<figure><img src="image_1.png" /></figure>

HAL
open science

# Moral commitment to gender equality increases (mis)perceptions of gender bias in hiring

Hualin Xiao, Antoine Marie, Brent Strickland

► To cite this version:

Hualin Xiao, Antoine Marie, Brent Strickland. Moral commitment to gender equality increases (mis)perceptions of gender bias in hiring. European Journal of Social Psychology, 2024, 54, pp.1211 - 1227. 10.1002/ejsp.3071 . hal-05421188

HAL Id: hal-05421188

https://hal.science/hal-05421188v1

Submitted on 17 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Moral commitment to gender equality increases (mis)perceptions of gender bias in hiring

Hualin Xiao<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,* | Antoine Marie<fnref n="2" />,<fnref n="4" />,* | Brent Strickland<fnref n="2" />,<fnref n="3" />,<fnref n="5" />

**Correspondence**

Antoine Marie, Institut Jean Nicod (ENS-PSL, EHESS, CNRS), Paris, France.

Email: antoine.marie.sci@gmail.com

Hualin Xiao, Laboratoire de Sciences Cognitives et Psycholinguistique (ENS-PSL, EHESS, CNRS), Paris, France.

Email: xhualin92@gmail.com

* Hualin Xiao and Antoine Marie: Joint first authorship. Name ordering was decided based on flipping of a coin.

**Funding information**

Carlsberg Foundation; Aarhus Universitet; European Research Council; Agence Nationale de la Recherche; China Scholarship Council

**Abstract**

Exploring what modulates people's trust in evidence of hiring discrimination is crucial to the deployment of corrective policies. Here, we explore one powerful source of variation in such judgments: moral commitment to gender equality (MCGE), that is, perceptions of the issue as a moral imperative and as identity-defining. Across seven experiments (N = 3579), we examined folk evaluations of scientific reports of hiring discrimination in academia. Participants who were more morally committed to gender equality were more likely to trust rigorous, experimental evidence of gender discrimination against women. This association between moral commitment and research evaluations was not reducible to prior beliefs, and largely explained a sex difference in people's evaluations on the issue. On a darker note, however, MCGE was associated with increased chances of fallaciously inferring discrimination against women from contradictory evidence. Overall, our results suggest that moral convictions amplify people's myside bias, bringing about both benefits and costs in the public consumption of science.

**KEYWORDS**

gender bias, morality, myside bias, science consumption, STEM

# 1 | INTRODUCTION

Despite the implementation of compensatory measures in science, technology, engineering and mathematics (STEM), women are still outnumbered by men in these fields (Shen, 2013), especially in senior positions (James et al., 2019). This fact has been attributed to multiple factors, from discriminatory hiring practices against women to gender differences in lifestyle and career choices, math abilities and market opportunities (see Ceci & Williams, 2011; Ceci et al.,

2009; Charlesworth & Banaji, 2019). Previous research has suggested that part of the gender disparities in academia is due to inequitable hiring favouring men over equally competent women. For example, Moss-Racusin et al. (2012) asked professors at research-intensive universities to evaluate the CVs of undergraduate students for a lab manager position. All professors received the exact same CVs, but applicants were randomly given either male or female names. Results revealed a significant bias in favour of the male students, which were rated as more competent and hireable, and were offered more

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. *European Journal of Social Psychology* published by John Wiley & Sons Ltd.

<footnote n="1">¹Laboratoire de Sciences Cognitives et Psycholinguistique (ENS-PSL, EHESS, CNRS), Paris, France</footnote>

<footnote n="2">²Institut Jean Nicod (ENS-PSL, EHESS, CNRS), Paris, France</footnote>

<footnote n="3">³School of Collective Intelligence, UM6P, Rabat, Morocco</footnote>

<footnote n="4">⁴Department of Political Science, Aarhus University, Aarhus, Denmark</footnote>

<footnote n="5">⁵Africa Business School, UM6P, Rabat, Morocco</footnote>
mentoring and a higher starting salary. Numerous studies in other professions have similarly observed biased hiring processes favouring male over female candidates despite comparable backgrounds (Begeny et al., 2020; Moss-Racusin et al., 2012; Régner et al., 2019; Reuben et al., 2014). It is important to note, however, that some studies using the CV evaluation method have found hiring preferences in academia *favouring* women (Breda & Hilton, 2016; Breda & Ly, 2015; Ceci et al., 2023; Williams & Ceci, 2015).

Although much research demonstrates the existence of sex-based
hiring discrimination in academic contexts, scholars have explored how
people vary in their reactions to this evidence, with a focus on sex dif-
ferences (Danbold & Huo, 2017; Handley et al., 2015; Moss-Racusin
et al., 2015). For instance, Handley et al. (2015, PNAS) asked partic-
ipants to rate the quality of Moss-Racusin et al.'s (2012) research
findings showing that male applicants tended to be judged as more
hireable than equally qualified females. The authors found that males
rated such research less favourably than female participants, and that
this sex difference in evaluations was more pronounced among STEM
faculty than in laypeople. Similarly, Moss-Racusin et al. (2015) found
more calls for social change in reaction to press articles reporting evi-
dence of hiring bias against women among females (than males), and
more justifications of gender bias among males (than females). These
results were interpreted as reflecting men's motivations to protect
their identity and privileges as the dominant group in STEM (Danbold
& Huo, 2017; Handley et al., 2015).

1.1 | Moral commitment to gender equality may increase myside bias in research evaluations

Here, we innovate by exploring a potentially more important source of
variation in folk evaluations of research on gender bias in hiring than
participants' sex: their degree of moral commitment to gender equality
(MCGE)<fnref n="1" />. MCGE refers to the extent to which individuals deem equal
treatment of men and women to be of unnegotiable importance and as
central to their moral identity (Skitka, 2010; Skitka et al., 2005; we also
say moral conviction about gender equality interchangeably<fnref n="2" />). Strong
moral commitment to an issue is characterised by a collection of subjec-
tive and behavioural features such as perception of the issue or cause
as a matter of objective right and wrong; prioritisation of the cause
above all other values, and reluctance to trade it off against other goals;

centrality of the cause to the person's social identity or self-concept; rejection of messages, legislations and information sources suspected of impeding advancement of the cause; willingness to physically distance oneself from individuals with different views on the issue; and acceptance of violence to advance the issue (Finkel et al., 2020; Ginges, 2019; Graso et al., 2022; Skitka et al., 2021; Skitka & Morgan, 2014; Spring et al., 2018; Stanford, 2018; Van Zomeren et al., 2012). To illustrate, women and men who are strongly morally committed to gender equality might tend to see critics of feminism as political enemies who are just objectively wrong and need to be avoided, censored, cancelled and potentially slapped in the face.

Myside bias is the tendency to interpret new information in ways that are partial to existing expectations and convictions, at the potential cost of failing to update one's belief in the face of incongruent evidence (Nickerson, 1998; Stanovich, West & Toplak, 2013; see also Kunda, 1987, 1990). Our general suspicion was that MCGE would exaggerate the strength of people's myside bias with respect to evidence of hiring discrimination against women in academia, a male-dominated field. Specifically, we expected people who are more morally committed to gender equality to judge more favourably not just rigorous but also fallacious scientific reports that draw the congenial conclusion that women are being discriminated against in hiring.

With regard to the mechanisms, we saw stronger MCGE as being
likely to reinforce participants' myside bias on the issue through
two, non-mutually exclusive routes. A first possibility was that higher
moral commitment to gender equality might capture stronger prior
expectations—among both women and men—that women are discrim-
inated against in academic hiring processes in Western academia
(Pennycook, 2020; Tappin et al., 2020a, 2020b). People more deeply
persuaded that hiring discrimination against women is taking place
would (quite rationally) judge scientific research making this observa-
tion as more credible than people who have weaker priors (and thus
score lower on MCGE assuming MCGE captures prior beliefs).

Individuals, however, do not only have truth-seeking motives when evaluating new information: their judgment and communication is also influenced by strategic, social goals (Kunda, 1990), such as signalling commitments to causes and fitting in moral communities (Kahan 2011, 2016; Williams, 2023; Tetlock, 2002; Van Bavel & Pereira, 2018), and trying to influence other people to increase their investment in causes that benefit the agent or the community (Fitouchi & Singh, 2022; Marie & Petersen, 2022; Kurzban et al., 2010; Pinsof, Sears & Haselton, 2023; Tetlock, 2002). Accordingly, an alternative (and non-mutually exclusive) explanation to MCGE being associated with more positive evaluations of research showing hiring discrimination against women might be that such findings can (i) confirm gender equalitarians in their identity and (ii) constitute useful tools for raising awareness and mobilising other people against this form of discrimination. For this reason, individuals higher in MCGE may react with more *motivated enthusiasm* to such findings than people lower in MCGE—a form of motivated thinking—beyond stronger prior expectations of gender bias. Conversely, under the motivated thinking hypothesis, strong gender egalitarians might display greater activist and identity-based motivations to discard the finding of hiring bias *favouring* females,

<footnote n="1">¹ In some contemporary language uses, 'gender' is used to refer to how individuals subjectively identify as more or less feminine or masculine (or else), with the implication that gender is somewhat of a social construction, whereas 'sex' refers to the biological distinction between males and females (and potentially, non-binary people). In some scientific literatures, by contrast, 'gender' can be used to mean 'sex'. It is this latter linguistic use that we make of the expression 'gender equality' in this paper. We use 'gender equality' to refer primarily to equality between men and women (the two dominant sexes and genders in the population), and assume that our participants understood the expression this way in the surveys.</footnote>

<footnote n="2">² We use these two expressions interchangeably here, but acknowledge that they might emphasise different facets of attitudes of moralisation of a cause. 'Moral commitment' might stress more the prioritisation of the cause above all else, the reluctance to trade off the cherished moral cause against other values. 'Moral conviction', by contrast, might emphasise the subjective, emotional dimension of caring highly about a cause, and its importance to someone's social identity or self-concept. These nuances need not matter in the present context, however.</footnote>
as such findings may be perceived as undermining mobilisation efforts.

