mostly

eradicated in the U.S. (e.g., polio; Betsch & Sachse, 2013; Dubé, 2015). Because of this, studies

examining attitudes towards vaccination have frequently relied on retrospective reports and

lacked an element of current threat. Although this is warranted given the pace of typical research

progression and the importance of understanding vaccine intentions during a pandemic (Harrison

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& Wu, 2020; Padhi, 2020; Peretti-Watel et al., 2020), it is also imperative to understand

predictors of vaccination intentions under the conditions of immediate threat involving a real and

dangerous pathogen. Vaccines for this pathogen were developed in record time and might

therefore seem particularly untrustworthy.

In summary, previous work has focused on broad, external reasons for vaccine hesitancy

(financial, cultural, and retrospective). Recent efforts towards increasing containment-related

behaviors, encouraging trust in the medical community, and pushing for collective responsibility

have introduced a sense of urgency in researchers hoping to find ways to manage the current

pandemic. Our work is therefore not only timely, but fills an important gap in the literature by

capturing people’s attitudes towards vaccination during an ongoing pandemic that represents a

clear and ongoing threat to both public health and economic well-being, at a time when large-

scale immunity has not yet been achieved. Moreover, it examines these attitudes as a function of

a number of relevant individual-difference predictors (e.g., past vaccination behavior, universal

vaccine hesitancy, conspiracy theory beliefs).

The Present Research

The current research aims to better understand attitudes towards the COVID-19 vaccine

by examining a range of potential predictors of vaccine hesitancy. Taking this approach—by

conducting studies during an ongoing pandemic—allows for clearer understanding of important

issues because the focal dependent measure involves a serious and real threat. Thus, this work

has the potential to provide novel insights into a range of factors associated with anti-vaccination

tendencies during a period when a vaccine is sorely needed and is only beginning to be

distributed across the world.

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Specifically, our research examines relationships between vaccine intention (COVID-19

vaccine and a hypothetical vaccine) and flu shot acquisition, the 5C psychological antecedents of

vaccination (adapted from Betsch et al., 2018), perceived vaccine danger (Jolley & Douglas,

2014), disease vulnerability (adapted from Duncan, 2009), political ideology, vaccine

knowledge, analytical thinking (adapted from Cacioppo & Petty, 1982), self-other overlap

(adapted from Aron et al., 1992), conspiracy beliefs (adapted from Jolley & Douglas, 2014), and

mistrust in science (adapted from Nandelson, 2014). In addition to studying intentions regarding

the COVID-19 vaccine, we also use a different measure of vaccine intentions (i.e., a hypothetical

scenario used in past work; Betsch & Sachse, 2013, Jolley & Douglas, 2017, and Haase, 2015).

We hypothesized that the following would be positively related to a) COVID19 vaccine

intention and b) intentions regarding protecting a child from a hypothetical disease: getting a flu

shot, vaccine confidence, collective responsibility, vaccine knowledge, rational thinking, self-

other overlap, perceived vulnerability to disease, and greater ideological liberalism. We also

hypothesized that the following measures would be negatively related to the same dependent

variables: anti-vaccine conspiracy beliefs, perceived dangers of vaccines, vaccine

constraints/complacency/calculation, mistrust in science, optimism bias, and greater ideological

conservatism.

Study 1 (N=346 undergraduate college students) was administered online as the

pandemic was escalating in the U.S. (February-April 2020). During this time there was confusion

and anxiety as the spread of the virus intensified and students/professors widely transitioned to

remote learning. As this was an initial study of a relatively unexplored topic (i.e., because the

pandemic was novel), a number of additional exploratory measures were included and are

presented in the appendix. Study 2 (N=676) was preregistered and was administered to an age-

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diverse panel of US adults in July, 2020. This study included those measures identified in Study

1 as important, but again also included several other exploratory measures that are presented in

the appendix.

Study 1: Undergraduate Sample

Methods

Participants

Sample size for Study 1 was determined by availability of credits during the spring

semester of 2020. Ultimately, 346 undergraduate psychology students who obtained course

credit for participating were recruited. A sensitivity analysis showed that with α = .05 (two-

tailed), this sample size (N=346) had 80% power to detect correlations of |0.15| or larger (two-

tailed α = .05). Those who incorrectly answered attention check questions (n = 38) were

excluded from analysis.1 Self-reported gender was 76.95% female and 22.73% male participants;

remaining participants reported other/prefer not to say. Age ranged from 18 to 27 years

(M=19.65 ; SD=1.35). Participants identified as White/European (49.35%), Asian/Asian

American (18.51%), Hispanic/Latino(a) (17.21%), Black/African American (5.52%), more than

one (7.14%), and other/prefer not to say (2.27%). On political ideology (1=extremely liberal,

4=middle of the road, 7=extremely conservative), the sample mean was 3.18 (SD=1.49),

representing a slightly liberal-leaning cohort. Slightly more than half of the sample indicated

having received a flu shot (56.82%); remaining participants either reported not receiving the

vaccine or choose not to disclose this information.

Procedure and Measures

1 Including all participants did not substantively alter their results.

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Verbatim questions for this study’s unique measures (e.g. the COVID-19 vaccine

intention measure, the previous vaccine knowledge measure, etc.), can be found in the appendix.

Measures that were previously published are simply listed. All items within each measure were

randomized and participants completed these measures in the order in which the measures are

described below and are presented in the appendix. After providing consent, participants

responded to a number of measures before providing demographics and being debriefed about

the goals of the study. The 5C Psychological Antecedents of Vaccination measure (Betsch et al.,

2018) used 7-point scales (1=Strongly disagree to 7=Strongly agree), with three items assessing

each subscale: confidence (e.g., “I am completely confident that vaccines are safe”), collective

responsibility (e.g., “When everyone is vaccinated I don’t have to get vaccinated too”),

complacency (e.g., “Vaccination is unnecessary because vaccine-preventable diseases are not

common anymore”), constraints (e.g., “Everyday stress prevents me from getting vaccinated”),

and calculation (e.g., “When I think about getting vaccinated, I weigh benefits and risks to make

the best decision”). Items in each subscale were averaged to form composite scores (α=.61;

collective responsibility2 r=.60; complacency α=.52; constraints α=.69; calculation α=.78).

Participants then responded to fictitious disease and COVID-19 vaccine intention

measures. Participants first read about dysomeria—a (fictitious) disease spread by droplet

infection (see Haase et al., 2019; Jolley & Douglas, 2014). Participants were asked to imagine

that they were the parent of an infant and were informed that vaccination against dysomeria was

recommended by the CDC for people of all ages but that adverse events were reported 12% of

the time. Participants then indicated their intention to vaccinate their hypothetical child: “If you

2 The item “I am getting vaccinated to protect myself against diseases, not to protect others who are not vaccinated”

(reverse-coded) from Betsch et al.’s (2018) original scale was presented due to programming error. This item was

not used to compute the collective responsibility composite. This issue was fixed in Study 2 by using the correct

item from the final scale, “I get vaccinated because I can also protect people with a weaker immune system.”

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had the opportunity to vaccinate your child (Sophie, 8 months old) against dysomeria next week,

what would you decide?” (1=Definitely not vaccinate, 7=Definitely vaccinate).

To capture COVID-19 vaccine intentions, participants read CDC-provided information

about COVID-19, including the number of cases and deaths in the United States. Until March 16,

2020, the number of cases (19) and deaths (0) remained constant, but after that date cases and

deaths were updated every few days (see Table 1). Participants then responded to the question:

“There is no specific antiviral treatment for COVID-19, and there is currently no vaccine to

protect against COVID-19. However, if such vaccine were made available to prevent infection,

would you receive it?” Response options were 1=I would not receive the vaccination even if it’s

free ($0), 2=I would receive the vaccination only if it’s free ($0), 3=I would pay up to $10 to

receive the vaccination, 4=I would pay up to $25 to receive the vaccination, 5=I would pay up to

$50 to receive the vaccination, 6=I would pay up to $100 to receive the vaccination, and 7=I

would pay more than $100 to receive the vaccination.

