The Effects of Gender-Blind Sexism on Rape Myth Acceptance: Results From a Nationally Representative Study

Abstract

This study uses a diverse sample that is nationally representative with regards to race and gender (N = 2,000) in an attempt to replicate and confirm Stoll, Lilley, and Pinter’s previous finding that gender-blind sexism is correlated with rape myth acceptance. As in the original study, we hypothesized that higher scores on the Gender-Blind Sexism Inventory (GBSI) would be predictive of higher scores on Stoll et al.’s Rape Myth Acceptance Scale (RMA). Gender-blind sexism builds on previous models of contemporary sexism such as hostile and benevolent sexism, modern sexism, and neosexism. It also represents an extension of racialized social system theory that explores the ways contemporary sexism operates in an era of post-racial and post-gender politics via four frames: abstract liberalism, naturalization, cultural sexism, and minimization of sexism. Unlike in the original study, however, our sample also allowed us to control for scores on the Ambivalent Sexism Inventory (ASI), the Modern Sexism Scale (MS), and the Neosexism Scale (NS) in testing this relationship. Our analysis confirmed the hypothesis that gender-blind sexism is predictive of higher rape myth acceptance among participants. Moreover, this study indicates that the GBSI offers additional value over the ASI, MS, and NS, as it was the only index of sexism tested that revealed gender-group differences within its relationship to RMA. Compared to men, women’s acceptance of rape myths was more responsive to shifts in the GBSI. We discuss the implications of our findings in terms of rape and sexual assault prevention and policy. We also provide some suggestions for how the GBSI could be used in future studies.

Keywords

rape and sexual assault rape myth acceptance sexism intervention

Rape myths are “attitudes and beliefs that are generally false but are widely and persistently held, and that serve to deny and justify male sexual aggression against women” (Lonsway & Fitzgerald, 1994, p. 134, emphasis in original). This study extends previous research measuring the association between rape myth acceptance (RMA) and gender-blind sexism among college students at a university in the Midwestern United States utilizing the Gender-Blind Sexism Inventory (GBSI) (Stoll, Lilley, & Pinter, 2017). The GBSI developed by Stoll et al. (2017) draws on the strengths of previous models of contemporary sexism that have been correlated with rape myth acceptance, including modern sexism (Swim, Aikin, Hall, & Hunter, 1995), neosexism (Tougas, Brown, Beaton, & Joly, 1995), and ambivalent sexism (Glick & Fiske, 1996). Gender-blind sexism also represents an extension of racialized social system theory (Bonilla-Silva, 1997) that explores the ways contemporary sexism operates in an era of post-racial and post-gender politics via four frames.

The first frame, abstract liberalism, assumes gender equality to be a zero-sum game. It is based on a belief in individualism and meritocracy with the conviction that no group should be singled out for special treatment and that individual freedoms must be protected above all. Ironically, this allows members of privileged groups to protect the status quo and their own interests by problematizing the needs of targeted minority groups. According to Bonilla-Silva (2006), the naturalization frame assumes segregation is not the result of racism but “biological” or “natural” phenomena but because “like attracts like.” There is also widespread appeal for using this same logic when it comes to gender, although in the case of gender, there tends to be far less stigma for privileging biological, binary explanations of social differences. Cultural sexism relies on culturally based arguments to explain gender differences and views differences as the result of social processes that distinguish certain types of men and women. This frame is generally called on to justify the unequal station of boys and girls and women and men in the larger society, as well as the unequal station of those who are straight and those who are gay, lesbian, bisexual, transgender, or otherwise gender or sexual nonconforming. Finally, minimization of sexism assumes gender inequality is a thing of the past. When gender inequalities are acknowledged, the logic of this frame excludes institutional sexism as a potential explanation. Instead, other frames of gender-blind sexism are often employed to account for such disparities.

Contemporary gender-blind sexism operates in a political climate in which blatant sexism is supposedly rejected, yet sexist ideologies, policies, and practices continue. This type of sexism is predicated on the assumption that because society is now “post-gender,” (e.g., there are no material consequences attached to gender identity/expression nor does gender identity/expression affect life chances) what sexism remains resides only in individual acts of prejudice or discrimination on the part of sexist persons who are out of touch with mainstream beliefs about gender. Taken together, the frames of gender-blind sexism reflect commonsense notions about gender that are used to justify and explain contemporary gender inequality (Stoll, 2013; Stoll et al., 2017). In this way, gender-blind sexism reflects and perpetuates a patriarchal social system. The purpose of this study is to utilize a diverse and nationally representative sample with regards to race and gender (N = 2,000) in an attempt to replicate and confirm Stoll et al.’s (2017) previous finding that gender-blind sexism is correlated with rape myth acceptance.