In parallel, we expected potential sex differences in evaluations of research documenting sex-based hiring discrimination against women in academia to be largely explained by differences in MCGE. Indeed, Handley et al.'s (2015) observation that men tend to judge evidence of hiring discrimination against women less positively than women might be largely reducible to men being on average lower in MCGE than women due to differences in personal experiences. As long as academic professions tend to favour male candidates, men are unlikely to suffer from sex-based hiring bias against them, and may be less likely to hear about this discrimination because it does not negatively impact (but rather benefits) them. This relative deficit in knowledge and lower personal stakes in fighting sex-based discrimination against women might naturally lead men to be on average lower in MCGE than women.<fnref n="3" />

**1.2 | Overview of the current research**

Our investigation started by documenting an epistemically desirable consequence of the amplification of myside bias by MCGE: increasing respondents' trust in rigorous, experimental research showing evidence of hiring discrimination against women (Experiments 1a-c, 2 and 3). We further hypothesied that differences in MCGE would largely explain Handley et al.'s (2015) finding that women judge evidence of hiring discrimination against them in academia more positively than men. If this hypothesis was supported, this would entail that efforts to raise people's awareness of hiring bias against women would have to target individuals who have less moral concerns or who are less informed about the issue, rather than men as members of a sex group.

We then turned our attention towards a potentially darker side of moral commitment amplifying myside bias: greater chances of neglecting the steps leading to an ideologically congenial but *fallacious* conclusion. Experiments 4a and 4b exposed respondents to a fallacious (and fictitious) scientific study that, while firmly concluding to the existence of hiring discrimination against women in STEM, shows results that clearly contradict this conclusion. Here, we hypothesised that individuals more morally committed to gender equality would more readily accept that contextually wrong conclusion because it matches what they expect to see or validates their activist commitment. If supported, our hypothesis would entail that advocates of gender equality in the workplace should be reminded that their (laudable) social agenda can lead them to jump to inaccurate conclusions about the occurrence of bias.

Overall, our approach expands knowledge on the consequences of moral convictions by exploring further how it can amplify myside bias in the public consumption of science.

**2 | EXPERIMENTS 1 a-c**

Experiments 1a-c started by exploring a potential epistemically desirable effect of MCGE on myside bias. The three experiments were inspired by Handley et al. (2015, PNAS), who found that women rated experimental research demonstrating hiring discrimination against women more favourably than men. Handley et al., however, did not explore any influence of moral attitudes. We drew on their design to test our hypothesis that more morally committed individuals would assess robust, experimental findings demonstrating hiring discrimination against women more positively than individuals less committed. Furthermore, we expected differences in MCGE to largely account for any sex differences in research evaluations.

**2.1 | Methods**

*Transparency statement:* In all studies contained in this manuscript, we report how we determined our sample size, all data exclusions (if any), manipulations and measures. All questionnaires were designed in Qualtrics. Participants took part in the studies via their own electronic devices.

**2.1.1 | Participants**

*Statement of informed consent:* All participants who took part in the studies reported in this manuscript gave their informed consent to participate. In all studies, respondents were paid £0.50–£0.80 depending on the duration of the survey.

Table 1 provides the main characteristics of the samples used in Experiments 1a-c, as well as in the other experiments reported in this paper. Details about invalid or incomplete responses that were excluded are reported in the Supporting Information, under Materials and Procedure.

**2.1.2 | Pre-registrations**

All designs, measures, hypotheses, sample sizes and analysis plans of Experiments 1a-c were preregistered on OSF (Experiment 1a: https://osf.io/7uw85/; 1b: https://osf.io/ugpe4/; 1c: https://osf.io/2ug95/).

**2.1.3 | Materials and procedure**

*Stimulus research summary.* Experiments 1a-c exposed participants to the summary of a randomised-controlled trial (RCT) study by Moss-Racusin et al. (2012) that reported evidence of gender bias in hiring against females for a lab manager position at universities. The stimulus summary used in our three experiments differed slightly from the abstract of Moss-Racusin et al. (2012) originally employed by Handley et al. (2015) when they found a sex effect on research evaluations.

<footnote n="3">³ Unless, of course, some large-scale progressive efforts are undertaken in society—typically by women—to sensitise men to the fate of women being discriminated against in hiring in a number of professions despite identical credentials. In this case, men might get educated to vicariously feel the injustice done to women, to 'put themselves in their shoes', and thus become morally committed to gender equality to the same extent as women are.</footnote>
<table><caption>TABLE 1 Characteristics of the samples used in all the experiments reported in this paper.</caption><thead><tr><th>Experiment</th><th>Recruitment</th><th>Sample recruited</th><th>Final sample</th><th>A priori power analysis</th><th>Sensitivity power analysis on final sample</th></tr></thead><tbody><tr><td>Experiment 1a</td><td>UK, Prolific</td><td>N = 483</td><td>N = 467<br>233 women 234 men<br>Age: M = 35, SD = 11.8</td><td>N = 466 suggested by G*Power (Faul et al., 2009) to achieve 90% power for a small effect size 0.03 (f²) at .05 alpha level.</td><td>An effect size of β = .15 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 1b</td><td>UK, Prolific</td><td>N = 288</td><td>N = 268<br>171 women 97 men<br>Age: M = 35, SD = 13.</td><td>N = 250 suggested by power analysis for mediating effect using the applications 'mc_power_med' and 'schoam4' in R. This sample size would allow us to achieve 87% power.</td><td>An effect size of β = .20 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 1c</td><td>US, Amazon<br>Mechanical Turk</td><td>N = 768</td><td>N = 419<br>166 women 253 men<br>Age: M = 38, SD = 12.9</td><td>N = 415 suggested by G*Power (Faul et al., 2009) to achieve 90% power for a small effect size 0.03 (f²) at .05 alpha level.</td><td>An effect size of β = .16 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 2</td><td>UK, Prolific</td><td>N = 660</td><td>N = 636<br>319 women 317 men<br>Age: M = 36, SD = 13.2</td><td>N = 618 suggested by G*Power (Faul et al., 2009) to achieve 90% power for a small effect size 0.02 (f²) at .05 alpha level.</td><td>An effect size of β = .12 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 3</td><td>UK, Prolific</td><td>N = 520</td><td>N = 517<br>265 women 252 men<br>Age: M = 36, SD = 13</td><td>N = 502 suggested by G*Power (Faul et al., 2009) to achieve 90% power for a small to medium effect size 0.06 (f²) at .05 alpha level.</td><td>An effect size of β = .16 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 4a</td><td>UK, Prolific</td><td>N = 620</td><td>N = 554<br>277 women<br>277 men<br>Age: M = 39, SD = 13.2</td><td>N = 550 suggested by G*Power (Faul et al., 2009) to achieve 80% power for a small effect size 0.02 (f²) at .05 alpha level.</td><td>An effect size of β = .16 or greater could be detected at conventional alpha and beta levels</td></tr><tr><td>Experiment 4b</td><td>UK, Prolific</td><td>N = 821</td><td>N = 718<br>360 women<br>358 men<br>Age: M = 41.9, SD = 13.6</td><td>N = 713 suggested by G*Power (Faul et al., 2009) to achieve 90% power for a small effect size 0.02 (f²) at .05 alpha level.</td><td>An effect size of β = .13 or greater could be detected at conventional alpha and beta levels</td></tr></tbody></table>

We reasoned that if a sex effect on evaluations (as found by Handley et al., 2015) was strong enough to remain after controlling for individual differences in MCGE—our hypothesis—then a close but not strictly identical design as Handley et al.’s, 2015 should be able to reveal this sex effect. See Figure 1 for the verbatim of the summary used in Experiments 1a and 1c and the Supporting Information (Stimulus summaries).

**Measures of research evaluations.** The main goal of Experiments 1a, 1b and 1c was to gauge the robustness of a hypothetical effect of MCGE on evaluations of the presented research summary throughout a diversity of measures. In Experiment 1a, we began by employing the same composite measure of overall perceived research quality as used by Handley et al. (2015). It was based on the following items: (1) ‘To what extent do you agree with the interpretation of the research results?’; (2) ‘To what extent are the findings of this research important?’; (3) ‘To what extent was the abstract well written?’; (4) ‘Overall, my evaluation of this abstract is favourable’. As in the original study, responses were collected on 6-point Likert scales ranging from 1 = Not at all to 6 = Very much. We then used a more specific pair of outcome variables of our own in Experiments 1b–c. First, ‘To what extent do you think these research findings are likely to be accurate?’ ( slider

scale from 0 = Extremely unlikely to 10 = Extremely likely, with 5 = Neither likely nor unlikely as default slider position). Second, ‘To what extent do you find the research methods reliable?’ (from 0 = Not at all reliable to 10 = Very much reliable, with 5 = Uncertain as default slider position). The two questions were presented in random order. We used those two latter outcome measures in Experiments 1b–c to see whether lay participants would be able to distinguish between a study’s findings and its methods, which professional scientists know to be orthogonal.

**Measures of MCGE.** Our key independent variable in all three studies was respondents’ MCGE. MCGE was captured with a three-item measure inspired from Skitka et al. (2005) in Experiments 1a and 1c. Because of high consistency, we also used these three items in the other studies in this paper (see the Supporting Information, Materials and Procedure). The three items were: (1) ‘Achieving gender equality in society is an absolute moral imperative’; (2) ‘The conviction that we need to fight for gender equality is central to my identity’; and (3) ‘Furthering gender equality should be the government’s utmost priority’. Response scales ranged from 0 = Strongly disagree to 10 = Strongly agree, with 5 = I do not mind as the default slider position. By contrast, Experiment 1b, run chronologically before Experiments 1a and 1c, used only the first item of the three as a measure of MCGE.
Science faculty's subtle gender biases favor male students

Moss-Racusin et al.
Yale University, New Haven, CT 06520

**Background:**

Despite efforts to recruit and retain more women, a stark gender disparity persists within academic science. Abundant research has demonstrated gender bias in many demographic groups, but has yet to experimentally investigate whether science faculty specifically exhibit a bias against female students that could contribute to the gender disparity in academic science.

