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Hegemonic masculinity predicts 2016 and 2020 voting and candidate evaluations

Significance Donald J. Trump’s history-making ascension from nonpolitician to president of the United States has been attributed to the antiestablishment, antielitist, and nativist populism of Trump voters, as well as to sexism, racism, homophobia, and xenophobia. Based on the findings of seven studies involving 2,007 people, men’s and women’s endorsement of hegemonic masculinity predicted support for Trump over and beyond the aforementioned factors, even when controlling for political party affiliation. Results highlight the importance of looking beyond social identity–based conceptualizations of masculinity to fully consider how men’s and women’s endorsement of cultural ideologies about masculinity legitimate patriarchal forms of dominance and reify gender-, race-, and class-based hierarchies.

In Study 4, 286 undergraduates were recruited from the psychology subject pool at Penn State approximately one month after the 2016 election (December 2 nd -December 5 th , 2016). After excluding the responses of students who did provide voting information (N = 18), the working data set was comprised of the responses of 268 participants including 55 cis-gender males, 212 cis-gender females, and 1 Transgender Female (Mage = 20.22,sd = 5.69;75.7 % White,7.9% Black, 10.5% Asian, 5.9% Other Non-White Identity).
In Study 5, 165 undergraduates were recruited from the psychology subject pool at Penn State approximately one month after the 2016 election (December 7 th -December 9 th , 2016). After excluding the responses of students who did not provide voting information (N = 11), and who were queer/gender non-identified (N = 1), the working data set then contained the responses of 153 participants including 39 cis-gender males, and 114 cis-gender females. (Mage = 18.93, sd = 1.62; 80.1 % White, 4.6% Black, 7.9% Asian, 7.3% Other Non-White Identity).
In Study 6, 406 undergraduates were recruited from the psychology subject pool at Penn State approximately a year after the 2016 election (November 29 th -December 6 th , 2017). After excluding the responses of participants who did not provide voting information (N = 26), who failed one or more attention checks (N = 22), and who were queer/gender non-identified (N = 1), our working data set was comprised of the responses of 357 participants including 168 cis-gender males, 186 cis-gender females, 1 Transgender Male, and 2 Transgender Females(Mage = 19.13, sd = 2.41; 80.3% White, 3.1% Black, 10.1% Asian, 4.3% Bi/Multiracial, 2.2% Other Non-White Identity).
Representative Sample. In Study 7, 302 participants were recruited from Prolific 50 days before the 2020 Presidential election (September 14, 2020). After excluding participants who reported that they would not be voting in the 2020 election (N = 12), the working data set was comprised of the responses of 290 participants. Using Prolific's built-in feature, we recruited a nationally representative sample of the U.S. population which included 146 cis-gender men, 142 cis-gender women, and 1 transgender male (Mage = 45.96,sd = 15.71;70.6% White,15.6% Black,8.7% Asian, 2.4% Bi/Multiracial, 2.8% Other Non-White Identity).

Measures.
Hegemonic Masculinity. In all seven studies, using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), participants completed the 26 item Male Role Norms scale (MRNS; 1). The MRNS assesses beliefs that, to be a good man, one must be (1) high in power and status (e.g. "it is essential for a man to always have the respect and admiration of everyone who knows him"); (2) physically, emotionally, and mentally tough (e.g. "a good motto for a man would be 'when the going gets tough, the tough get going'"); and (3) nothing like women (e.g. "it is a bit embarrassing for a man to have a job that is usually filled by a woman"). After reverse scoring appropriate items, we averaged across responses to create an endorsement of hegemonic masculinity (all αs > .90); higher numbers reflected stronger endorsement of hegemonic masculinity.
Precarious Masculine Identity. In Studies 1 -5 and Study 7, using a 5-point Likert scale (1 = not at all stressful, 5 = extremely stressful, participants completed the 40 item Male Gender Role Stress Scale (MGRS; 2). Participants rate the stress they imagine feeling at violating 5 elements of masculinity: (1) physical inadequacy (e.g. "losing in a sports competition"), (2) emotional inexpressiveness (e.g. "admitting that you are afraid of something"), (3) subordination (e.g. "being outperformed by a women at work"), (4) intellectual inferiority (e.g. "having to ask for directions when you are lost"), and (5) performance failure (e.g. "not making enough money"). After reverse scoring appropriate items, a single MGRS variable was created with higher numbers indicating greater precarious masculine identity (all αs > .89) Political Identity and Voting. Participants indicated their political party (1 = Democrat, 3 = Independent, 5 = Republican) and their political ideology (1 = very liberal, 5 = very conservative). For all analyses, the political party variable was used. Participants also indicated for whom they voted in the 2016 election. If participants did not vote, they instead indicated for whom they would have most likely voted. A dummy-coded variable was created to differentiate between participants who voted (or would have voted) for Trump (coded 1) and those who voted (or would have voted) for Clinton or another candidate (coded 0).