Previous Research

Over the years, an extensive body of research documenting the pervasiveness of rape myth acceptance has emerged. Drawing on the work of Brownmiller (1975), Burt’s (1980) Rape Myth Acceptance Scale (RMA) constituted the “first effort to provide an empirical foundation for a combination of social psychological and feminist theoretical analysis of rape attitudes and their antecedents” (229). Nearly 15 years after the completion of that scale, Lonsway and Fitzgerald (1994) completed a review of rape myth research and constructs, identifying several shortcomings of existing measurements. For example, the RMAS is a gender specific scale geared toward measuring sexual assault of women which led the authors to conclude that the RMAS essentially measured hostility toward women more than RMA (Suarez & Gadalla, 2010). In response, Payne, Lonsway, and Fitzgerald (1999) developed the gender-neutral Illinois Rape Myth Acceptance Scale (IRMA). The IRMA comprised seven components that measure a respondent’s understanding of the frequency, severity, sense of blame, denial, or minimization of sexual assault. These components include the following: “She asked for it,” “It wasn’t really rape,” “He didn’t mean to,” “She wanted it,” “She lied,” “Rape is a trivial event,” and “Rape is a deviant event” (37).

Rape myth acceptance has been correlated with traditional notions of masculinity and femininity (Kassing & Prieto, 2003; see also Chapleau, Oswald, & Russell, 2008; White & Robinson Kurpius, 2002) and homophobia (Kassing, Beesley, & Frey, 2005). A meta-analysis of 37 studies conducted by Suarez and Gadalla (2010) found a marked gender gap in rape myth acceptance, with men routinely showing higher acceptance compared to women, and confirmed that measures of sexism and hostility toward women were significant predictors of rape myth acceptance (see also Aosved & Long, 2006; Chapleau et al., 2008; Ståhl, Eek, & Kazemi, 2010). Rape myth acceptance is associated with a higher proclivity to rape (Chiroro, Bohner, Viki, & Jarvis, 2004), how people perceive rape and assign blame (Basow & Minieri, 2011; see also Chapleau & Oswald, 2013; Duff & Tostevin, 2015) can prevent women from defining unwanted sexual encounters as rape (Peterson & Muehlenhard, 2004; see also Eyssel & Bohner, 2011; Heath, Lynch, Fritch, & Wong, 2013; Lemaire, Oswald, & Russell, 2016) and make bystanders less likely to intervene in potentially threatening situations (McMahon, 2010).

Drawing on the racial ambivalence work of Katz and colleagues (Katz & Hass, 1988; Katz, Wackenhut, & Hass, 1986), Glick and Fiske (1996) made the argument that sexism operates in more ways than a hostile antipathy toward women, or a hostile sexism (HS). Through explanatory and confirmatory factor analyses, they demonstrated that sexism also included what they termed benevolent sexism (BS). Together, BS and HS created an expression of “ambivalent sexism” that better captured how sexism operates post-second-wave feminism (493-494). When Glick and Fiske (1996) looked at the relationship between the Ambivalent Sexism Inventory and rape myth acceptance they found that HS, but not BS, was significantly correlated with RMA.

In 2017, Stoll et al. offered the GBSI, a new scale for studying gender-blind sexism and rape myth acceptance that tapped components of sexism currently measured separately by the Ambivalent Sexism Inventory, Modern Sexism Scale, and Neosexism Scale. The GBSI went further than simply combining the relative strengths of these previous scales; it offered a new set of frames with which to analyze the ways that contemporary sexism operates in an era of post-gender politics: abstract liberalism, naturalization, cultural sexism, and minimization of sexism. In their initial study, Stoll et al. found empirical support for their model of gender-blind sexism. However, their findings were limited by a homogeneous sample of college students and a survey instrument that did not allow them to compare the robustness of gender-blind sexism with the Ambivalent Sexism Inventory, Modern Sexism Scale, and Neosexism Scale. In this article, we utilize a nationally representative sample with regards to race and gender to test the effects of the GBSI, Ambivalent Sexism Inventory, Modern Sexism Scale, and Neosexism Scale on rape myth acceptance.

Method

Participants

During the spring of 2015, we partnered with Qualtrics, a third-party online survey company, to administer the survey instrument we created to a sample of participants 18 years of age or older living in the United States (N = 2,000). The sample was nationally representative in terms of race and gender (see Table 1).¹ Samples for Qualtrics panels are obtained through partnerships with research companies like Research Now, Toluna, or Clearvoice (Devlin, 2018) that recruit individuals via online platforms to complete surveys. Invited participants for this study were randomly selected from a larger group of U.S. adults who double opted in to participate in a panel. To increase accuracy, Qualtrics uses several quality assurance measures including survey validation, attention filters, prevention of ballot stuffing, and a soft launch phase to identify and address any potential issues which might compromise the integrity of the data before full survey launch. Qualtrics also guarantees complete data sets with no missing data. Participants who completed the survey received compensation in various forms, including gift cards and airline miles. Compensation type varied according to several factors, including recruitment mode and participant preference.

Table 1.

Sample Versus U.S. Population Demographics.