**Design:**

The present study asked science faculty members (N = 127) from research-intensive universities to rate the application materials of a student for a laboratory manager position (which, in scientific labs, is often occupied by a student). The student applicant was randomly given a male or female name, but was described as having exactly the same skills in both groups.

In both groups, faculty members were asked to: rate the student's perceived competence and hireability, propose a starting salary, and offer a certain amount of career mentoring to the student. A comparison between average ratings of student's perceived competence, hireability, proposed salary and career mentoring was conducted between the male student and the female student groups.

**Results:**

<figure><img src="image_3.png" /></figure>

* Science faculty members rated the male student as significantly more competent and hireable than the (identical) female student.

* Faculty members also offered more career mentoring to the male student and selected a higher starting salary.

* The gender of the faculty did not affect their ratings, such that female and male faculty were equally likely to exhibit bias against the female student.

**Conclusions:**

These results suggest that well-documented bias against females also exists in academia.

<caption>**FIGURE 1** Research summary used in Experiments 1a and 1c.</caption>

**Sex and gender.** Participants reported their biological sex by choosing from three options: 'Male', 'Female' and 'Other'. They also reported their gender identity using a continuous scale (0 = *Strongly masculine* to 5 = *Gender neutral* to 10 = *Strongly feminine*).

**Presentation order.** In Experiment 1a, the order in which participants saw the stimulus summary and the moral commitment items was counter-balanced across participants. In Experiments 1b and 1c, conducted chronologically before Experiment 1a, participants always responded to the research evaluation questions before seeing the moral commitment items.

2.2 | Analyses

Data from Experiments 1a-c were fit with linear regression models (see Tables 1.2, 2.1, 2.2, 3.1 and 3.2 in the Supporting Information, Statistical analyses for Experiments 1a, 1b and 1c). Ratings on the four items of overall research quality (α = .86) in Experiment 1a were averaged to create a single dependent variable. Ratings on the three items of MCGE in Experiment 1a (α = .86) and Experiment 1c (α = .89) were also averaged. In all studies reported in this paper, continuous variables (MCGE, results accuracy, methods reliability and
subjective gender identity) were standardised, using the *scale* function in R (R Core Team, 2023), and categorical variables (e.g. biological sex) were contrast coded. We report 95% confidence intervals of the standardised estimates in squared brackets.

Our analyses test for effects of participants' biological sex (male, female), as in Handley et al. (2015), but also subjective gender identity (a continuous measure of participants' self-identification as more or less masculine or feminine) on research evaluations. This is because we were interested in contrasting the effects of MCGE with sex and gender identity, without assuming that the presence of one effect (e.g. sex) would entail presence of the other (e.g. gender identity). Moreover, subjective gender had the advantage of being continuous like MCGE. As shown in the Supporting Information, biological sex and gender identity ended up being highly correlated and their influence on research evaluations was similar in pattern. In all the analyses reported in this paper, just like Handley et al. (2015), we adopted a simplified approach to the sex variable, with the ratings of only male and female respondents being compared, and excluding responses of potential non-binary respondents reporting their sex as 'other'. We made this choice to increase statistical power and limit the number of comparisons.

## 2.3 | Results

Figure 2 displays participants' research evaluations as a function of MCGE in Experiment 1c. In Figures in the Supporting Information, readers can also find figures for Experiment 1a (Figure 7) and 1b (Figure 8). Tables 1.3, 2.3 and 3.3 in the Supporting Information present correlation matrices of Experiments 1a-c's main variables.

**Effects of sex and gender.** Sex and gender effects on evaluations were only observed in Experiment 1a, not Experiments 1b and 1c. Separate simple linear regressions with sex and gender as the only predictor of evaluations (excluding MCGE) showed that participants who identified as male/masculine gave lower overall evaluations of the research summary experimentally demonstrating a bias against women than female/feminine participants in Experiment 1a (Sex: $\beta$ = -.13, [-.22, .04], t(465) = -2.83, *p* = .005; Gender: $\beta$ = -.14, [-.23, .05], t(465) = -3.10, *p* = .002). That said, the effect size of biological sex in Experiment 1a (*d* = .26, [.08, .44]), run in 2020, was markedly smaller than in Handley et al.'s PNAS 2015 study (*d* = .45). Importantly, moreover, both the sex and gender effects in Experiment 1a vanished when MCGE was included in the model (*p*'s = .272–.452; see Table 1.2 in the Supporting Information), suggesting that MCGE largely explained the sex difference.

Let us highlight that no sex or gender effects on evaluations were found at all in Experiment 1b (*p*'s = .091–.881; see Tables 2.1 and 2.2 in the Supporting Information) and Experiment 1c (*p*'s = .255–.897; see Tables 3.1 and 3.2 in the Supporting Information), regardless of whether moral commitment was controlled for or not.

**Effects of moral commitment.** Whenever an effect of MCGE on research evaluations is reported, two models were used: one with sex and the other with gender as controls. This was done to avoid

collinearity between sex and gender in all experiments reported in the paper. Results of Experiment 1a showed that the higher in MCGE respondents were, the higher overall quality they ascribed to the summary reporting experimental evidence of bias against women in STEM ($\beta$'s = .49, [.41, .57], t's(459) = 12.1, *p*'s < .001). Similarly, in Experiments 1b and 1c, greater MCGE predicted higher perceived accuracy of the findings (Experiment 1b: $\beta$'s = .31, [.19, .43], t's(264) = 5.21–5.31, *p*'s < .001; Experiment 1c: $\beta$'s = .45–.46, [.36, .55], t's(415) = 9.79–10.5, *p*'s < .001). In Experiments 1b and 1c, greater MCGE also predicted higher perceived reliability of the methods of the study experimentally demonstrating bias against women (Experiment 1b: $\beta$'s = .18–.19, [.06, .31], t's(264) = 3.00–3.15, *p*'s = .002–.003; Experiment 1c: $\beta$'s = .36–.37, [.26, .46], t's(415) = 7.50–8.06, *p*'s < .001).

In Experiment 1a, MCGE also showed a stronger positive association with research evaluations among male/masculine participants than among female/feminine participants (Commitment × Sex on evaluations: $\beta$ = .11, [.03, .19], t(459) = 2.67, *p* = .008; Commitment × Gender on evaluations: $\beta$ = .12, [.05, .19], t(459) = 3.20, *p* = .001), an effect possibly explained by men lower in MCGE giving less positive

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** Participants' evaluations (i.e. accuracy of the results and reliability of the methods) of a rigorous study experimentally demonstrating gender bias *against* women in hiring as a function of MCGE in Experiment 1c (N = 419).</figcaption></figure>
evaluations than their female counterparts. However, the effect of MCGE did not interact with the sex and gender of respondents in Experiment 1b (p's = .176–.439; see Tables 2.1 and 2.2 in the Supporting Information) and Experiment 1c (p's = .112–.465; see Tables 3.1 and 3.2 in the Supporting Information).

## 2.4 | Discussion

The more participants were morally committed to gender equality, the more they trusted a study providing strong, experimental evidence of hiring bias against women in STEM. This was the case whether research evaluations were measured as a composite index of quality, as the perceived accuracy of its findings, or as the perceived reliability of its methods. These associations highlight a positive aspect of myside bias as amplified by moral commitment. Moreover, as expected, the only time the sex difference in research evaluations documented by Handley et al.’s (2015) study occurred in our 2020 studies, it was smaller, and the effect was largely reducible to differences in MCGE between men and women. We thus explain an important result published in PNAS with a new moral covariate, previously uncontrolled for.

## 3 | EXPERIMENT 2

Experiment 2 widened the scope beyond Experiments 1a-c's findings. It asked what possible associations between moral commitment to gender equality and research evaluations would be observed when the results suggest women are actually *favoured* in hiring, rather than *discriminated against*. The dominant narrative in Western English-speaking countries is that women are victims of discrimination, and substantial sociological evidence supports this notion (e.g. Begeny et al., 2020; Moss-Racusin et al., 2012; Régner et al., 2019; Reuben et al., 2014). We thus had two main expectations. First, that the 'women being favoured' finding would be met with more scepticism overall by our lay participants than the 'women being discriminated against' finding. Second, we expected that higher MCGE would predict more positive evaluations of the 'women being discriminated against' result, but greater scepticism towards the 'women being favoured' finding.

### 3.1 | Methods

#### 3.1.1 | Pre-registration

Experiment 2 was fully preregistered on OSF (https://osf.io/w9kus).

#### 3.1.2 | Materials and procedure

We used four stimulus summaries in Experiment 2, two of them describing a hiring bias *against* women and the other two a hiring bias *favouring* women. The goal of having two summaries for each reported

bias direction was to widen the stimuli database and draw more general conclusions. However, using four completely different summaries could have introduced confounds because of random wording features. We avoided this issue by creating two counter-factual summaries by simply flipping the results of the two original summaries reporting a hiring bias against and favouring women, respectively (i.e. original: *against* women → counter-factual: *favouring* women; original: *favouring* women → counter-factual: *against* women) and adapting the graphs. Respondents were randomly presented with one of the four summaries in a 2 (hiring bias against vs. favouring women) × 2 (original vs. counter-factual summaries) between-subjects design. Participants who saw the counter-factual summaries were debriefed on their nature. We counter-balanced the order in which participants saw the stimulus summary and the moral commitment items. Otherwise, the procedure was identical to that of Experiment 1c.

