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Running head: POLITICS AND COVID-19 HEALTH BEHAVIORS
Manuscript not yet peer-reviewed
The Political Dimension of COVID-19 Health-Protective Behavior in the United
States
Wolfgang Stroebe1
Michelle R. vanDellen2*
Georgios Abakoumkin3
Edward P. Lemay, Jr.4
William Schiavone2
Maximillian Agostini1
Jocelyn J. Bélanger5
Ben Gützkow1
Anita C. Keller1
Jannis Kreienkamp1
Anne Margit Reitsema1
Jamilah H. B. Abdul Khaiyom6
Vjolica Ahemdi7
Handan Akkas8
Carlos A. Almenara9
Anton Kurapov10
Moshin Atta11
2
Sabahat Cigdem Bagci12
Sima Basel5
Edona Berisha Kida7
Nicholas Buttrick13
Phatthanaki Chobthamkit14
Hoon-Seok Choi15
Miora Cristea16
Sára Csaba17
Kaja Damnjanovic18
Ivan Danyliuk10
Arobindu Dash19
Daniela Di Santo20
Karen M. Douglas14
Violeta Enea21
Daiane Gracieli5
Gavan Fitzsimons22
Alexandra Gheorghiu21
Ángel Gómez23
Qing Han24
Mai Helmy25
Joevarian Hudiyana26
Bertus F. Jeronimus1
Ding-Yu Jiang27
Veljko Jovanovic28
Željka Kamenov29
3
Anna Kende30
Shian-Ling Keng31
Tra Thi Thanh Kieu32
Yasin Koc1
Kamila Kovyazina33
Inna Kozytska10
Joshua Krause1
Arie W. Kruglanksi4
Maja Kutlaca34,35
Nóra Anna Lantos17
Cokorda Bagus Jaya Lemsmana36
Winnifred R. Louis37
Adrian Lüders38
Najma Malik11
Anton Martinez39
Kira McCabe40
Jasmina Mehulic29
Mirra Noor Milla26
Idris Mohammed41
Erica Molinario4
Manuel Moyano42
Hayat Muhammad43
Silvana Mula20
Hamdi Muluk26
Solomiia Myroniuk1
4
Reza Najafi44
Claudia F. Nisa5
Boglárka Nyúl17
Paul A. O’Keefe31
Jose Javier Olivas Osuna45
Evgeny Osin46
Joonha Park47
Gennaro Pica20
Antonio Pierro20
Jonas Rees48
Elena Resta20
Marika Rullo49
Michelle K. Ryan50
Adil Samekin51
Pekka Santtila52
Edyta Sasin5
Birga Marie Schumpe5
Heyla A. Selim53
Michael Vicente Stanton54
Samiah Sultana1
Robbie M. Sutton14
Eleftheria Tseliou3
Akira Utsgugi47
Jolien Anne van Breen50
Caspar J. van Lissa55
5
Kees Van Veen1
Alexandra Vázquez23
Robin Wollast38
Victoria Yeung56
Somayeh Zand44
Iris Zezelj18
Bang Zheng57
Andreas Zick48
Claudia Zuniga58
N. Pontus Leander1
Word Count: 8364
*Corresponding Author: Michelle vanDellen, Psychology Building, Athens, GA,
30606, mvd@uga.edu
**Full author list named in submission system
1. University of Groningen
2. University of Georgia
3. University of Thessaly
4. University of Maryland
5. New York University Abu Dhabi
6. International Islamic University Malaysia
7. Pristine University
8. Ankara Science University
9. Universidad Peruana de Ciencias Aplicadas
10. Taras Schevchenko National University of Kyiv
11. University of Sargodha
12. Sabanci University
13. University of Virginia
14. University of Kent
15. Sungkyunkwan University
16. Heriot Watt University
17. Eotvos Lorand University
18. University of Belgrade
19. International University of Business Agriculture and Technology
20. University of Rome La Sapienza
21. Alexandru Ioan Cuza University
22. Duke University
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23. Universidad Nacional de Educacion a Distancia
24. University of Bristol
25. Menoufia University
26. Universitas Indonesia
27. National Chung Cheng University
28. University of Novi Sad
29. University of Zagreb
30. Eotvos Lorand University
31. Yale-NUS College
32. Ho Chi Minh City University of Education
33. Independent Researcher
34. Universitat Osnabruck Fachbereich & Humanwissenschaften
35. Durham University
36. Udayana University
37. The University of Queensland
38. Université Blaise-Pascal
39. The University of Sheffield
40. Vanderbilt University
41. Usmanu Danfodiyo University
42. University of Cordoba
43. University of Peshawar
44. Islamic Azad University Rasht Branch
45. National Distance Education University
46. National Research University
47. Nagoya University
48. Bielefeld University
49. University of Siena- Arezzo Campus
50. University of Exeter
51. International Islamic Academic of Uzbekistan
52. New York University Shanghai
53. King Saud University
54. California State University East Bay
55. Utrecht University
56. Lingnan University
57. Imperial College London
58. Universidad de Chile
7
Abstract
According to health behavior theories, perceived vulnerability to a health threat and
perceived effectiveness of recommended health-protective behaviors determine
motivation to follow these recommendations. Because the U.S. President Trump and
U.S. conservative politicians downplayed the risk and seriousness of contracting
COVID-19 and the effectiveness of recommended actions, we predicted that
politically conservative Americans would be less likely than liberals to enact
recommended health-protective behaviors. We further predicted that these effects
would be mediated by perceived health risk, perceived infection severity and
perceived action effectiveness. In two studies of U.S. residents, political conservatism
was inversely associated with perceived health risk and enactment of health-protective
behaviors. Furthermore, perceived risk of infection (both studies), perceived severity
of infection (Study 2), and perceived effectiveness of behaviors (Study 2), mediated
effects of political orientation on health-protective behaviors. These effects were
stronger for participants living in the U.S. (N=10,923) than outside the U.S.
(N=51,986).
Keywords: COVID-19, Corona Pandemic, health behavior, political orientation
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The Political Dimension of COVID-19 Health-Protective Behavior in the United
States
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According to most theories of health behavior, people’s compliance with these
recommendations would depend on their perceptions of their infection risk, the
anticipated severity of such an infection, and the perceived effectiveness of
recommended health behaviors (e.g., de Hoog et al., 2005, 2007; Janz & Becker,
1984; Rogers, 1983; Sheeran & Abraham, 2005). People should take actions to
prevent a COVID-19 infection to the extent they believe they could become infected
and consider such an infection to be a serious threat to their health. But whether
people will adopt recommended health-protective behaviors would also be influenced
by the perceived effectiveness of that behavior; people are only likely to behave in
ways they believe will protect them against the health threat. Two large meta-analyses
on the effectiveness of fear-arousing communications provide empirical support for
the role of perceived threat and perceived behavioral effectiveness in predicting health
behaviors from both experimental and observational studies (de Hoog et al., 2007;
Tannenbaum et al., 2015).
