ArticlePDF Available

Donald Trump as a Cultural Revolt Against Perceived Communication Restriction: Priming Political Correctness Norms Causes More Trump Support


Abstract and Figures

Donald Trump has consistently performed better politically than his negative polling indicators suggested he would. Although there is a tendency to think of Trump support as reflecting ideological conservatism, we argue that part of his support during the election came from a non-ideological source: The preponderant salience of norms restricting communication (Political Correctness – or PC – norms). This perspective suggests that these norms, while successfully reducing the amount of negative communication in the short term, may produce more support for negative communication in the long term. In this framework, support for Donald Trump was in part the result of over-exposure to PC norms. Consistent with this, on a sample of largely politically moderate Americans taken during the General Election in the Fall of 2016, we show that temporarily priming PC norms significantly increased support for Donald Trump (but not Hillary Clinton). We further show that chronic emotional reactance towards restrictive communication norms positively predicted support for Trump (but not Clinton), and that this effect remains significant even when controlling for political ideology. In total, this work provides evidence that norms that are designed to increase the overall amount of positive communication can actually backfire by increasing support for a politician who uses extremely negative language that explicitly violates the norm.
Content may be subject to copyright.
Original Research Reports
Donald Trump as a Cultural Revolt Against Perceived Communication
Restriction: Priming Political Correctness Norms Causes More Trump
Lucian Gideon Conway III*a, Meredith A. Repkea, Shannon C. Houckb
[a] Psychology Department, University of Montana, Missoula, MT, USA. [b] Psychology Department, Syracuse University, Syracuse, NY,
Donald Trump has consistently performed better politically than his negative polling indicators suggested he would. Although
there is a tendency to think of Trump support as reflecting ideological conservatism, we argue that part of his support during
the election came from a non-ideological source: The preponderant salience of norms restricting communication (Political
Correctness – or PC – norms). This perspective suggests that these norms, while successfully reducing the amount of negative
communication in the short term, may produce more support for negative communication in the long term. In this framework,
support for Donald Trump was in part the result of over-exposure to PC norms. Consistent with this, on a sample of largely
politically moderate Americans taken during the General Election in the Fall of 2016, we show that temporarily priming PC
norms significantly increased support for Donald Trump (but not Hillary Clinton). We further show that chronic emotional
reactance towards restrictive communication norms positively predicted support for Trump (but not Clinton), and that this effect
remains significant even when controlling for political ideology. In total, this work provides evidence that norms that are designed
to increase the overall amount of positive communication can actually backfire by increasing support for a politician who uses
extremely negative language that explicitly violates the norm.
Keywords: Donald Trump, political correctness, communication norms, culture, backfiring
Journal of Social and Political Psychology, 2017, Vol. 5(1), 244–259, doi:10.5964/jspp.v5i1.732
Received: 2016-10-28. Accepted: 2017-03-29. Published (VoR): 2017-05-10.
Handling Editor: Małgorzata Kossowska, Jagiellonian University, Kraków, Poland
*Corresponding author at: 143 Skaggs Building, University of Montana, MT, USA. E-mail:
This is an open access article distributed under the terms of the Creative Commons Attribution License
(, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
“I’ve been talking about negatives, and you’re up on him!” said an
astounded [Republican pollster] Frank Luntz. “That’s the story of Trump’s
poll numbers.” (From Guo, 2015)
Controversial United States President Donald Trump presents a bit of a puzzle. Since the beginning of his candi-
dacy during the election season, Trump’s poll numbers suggested that a lot of people did not like him, and yet in
actual election results he has garnered surprising levels of support that belie those negatives (Guo, 2015).
Journal of Social and Political Psychology | 2195-3325
This juxtaposition can be seen in Trump’s often alarming use of non-normatively negative language. From his
comments about Mexicans being “murderers” and “rapists” (Guo, 2015), to questioning POW John McCain as a
war hero (Noble, 2015), to his insult of former GOP rival Carly Fiorina: "Can you imagine that, the face of our next
president?... are we serious?" (Cohen, 2017), Trump has shown that he does not conform to typical political lan-
guage. Yet despite all of his often bracingly insensitive language, Trump won both the Republican Party nomination
for president and ultimately the General Election.
One explanation for Trump’s success involves a socio-historical discussion that is essentially exclusive to conser-
vative American politics (see, e.g., Oliver & Rahn, 2016). For example, evidence suggests that increased author-
itarianism, increased social dominance, and lower cognitive abilities were all predictive of Trump support during
the election (Choma & Hanoch, 2017). Although these dimensions are important in our understanding of Trump
support, they may also lead to a dismissal of Trump’s electoral success as a function solely of traits associated
with ideologically extreme conservatism in the U.S. And while it is true that his supporters are largely ideologically
conservative (e.g., Burnett, 2016;Pew Research Center, 2016;Thompson, 2016), that is not the whole story. In
the present article, we expand upon existing social psychological theories of deviance to argue that part of his
support came from a source that is not directly ideological in nature: Namely, that the surprising levels of support
for Trump were the result of the salience of restrictive communication norms. In order to establish the connection
between psychological theory about cultural norms and Donald Trump, we first discuss a theoretical framework
on what causes deviance from cultural norms in the first place.
The Backfiring of Cultural Norms
It is practically an axiom in social psychology that cultural norms influence behavior. If there is a cultural norm to
drive on the right side of the road, then most people drive on the right side. If there is a cultural norm to drive on
the left side, then most people become left-side drivers. And when we shift cultural contexts, we frequently learn
which side of the cultural road to drive on – and drive accordingly. However, it is clear that people do not always
follow cultural norms, and an emerging, theory-driven literature has sought to more clearly illuminate those conditions
under which cultural norms do and do not induce compliance.
One theoretical perspective focuses on the sometimes ironic consequences of top-down, heavy-handed norms.
This communication perspective suggests that while top-down cultural norms may often produce short-term
compliance, the communication of heavy-handed norms sometimes simultaneously produces a sense of forced
artificiality that causes them to ultimately backfire and instead produce long-term deviance (see, e.g., Conway &
Schaller, 2005;Conway & Schaller, 2007;Conway et al., 2009). Thus, the forced consensus that a cultural norm
produces can be like a pane of glass with a small, barely perceptible crack: As long as all else remains equal, it
will remain as a single piece of glass – but a little change in context or pressure, and the glass pane will break
(see Conway & Schaller, 2005;Conway et al., 2009).
Why Do Cultural Norms Backfire? Reactance and Informational Contamination
What specifically causes the cracks on the pane of cultural glass? Prior research and theory suggests that this
can be for two separate reasons. (1) First is emotional reactance (e.g., Brehm, 1966;Crawford, McConnell, Lewis,
& Sherman, 2002;Fuegen & Brehm, 2004). Freedom of choice is a valued psychological commodity, and so we
often will deviate from others’ expectations—both in belief and in action—in order to re-assert our right to choose.
This feeling may be suppressed in the short-term to avoid embarrassment or public sanction; but in the long-term,
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 245
any reason (such as a surprisingly-popular candidate who is not politically correct) to express it may draw it out.
(2) Second is informational contamination (Conway & Schaller, 2005;Conway et al., 2009; see also Fein et al.,
1990;Harkins & Petty, 1987;Maio & Esses, 1998). A heavy-handed norm doesn’t just produce a strong emotional
response – it also cognitively undermines the informational value of any emergent consensus by causing observers
to attribute the consensus to the coercion induced by the norm instead of the potential information contained in
the observed behavior.