### 3.2 | Results

Data from Experiment 2 were fit with linear mixed-effects models with a random intercept for each stimulus summary (see the Supporting Information, Tables 4.1–4.4, Statistical analyses for Experiment 2). See also Figure 3. As in Experiment 1, ratings on the three items of MCGE correlated highly ($\alpha$ = .85), so they were averaged. Analyses were run by pooling the original and the counter-factual summaries reporting a given bias direction together. Table 4.5 in the Supporting Information presents a correlation matrix of Experiments 2’s main variables.

**Effects of bias direction.** Overall, participants rated the experimental evidence of women being discriminated *against* more positively than the experimental evidence of women being *favoured* in hiring in STEM (results accuracy: $\beta$'s = .39, [.32, .47], t's(626) = 10.6, p's < .001; methods reliability: $\beta$'s = .29, [.22, .37], t's(626) = 7.53 to 7.57, p's < .001).

**Effects of sex and gender.** To test for a global effect of sex/gender on evaluations of the research (excluding MCGE), regression analyses were run with bias direction, sex/gender and their interaction terms as predictors (models were fitted separately with either sex or gender). Results showed no overall effect of sex/gender on research evaluations (p's = .289–.627; see Tables 4.1 and 4.2 in the Supporting Information). However, depending on the reported direction of the hiring bias in the summary, male/masculine and female/feminine participants differed in their research evaluations (Bias × Sex on results accuracy: $\beta$ = .13, [.06, .20], t(631) = 3.48, p = .001; Bias × Sex on methods reliability: $\beta$ = .09, [.01, .16], t(631) = 2.26, p = .024; Bias × Gender on results accuracy: $\beta$ = .09, [.01, .16], t(631) = 2.34, p = .020; Bias × Gender on methods reliability: $\beta$ = .05, [−.02, .13], t(631) = 1.40, p = .163). Specifically, follow-up analyses for each bias direction revealed that male/masculine participants gave lower ratings to the research summaries demonstrating a bias *against* women than female/feminine participants (Sex on results accuracy: $\beta$ = −.33, [−.53, −.13], t(632) = −3.21, p = .001; Sex on methods reliability: $\beta$ = −.21, [−.42, .00], t(632) = −1.96, p = .050; Gender on results accuracy: $\beta$ = −.11, SE = 0.05, t(632) = −2.09, p = .037; Gender on methods
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** Participants' research evaluations (i.e. accuracy of the results and reliability of the methods) as a function of MCGE across summaries demonstrating a bias *against* and *favouring* women in hiring in Experiment 2 (N = 636).</figcaption></figure>

reliability: $\beta = -.07, SE = 0.05, t(632) = -1.33, p = .186$. When the summary demonstrated a bias *favouring* women, however, no sex/gender effects on the research evaluations were found ($p$'s = .087–.517). These patterns of results remained similar after MCGE was controlled for. See more details in the Supporting Information, Additional Analyses Experiment 2.

**Effects of moral commitment.** The full models with bias direction, sex/gender, MCGE and their interaction terms, as well as task order as predictors showed that MCGE's effect on participants' evaluations was moderated by hiring bias direction (Bias × Commitment on results accuracy: $\beta$'s = -.09 to -.10, [-.17, -.01], $t$'s(626) = -2.29 to -2.62, $p$'s = .009–.022; Bias × Commitment on methods reliability: $\beta$'s = -.08 to -.09, [-.17, .00], $t$'s(626) = -1.96 to -2.24, $p$'s = .026–.050). Specifically, in restricted analyses on each condition separately, greater MCGE predicted higher trust in the summaries reporting experimental demonstrations of bias *against* women, replicating results from Experiments 1a-c (Commitment on results accuracy: $\beta$'s = .23–.24, [.13, .34], $t$'s(313) = 4.55–4.83, $p$'s < .001; Commitment on methods reliability: $\beta$'s = .19–.20, [.08, .31], $t$'s(313) = 3.42–3.56, $p$'s < .001). By contrast,

and against our expectation, MCGE was *not* correlated overall, neither positively nor negatively, with trust in the summaries reporting experimental evidence of a bias *favouring* women (Commitment on results accuracy: $\beta$'s = .03–.04, [-.08, .15], $t$'s(313) = 0.61–0.75, $p$'s = .452–.539; Commitment on methods reliability: $\beta$'s = .02–.03, [-.09, .14], $t$'s(313) = 0.27–0.46, $p$'s = .643–.786).

With regard to the summary demonstrating a hiring bias *against* women, the positive effect of MCGE on research evaluations was not modulated by sex nor gender (MCGE × sex/gender: $p$'s = .23–.38; see Table 4.3 in the Supporting Information). In the condition where the summary described a bias *favouring* women, the effect of MCGE on evaluations tended to be positive for women but negative for men, although the MCGE × sex interaction was only marginally significant (see Additional analyses for Experiment 2 in the Supporting Information).

## 3.3 | Discussion

As we expected, participants trusted the studies reporting experimental evidence of hiring bias *favouring* women less than the ones reporting a hiring bias *against* women—either because the latter outcome was more expected, or more congenial to most participants’ ideologies, or both. Moreover, in line with Experiments 1a-c, Experiment 2 found that people who had strong moral convictions about gender equality were also more likely to trust rigorous, experimental evidence of women being discriminated *against* in hiring—thereby highlighting a beneficial effect of myside bias. Consistent with the results of Experiments 1, MCGE again largely explained the sex difference in participants’ evaluations of the summary demonstrating a hiring bias against women.

Interestingly, however, MCGE had no overall association with evaluations of a study reporting hiring discrimination *favouring* women. One could have expected MCGE to amplify morally motivated scepticism towards such a finding among supporters of gender equality (women and men), as it might be perceived as implausible, identity-threatening, or as delegitimising the struggle for gender equality. Instead, MCGE seemed associated with increased trust from women towards experimental evidence of bias favouring females. A possible interpretation is that female participants might have welcomed these results as encouraging news for their long-term goal of achieving equality.

# 4 | EXPERIMENT 3

The goal of Experiment 3 was to analyse the mechanisms underpinning the association between MCGE and more positive evaluations of experimental demonstrations of gender bias against women found in Experiments 1–2. One possibility is that this relationship could be reduced to people's prior beliefs about how frequent or large such bias is in academia (Koehler, 1993; Pennycook, 2020; Tappin et al., 2020a, 2020b). Indeed, the degree to which people want to correct a given type of social inequality should be correlated with the
factual beliefs they hold about, for example, how prevalent this inequal-
ity is, how many victims it causes, and so on. Effects of MCGE on
research evaluations might thus disappear when controlling for prior
beliefs.

On the other hand, information about a social inequality one is concerned about is likely to activate *strategic concerns* and *emotional reactions* that might affect evaluations of the research, above and beyond prior expectations. For gender equalitarians, demonstrations of hiring bias against women can be seen as a tool for mobilising others to act against the discrimination, and as comforting them in their moral identity. As a result, the more committed to gender equality individuals are, the more they might react with *motivated enthusiasm* towards such finding, which might inflate evaluations above and beyond prior beliefs (Kunda, 1987, 1990). This would entail that a statistical effect of MCGE on research evaluations would remain even when controlling for prior beliefs.

**4.1 | Methods**

4.1.1 | Pre-registration

Experiment 3 was fully preregistered on OSF (https://osf.io/bvkjc).

4.1.2 | Materials and procedure

As in Experiment 2, Experiment 3 used a between-subjects design in which participants were randomly exposed to a research summary reporting experimental evidence either of gender bias *against* or *favouring* women. The former summary was based on the article of Moss-Racusin et al. (2012), and the latter on that of Williams and Ceci (2015). Contrary to Experiment 2 however, only the two original summaries were used (no counter-factual scenarios were employed). Moreover, each condition was composed of two tasks: first *prediction* and then *evaluation* of the results. First, participants were shown the summary of a scientific study, described as being about to be conducted, on the issue of gender discrimination in hiring in academic sciences and were asked to *predict* its likely results. We assumed that participants would make predictions based on their expectations about hiring biases in the academic context. Thus, we measured participants’ predictions about the results of the given study as a proxy for their *prior beliefs* about how much hiring bias against women they think is taking place in academia. The stimulus summaries were the same as in previous experiments, except that the study’s methods were described in the future tense and no results were shown. Participants’ predictions were collected as responses to the question: ‘Now please make predictions about the likely results of the study. What do you think will be the average ratings of the two groups?’; ‘Average rating for the female applicant’; ‘Average rating for the male applicant’, choices 0 to 100, with 0 as default slider position.

Then, came the *evaluation* task. Immediately after having made their
predictions, participants saw the following message: 'The study that

you just read about has been recently run by researchers. The research
findings were published as an article in a scientific journal. On the next
page, you are going to see a summary of the published article. Please
read carefully and answer two questions". This time, participants saw
the full research summary including its results (both in text and graph-
ical format), worded in the past tense. Following Experiments 1b, 1c
and 2, they were asked to rate how accurate they found the research
summary's results, and how reliable they found its methods to be.

As in Experiment 2, the MCGE items came before the two tasks (pre-
diction + evaluation, always in that order) 50% of the time, and after the
two tasks 50% of the time.

4.2 | Results

All analyses run in Experiment 3 can be found in Tables 5.1–5.6, Statisti-
cal analyses for Experiment 3 in the Supporting Information. As before,
ratings on the three items of MCGE (α = .84) were averaged. Main
results are also displayed in Figures 9 and 10 in the Supporting Infor-
mation. Table 5.7 in the Supporting Information presents a correlation
matrix of Experiments 3’s main variables.

**Effects of bias direction.** As observed in Experiment 2, partici-
pants trusted less the summary experimentally demonstrating a hir-
ing bias *favouring* women than the summary demonstrating a bias
*against* women (results accuracy: *β*′ = −.36 to −.37, [−.45, −.28],
t's(503) = −8.79 to −8.89, *p*'s < .001; methods reliability: *β*′ = −.25 to
−.26, [−.35, −.18], t's(503) = −5.89 to −6.03, *p*'s < .001).

