She Asked for It: Hardcore Porn,Sexism,and Rape Myth Acceptance

Abstract

The present study examined pornography viewing, rape myth acceptance, and sexist attitudes. Data came from 392 male and 903 female participants. Multigroup SEM indicated neither pornography viewing, nor hardcore pornography viewing, were related to rape myth acceptance when controlling for sexist attitudes among men. Wald tests indicated hostile sexism to be a significantly stronger predictor of all rape myths examined compared to pornography viewing or hardcore pornography viewing in men and women. Latent variable interaction analyses suggested hardcore pornography viewing as a significant exacerbating factor for the relationship between hostile sexism and “she asked for it” rape myths across genders.

Keywords

pornography hostile sexism rape myth acceptance hardcore pornography gender differences

Sexual assault is a considerable public health problem (Fedina et al., 2018; Perilloux et al., 2012; Smith et al., 2018). According to the Centers for Disease Control and Prevention, 19.3% of women and 1.7% of men are raped in their lifetime (Breiding et al., 2014). Lifetime prevalence of other forms of sexual assault (i.e., unwanted sexual contact, sexual coercion, noncontact sexual experiences) ranges from 44% to 75% of women (Smith et al., 2018; Walters et al., 2013). Several researchers have argued that understanding rape myth acceptance is pivotal to addressing sexual assault more broadly (Suarez & Gadalla, 2010; Yapp & Quayle, 2018).

Consistent with this position, a growing area of scientific interest has identified pornography viewing as a key activity positively linked with acceptance of rape myth beliefs, such as believing a victim is responsible for being assaulted or is lying about the assault (Allen et al., 1995; Brosi et al., 2011; Davis et al., 2006; Foubert et al., 2011). Hardcore forms of pornography are particularly associated with attitudes supporting violence against women (Hald et al., 2010; Malamuth et al., 2012). However, much of the extant research has failed to control for underlying sexist perspectives that might be confounding the relationship between pornography viewing and rape myth acceptance. Moreover, consistent with social script models (Wright, 2011; Wright & Bae, 2016), it is possible that viewing hardcore forms of pornography (cf., Bridges et al., 2010; Fritz & Paul, 2017; Shor & Golriz, 2019; Sun et al., 2008) may be promulgating or maintaining rape myth beliefs at disproportionately higher rates for those with underlying sexist belief systems. However, researchers have yet to formally explore these positions scientifically.

The present study sought to bridge gaps in the literature by examining the separate and combined contributions of sexism and pornography viewing as predictors of rape myth acceptance and determining whether the associations between sexism and rape myth acceptance are exacerbated (i.e., moderated) by hardcore pornography viewing. Furthermore, although the majority of research examining pornography use has focused on male participants, both men and women view and experience problems related to pornography (Borgogna et al., 2018; Price et al., 2016). Researchers have also noted that men and women can (and do) espouse rape myth acceptance, as well as sexist ideologies (Glick & Fiske, 1996; McDermott et al., 2019; McMahon, 2010), though comparatively little research exists examining potential gender differences and the interrelationships across these variables. Thus, we further sought to examine these connections across men and women separately.

Rape Myths

Rape myths are erroneous narratives that shift the blame for a sexual assault from the perpetrator to the victim (Burt, 1980; McMahon & Farmer, 2011; Payne et al., 1999). Common rape myths include themes suggesting a victim acted in a manner that instigated the rape (e.g., “If a girl initiates kissing or hooking up, she should not be surprised if a guy assumes she wants to have sex”; Payne et al., 1999) or the victim lied about being raped (e.g., “Girls who are caught cheating on their boyfriends sometimes claim it was rape”; Payne et al., 1999). Rape myths place blame on the victim by implying they had malicious or nefarious intentions, and excuse the perpetrator by implying they simply misunderstood the situation, and thus, acted innocently. The promulgation of rape myths and the acceptance of rape myth beliefs are linked to many problematic societal outcomes (Hockett et al., 2016; Suarez & Gadalla, 2010; Yapp & Quayle, 2018) including but not limited to: racism, classism, religious intolerance, and homophobia (Aosved & Long, 2006), as well as increased proclivity to perpetrate sexual assault (Davis et al., 2015; Tharp et al., 2013) and sexual harassment. Thus, understanding the nature of rape myths is pivotal to solving the broader social problem of sexual assault (Payne et al., 1999; Suarez & Gadalla, 2010).