Evaluations of Major Party
Candidates. Across studies, one of two scales was used to measure candidate evaluations. In Studies 1, 2, 4, 5 and 7, using 5-point scales (1 = far below average, 5 = far above average), participants evaluated each candidate on 12 dimensions. These include: (1) having overall presidential qualities, (2) qualified for the job, (3) honest, (4) ability to perform in war situations, (5) respectability, (6) willing to work the amount of hours needed, (7) ability to stand up to adversity, (8) control of national security, (9) good health, (10) willingness to fight ISIS, (11) ability to have control over any situation, and (12) intelligence. Evaluations of Trump and Clinton on these 12 dimensions were submitted to separate principle components factor analyses using a varimax rotations. In each of the four studies including Clinton, scree plots indicated a single factor solution, which accounted for more than 62.02% of the variance for evaluations Trump and more than 56.29% of the variance for evaluations Clinton. Therefore, we averaged across appropriate items to create an evaluation of Trump variable (αs ³ .94) and an evaluation of Clinton variable (αs > .93); higher numbers indicated more positive candidate evaluations. In Study 7, a single factor solution emerged for both Trump (accounting for 78.97% of the variance) and Biden (accounting for 78.16% of the variance). Again, we averaged across appropriate items to create an evaluation of Trump variable (α = .97) and an evaluation of Biden variable (α = .97). In Studies 2 and 5, participants completed the Leadership Trait Questionnaire (LTQ; 3); specifically, using 5-point scales (1 = strongly disagree, 5 = strongly agree) rated Trump and Clinton on 14 leadership traits (e.g. articulate, perceptive, self-confident). Again, parallel factor analyses revealed single factor solutions, which accounted for more than 50.94% of the variance in evaluations of Trump across studies and for more than 50.18% of the variance in evaluations of Clinton. Again, an evaluation of Trump variable (αs > .92) and an evaluation of Clinton variable (αs > .92) were created, with higher scores reflecting more positive evaluations.
Trust in the Government. In Studies 2 and 7, participants were asked to think back to the months leading up to the 2016 presidential election and indicate their trust in the government. Using items from the American National Election Studies (4), participants completed four items with either a 0 (no trust in government) or a 100 (complete trust in government). Participants reported how much of the time they thought they could trust the government to do what was right (100 = always, 0 = never), whether they felt the government was run by a few big interests looking out for themselves (0) or that it was run for the benefit of all (100), if they felt that people in the government wasted tax dollars (0 = wasted a lot of money, 100 = didn't waste very much of it), and if they felt the people who ran the government were crooked (0) or not at all crooked (100). We created an average score across the four items (αs > .72) with higher scores reflect more trust in the government.
Sexism. In Study 3, participants completed the 22-item, Ambivalent Sexism Inventory (ASI; 5) using 7-point scales (1 = strongly disagree, 7 = strongly agree). We averaged across 11 subscale items (e.g. "A good woman should be put on a pedestal by her man") to create a benevolent sexism variable (α = .80) with higher numbers reflecting stronger beliefs that good women should be protected and cherished. We averaged across the remaining 11 items (e.g. "Women seek to gain power by getting control over men") to create a hostile sexism variable (α = .87) with higher numbers reflecting more hostility toward non-traditional women. In Study 7, participants completed the short, 12-item, version of the Ambivalent Sexism Inventory (6). Items still measured both benevolent (e.g. "Many women have a quality of purity that few men possess) and hostile (e.g. "Women exaggerate problems they have at work") sexism. We averaged across appropriate items to create both a benevolent sexism variable (α = .85) and a hostile sexism variable (α = .90). (7) using 7-point scales (1 = strongly disagree, 7 = strongly agree). We averaged across 10 subscale items (e.g. "Black people do not have the same employment opportunities that Whites do") to create a pro-black attitudes variable (αs > .84) with higher numbers reflecting more sympathy toward Black Americans as victims of injustice. We averaged across the remaining 11 items (e.g. "One of the biggest problems for a lot of Blacks is their lack of self-respect") to create an anti-black variable (αs > .83) with higher numbers reflecting antipathy toward Black Americans who are blamed for the relative lower social status of their racial group.

Racism. In Studies 4 and 7, participants completed the Pro-Black and Anti-Black scale
Xenophobia and Homophobia. In Studies 5 and 7, participants completed measures of xenophobia and homophobia, in random order. Participants, using a 7-point scale, (1 = strongly disagree, 7 = strongly agree) completed a xenophobia scale (8) reporting their fears about immigration (e.g. "interacting with immigrants makes me uneasy"). In addition, homophobia was assessed using the Homonegativity Scale (9); participants reported their attitudes toward gay men (e.g., "gay men should be avoided whenever possible") using 5-point scales 1 (1 = strongly disagree, 5 = strongly agree). After reverse scoring appropriate items, we averaged across items of a given scale to create a homophobia variable (αs > .93) and a xenophobia variable (αs > .85); higher numbers indicate more negative attitudes.
Islamophobia. In Study 7, participants completed the Islamophobia Scale (10). Using 7-point scales (1 = strongly disagree, 7 = strongly agree), participants completed 14 items assessing their attitudes toward Islam (e.g. "Islam is a dangerous religion") and Muslims (e.g. "If I could, I would avoid contact with Muslims"). We averaged across items to create an Islamophobia variable (α = .98); higher numbers indicate more negative attitudes.