	Sample	U.S. Population^a (%)
Gender
Men	49% (n = 980)	49
Women	51% (n = 1,020)	51
Race
White	72.5% (n = 1,449)	72
Black or African American	10% (n = 200)	13
Latino(a)/Hispanic^b	10% (n = 200)	16
Asian/Pacific Islander	5% (n = 100)	5
Other	2.5% (n = 51)	4

Source. U.S. Census Bureau (2011).

Percentages do not total 100% because of the overlap between racial and ethnic groups.

The percentage of respondents who identified as Latino(a)/Hispanic in this survey is below the U.S. Census percentage, however, the category “White” may also include individuals who are of Latinx or Hispanic origin.

Measures

Rape Myth Acceptance Survey

The online survey that was administered by Qualtrics included a number of items used to measure RMA (Table 2). These measures came from the survey developed by Stoll et al. (2017). The items in their scale were either taken or adapted from the IRMA (Payne et al., 1999) or developed based on the suggestions of other scholars (e.g., Remick, 1993; Sivakumaran, 2005). For example, research has found RMA is correlated with systems of inequality other than sexism, including racism, classism, and heterosexism (Suarez & Gadalla, 2010). Therefore, Stoll et al. created a number of items to capture rape myths related to race, class, gender, and sexuality, distinguishing their measure of RMA from other commonly used instruments, including the IRMA. In sum, the RMA scale used in this study contained 27 items. Response categories ranged from 1 (strongly disagree) to 5 (strongly agree). Internal consistency for our overall RMA scale resulted in an alpha of .92.

Table 2.

Rape Myths.

Q1: Rape does not occur in lesbian relationships.
Q2: Rapists are usually “criminal types.”
Q3: Rape usually occurs in inner city urban areas.
Q4: Many so-called rape victims are actually women who had sex and “changed their minds” afterward.
Q5: Rape accusations are often used as a way of getting back at men.
Q6: When men get raped, the victims are almost always gay.
Q7: Rape is most traumatic for girls from upper-class neighborhoods.
Q8: Men from middle-class homes almost never rape.
Q9: A woman who goes to the home or apartment of a man on the first date is implying that she wants to have sex.
Q10: Women of color are more likely than White women to be raped because they tend to have more sexual partners.
Q11: White women who date men of color are more likely to be raped.
Q12: A woman who dresses in skimpy clothes should not be surprised if a man tries to force her to have sex.
Q13: Lesbian women that dress in masculine clothing are rarely raped.
Q14: If a woman chooses to take the train at night, she is putting herself at risk of being raped.
Q15: A gay man that ejaculates during a sexual encounter cannot be considered a rape victim during the same encounter.
Q16: Women that live in bad neighborhoods are more likely to be raped.
Q17: When a man is very sexually aroused, he may not even realize that a woman is resisting.
Q18: Upper-class people have more control over their sexual urges.
Q19: Although most women wouldn’t admit it, they generally find being physically forced into sex a real “turn-on.”
Q20: White women are more likely than women of color to fantasize about being raped.
Q21: Prostitutes cannot be raped.
Q22: Rape isn’t as big a problem as some feminists would like people to think.
Q23: Being raped isn’t as bad as being mugged and beaten.
Q24: If a woman claims to have been raped but has no bruises or scrapes, she probably shouldn’t be taken too seriously.
Q25: If the rapist doesn’t have a weapon, you really can’t call it a rape.
Q26: Women tend to exaggerate how much rape affects them.
Q27: Since the women’s rights movement, rape is no longer that big of an issue.

Ambivalent Sexism Inventory

The ASI (Glick & Fiske, 1996) includes a 22-item scale that consists of two 11-item subscales that measure (a) hostile sexism and (b) benevolent sexism. Sample items for hostile sexism include “Most women interpret innocent remarks or acts as being sexist,” and “Women are too easily offended.” Sample items for benevolent sexism include, “A good woman should be set on a pedestal” and “Women have a quality of purity few men possess.” Response categories for all items ranged from 1 (disagree strongly) to 5 (strongly agree). Glick and Fiske (1996) found internal consistency for the ASI across six studies. The lowest alpha reported was .83 and the highest .92. Internal consistency was also calculated for our sample and resulted in an alpha of .85.

Modern Sexism Scale

The Modern Sexism (MS) scale (Swim et al., 1995) includes a 13-item scale that consists of two subscales that measure (a) old-fashioned sexism and (b) modern sexism. Sample items for old-fashioned sexism include, “Women are generally not as smart as men” and “When both parents are employed and their child gets sick at school, the school should call the mother rather than the father.” Sample items for modern sexism include “On average, people in our society treat husbands and wives equally” and “It is rare to see women treated in a sexist manner on television.” Response categories for all items ranged from 1 (strongly disagree) to 5 (strongly agree). Swim et al. (1995) found internal consistency for each of the MS subscales with reported alphas of .65 to .66 for old-fashioned sexism and reported alphas of .75 to .84 for modern sexism. Internal consistency for the entire 13-item scale was also calculated for our sample and resulted in an alpha of .82.