Sexism

A considerable body of evidence has linked rape myth acceptance to underlying sexist beliefs (Aosved & Long, 2006; Chapleau et al., 2007; Davies et al., 2012; Rollero & Tartaglia, 2019; Stoll et al., 2021; Viki & Abrams, 2002). Ambivalent sexism theory (Glick & Fiske, 1996, 2001) may be particularly important to understand connections between pornography use and rape myth acceptance. Ambivalent sexism theory suggests two primary forms of sexism: hostile and benevolent (Glick & Fiske, 1996). Hostile sexist beliefs are perspectives that women should be subservient to men, and those who are not use nefarious means to establish power (e.g., “Women seek to gain power by getting control over men”; Glick & Fiske, 1996). By contrast, benevolent sexist beliefs regard women in a superficially positive and morally superior way that is indirectly marginalizing (e.g.,“A good woman should be set on a pedestal by her man”; Glick & Fiske, 1996). Both forms of sexism have been positively associated with rape myth acceptance (Chapleau et al., 2007); however, hostile sexism has been reported as a much stronger correlate (Chapleau et al., 2007; Rollero & Tartaglia, 2019; Viki & Abrams, 2002). Although benevolent sexist factors are still positively related (Viki & Abrams, 2002), those with hostile sexist beliefs may be more susceptible to accept rape myths given these individuals demonstrate outwardly negative appraisals of women (Suarez & Gadalla, 2010). Moreover, those holding hostile sexist beliefs may also be more strongly influenced by pornography, as pornography often promulgates sexist gender differences (Dines, 1998; Malamuth et al., 2012; Mattebo et al., 2012; McKenzie-Mohr & Zanna, 1990). Thus, while hostile sexist beliefs are likely a stronger proximal covariate of pornography use and rape myth acceptance, it is still important to examine and control for benevolent sexist attitudes when exploring the relationships among pornography and rape myth acceptance.

Pornography

Although researchers have noted that pornography use is not an inherently problematic activity (Campbell & Kohut, 2017; Hald & Malamuth, 2008; Kohut et al., 2018) and not all viewers hold sexist attitudes (Ciclitira, 2004; Garos et al., 2004; Kohut et al., 2016), a breadth of research has linked pornography viewing to rape myth acceptance (Allen et al., 1995; Brosi et al., 2011; Davies et al., 2012; Foubert et al., 2011), violence perpetration (Brem et al., 2021; Stanley et al., 2018), and sexist attitudes (Hald et al., 2013; Wright & Funk, 2014; Wright & Tokunaga, 2016). Moreover, recent findings have found pornography use to be associated with conformity to sexist norms (i.e., men having “power over women”; Borgogna et al., 2019) and male dominance ideologies (Borgogna et al., 2019).

Findings linking pornography viewing to sexist attitudes and behaviors are consistent with social script models (cf., Simon & Gagnon, 1986), particularly the components in which viewers “activate” or model the sexual interactions they watch (Wright, 2011; Wright & Bae, 2016). Indeed, findings have shown that men who view pornography are more likely to request the acts they watch (Sun et al., 2016). Sexual scripts have also been shown to mediate the connection between pornography viewing and sexually coercive behaviors (Marshall et al., 2021). Similarly, the connection between pornography viewing and acceptance of violence towards women has been shown to be mediated by objectification (Wright & Tokunaga, 2016). By extension, hardcore and more degrading forms of pornography, such as sadomasochistic and rape pornography, have been shown to be associated with rape myth acceptance (Brosi et al., 2011; Foubert et al., 2011).

While previous research suggests a positive association between pornography viewing and rape myth acceptance, it is not clear whether prior findings are confounded by the viewers holding hostile sexist beliefs. For example, some researchers have documented samples in which pornography viewing was associated with increased egalitarian views (Kohut et al., 2016). Thus, further research is needed to examine the relationship between pornography viewing and rape myth acceptance while controlling for the potential confounding variables of hostile and benevolent sexism.

Additionally, several studies have indicated that pornography is highly heterogeneous in content and genre viewership (Fritz & Paul, 2017; Hald & Štulhofer, 2016; Kohut et al., 2016; Paul, 2009; Sabina et al., 2008). For example, pornography might range from general erotica (i.e., sexualized images) to graphic scenes featuring nonnormative forms of penetration (Bridges et al., 2010; Hald & Štulhofer, 2016; Paul, 2009). Of note, many popular forms of pornography involve women being the targets of aggressive acts (e.g., hitting, spitting, choking; Bridges et al., 2010). Although some acts shown during these pornographic scenes are explicitly consensual (Fritz & Paul, 2017; Shor, 2019), they still likely provide a nonegalitarian script for viewers that may promulgate rape myths. In other words, if a scene depicts a man choking and/or spitting on a woman, it promotes a sexist gender differential even if the women consents to the acts. Therefore, it is possible that pornographic genre functions as a critical moderating variable, with harder forms of pornography potentially evidencing stronger associations with rape myth acceptance. However, further research is necessary to examine this position.