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Table 1

Displayed Number of COVID-19 Cases and Deaths (Study 1)

Cases Deaths

Feb 14 – March 16 19 0

March 16 – March 20 3,487 68

March 20 – March 23 15,219 201

March 23 – March 24 33,404 400

March 24 – March 25 44,183 544

March 25 – March 26 54,453 737

March 26 – March 27 68,440 994

March 27 – March 30 85,356 1,246

March 30 – March 31 140,904 2,405

March 31 – April 1 163,539 2,860

April 1 – April 2 186,101 3,603

April 2 – April 4 213,144 4,513

April 4 – April 6 239,279 5,443

April 6 – April 7 330,891 8,910

April 7 – April 8 374,329 12,064

April 8 – April 9 395,011 12,754

April 9 – April 10 427,460 14,696

April 10 – April 14 459,165 16,570

April 14 – April 15 579,005 22,252

April 15 – April 16 605,390 24,582

April 16 – April 17 632,548 31,071

April 17 – April 18 661,712 33,049

Participants then completed remaining measures. Three questions assessed vaccine

knowledge compared to peers (e.g., “How vaccines work”; α=.88) using 7-point scales (1=Not at

all knowledgeable, 7=Extremely knowledgeable). A single item then asked about influenza

vaccination: “This past season, did you receive the flu shot?” (no, yes, prefer not to answer).

Anti-vaccine conspiracy beliefs (Jolley & Douglas, 2014) were assessed with 7 items3 (e.g.,

“Immunizations allow governments to track and control people”; α=.83) using 7-point scales

(1=Strongly disagree, 7=Strongly agree). Perceived vaccine dangerousness (Jolley & Douglas,

3 One item from the original measure (“the flu vaccine allows the government to monitor the elderly through the

implantation of tiny tracking devices”) was omitted due to the study’s original focus being centered around general

vaccine hesitancy rather than vaccine-hesitancy related to the flu.

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2014) was assessed with 8 items (e.g., “I feel uncertain about the potential side-effects of

immunizations”; α=.90) using the same 7-point scales.

Five items, representing those with the highest inter-item correlations, were drawn from

the Inventory of Trust in Science and Scientists (Nandelson, 2014) to assess science/scientist

mistrust (α=.86). These items (e.g., “Scientists ignore evidence that contradicts their work.”)

were measured on 5-point scales (1=Strongly disagree, 5=Strongly agree). The ten highest-

loading items of the rational subscale of the Rational-Experimental Multimodal Inventory

(REIm; Norris & Epstein, 2011) were used to assess analytic thinking (e.g., “I enjoy intellectual

challenges”; α=.83) and were measured on 5-point scales (1=Completely false, 5=Completely

true). An adapted version of the Inclusion of Other in the Self scale (IOS; Aron et al., 1992)

asked participants to select one of seven Venn-diagrams that best represented their relationship

with acquaintances. Higher numbers indicate greater self-acquaintance overlap. Perceived

vulnerability to disease was measured with the perceived infectibility subscale of the Perceived

Vulnerability to Disease Scale (Duncan, 2009). This subscale has seven items (e.g., “I have a

history of susceptibility to infection disease”; α=.88) measured on 7-point scales (1=Strongly

disagree, 7=Strongly agree).

Results and Discussion

Below, results are presented in tables. Specifically, we outline the rates of vaccine

intentions (Table 2), means and standard deviations of all variables (Table 3), correlations among

all variables (Table 4), and report results of multiple regression analysis predicting COVID-19

and hypothetical vaccine intention (Table 5).

Most student participants (94.48%) indicated that they would receive the COVID-19

vaccine, although some (5.52%) appeared to be completely unwilling to receive the vaccine.

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Despite the willingness of the majority to be vaccinated, there was variability in the amount that

people were willing to pay, suggesting that our proxy for “hesitancy” was capturing real

variability in willingness to be vaccinated (Table 2). We also found that most (~80%) student

participants indicated that they would definitely vaccinate their hypothetical daughter against a

hypothetical disease, suggesting a ceiling effect (Table 3).

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Table 2

COVID-19 Vaccine Intention in Student Sample (Study 1)

Would you receive COVID-19

vaccine?

Raw N %

$0 (I would not) 17 5.52

$0 20 6.49

$10 21 6.82

$25 46 14.94

$50 56 18.18

$100 48 15.58

$100+ 100 32.47

Total 308 100

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Table 3

Means and Standard Deviations of All Variables (Study 1)

M SD

1. COVID-19 Vaccine 5.10 1.83

2. Hypothetical 6.36 1.09

3. Confidence 5.74 0.95

4. Collective 6.42 0.93

5. Calculation 3.86 1.66

6. Complacency 2.04 1.05

7. Constraint 1.94 1.08

8. Self-Other Overlap 4.28 1.56

9. Vaccine Knowledge 4.97 1.17

10. Rational Thinking 3.78 0.58

11. Conspiracy Belief 1.95 0.90

12. Perceived Danger 2.78 1.18

13. Science Mistrust 1.77 0.64

14. Vulnerability 3.73 1.17

15. Political Ideology 3.18 1.49

More important than these summaries, however, is the examination of what actually

predicts any hesitancy that exists. Of the 13 predictors of hesitancy, 9 had significant zero-order

correlations with COVID-19 hesitancy, and 11 significantly correlated with the hypothetical

measure. This suggests that each of these variables might play an important role in why people

would choose to not vaccinate. Also of interest, the correlation between real and hypothetical

vaccination intentions, while significant, was relatively small and showed only 9% of shared

variables between the measures. This suggests that although willingness to give a child a vaccine

for a hypothetical disease shares similarities with willingness to vaccinate oneself during a real

pandemic, there may also be substantial differences between these types of measures (Table 4).

The correlations between intention to vaccinate for COVID and age, cases, deaths, and time were

not significant (rs < |.09|, ps > .117).

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Table 4

Correlations Between Study Variables (Study 1)

Note. All bolded correlations are significant at p < .05. † p < .01. * p < .001.

Multiple regression analyses that were conducted to see which variables uniquely predicted hesitancy above and beyond other

predictors revealed a few strong, unique predictors. In particular, for both real and hypothetical scenarios, receiving the flu shot,

confidence in vaccination, collective responsibility, calculation regarding benefits/pitfalls of vaccination, and perceived danger were

significant predictors (Table 5).

1. 2. 3 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

1. COVID-19 -

2. Hypothetical .31* -

3. Confidence .41* .42* -

4. Collective .38* .49* .52* -

5. Calculation -.21* -.29* -.23* -.25* -

6. Complacency -.23* -.37* -.34* -.51* .25* -

7.Constraint -.19* -.27* -.27* -.40* .18† .38* -

8. Self-Other Overlap .09 -.08 -.01 .06 .07 -.09 -.06 -

9. Vaccine Knowledge .22* .19* .37* .23* .03 -.18† -.29* .12† -

10. Rational Thinking .08 .17† .19* .10 -.01 -.03 -.18† .09 .38* -

11. Conspiracy Belief -.32* -.34* -.50* -.50* .18† .35* .27* -.02 -.33* -.21* -

12. Perceived Danger -.33* -.46* -.60* -.47* .25* .36* .33* -.03 -.40* -.25* .70* -

13. Science Mistrust -.27* -.24* -.45* -.36* .13 .30* .26* -.10 -.36* -.30* .53* .56* -

14. Vulnerability .05 .06 .04 .11 .005 -.27* -.02 .03 .03 -.06 .02 .02 -.02 -

15. Political Ideology -.05 -.21 -.21* -.17† .16† .36* .14 .07 -.02 -.06 .10 .18† .12 -.15 -

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Table 5

Results of Simultaneous Multiple Regression Analysis Predicting COVID-19 and Hypothetical Child Vaccine Intention from All

Predictors (Study 1).

Note. All bolded coefficients are significant at p < .05.