Neosexism Scale

The Neosexism (NS) scale (Tougas et al., 1995) includes an 11-item scale. Sample items include “I consider the present employment system to be unfair to women” and “Women will make more progress by being patient and not pushing too hard for change.” Response categories ranged from 1 (strongly disagree) to 5 (strongly disagree). Tougas et al. (1995) found internal consistency for the NS scale with reported alpha of .78. Internal consistency was also calculated for our sample and resulted in an alpha of .83.

GBSI

The GBSI (Stoll et al., 2017) includes a 12-item scale² that consists of four 3-item subscales measuring (a) abstract liberalism, (b) naturalization, (c) cultural sexism, and (d) minimization of sexism (Table 3). Response categories for all items ranged from 1 (strongly disagree) to 5 (strongly agree). Stoll et al. found internal consistency for the GBSI with a reported alpha of .80. Internal consistency was also calculated for the present sample and resulted in an alpha of .81.

Table 3.

Gender-Blind Sexism Inventory.

Abstract Liberalism
Q1: When women earn less than men, it is because they make different choices.
Q2: Affirmative action policies benefit women at the expense of men.
Q3: If a public policy focuses specifically on women, it is not fair to men.
Naturalization
Q1: Women are naturally more emotional than men.
Q2: Men are naturally more aggressive than women.
Q3: Nature is more important than nurture in explaining the differences between men and women.
Cultural Sexism
Q1: It is important to teach children to behave in gender appropriate ways.
Q2: It is more acceptable for a girl to behave like a tomboy than it is for a boy to behave like a sissy.
Q3: It is better to socialize girls to be caregivers and boys to be breadwinners than vice versa.
Minimization of Sexism
Q1: Sexism is not a major problem in today’s society.
Q2: Today, women have all the opportunities that men have.
Q3: Gender inequality in the United States is not as bad as it used to be.

Grouping variables

The survey also contained several demographic measures we used as grouping variables. These included gender (0 = woman, 1 = man), race (0 = white, 1 = non-White), sexuality (0 = heterosexual, 1 = nonheterosexual), and education level (0 = college or higher; 1 = less that college). To measure gender, participants were asked to identify the term that best described their gender. Response categories included either “woman” or “man.” To measure race, participants were asked to identify their race/ethnicity. Response categories included, “White,” “Black or African American,” “Latino(a)/Hispanic,” “Asian and Pacific Islander,” and “Other.” To measure sexuality, participants were asked to identify the term that best described their sexual orientation. Response categories included, “Asexual,” “Bisexual,” “Gay, Lesbian, or Queer,” “Heterosexual or Straight,” “Pansexual,” and “Questioning.” To measure education level, participants were asked to identify the highest level of education they had completed. Response categories included, “less than high school,” “high school,” “some college,” “business/technical school certificate,” “associate’s degree,” “bachelor’s degree,” “master’s degree,” and “doctoral degree.”

Control variables

We used two items as control variables: social class and age. Social class was measured by asking participants to identify their class position. Response categories ranged from 1 (poor) to 7 (upper class). Age was measured by asking participants to provide their numerical age. Response categories included “18-25,” “26-34,” “35-54,” “55-64,” and “65 or over.”

Expectations

Based on Stoll et al.’s (2017) previous findings, we expected to observe a positive relationship between gender-blind sexism and rape myth acceptance. As values on the GBSI increased, we anticipated the scores that respondents received on the RMA index would rise as well. Conversely, respondents who scored lower on RMA would tend to express weaker levels of gender-blind sexism.

Analyses

We used multiple regression analysis to explore the impact of sexist attitudes on RMA.³ Specifically, our regression model took the following functional form: RMA = β0 + β1 (GBSI) + β2 (ASI) + β3 (MS) + β4 (NS) + β5 (Respondents’ Age) + β6 (Self-Reported Social Class). The dependent variable was rape myth acceptance (RMA). The main independent variable in this model was Stoll et al.’s (2017) measure of gender-blind sexism (GBSI). To compare the influence of this measure of sexism (as a predictor of RMA) to other indices in the extant literature, we included the ASI, MS, and NS in our model. Stated formally, our hypothesis based on our regression model is that β₁ > 0, after controlling for the impact of the other measures of sexism. The null hypothesis is that β₁ = 0. Moreover, we also assumed that the impact of GBSI scores on RMA might differ across gender/race/sexuality/education level. These additional expectations suggested that β₁ (Women) ≠ β₁ (Men), β₁ (Whites) ≠ β₁ (Non-Whites), β₁ (Straight) ≠ β₁ (lesbian, gay, bisexual, and queer, LGBQ), and β₁ (No College) ≠ β₁ (College and Beyond). The null hypothesis in this case would be that there would be no demographic group differences in the impact of GBSI on RMA (β₁ [Gender] = β₁ [Race] = β₁ [Sexuality] = β₁ [Education Level] = 0). Finally, we took advantage of the large and diverse sample size of our survey and tested for demographic group differences in the results. This test involved two steps. In the first step, we included measures of age and social class. In the second step, we sorted our regression analyses by several “grouping variables.” In this case, we used binary measures of gender (men vs. women), race (Whites vs. non-Whites), sexuality (straight vs. LGBQ), and education level (no college education vs. college education and beyond). An overall summary of the variables we included in our regression models appear in Table 4.