Thus, a heavy-handed norm may induce short term compliance, but it simultaneously sows the seeds of its own
later undoing: It makes people feel bad about the norm emotionally, and (independent of that) it cognitively con-
taminates the informational value that the norm otherwise might have produced.
Restrictive Communication Norms and Donald Trump
Viewed from this norm communication perspective, Donald Trump is not the cause of cultural deviance – rather,
support for him is (in part) the product of the salience of restrictive communication norms. To illustrate, we discuss
the specific set of communication norms in question and Donald Trump’s overlap with those norms.
The Backfiring of Positively-Aimed Communication Norms
An extremely powerful set of norms in North American society, often called “political correctness” (or “PC”) norms,
explicitly attempts to remove negative group-relevant language (see Conway et al., 2009). As a result, in situations
where the norms are in evidence, they create particularly strong public pressure to restrict one’s communication.
Although sometimes derided, few academics would disagree that the practical goal of the political correctness
movement is well-aimed.
One of the distinguishing features of modern theories of norm backfiring is that they do not require a norm to have
a negative aim itself in order to ultimately show negative effects. Indeed, some research suggests that over-
salience of PC norms may actually undermine its positive goal and produce more negative communication in the
long-term. In one set of research studies involving fictitious scenarios, the stated presence of a heavy-handed
PC norm caused participants to later report that they would communicate more negatively about a stereotyped
fraternity to a fictional “friend” in the scenarios (Conway et al., 2009; see also Conway & Schaller, 2005).
Donald Trump as Cultural Revolt Against Restrictive Communication Norms
“It’s not just that Trump is willing to be provocative – he’s exciting to
many people because he says things they feel they can’t say.”
(From Guo, 2015)
So there is reason to suspect that consistent salience of PC norms might cause a crack on the cultural pane of
glass. Is there reason to suspect that Donald Trump might be a product of that crack? Yes. As the above quote
suggests, it may be in part because of his politically incorrect rhetoric that he garnered support. At a general level,
evidence suggests that Trump’s grandiose rhetorical style was one of the reasons he won the Republican primary
(Ahmadian, Azarshahi, & Paulhus, 2017). Further and more specifically, polls from the election cycle suggested
that people liked his provocative language (Guo, 2015;Thompson, 2016) and that feeling voiceless better predicted
Trump support than multiple other variables, some of which include age, race, and attitudes towards Muslims, il-
legal immigrants, and Hispanics (Thompson, 2016). And indeed, Trump himself has publically recognized the
value of this anti-communication norm stance. As he said at the Republican Primary debate in August 2015: "I
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 246
think the big problem this country has is being politically correct. I’ve been challenged by so many people and I
don’t, frankly, have time for total political correctness” (quoted in Guo, 2015).
The Present Research
In summary, to date evidence exists that (1) restrictive communication norms can sometimes backfire in fictitious
scenarios (e.g. Conway et al., 2009), and (2) some people report they like Trump’s willingness to violate cultural
communication norms (Guo, 2015;Thompson, 2016).
However, this leaves many gaps to fill. (1) First, prior scientific work on the backfiring of PC norms has manipulated
norm salience only in hypothetical role-playing contexts where participants imagined their behavior in fictitious
scenarios using fictitious target groups. (2) Further, although polling and survey data suggest reasons to believe
that Trump is an iconic representative of anti-communication norms, no scientific test has directly tied support for
Trump to political correctness norms in a controlled experimental environment. (3) There is an implication in polling
data and public discourse to focus on support for Trump as a conservative ideological issue (e.g., Burnett, 2016;
Pew Research Center, 2016), and yet some academic analyses also suggest that Trump’s supporters may not
be true ideological conservatives (Noel, 2016). Thus, it is important to parse out the effects of restrictive commu-
nication norms from that of political ideology.
To accomplish these goals, in the midst of the 2016 U.S. Presidential election, we manipulated the salience of
PC norms by randomly assigning some participants to a restrictive communication prime where they read and
responded to a brief description of the benefits of PC norms. We then measured (a) support for Trump and Clinton,
(b) chronic concern with restrictive communication norms, and (c) political conservatism. We did this on a slightly
left-leaning (M= 4.36 on a 1-9 conservatism scale) sample of non-college adults (Mage = 34.38).
We expected that (1) participants’ chronic feelings of concern with restrictive communication norms would predict
support for Trump, and (2) priming restrictive communication norms would increase support for Trump. We further
expected that these effects would either not be in evidence – or be reversed – for Hillary Clinton.
Participants and Disclosures
Three hundred and twelve participants were recruited through Mechanical Turk. We chose Mechanical Turk in
part because it is been particularly validated for use as a representative sample for research related to politics
and political ideology (see, e.g., Clifford, Jewell, & Waggoner, 2015) and generally shows similar results as other
samples (for an example, see Houck, Conway, & Repke, 2014). Participation occurred in early-to-mid September,
a period where the race between Clinton and Trump had tightened, but Clinton still maintained a lead in the polls
(see We excluded seven participants who failed to
answer an attention-check question accurately, leaving 305 for final analyses. No data collection occurred after
data analysis, and all relevant manipulations, measures, and exclusions are disclosed.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 247
Restrictive Communication Prime Manipulation
Prime Condition
Some participants (n= 95) were randomly assigned to the Restrictive Communication Prime Condition. Participants
in this condition read the following introduction:
“First, we would like to get your opinions on societal norms. In our modern society, we have norms that
dictate that we refrain from saying negative things – especially those things deemed as politically incorrect
to say. These norms state that it is better to have rules that constrain us from anything that might sound
too-negative or might be offensive to members of particular groups. These social norms that discourage
too-negative conversation have many good benefits, and we first want to get your opinion on these norms
before moving forward.”
Participants were then asked to respond to four questions expressing either support for or opposition to those
norms. These questions (on a scale anchored by 1 and 7) asked participants the degree they were in favor of
such norms, that such norms have value, that they oppose societal pressures to restrict communication, and that
they believe in the value of norms governing communication.
Control Conditions
The present study used two different control conditions. First, some participants (n= 91) were randomly assigned
to receive a description parallel to Restrictive Communication Prime participants; however, their description con-
tained only positively-framed suggestions rather than negative restrictions (and made no reference to political
correctness). Participants in this condition also answered four questions in support of norms that suggest we
should be positive, sensitive, and respectful in our communication.
A second group of participants (n= 119) was randomly assigned to receive no introduction about communication
or complete any questions relevant to communication norms.
These two control conditions showed largely identical patterns in the present work; for simplicity, we combined
them into one Control Condition.i
Dependent Measures: Support for Trump and Clinton
Voting Intent
In two separate items, participants were first asked to report on a 7-point rating scale how likely they were to vote
for Trump and Clinton, respectively.
Trait Positivity Ratings
Participants were then asked to rate each candidate on a series of parallel trait rating scales drawn from prior
work (e.g., Conway et al., 2012). We organized these by semantic meaning: Five questions pertained to honesty
(honest, liar [reverse], speaks mind, goes against norms, truthful; alphas = .92 and .87), four pertained to adapt-
ability (flexible, thoughtful, complex, smart; alphas = .87 and .90), three pertained to strength (strong leader, tough,
consistent; alphas = .85 and .88), two to general likability (like, respect; alphas = .93 and .91), and one each to
kindness and attractiveness.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 248
In order to produce an overall trait positivity rating that was unbiased by this semantic organization, we converted
all sixteen trait rating items to z-scores and averaged them into a single trait positivity score for each candidate
(alphas = .96).