**Effects of sex and gender.** Just as in Experiment 2, we per-
formed regression analyses with bias direction, sex/gender (in sep-
arate models) and their interaction terms as predictors to test
for effects of sex/gender on evaluations of the experimental study
(excluding MCGE). Again, results showed that male/masculine and
female/feminine participants differed in their research evaluations,
and that this difference depended on the bias direction (Bias × Sex
on results accuracy: β = .15, [.07, .23], t(513) = 3.64, *p* < .001;
Bias × Sex on methods reliability: β = .12, [.04, .20], t(513) = 2.81,
*p* = .005; Bias × Gender on results accuracy: β = .13, [.06, .21],
t(513) = 3.33, *p* = .001; Bias × Gender on methods reliability: β = .13,
[.05, .22], t(513) = 3.19, *p* = .001). Specifically, follow-up analyses
revealed that male/masculine participants trusted less the research
summaries experimentally demonstrating a bias *against* women than
female/feminine participants (Sex on results accuracy: β = −.37, [−.60,
−.15], t(513) = −3.28, *p* = .001; Sex on methods reliability: β = −.20,
[−.43, .03], t(513) = −1.69, *p* = .092; Gender on results accuracy:
β = −.14, SE = 0.06, t(513) = −2.54, *p* = .011; Gender on methods
reliability: β = −.09, SE = 0.06, t(513) = −1.62, *p* = .106). However,
male/masculine participants trusted more the research experimentally
demonstrating a bias *favouring* women than female/feminine partic-
ipants (Sex on results accuracy: β = .21, [−.01, .44], t(513) = 1.87,
*p* = .063; Sex on methods reliability: β = .27, [.04, .51], t(513) = 2.29,
*p* = .022; Gender on results accuracy: β = .12, SE = 0.06, t(513) = 2.17,
*p* = .030; Gender on methods reliability: β = .17, SE = 0.06, t(513) = 2.87,
*p* = .004).
Importantly, when MCGE was included in the analyses, the sex and gender effects on the two outcome measures disappeared in the condition where a bias *against* women was reported (*p*'s = .282–.833; see Table 5.3 in the Supporting Information). When controlling for MCGE, in the condition where a bias *favouring* women was described, only the effects of sex and gender on the perceived reliability of the methods (not on results’ accuracy) remained (Sex on methods reliability: $\beta$ = .13, [.00, .25], t(252) = 2.00, *p* = .046; Gender on methods reliability: $\beta$ = .17, [.04, .29], t(252) = 2.59, *p* = .010). See the Supporting Information, Additional Analyses for Experiment 3. These results suggest that sex and gender differences in evaluations were largely explained by differences in MCGE in Experiment 3.

**Effects of moral commitment.** Replicating results of Experiment 2, the full models (with sex/gender, MCGE, prior beliefs and order included) showed that the influences of MCGE on evaluations were less pronounced in the summary demonstrating a hiring bias *favouring* women than in the summary demonstrating a bias *against* women (Bias × Commitment on results accuracy: $\beta$'s = -.12, [−.20, −.03], t's(503) = −2.75 to −2.80, *p*'s = .005–.006; Bias × Commitment on methods reliability: $\beta$'s = −.08 to −.09, [−.17, .01], t's(503) = −1.66 to −1.92, *p*'s = .056–.097). Like in Experiment 2, analyses restricted to each bias direction condition showed that greater MCGE predicted higher trust in the summaries reporting experimental evidence of bias *against* women (Commitment on results accuracy: $\beta$'s = .22–.23, [.11, .33], t's(255) = 4.19–4.39, *p*'s < .001; Commitment on methods reliability: $\beta$'s = .20, [.08, .31], t's(255) = 3.36–3.38, *p*'s < .001). By contrast, no overall effect of MCGE on evaluations, neither negative nor positive, was observed for the summary reporting experimental evidence of a bias *favouring* women (Commitment on results accuracy: *p*'s = .876–.938; Commitment on methods reliability: *p*'s = .572–.681; see Table 5.4 in the Supporting Information).

**Effects of prior beliefs.** Similar to effects of MCGE, the effects of people's prior beliefs about the size of gender bias in hiring in academia (while controlling for MCGE) were less pronounced in the summary demonstrating a hiring bias *favouring* women than in the summary showing bias *against* women (Bias × Prior beliefs on results accuracy: $\beta$'s = −.14, [−.22, −.06], t's(503) = −3.36 to −3.49, *p*'s = .001; Bias × Prior beliefs on methods reliability: $\beta$'s = −.15, [−.24, −.07], t's(503) = −3.50, *p*'s < .001). Specifically, restricted analyses showed that prior beliefs about greater gender discrimination against women predicted higher trust in the summary experimentally demonstrating a bias *against* women (Prior beliefs on results accuracy: $\beta$'s = .23–.24, [.12, .35], t's(255) = 4.23–4.44, *p*'s < .001; Prior beliefs on methods reliability: $\beta$'s = .21, [.09, .33], t's(255) = 3.44–3.46, *p*'s = .001). By contrast, for the summary reporting a bias *favouring* women, no effects of prior beliefs were found (Prior beliefs on results accuracy: *p*'s = .280; Prior beliefs on methods reliability: *p*'s = .082–.084; see Table 5.4 in the Supporting Information).

**Effects of moral commitment while controlling for prior beliefs.** In the condition where a hiring bias *against* women was described, we compared the raw effects of MCGE (when prior beliefs were factored out) to the effects of MCGE when prior beliefs were factored in. Controlling for individual differences in prior beliefs caused the coefficients of

MCGE to slightly decrease (prior beliefs factored in: Commitment on results accuracy: $\beta$ = .23, [.13, .33], t(258) = 4.72, *p* < .001; Commitment on methods reliability: $\beta$ = .19, [.08, .30], t(258) = 3.47, *p* < .001), compared to when prior beliefs were factored out (Commitment on results accuracy: $\beta$ = .28, [.18, .37], t(258) = 5.53, *p* < .001; Commitment on methods reliability: $\beta$ = .23, [.12, .34], t(258) = 4.15, *p* < .001). Still, the effects of MCGE remained largely significant after controlling for prior beliefs. This suggests that the influence of MCGE on trust in evidence of bias *against* women is not reducible to participants’ prior beliefs about the size of gender bias in academic hiring. Rather, research evaluations of this summary also seem to be influenced by some motivated enthusiasm for the finding springing from high moral commitment—a finding congenial to gender equalitarians’ identity and concern to mobilise others to act against discrimination (Marie & Petersen, 2022; Tooby & Cosmides, 2010).

We did not examine how effects of MCGE were affected by controlling for prior beliefs in the condition showing a hiring bias *favouring* women, because no effects of MCGE or prior beliefs were found.

## 4.3 Discussion

Experiment 3 again found a positive association between MCGE and trust in rigorous, experimental evidence of hiring bias *against* women. Differences in MCGE also explained the difference between women and men in evaluations of the presented experimental research, both when the reported bias was favouring and went against females. Importantly, the positive influence of MCGE on evaluations of the study reporting a bias *against* women did not seem to be reducible to respondents' specific prior beliefs about how much sex-based discrimination is taking place in academia. Other processes than prior expectations alone likely explain the association, such as emotional sympathy for a research finding that is ideologically congenial to gender egalitarian participants' moral identity and concern to mobilise others against discrimination. Overall, results from Experiment 3 suggest that MCGE reinforces participants' myside bias with respect to research on gender bias, through mechanisms involving stronger expectations and motivated reactions.

## 5 EXPERIMENTS 4a-b

Our experiments so far shed light on what may be considered a desirable consequence of MCGE: increasing people's trust in strong, experimental evidence of hiring bias against women. We now turn our attention towards a potentially dark side of moral commitment to an issue and its influence on myside bias: greater inclinations to *accept faulty reasoning* supporting a conclusion that fits moral goals or entrenched beliefs. Experiments 4a-b used biased scientific reports that *fallaciously* infer hiring discrimination against women in academic contexts from data that, despite showing an underrepresentation of women, clearly undermines any hiring discrimination inference. We expected people higher in MCGE to show a stronger myside bias on the
issue, and consequently to be more likely to endorse this congenial but misguided conclusion.

## 5.1 | Methods

### 5.1.1 | Pre-registrations

Both experiments were fully preregistered on OSF (4a: https://osf.io/n3zsy; 4b: https://osf.io/a9e82).

### 5.1.2 | Materials and procedure

Experiments 4a-b broke off from the rigorous evidence presented so far by presenting the summary of a highly misleading and fictitious study examining the gender distribution of scientists in STEM in the United Kingdom (inspired from the non-fallacious study of Sheltzer & Smith, 2014). Specifically, the summary's results section indicated that the labs were found to comprise fewer employed female (30%) than male (70%) researchers. This only shows women are underrepresented in the labs compared to their base rate in the population (about 50%). It provides no evidence of hiring processes being biased against women because women may be less numerous in a profession because they are less interested in the job, for instance, and apply less. What is more, the text and the graph specified that the percentage of women increased after hiring (30%) compared to the percentage of women applying to the jobs (20%). Women, despite being outnumbered, were thus benefitting from hiring processes favouring them over men. Still, we formulated the summary's conclusion such that it fallaciously concluded to the presence of a sex-based hiring bias against women. See Figure 4 for the verbatim of the summary used in Experiment 4b. See also the Supporting Information, Stimulus Summaries.

We expected MCGE in both studies to be associated with an increased propensity to accept the summary's fallacious conclusion. Additionally, we hypothesised that average fallacy acceptance, as well as the positive association between MCGE and fallacy acceptance, would be greater on the topic of gender-based discrimination than on control topics (discrimination against left-handed (Experiment 4a) and shorter-than-average (Experiment 4b) applicants). Those control topics were selected to constitute forms of (hypothetical) hiring discrimination on which respondents could be expected to have less strong moral convictions or expectations than on gender. Both studies employed a between-subjects design, presenting either a version of the fallacious gender-related summary in Experiments 4a and 4b, or the control condition. All research summaries presented the exact same pre-hiring and post-hiring proportions numerically and graphically.