Gender

While understanding the relationships across sexism, rape myth acceptance, and pornography viewing are important, it is also essential to determine how these relationships may differ between genders. Indeed, the overarching sexist structures that dictate rape myths are theorized to be rooted in male-driven power structures (Glick & Fiske, 2001). Further, most modern pornography is aimed at male audiences. Men also view pornography significantly more often than women, experience more arousal when viewing, and experience more pornography-related problems (Borgogna et al., 2019; Paul, 2009; Price et al., 2016). Of note, while some women do hold sexist attitudes and endorse rape myths, they do so at significantly lower rates than men (Glick & Fiske, 1996; McDermott et al., 2019; McMahon, 2010; McMahon & Farmer, 2011). Thus, gender may serve as an additional key moderating variable when examining the relationships across sexism, rape myth acceptance, and pornography viewing.

The Present Study

To address the noted gaps in the literature, a large quantitative analysis was conducted on data gathered across heterosexual cisgender men and women. Specifically, we examined how pornography viewing frequency, hardcore pornography viewing frequency, and ambivalent sexism domains predict rape myths. Two research questions guided our analyses. Based on conceptual assertions that the connection between pornography viewing and rape myth acceptance is associated with hostile sexist ideologies, we explored whether hostile sexism would act as a significantly stronger predictor of rape myth acceptance compared to pornography viewing (and hardcore viewing) (R1). Based on research suggesting those with hostile masculinity have stronger positive correlations between pornography use and attitudes supporting violence against women (Malamuth et al., 2012; Vega & Malamuth, 2007) and research suggesting that pornography can act as a sexual script (Sun et al., 2016; Wright & Bae, 2016), we explored whether hardcore pornography viewing would exacerbate (i.e., moderate) the relationship between hostile sexism and rape myths (R2).

Method

Participants/Procedure

After IRB approval, data were collected during a large study on sexist beliefs and violence perpetration (N = 1,997) between October 2017 and December 2018. Data were gathered online via a subject pool located at a mid-size public university in the southeastern United States, with snowball sampling through social media formats, Greek society forums, and postings on Craigslist. Participants recruited through the subject pool were offered extra-credit, while those participating through the snowball procedure could optionally enter a raffle for a $100 Visa gift card. All measures were presented in a randomized fashion.

Inclusion criteria included: identifying as heterosexual and cisgender, completing at least 80% of each measure, passing two attention checks, and selecting a response stating their responses accurately represented their true feelings/behaviors. After data cleaning, 1,295 participant responses were fit for analyses (n = 392 men, n = 903 women). No data from the overarching dataset had been submitted for publication when this article was written. Demographics are shown in Table 1.

Table 1.

Demographic Characteristics.

	Men (n = 392)	Women (n = 903)
Race
Caucasian	67.3% (n = 264)	67.9% (n = 613)
Black/African American	14.5% (n = 57)	18.4% (n = 166)
Hispanic/Latinx	4.3% (n = 17)	5.4% (n = 49)
Asian	4.1% (n = 16)	4.7% (n = 42)
Middle Eastern	1.8% (n = 7)	0.8% (n = 7)
Asian-Indian	2.0% (n = 8)	0.4% (n = 4)
Native American	1.3% (n = 5)	0.9% (n = 8)
Pacific Islander	1.0% (n = 4)	0.1% (n = 1)
Other	3.6% (n = 14)	1.4% (n = 13)
Religion
Christianity	72.8% (n = 283)	81.5% (n = 726)
Agnostic	22.1% (n = 86)	15.3% (n = 136)
Muslim	1.8% (n = 7)	1.1% (n = 10)
Jewish	1.8% (n = 7)	0.8% (n = 7)
Hindu	1.3% (n = 5)	0.2% (n = 2)
Buddhist	0.3% (n = 1)	1.1% (n = 10)
Mean age (SD)	22.15 (8.13)	21.36 (7.51)

Measures

Sexism

The Ambivalent Sexism Inventory (ASI; Glick & Fiske, 1996) was used to measure hostile and benevolent sexism. The ASI includes two 11-item scales of sexism (hostile sexism, e.g., “When women lose fairly, they claim discrimination.”; benevolent sexism, e.g., “Women should be cherished and protected by men.”). Items are scored on a scale ranging from 1 (Strongly Disagree) to 6 (Strongly Agree), with higher scores indicating greater levels of sexism. Cronbach's alpha was 0.79 for benevolent sexism and 0.88 for hostile sexism in the current study. A considerable body of validity evidence has been generated on the ASI. For example, as of 2019, the validation paper had been cited over 3,900 times according to Google Scholar.

Rape Myth Acceptance

Rape myth acceptance was assessed via two factors of the Updated Rape Myth Acceptance Scale (UIRMA; McMahon & Farmer, 2011): “She asked for it” (six items, e.g., “If a woman is raped while she is drunk, she is at least somewhat responsible for letting things get out of hand.”) and “She lied” (five items, e.g., “A lot of times, women who say they were raped often led a man on and then had regrets.”). In the current study, “She asked for it” and “She lied” subscale scores were used; individual items were rated on a Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher scores representing greater rape myth acceptance. Only two of the UIRMA scales were administered because the data for the present study were gathered as part of much larger study that primarily utilized short-form measures. Cronbach's alpha was 0.82 for “She asked for it” myths and 0.85 for “She lied” myths in the current study.