9
In the U.S., perceptions of both the health threat and the effectiveness of the
recommended behaviors are likely to be related to political orientation because
liberals (i.e., Democratic leaning Americans) and conservatives (i.e., Republican
leaning Americans) tend to rely on different sources of information and differ in their
perception of the credibility of these sources (PEW Research Center, June 29, 2020).
Although most liberals view health experts as the most credible sources for COVID-
19 information, a majority of conservatives find the White House more credible.
According to a recent survey by the PEW Research Center (June 29, 2020), 76% of
liberals say that the CDC and other public health experts “get the facts right almost all
of the time” when it comes to the COVID-19 outbreak, but only 51% of conservatives
(i.e., Republican leaning Americans) agreed with this statement. In contrast, 54% of
conservatives believe that the White House gets its facts right compared to 9% of
liberals.
Source credibility can be an important determinant of the impact of
communication (Hovland & Weiss, 1951; Pornpitakpan, 2004), particularly if
respondents’ motivation and ability to scrutinize communications is low (Petty et al.,
1981). Because people who consider their vulnerability to a health threat as low are
not motivated to carefully scrutinize health communications (de Hoog et al., 2005;
2007), conservatives in the United States may be particularly sensitive to information
from political rather than scientific sources.
These differences in the perception of the seriousness of the COVID-19
pandemic may have been increased by the fact that President Trump has made
repeated statements in which he both downplayed the seriousness of COVID-19 as
well as the risk of getting infected. To give only a few examples, he stated during a
meeting with U.S. governors at the White House on February 10th, that “A lot of
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people think that it goes away in April with the heat – as the heat comes in” (Forbes,
September 10, 2020). On February 26th, he publicly dismissed concerns about the
lethality of the virus at a White House News conference, saying “It is a little like the
regular flu that we have flu shots for…And we’ll essentially have a flu shot for this in
a fairly quick manner” (Forbes, September 10, 2020). He repeatedly said the U.S. had
very few cases, implying the risk of getting infected was fairly low. For example, on
February 25th, he commented: "You may ask about the coronavirus, which is very
well under control in our country. We have very few people with it…” (Forbes,
September 10, 2020).1 It is therefore not surprising that 68% of the people who relied
on the Trump task force for COVID-19 news believed the pandemic had been
overhyped, compared to 24% who relied on national news outlets. Even after
President Trump had contracted the infection himself and was ill enough to have been
hospitalized and received aggressive experimental treatments (October 2nd, 2020), he
continued to downplay the seriousness of such an infection. He stated after his
release: “One thing that’s for certain; don’t let it dominate you; don’t be afraid. You’re
going to beat it” (New York Times, October 7th, 2020), suggesting conservatives
might persist in their beliefs that the virus poses a low risk despite President Trump
contracting (at least) a moderately severe case of the virus.
President Trump and other leading Republicans have also repeatedly publicly
disregarded mask-wearing and social distancing recommendations. For example,
when Vice President Pence visited the Mayo Clinic on April 28th, he did not wear a
facemask, even though he was told about the Mayo Clinic policy requiring facemasks
$ In contrast, in Germany, a country that coped much better with the pandemic than
the U.S., the chancellor Angela Merkel warned already on March 11, that up to 70%
of the country’s population could contract the virus. She further stated that since there
was no known cure, the focus would fall on winning time (BBC News, 11 March,
2020).
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and even though everybody else in the building wore a mask (CNN, April 29th, 2020).
President Trump has also been consistently shown on TV not wearing a mask and as
CBC News criticized, “Trump’s mixed messaging on facemasks has hurt U.S.
Coronavirus response” (CBC, August 10th, 2020). After his release from the hospital
on October 5th, 2020, when he was still highly infectious, he demonstratively took off
his facemask and kept it off before entering the White House, despite the presence of
others in his area (CBC, August 10th, 2020).
President Trump also continued holding indoor rallies defying state regulations
and recommendations from the CDC to wear face coverings indoors (e.g., The
Guardian, September 14th, 2020). For example, on September 14th he held an indoor
rally near Las Vegas in which most of the thousands of attendees did not wear masks,
even though at that time Nevada state regulations prohibited gatherings larger than 50
people and mandated face coverings in public to prevent the spread of COVID-19
(New York Times, September 14th, 2020). The fact that the leader of a country
publicly disregards governmental health behavior recommendations conveys the
message that the recommended behavior is neither necessary nor effective. It is
therefore not surprising that in a recent survey only 44% of conservatives reported
wearing a facemask always or sometimes, compared to 94% of liberals (Gallup, July
13th, 2020). Several studies demonstrate such differences across political party
affiliations in perception of the health risk associated with COVID-19 and in
compliance with some of the recommended health-protective (Calvillo et al., 2020;
Gollwitzer et al., 2020; Kushner et al., 2020; Rothgerber et al., 2020).
In addition to turning to President Trump as a source of information about
COVID-19, conservative Americans are also more likely than liberals to rely on Fox
News as a trusted source. Indeed, 76% of conservative Americans name Fox News as
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their main source of political news, compared to only 1% of liberals. In fact, 61% of
liberals say they distrust Fox News (Pew Research Center, April 1st, 2020). This
difference is important because Fox News has been accused of spreading
misinformation about the COVID-19 pandemic. In April, 74 journalism professors
signed an open letter stating: “Yet by commission as well as omission – direct
uncontested misinformation as well as failure to report the true dimension of the
crisis – Fox News has been derelict in their duty to provide clear and accurate
information about COVID-19. As the virus spread across the world, Fox News hosts
and guest minimized the danger, accusing the Democrats and the media of inflating
the dangers…” (Midiaite, April 2nd, 2020). Although not all news anchors at Fox
News downplayed the COVID-19 danger, two popular news anchors, Sean Hannity
and Trish Regan, were calling the COVID-19 pandemic a hoax as late as March 9th,
2020 (Vox, March 20th, 2020). It is therefore not surprising that 79% of those whose
main news source is Fox News said that the media has exaggerated the risk of the
pandemic, compared to only 35% of viewers of MSNBC (Pew Research Center, April
1st, 2020). Academic research has also confirmed the link between political
conservatism and believing the media has exaggerated the impact of COVID-19
(Calvillo et al., 2020). Although Fox News eventually changed their reporting
towards- painting a more realistic picture of the COVID-19 pandemic (Washington
Post, March 16th, 2020), their programming may have reduced risk perceptions for
many viewers during early stages of the pandemic (Simonov et al., 2020). One study
even demonstrated that Fox News audiences (compared to people who preferred other
networks) were less likely to engage in social distancing (Gollwitzer et al., 2020).