Interpretation of Interactions
Simple slope analyses (11) were conducted for all interactions if they emerged as significant in the final step of a regression model (a) that was associated with a significant ΔR 2 , or (b) in more than one study showing the effect on a given variable, if the ΔR 2 was not significant. All continuous variables were mean centered and references to high and low values refer to one standard deviation above and below the mean, respectively.
Interactions are presented by effect and in the order in which the analyses from which they emerge are discussed in the text.

Hegemonic Masculinity (HM) x Race Interactions:
There were no consistent findings showing the moderation of HM effects by race. HM and Race interacted on evaluations of Trump in Studies 4 and 7 and on voting in Study 3 but were related with inconsistent findings. In Study 4, HM predicted more positive evaluations of Trump for non-White (but not White) participants. Findings from the nationally representative sample (Study 7) revealed the opposite: HM predicted more positive evaluations of Trump for White (but not non-White) participants. Effects are fully described below.

On Evaluations of Trump (Studies 4 & 7)
A significant HM x Race interaction emerged on evaluations of Trump in study 4, which was significant when sexism was included in the model [

Hegemonic Masculinity (HM) X Political Party Interactions:
HM and Political Party Affiliation interacted to influence evaluations of Trump, over and above Trust in Government (Study 3 and Study 7). Over and above Trust in Government, HM predicted more positive evaluations of Trump among respondents who were republican leaning, but possibly independents, but not respondents who were democrats. There was also some evidence of a parallel effect over and above racism (Study 5), however, that effect did not similarly replicate in the representative national sample.
Below, we show the scale used to assess political party affiliation. We also show the means and standards on political party affiliation that emerged in each study in which there were significant HM X Political Party interactions.
As shown below, our samples were skewed toward Democrats, even in the nationally representative sample (Study 7). In addition, given the standard deviation used to create groups for comparison in simple slopes analyses, we performed simple slope analyses on respondents who were Democrats or Republican leaning.

"Please indicate your personal affiliation"
Democrat

On Evaluations of Trump (Study 3, Study 5, and Study 7)
In each Study, HM predicted more positive evaluations of Trump among: Respondents who were Republican leaning

Precarious Masculine Identity (PMI) X Gender Interactions:
PMI and Gender interacted on evaluations of Trump in Study 4 and on voting in Study 5 but provided no evidence of a reliable and expected PMI effect. On evaluations of Trump, the pattern among men was consistent with a precarious masculinity effect, though marginally significant, perhaps due to a small N. The opposite pattern emerged for women. However, these effects did not emerge as significant in any other study. In addition, on voting, the effects were opposite of precarious masculinity predictions. Effects are shown and fully described below

On Evaluations of Trump (Study 4)
A significant PMI X Gender interaction emerged on evaluations of Trump in Study 4, which was significant when sexism was excluded from the model (see Table S2), t(205) = 2.42, p = .016, and when sexism was included in the model (Table 4), t(195) = 2.66, p = .008.
When sexism is omitted from the analysis ( Figure S1,

On Voting for Trump (Study 5)
A significant PMI X Gender interaction emerged on voting in Study 5, which was significant when racism was excluded from the model [ Table S1, Wald = 3.89, p = .049] and included in the model [ Table 5, Wald = 4.07, p = .044].
When racism omitted, PMI did not predict voting for either men or women [ps = .100].
When racism was included, PMI predicting voting for men in a direction opposite to precarious masculine identity effect; PMI predicted less likelihood of voting for Trump for men [OR < .000, Wald = 4.26, p = .039] and did not predict voting for women [p = .332].

Precarious Masculine Identity (PMI) X Hegemonic Masculinity (HM) Interactions:
PMI and HM interacted on evaluations of Trump in Study 3 (see Table S1 and Table 3). Increases in PMI predicted more positive evaluations of Trump for those who are low in HM, but not high in HM (see Figure  S2).
HM predicted more positive evaluations of Trump for those low in PMI Excluding Trust in government [b = .34, t(280)

The PMI X HM interaction was qualified by Education when omitting Trust in Government
The pattern shown in Figure S2 held among people relatively low in education, but not high in education.

Precarious Masculine Identity (PMI) x Race Interaction:
Contrary conceptualizations of PMI, PMI and Race interacted to influence voting in Study 5 when racism is included in the analysis (see Table 5), such that increases in PMI were unrelated to voting for Trump for White participants [OR < .000, Wald = 6.08, p = .014] but PMI did not predict voting for non-White participants [p = .484].