COVID-19 Vaccine Intention (R2 = .21) Hypothetical Child Vaccine Intention (R2 = .32)

b SE p 95% CI b SE p 95% CI

Intercept 4.84 .15 <.001 4.6, 5.1 6.36 .08 <.001 6.20, 6.52

Flu shot (1=received) .46 .20 .025 .08, .86 -.001 .11 .991 -.22, .22

5C-Confidence .43 .13 .002 .16, .69 .11 .07 .148 -.04, .25

5C-Collective .35 .14 .011 .08, .62 .35 .08 <.001 .20, .50

5C-Calculation -.12 .06 .078 -.23, .01 -.07 .03 .033 -.14, -.01

5C-Complacency .02 .11 .867 -.21, .25 -.11 .06 .087 -.24, .02

5C-Constraint .03 .10 .762 -.17, .23 -.003 .06 .957 -.11, .11

Self-Other Overlap .08 .06 .194 -.04, .20 -.07 .03 .039 -.13, -.003

Vaccine Knowledge .07 .10 .448 -.12, .26 -.01 .05 .900 -.11, .10

Rational Thinking -.10 .18 .574 -.45, .25 .20 .10 .040 .01, .39

Conspiracy Belief -.14 .16 .350 -.45, .16 .10 .08 .252 -.07, .26

Perceived Danger .03 .13 .801 -.22, .29 -.26 .07 <.001 -.40, -.12

Science Mistrust -.16 .19 .413 -.53, .22 .17 .10 .114 -.04, .37

Disease Vulnerability .05 .08 .556 -.12, .22 .01 .05 .839 -.08, .10

Political Ideology .06 .07 .360 -.07, .20 -.03 .04 .421 -.10, .04

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OLS regression analysis for COVID-19 vaccine intention and hypothetical vaccine

intention revealed that significant predictors differed between the two measures. While COVID-

19 vaccine hesitancy was predicted by receiving the flu shot, confidence in the safety and

effectiveness of vaccines (b = .43) and belief in collective responsibility towards health (b =

.35), hypothetical vaccine intentions had no relationship with receiving the flu shot or

confidence. Instead, significant predictors were calculation/complex thinking regarding

vaccination (b = -.07), self-other overlap (b = -.07), rational thinking (b = .20), perceived danger

of vaccinations (b = -.26), and collective responsibility towards health (b = .35).

Overall, many of the variables included in this study had some significant relation to both

real and hypothetical vaccine intentions, suggesting there might be multiple routes to effective

intervention. However, not all of these variables uniquely predicted vaccine hesitancy once

accounting for shared variance among the larger set. Despite this, the set of predictors as a whole

worked well in explaining vaccine hesitancy (respectively explaining 21% and 32% of the

variance in real and hypothetical vaccine intentions). Moreover, given the moderate correlations

of many predictors with these outcomes and the moderate correlations of predictor variables with

one another, it seems plausible that vaccine hesitancy might be targeted using a number of

approaches, and that targeting multiple fronts at once could be even more useful for increasing

people’s willingness to vaccinate.

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Study 2: Age-Diverse Panel of U.S. Adults

Study 2 replicated and extended the findings of Study 1, using an age-diverse panel of US

adults. Hypotheses were the same as in Study 1.

Method

Participants

To recruit at least 652 participants, which preregistered sensitivity analyses suggested

would provide 80% power to detect a correlation of |.11| with two-tailed α = .05, 848 participants

were recruited from an age-diverse TurkPrime sample. Participants who incorrectly answered

one or more attention check questions (n = 167) or spent an average of less than 10 seconds on

each measure (n = 5) were excluded from analysis, leaving a final sample size of N = 676

(Mage=51.95 ; SD=18.05, range 18-95; 62.87% female, 36.69% male; remaining participants

reported other/decline to respond). Participants identified as White/European (79.29%),

Asian/Asian American (6.07%), Hispanic/Latino(a) (4.14%), Black/African American (6.07%),

more than one (1.87%), and other/prefer not to say (0.74%). Average political ideology was 4.00

(SD=1.89) (1=extremely liberal, 4=middle of the road, 7=extremely conservative). Over half the

sample (55.78%) indicated that they had received a flu vaccine that season. A majority (60.21%)

reported being parents, and most (82.21%) reported a yearly household income of less than

$100K.

Procedures and Measures

After consenting to participate, individuals responded to many of the same measures

from Study 1. Any additional exploratory measures can be found in the appendix.These included

the COVID-19 vaccine intention measure and the hypothetical vaccine intention (Haase et al.,

2019; Jolley & Douglas, 2014) as the main dependent variables. Predictor variables included

22

reasons for vaccine intention (α = .83), subscales from the 5C Psychological Antecedents of

Vaccination scale (Betsch et al., 2018): confidence (α = .73), collective (α = .71), calculation (α

= .78), complacency (α = .82), constraint (α = .84), self-other overlap (IOS; Aron et al., 1992),

previous vaccine knowledge (α = .92), anti-vaccination conspiracy belief (Jolley & Douglas,

2014; α = .91), perceived danger of vaccines (Jolley & Douglas, 2014; α = .92), mistrust in

science (Nandelson, 2014; α = .94), analytical thinking (REIm; Norris & Epstein, 2011; α = .88),

and perceived vulnerability to disease (Duncan, 2009; α = .84). The fictitious disease measure

was the same as from Study 1. The COVID-19 vaccine intention question was similar, except

participants were presented with new COVID-19 data (i.e., that there had been 3,698,161 cases

and 139,659 deaths in the U.S. by July 20, 2020, the day that all data were collected). The

measure was also slightly updated, with participants being instructed: “There is no specific

antiviral treatment for COVID-19, and there is currently no vaccine to protect against COVID-

19. However, if such vaccine were made available to prevent infection, would you receive it?”

Three responses were possible: 1=I would not receive the vaccination even if it’s free ($0), 2=I

would receive the vaccination only if it’s free ($0); if I need to pay money I would not receive

the vaccination, and 3=I would pay money to receive the vaccination. A second question also

asked, “I would pay up to $_____ to receive the COVID-19 vaccination.” Possible responses

could range from $0 to $500. A slightly modified flu shot question asked, “This past flu season

(October 2019 – April 2020) did you receive the flu shot?” Possible responses were “yes” or

“no”.

23

Results and Discussion

Below, results are presented in tables. Specifically, we outline vaccine intention rates

(Table 6), means and standard deviations of all variables (Table 7), correlations among all

variables (Table 8), and results of a multiple regression analysis simultaneously predicting

COVID-19 and hypothetical vaccine intention from all predictor variables (Table 9).

A majority of participants (81.95%) claimed that they would receive the COVID-19

vaccine, although some (18.05%) appeared to be completely unwilling to receive the vaccine,

regardless of cost. Although most participants were willing to vaccinate, there was variability in

the amount that people were willing to pay, with many claiming that they would only receive the

vaccine if it was free (27.51%). This suggests that our proxy for “hesitancy” was again capturing

real variability in desire to be vaccinated (Table 6). Additionally, most adult participants

indicated that they would definitely vaccinate (~70%) or would most likely vaccinate

(demarcating 6 on a 7 point scale) their hypothetical daughter against a hypothetical disease (M

= 5.65), again suggesting a ceiling effect (Table 7).

Table 6

COVID-19 Vaccine Intention in Age-Diverse Adult Sample Sample (Study 2)

I would not receive the

vaccine, even if it was free

(NO)

I would only receive the

vaccine if it was free (FREE)

I would receive the vaccine,

even if it was not free (YES)

121 186 368

18.05% 27.51% 54.44%

24

Table 7

Means and Standard Deviations of the Study Variables (Study 2)

M SD










10. Rational Thinking 3.67 .77



13. Science Mistrust 2.22 1.05



As in Study 1, our main goal was to determine correlates of vaccine hesitancy. Several

similarities emerged between Study 1 and Study 2. For example, of 13 predictors, 11 and 10

respectively had significant zero-order correlations with real and hypothetical vaccine intentions

(Table 8). In addition, multiple regression analyses showed that some of the same predictors

were able to account for unique variance in real vaccine intentions across both studies. However,

some unique predictors were significant in Study 2 that were not in Study 1. These included

complacency, constraint, self-other overlap, previous vaccine knowledge, and perceived

vulnerability to disease. While most of these correlations are consistent with hypotheses, the

positive association between constraint in receiving the vaccine and vaccination intentions was

opposite from predicted. However, it is worth noting that this relationship was relatively weak

(Table 9).

25

Table 8

Correlations Between Study Variables (Study 2)


1. 2. 3 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

1. COVID-19 -


3.Confidence .50* .56* -

4. Collective .37* .51* .59* -

5. Calculation -.06 -.03 -.03 -.02 -

6. Complacency -.21* -.42* -.37* -.64* .10† -

7. Constraint -.07 -.22* -.16* -.40* .06 .54* -

8. Self-Other Overlap .18* -.08 .23* .23* .08 .00 -.09† -

9. Vaccine Knowledge .23* .19* .29* .18* .14* -.01 -.16* .25* -

10. Rational Thinking .10† .17† .11† .17* .04 -.12† -.25* .17* .37* -

11. Conspiracy Belief -.27* -.34* -.47* -.62* .13* .70* .54* -.05 -.09† -.19* -

12. Perceived Danger -.36* -.46* -.55* -.59* .23* .60* .50* -.10 -.21* -.23* .78* -

13. Science Mistrust -.32* -.24* -.49* -.55* .15* .56* .40* -.10 -.14* -.26* .68* .67* -

14. Vulnerability .16* .06 .11† .15* -.07 -.22* .06 -.11† -.04 -.15* -.04 -.03 -.05 -

15. Political Ideology -.23* -.21 -.28* -.25* .10† .22* .05 .02 -.04 -.10† .25* .29* .45* -.16* -

26

Table 9

Results of Regression Analysis Predicting COVID-19 and Hypothetical Child Vaccine Intention (Study 2).