Several reasons lead us to first run Experiment 4a, and then Experiment 4b. First, to replicate any positive effect of MCGE on acceptance of the fallacy. Second, to gauge the extent to which a positive effect of MCGE on fallacy acceptance on the topic of gender would extend to two distinct topics, handedness- and height-based discrimination.

Third, discrimination against shorter-than-average applicants (but not against left-handed applicants) had the advantage of allowing a definition of 'shortness' defined by a median split (being <1.62 m in height), entailing the same population base rates as in the gender condition (i.e. 50% female/short, 50% male/tall). Other, minor improvements (e.g. in wording) introduced in Experiments 4b are detailed in the Supporting Information. The demographic questions and measures of MCGE (three items) were the same in both studies.

## 5.2 | Results

We report below and in Figures 5 and 6 the results answering the research questions presented in the introduction to Experiments 4a-b, and leave other results to the Supporting Information (cf. Additional analyses for Experiment 4a, and Tables 6.1–6.10 and 7.1–7.4, Statistical analyses for Experiments 4a-b). Ratings on the three items of MCGE (Experiment 4a: $\alpha = .86$; Experiment 4b: $\alpha = .88$) were averaged, as were the fallacy acceptance ratings in Experiment 4b (gender condition: $\alpha = .89$; height condition: $\alpha = .93$).

**Effects of summary topic.** Participants accepted more the fallacious conclusion of the study on the highly moralised issue of gender than on the control issue of handedness in Experiment 4a (conclusion: $\beta$'s = .33, [.26, .41], t's(544) = 8.70–8.72, p's < .001). However, participants accepted to the same extent the fallacious conclusion of the study on gender and height in Experiment 4b (conclusion: $\beta$'s = .03, [–.04, .10], t's(712) = .89–.91, p's = .365–.373).

**Effects of moral commitment.** In Experiment 4a, pooling all conditions together (gender vs. handedness, with vs. without base rates), higher MCGE predicted more acceptance of the fallacy (Commitment on fallacy acceptance: $\beta$'s = .20, [.12, .27], t's(544) = 4.84–4.88, p < .001). Importantly, the effect of MCGE on fallacy acceptance was more pronounced in the gender conditions than in the handedness conditions in this study (Topic x Commitment: $\beta$'s = .10–.11, [.02, .19], t's(544) = 2.46–2.69, p's = .007–.014). In restricted analyses on each summary topic condition, people higher in MCGE were more likely to accept the fallacious conclusion on the issue of gender-based hiring discrimination ($\beta$'s = .30–.31, [.19, .42], t's(279) = 5.33–5.49, p's < .001). But there was no effect of MCGE on fallacy acceptance on the issue of handedness-based hiring discrimination ($\beta$'s = .09–.10, [–.03, .21], t's(267) = 1.50–1.70, p's = .090–.135). The effect of MCGE on fallacy acceptance was not moderated by the presence (vs. absence) of base rates in the summaries of Experiment 4a (Base rates x Commitment: $\beta$'s = –.06, [–.14, .01], t's(544) = –1.62 to –1.65, p's = .100–.107).

In Experiment 4b, MCGE also predicted more acceptance of the fallacy when pooling the gender and height conditions together (Commitment on fallacy acceptance: $\beta$'s = .18, [.10, .25], t's(712) = 4.60–4.68, p's < .001). Like in Experiment 4a, the effect of MCGE on fallacy acceptance was more pronounced in the gender condition than in the control condition of height, although the interaction was only marginally significant (Topic x Commitment: $\beta$'s = .07, [.00, .14], t's(712) = 1.84–1.85, p's = .065–.07). Restricted analyses on each summary topic showed that greater MCGE was associated with increased fallacy acceptance on
# Fewer women than men work in universities' STEM departments

McGuire, A. & S. Philipps

Department of Social Sciences, University of Oxford

## Overview

The lack of sex equality is a pressing problem for the fields of science, technology, engineering and mathematics (STEM). There are ongoing debates on whether sex-based hiring discrimination against women accounts for their under-representation in STEM professions in the UK.

The current study examined the sex ratios of researchers in the STEM departments of various universities. We found that the departments investigated comprised more male than female researchers.

## Method

In order to examine the sex ratios of researchers in STEM departments, we collected information on job applicants and hired researchers in 39 departments at 24 of the highest-ranked universities in the UK over the last five years.

## Results

<figure><img src="image_6.png" /></figure>

The study found that there was a smaller proportion of females (20%) than males (80%) among the job applicants. However, the proportion of female researchers who eventually got hired (30%) was greater than the proportion of females who applied for the jobs (20%).

## Conclusion

Our findings provide clear evidence that women face hiring discrimination in STEM departments because of their sex. This hiring discrimination likely accounts for their under-representation in STEM professions.

<caption>**FIGURE 4** Fallacious research summary used in Experiment 4b.</caption>

both gender (β's = .23, [.13, .34], t(353) = 4.42 - 4.51, p's < .001) and height (β's = .12, [.01, .23], t's(357) = 2.17-2.20, p's = .028-.031).

The observation that fallacy acceptance was always stronger on the highly moralised issue of gender than on control topics suggests that being morally committed to an issue predicts greater vulnerability to congenial but fallacious conclusions. In other words, moral commit-

ment to an issue seems associated with stronger myside bias on that issue.

**Effects of base rates (Experiment 4a only).** Showing base rates (i.e. the distribution of men vs. women and that of left- vs. right-hander applicants *before* hiring) did not overall change participants' fallacy acceptance (Base rates on fallacy acceptance: β's = -.02, [−.10, .05],
<figure><img src="image_7.png" /><figcaption>**FIGURE 5** Participants' tendency to make a fallacious inference of hiring bias as a function of MCGE across summaries reporting a gender- and handedness-based imbalance in Experiment 4a (N = 554).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**FIGURE 6** Participants' tendency to make a fallacious inference of hiring bias as a function of MCGE across summaries reporting a gender- and height-based imbalance in Experiment 4b (N = 718).</figcaption></figure>

t's(544) = -0.58 to -0.60, p's = .550-.565). However, depending on the topic of the summary, showing base rates differentially affected participants' acceptance of the fallacy (Topic × Base rates: β's = -.08, [-.15, .00], t's(544) = -2.06 to -2.07, p's = .039-.040). Restricted analyses on each summary topic condition revealed that seeing (vs. not seeing) the base rates slightly reduced participants' tendency to accept the fallacious conclusion when the summary touched on gender, even though the effects were only marginally significant (Base rates on acceptance of conclusions: β's = -.10, [-.21, .01], t's(279) = -1.77 to -1.79, p's = .075-.077). Seeing (vs. not seeing) the base rate had no effect on participants' inclination to accept the fallacious conclusion in the handedness conditions (Base rates on acceptance of conclusions: β's = .05-.06, [-.05, .16], t's(267) = 1.04-1.07, p's = .285-.297). Those weak-to-null effects of base rates suggest that respondents tended not to process the contradiction between the results and the conclusion even in the conditions including base rates. This provides stronger evidence that strong moral convictions about an issue are associated with dispositions to neglect data and arguments supporting an ideologically congenial or expected conclusion.

**Effects of sex and gender.** In all conditions of Experiments 4a and 4b (when MCGE was excluded), male/masculine participants were less likely to accept the fallacious conclusions than female/feminine participants (Sex on acceptance of conclusions in Experiment 4a: $\beta$ = -.16, [-.23, .08], t(547) = -3.94, *p* < .001; Experiment 4b: $\beta$ = -.12, [-.19, -.04], t(714) = -3.15, *p* = .002; Gender on acceptance of conclusions in Experiment 4a: $\beta$ = -.16, [-.24, -.08], t(547) = -3.99, *p* < .001; Experiment 4b: $\beta$ = -.12, [-.19, -.05], t(714) = -3.26, *p* = .001). Effects of sex and gender on fallacy acceptance in Experiment 4a were robust to controlling for MCGE, but they decreased in size (Sex on acceptance
of conclusion: $\beta = -.10$, [−.18, −.02], $t(544) = -2.48$, $p = .013$; Gender on acceptance of conclusion: $\beta = -.10$, [−.18, −.02], $t(544) = -2.56$, $p = .011$. Effects of sex and gender on fallacy acceptance became insignificant in Experiment 4b after MCGE was controlled for (Sex on acceptance of conclusion: $\beta = -.07$, [−.14, .01], $t(544) = -1.77$, $p = .077$; Gender on acceptance of conclusion: $\beta = -.06$, [−.14, .01], $t(544) = -1.63$, $p = .104$).

## 5.3 | Discussion

Experiments 4a-b confirmed our fear of moral commitment amplifying myside bias. Individuals high in MCGE were more likely to fallaciously infer the existence of sex-based hiring bias against women from the mere observation that women are less numerous among employed researchers—despite the data unambiguously showing a hiring bias *favouring* women. Acceptance of the fallacy was greater among women than men. This might reflect that women are more likely to expect hiring processes to be biased against them than men do, but also that women might be more motivated to believe this conclusion, even when it is not supported (Pew Research Center, 2018). Finally, the effect of moral commitment on fallacy acceptance was greatest on the uniquely moralised issue of gender discrimination. It extended to height-based but not handedness-based discrimination, a result possibly explained by height-based discrimination appearing more plausible or similar in kind as gender-based discrimination.

# 6 | GENERAL DISCUSSION

Uncovering the roots of people's diverging reactions to evidence of hiring discrimination against women is essential to the deployment of corrective policies. Across seven online experiments (N = 3579), we investigated the relationship between people's moral commitment to gender equality and their propensity to succumb to the myside bias when evaluating research on gender bias in hiring against women in academia.