Pornography Viewing Frequency

Pornography consumption was measured via the following item: “How often do you intentionally view pornography? 1. Haven’t accessed pornography in the past 12 months, 2. A few times in the past year, 3. A few times a month, 4. A few times a week, 5. About daily.” This scale has been used in previous studies measuring pornography viewing frequency (e.g., Borgogna & McDermott, 2018). The item has been shown to be positively correlated with problematic pornography viewing behaviors and promiscuous male role norms, and negatively correlated with self-esteem (Borgogna & McDermott, 2018; Borgogna et al., 2019).

Hardcore Pornography Viewing Frequency

Hardcore pornography consumption was measured utilizing an author-modified version of the previous question: “How often do you intentionally view ‘Hardcore,’ ‘BDSM,’ or ‘Fetish,’ pornography? 1. Haven’t accessed pornography in the past 12 months, 2. A few times in the past year, 3. A few times a month, 4. A few times a week, 5. About daily.”

Analytic Plan

First, data were examined for normality issues, missing data, and outliers. Bivariate correlations, split by gender, were then conducted in addition to a series of t-tests to assess descriptive differences between gender samples. To test research questions, multi-group structural equation modeling (SEM) was utilized. This involved constructing a measurement model to ensure that all latent variables were adequately represented by their manifest indicators. To ensure any differences between genders were not due to measurement bias, configural and metric invariance were tested (cf., Kline, 2016; Vandenberg & Lance, 2000). To evaluate measurement invariance in each model, a scaled χ² difference test was conducted, with a nonsignificant χ² difference providing support for invariance (Kline, 2016). Because the χ² difference test is highly sensitive to sample size (Kline, 2016), a p-value = .001 was adopted as a more rigorous statistical significance cut-off. For evaluating model-fit across models, comparative fit index (CFI), Tucker-Lewis index (TLI; values above 0.95 indicate good fit for both the CFI and TLI), root-mean-square error of approximation (RMSEA) with 90% confidence intervals (CIs; low values of 0.06 or less and high values less than 0.10 indicate acceptable fit), and standardized root-mean-square residual (SRMR; values of 0.08 or less indicate a good fit) were used. The chi-square test statistic was also reported; however, it was interpreted with caution, given sensitivity to sample size.

To address our first research question (R1), we constructed a multi-group structural model in which pornography viewing frequency, hardcore pornography viewing frequency, benevolent sexism, and hostile sexism simultaneously predicted “she asked for it” and “she lied” rape myths across genders. We employed a series of Wald tests of equality constraints between hostile sexism and pornography viewing and hardcore pornography viewing predictive paths to examine whether the two paths were statistically equivalent. In the current model, a significant Wald test indicated a path was significantly different (i.e., stronger or weaker) than the comparison path.

To examine our second research question (R2), a series of latent variable interaction models (Klein & Moosbrugger, 2000) were constructed. This involved testing interactions while controlling for all paths within the multigroup structural model. Interaction terms were specified with hardcore pornography viewing frequency acting as a moderator between hostile sexism and “she asked for it” and “she lied” rape myths. For any significant interactions, we examined the follow-up simple slopes, which signified the path strength between hostile sexism and rape myth acceptance at high (1 SD above the mean) and low (1 SD below the mean) levels of hardcore pornography viewing frequency.

Results

Preliminary Analyses

Of the 1,295 participants, few had missing responses (no item had more than three missing inputs). Nineteen univariate outliers (z > 3.29) were identified on our measure of hardcore pornography viewing; no other indicators evidenced univariate outliers. Few (< 1.47%) multivariate outliers were identified via examination of Mahalanobis distances in the total sample. All outliers were retained given their infrequency and distribution. Moreover, a full information maximum likelihood estimator with robust standard errors was used in our primary analyses to fit the model taking into account any potential normality violations and missing values. Table 2 displays the full bivariate correlation matrix, means, standard deviations, t-tests, and Cohen's d's across men and women. Notably, men evidenced significantly higher scores across all variables of interest. Inconsistent with previous research (e.g., Foubert et al., 2011), men's pornography use and hardcore use was not significantly correlated with acceptance of “she asked for it” or “she lied” rape myths at the bivariate level. Of note, women's pornography viewing was weakly (but significantly) negatively correlated with acceptance of both rape myths.

Table 2.

Correlations, Means, and Standard Deviations.