COVID-19 Vaccine Intention (R2 = __.30__) Hypothetical Child Vaccine Intention (R2 = __.39__)


Intercept 2.98 .10 <.001 4.6, 5.1 5.64 .09 <.001 5.47, 5.82

Flu shot (1=received) .45 .15 .002 .16, .73 .00 .13 .972 -.25, .26

5C-Confidence .40 .06 <.001 .28, .52 .43 .05 <.001 .33, .54

5C-Collective .14 .07 .062 -.01, .28 .18 .06 .005 .06, .31

5C-Calculation -.04 .05 .464 -.13, .24 .01 .04 .750 -.07, .10

5C-Complacency .09 .07 .193 -.04, .18 -.15 .06 .013 -.27, -.03

5C-Constraint .12 .06 .026 .01, .15 .03 .05 .513 -.06, .13

Self-Other Overlap .08 .04 .021 .01, .15 -.03 .03 .344 -.09, .03

Vaccine Knowledge .07 .05 .209 -.04, .18 .15 .05 .002 .06, .25

Rational Thinking .06 .09 .540 -.12, .24 -.09 .08 .279 -.25, .07

Conspiracy Belief .05 .08 .586 -.12, .21 -.12 .07 .113 -.26, .03

Perceived Danger -.18 .08 .027 -.33, -.02 -.00 .07 .971 -.14, .14

Science Mistrust -.07 .09 .465 -.25, .12 -.05 .08 .524 -.22, .11

Disease Vulnerability .17 .06 .002 .06, .29 .05 .05 .316 -.05, .15

Political Ideology -.07 .04 .078 -.14, .01 -.04 .03 .252 -.10, .03

27

Results from OLS regression revealed differences in the unique predictors of COVID-19

vaccine intention and hypothetical vaccine intention. The only consistent unique predictor of

both variables was confidence. This again suggests that although hypothetical vaccination

scenarios may have utility in understanding vaccine intentionality, different factors may be more

important in understanding people’s intentions to receive real vaccines.

General Discussion

Since the World Health Organization (WHO) officially declared the COVID-19 outbreak

a pandemic on March 11, 2020, people have been forced to adapt to the health crisis with an

urgency that has not been seen in the United States since the 1918 H1N1 pandemic (CDC 2019).

So far, over 525 thousand lives in the US and more than two million lives have been lost to

COVID-19 worldwide. These numbers continue to increase by the hour. Beyond impacting our

physical health, continued efforts toward mitigation have negatively impacted the mental health

of many people (Farkhad & Albarracin, 2021). Offering a ray of hope, however, highly

efficacious vaccines that were developed in record time are now becoming more widely

available. Despite this, achieving herd immunity—which should allow humans to broadly and

safely return to the same levels and types of activity they enjoyed before the pandemic—will

require that a large majority of people across the world are vaccinated. That is, although herd

immunity might also be reached if enough people contract the virus to build high levels of

natural immunity in the population, many millions would die in the process. Equally important,

if rates of new infections continue to be high, new, more infectious, and potentially deadlier

strains of the virus—such as the B.1.1.7 and 501.V2 variants that have already emerged—might

continue both the length and severity of the pandemic. Thus, getting safe and effective vaccines

into people’s arms appears to be the best hope for future normalcy.

28

As of September 2020, the Pew Research Center reported that only approximately 50%

of Americans indicated a willingness to get a COVID-19 vaccine. More recent polls have

captured similar numbers (Galewitz, 2021; Leonhardt, 2021), suggesting that even for vaccines

have been shown in rigorous clinical trials to be safe and effective, substantial vaccine hesitancy

persists. This work therefore remains timely in its identification of correlates of vaccine

hesitancy, particularly since new pandemics are likely to emerge over the years, and widespread

outbreaks of new diseases from novel pathogens might only be contained if people are willing to

be vaccinated against them, should vaccines become available.

Study 1 and Study 2 both indicate that a number of variables predict both actual and

hypothetical hesitancy. In fact, in Study 1, only self-other overlap, rational thinking, perceived

vulnerability to disease, and political ideology failed to be associated with COVID-19 vaccine

hesitancy. Likewise, only self-other overlap and perceived vulnerability to disease failed to be

associated with hesitancy in the hypothetical child scenario. In Study 2, calculation and

constraint were the only two variables that were not significantly associated with COVID-19

vaccine intention, and for hypothetical vaccine intentions, only self-other overlap and perceived

vulnerability to disease had non-significant correlations.

When examining all variables simultaneously, a substantial amount of variance in actual

and hypothetical hesitancy was accounted for by the set of predictors. In Study 1, the set

accounted for 21% of variance in COVID-19 and 32% in hypothetical vaccine intentions. In

Study 2, these percentages were respectively 30% and 39%. This suggests that as a set, the

variables selected for these studies had substantial predictive power. However, only a few

variables were reliably associated with hesitancy after controlling for all other variables.

Together, the zero-order correlations and multiple regression results suggest two things: First,

29

that there is substantial overlap in the predictive power of the variable set. Second, that a few

different predictors can predict unique aspects vaccine hesitancy. These variables were receiving

the flu shot, confidence in vaccine safety/effectiveness and collective responsibility in Study 1

for an actual vaccine and collective responsibility, calculation, self-other overlap (empathy),

rational thinking, and perceived vulnerability to disease for a hypothetical vaccine. In Study 2,

receiving the flu shot, confidence, perceived danger of vaccination, and perceived vulnerability

to disease were able to predict unique variance in COVID hesitancy above and beyond other

strong predictors (e.g., whether participants had received a flu vaccine), and similarly,

confidence, collective responsibility, complacency, and previous vaccine knowledge were unique

predictors of hypothetical hesitancy. Looking across both studies, this suggests that beyond prior

vaccination history (i.e., for the flu), confidence in the safety/effectiveness of vaccines was a

reliable unique predictor. However, given the strong overall predictive power of the variable set

as a while, alongside the robust correlations of most variables with these dependent measures, it

appears that there are multiple routes to tackling hesitancy, including collective responsibility,

factors of constraint, empathetic tendencies (self-other overlap), perceived danger of vaccines

and vulnerability to disease.

Moreover, because confidence was a consistent unique predictor, one relatively simple

strategy that might have high payoff would be to find ways to clearly and accurately convey the

scientific foundations of vaccination to the general population. This might be accomplished

through, for example, asking people (including influencers) to share their reasons for having

been vaccinated with friends and family. Using easy to understand graphics rather than text

might also help increase confidence that vaccines are not only safe, but effective.

30

One thing worth considering is the differences that were found in unique predictors of

vaccine intention for a fictitious disease and for COVID-19 across both studies. Plausibly, these

different measures are capturing different reasons people feel hesitant about vaccination,

although there are important differences in the measures themselves that might make this

conclusion hasty. For example, the hypothetical vaccine measure was for a fictional disease and

the person it asked about vaccinating was a hypothetical child, which for younger participants

(e.g., most college students in Study 1) might bear little relation to how the same participants

would react if they were actually parents and the disease being considered was real. In contrast,

the COVID-19 measure was given at different times during the pandemic, such as at its start

(February – April 2020) and again during the initial quarantine (July 2020). During these times,

no vaccines were available and panic/uncertainty was growing rapidly, suggesting that responses

to this measure may have been a better index of how people actually feel about vaccination under

threat. In any case, by using responses capturing hesitancy toward a real vaccine and comparing

them with responses to a measure similar to those used in past research, we can begin to address

questions about similarities and differences in the measures and their ability to predict real-world

behavior.

In the larger scope of public health movements, this study might be used to inform

current and future efforts to increase overall vaccination rates. Even once this pandemic has

ended, knowing what predicted hesitancy during the crisis, when people were under threat and

the vaccine being considered was still being developed and had not yet been distributed, will

help in developing public health policies that encourage vaccination and the development of herd

immunity.