Table 4.

Descriptive Statistics for the Variables Used in the Analyses.

	Obs	M	SD	Minimum	Maximum
Dependent Variable
Factor Index Scores for RMA	2,000	0.00	14.49	−28.50	60.50
Theoretically Central Independent Variable
Factor Index Scores for GBSI (centered)	2,000	0.00	0.91	−2.74	3.09
Control Variables
Factor Index Scores for ASI (centered)	2,000	0.00	0.94	−3.00	3.14
Factor Index Scores for MS (centered)	2,000	0.00	0.92	−1.99	3.42
Factor Index Scores for NS (centered)	2,000	0.00	0.93	−1.86	3.41
Age (In Years) (centered)	2,000	0.00	0.27	−0.58	0.42
Social Class (0 = Poor, 1 = Upper Class) (centered)	2,000	0.00	0.24	−0.51	0.49
Grouping Variables
Gender (1 = Woman, 0 = Man)	2,000	0.51	0.50	0	1
Race (1 = Non-White, 0 = White)	2,000	0.28	0.45	0	1
Sexuality (1 = LBGQ, 0 = Straight)	2,000	0.91	0.28	0	1
Education Level (1 = Some College and Beyond, 0 = No College)	2,000	0.53	0.50	0	1

Note. RMA = Rape Myth Acceptance Scale; GBSI = Gender-Blind Sexism Inventory; ASI = Ambivalent Sexism Inventory; MS = Modern Sexism Scale; NS = Neosexism Scale; LGBQ = lesbian, gay, bisexual, and queer; obs = observed.

Results

We used ordinary least squares (OLS) regression to explore the impact of gender-blind sexism (Stoll et al., 2017) on rape myth acceptance, controlling for the effect of other measures of sexism and respondents’ demographic background (see Figure 1 and Table 5).⁴ All regression coefficients (displayed as dots) include a measure of uncertainty; in this case, the horizontal lines extending from the regression estimates are 95% confidence intervals (CIs). These uncertainty parameters show that the “true result” can fall within a range of potential values. If the lower and upper bounds of a CI are of different signs, then that result is statistically nonsignificant. This is because the interval of numbers surrounding the coefficient also contains the value of zero (marked in each figure with a vertical dashed line), and this value represents the null hypothesis that the independent and dependent variables are unrelated. Conversely, estimates with CIs that do not include zero (i.e., those for which the upper and lower bounds are the same sign) are statistically significant at the .05 level.

Figure 1.

Overall models of the impact of gender-blind sexism on rape myth acceptance.

Table 5.

OLS Regression Results.

Models of the Impact of GBSI on RMA	No Grouping Variables	With Grouping Variables
Factor Index Scores for GBSI (centered)	3.80***	3.82***
Factor Index Scores for GBSI (centered)	[2.99, 4.61]	[3.01, 4.63]
Factor Index Scores for ASI (centered)	2.94***	2.75***
Factor Index Scores for ASI (centered)	[2.18, 3.69]	[1.98, 3.51]
Factor Index Scores for MS (centered)	1.11**	0.95*
Factor Index Scores for MS (centered)	[0.29, 1.94]	[0.11, 1.78]
Factor Index Scores for NS (centered)	4.26***	4.31***
Factor Index Scores for NS (centered)	[3.33, 5.19]	[3.37, 5.24]
Age (In Years) (centered)	6.03***	5.83***
Age (In Years) (centered)	[4.35, 7.71]	[4.05, 7.62]
Social Class (0 = Poor, 1 = Upper Class) (centered)	0.96	1.47
Social Class (0 = Poor, 1 = Upper Class) (centered)	[–0.98, 2.90]	[–0.55, 3.50]
Gender (1 = Woman, 0 = Man)		−1.49**
Gender (1 = Woman, 0 = Man)		[–2.47, –0.51]
Race (1 = Non-White, 0 = White)		0.035
Race (1 = Non-White, 0 = White)		[–1.05, 1.12]
Sexuality (1 = LGBQ, 0 = Straight)		−1.18
Sexuality (1 = LGBQ, 0 = Straight)		[–2.85, 0.49]
Education Level (1 = Some College, 0 = No College)		−0.93
Education Level (1 = Some College, 0 = No College)		[–1.89, 0.035]
Constant	55.5***	57.8***
Constant	[55.0, 56.0]	[56.1, 59.6]
Observations	2000	2000
R ²	.483	.487

Note. 95% confidence intervals in brackets. OLS = ordinary least squares; GBSI = Gender-Blind Sexism Inventory; RMA = Rape Myth Acceptance Scale; ASI = Ambivalent Sexism Inventory; MS = Modern Sexism Scale; NS = Neosexism Scale; LGBQ = lesbian, gay, bisexual, and queer.