							Men		Women
Variable	1	2	3	4	5	6	M	SD	M	SD	t	d
1. Hostile	–	.21***	.44***	.57***	.06	.02	3.53	0.96	3.12	0.97	7.06***	0.43
2. Benevolent	.38***	–	.15**	.08	−.12*	−.13*	3.68	0.80	3.48	0.83	3.91***	0.25
3. She asked	.47***	.26***	–	.61***	.00	.08	2.10	0.80	1.78	0.79	6.73***	0.40
4. She lied	.53***	.21***	.67***	–	.06	.07	2.49	0.94	2.02	0.90	8.32***	0.51
5. Porn Freq	−.12***	−.11**	−.14***	−.08*	–	.50***	3.06	1.26	1.59	0.89	20.97***	1.35
6. HC porn Freq	−.04	−.09**	−.03	.00	.54***	–	1.62	1.06	1.17	0.53	8.02***	0.54

Note: Men's correlation are presented above the diagonal, women's are presented below.

* p < .05. **p < .01. ***p < .001.

Primary Analysis

After preliminary analyses, the specified SEM measurement and structural models were constructed in Mplus version 8 (Muthén & Muthén, 2016). Individual items were used to form the “she asked for it” and “she lied” rape myth latent variables. However, given that the hostile and benevolent sexism scales have 11-items each, we used item parcels (3 per latent variable) to form hostile and benevolent sexism latent variables (cf., Little et al., 2002). The configural measurement invariance model, in which all paths were freely estimated between men and women, provided an acceptable fit overall, χ ² (226) = 489.80, p < .001, CFI = 0.968, TLI = 0.962, RMSEA = 0.042 (90% CI = 0.037, 0.048), and SRMR = 0.038. Next, a metric invariance measurement model was tested by constraining the factor loadings to be equal across men and women to ensure any differences in the multi-group structural model were not due to measurement biases between genders. The constrained model provided acceptable fit, χ² (239) = 522.30, p < .001, CFI = 0.966, TLI = 0.961, RMSEA = 0.043 (90% CI = 0.038, 0.048), and SRMR = 0.043. Since the scaled χ² difference test indicated that the metric invariance model was not significantly different at p < .001 (given our N > 1,000) than the freely estimated model, χ² (13) = 32.73, p = .002, we determined that metric invariance was achieved (additionally, the change in CFI was 0.002 suggesting invariance regardless of any changes in the χ²; Meade et al., 2008). Importantly, all factor loadings were large and statistically significant (contact first author for specific coefficients).

Structural Model

The structural model was examined with hostile sexism, benevolent sexism, pornography viewing frequency, and hardcore pornography viewing frequency simultaneously predicting “she asked for it” and “she lied” rape myths. The configural structural model provided acceptable fit, χ ² (304) = 660.53, p < .001, CFI = 0.959, TLI = 0.954, RMSEA = 0.043 (90% CI = 0.038, 0.047), and SRMR = 0.043. Bias-corrected bootstrap samples (n = 1,000) were then used to estimate the confidence intervals of each path. Table 3 displays unstandardized, standard error, and standardized coefficients for each path, as well as the 95% bias-corrected CI's.

Table 3.

Standardized and Unstandardized Structural Model Results.

Gender	Predictor variables →	Criterion variables	Direct effect B	SE	Standardized direct effect β	95% CI
Men	Hostile →	She lied	0 . 80	0 . 07	0.64***	(0.553, 0.714)
Women	Hostile →	She lied	0 . 67	0 . 05	0.60***	(0.526, 0.673)
Men	Hostile →	She asked	0 . 39	0 . 05	0.50***	(0.374, 0.592)
Women	Hostile →	She asked	0 . 36	0 . 04	0.49***	(0.415, 0.560)
Men	Ben →	She lied	−0.05	0.05	−0.04	(−0.130, 0.068)
Women	Ben →	She lied	−0.04	0.05	−0.03	(−0.110, 0.045)
Men	Ben →	She asked	0.07	0.05	0.08	(−0.039, 0.192)
Women	Ben →	She asked	0.07	0.03	0.09*	(0.004, 0.116)
Men	Porn →	She lied	−0.03	0.04	−0.04	(−0.142, 0.057)
Women	Porn →	She lied	−0.02	0.03	−0.02	(−0.083, 0.041)
Men	Porn →	She asked	−0.04	0.03	−0.09	(−0.209, 0.035)
Women	Porn →	She asked	−0.07	0 . 02	−0.11***	(0.167, 0.034)
Men	HC porn →	She lied	0.06	0.05	0.06	(−0.042, 0.180)
Women	HC porn →	She lied	0.05	0.06	0.03	(−0.044, 0.092)
^a Men	HC porn →	She asked	0.07	0 . 04	0.12*	(0.005, 0.235)
Women	HC porn →	She asked	0.05	0.04	0.05	(−0.131, 0.039)

Given the B-to-SE ratio, this path was interpreted as nonsignificant.

*p < .05. **p < .01. ***p < .001.

Bold signifies a statistically significant path.