31

With the very first COVID-19 vaccine administration occurring on December 14, 2020,

there is promise for the eradication of this virus and the conclusion of this pandemic. However,

because of lingering hesitancy, pressure remains for government, in conjunction with public

health organizations, to find ways to get vaccine into people’s bodies as quickly and efficiently

as possible. For example, a Pew Research Survey conducted in November 2020 indicated that

COVID-19 vaccine intention had risen to 60% as “confidence in research and development

process increases.” However, despite this increase from September 2020, 20% of those surveyed

continued to claim that they would not receive the vaccine, even when more information was

made available. Additionally, of the 60% willing to receive the vaccine, 37% claimed that they

would not like to be the first recipient, hence still providing indication of hesitancy (Tyson &

Funk, 2020). And beyond the current pandemic, when the next public health crisis emerges and

preventative measures can limit spread of disease only if people adopt recommendations quickly,

we will need every tool we can muster to convince people to trust science and take the

recommended steps that will protect everyone. Among other research, the findings of the current

study will help us achieve these goals.

32

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37

Study 1 Appendix

Participants (Full Sample, N = 346)

Participants were 346 undergraduate psychology students who obtained course credit for

participating. The results reported for this sample is without exclusion of participants that failed

attention checks. Of those who reported their gender, there were 76.59% female and 22.83%

male participants; their age ranged from 18 to 27 years (M=19.64 ; SD=1.32). The sample

consisted of participants who identified as White/European (47.98%), Asian/Asian American

(18.21%), Hispanic/Latino(a) (17.63%), Black/African American (6.65%), more than one

(6.94%), and other/prefer not to say (1.16%). On political ideology (1=extremely liberal,

4=middle of the road, 7=extremely conservative), the sample mean was 3.21 (SD=1.50). Of the

sample of participants that shared whether or not they received the flu shot, more than half of the

responses indicated having received the vaccine (57.51%) and while the rest of them indicated

not having received the vaccine (38.43%). The time of participation (before vs. after spring

break) indicated that the vast majority of participants (74.57%) recorded their responses before

spring break, while the rest (25.43%) participated during the chaos of spring break and the

transition to online learning.

38

Measures

The order in which measures were displayed to participants:

5C Psychological Antecedents of Vaccine Hesitancy (Betsch et al., 2018)

Hypothetical (Haase et al., 2019; Jolley & Douglas, 2014)

Explain

Please let us know how you feel about vaccinations. For example, you can explain in a few

sentences why you responded the way you did in the previous questions regarding vaccinations.

Corona

A recent outbreak of a respiratory illness caused by a novel coronavirus (COVID-19) is

spreading rapidly. There have been hundreds of thousands of confirmed cases and thousands of

deaths around the world. As of April 17, 2020, 661,712 confirmed cases in the United States

have been reported, along with 33,049 deaths. The Centers for Disease Control and Prevention

(CDC) confirmed on January 30, 2020 that the COVID-19 has spread between two people in

Illinois, representing the first instance of person-to-person spread with this new virus in the

United States. Much is unknown about how COVID-19 spreads. Current knowledge is largely

based on what is known about similar coronaviruses (e.g., SARS, MERS). Person-to-person

spread is thought to occur mainly via respiratory droplets produced when an infected person

coughs or sneezes, similar to how influenza and other respiratory pathogens spread. These

droplets can land in the mouths or noses of people who are nearby or possibly be inhaled into the

lungs. It is recommended to regularly wash one’s hands and disinfect hard surfaces as the virus

could be transmitted through these routes by touching a surface or object that has the virus on it

and then touching their own mouth, nose, or possibly their eyes. Symptoms can range from mild

illness to severe illness and death and are accompanied by fever, cough, and shortness of

breath. Source: Centers for Disease Control and Prevention There is no specific antiviral

treatment for COVID-19, and there is currently no vaccine to protect against COVID-19.

39

However, if such vaccine were made available to prevent infection, would you receive it?

Please select only one of the following:

o I would not receive the vaccination even if it's free ($0) (1)

o I would receive the vaccination only if it's free ($0) (2)

o I would pay up to $10 to receive the vaccination (3)




o I would pay more than $100 to receive the vaccination (7)

40

Required Immunizations

Illinois requires all college students to be immunized against certain vaccine-preventable

diseases. Listed below are required vaccinations that are offered at UIUC McKinley Health

Center along with a brief description of the diseases that they prevent. Please take a moment to

read and review the immunizations as you answer the questions that follow.

MMR (Measles, Mumps, Rubella): starts as simple cold symptoms, but escalates into severe

rashes on the face and body, possible permanent hearing loss, damage to the brain and spinal

cord, and death

T-Dap (Tetanus, Diphtheria, Pertussis): bacterial infection that can cause painful tightening of

the muscles, breathing difficulties, heart failure, paralysis, whooping cough, and possibly death

Extremely

Unlikely (1) Unlikely (2) Neutral (3) Likely (4)

Extremely

Likely (5)

I would receive

the MMR

vaccination even

if it was not

required to

attend UIUC

(mmr.receive)

o o o o o

I would

encourage my

peers to receive

the MMR

vaccination even

if it was not

required by

UIUC

(mmr.encourage)

o o o o o

I would receive

the Tdap

vaccination even

if it was not

required to

attend UIUC

(tdap.receive)

o o o o o

I would

encourage my

peers to receive

the Tdap

vaccination even

if it was not

required by

UIUC

(tdap.encourage)

o o o o o

41

Recommended Immunizations

Illinois recommends all college students to be immunized against certain vaccine-preventable

diseases. Listed below are recommended vaccinations that are offered at UIUC McKinley

Health Center along with a brief description of the diseases that they prevent. Please take a

moment to read and review the immunizations as you answer the questions that follow.

Hepatitis A: Liver infection that results in symptoms of fatigue, low appetite, stomach pain,

nausea, and jaundice, that takes about 2 months to be resolved

Meningitis B: bacterial infection that attacks the brain and spinal cord and cause swelling in

those areas. This can be fatal as quickly as 24 hours after the appearance of symptoms

Varicella (Chicken Pox): Highly contagious disease resulting in skin rashes and small, itchy

blisters all over the face and body

42

Extremely

Unlikely (1) Unlikely (2) Neutral (3) Likely (4)

Extremely

Likely (5)

I would receive

the Hepatitis A

vaccination even

if it was not

recommended by

UIUC

(HepA.receive)

o o o o o

I would encourage

my peers to

receive the

Hepatitis A

vaccination even

if it was not

recommended by

UIUC

(HepA.encourage)

o o o o o

I would receive

the Meningitis B

vaccination even

if it was not

recommended by

UIUC (MenB.receive)

o o o o o

I would encourage

my peers to

receive the

Meningitis B

vaccination even

if it was not

recommended by

UIUC

(MenB.encourage)

o o o o o

I would receive

the Varicella

vaccination even

if it was not

recommended by

UIUC

(Var.receive)

o o o o o

I would encourage

my peers to

receive the

Varicella

vaccination even

if it was not

recommended by

UIUC

(Var.encourage)

o o o o o

43

Previous Knowledge

Compared to your peers, how much do you know about:

I am not at all

knowledgeable

1 (1)

2 (2) 3 (3) 4 (4) 5 (5) 6 (6)

I am extremely

knowledgeable

7 (7)

How

vaccines

work

(previous1) o o o o o o o

The benefits

of

vaccinations


Real facts

about

vaccinations


Flu Shot

This past season, did you receive the flu shot?

o yes (1)

o no (2)

o prefer not to answer (4)

Flushot.yes

You indicated that you received the influenza vaccine this past flu season. Please explain the

factors that influenced this decision.

Flushot.no

You indicated that you did not receive the influenza vaccine this past flu season. Please

explain the factors that influenced this decision.

44

Anti-Vax Conspiracy Belief (Jolley & Douglas, 2014)

Participants were asked to rate their level of agreement on a 7-point likert scale to assess the first

7 of 8 conspiracy belief items.

Perceived Danger (Jolley & Douglas, 2014)

This measure was derived verbatim from Jolley & Douglas, 2014.

Trust in Science (Nandelson, 2014)

Participants were asked to indicate their agreement on a five-point scale for the items that had an

inter-item correlation greater than 0.5.

Rational/Experimental Multimodal Inventory – Rational Items (Norris & Epstein, 2011)

Participants indicated their agreement of the 10 rational items in the REIm on a five point scale.

Interpersonal Reactivity Index – Empathy measures (David, 1980)

This was a 5 point scale that asked participants to indicate how well the statement applied to

him/her. Only the empathetic concern items were displayed.

The Inclusion of Other in Self (IOS) (Aron et al., 1992)

Gratification (Hoerger, Quirk, Weed, 2012)

45

Participants indicated how well each item in the DGI-10 short form composite described them on

a 5-point scale.