The Present Research
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Given that conservatives in the U.S. have greater exposure to - and trust of -
sources that communicate low risk and severity of contracting the COVID-19 virus
and also that some prominent conservative leaders publicly downplay recommended
health behaviors, especially the wearing of facemasks, we expected that conservatives
would perceive the risk of becoming personally infected and the protective effects of
health behaviors as lower. As a consequence, they should - according to health
behavior models - be less motivated to engage in recommended health-protective
behaviors. Most importantly, we further predicted this difference in the adoption of
health-protective behavior would be mediated by differences in perceived risk of
contracting the virus, the perceived severity of the consequences of such an infection,
and the perceived effectiveness of the recommended health-protective behaviors.
We conducted two studies with samples of participants living in the U.S. (the
second study included an international sample) to test these assumptions. In both
studies, we assessed political orientation (conservative vs. liberal), perceived risk of
getting infected, and willingness to engage in recommended health-protective
behaviors. In Study 2, we additionally assessed perceived severity of getting infected,
the perceived effectiveness of wearing a facemask as well as information about
participants’ area of residence (i.e., population in county of residence, virus spread in
county of residence). In both studies, participants were resampled for several weeks,
allowing for examination of the stability of the associations throughout the early
stages of the pandemic (5 time points in Study 1 and 13 time points in Study 2).
Finally, Study 2 also allowed for a comparison of the relationship between politics
and health-protective behavior in the U.S. relative to other countries. This comparison
should enable us to rule out the possibility that the association between political
orientation and virus perception in the U.S. could merely be the result of different
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worldviews. To the extent that the effects of political conservatism on lower risk
perceptions and health-protective behaviors are due to sources of influence that are
relatively localized to the U.S., these effects should be stronger in the U.S. relative to
in other countries.
Study 1
Method
Participants and Procedure
This study involved longitudinally tracking participants’ attitudes and self-
reported behaviors across five time points. Wave 1 (Baseline) was launched on March
10th, one day before the WHO declared the COVID-19 outbreak a pandemic. To
capture potentially acute and relatively long-term changes, we followed up with
participants at three time points in close succession (March 20th, March 28th, April
11th) as well as a longer-term follow-up on June 16th.
Participants were Amazon MTurk respondents. They were recruited to “fill out
five surveys across the next months asking questions about recent events in society.”
Current residence in the U.S. was an eligibility criterion, and we used an IP address
filter to ensure fulfillment of this requirement. At Baseline, 1,056 MTurk respondents
participated in the study. Seventeen individuals were excluded from analyses due to
suspicion of data invalidity (e.g., double MTurk ID, survey completion in less than
five minutes); thus, the final sample size was N = 1,039. Table 1 reports
characteristics of these participants. At Wave 2, 649 participants yielded valid data
(data from seven individuals were excluded), at Wave 3, there were 642 participants
with valid data (seven individuals were excluded), there were 547 participants at
Wave 4 (nine were excluded), and 462 participated in Wave 5 (one was excluded).
Effect sizes were not anticipated for the analyses relevant to this paper prior to data
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collection. A sample size of 1000 participants offered 80% power (two-tailed alpha = .
05) to observe effect sizes of r > .085 to be statistically significant.
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Table 1
Demographic information at Baseline for Participants in Studies 1 and 2
Study 1 (US)
N
Study 2 (US)
N
Study 2 (Non-US)
N
Gender
Male 463 4043 19732
Female 529 6773 31704
Other 6 81 223
Did not report 41 26 327
Age
18-24 62 1670 12746
25-34 367 3244 11991
35-44 256 2446 9554
45-54 153 1534 7518
55-64 111 1211 5739
65+ 49 784 4086
Did not report 41 34 352
Education
Some High School or less 7 360 547
High School graduate/GED 85 1637 12601
Some College 211 2146 12549
College Graduate 415 4229 14834
Graduate Degree 261 2512 11044
Did not report 60 39 411
Measures
Perceived Infection Risk. Perceived infection risk was assessed at all five
time points with the item “How likely is it that the following will happen to you in the
next few months? … You will get infected with the Coronavirus.” (1 = Not at all
likely; 5 = Extremely likely).
Health-Protective Behaviors. In this study, we assessed health-protective
behaviors through three virus mitigation behaviors recommended by WHO at the time
of the start of this study. These behaviors were assessed at all five time points.
Following the text “To minimize my chances of getting Coronavirus, I would…” the
relevant items read “...wash my hands more often.”, “...avoid crowded spaces.”, and
“...put myself in quarantine,” (-3 = Strongly disagree; +3 = Strongly agree). The items
were averaged to build a health-protective behaviors scale. The scale had satisfactory
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internal consistency (αs from .69 to .84 across time points). Descriptive statistics at
each wave are presented in Table 2.
Political Orientation. Political orientation was measured at Baseline with the
item “What is your political orientation?” (1 = Extremely conservative; 9 = Extremely
liberal; M = 5.72, SD = 2.39).
Results
Political Orientation and Perceived Health Risk
To examine whether political orientation was associated with perceived health
risk, we calculated correlations between political orientation at Baseline and perceived
health risk at all five time points. These correlations (see Table 2) show consistently
across all time points that the more participants describe their political orientation as
conservative, the lower they perceived their risk of infection. We also calculated
partial correlations between the focal variables controlling for gender, age, and
education separately as well as controlling for all these three variables concurrently.
The correlations between political orientation and perceived health risk remained
significant in all models.
Political Orientation and Health Protective Behaviors
To examine whether political orientation was associated with health-protective
behaviors, we calculated correlations between political orientation at Baseline and
WHO-recommended virus mitigation behaviors at all five time points. The
correlations, which are depicted in Table 2, show a small but consistent pattern over
time: the more participants described themselves as conservative, the less they
enacted health-protective behaviors. In addition, partial correlations controlling for
gender, age, and education separately as well as for all these three variables
simultaneously, did not alter the above pattern.