Cumulative Trump and Clinton Support Measures
To capture the overall degree across all measures that participants supported Trump and Clinton, we converted
each candidate’s voter preference and trait positivity scores to z-scores and averaged them into cumulative Trump
Support (alpha = .92) and Clinton Support (alpha = .88) measurements.ii
Restrictive Communication Concern
We measured two constructs related to restrictive communication concern by adapting items from prior studies
(Conway & Schaller, 2005;Conway et al., 2009). Three items measured reactance (alpha = .55): “I often feel
pressured by society to keep my own negative political opinions to myself – and when I do, it makes me feel like
I want to say my opinions anyway so no one can tell me how to think or talk,” “I often feel like political pundits are
trying to make me fit their own narrow views and it makes me upset,” and “Political correctness norms generally
aggravate me.”
Six items measured informational contamination (alpha = .71):“I believe that a lot of what politicians say is just
to avoid saying things that might offend particular groups,” “I distrust a lot of what politicians say because I assume
it is reflective of some political agenda,” “I believe that a lot of what politicians say is just to avoid saying things
that might offend particular groups,” “I believe that politicians generally keep their real opinions to themselves
because if they didn’t, they would be in trouble with powerful groups in society,” “I believe that politicians do not
respond to social norms telling them what to say and, therefore, what they say is what they generally really believe”
(reverse-scored), and “I distrust most of what members of the Democratic party say because I assume they are
just manipulating people,” and “I distrust most of what members of the Republican party say because I assume
they are just manipulating people.” Reactance and informational contamination were modestly correlated (r= .43).
Political Ideology
Participants completed two standard ideology items anchored by democrat/republican and liberal/conservative
on 1-9 rating scales (see, e.g., Conway, Gornick, et al., 2016;Conway, Houck, Gornick, & Repke, 2016). We av-
eraged them into a single measurement of political conservatism (alpha = .92, M= 4.36; SD = 2.30).
Chronic Concern With Restrictive Communication Predicting Support for Trump and
We correlated our two primary measurements of restrictive communication concern (reactance, informational
contamination) with measurements of support for Trump and Clinton. Table 1 presents the outcome of these
analyses both for zero-order predictions as well as those which control for political ideology.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 249
Table 1
Chronic Restrictive Communication Concern Predicting Support for Trump and Clinton
Informational ContaminationReactance
Controlling for IdeologyZero-OrderControlling for IdeologyZero-Order
Trump Trait Ratings
Honest .10-.09.20***.40***
Strong .02-.12*.18***.37***
Kind .13*-.04.18**.37***
Adaptable .04-.11.12*.32***
Attractive .18***-.07-.04.19***
General Liking .07-.10.25***.43***
Trump Total Trait Positivity .09-.09.18***.39***
Trump Voting Likelihood .14*-.08.10.35***
Total Trump Support .12*-.09.16**.38***
Clinton Trait Ratings
Honest .26***-.33***-.20***-.33***-
Strong .08-.18**-.22***-.37***-
Kind .23***-.30***-.22***-.36***-
Adaptable .01-.11-.16**-.30***-
Attractive .22***-.28***-.19***-.30***-
General Liking .17**-.26***-.21***-.38***-
Clinton Total Trait Positivity .17**-.26***-.23***-.37***-
Clinton Voting Likelihood .12*-.23***-.20***-.39***-
Total Clinton Support .16**-.26***-.24***-.40***-
Note. N = 305.
*p≤ .05. **p≤ .01. **p≤ .001.
As can be seen there, a pattern emerged for Reactance consistent with expectations: Persons who felt chronic
reactance at restrictive communication norms were significantly more likely to support Trump (overall support r=
.38) and significantly less likely to support Clinton (overall support r= -.40) – and both of those effects remained
significant even when controlling for political ideology (p’s < .01).
The pattern for informational contamination was less consistent. Informational contamination weakly but positively
predicted support for Trump, but this pattern was reversed when controlling for ideology. Support for Clinton was,
consistent with expectations, significantly negatively predicted by informational contamination (and this remained
significant when controlling for ideology).iii
Overall, these data suggest qualified support for the perspective outlined here: It is clear that support for Trump
(and opposition to Clinton) is especially likely amongst people who feel emotional reactance to restrictive commu-
nication norms – and importantly, this effect goes beyond political ideology. To a lesser degree, persons who express
concern about informational contamination conformed to the expected pattern for zero-order correlations; but
while a significant effect remained for Clinton in the expected direction when controlling for ideology, the effect
for Trump actually reversed (albeit weakly). We return to what these differences might mean in the Discussion.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 250
Priming of Restrictive Communication Norms
A series of One-Way ANOVAs compared the Restrictive Communication Prime versus Control Conditions. Table
2presents these results in full. As can be seen there, these results provided clear support for the norm backfiring
perspective outlined in the introduction: Participants made to think about restrictive communication norms showed
more support for Trump than controls (cumulative Trump Support F[1,304] = 5.39, p=.02). Also consistent with
expectations, this effect was exclusive to Trump (cumulative Clinton Support F[1,303] = 0.05, p=.82).iv
Table 2
Effects of Restrictive Communication Prime on Support for Trump and Clinton via One-Way ANOVAS
Difference p-valueEffect Size rRestrictive PrimeControl Conditions
Trump Trait Ratings
Honest .009*.15**.19.09-
Strong .
Kind .020*.13*.20.09-
Adaptable .
Attractive .398.05.07.03-
General Liking .047*.11*.16.08-
Trump Total Trait Positivity .039*.12*.14.06-
Trump Voting Likelihood .019*.13*.20.09-
Total Trump Support .021*.13*.19.09-
Clinton Trait Ratings
Honest .418.05.06.03-
Strong .852.01-.01-.01
Kind .667.03-.04-.02
Adaptable .560.04.05.02-
Attractive .872.01.01.01-
General Liking .920.01.01.00-
Clinton Total Trait Positivity .760.02.02.01-
Clinton Voting Likelihood .556.03-.05-.02
Total Clinton Support .823.01-.01-.01
Note. N = 305.
*p≤ .05. **p≤ .01. **p≤ .001.
Moderation and Mediation of Priming Effect
We expected that the effect of the prime would likely be especially in operation for persons who had higher levels
of chronic concern with restrictive norms. To test this, we performed simultaneous regression analyses (using the
SPSS macro developed by Hayes, 2013) entering (a) the dummy-coded prime, (b) each moderator, and (c) the
interaction of each moderator with the prime (for exemplars, see e.g., Conway & Schaller, 2005; Conway et al.,
2009). For brevity, we focus here on analyses predicting the cumulative support measures which incorporate all
the support-based measurements.
These results revealed that reactance was a significant moderator for the Prime – Trump Support effect (interaction
beta = .16, p= .03, lower CI = .01, upper CI = .32). Subsequent analyses (Hayes, 2013) revealed the expected
pattern: When participants felt low levels of reactance against restrictive norms, the effect of the prime on Trump
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 251
Support was small (beta = .13, p= .36); but at high levels, this effect was much larger and significant (beta = .57,
p< .001). This same moderating pattern held for informational contamination on Trump Support; however, the
interaction did not approach statistical significance (beta = .16, p=.184, LCI = -.08, UCI = .39).