Experiments 1-3 began by consistently showing that higher MCGE predicts more positive evaluations of a summary reporting strong, experimental evidence of hiring bias against women. This observation was made across a diversity of UK and US participant populations<fnref n="4" /> and with various measures of trust in the research summaries. Experiments 4a-b then turned the spotlight to a potential dark side of moral commitment to an issue: acceptance of a faulty conclusion when it is ideologically desirable or expected. As we feared, we found that MCGE was associated with higher chances of *fallaciously* accepting the conclusion that women are the victims of sex-based discrimination in hiring from the mere observation that women are less numerous than men

in academia. And yet, the presented data clearly showed that women were in fact *more* likely to be hired than men.

Taken together, these results point to the idea that moral commitment to gender equality may amplify myside bias in folk evaluations of science documenting gender bias in hiring against women. Insights from Experiments 4a-b even make one suspect that in Experiments 1-3, the more morally committed respondents were, the more *epistemic luck* (Engel, 1992) they had in trusting rigorous evidence of hiring bias against women—evidence that could well have been phony like in Experiments 4a-b. With regard to the causal nature<fnref n="5" /> of the influence of moral commitment on research evaluations, results from Experiment 3 suggest that it was not reducible to stronger prior beliefs on the issue of biased hiring (see Pennycook, 2020; Tappin et al., 2020a, 2020b). Instead, MCGE's influence on evaluations might be partly underpinned by higher motivated enthusiasm for the conclusion that women are discriminated against (Kunda, 1987, 1990), potentially because this conclusion can constitute useful material to raise awareness and mobilise others against discrimination (Marie & Petersen, 2022; Tooby & Cosmides, 2010), and because it confirms gender egalitarians in their moral identity (Van Bavel & Pereira, 2018).

What are the broader implications of our findings? Rigorous inductive reasoning is required for accurate public opinion formation. So, citizens ought to make room for the possibility that a sex imbalance can occur through mechanisms other than hiring bias—for example, because women may be less interested in pursuing STEM jobs (Pew Research Center, 2018), which of course does not undermine ideals of gender equality. To be clear, we are convinced that progressive moral commitments can motivate individuals to advance equality. Still, acknowledging the reasoning fallacies potentially amplified by well-intended moral or activist commitments (illustrated by Experiments 4a-b) is a social responsibility for scientists and educators. With more awareness of the dark side of morality, citizens and activists may be inspired to reason with more intellectual humility (Porter et al., 2022) on complex policy issues such as gender inequality, which may help reduce political polarisation (Finkel et al., 2020)—without this humility necessarily undermining progressive moral commitments.

Our observation that MCGE amplifies myside bias and can lead to negative consequences in science consumption dovetails with recent findings that people disposed to trust scientists' advice highly are more likely to endorse valid scientific claims, but also, occasionally, unsupported claims that seem to align with scientists' advice (Graso et al., 2022), and false claims adorned with scientific references (O'Brien et al., 2021). We also see our studies as an important contribution to the theory of moral convictions (Skitka, 2010; Skitka et al., 2005; Skitka et al., 2021), which tended to leave effects of moral attitudes on science consumption rather unexplored, focusing instead on folk objectivism, trade-off insensitivity, and so on.

<footnote n="4">⁴ That said, all our respondents were from Western, Educated, Industrialised, Rich, and Democratic societies (Henrich, Heine, Norenzayan, 2010). Replicating the main associations found in our work on other issues and in countries from the Global South would increase confidence about the cross-cultural generality of moral commitment reinforcing myside bias.</footnote>

<footnote n="5">⁵ We acknowledge that experimental designs could in theory better advance our understanding of the causal relations between moral convictions about gender equality and research evaluations. However, it is notoriously difficult to manipulate deep and stable moral convictions, hence our resorting to observational designs in which only the message being presented to respondents was experimentally manipulated, as is standard within research on individual differences and science consumption (e.g. Tappin et al., 2020a, 2020b; Kahan et al., 2011, 2016; Lord et al., 1979).</footnote>
In our studies, respondents trusted less the studies when they found a hiring bias *favouring* women than *against* women. Given the dominant narrative in the West that women are the victims of biased hiring processes, an isolated finding that they may be beneficiaries was likely unexpected and/or unwelcome for gender egalitarians. Still, MCGE did *not* amplify scepticism towards findings of women being favoured in hiring. This absence of negative effect of MCGE adds nuance to findings of classic studies (e.g. Edwards & Smith, 1996; Lord, Ross & Lepper, 1979) that pre-existing convictions make people more likely to discard counter-attitudinal scientific information. One possible interpretation of this absence of negative effect of MCGE is that respondents highly committed to gender equality acknowledged that results that women are favoured in hiring are encouraging, in a way, for their long-term desire to reach gender equality, which may have lowered their motivation to discount them. People can have conflicting desires (Kurzban & Aktipis, 2007), such as an instrumental concern to emphasise discrimination to mobilise; and simultaneously, the deeper aspiration that discrimination ultimately will disappear.

While Handley et al. (2015) had found evidence of women being discriminated against in hiring in academia less positively than females, our 2020 studies found those sex differences in evaluations—when they occurred—to be largely explained by corresponding differences in MCGE. This means that Handley et al.’s (2015) highly cited finding published in *PNAS* can be parsimoniously explained in terms of a more proximate moral variable. With regard to the raw sex effects we found on evaluations, they were also smaller than Handley et al.’s (2015), possibly due to men having been educated to become more aware of gender-based discriminations against women since 2015 (e.g. thanks to the Women’s Marches and the #MeToo movement).

## AUTHOR CONTRIBUTIONS

Hualin Xiao, Antoine Marie and Brent Strickland designed the experiments. Hualin Xiao and Antoine Marie created experimental materials. Hualin Xiao collected data and performed statistical analyses. Antoine Marie and Hualin Xiao wrote the manuscript and responded to reviewers’ requests. Brent Strickland provided revisions. All authors approved the final state of the manuscript.

## ACKNOWLEDGEMENTS

We thank Anne Skorkjær Binderkrantz for useful feedback on the manuscript. The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grants 324115-FRONTSEM (PI: Schlenker) and 313610-SEMEXP (PI: Chemla), and ERC H2020 Grant Agreement No. 788077-Orisem (PI: Schlenker), as well as China Scholarship Council (Hualin Xiao’s doctoral research). Research was conducted at Département d’Etudes Cognitives (ENS), which is supported by Grants ANR-10-IDEX-0001-02 PSL*, ANR-10-LABX-0087 IEC and ANR-17-EURE-0017 FrontCog. Two of the experiments were funded by an endowment from Aarhus University’s Department of political science to Antoine Marie, and his salary was provided by the Carlsberg Foundation from March 2021 to March 2024.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## ETHICS STATEMENT

IRB approval number 20152900001072 was given to our studies by the Comité d'éthique de la recherche en santé (CERES) from Université Paris Descartes. All participants who took part in these studies gave their informed consent to participate.

## DATA TRANSPARENCY STATEMENT

All results are reported honestly, the studies were conducted ethically and the submitted work is original. All data and code are available on the Open science framework (OSF) at: https://osf.io/h9nvp

## ORCID

Hualin Xiao https://orcid.org/0000-0003-2471-7084

Antoine Marie https://orcid.org/0000-0002-7958-0153

Brent Strickland https://orcid.org/0000-0002-7573-3722

## REFERENCES

Begeny, C. T., Ryan, M. K., Moss-Racusin, C. A., & Ravetz, G. (2020). In some professions, women have become well represented, yet gender bias persists—Perpetuated by those who think it is not happening. *Science Advances*, 6(26), eaba7814. https://doi.org/10.1126/sciadv.aba7814

Breda, T., & Hillion, M. (2016). Teaching accreditation exams reveal grading biases favor women in male-dominated disciplines in France. *Science*, 353(6298), 474-478. https://doi.org/10.1126/science.aaf4372

Breda, T., & Ly, S. T. (2015). Professors in core science fields are not always biased against women: Evidence from France. *American Economic Journal: Applied Economics*, 7(4), 53–75. https://doi.org/10.1257/app.20140022

Ceci, S. J., & Williams, W. M. (2010). Sex differences in math-intensive fields. *Current Directions in Psychological Science*, 19(5), 275-279. https://doi.org/10.1177/0963721410383241

Ceci, S. J., & Williams, W. M. (2011). Understanding current causes of women's underrepresentation in science. *Proceedings of the National Academy of Sciences*, 108(8), 3157-3162. https://doi.org/10.1073/pnas.1014871108

Ceci, S. J., Kahn, S., & Williams, W. M. (2023). Exploring gender bias in six key domains of academic science: An adversarial collaboration. *Psychological Science in the Public Interest*, 24(1), 15-73. https://doi.org/10.1177/15291006231163179

Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women's underrepresentation in science: Sociocultural and biological considerations. *Psychological Bulletin*, 135(2), 218-261. https://doi.org/10.1037/a0014412

Charlesworth, T. E., & Banaji, M. R. (2019). Gender in science, technology, engineering, and mathematics: Issues, causes, solutions. *Journal of Neuroscience*, 39(37), 7228-7243. https://doi.org/10.1523/JNEUROSCI.0475-18.2019

Danbold, F., & Huo, Y. J. (2017). Men's defense of their prototypicality undermines the success of women in STEM initiatives. *Journal of Experimental Social Psychology*, 72, 57-66. https://doi.org/10.1016/j.jesp.2016.12.014

Edwards, K., & Smith, E. E. (1996). A disconfirmation bias in the evaluation of arguments. *Journal of Personality and Social Psychology*, 71(1), 5-24. https://doi.org/10.1037/0022-3514.71.1.5

Engel, M. (1992). Is epistemic luck compatible with knowledge? *The Southern Journal of Philosophy*, 30(2), 59-75.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. *Behavior research methods*, 41(4), 1149-1160.
Finkel, E. J., Bail, C. A., Cikara, M., Ditto, P. H., Iyengar, S., Klar, S., Mason, L., McGrath, M. C., Nyhan, B., Rand, D. G., Skitka, L. J., Tucker, J. A., Van Bavel, J. J., Wang, C. S., & Druckman, J. N. (2020). Political sectarianism in America. *Science*, **370**(6516), 533–536.