$6
Table 2
Relationship of Political Orientation with Perceived Health Risk and Health Protective
Behaviors: Study 1
Date Perceived health risk WHO Virus mitigation behaviors
M (SD), N r (N) M (SD), N r (N)
March 10 2.55 (1.13), 1029 .138*** (1001) 1.84 (1.04), 1021 .093** (1001)
March 20 2.73 (1.08), 646 .157*** (640) 2.26 (0.89), 642 .085* (636)
March 28 2.75 (1.05), 634 .195*** (627) 2.34 (0.91), 634 .089* (627)
April 11 2.56 (1.03), 547 .118** (540) 2.34 (0.95), 547 .141** (540)
June 16 2.47 (0.97), 456 .158** (452) 2.17 (1.10), 456 .183*** (452)
* p < .05, ** p < .01, *** p < .001
Note. Higher scores on this measure of political orientation correspond to more liberal attitudes.
Mediational Analyses
To examine whether perceived infection risk mediated the relationship between political
orientation and health-protective behaviors, we conducted five bootstrapping analyses
(Hayes, 2013; PROCESS macro, Model 4, 5,000 bootstrap samples), one for each assessment
wave. Note, that political orientation was measured at baseline, whereas perceived health risk
and health-protective mitigation behaviors were measured at each time point. In support of
our hypothesis, the indirect effects of political orientation on health-protective behaviors via
perceived health risk were significant for four out of five assessment waves (see Table 3;
additional path coefficients are presented in Table S1).
$%
Table 3
Tests of the Mediational Model in Five Time Points: Study 1
Date Direct Effect: Political Orientation on
WHO Virus Mitigation Behaviors
Indirect Effect: Political Orientation on
WHO Virus Mitigation Behaviors
through Perceived Risk
B SE CI ab SE CI
March 10 .037 .014 .009, .064 .004 .002 .001, .009
March 20 .028 .015 -.001, .057 .003 .003 -.001, .010
March 28 .024 .015 -.006, .054 .009 .004 .003, .018
April 11 .047 .017 .016, .081 .006 .003 .001, .015
June 16 .072 .020 .031, .112 .009 .005 .002, .020
Note. CI = 95% bootstrap confidence interval. The a and b pathways are presented in
Table S1.
Study 2
The data we collected for Study 1 only allowed us to test the meditating role
of perceived risk. As we outlined in our introduction, we expected perceived severity
of the consequences of an infection and perceived effectiveness of recommended
health-protective behaviors would also act as mediators, as both of these facets of
health models were implicated in the words and behaviors of conservatives in the U.S.
In addition, we did not test whether the association between political orientation and
health-protective behavior was specific to the situation in the U.S. We addressed these
questions in Study 2. We extended the health-protective behaviors we assessed by
including measures of wearing a face covering in public as they became more clearly
recommended by WHO (and the U.S. CDC). In addition, given that a COVID-19
vaccine may be a promising health-protective mitigation behavior once it is
developed, we also examined willingness to be vaccinated against COVID-19.
Method
Participants and Procedure
4
Participants from the U.S. as well as from 114 non-U.S. countries were
recruited into a longitudinal survey. Assessments began on March 19, 2020. The
current results report data collected through July 6th from 62,909 individuals. The
survey was distributed online through a combination of convenience sampling,
snowball sampling, and paid recruitment procedures. Members of the research team
distributed the survey via different means: social media campaigns, academic
networks and press releases. When respondents completed the survey and were
debriefed, a final screen invited them to distribute the survey link to their networks
and to participate in weekly (unpaid) follow-ups. Follow-up surveys with rotating
questions were administered from March 27th to July 13th. To avoid the questionnaire
becoming too long, some measures were excluded from particular survey
administrations. Response rates for follow-up surveys varied; participants completing
the survey at Baseline matriculated into follow-up surveys (no follow-up surveys
included participant payment) after they completed the initial survey. Characteristics
of participants at baseline are reported in Table 1. Because this project was a large-
scale project covering a broad-range of psychological factors, effect sizes for the
research questions examined in this paper were not estimated a priori. Sensitivity
power analyses indicate that we had ample power to observe effects as small as r = .
03 as statistically significant for the largest sample (i.e., baseline). At other time
points, we had 80% power (using two-tailed hypotheses) to observe effects (r) as
small as .03-.12 as significant. Comparisons between the US and non-US countries
had 80% power to find very small differences between countries to be significant (ƞ2
values between .0002 and .0006, depending on Study wave).
Measures
$
Political Orientation. We assessed political orientation using the political
compass (https://www.politicalcompass.org/analysis2). This measure was chosen for
its adaptability across diverse political frameworks. For the purposes of the present
study, we used the left to right continuum to capture conservatism. Participants were
specifically prompted to click on the position on a graphic that represents their
political orientation from economically left (-200) to economically right (+200; MUS =
-16.04, SD = 80.68; Mnon-US = -4.83, SD = 67.03). To maintain consistency, we use the
labels “conservative” and “liberal” to refer to economic right and left orientations,
respectively.
Perceived Risk of Infection. As in Study 1, we assessed perceived risk of
infection. This item was included in 10 surveys over time. At each point, participants
rated a single item about their perceived likelihood of becoming infected with
coronavirus in the next few months (1 = exceptionally unlikely; 7 = all but certain).
An additional response choice allowed participants to indicate if they had already
become infected with the coronavirus. As the analyses focused on perceptions of risk,
participants who selected this response were excluded from analyses, with
participants becoming excluded at later time points as they identified as having been
diagnosed with COVID-19.
Consequences of Contracting the Virus. To assess the perceived severity of
getting infected, we asked respondents (at Baseline) to indicate the consequences of
contracting the virus. Participants rated how disturbing they would find it to contract
the virus (1 = not disturbing at all; 5 = extremely disturbing).
Perceived Effectiveness of Health-Protective Behaviors. Beliefs that health
behaviors are effective at protecting health were assessed about social distancing (at
three time points) and wearing a mask (at four time points). Participants reported their
beliefs about the effectiveness of social distancing by agreeing with the statement, ‘In
the absence of an effective medical treatment or vaccine, social distancing measures
are the most effective means of controlling the pandemic’. Participants reported
beliefs about the effectiveness of wearing a mask or face covering by indicating their
agreement with the statement ‘I believe that wearing a mask protects myself.’ Both
efficacy belief items used the same scale (-2 = strongly disagree; +2 = strongly agree).
Health-Protective Behaviors. We assessed three forms of health-protective
behavior (for correlations between behavior, see Table S2).
WHO Virus Mitigation Behaviors. As in Study 1, we assessed three behaviors
recommended by the WHO (i.e., hand washing, avoiding crowds, self-isolating) at
Baseline, Wave 4, Wave 11, and Wave 12. We used the same items and scaling as in
Study 1. Internal consistency was acceptable (αs = .62-.74).