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

We present two regression models in Figure 1 (see also Table 5). The darker-colored estimates and CIs represent the results for a regression model run without the grouping variables. Put differently, these results include only the centered versions of the indices for ASI, MS, NS, age and social class as predictors of RMA. Conversely, the lighter-colored dots and error bands report the regression analyses that include our demographic grouping variables: race, gender, sexuality, and education level. Holding other variables constant, a one-unit shift in the standard deviation of the GBSI corresponds with an increase in RMA. In the model that excludes grouping variables the increase is 3.80, with a CI of [2.99, 4.61]. The values representing the lower and upper bound of the CIs are the same sign, which tells us that the effect is statistically significant at the .05 level. The increase in RMA as a function of GBSI is comparable in the model that includes those demographic groups (β₁ = 3.82, CI = [3.01, 4.63]). Consistent with Stoll et al. (2017, p. 38), it is clear that gender-blind sexism is positively related to rape myth acceptance. This pattern remains true, regardless of how we specify our regression models, and, because of the consistency of these results, we can reject the null hypothesis that β₁ = 0. It is also important to note, however, that the ASI, MS, and NS also had a statically significant impact on RMA.

Having confirmed the association between GBSI and RMA, we then analyzed group differences in this relationship. Specifically, we examined the potential for gender, race, sexuality, and education level to serve as moderating variables. We got similarly consistent results in the sense that the GBSI was always a positive and statistically significant predictor of RMA. However, there was only one instance in which we detected demographic group differences on the GBSI → RMA relationship that is not present in the relationships between RMA and the ASI, MS, or NS and that was with gender. As seen in Table 6 and Figure 2, the influence of GBSI on the RMA was greater for women (β₁ = 4.52, CI = [3.30, 5.74]) than it for men (β₁ = 2.68, CI = [1.32, 3.05]). Because the CIs for the regression model ran on the women-only sample do not overlap with the intervals for the men-only sample, we can conclude that this gender difference is statistically significant.

Table 6.

Gender Differences in the Impact of GBSI on RMA.

	Men	Women
Factor Index Scores for GBSI (centered)	2.68***	4.52***
Factor Index Scores for GBSI (centered)	[1.32, 3.05]	[3.30, 5.74]
Factor Index Scores for ASI (centered)	3.93***	2.13***
Factor Index Scores for ASI (centered)	[2.29, 5.18]	[1.06, 3.20]
Factor Index Scores for MS (centered)	1.61*	0.44
Factor Index Scores for MS (centered)	[0.26, 2.96]	[–0.80, 1.68]
Factor Index Scores for NS (centered)	4.49***	4.00***
Factor Index Scores for NS (centered)	[2.83, 6.14]	[2.58, 5.41]
Age (In Years) (centered)	4.38***	7.05***
Age (In Years) (centered)	[1.93, 6.83]	[4.69, 9.41]
Social Class (0=Poor ==> 1=Upper Class) (centered)	1.14	0.76
Social Class (0=Poor ==> 1=Upper Class) (centered)	[–1.57, 3.85]	[–1.97, 3.48]
Constant	56.0***	54.6***
Constant	[55.4, 56.7]	[53.9, 55.4]
Observations	980	1020
R ²	.479	.426

Note. 95% confidence intervals in brackets. GBSI = Gender-Blind Sexism Inventory; RMA = Rape Myth Acceptance Scale; ASI = Ambivalent Sexism Inventory; MS = Modern Sexism Scale; NS = Neosexism Scale.

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

Figure 2.

Gender-group differences in the impact of gender-blind sexism on rape myth acceptance.

To contextualize the gender-group differences, we used the CLARIFY statistical software package (King, Tomz, & Wittenberg, 2000; Tomz, Wittenberg, & King, 2001) to generate additional results from the regression models in Figure 3. Specifically, we simulated changes in the levels of RMA (the y-axis) across values of the GBSI (x-axis), holding the effects of other variables constant. The light- and dark-colored slopes in the figure summarize these changes in simulated values for the female and male respondents, respectively. While the level of RMA was virtually identical at the lowest values of the GBSI (estimate for women = −25.69, estimate for men = −25.39), RMA was noticeably higher for women (32.06) than for men (25.24) at the highest value of GBSI. The difference in the steepness of these slopes confirms what we saw in Figure 2: compared to men, women’s acceptance of rape myths is more responsive to shifts in the GBSI.

Figure 3.

How gender moderates the impact of gender-blind sexism on rape myth acceptance.

Discussion and Conclusion

This article is not without its limitations. By utilizing a third-party online survey company, we could not include individuals without Internet access. Because we dichotomize both race and gender, we are unable to account for any potential differences among racial groups or among individuals who do not identify with the gender binary. Thus, our ability to fully capture all the relationships related to our independent and dependent variables with regards to race, class, and gender are somewhat limited.

Our research confirms the frames of gender-blind sexism (Stoll et al., 2017) are correlated with RMA. We were also able to test gender-blind sexism alongside other indices of sexism that are well established in the literature with the benefit of a nationally representative and demographically diverse sample. While our research confirms the statistical significance of the ASI, NS, and MS as predictors of RMA, it also confirms that the GBSI offers additional value. For example, GBSI is the only index of sexism tested that revealed gender-group differences within its relationship to RMA.