A conceptually identical pattern emerged for Clinton Support, although which restrictive communication concern
variable was the strongest moderator was reversed: Informational Contamination was a significant moderator
(interaction beta = -.24, p= .034, lower CI = -.47, upper CI = -.02). Subsequent analyses revealed a pattern
largely consistent with expectations: When participants felt low levels of informational contamination, the effect
of the prime on Clinton Support was negative (beta = -.27); but at high levels, this effect was actually somewhat
reversed (beta = .22). This same moderating pattern held for reactance on Clinton Support; however, the interaction
did not approach statistical significance (beta = -.08, p= .33, LCI = -.23, LCI = .08).v,vi
To test the mediating effect of reactance and informational contamination, we tested their indirect effects in a series
of A(Prime)→B(Mediator)→C(Candidate Support) analyses (Hayes, 2013). These analyses revealed weak and
non-significant indirect effects of the two general concern measurements with restrictive communication norm
measurements (all Sobel tests of indirect effect ps > .16)vii – indeed, the prime did not significantly affect either
reactance or informational contamination, making the A→B→C path untenable. We return to what this might mean
for interpretation in the discussion.viii
General Discussion
Taken in total, these results provide support for a connection between the salience of restrictive communication
norms and support for Donald Trump. Participants primed to think about restrictive communication norms showed
more support for Trump than participants who were not so primed. Further, participants who felt chronic reactance
to communication norms were especially likely to express support for Trump (above and beyond their political
ideology), and were further especially likely to respond to the prime by increasing Trump support. In the main,
these results either did not hold – or were reversed – for supporting Clinton.
These results are not without their limitations and interpretational ambiguities, however. We first address those
limitations and then discuss, in larger perspective, what we can learn from these results.
Interpretation Obstacles
Lack of Mediation by Chronic Concern Measurements
One of the interpretational ambiguities in the present results involves the fact that, although chronic concern
measurements largely predicted support for Trump (and opposition to Clinton) across the whole sample, these
measurements did not also mediate the relationship between the Restrictive Communication Prime and Trump
Support. If the Prime operates by heightening the salience of restrictive communication norms, why would these
measurements not partially account for the effect of the prime on candidate preference?
A few points suggest that this lack of mediation was likely the result of our reactance and informational contami-
nation measurements being too stable to effectively serve as the mediator for the effect of a temporary prime.
First, at face value, the questions were written at a very general level to apply to any political group, norm, or
politician. Prior work that showed the mediation of emotional reactance and informational contamination on PC
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 252
norm priming was written specifically for a very clearly identified scenario – thus the overall psychological distance
between the prime, proposed mediator, and dependent measure was far less in that work than in the present
study (see Conway et al., 2009). As an initial test of the possibility that this helps account for our results, we took
the three questions from the trait positivity rating that were most related to scenario-specific concern with commu-
nication norms and combined them into a single measurement of scenario-specific concern with communication
norms (all reverse-scored; alpha = .87). The three items were: “I appreciate that Donald Trump seems to say
whatever is on his mind, even if it upsets people,” “For the most part, I trust Donald Trump to speak truthfully,”
and “Donald Trump goes against cultural norms that restrict free speech, and that’s a good thing.” We then used
this measurement as the mediator an A(Prime)→B(Mediator)→C(Outcome) analysis of indirect effects. (To avoid
too much overlap between related measurements, we used only voting preference as the outcome measurement.
Results are identical if we use the overall Trump Support measurement). Results suggested a significant indirect
effect of the newly-constructed, Trump-specific communication norms measurement (beta = .50; lower CI = .04;
upper CI = .96; Sobel test z= 2.16; p=.03). We recognize that this measurement has some method overlap (and
that is one of the reasons why we did not use it as the primary measurement of the construct in the first place)
and it should be interpreted cautiously. However, it is at least consistent with the notion that there was a more
transient communication-relevant mediator of the present results.
Second, the chronic measurements of emotional reactance and informational contamination did interface with the
prime; however, they interfaced as one would expect a measurement that is more chronic and thus less subject
to direct temporal fluctuations. Specifically, these measurements served as fairly consistent moderators of the
prime→support relationship.
Third, these chronic measurements were predictive overall of support for Trump (and especially, opposition to
Clinton) in ways that are generally consistent with predictions (with some caveats we return to later in the discus-
sion). This is again consistent with their status as a more stable measurement that is relevant to the proposed
outcomes at a larger, more stable level.
Finally, these results do not stand alone. Although they are unique in their application of political correctness
priming to Trump support, other work shows the potential backfiring of communication norms and artificial consensus
in other domains (Conway & Schaller, 2005;Conway et al., 2009). As a result, in the larger theoretical landscape,
there is reason to believe that concern with restrictive communication norms may have an impact in the present
Taken together, this evidence suggests that while the lack of direct mediation by those measurements is a
weakness in the present results, we feel there is ample reason to nonetheless trust that the larger picture
emerging here is one best captured by a theoretical angle focusing on concern with restrictive communication.
Inconsistency of Chronic Concern With Communication Norms?
Another interpretational ambiguity lies in the fact that Trump Support was predicted by chronic emotional reactance
but not chronic informational contamination. What are we to make of this inconsistency?
One possibility is that Trump simply evokes more emotional deviance than cognitive deviance. Reactance is more
purely emotive (see Knowles & Linn, 2004); informational contamination is more purely cognitive (see Conway &
Schaller, 2005;Conway et al., 2009). Recall that in prior research and theory these two types of deviance are in-
dependent predictors of cultural norms backfiring. Every theoretically sound predictor cannot predict every phe-
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 253
nomenon – and in this case, it may be rather instructive that it is emotional reactance, and not informational con-
tamination, that predicts Trump support. To the degree this is true, it suggests that it is more of a guttural emotive
dislike of restriction than a cognitive erosion of information that is behind Donald Trump’s support.
Indeed, it is noteworthy that these results actually show an interesting set of consistencies across analyses that
partially support this conclusion. (1) First, not only was emotional reactance a significant predictor of Trump support
overall, it (and not informational contamination) was a significant moderator of the effect of the prime on Trump
support. This suggests the prime is having its effect on people who have a chronic emotional dislike of restrictive
communication norms, more so than people who are cognitively concerned with informational contamination. (2)
Equally as interestingly, informational contamination had much more of an impact on support for Clinton (compared
to support for Trump) in the opposite direction, both as a direct predictor and as a moderator of the prime→Clinton
support relationship. While it is difficult to fully know how to interpret this and any interpretation must remain
speculative, it is at least consistent with the notion that support for Clinton is more of a function of cognitive concerns
than is support for Trump.
What Can We Learn?
At a minimum, these results provide evidence that (a) priming restrictive communication norms and (b) chronic
emotional reactance against those norms both increased support for Donald Trump during the general election.
These results help us in our theoretical understanding of culture in multiple ways. First, they provide for the first
time to the authors’ knowledge an experimentally-manipulated example of a cultural norm’s ironic effect on a real-
world political example. Indeed, a reasonable criticism of much of the past work on the backfiring effect of cultural
norms is that it focuses on hypothetical scenarios that are removed from the real world (see, e.g., Conway et al.,
2009). While this prior work has value, it is important to establish any set of effects in real contexts that truly
matter to people, and the present study does just that.
Second, these results also contribute to the larger theoretical discussion about how cultures emerge, change,
and have influence. It is noteworthy that, unlike in prior research on the backfiring of communication norms, the
prime in this study bore little direct linguistic overlap with the outcome measurement (support for Donald Trump).
As a result, it suggests the pervasive value of understanding the eventual emergence of controversial political
figures through the lens of communication-based theories of cultural norms and cultural deviance.