Willingness to be Vaccinated. At three time points (Waves 4, 11, and 12),
participants reported their willingness to get a vaccine in response to the question
‘How likely are you to get vaccinated against coronavirus once a vaccine becomes
available?’ on a five point scale (-2 = extremely unlikely; +2 = extremely likely).
Wearing a Mask. Although wearing a facemask is now considered a virus
mitigation behavior, it was not initially recommended by WHO and was not included
at baseline. As it became evident that mask wearing would be a critical behavior in
responding to the COVID-19 pandemic, we added a measure of it to our longitudinal
survey. At four time points (Waves 6, 8, 10, and 12), participants were asked about
their frequency of wearing a mask/face covering in public. Participants saw the
statement ‘In the past week, I have covered my face in public places,’ using a five
point scale (1 =[almost] never; 5 = [almost] always).
Results
7
We first evaluated the zero-order correlations between political orientation and
each outcome (i.e., perceived risk, health behaviors) within and across country. To
compare correlations across countries, we used a general linear model with country
(U.S. = 0; non-U.S. = 1) as a categorical between-subjects factor and political
orientation treated as a continuous between-subjects factor. A test of the interaction
between country and political orientation served as a test of whether associations
between political orientation and outcomes were different for participants living
inside versus outside of the United States. These parsimonious models allow for easy
interpretation of effects and effect size; however, the nested data (i.e., participants
nested within country) could create biased significance tests, which can be corrected
using multi-level models (Bryk & Raudenbush, 1992). Additionally, political
orientation was (albeit weakly) associated with age (r = .04, p < .0001), education (r =
-.09, p < .0001), and gender (r = -.04, p < .0001) at baseline. Thus, we conducted
robustness checks using multi-level modeling in which participants were nested into
country (with intercepts modeled as randomly varying across countries) and
controlling for age, education, and gender. Combined, these robustness checks
allowed us to account for interdependence of data, alternative demographic covariates
and the alternative account that perceptions of risk might be due to demographic
factors.
Does Perceived Risk Differ by Political Orientation?
As Table 4 shows, in the US, political orientation was associated with
perceived risk of infection such that more conservative individuals reported a lower
likelihood of becoming infected. The correlations between political orientation and
perceived risk were stronger in the US than in the non-US sample, with the exception
of the fifth follow-up assessment (Early/Mid May), when the effects were in the same
8
direction but did not significantly differ across countries. The observed interactions
between country and political orientation was significant during our multi-level model
robustness checks that nested participants within country and controlled for age,
education and gender at all time points, p’s < .001 (note that in the one instance where
the interaction effect indicated associations between political orientation and risk did
not differ between US and non-US participants, this interaction term became
significant when covariates were included, p = .012).
In our cross-sectional baseline questionnaire, political orientation was also
negatively associated with expected severity of infection such that more conservative
individuals expected a COVID-19 infection to be less severe if one were to contract
the disease. Here, the association of political orientation and perceived severity
reversed direction for participants living outside of the U.S. As with perceived risk,
the interaction between country of residence and political orientation was significant
and remained so after our robustness checks.
Does Perceived Effectiveness Differ by Political Orientation?
Across all assessment waves, political orientation was also associated with the
perceived effectiveness of measured health-protective behaviors (i.e., social
distancing, wearing a face covering; see Table 4) such that more conservative
individuals perceived these behaviors as less useful. The significant interactions with
country indicate that these effects were stronger for participants in the US relative to
non-US participants, and these interactions persisted after our robustness checks.
Does Political Orientation Predict Health-Protective Behaviors?
As Table 4 shows, political orientation was associated with the WHO
recommended health-protective behaviors, including wearing a face covering, such
that more conservative individuals engaged in less health behaviors. Conservatives
9
also indicated less willingness to be vaccinated. These effects were larger among US
participants versus non-US participants at every time point, and the interactions
between political orientation and country held during our robustness checks, all p’s <=
.001
:
Table 4
Correlations between political orientation and perceived risk, perceived effectiveness, and
health-protective behaviors
Date US r(N) Non-US r(N) U.S. vs. Non-US
Comparison
Perceived Risk
Baseline (March-July) -.13*** (10912) -.08*** (51570) F = 5.87* ƞ2 <.001
Late March/Early April -.25*** (541) .01 (983) F = 18.24*** ƞ2= .011
Mid-April -.18*** (2679) -.07*** (3527) F = 12.46*** ƞ2= .002
Mid/Late April -.17*** (1863) -.06*** (3633) F = 9.67** ƞ2= .002
Late April/Early May -.22*** (1361) -.08*** (6608) F = 17.18*** ƞ2= .002
Early/Mid May -.15*** (1037) -.08*** (6252) F = 3.18 ƞ2 < .001
Mid-May -.20*** (603) -.06*** (4672) F = 8.88** ƞ2= .002
Late May/Early June -.17*** (746) -.05*** (4066) F = 7.52** ƞ2= .002
Mid-June -.22*** (772) -.05** (4145) F = 18.23*** ƞ2= .004
Mid-July -.19*** (693) -.06*** (3617) F = 7.80** ƞ2= .002
Severity of Contracting the Virus
Baseline (March-July) -.08*** (10914) .03*** (51684) F = 87.65***, ƞ2= .001
Effectiveness of Social Distancing
Early/Mid-April -.19*** (2679) -.03 (3528) F = 27.20*** ƞ2= .004
Mid-April -.23*** (1864) -.04* (3636) F = 33.27*** ƞ2= .004
Late April -.25*** (1362) -.04** (6608) F = 50.20*** ƞ2= .006
Effectiveness of Wearing a Mask/Face Covering
Mid-May -.13*** (966) .08*** (5592) F = 38.23*** ƞ2= .006
Late May -.19*** (838) .06*** (4516) F = 44.06*** ƞ2= .008
Early June -.11* (552) .07*** (3598) F = 16.10*** ƞ2= .004
Mid-July -.17*** (693) .03 (3618) F = 22.35*** ƞ2= .005
WHO Virus Mitigation Behaviors
March-June -.13*** (11030) -.03*** (52072) F = 76.38*** ƞ2= .001
Late April/Early May -.19*** (1362) -.02** (6610) F = 34.81*** ƞ2= .004
Mid-June -.28*** (772) .02 (4144) F = 53.11*** ƞ2= .011
Mid-July -.26*** (693) .03 (3621) F = 42.98*** ƞ2= .010
Willingness to be Vaccinated
Late April/Early May -.31*** (1362) -.06*** (6534) F = 65.37*** ƞ2= .008
Mid-June -.29*** (772) -.05** (4115) F = 33.71*** ƞ2= .007
Mid-July -.30*** (693) -.09*** (3597) F = 20.14*** ƞ2= .005
Wearing a Mask
Mid-May -.23*** (888) .06*** (5050) F = 46.34*** ƞ2= .008
Late May -.32*** (807) .02 (4314) F = 49.03*** ƞ2= .009
Early June -.21*** (530) .00 (3417) F = 11.37*** ƞ2= .003
Mid-July -.30*** (693) -.02* (3329) F = 11.84*** ƞ2= .003
*p <.05, **p<.01, ***p<.001
Note. The baseline assessment was a cross-sectional survey that participants completed
before matriculation into the longitudinal component of the study.