Women’s acceptance of rape myths was more responsive to shifts in the GBSI. While this may seem counterintuitive given the body of empirical evidence that finds men tend to have higher rates of RMA than women, we suggest two possible explanations for this finding. First, because women are more likely than men to experience rape and sexual assault, women’s scores on the GBSI may reflect an inclination to view victimization as within women’s control. As Yodanis (2004) points out, “Not every woman needs to be a victim of violence for violence to control the lives of women” (671-672). A parallel can be drawn from Bonilla-Silva’s (2003) work. While he finds that Whites are most likely to make full use of the frames of colorblind racism, people of color are also subject to this ideology, albeit in different ways. Specifically, he shows that while Black people may not minimize or dismiss structural inequality, the frame of abstract liberalism in particular helps shape how some make sense of it. In the same fashion, gender-blind sexism may resonate with women more than other models of contemporary sexism because it is more multidimensional as discussed below. Second, the gender differences we observe may also reflect what scholars who do experimental research call “ceiling effects” (see Lammers & Badia, 2005, Chapter 4). In other words, because the mean scores for men’s RMA were already high (M = 59.3), subsequent expression of RMA may be minimal. Conversely, women’s scores started out lower on the scale (mean 51.9). Therefore, there was more room for women’s RMA scores to vary.

In sum, the difference in responsiveness between men and women is not extremely large in magnitude. However, as we saw in Figure 2, the difference is statistically significant. We credit our ability to isolate this small but important difference to the precision of this research design as compared with the original study (Stoll et al., 2017). If not for the GBSI that we replicate from Stoll et al. (2017) and the large, nationally representative, and demographically diverse sample, we would not have been able to detect such a group difference.

Gender-blind sexism as a theoretical model calls attention to the ways contemporary gender inequality is structural in nature. When Glick and Fiske (1996, p. 509) were developing the ASI, they suggested that

. . . the Modern Sexism and Neo-Sexism Scales may have greater predictive utility for exploring gender-related political attitudes, whereas the ASI may be of particular interest in the interpersonal relationships area . . .

However, as Stoll et al. (2017) contend, contemporary sexism and rape myth acceptance are both interpersonal and political in nature. Drawing inspiration from Bonilla-Silva’s (2006) racialized social system theory, gender-blind sexism situates the frames of abstract liberalism, naturalization, cultural sexism, and minimization of sexism as a reflection and perpetuation of a patriarchal social system.

Rape and sexual assault can best be viewed as part of this system. Rape myth acceptance reflects commonsense notions about gender that operate like the aforementioned “cul-de-sacs” (e.g., Bonilla-Silva, 2006, p. 26) and serve to explain and justify women’s subordination. Dominant group members have a vested interest in developing ideologies to explain gender inequalities in a society they purport to be “post-gender.” As Bonilla-Silva (2006) points out, if the ultimate goal of the dominant race is to maintain their position of privilege within a racialized society, they must develop rationalizations to account for the status of minorities. This is also true with patriarchy. To maintain power, authority, and privilege, dominant group members must develop rationalizations to account for the status of women, men of subordinated masculinities, and any other persons who are gender or sexual nonconforming.

Certainly, sexual assault can best be understood via an ecological framework (see, for instance, Belsky, 1980) that situates individuals within relational contexts that are, themselves nested within larger structural realities. But because the politics of gender-blind sexism, and most specifically the frame of abstract liberalism, relegate rape and sexual assault to individual-level problems, the solutions offered to deter both are almost exclusively rooted in individual-level approaches. Sexual assault prevention programs still routinely emphasize risk-reduction information, teaching women safety measures and techniques to avoid becoming a victim of assault. Increasingly, these programs also focus on issues of consent, attempting to move beyond a “no means no” orientation toward an understanding of what means “yes.” But, while risk-reduction and consent-based approaches can no doubt help to deter some offenses, the present study, along with a long line of work from scholars such as Schur (1988), Crenshaw (1991), Bumiller (2008), and Feimster (2009), demonstrates and confirms that attitudes toward rape and sexual assault are also structurally contingent. It is this structural analysis that we argue is largely missing from our discursive representations of sexual violence.

As such, our findings lead us to call for contextualizing rape and sexual assault as a structural issue in tandem with efforts to dismantle other forms of institutional sexism. This includes addressing gendered occupational clustering and segregation, barriers to promotion, as well as the disparity in wages faced by women in the paid labor force. It includes addressing the disproportionate experience of sexual harassment and gender discrimination women face in the workplace, and ending the forced arbitration, and enforced nondisclosure, of such cases. Our call also includes addressing the disproportionate amount of unpaid, domestic labor done by women, and their continued underrepresentation and disenfranchisement in politics. The same rationalizations utilized to explain away these inequities are utilized in explaining away women’s sexual victimization.