Perceived Vulnerability to Disease (PVD) (Duncan, 2009)

Participants were asked to rank the seven perceived infectibility items phrases on a seven-point

scale.

Domain-Specific Risk-Attitude Scale – Health Domain (Weber et al., 2002)

This was a 5-point scale on which participants ranked the likelihood they would engage in the

listed activities. Only the health/safety items were presented.

Behavior Identification Form (Vallacher, 1989)

This measure was derived verbatim from Vallacher, 1989

Demographic Questions

The final portion of the questionnaire requested participants to provide their demographics:

gender, age, ethnicity, and political ideology.

46

Results

Study 1 Full Sample

Table S1

Means and Standard Deviations of the Study Variables (Study 1)

M SD










10. Rational Thinking 3.78 .58



13. Science Mistrust 1.86 .72



16. Receive 4.48 .79

17. Encourage 4.18 .97

18. Empathy 3.94 .66

19. Gratification 3.68 .49

20. BIF Sum 13.29 5.49

21. Corona Deaths 2379.3 7059.12

22. Corona Cases 64246.43 164814.18

23. Time (days since start of data collection) 16.80 14.62

47

Table S2

Correlations between study variables (study1 full sample)

1. 2. 3. 4 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

1. COVID-19 Vaccine -


3. Flu Shot .25* .12† -

4. Confidence .38* .43* .26* -

5. Collective .37* .53* .16* .57* -

6. Calculation -.23* -.29* -.14† -.22* -.24* -

7. Complacency -.24* -.40* -.15† -.37* -.53* .26* -

8. Constraint -.20* -.32* -.23* -.32* -.45* .19* .43* -

9 Self-Other Overlap .12 -.04 .09 .02 .11 .07 -.09 -.11 -

10. Vaccine Knowledge .21* .17† .25 .31* .18* .04 -.14† -.20* .10 -

11. Rational Thinking .12 .20* .05 .24* .19 -.02 -.09 -.25† .12 .30* -

12. Conspiracy Belief -.32* -.38* -.16† -.55* -.54* .17† .37* .32* -.01 -.28* -.27* -

13. Perceived Danger -.34* -.47* -.26* -.58* -.50* .24* .39* .33* -.02 -.37* -.28* .70* -

14. Science Mistrust -.29* -.33* -.08 -.48* -.47* -.47* .38* .32* -.09 -.29* -.35* .62* .62* -

15. Vulnerability .06 .05 .004 .02 .07 .01 -.21* .03 .04 .02 -.07 .03 .05 .004 -

16. Political Ideology -.07 -.23* -.05 -.24* -.20* .15† .35* .14† .06 -.01 -.07 .12 .18* .14 -.14† -


48

Table S3

Correlations between Study Variables & Supplementary variables, Study 1 (N = 346)

COVID Hypothetical

1. COVID-19 Vaccine - .31*


3. Flu shot .25* .12†

4. Confidence .38* .43*

5. Collective .37* .53*

6. Calculation -.23* -.29*

7. Complacency -.24* -.40*

8. Constraint -.20* -.32*

9. Self-Other Overlap .12 -.04

10. Vaccine Knowledge .21* .17†

11. Rational Thinking .12 .20*

12. Conspiracy Belief -.32* -.38*

13. Perceived Danger -.34* -.47*

14. Science Mistrust -.29* -.33*

15. Vulnerability .06 .05

16. Political Ideology -.07 -.23*

17. Receive .39* .54*

18. Encourage .41* .43*

19. Empathy .04 .10

20. Gratification .04 .09

21. BIF Sum -.06 -.05

22. Corona Deaths -.09 -.01

23. Corona Cases -.09 -.03

24. Time (days since start of data collection) -.06 -.03

25. log.deaths -.09 -.05

26. log.cases -.08 -.05


49

Table S4

Prediction of COVID Intention – Full (N = 346) vs. excluded sample (N = 308)


COVID-19 Vaccine Intention (R2 = __.21__)

N = 346


N = 308

b SE P 95% CI b SE p 95% CI

Intercept 4.77 .14 <.001 4.49, 5.05 4.84 .15 <.001 4.6, 5.1

Flu shot (1=received) .48 .19 .014 .10, .86 .46 .20 .025 .08, .86

5C-Confidence .28 .12 .026 .03, .52 .43 .13 .002 .16, .69

5C-Collective .30 .12 .014 .06, .55 .35 .14 .011 .08, .62

5C-Calculation -.15 .06 .013 -.27, -.03 -.12 .06 .078 -.23, .01

5C-Complacency .01 .11 .950 -.21, .22 .02 .11 .867 -.21, .25

5C-Constraint .04 .09 .668 -.14, .22 .03 .10 .762 -.17, .23

Self-Other Overlap .10 .06 .087 -.01, .21 .08 .06 .194 -.04, .20

Vaccine Knowledge .11 .09 .225 -.07, .28 .07 .10 .448 -.12, .26

Rational Thinking -.07 .17 .683 -.41, .27 -.10 .18 .574 -.45, .25

Conspiracy Belief -.09 .14 .487 -.36, .17 -.14 .16 .350 -.45, .16

Perceived Danger -.06 .12 .613 -.29, .17 .03 .13 .801 -.22, .29



Political Ideology .05 .07 .460 -.08, .18 .06 .07 .360 -.07, .20

50

Table S5

Prediction of Hypothetical Intention – Full (N = 346) vs. excluded sample (N = 308)


Hypothetical Child Vaccine Intention (R2 = __.32__)

N = 346


N = 308


Intercept 6.32 .08 <.001 6.17, 6.47 6.36 .08 <.001 6.20, 6.52

Flu shot (1=received) -.07 .11 .528 -.30, .14 -.001 .11 .991 -.22, .22

5C-Confidence .09 .07 .174 -.04, .22 .11 .07 .148 -.04, .25

5C-Collective .34 .07 <.001 .21, .48 .35 .08 <.001 .20, .50

5C-Calculation -.08 .03 .019 -.14, -.01 -.07 .03 .033 -.14, -.01

5C-Complacency -.10 .06 .096 -.21, .02 -.11 .06 .087 -.24, .02

5C-Constraint -.04 .05 .449 -.14, .06 -.003 .06 .957 -.11, .11

Self-Other Overlap -.06 .03 .047 -.12, .00 -.07 .03 .039 -.13, -.003

Vaccine Knowledge -.01 .06 .824 -.08, .10 -.01 .05 .900 -.11, .10

Rational Thinking .16 .05 .088 -.02, .34 .20 .10 .040 .01, .39

Conspiracy Belief .05 .03 .503 -.10, .19 .10 .08 .252 -.07, .26

Perceived Danger -.21 .05 .001 -.33, -.08 -.26 .07 <.001 -.40, -.12

Science Mistrust .10 .09 .281 -.09, .29 .17 .10 .114 -.04, .37



51

Study 1 Supplementary Variable Analysis (N = 308)

Table S6

Means and Standard Deviation of Supplementary Variables

M SD

Receive 4.54 .73

Encourage 4.23 .96

Empathy 4.00 .64

Gratification 3.72 .48

Gratification.omit 3.78 .50

Risk 2.27 .57

BIF Sum 13.15 5.58

Note: Item physical.17 was omitted in “gratification.omit”

Table S7

Correlations between DVs and Supplementary Variables

COVID Hypothetical

Receive .39* .52*

Encourage .41* .40*

Empathy .02 .05

Gratification .01 -.00

Gratification.omit -.02 -.02

Risk -.03 .09

BIF Sum -.06 -.03


Note: Item physical.17 was omitted in “gratification.omit”

52

Table S8

Prediction of COVID Intention Controlling for Gender



N = 308


N = 308

Controlled for Gender


Intercept 4.84 .15 <.001 4.6, 5.1 4.73 .23 <.001 4.3, 5.2


5C-Confidence .43 .13 .002 .16, .69 .44 .14 .001 .17, .71

5C-Collective .35 .14 .011 .08, .62 .34 .14 .013 .07, .61

5C-Calculation -.12 .06 .078 -.23, .01 -.11 .06 .069 -.23, .01

5C-Complacency .02 .11 .867 -.21, .25 .03 .12 .806 -.20, .26

5C-Constraint .03 .10 .762 -.17, .23 .03 .10 .738 -.17, .23





Perceived Danger .03 .13 .801 -.22, .29 .03 .13 .827 -.23, .28




Gender - - - - .15 .24 .520 -.32, .63

53

Table S9

Prediction of COVID Intention Controlling for Gender and Time of Participation (Before vs. After Spring Break)