Cultures emerge and change for many reasons. These can be considered in two basic categories: (1) Top-down
ecological or political pressures – such as frontier topography or dictatorial political systems – may directly constrain
how culture emerges (e.g., Conway et al., 2014;Kitayama, Conway, Pietromonaco, Park, & Plaut, 2010;Murray
& Schaller, 2014;Van de Vliert, 2013). (2) However, often communication processes operate independent of
these top-down pressures to shape the emergence and influence of culture (e.g., Conway, 2004;Conway &
Schaller, 2007;Conway, Sexton, & Tweed, 2006;Schaller, Conway, & Tanchuk, 2002). The present study falls
squarely at the intersection of these two categories. It suggests that the top-down pressures that often shape
culture are themselves subject to communication and psychological processes that direct their influence. If the
top-down communication norms are too heavy-handed, they may indeed create a forced consensus; but that
consensus is psychologically fragile and prone to break. The present results thus complement prior work on hy-
pothetical scenarios, providing a very important bellwether test for the degree that large-scale cultural norms might
ironically rebound. They suggest that instead of producing the niceness that they are intended to produce, these
norms instead might lead to more nasty political discourse in the long-term.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 254
Third, these results contribute to the larger literature on the importance of the immediate context to political deci-
sions. Consider, for example, work on framing that demonstrates the specific way a particular finding is discussed
– or framed – can have a great deal of impact on political attitudes and behaviors (Bizer, Larsen, & Petty, 2011;
Bizer & Petty, 2005;Brewer & Gross, 2005;de Vreese, Boomgaarden, & Semetko, 2011). Some of this works
suggests that positive framing of news articles can increase the likelihood of endorsement of a specific opinion
(e.g., de Vreese et al., 2011). Other work shows that framing an attitude as an “opposition” to something increases
the subsequent longevity of the opinion and its strength (Bizer & Petty, 2005;Bizer et al., 2011).ix
Like this prior work on framing, our work also suggests a mechanism by which the immediate context can influence
political attitudes. Specifically, our work suggests that the immediate salience of chronic communication norms
may aid candidates who appear to violate those norms. Thus, communication norms can function psychologically
in much the same way that frames can by focusing persons on a part of the context that ultimately shapes their
attitudes and decisions.
Finally, these results also demonstrate the value of applying both existing social psychological theory and exper-
imental methods to understand puzzles in the modern world. A descriptive illustration of this power can be seen
in Figure 1. Looking at the raw mean scores for voting preference – the clearest marker in our study of the likelihood
of potential cultural change – reveals a powerful pattern: In control conditions, Clinton was soundly preferred to
Trump (Clinton M= 3.66, SD = 2.41; Trump M= 2.63, SD = 2.28), but when Political Correctness norms were
made salient, this gap virtually disappeared (Clinton M= 3.48, SD = 2.51; Trump M= 3.32, SD = 2.51).
Figure 1. Voting likelihood for Trump and Clinton by restrictive communication prime.
Concluding Thoughts
Although Donald Trump presents an interesting paradox of sorts to modern political pundits, his emergence is
precisely what a theory focusing on the backfiring of social norms would expect. It is a paradox, but a theoretically-
expected one: As restrictive norms become ever more salient and heavy-handed, the more they will work in the
short-term. But in the long-term, this salient heavy-handedness increases the likelihood that they will ultimately
backfire. And this backfiring doesn’t just occur for norms that are genuinely repressive to political freedom – it also
occurs for norms that have a clearly good and noble aim.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 255
The present study suggests that communication norms that are designed to increase the amount of positive
communication may ultimately backfire in political figures like Donald Trump – figures who do anything but increase
the amount of positive communication. His emergence should serve as a lesson for students of cultural change
and deviance. He is not the first and will certainly not be the last example of popular figures emerging in response
to restrictive norms – and the present work illustrates specific psychological processes that help us better understand
why that occurs and when it will occur.
i) Originally, we included the positive communication control as a way of (a) delineating the exact psychological locus of any
differences between the Restrictive Communication Prime and the no-introduction Control (e.g., was a negative restrictive
tone necessary to find the effect as we suspected?), and (b) accounting for the general introduction confound (e.g., the positive
communication control, unlike the no introduction control, contains instructions and questions parallel in structure to the Prime,
but differing in content). Because our predictions for the positive communication Control were less clear than those for the no
introduction Control, this leaves open the possibility that lumping the positive communication Control with the no introduction
Control artificially inflates power. To account for this, we also performed analyses for major findings that excluded the positive
communication Control condition. We report these analyses in footnotes. As will be seen there, these analyses reveal a pattern
largely identical, both descriptively and inferentially, to that reported in the main text. Because we believe the positive
communication Control adds value to the manuscript (see discussion), we opted to include it here. However, excluding it would
not change the basic conclusions drawn from this study.
ii) We also included a categorical measurement of who participants indicated they would vote for. This measurement was
collected largely for sample description purposes and was never analyzed by condition for hypothesis testing. Like the continuous
measurement, it revealed a sample that was pro-Clinton (43% Clinton; 25% Trump); but it also revealed that 32% of the sample
reported that they were going to vote for a third party, would not vote at all, or were undecided.
iii) These results remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control. Reactance was positively correlated with cumulative Trump Support, both zero-order and while controlling for ideology
(zero-order r= .38, p< .001; partial r= .19, p = .006). Neither zero-order nor partial correlations were significant for Informational
Contamination predicting Trump Support (zero-order r= .08, p= .27; partial r= -.11, p = .12). Both Reactance and Informational
Contamination were negatively related to Clinton support at for both zero-order and partial correlations controlling for ideology
(r’s < -.22, p’s ≤ .001).
iv) These results remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control: The effect of the Prime on overall Trump Support remained significant (F[1, 212] = 4.51, p= .03), and the effect of
the Prime on overall Clinton Support remained non-significant (F[1,212] = 0.33, p= .56).
v) We also performed parallel moderation analyses for political ideology. These results revealed that ideology did not significantly
moderate the effect of the prime on either Trump Support (interaction p= .271) or Clinton Support (interaction p= .49), although
– descriptively speaking – the Prime worked much more effectively on political conservatives than on political liberals.
vi) These effects remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control: The Prime X Reactance interaction remained significant for Trump Support (p= .02) and the Prime X Informational
Contamination interaction was nearly-significant for Clinton Support (p= .05). The Prime X Informational Contamination on
Trump Support and Prime X Reactance Interaction on Clinton Support were in the same direction descriptively but, as in the
main text analyses including all participants, were not significant.
vii) When excluding the positive communication Control, these results were virtually identical (all Sobel test p’s > .34).
viii) Interestingly and unexpectedly, the Prime made people more report more general conservatism based on our self-report
political ideology measure, and this ideology measurement showed a significant indirect effect in an A→B→C model for both
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 256
Trump Support and Clinton Support (Sobel test p’s < .03). This suggests the interesting possibility that the Prime’s effects on
Trump and Clinton are being partially carried through a general shift in conservatism.
ix) Although it was not our primary focus, one might draw a parallel in our study to positive versus negative framing. In particular,
as we suspected, a control condition that had a parallel, softer, “positive” discussion of communication norms showed essentially
the same effects as a no-introduction control. This is further consistent with the fact that the results for Trump are more a
function of emotional reactance than informational contamination, because emotional reactance (unlike informational
contamination) is more dependent on the emotional tone of a message (see Conway & Schaller, 2005;Conway et al., 2009).
Thus, it is in the heavy-handed restrictive framing of the norm that people find a desire to culturally revolt and support Trump.
This corroborates the basic lesson of this study – and provides hope that softer, more positively-framed instantiations of
communication norms may be more effective.
The authors have no funding to report.
Competing Interests
The authors have declared that no competing interests exist.
The authors have no support to report.