;
Mediational Analyses
As in Study 1, we used the PROCESS macro (Hayes, 2013; seed = 31216) to
examine whether the relationship between political orientation and health-protective
behaviors was mediated by perceived health risk, perceived severity of contracting the
virus, or perceived efficacy of health behaviors (Model 4), as well as whether indirect
effects were moderated by country (Model 7). Note, that political orientation was
exclusively measured at Baseline, whereas perceived health risk and health-protective
behaviors were measured across multiple time points. When building mediational
models, we used concurrent reports of risk and health behavior, thus maximizing
sample size for each test. When concurrent reports were not available, we used the
report of perceived risk from the most recent wave prior to the health behavior
assessment (e.g., behavior in Wave 8, perceived risk in Wave 7). Because all
correlations remained after our robustness checks, tests of indirect effects neither
included covariates nor nested participants into country.
Across all health behaviors and mediators, we observed consistent patterns of
a) mediation of health behaviors by perceived risk, perceived consequences, and
effectiveness of relevant health behaviors and b) differences in the magnitude of the
indirect effect in the US participants relative to the non-US participants. All
mediational models were supported (see Tables S3-4 for full details). To illustrate the
differences in magnitude of the indirect effect, Figures 1 and 2 show the indirect
pathways (i.e., a x b coefficients) of political orientation through the mediators of
perceived risk (Figure 1) or perceived effectiveness (Figure 2) on relevant health
behaviors. Notably, the associations between political orientation and perceived
effectiveness of health behaviors were negative for participants living in the United
States, but positive for participants living outside of the US, consistent with our
6
hypothesis regarding the unique effects of political orientation in the US. In addition
to these effects, we evaluated whether perceiving social distancing to be effective
(Wave 4) mediated the link between political orientation and WHO-recommended
virus mitigation behaviors (which include two items relevant to maintaining social
distance). As with other pathways, we observed significant moderated mediation (B
= .0010; 95% CI: .0007, .0013), such that the indirect effect was larger for participants
living in the US (B = -.0011; 95% CI: -.0014, -.0009) than for participants living
outside the US (B = .-.0002; 95% CI: -.0003, -.0001).
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Figure 1
Indirect effects (a x b coefficients) of political orientation on health-protective behaviors via perceived risk
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Figure 2
Indirect effects (a x b coefficients) of political orientation on wearing a face covering
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General Discussion
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In Study 1, we assessed political orientation (on March 10th) as well as
perceived risk of getting COVID-19 and health-protective behaviors (i.e., WHO-
recommended virus mitigation behaviors) during five time points over two months in
a MTurk sample of people living in the U.S. Consistent with our predictions,
correlations showed that more conservative respondents perceived lower risk of
getting infected by COVID-19 and were less likely to enact recommended health-
protective behaviors. A mediation analysis supported our theory-based prediction that
the association between political orientation and compliance with health behavior
recommendations was mediated by the difference in perceived risk of infection in all
but one wave.
In Study 2, we collected a large sample of participants living in the U.S. as
well as in several other countries. This procedure allowed us not only to test our
predictions about the association of political orientation with risk perception and
various manifestations of health behavior (i.e., social distancing, mask wearing, and
vaccination intentions), but also to assess whether this association was specific to the
political situation in the U.S. This study was also longitudinal, with participants being
resurveyed frequently between March 19th and July 13th, of 2020, allowing us to
observe whether effects were consistent over time. In line with predictions, political
orientation was negatively associated with perceived risk, expected severity of a
COVID-19 infection, and the perceived effectiveness of social distancing and wearing
7
a face covering. Because U.S. President Trump and other U.S. conservatives regularly
refused to wear masks and held widely publicized and large political rallies where
social distancing recommendations were ignored, they sent explicit and implicit
messages that these behaviors were not effective. Conservative American residents
were less likely to engage in any of our measured behaviors than were liberal
American residents. In line with predictions from health behavior models, these
health-protective behaviors were indirectly predicted by perceived risk, anticipated
illness severity, and effectiveness of the behaviors. Moreover, these associations were
a) significantly stronger in the U.S. than in the combined sample of Non-US countries
and b) persisted beyond other objective measures of actual risk (i.e., local population,
local virus outbreak). These patterns are consistent with our prediction that deleterious
effects of political orientation on health behaviors are specific to the U.S. and to the
present conservative leadership. Indeed, outside of the U.S., conservatives were more
likely than liberals to believe masks would provide personal protection (and were
consequently more likely to report wearing a face covering). Moreover, these patterns
are different than what might be expected by evidence that conservatives are more
sensitive to threats (especially physical threats) than liberals (Jost et al., 2003;
Pedersen et al., 2018) and that conservatives in the U.S. (relative to liberals in the
U.S.) expressed more concern about a pandemic happening under other political
leadership; Pew Research Center, October 21, 2014). Thus, although we did not
empirically assess attention to or agreement with conservative leadership and news
sources, the patterns we observe are different than what might be expected from past
research on conservative responses to virus threats, pointing to the unique role present
conservative leadership might be playing.
77
Because our data are correlational—as must be the case in a study of the
impact of historical events—we cannot draw causal conclusions. What we can show,
however, is that the pattern of our data is consistent with such a causal interpretation.
And such support is evidenced by the consistent pattern of a mediation of health
behaviors by perceived risk of getting infected, perceived severity of the
consequences of such an infection, and perceived efficacy of relevant health behaviors
in preventing such negative outcomes from happening. Finally, the difference in the
magnitude of the indirect effects between the U.S. and the non-U.S data suggest that
these effects are specific to the situation in the U.S. Figures 2 1 and 2, which show the
indirect effect coefficients for U.S. and Non-US participants, illustrate the uniqueness
of the U.S. effects. We also found parallel patterns for the perceived effectiveness of
socially distancing. Political orientation predicted willingness to socially distance and
this association was mediated by perceived effectiveness, with the mediation effect
again being moderated by U.S. versus Non-US location.