N = 308


N = 308

Controlled for Gender & Time of Participation


Intercept 4.84 .15 <.001 4.6, 5.1 4.78 .24 <.001 4.3, 5.2


5C-Confidence .43 .13 .002 .16, .69 .45 .14 .001 .18, .72

5C-Collective .35 .14 .011 .08, .62 .33 .14 .015 .07, .60

5C-Calculation -.12 .06 .078 -.23, .01 -.11 .06 .061 -.24, .01

5C-Complacency .02 .11 .867 -.21, .25 .03 .12 .795 -.20, .26

5C-Constraint .03 .10 .762 -.17, .23 .03 .10 .752 -.17, .23





Perceived Danger .03 .13 .801 -.22, .29 .03 .13 .797 -.22, .29




Gender - - - - .16 .24 .504 -.31, .63

Spring Break - - - - -.22 .22 .330 -.66, .22

54

Table S10

Prediction of Hypothetical Vaccine Intention Controlling for Gender



N = 308


N = 308

Controlled for Gender


Intercept 6.36 .08 <.001 6.20, 6.52 6.31 .13 <.001 6.06, 6.56


5C-Confidence .11 .07 .148 -.04, .25 .11 .07 .132 -.03, .26

5C-Collective .35 .08 <.001 .20, .50 .35 .07 <.001 .20, .49

5C-Calculation -.07 .03 .033 -.14, -.01 -.07 .03 .030 -.14, -.01

5C-Complacency -.11 .06 .087 -.24, .02 -.12 .06 .102 -.23, .02

5C-Constraint -.003 .06 .957 -.11, .11 -.002 .06 .977 -.11, .11

Self-Other Overlap -.07 .03 .039 -.13, -.003 -.07 .03 .037 -.14, -.004


Rational Thinking .20 .10 .040 .01, .39 .20 .10 .038 .01, .39


Perceived Danger -.26 .07 <.001 -.40, -.12 -.26 .07 <.001 -.40, -.12




Gender - - - - .06 .13 .628 -.20, .32

55

Table S11

Prediction of Hypothetical Vaccine Intention Controlling for Gender and Time of Participation (Before vs. After Spring Break)



N = 308


N = 308

Controlled for Gender and Time of Participation


Intercept 6.36 .08 <.001 6.20, 6.52 6.30 .13 <.001 6.04, 6.55


5C-Confidence .11 .07 .148 -.04, .25 .11 .07 .139 -.04, .26

5C-Collective .35 .08 <.001 .20, .50 .35 .08 <.001 .20, .50

5C-Calculation -.07 .03 .033 -.14, -.01 -.07 .03 .033 -.14, -.01

5C-Complacency -.11 .06 .087 -.24, .02 -.12 .06 .102 -.23, .02

5C-Constraint -.003 .06 .957 -.11, .11 -.001 .06 .985 -.11, .11

Self-Other Overlap -.07 .03 .039 -.13, -.003 -.07 .03 .038 -.14, -.004


Rational Thinking .20 .10 .040 .01, .39 .20 .10 .036 .01, .40


Perceived Danger -.26 .07 <.001 -.40, -.12 -.26 .07 <.001 -.40, -.12




Gender - - - - .06 .13 .638 -.20, .32

Spring Break - - - - .06 .12 .604 -.17, .30

56

Study 2 Appendix

Measures

Corona DV 1

A recent outbreak of a respiratory illness caused by a novel coronavirus (COVID-19) is

spreading rapidly. The Centers for Disease Control and Prevention (CDC) confirmed on January

30, 2020 that COVID-19 has spread between two people in Illinois, representing the first

instance of person-to-person spread with this new virus in the United States. On March 11, 2020,

the World Health Organization (WHO) officially declared the outbreak a pandemic. There have

been millions of confirmed cases and hundreds of thousands of deaths around the world. As of

July 20, 2020: 3,698,161 confirmed cases in the United States have been reported, along with

139,659 deaths. The virus that causes COVID-19 is thought to spread mainly from person to

person through respiratory droplets produced when an infected person coughs, sneezes, or talks.

These droplets can land in the mouths or noses of people who are nearby or possibly be inhaled

into the lungs. Spread is more likely when people are in close contact with one another (within

about 6 feet). Symptoms can range from mild illness to severe illness and death and are

accompanied by fever, cough, and shortness of breath, among others. There is much more to

learn about the transmissibility, severity, and other characteristics associated with COVID-19,

and investigations are ongoing.

Source: Centers for Disease Control and Prevention

There is no specific antiviral treatment for COVID-19, and there is currently no vaccine to

protect against COVID-19. However, if such vaccine were made available to prevent

infection, would you receive it?

o I would not receive the vaccination even if it's free ($0) (1)

o I would receive the vaccination only if it's free ($0); If I need to pay money, I would not

receive the vaccination (2)

o I would pay money to receive the vaccination (3)

57

Corona DV 2

Please indicate the maximum amount of money that you would personally pay to receive

the COVID-19 vaccination.

$0 $500

0 500

I would pay up to $___ to receive the

COVID-19 vaccination. ()

Hypothetical (Haase et al., 2019; Jolley & Douglas, 2014)

Reasons for Corona DV response

In the earlier question asking about the COVID-19 vaccine, you indicated:

${corona1/ChoiceGroup/SelectedChoices}.

Listed below are some concerns that people may have about the vaccine.

1. Please rate your agreement with each statement.

2. Then, indicate whether each statement matters to your decision about getting COVID-19

vaccination.

3. If you did not initially think about the reason when you answered the question earlier,

please let us know by checking the box on the right.

Participants ranked their agreement on a 7-point likert scale, with additional options to indicate

whether or not the reason mattered to the decision for vaccination (yes or no) and if the

individual did not originally think about the reason all together.

The items are as follows:

1.The vaccine is too new

2.I worry about the side effects

3.The vaccine will not protect me

4.I avoid most vaccines

5.COVID-19 is not severe enough to concern me

6.A doctor has recommended no vaccines

7.I will not have access to the vaccine

8.My religion prevents vaccination

58

Flu Shot

This past flu season (October 2019 - April 2020), did you receive the flu shot?

o no (0)

o yes (1)

5C Psychological Antecedents of Vaccine Hesitancy (Betsch et al., 2018)

Previous Knowledge

This is the same measure provided in the Study 1 appendix.

News Sources

There are many resources available to stay informed on COVID-19. Which sources are you

using to receive Coronavirus-related information? Please select all that apply.

Please check the box if you receive COVID-related information from this source

ABC News

BBC

CBS News

CDC (Centers for Disease Control and Prevention)

CNN

FOX News

MSNBC

NBC News

NPR

PBS

Social Media (e.g. Twitter, Facebook, Instagram, TikTok)

State-government websites

The Guardian

The New York Times

The Rush Limbaugh Show (radio)

The Sean Hannity Show (radio)

The Wall Street Journal

The Washington Post

Univision

USA Today

WHO (World Health Organization)

Other (please specify)

59

Anti-Vax Conspiracy Belief (Jolley & Douglas, 2014)

Perceived Danger (Jolley & Douglas, 2014)

Rational/Experimental Multimodal Inventory – Rational Items (Norris & Epstein, 2011)

The Inclusion of Other in Self (IOS) (Aron et al., 1992)

In Study 2, the question stem was revised to ask participants to discern which picture describes

the individual’s relationship with other members in their community.

Perceived Vulnerability to Disease (PVD) (Duncan, 2009)

Trust in Science (Nandelson, 2014)

More COVID Measures

Optimism

In the following questions we will ask you to answer questions for you and an average

person similar to you. By "average person similar to you" we mean someone of the same gender

and ethnicity, roughly your age, who lives in the same town/city/area. When we say average

person in the following, please think of this person.

(0%) Definitely not Definitely (100%)

0 100

What do you think is the probability that an

average person will be infected with the

novel coronavirus in the next 2 months? ()

If an average person were to be infected with

the new coronavirus, how probable would it

be that they get only mild symptoms like a

common cold? ()

What do you think is the probability that you

will be infected with the novel coronavirus in

the next 2 months? ()

If you were to be infected with the new

coronavirus, how probable would it be that

you get only mild symptoms like a common

cold? ()

If you are reading this, please select 67 on

this sliding scale. ()

60

Social Distancing

To what extent do you believe it is important to behave in the following ways?