Ahmadian, S., Azarshahi, S., & Paulhus, D. L. (2017). Explaining Donald Trump via communication style: Grandiosity, informality,
and dynamism. Personality and Individual Differences, 107, 49-53. doi:10.1016/j.paid.2016.11.018
Bizer, G. Y., Larsen, J. T., & Petty, R. E. (2011). Exploring the valence-framing effect: Negative framing enhances attitude
strength. Political Psychology, 32, 59-80. doi:10.1111/j.1467-9221.2010.00795.x
Bizer, G. Y., & Petty, R. E. (2005). How we conceptualize our attitudes matters: The effects of valence framing on the resistance
of political attitudes. Political Psychology, 26(4), 553-568. doi:10.1111/j.1467-9221.2005.00431.x
Brehm, J. (1966). Psychological reactance: A theory of freedom and control. New York, NY, USA: Academic Press.
Brewer, P. R., & Gross, K. (2005). Values, framing, and citizens’ thoughts about policy issues: Effects on content and quantity.
Political Psychology, 26, 929-948. doi:10.1111/j.1467-9221.2005.00451.x
Burnett, B. (2016, August 5). “Who are Trump Voters?” [Blog post]. The Huffington Post: The Blog. Retrieved from
Choma, B. L., & Hanoch, Y. (2017). Cognitive ability and authoritarianism: Understanding support for Trump and Clinton.
Personality and Individual Differences, 106, 287-291. doi:10.1016/j.paid.2016.10.054
Clifford, S., Jewell, R. M., & Waggoner, P. D. (2015). Are samples drawn from Mechanical Turk valid for research on political
ideology? Research and Politics, October-December 2015, 1-9. doi:10.1177/2053168015622072
Cohen, C. (2017, January 20). Donald Trump sexism tracker: Every offensive comment in one place. The Telegraph. Retrieved
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 257
Conway, L. G., III (2004). Social contagion of time perception. Journal of Experimental Social Psychology, 40, 113-120.
Conway, L. G., III, Gornick, L. J., Burfeind, C., Mandella, P., Kuenzli, A., Houck, S. C., & Fullerton, D. T. (2012). Does simple
rhetoric win elections? An integrative complexity analysis of U.S. presidential campaigns. Political Psychology, 33, 599-618.
Conway, L. G., III, Gornick, L. J., Houck, S. C., Anderson, C., Stockert, J., Sessoms, D., & McCue, K. (2016). Are conservatives
really more simple-minded than liberals? The domain specificity of complex thinking. Political Psychology, 37, 777-798.
Conway, L. G., III, Houck, S. C., & Gornick, L. J. (2014). Regional differences in individualism and why they matter. In P. J.
Rentfrow (Ed.), Geographical psychology: Exploring the interaction of environment and behavior (pp. 31-50). Washington,
DC, USA: American Psychological Association.
Conway, L. G., III, Houck, S. C., Gornick, L. J., & Repke, M. A. (2016). Ideologically-motivated perceptions of complexity:
Believing those who agree with you are more complex than they are. Journal of Language and Social Psychology, 35,
708-718. doi:10.1177/0261927X16634370
Conway, L. G., III, Salcido, A., Gornick, L. J., Bongard, K. A., Moran, M. A., & Burfiend, C. (2009). When self-censorship norms
backfire: The manufacturing of positive communication and its ironic consequences for the perceptions of groups. Basic
and Applied Social Psychology, 31, 335-347. doi:10.1080/01973530903317169
Conway, L. G., III, & Schaller, M. (2005). When authority’s command backfire: Attributions about consensus and effects on
deviant decision-making. Journal of Personality and Social Psychology, 89, 311-326. doi:10.1037/0022-3514.89.3.311
Conway, L. G., III, & Schaller, M. (2007). How communication shapes culture. In K. Fiedler (Ed.), Frontiers of Social Psychology:
Social communication (pp. 107-127). New York, NY, USA: Psychology Press.
Conway, L. G., III, Sexton, S. M., & Tweed, R. G. (2006). Collectivism and governmentally initiated restrictions: A cross-sectional
and longitudinal analysis across nations and within a nation. Journal of Cross-Cultural Psychology, 37, 20-41.
Crawford, M. T., McConnell, A. R., Lewis, A. C., & Sherman, S. J. (2002). Reactance, compliance, and anticipated regret.
Journal of Experimental Social Psychology, 38, 56-63. doi:10.1006/jesp.2001.1481
de Vreese, C. H., Boomgaarden, H. G., & Semetko, H. A. (2011). (In)direct framing effects: The effects of news media framing
on public support for the Turkish membership in the European Union. Communication Research, 38, 179-205.
Fein, S., Hilton, J. L., & Miller, D. T. (1990). Suspicion of ulterior motivation and the correspondence bias. Journal of Personality
and Social Psychology, 58, 753-764. doi:10.1037/0022-3514.58.5.753
Fuegen, K., & Brehm, J. W. (2004). The intensity of affect and resistance to social influence. In E. S. Knowles & J. A. Linn
(Eds.), Resistance and persuasion (pp. 39-64). Mahwah, NJ, USA: Mahwah Press.
Guo, J. (2015). The real reasons Donald Trump’s so popular — for people totally confused by it. [Blog post]. The Washington
Post: Wonkblog. Retrieved from
Harkins, S. G., & Petty, R. E. (1987). Information utility and the multiple source effect. Journal of Personality and Social
Psychology, 52, 260-268. doi:10.1037/0022-3514.52.2.260
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Donald Trump as Cultural Revolt 258
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach.
New York, NY, USA: The Guilford Press.
Houck, S. C., Conway, L. G., III, & Repke, M. A. (2014). Personal closeness and perceived torture efficacy: If torture will save
someone I’m close to, then it must work. Peace and Conflict, 20, 590-592. doi:10.1037/pac0000058
Kitayama, S., Conway, L. G., III, Pietromonaco, P. R., Park, H., & Plaut, V. C. (2010). Ethos of independence across regions
in the United States: The production-adoption model of cultural change. The American Psychologist, 65, 559-574.
Knowles, E. S., & Linn, J. A. (2004). The importance of resistance to persuasion. In E. S. Knowles & J. A. Linn (Eds.), Resistance
and persuasion (pp. 3-9). Mahwah, NJ, USA: Mahwah Press.
Maio, G. R., & Esses, V. M. (1998). The social consequences of Affirmative Action: Deleterious effects on perceptions of
groups. Personality and Social Psychology Bulletin, 24, 65-74. doi:10.1177/0146167298241005
Murray, D. R., & Schaller, M. (2014). Pathogen prevalence and geographical variation in traits and behavior. In P. J. Rentfrow
(Ed.), Psychological geography (pp. 51-70). Washington, DC, USA: American Psychological Association.
Noble, J. (2015, July 18). Trump comments on McCain war record spark outrage. USA Today. Retrieved from
Noel, H. (2016). Ideological factions in the Republican and Democratic parties. The ANNALS of the American Academy of
Political and Social Science, 667, 166-188. doi:10.1177/0002716216662433
Oliver, J. E., & Rahn, W. M. (2016). Rise of the Trumpenvolk: Populism in the 2016 election. The ANNALS of the American
Academy of Political and Social Science, 667, 189-206. doi:10.1177/0002716216662639
Pew Research Center. (2016). 2016 campaign: Strong interest, widespread dissatisfaction. Retrieved from
Schaller, M., Conway, L. G., III, & Tanchuk, T. (2002). Selective pressures on the once and future contents of ethnic stereotypes:
Effects of the ‘communicability’ of traits. Journal of Personality and Social Psychology, 82, 861-877.