Fitouchi, L., & Singh, M. (2022). Supernatural punishment beliefs as cogni-
tively compelling tools of social control. *Current opinion in psychology*, **44**,
252–257.

Ginges, J. (2019). The moral logic of political violence. *Trends in Cognitive Sciences, 23*(1), 1–3.

Graso, M., Henwood, A., Aquino, K., Dolan, P., & Chen, F. X. (2022). The dark side of belief in COVID-19 scientists and scientific evidence. *Personality and Individual Differences*, **193**, 111594.

Handley, I. M., Brown, E. R., Moss-Racusin, C. A., & Smith, J. L. (2015). Quality of evidence revealing subtle gender biases in science is in the eye of the beholder. *Proceedings of the National Academy of Sciences*, **112**(43), 13201–13206. https://doi.org/10.1073/pnas.1510649112

James, A., Chisnall, R., & Plank, M. J. (2019). Gender and societies: A grass-
roots approach to women in science. *Royal Society Open Science*, **6**(9),
190633. https://doi.org/10.1098/rsos.190633

Kahan, D. M. (2016). The politically motivated reasoning paradigm, Part 1: What politically motivated reasoning is and how to measure it. In R. Scott & S. Koslyn (eds.) *Emerging Trends in the Social and Behavioral Sciences* (pp. 1–16). Wiley.

Kahan, D. M., Jenkins-Smith, H., & Braman, D. (2011). Cultural cognition of scientific consensus. *Journal of Risk Research*, **14**(2), 147–174. https://doi.org/10.1080/13669877.2010.511246

Kahneman, D., Slovic, P., & Tversky, A. (1982). *Judgment under uncertainty: Heuristics and biases*.

Koehler, J. J. (1993). The influence of prior beliefs on scientific judgments of evidence quality. *Organizational Behavior and Human Decision Processes*, **56**(1), 28–55. https://doi.org/10.1006/obhd.1993.1044

Kunda, Z. (1987). Motivated inference: Self-serving generation and evaluation of causal theories. *Journal of Personality and Social Psychology*, **53**(4), 636–647. https://doi.org/10.1037/0022-3514.53.4.636

Kunda, Z. (1990). The case for motivated reasoning. *Psychological Bulletin*, **108**(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480

Kuran, T. (1997). *Private truths, public lies*. Harvard University Press.

Kurzban, R., & Athena Aktipis, C. (2007). Modularity and the social mind: Are psychologists too selfish? *Personality and Social Psychology Review*, **11**(2), 131–149. https://doi.org/10.1177/108868306294906

Kurzban, R., Dukes, A., & Weeden, J. (2010). Sex, drugs and moral goals: reproductive strategies and views about recreational drugs. *Proceedings of the Royal Society B: Biological Sciences*, **277**(1699), 3501–3508. https://doi.org/10.1098/rspb.2010.0608

Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. *Journal of Personality and Social Psychology*, **37**(11), 2098–2109. https://doi.org/10.1037/0022-3514.37.11.2098

Marie, A., & Petersen, M. B. (2022). Political conspiracy theories as tools for mobilization and signaling. *Current Opinion in Psychology*, **48**, 101440. https://doi.org/10.1016/j.copsyc.2022.101440

Mazzuca, S., Moscatelli, S., Menegatti, M., & Rubini, M. (2022). Men's reactions to gender inequality in the workplace: From relative deprivation on behalf of women to collective action. *Frontiers in Psychology*, **13**, 999750.

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty's subtle gender biases favor male students. *Proceedings of the National Academy of Sciences*, **109**(41), 16474–16479. https://doi.org/10.1073/pnas.1211286109

Moss-Racusin, C. A., Molenda, A. K., & Cramer, C. R. (2015). Can evidence impact attitudes? Public reactions to evidence of gender bias in STEM fields. *Psychology of Women Quarterly*, **39**(2), 194–209. https://doi.org/10.1177/0361684314565777

Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomena in many guises. *Review of General Psychology*, **2**(2), 175–220.

O'Brien, T. C., Palmer, R., & Albarracin, D. (2021). Misplaced trust: When trust in science fosters belief in pseudoscience and the benefits of critical evaluation. *Journal of Experimental Social Psychology*, **96**, 104184. https://doi.org/10.1016/j.jesp.2021.104184

Pennycook, G. (2020). Belief bias and its significance for modern social science. *Psychological Inquiry*, **31**(1), 57–60. https://doi.org/10.1080/1047840X.2020.1722577

Pew Research Center. (2018). Women and men in STEM often at odds over workplace equity. https://www.pewresearch.org/social-trends/2018/01/09/women-and-men-in-stem-often-at-odds-over-workplace-equity/

Pew Research Center. (2017). Mixed messages about public trust in science. https://www.pewresearch.org/science/2017/12/08/mixed-messages-about-public-trust-in-science/

Pinsof, D., Sears, D. O., & Haselton, M. G. (2023). Strange bedfellows: The alliance theory of political belief systems. *Psychological Inquiry*, **34**(3), 139–160. https://doi.org/10.1080/1047840x.2023.2274433

Porter, T., Elnakouri, A., Meyers, E. A., Shibayama, T., Jayawickreme, E., & Grossmann, I. (2022). Predictors and consequences of intellectual humility. *Nature Reviews Psychology*, **1**(9), 524–536.

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Régner, I., Thinus-Blanc, C., Netter, A., Schmader, T., & Huguet, P. (2019). Committees with implicit biases promote fewer women when they do not believe gender bias exists. *Nature Human Behaviour*, **3**(11), 1171–1179. https://doi.org/10.1038/s41562-019-0686-3

Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women's careers in science. *Proceedings of the National Academy of Sciences*, **111**(12), 4403–4408. https://doi.org/10.1073/pnas.1314788111

Sheltzer, J. M., & Smith, J. C. (2014). Elite male faculty in the life sciences employ fewer women. *Proceedings of the National Academy of Sciences*, **111**(28), 10107–10112. https://doi.org/10.1073/pnas.1403334111

Shen, H. (2013). Inequality quantified: Mind the gender gap. *Nature*, **495**(7439), 22–24. https://doi.org/10.1038/495022a

Skitka, L. J. (2010). The Psychology of moral conviction. *Social and Personality Psychology Compass*, **4**(4), 267–281. https://doi.org/10.1111/j.1751-9004.2010.00254.x

Skitka, L. J., & Morgan, G. S. (2014). The social and political implications of moral conviction. *Political Psychology*, **35**, 95–110.

Skitka, L. J., Bauman, C. W., & Sargis, E. G. (2005). Moral conviction: Another contributor to attitude strength or something more? *Journal of Personality and Social Psychology*, **88**(6), 895–917. https://doi.org/10.1037/0022-3514.88.6.895

Skitka, L. J., Hanson, B. E., Morgan, G. S., & Wisneski, D. C. (2021). The psychology of moral conviction. *Annual Review of Psychology*, **72**, 347–366.

Spring, V. L., Cameron, C. D., & Cikara, M. (2018). The upside of outrage. *Trends in Cognitive Sciences*, **22**(12), 1067–1069.

Stanford, P. K. (2018). The difference between ice cream and Nazis: Moral externalization and the evolution of human cooperation. *Behavioral and Brain Sciences*, **41**, e95. https://doi.org/10.1017/S0140525X17001911

Stanovich, K. E., West, R. F., & Toplak, M. E. (2013). Myside bias, rational thinking, and intelligence. *Current Directions in Psychological Science*, **22**(4), 259–264. https://doi.org/10.1177/0963721413480174

Tappin, B. M., Pennycook, G., & Rand, D. G. (2020a). Thinking clearly about causal inferences of politically motivated reasoning: Why paradigmatic study designs often undermine causal inference. *Current Opinion in Behavioral Sciences*, **34**, 81–87. https://doi.org/10.1016/j.cobeha.2020.01.003
Tappin, B. M., Pennycook, G., & Rand, D. G. (2020b). Bayesian or biased? Analytic thinking and political belief updating. *Cognition*, 204, 104375. https://doi.org/10.1016/j.cognition.2020.104375

Tetlock, P. E. (2002). Social functionalist frameworks for judgment and choice: Intuitive politicians, theologians, and prosecutors. *Psychological Review*, 109(3), 451–471. https://doi.org/10.1037/0033-295x.109.3.451

Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R package for causal mediation analysis. *Journal of Statistical Software*, 59(5), 1–38.

Tooby, J., & Cosmides, L. (2010). Groups in mind: The coalitional roots of war and morality. In H. Hogh-Olesen (Ed.), *Human morality and sociality: Evolutionary and comparative perspectives* (pp. 191–234). Palgrave MacMillan.

Van Bavel, J. J., & Pereira, A. (2018). The Partisan brain: An identity-based model of political belief. *Trends in Cognitive Sciences*, 22(3), 213–224. https://doi.org/10.1016/j.tics.2018.01.004

Van Zomeren, M., Postmes, T., & Spears, R. (2012). On conviction's collective consequences: Integrating moral conviction with the social identity model of collective action. *British Journal of Social

Psychology, 51(1), 52–71. https://doi.org/10.1111/j.2044-8309.2010.02000.x

Williams, W. M., & Ceci, S. J. (2015). National hiring experiments reveal 2: 1 faculty preference for women on STEM tenure track. *Proceedings of the National Academy of Sciences*, 112(17), 5360–5365. https://doi.org/10.1073/pnas.1418878112

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Xiao, H., Marie, A., & Strickland, B. (2024). Moral commitment to gender equality increases (mis)perceptions of gender bias in hiring. *European Journal of Social Psychology*, 54, 1211–1227. https://doi.org/10.1002/ejsp.3071