With regard to R1, hostile sexism emerged as a strong positive predictor of acceptance of “she asked for it,” β = 0.50 (95% CI = 0.290, 0.487) and “she lied,” β = 0.64 (95% CI = 0.664, 0.924) rape myth beliefs in men. Similarly, hostile sexism emerged as a strong positive predictor of acceptance of “she asked for it,” β = 0.49 (95% CI = 0.295, 0.435) and “she lied,” β = 0.60 (95% CI = 0.587, 0.777) rape myths in women. Pornography viewing frequency did not significantly predict “she asked for it” or “she lied” rape myths in men. Pornography viewing frequency did weakly negatively predict “she asked for it” rape myths in women, β = − 0.11 (95% CI = − 0.114, − 0.022). Hardcore viewing frequency was a significant predictor of “she asked for it” rape myths in men, β = 0.12 (95% CI = 0.005, 0.235), representing a small suppression effect (e.g., the bivariate correlation was nonsignificant, but the link became significant in the multivariate model). The suppression effect should be interpreted with caution, as the standard error (0.04) of the path was over half the size of the unstandardized coefficient (0.07). Moreover, the fact that the path became significant may be the result of reduced measurement error associated with SEM analyses (cf., Kline, 2016) instead of a change in path strength. Further, the strength of the coefficient was below what is often considered “practically” significant in terms of effect size (Ferguson, 2009).

Wald tests of the equality of paths between hostile sexism and pornography viewing and hardcore pornography viewing were significant. Specifically, hostile sexism emerged as a significantly stronger predictor of both “she asked for it” and “she lied” rape myths across genders (see Table 4). Cumulatively, the base structural model accounted for 28% of the variance in acceptance of “she asked for it” rape myths and 40% of the variance in acceptance of “she lied” rape myths in men. In women, the model accounted for 31% of the variance in acceptance of “she asked for it” rape myths and 34% of the variance in “she lied” rape myths.

Table 4.

Wald Tests of the Equality of Standardized Regression Parameters.

Gender	Hostile sexism	Porn frequency	HC porn frequency	Outcome variable	Wald Test
Men	0 . 64	−0.04	–	She lied	χ² (1) = 92.39***
Women	0.63	0.05	–	She lied	χ² (1) = 205.37***
Men	0.64	–	0.06	She lied	χ² (1) = 71.97***
Women	0.63	–	0.04	She lied	χ² (1) = 148.82***
Men	0.49	−0.09	–	She asked	χ² (1) = 33.41***
Women	0.52	−0.05	–	She asked	χ² (1) = 117.11***
Men	0.49	–	0.08	She asked	χ² (1) = 21.15***
Women	0.52	–	0.08	She asked	χ² (1) = 66.48***

Note: Bolded font indicates the path is significantly stronger than the compared path.

* p < .05. **p < .01. ***p < .001.

To address R2, latent variable interaction models were constructed. Hardcore pornography viewing frequency exacerbated the relationship between hostile sexism and “she asked for it” rape myths in men (Figure 1). Notably, the relationship was only significant for men who reported high (1 SD above the mean) hardcore pornography viewing frequencies (see Figure 1).

Figure 1.

Interaction effect with hardcore pornography viewing exacerbating the relationship between hostile sexism and she asked for it rape myths in men. Simple slopes are shown for men high (1 SD above the mean) and low (1 SD below the mean) hardcore pornography viewing frequencies.

A similar interaction was also observed in women, with hardcore pornography viewing exacerbating the relationship between hostile sexism and acceptance of “she asked for it” rape myths in women (see Figure 2). Additionally, the interaction in the sample of women accounted for an additional 1% unique variance in “she asked for it” rape myths, although the significant interaction did not account for an increase in accounted variance for men.

Figure 2.

Interaction effect with hardcore pornography viewing exacerbating the relationship between hostile sexism and she asked for it rape myths in women. Simple Slopes are shown for women high (1 SD above the mean) and low (1 SD below the mean) hardcore pornography viewing frequencies.

All interactions were nonsignificant when predicting “she lied” rape myths for both men and women. As a means of comprehensive examination, a series of commensurate interactions between hardcore pornography viewing and benevolent sexism were also tested in men and women post-hoc; however, none were significant. Further, interactions between hostile sexism and general pornography viewing frequency were also tested across men and women; however, none were significant.

Discussion

The present study sought to examine the relationships across sexism, pornography use, hardcore pornography use, and rape myth acceptance in cisgender heterosexual men and women. Two research questions guided our analyses. Based on conceptual assertions that the connection between pornography viewing and rape myth acceptance is associated with hostile sexist ideologies, we explored whether hostile sexism would act as a significantly stronger predictor of rape myth acceptance compared to pornography viewing (R1). Based on research suggesting those with hostile masculinity to have stronger positive correlations between pornography use and attitudes supporting violence against women (Malamuth et al., 2012; Vega & Malamuth, 2007) and research suggesting that pornography can act as a sexual script (Sun et al., 2016; Wright & Bae, 2016), we explored whether hardcore pornography viewing would exacerbate the relationship between hostile sexism and rape myths (R2).