Thompson, D. (2016, March 1). Who are Donald Trump's supporters, really? Four theories to explain the front-runner’s rise
to the top of the polls. The Atlantic. Retrieved from
Van de Vliert, E. (2013). Climato-economic habitats support patterns of human needs, stresses, and freedoms. Behavioral
and Brain Sciences, 36, 465-480. doi:10.1017/S0140525X12002828
PsychOpen is a publishing service by Leibniz Institute
for Psychology Information (ZPID), Trier, Germany.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
Conway, Repke, & Houck 259
... ulation 1 under the signifier "Mexicans", from which the U.S. society needed to be guarded. As Trump gained prominence, he systematically violated emergent norms promoting social diversity (Conway, Repke, and Houck 2017), criticizing them as partisan values, while supporting elements of "white identity politics" (Sides, Tesler, and Vavreck 2016): Trump's rhetoric suggested a zero-sum game in which the empowerment of ethnic, social and gender minorities results in discrimination against white Americans. This position found surprising levels of electoral support, and Trump's 2016 election can be seen as part of a "cultural backlash" (Inglehart and Norris 2016). ...
Conference Paper
In this study, we investigate the role of the US online media ecosystem in Donald Trump's rise and consolidation to power (2013-2019). We analyze over 54 million articles from online U.S. media and locate a media narrative shift related to three issues that Trump focused on during his 2016 presidential campaign: immigration, Latin people, and identity politics. Given this, we develop Natural Language Processing techniques based on word embeddings to quantify biased representations of minorities in the media across time. We locate an increase in biased speech that parallels Trump's rise to power, and a clear partisan pattern to this bias. Comparing articles related to Latinos/as, African Americans, Asian Americans, and Jewish Americans, we show that the most biased representations in terms of stereotypes and prejudice are found when the media uses the term "Mexicans," which Trump used as a blanket term for a diverse Latin and Hispanic population. We develop econometric models to understand the narrative shift and to associate media reporting with real-world dynamics. We find that the media's focus on the new narratives and the intensity of biased representations are statistically associated with hate-crime incidents at the state level. These results illustrate how media amenability to politi-cians' agenda-building can result in the discrimination of social groups, as well as how problematic media reporting is linked to real-world harms. Consequently, we reflect on the role of the media as gatekeepers of public discourse and discuss the conditions for a diverse and inclusive media ecosystem .
This paper builds on Smith and Hanley’s finding that Trump’s supporters were not solely driven by demographics and economic distress, but predominantly by prejudices and preference for an overt authoritarian leadership. Our longitudinal study of the 2020 US Presidential election extended their study to test additional propositions about tribalism by considering two intergroup factors: an orientation to protestors and minorities and conservative vs liberal ingroups. While there was a strong negative correlation between attitude to protestors and to minorities, the strength of correlation between liberal and conservative ingroup ‘membership’ and support/vote for Trump/Biden was more telling. Essentially, because tribalism factors overpowered almost every variable including political orientation, we conclude that identity-based tribalism is now the primary basis of political allegiance.
Extrajudicial, extraterritorial killings of War on Terror adversaries by the US state have become the new normal. Alongside targeted individuals, unnamed and uncounted others are maimed and killed. Despite the absence of law's conventional sites, processes, and actors, the US state celebrates these killings as the realization of 'justice.' Meanwhile, images, narrative, and affect do the work of law; authorizing and legitimizing the discounting of some lives so that others – implicitly, American nationals – may live. How then, as we live through this unending, globalized war, are we to make sense of law in relation to the valuing of life? Adopting an interdisciplinary approach to law to excavate the workings of necropolitical law, and interrogating the US state's justifications for the project of counterterror, this book's temporal arc, the long War on Terror, illuminates the profound continuities and many guises for racialized, imperial violence informing the contemporary discounting of life.
This chapter describes seven distinct cultural workplace differences between the United States and the Netherlands and discusses practical implications. These differences are directness in language, work life mentality, levels of collaboration, consensus, part-time work, maternity and paternity leave, and how success is defined in the United States versus the Netherlands. Interviews with six expatriates offer additional insights into these differences. The chapter then looks at the effects on women and their career potential and suggests that the Netherlands can offer a more conducive workplace for women than the United States. However, once a woman enters motherhood, our research indicates a shift in Dutch ideology. A mother’s desire to work full-time, part-time, or stay at home can influence which of the two countries better supports her career progression. The United States’ work culture norms better align in the case of a mother that would like to continue to work full-time.KeywordsNetherlandsUnited StatesWorkplaceCultureWomenCareerComparative study
Despite the well-documented harmful effects of Native-themed mascots, Native-themed mascots have many supporters who decry the politically correct efforts to remove these mascots. Although ostensibly unrelated to race/racism, we reasoned that invoking anti-PC attitudes allow prejudiced people to indirectly support Native-themed mascots while minimizing the appearance of being biased. Three studies ( N = 587) found that anti-Native bias predicted anti-PC attitudes and, in turn, Native-themed mascot support. In Studies 2 and 3, participants varying in anti-PC attitudes considered a university changing their Native-themed mascot for PC or non-PC reasons. Anti-PC attitudes predicted opposition to changing Native-themed mascots in both conditions. However, the effect of anti-PC attitudes was stronger in the PC condition where social justice norms were salient. These results suggest that, for many, anti-PC attitudes reflect more than just opposition to political correctness and are used by prejudiced people to indirectly defend controversial mascots without appearing prejudiced.
The arrest and subsequent death of George Floyd are often cited as pivotal events in the evolution of police-citizen relationships. They were also the pinnacle of the “new visibility of policing” in that they were filmed by multiple cameras, and video recordings of the arrest of George Floyd played a central role in the trial of the police officer who killed Floyd. Although empirical work in other fields has repeatedly shown that how information is conveyed (the container) influences our perceptions and opinions sometimes as much as the information itself (the content), criminologists have largely neglected the effect of cognitive biases on perceptions of the police. The present study investigates both camera perspective and audio biases by reporting the results of three related viewings of controversial police interventions involving the use of force, filmed from the perspectives of a body-worn camera, a surveillance camera, and a cellphone. Results inconsistently support the existence of both biases but still point toward a concerning conclusion: technical features of the videos presented are associated with significantly different opinions. Implications for the public release of video footage are discussed.
A set of ‘New Culture Wars’ over questions of majority identity protection and free speech have become important in American politics, but have not received attention from empirical political science Compare the relative size of partisan differences on issues of ‘Cancel Culture’ and ‘Critical Race Theory’. Logistic regression models using attitudes toward real‐world Cancel Culture and Critical Race Theory examples to predict partisanship. Data show that Republican voters are no more likely to fear career consequences or dismissal for speech than Democrats. Republicans are also more opposed to teaching critical perspectives on race and history in schools than they are to employees being fired for dissenting speech within organizations. Strong white identifiers are both more opposed to diversity training which emphasizes white racism and less opposed to firing people for disputed cases of racist or sexist speech. Due to the distinctive moral foundations of conservative voters, this paper argues that perceived attacks on white and American identity are a more powerful source of grievance for Republican voters than concerns over freedom of expression. It is hypothesized that the conservative moral foundation of group loyalty helps to explain these findings.
Politicians are increasingly relying on outrage to engage the public. President Donald Trump used outrage to fuel his unlikely 2016 presidential victory and 2020 reelection effort. Was Trump's outrageous behavior a boon to his political fortunes or political malpractice? I draw on research in political psychology, populism, and presidential appeals to examine President Trump's outrageous behavior and its effect on the public. Using a series of original experiments, I find support for my expectation that Trump's outrageous behavior is a politically advantageous public relations strategy. My results show that Trump's outrageous behavior increases his support among self-identified Independents. However, contrary to popular conception, the results do not support claims that racial resentment or affinity for populism makes individuals more amenable to Trump's outrageous behavior.