As all research, our research has strengths and weaknesses. One weakness
is that we used suboptimal measures of political orientation, particularly in Study 2.
Whereas Republican-leaning Americans are likely to rate themselves as more
conservative than would Democrat-leaning Americans, this association is less clear
for the economic dimension of the political compass used in Study 2. The political
compass measure was chosen in order to make the political orientation data
comparable across countries. However, the fact that the correlations are of a similar
magnitude in both studies suggests that the right to left dimension was similar to the
conservative to liberal continuum. Most likely, Republican-leaning conservatives
would identify as right-leaning relative to Democratic-leaning liberals. However, not
all conservatives are Republican, Trump supporters, or viewers of Fox News, and
78
these characteristics may more directly index, relative to conservativism, exposure to
messages that have downplayed COVID-19, as well as susceptibility to influence by
such messages. Thus, if we had directly asked respondents whether they support
President Trump or attend primarily to news sources such as Fox News, News, these
variables may have predicted our health outcomes more strongly than conservativism.
A second potential weakness is the fact that we did not measure all variables during
all waves. However, the consistency of patterns found across waves suggests that this
would have made very little difference.
Our studies also have several strengths. They illustrate both the applicability
of social- and health-psychological theories to address a real-world issue and the use
of a real-world problem to test psychological theories. The starting point of our
analysis is the political situation in the U.S., where the president as well as leading
conservative politicians have consistently downplayed the severity of the COVID-19
pandemic and belittled the effectiveness of the scientifically-based recommendations
regarding health-protective behavior. Like other researchers before us, as well as
opinion surveys, we showed that political orientation is associated with compliance
with recommended behaviors. We then tested and supported the theory-based
prediction that this association was mediated by risk perception, perceived severity of
the infection as well the perceived effectiveness of the recommended health-
protective behaviors.
Another major strength of our studies is the size of our samples and their
longitudinal design. Both studies utilized large samples and were longitudinal,
allowing us to examine the stability of associations over five waves in Study 1 and ten
waves in Study 2. The fact that the associations hardly changed over the months
during which these surveys were conducted indicates their high stability over the
79
course of a changing pandemic. Even in Study 2, we observed consistency across
different health behaviors. Study 2 also permitted a comparison of the U.S. with many
other countries, which demonstrated the operation of influences that are specific to the
USA, as opposed to processes that are fundamental to conservative beliefs.
Admittedly, the effect sizes representing the association between political
orientation and compliance with health behavior recommendation observed in our
samples are small. However, weak effects on an individual level can still have
powerful impact on a population level. For example, even though smokers run a much
greater risk of lung cancer than non-smokers, the 10-year absolute risk of lung cancer
for 35-year old man who is a heavy smoker is only about 0.9% (Jeffery, 1989). And
yet, these small effects have great impact on a population level. In a group of 1
million heavy smokers aged 35, nearly 10,000 will die prematurely before the age of
45 due to smoking (Jeffery, 1989). Seven months after the WHO declared COVID-19
a pandemic, there is still no alternative to lowering population level risk than the
behaviors they initially recommended (c.f., the addition of masks or face coverings).
During this interim, communications by a trusted source that downplays the risk or
consequences of a COVID-19 infection, or the effectiveness of recommended
behaviors to prevent infection, will likely decrease the probability of engaging in
these behaviors. Such communications may have rather dire consequences.
Individuals who fail to comply with health behavior recommendations increase their
chances of contracting COVID-19, dying or suffering long-term effects from the
disease, and spreading it to others. Indeed, U.S counties that voted for Donald Trump
over Hillary Clinton in 2016 have not only exhibited less social distancing, but this
reduction in social distancing was associated with subsequently higher COVID-19
infections and fatalities (Gollwitzer et al., 2020). In the case of a pandemic, slowing
7:
or stopping the spread can have cumulative public health as well as economic and
social benefits. Communication from leaders who downplay risks can lead to
increased spread of the disease and ultimately increase the death toll.
7;
References
Alfano, V., & Ercolano, S. (2020). The efficacy of lockdown against COVID-19: A
cross-country panel analysis. Applied Health Economics and Health Policy, 18,
509-517. https://doi.org/10.1007/s40258-020-00596-3
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications
and data analysis methods. Thousand Oaks, CA: Sage.
Calvillo, D. P., Ross, B. J., Garcia, R. J. B., Smelter, T. J., & Rutchik, A. M. (2020).
Political ideology predicts perceptions of the threat of COVID-19 (and
susceptibility to fake news about it). Social Psychological and Personality
Science. Advance online publication.
https://doi.org/10.1177/1948550620940539
CBS New (August 10th, 2020). Unmasked: How Trump’s mixed messaging on face-
covering hurt U.S. coronavirus response. Retrieved from:
https://www.nbcnews.com/politics/donald-trump/calendar-confusion-february-
august-trump-s-mixed-messages-masks-n1236088.
CNN (29th April, 2020). Pence flouts Mayo Clinic policy on masks- which is to wear
one. Retrieved from: https://edition.cnn.com/2020/04/28/politics/mike-pence-
mayo-clinic-mask/index.html
de Hoog, N., Stroebe, W., & de Wit, J. B. F. (2005). The impact of fear appeals on
processing and acceptance of action recommendations. Personality and Social
Psychology Bulletin, 31, 24-33. https://doi.org/10.1177/0146167204271321
de Hoog, N., Stroebe, W., & de Wit, J. B. F. (2007). The impact of vulnerability to and
severity of a health risk on processing and acceptance of fear-arousing
communications: A meta-analysis. Review of General Psychology, 11, 258-285.
https://doi.org/10.1037/1089-2680.11.3.258
76
Gallup (July 13th, 2020). American’s facemask usage varies greatly according to
demographics. Retrieved from: https://news.gallup.com/poll/315590/americans-
face-mask-usage-varies-greatly-demographics.aspx
Gimpel, J. G., Lovin, N., Moy, B., & Reeves, A. (2020). The urban–rural gulf in
American political behavior. Political Behavior, 1-26.
https://doi.org/10.1007/s11109-020-09601-w
Forbes (September 10th, 2020). All the times Trump compared COVID-19 to the Flu,
even after he knew COVID-19 was far more deadly. Retrieved from:
https://www.forbes.com/sites/tommybeer/2020/09/10/all-the-times-trump-
compared-covid-19-to-the-flu-even-after-he-knew-covid-19-was-far-more-
deadly/#68e832b8f9d2
Gollwitzer, A., Martel, C., Brady, W. J., Knowles, E. D., & Van Bavel, J. (2020).