Pornography viewing and hardcore pornography viewing were not related to acceptance of “she asked for it” or “she lied” rape myths at the bivariate level in men. General pornography viewing was significantly negatively correlated with “she asked for it” and “she lied” rape myths in women (though the effect size was quite small). These findings are inconsistent with previous research suggesting a positive relationship between pornography viewing and rape myth acceptance in both men and women (Brosi et al., 2011; Foubert et al., 2011). When all variables were simultaneously entered into a structural model, pornography viewing and hardcore viewing remained nonsignificant in men with the path from pornography viewing to “she asked for it” rape myths remaining significantly negatively correlated in women. Moreover, results from the Wald tests indicated that hostile sexism was a significantly stronger positive correlate of both “she asked for it” and “she lied” rape myths among men and women than either pornography viewing indicator. Consistent with previous findings (Suarez & Gadalla, 2010), the effect size of the path was remarkably large, indicating hostile sexism as a strong positive predictor of rape myth acceptance regardless of gender and pornography viewing frequencies.

All pornography viewing bivariate correlations were either nonsignificant or negatively related to rape myths and ambivalent sexism scales. Although these results are inconsistent with much of the previous work on pornography viewing from the past several decades (Allen et al., 1995; Brosi et al., 2011; Foubert et al., 2011), our findings are consistent with recent research indicating that viewing pornography is associated with egalitarian views towards women (Kohut et al., 2016), as well as distally consistent with recent research suggesting violent forms of pornography to be less favorably rated by viewers (Shor & Seida, 2019). Taken together, pornography viewing ostensibly may not be related to rape myth acceptance.

Results were also inconsistent with prior accounts linking sexist ideologies to pornography viewing (Borgogna et al, 2017). Because benevolent sexist attitudes tend to conceptualize women as pure and chaste (Glick & Fiske, 1996; Levant et al., 2007), findings that benevolent sexism was negatively related to pornography viewing and hardcore viewing for men and women were not necessarily surprising. However, the finding that hostile sexist beliefs were not significantly related to pornography viewing or hardcore pornography viewing in men is inconsistent with previous research.

A key difference between the current article and many previous studies was the examination of pornography viewing versus problematic pornography viewing. For instance, Borgogna et al. (2019) examined associations between traditionally masculine ideology and problematic pornography viewing dimensions in men and women, where it was found that men's dominance and avoidance of femininity ideologies were positively associated with subjective problems such as excessive pornography use. Similarly, Borgogna et al. (2019) demonstrated that men who conform to “power over women” norms reported multiple problematic outcomes associated with their pornography viewing (viewing frequencies were controlled). Therefore, pornography viewing ostensibly may not be related to sexism, just as pornography viewing was not ostensibly related to rape myth acceptance in the current study. Rather, sexist beliefs may be a significant factor for those who report problematic behaviors associated with pornography use (e.g., Brem et al., 2021; Borgogna et al., 2019). An important area of future research is replicating current findings while controlling for problematic pornography viewing behaviors compared to mere viewing frequencies.

Furthermore, our results may also reflect a societal shift. Pornography viewing has become extremely widespread across younger generations (Price et al., 2016; Willoughby et al., 2018). This could be leading to a saturation effect, especially for harder forms of pornography. In other words, in past generations pornography was substantially more difficult to obtain. Only those with a strong desire to view such materials (likely those with underlying sexist beliefs) would obtain pornography. However, today pornography, including aggressive forms of pornography, are almost universally viewed at least at some point in development. Therefore, pornography viewing has lost some predictive validity in connection to rape myth acceptance and sexism, in conjunction with its prevalence.

That said, results from our interaction analyses further elucidated the relationships across sexism, hardcore pornography viewing, and rape myth acceptance. Specifically, hardcore pornography viewing exacerbated the relationship between hostile sexism and acceptance of “she asked for it” rape myths in men and women. The significant interactions were consistent with findings that pornography may exacerbate attitudes supporting violence against women, but only in those with underlying predispositions (Brem et al., 2021; Hald et al., 2013; Hald & Malamuth, 2015; Malamuth et al., 2012). Moreover, our results provide additional support for social script models of pornography viewing (Wright & Bae, 2016), as the interactions were only significant when hardcore pornography viewing was the moderator, and not for general pornography use. These findings are also consistent with research implicating a connection between men's dominance ideologies and functional (e.g., relationship) problems associated with pornography use (Borgogna et al., 2019).