Full-text available
The current studies (N = 1,709) explore why demographic composition of place matters. First, this work demonstrates that relative level of group representation affects one’s experience of place in the form of self-definition ( self-categorization ), perceptions of place being representative or characteristic of factors that distinguish the group from others ( place-prototypicality ), and sense of belonging ( place-identification; Studies 1a-1e; Studies 2a & 2b). Second, the studies illustrate that group representation within place shapes the way group member’s approach (i.e., expectations of group-based treatment and procedural justice; Studies 2a-2c), understand (i.e., attribution for group-based events, Study 2b; responsiveness to bias-reduction intervention, Study 4a; sense of solidarity, Study 4b), and behave (i.e., prejudice, Studies 3a & 3b; collective action, Study 4c). More broadly, I present a S ocial identity Pa radigm for C ontextualized E xperience (SPACE) that provides an organizing framework for the study of the impact of characteristics of place on social identity-based contextualized experience and (in turn) collective behavior. Taken together, the findings provide evidence of distinct psychological experience and orientation as a function of minority versus majority-group status within place, as well as for a group-based approach to place. Implications for the study of collective and intergroup behavior are discussed.
Full-text available
Left‐wing authoritarianism (LWA) has a controversial history in psychology. Some researchers have expressed skepticism about the existence of LWA, whereas others have argued that LWA is a valid construct. In the present article, we offer a framework to reconcile these two perspectives by proposing that ideologically based authoritarian norms are sometimes in conflict with the processes that create authoritarian individuals. In Western political contexts, authoritarian norms are more likely to occur on the conservative side of the political spectrum; but authoritarian attributes can occur in both conservatives and liberals. In our model, left‐wing authoritarians thus often occupy the space where forces influencing authoritarianism are in conflict. We review existing evidence related to the model, present novel evidence related to the model, derive four hypotheses from the model, and discuss criteria for falsifying the model. We conclude by considering the model's place in current research on the complexities of ideology.
Full-text available
Amazon’s Mechanical Turk (MTurk) is an increasingly popular tool for the recruitment of research subjects. While there has been much focus on the demographic differences between MTurk samples and the national public, we know little about whether liberals and conservatives recruited from MTurk share the same psychological dispositions as their counterparts in the mass public. In the absence of such evidence, some have argued that the selection process involved in joining MTurk invalidates the subject pool for studying questions central to political science. In this paper, we evaluate this claim by comparing a large MTurk sample to two benchmark national samples – one conducted online and one conducted face-to-face. We examine the personality and value-based motivations of political ideology across the three samples. All three samples produce substantively identical results with only minor variation in effect sizes. In short, liberals and conservatives in our MTurk sample closely mirror the psychological divisions of liberals and conservatives in the mass public, though MTurk liberals hold more characteristically liberal values and attitudes than liberals from representative samples. Overall, our results suggest that MTurk is a valid recruitment tool for psychological research on political ideology.
It is hypothesized that traits that are most likely to be the subject of social discourse (i.e., most communicable) are most likely to persist in ethnic stereotypes over time and that this effect is moderated by the extent to which an ethnic group is the subject of social discourse. Study 1 yielded communicability ratings of 76 traits. Study 2 tested the relation between a trait's communicability and its presence in stereotypes of 4 Canadian ethnic groups. Study 3 tested the relation between a trait's communicability and its persistence over time in stereotypes of 8 American ethnic groups. Results supported the hypotheses. A communication-based analysis of stereotypes appears helpful in predicting persistence and change in the contents of stereotypes of real groups in the real world.
How did Donald Trump dominate his more experienced competitors in the primaries? We suspected the answer might lie in his communication style rather than his platform details. Hence, we analyzed the announcement speeches of the top nine Republican contenders as of October, 2015. We transcribed 27 speech segments each and applied Pennebaker's Linguistic Inquiry and Word Count (LIWC), a computerized text analysis software. We also conducted acoustic analyses of the speech recordings and had them coded for grandiosity by trained (but blind) raters. Trump scored highest on (a) grandiosity ratings, (b) use of first person pronouns, (c) greater pitch dynamics, and (d) informal communication (including Twitter usage of all 17 candidates). With number of primaries won as the criterion, our results suggest that Trump benefited from all these aspects of campaign communication style. It remains to be seen whether this same communication profile will help or hinder success in a general election.
With Donald Trump the Republican nominee and Hillary Clinton the Democratic nominee for the 2016 U.S. Presidential election, speculations of why Trump resonates with many Americans are widespread - as are suppositions of whether, independent of party identification, people might vote for Hillary Clinton. The present study, using a sample of American adults (n = 406), investigated whether two ideological beliefs, namely, right-wing authoritarianism (RWA) and social dominance orientation (SDO) uniquely predicted Trump support and voting intentions for Clinton. Cognitive ability as a predictor of RWA and SDO was also tested. Path analyses, controlling for political party identification, revealed that higher RWA and SDO uniquely predicted more favorable attitudes of Trump, greater intentions to vote for Trump, and lower intentions to vote for Clinton. Lower cognitive ability predicted greater RWA and SDO and indirectly predicted more favorable Trump attitudes, greater intentions to vote for Trump and lower intentions to vote for Clinton.
Despite the wide application of the label “populist” in the 2016 election cycle, there has been little systematic evidence that this election is distinctive in its populist appeal. Looking at historical trends, contemporary rhetoric, and public opinion data, we find that populism is an appropriate descriptor of the 2016 election and that Donald Trump stands out in particular as the populist par excellence. Historical data reveal a large “representation gap” that typically accompanies populist candidates. Content analysis of campaign speeches shows that Trump, more so than any other candidate, employs a rhetoric that is distinctive in its simplicity, anti-elitism, and collectivism. Original survey data show that Trump’s supporters are distinctive in their unique combination of anti-expertise, anti-elitism, and pronationalist sentiments. Together, these findings highlight the distinctiveness of populism as a mechanism of political mobilization and the unusual character of the 2016 race.
Both the Republican and Democratic parties are internally divided. Each contains a party regular wing, which is interested in winning office and in the compromises necessary to govern. And each contains an ideological wing, which is interested in close adherence to the core coalition of the party. But the nature of the cleavage is very different within the parties. Among Democrats, the cleavage is mild, with most members belonging to the party regular camp, to the chagrin of ideologues, who are for the most part Bernie Sanders supporters. The cleavage among Republicans, though, is so deep that the party could not find a way to bridge it in the so-called invisible primary for 2016, creating an opening for Donald Trump, who is from neither camp.
While prior research has found linguistic complexity to be predictive across multiple domains, little research has examined how people perceive—or misperceive—linguistic complexity when they encounter it. Drawing from a model of the motivated ideological lens through which people view linguistic complexity, two studies examined the hypotheses that (a) participants are more likely to overestimate the complexity of political candidates when they believe they align with their own political views and (b) this complexity overestimation effect will be particularly strong for political liberals. Both studies presented participants with paragraphs from political candidates that varied in their actual integrative complexity levels and asked them to estimate the complexity of the paragraph. Consistent with expectations, Study 1 found that participants were significantly more likely to overestimate complexity levels for political candidates with whom they shared ideological beliefs and that this effect was particularly in evidence for political liberals. Study 2 replicated this basic pattern and further demonstrated that this effect was dependent on participants’ knowledge of their ideological agreement with the paragraph author. Because people misperceive linguistic complexity, researchers should move beyond thinking solely about how complex political rhetoric is; we have to also consider the degree that the intended audience may over- or underestimate complexity when they see it.