Partisan Differences in Physical Distancing Predict Infections and Mortality
During the Coronavirus Pandemic. Available at SSRN 3609392.
Guardian (September 14th, 2020). Trump’s first indoor rally since June defies Covid
laws, attacks Biden. Retrieved from https://www.theguardian.com/us-
news/2020/sep/14/trumps-first-indoor-rally-since-june-defies-covid-laws-
attacks-biden?CMP=Share_iOSApp_Other
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process
analysis. A regression-based approach. New York, NY: Guilford.
Hovland, C. I., & Weiss, W. (1951). The influence of source credibility on
communication effectiveness. Public Opinion Quarterly, 15, 635-650.
https://doi.org/10.1086/266350
Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health
Education Quarterly, 11, 1-47. https://doi.org/10.1177/109019818401100101
7%
Jeffery, R.W. (1989) Risk behavior and health: contrasting individual and population
perspectives. American Psychologist, 44, 1194-1202.
https://doi.org/10.1037/0003-066X.44.9.1194
Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. J. (2003). Political conservatism
as motivated social cognition. Psychological Bulletin, 129(3), 339.
https://doi.org/10.1037/0033-2909.129.3.339
Kushner, S., Goodman, S. W., & Pepinsky, T. B. (2020). Partisanship, health behavior,
and policy attitudes in the early stages of the COVID-19 pandemic. Retrieved
from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3562796
Mediaite.com/news (April 2, 2020). 74 Journalism Professors sign letter slamming Fox
News over Coronvirus ‘Misinformation’: a danger to public health. Retrieved
from: https://www.mediaite.com/news/74-journalism-professors-sign-letter-
slamming-fox-news-over-coronavirus-misinformation-a-danger-to-public-
health/
Motta, M. (2018). The dynamics and political implications of anti-intellectualism in the
United States. American Politics Research, 46, 465-498.
https://doi.org/10.1177/1532673X17719507
New York Times (March 27th, 2020). Analyzing the patterns in Trump’s falsehoods
about Coronavirus. Retrieved from:
https://www.nytimes.com/2020/03/27/us/politics/trump-coronavirus-
factcheck.html
New York Times (September 14th, 2020). Trump defends indoor rally, but aides express
concern. Retrieved from:
https://www.nytimes.com/2020/09/14/us/politics/trump-rally.html?
referringSource=articleShare
84
New York Times (October 7th, 2020). Trump returns home after downplaying disease,
but doctor says he isn’t ‘out of the woods’. Retrieved from:
https://www.nytimes.com/live/2020/10/05/world/covid-trump
Pechar, E., Bernauer, T., & Mayer, F. (2018). Beyond political ideology: The impact of
attitudes towards government and corporations on trust in science. Science
Communication, 40, 291-313. https://doi.org/10.1177/1075547018763970
Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a
determinant of argument-based persuasion. Journal of Personality and Social
Psychology, 41, 847- 855. https://doi.org/10.1037/0022-3514.41.5.847
Pew Research Center (October 21, 2014). Ebola worries rise, but most are ‘fairly’
confident in government, hospitals to deal with disease. Retrieved from:
https://www.pewresearch.org/politics/2014/10/21/ebola-worries-rise-but-most-
are-fairly-confident-in-government-hospitals-to-deal-with-disease/
Pew Research Center (April 1st, 2020). Cable TV and COVID-19: How Americans
perceived the outbreak and view media coverage differ by main sources.
Retrieved from: https://www.journalism.org/2020/04/01/cable-tv-and-covid-19-
how-americans-perceive-the-outbreak-and-view-media-coverage-differ-by-
main-news-source/
Pew Research Center (April, 8, 2020). 5 facts about Fox News. Retrieved from
https://www.pewresearch.org/fact-tank/2020/04/08/five-facts-about-fox-news/
Pew Research Center (June 29, 2020). Americans rate CDC highly, Trump and his
administration poorly, on getting the facts right about COVID-19. Retrieved
from: https://www.journalism.org/2020/06/29/americans-rate-cdc-highly-trump-
and-his-administration-poorly-on-getting-the-facts-right-about-covid-19/
8$
Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of
five decades' evidence. Journal of Applied Social Psychology, 34, 243-281.
https://doi.org/10.1111/j.1559-1816.2004.tb02547.x
Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and
attitude change: a revised theory of protection motivation. In J. T. Cacioppo &
R. E. Petty (Eds.) Social Psychophysiology: A Source Book (pp. 153-176).
Guilford Press.
Rothgerber, H., Wilson, T., Whaley, D., Rosenfeld, D. L., Humphrey, M., Moore, A., &
Bihl, A. (2020). Politicizing the covid-19 pandemic: Ideological differences in
adherence to social distancing. Retrieved from: https://psyarxiv.com/k23cv/
Sheeran, P., & Abraham, C. (2005). The health belief model. In M. Conner & P.
Norman, Predicting Health Behaviour (pp. 28-80). Open University Press.
Simonov, A., Sacher, S.K., Dube’, J-P. & Biswas, S. (2020). The persuasive effect of
Fox News: Non-Compliance with social distancing during the COVID-19
Pandemic. NBER Working Paper No. 27237. Retrieved from:
https://www.nber.org/papers/w27237
Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., &
Albarracín, D. (2015). Appealing to fear: A meta-analysis of fear appeal
effectiveness and theories. Psychological Bulletin, 141, 1178–1204.
https://doi.org/10.1037/a0039729
Vinceti, M., Filippini, T., Rothman, K. J., Ferrari, F., Goffi, A., Maffeis, G., & Orsini,
N. (2020). Lockdown timing and efficacy in controlling COVID-19 using
mobile phone tracking. EClinicalMedicine, 25, 100457.
https://doi.org/10.1016/j.eclinm.2020.100457
8
Vox (March 20th, 2020). Hannity claims he’s “never called the virus a hoax” 9 days
after decrying Democrats’ “new hoax”. Retrieved from
https://www.vox.com/2020/3/20/21186727/hannity-coronavirus-coverage-fox-
news
Washington Post (March 16th, 2020). On Fox News, suddenly a very different tune
about the Corona Virus. Retrieved from:
https://www.washingtonpost.com/lifestyle/media/on-fox-news-suddenly-a-very-
different-tune-about-the-coronavirus/2020/03/16/7a7637cc-678f-11ea-9923-
57073adce27c_story.html