It should be noted that the interaction analyses were nonsignificant when examining relations among hostile sexism, hardcore pornography viewing, and acceptance of “she lied” rape myths. These findings possibly reflect the nuance that exists across rape myths. Indeed, “she asked for it” myths (e.g., “When girls go to parties wearing slutty clothes, they are asking for trouble”; Payne et al., 1999) reflect victim-blaming conceptualizations of rape. Specifically, these myths purport that an assault does occur, but the victim was responsible for the circumstances (e.g., clothing, substance use, initiating sexual contact). Whereas, acceptance of “she lied” rape myths (e.g., “A lot of times, girls who claim they were raped have emotional problems”; Payne et al., 1999) reflect conceptualizations that the victim was not truthful about being raped or circumstances surrounding a rape due to nefarious intentions and/or emotional/personality problems.

It is possible that “she asked for it” myth acceptance is exacerbated by hardcore pornography viewing because most pornography involves sexual interactions where women are coy but teasing, and therefore “responsible” for sexual contact. By extension, scripts that women enjoy aggressive sexual contact are highly consistent with identified pornographic themes (Bridges et al., 2010; Fritz & Paul, 2017). Conversely, “she lied” myths lack the same degree of conceptual connection to hardcore pornography depictions.

Our results suggest that future researchers should seek to examine specific factors associated with individuals who strongly endorse hostile sexist attitudes and those who view considerable amounts of hardcore pornography. Our demographic findings suggested that most individuals do not endorse rape myths, nor watch hardcore forms of pornography frequently (though they do watch other forms of pornography more regularly). A select few were at risk (high in hostile sexism, high in rape myth acceptance, and high in hardcore pornography viewing). Identifying at-risk individuals, understanding what drives their behavior, and intervening early, are key areas of inquiry. Our results are consistent with a large body of research that has found these individuals to be the most likely to commit sexual assault (Brem et al., 2021; McDermott et al., 2015).

Limitations

As with all findings, results must be interpreted in light of limitations. Notably, the cross-sectional nature of the data precludes causal inference. Future longitudinal research is necessary to better ascertain the temporal order of relationships across variables. While this is one of only a few studies to distinguish hardcore forms of pornography from general pornography viewing, the current study did so in a broad manner. We included multiple genres of pornography in our indicator (BDSM, Hardcore, Fetish). Although related, it is unknown whether these genres are differentially related to rape myths and thus may have canceled out any associations between pornography viewing frequency and rape myth acceptance. For instance, non-BDSM hardcore pornography might be considered a stronger correlate of rape myth acceptance compared to BDSM-hardcore pornography, which tends to place higher value on consent (Beres & MacDonald, 2015).

It is also unclear the degree to which our participants perceived the content of hardcore pornography as differing from general pornography. Indeed, Bridges et al. (2010) suggest most mainstream pornography contains aggressive acts with men being the primary perpetrators and women being the primary targets. Moreover, it is possible that no underlying distinction existed in the actual content viewed. It is possible that something about considering one's pornography “hardcore” is in turn associated with a stronger hostile sexism—rape myth acceptance connection. Future studies could better address this problem by providing specific content domains, as well as providing clear operational definitions for what constitutes “hardcore pornography” (as well as what constitutes “pornography” more broadly; Short et al., 2012).

Similarly, we did not specifically examine participants' use of coercive forms of pornography. Recent research has begun to acknowledge withdrawal/tolerance effects associated with pornography use (Bőthe et al., 2018). It is possible that viewers start out viewing softer forms of pornography and gradually move towards more aggressive forms. Future research should attempt to better measure this process in conjunction with rape myth acceptance and sexual violence perpetration. It would also be important for future studies to explore how sexist beliefs and rape myth acceptance are associated with varying types of pornography. Additionally, the current study only examined two prominent rape myths, “she asked for it” and “she lied”; however, others exist, such as “it wasn’t really rape” and “he didn’t mean to” myths (Payne et al., 1999). Thus, future studies should examine correlations across additional rape myths in conjunction with sexist factors and pornography viewing.

Conclusions

Our findings suggest pornography viewing and hardcore pornography viewing are not ostensibly related to “she lied” and “she asked for it” rape myths. Rather, hostile sexism appears to be the proximal factor in relation to rape myth acceptance for men and women. However, frequent hardcore pornography viewing exacerbated the relationship between hostile sexism and “she asked for it” rape myths in men and women. These findings suggest hardcore pornography viewing may be an important behavioral risk factor for rape myth acceptance in those holding hostile sexist attitudes. By extension, interventions, awareness campaigns, and future research should continue to be aimed at understanding the development and promulgation of sexist belief systems, as well as developing means of reducing their prevalence and impact on society. Addressing the underlying roots of sexism will likely be key to reducing and eventually eliminating sexual assault.

Footnotes

Author's Note

Emma C. Lathan PhD is a post-doctoral fellow at Emory University School of Medicine.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Nicholas C. Borgogna

Emma C. Lathan

Author Biographies

Nicholas C. Borgogna, PhD, is an assistant professor at Texas Tech University.

Emma C. Lathan, PhD, is a post-doctoral resident at Emory University.

Ryon C. McDermott, PhD, is an associate professor at the University of South Alabama.

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