Not at all

important

1 (1)

2 (2) 3 (3) 4 (4) 5 (5) 6 (6)

Extremely

important

7 (7)

Avoid

visiting an

elderly

relative

(distancing1)

o o o o o o o

Keep 6 feet

of distance

between

yourself and

other people

while in an

enclosed

area (e.g., in

line at a

store)

(distancing2)

o o o o o o o

Wear a mask

while in an

enclosed

area with

others (e.g.,

in a store)

(distancing3)

o o o o o o o

Avoid

hosting an

indoor get-

together

with friends

(distancing4)

o o o o o o o

Avoid

visiting non-

essential

businesses

(e.g., nail

salon, hair

stylist,

bowling

o o o o o o o

61

Impact on Health and Economy

To what extent, in your view, has COVID-19 had a negative impact on health in the U.S? The

impacts of COVID-19 on U.S. health have been...

Not at all

bad/serious

1 (1)

2 (2) 3 (3) 4 (4) 5 (5) 6 (6)

Extremely

bad/serious

7 (7)

22

(impact.health) o o o o o o o

To what extent, in your view, has COVID-19 had a negative impact on economy in the

U.S.? The impacts of COVID-19 on U.S. economy have been...

Not at all

bad/serious

1 (1)

2 (2) 3 (3) 4 (4) 5 (5) 6 (6)

Extremely

bad/serious

7 (7)

(impact.econ) o o o o o o o

alley)

(distancing5)

Avoid travel

to other

locations

(e.g., out of

state) to visit

friends or

family

(distancing6)

o o o o o o o

62

In your opinion, which is more important: protecting public health in the U.S. or trying to revive

the U.S. economy?

I believe...

Protecting

public

health is

more

important

1 (1)

2 (2) 3 (3)

Both are

equally

important

4 (4)

5 (5) 6 (6)

Reviving

the U.S.

economy

is more

important

7 (7)

(health.econ) o o o o o o o

COVID-19 Test

Have you previously been tested for COVID-19?

o Yes (1)

o No (2)

o Prefer not to answer (3)

Display This Question:

If test = 1

Test Result

You indicated in the previous question that you were previously tested for COVID-19. If you

feel comfortable with sharing the results, please indicate if you tested positive, negative, or

prefer not to answer.

o Positive (1)

o Negative (2)

o Prefer not to answer (3)

63

Additional Demographic Questions

The same demographic questions from study 1 were used in study 2, with the addition of a few

more questions that asked participants to indicate whether they were a parent, the state in which

they reside, and their approximate household income.

Results

Table S12

Prediction of COVID Intention, Controlling for Income, Gender, State Cases, and State Deaths


COVID-19 Vaccine Intention

Demographic

Variables not

controlled

R2 = .30

Controlled

for Income

R2 = .38

Controlled

for Gender

R2 = .38

Controlled

for State

Cases

R2 = .38

Controlled

for State

Deaths

R2 = .38

Intercept 2.98 3.02 3.21 3.22 3.22

Flu shot (1=received) .45 .38 .40 .39 .39

5C-Confidence .40 .36 .34 .34 .34

5C-Collective .14 .15 .17 .17 .17

5C-Calculation -.04 -.04 -.03 -.03 -.03

5C-Complacency .09 .07 .06 .06 .06

5C-Constraint .12 .10 .08 .08 .08

Self-Other Overlap .08 .06 .06 .06 .06

Vaccine Knowledge .07 .01 .01 .02 .02

Rational Thinking .06 .01 -.02 -.02 -.02

Conspiracy Belief .05 .02 .02 .02 .02

Perceived Danger -.18 -.12 -.11 -.11 -.11

Science Mistrust -.07 -.10 -.11 -.11 -.12

Disease Vulnerability .17 .20 .20 .20 .20

Political Ideology -.07 -.07 -.08 -.08 -.08

Income - .29 .27 .28 .28

Gender - - -.31 -.31 -.31

State Cases (log) - - - -.05 -.05

State Deaths (log) - - - - .01

64

Table S13:

Prediction of COVID Intention (log) and COVID intention (pre-registered)


COVID-19 Vaccine Intention (R2 = __.30__) Pre-Registered COVID-19 Vaccine Intention (R2 = __.13__)


Intercept 2.98 .10 <.001 4.6, 5.1 82.38 7.94 <.001 66.78, 97.98

Flu shot (1=received) .45 .15 .002 .16, .73 13.48 11.29 .233 -8.68, 35.64

5C-Confidence .40 .06 <.001 .28, .52 15.01 4.80 .002 5.58, 24.43

5C-Collective .14 .07 .062 -.01, .28 6.73 5.69 .24 -4.45, 17.90

5C-Calculation -.04 .05 .464 -.13, .24 2.27 3.74 .544 -5.08, 9.62

5C-Complacency .09 .07 .193 -.04, .18 7.57 5.30 .154 -2.84, 17.98

5C-Constraint .12 .06 .026 .01, .15 12.95 4.29 ..003 4.52, 21.38

Self-Other Overlap .08 .04 .021 .01, .15 8.36 2.71 .002 3.04, 13.68

Vaccine Knowledge .07 .05 .209 -.04, .18 10.30 4.26 .016 1.94, 18.65

Rational Thinking .06 .09 .540 -.12, .24 6.04 7.17 .400 -8.04, 20.12

Conspiracy Belief .05 .08 .586 -.12, .21 12.19 6.48 .060 -.53, 24.90

Perceived Danger -.18 .08 .027 -.33, -.02 -12.74 6.28 .043 -25.07, -.42

Science Mistrust -.07 .09 .465 -.25, .12 -.306 7.29 .967 -14.62, 14.00

Disease Vulnerability .17 .06 .002 .06, .29 7.98 4.55 .080 -.94, 16.91

Political Ideology -.07 .04 .078 -.14, .01 -5.47 2.95 .064 -.11.27, .33

Infection Bias - - - - .082 .219 .709 -.25, .51

Symptom Bias - - - - .045 .211 .831 -.27, .46

65

Table S14

Prediction of Hypothetical Vaccine Intention, Controlling for Income, Gender, and

Parent hood


Hypothetical Vaccine Intention

Demographic

Variables not

controlled (R2 = __.39__)

Controlled

for Income (R2 = __.39__)

Controlled

for Gender (R2 = __.40__)

Controlled

for

Parenthood (R2 = __.40__)

Intercept 5.64 5.66 5.86 5.86

Flu shot (1=received) .00 -.02 .00 -.00

5C-Confidence .43 .42 .40 .40

5C-Collective .18 .19 .20 .20

5C-Calculation .01 .01 .02 .02

5C-Complacency -.15 -.16 -.16 -.16

5C-Constraint .03 .02 .01 .01

Self-Other Overlap -.03 -.04 -.04 -.04

Vaccine Knowledge .15 .13 .13 .13

Rational Thinking -.09 -.10 -.14 -.14

Conspiracy Belief -.12 -.13 -.13 -.13

Perceived Danger -.00 .02 .03 .03

Science Mistrust -.05 -.06 -.08 -.08

Disease Vulnerability .05 .06 .06 .06

Political Ideology -.04 -.04 -.05 -.05

Income - .10 .09 .09

Gender - - -.34 -.34

Parenthood - - - .01

66

Table S15

Prediction of COVID Intention (log) and COVID intention (pre-registered)


Hypothetical Vaccine Intention (R2 = __.39__) Pre-registered Hypothetical Child Vaccine Intention

(R2 = __.39__)


Intercept 5.64 .09 <.001 5.47, 5.82 5.64 .09 <.001 5.46, 5.82

Flu shot (1=received) .00 .13 .972 -.25, .26 .01 .13 .954 -.25, .26

5C-Confidence .43 .05 <.001 .33, .54 .43 .05 <.001 .33, .54

5C-Collective .18 .06 .005 .06, .31 .18 .06 .006 .05, .31

5C-Calculation .01 .04 .750 -.07, .10 .01 .04 .751 -.07, .10

5C-Complacency -.15 .06 .013 -.27, -.03 -.15 .06 .016 -.27, -.03

5C-Constraint .03 .05 .513 -.06, .13 .03 .05 .501 -.06, .13

Self-Other Overlap -.03 .03 .344 -.09, .03 -.03 .03 .363 -.09, .03

Vaccine Knowledge .15 .05 .002 .06, .25 .15 .05 .002 .06, .25



Perceived Danger -.00 .07 .971 -.14, .14 -.01 .07 .912 -.14, .13




Infection Bias - - - - .00 .00 .594 -.00, .01

Symptom Bias - - - - -.00 .00 .870 -.01, .00

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