Gender-blind sexism was originally developed in an institutional ethnography exploring elementary teachers’ perspectives of gender and gender inequality (Stoll, 2013). Its relationship to rape myth acceptance has also been tested quantitatively (Stoll et al., 2017) and this relationship has been confirmed in the current study. We believe gender-blind sexism can be used to study other manifestations of contemporary sexism. When exploring these lines of inquiry, we encourage scholars to do so using a mixed-methods approach, using both the GBSI as well as qualitative methodologies.

Footnotes

Acknowledgements

The authors would like to thank anonymous reviewers for their feedback and the College of Liberal Studies at the University of Wisconsin–La Crosse for a generous faculty research grant to complete this study.

Authors’ Note

Ray Block Jr. is also affiliated with The University of Kentucky, USA. An earlier version of this paper was presented at the Midwest Sociological Society Annual Meeting in Minneapolis, MN, in March 2018.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Laurie Cooper Stoll is an associate professor of Sociology at the University of Wisconsin–La Crosse. Her research focuses on social inequalities in institutions, particularly education.

Terry Glenn Lilley is a sociologist and assistant professor in the department of Women’s, Gender, and Sexuality Studies at the University of Wisconsin–La Crosse. His research and teaching focuses on social justice issues with a specific emphasis on gendered violence.

Ray Block Jr. is an associate professor of Political Science and Public Administration at the University of Wisconsin–La Crosse and associate professor of Political Science and African American & Africana Studies at The University of Kentucky. His research focuses on racial, ethnic, and gender differences in civic involvement; the formation and mutability of social identity; campaigns and elections; and racial attitudes and stereotypes.

References

Aosved

A. C.

Long

P. J.

(2006). Co-occurrence of rape myth acceptance, sexism, racism, homophobia, ageism, classism, and religious intolerance. Sex Roles, 55, 481-492. doi:10.1007/s11199-006-9101-4

Basow

S. A.

Minieri

(2011). “You owe me”: Effects of date cost, who pays, participant gender, and rape myth beliefs on perceptions of rape. Journal of Interpersonal Violence, 26, 479-497. doi:10.1177/0886260510363421

Belsky

(1980). Child maltreatment: An ecological integration. American Psychologist, 35, 320-325. doi:10.1037/0003-066X.35.4.320

Bonilla-Silva

(1997). Rethinking racism: Toward a structural interpretation. American Sociological Review, 62, 465-480. doi:10.1177/2332649216632242

Bonilla-Silva

(2003). Racism without racists: Color-blind racism and the persistence of racial inequality in the United States. Lanham, MD: Rowman & Littlefield.

Bonilla-Silva

(2006). Racism without racists: Color-blind racism and the persistence of racial inequality in the United States (2nd ed.). Lanham, MD: Rowman & Littlefield.

Brownmiller

(1975). Against our will: Men, women, and rape. New York, NY: Simon & Schuster.

Bumiller

(2008). In an abusive state: How neoliberalism appropriated the feminist movement against sexual violence. Durham, NC: Duke University Press.

Burt

M. R.

(1980). Cultural myths and supports for rape. Journal of Personality and Social Psychology, 38, 217-230. doi:10.1037/0022-3514.38.2.217

10.

Chapleau

K. M.

Oswald

D. L.

(2013). Status, threat, and stereotypes: Understanding the function of rape myth acceptance. Social Justice Research, 26, 18-41. doi:10.1007/s11211-013-0177-z

11.

Chapleau

K. M.

Oswald

D. L.

Russell

B. L.

(2008). Male rape myths: The role of gender, violence, and sexism. Journal of Interpersonal Violence, 23, 600-615. doi:10.1177/0886260507313529

12.

Chiroro

Bohner

Viki

G. T.

Jarvis

C. I.

(2004). Rape myth acceptance and rape proclivity: Expected dominance versus expected arousal as mediators in acquaintance-rape situations. Journal of Interpersonal Violence, 19, 427-442. doi:10.1177/0886260503262081

13.

Crenshaw

(1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43, 1241-1299.

14.

Devlin

A. S.

(2018). The research experience: Planning, conducting, and reporting research. Thousand Oaks, CA: Sage.

15.

Duff

Tostevin

(2015). Effects of gender, rape myth acceptance, and perpetrator occupation on perceptions of rape. Journal of Criminal Psychology, 5, 249-261. doi:10.1108/JCP-12-2014-0019

16.

Eyssel

Bohner

(2011). Schema effects of rape myth acceptance on judgments of guilt and blame in rape cases: The role of perceived entitlement to judge. Journal of Interpersonal Violence, 26, 1579-1605. doi:10.1177/0886260510370593

17.

Feimster

(2009). Southern horrors: Women and the politics of rape and lynching. Cambridge, MA: Harvard University Press.

18.

Glick

Fiske

S. T.

(1996). The Ambivalent Sexism Inventory: Differentiating hostile and benevolent sexism. Journal of Personality and Social Psychology, 70, 491-512. doi:10.1111/j.1471-6402.1997.tb00104.x

19.

Gonzalez-Barrera

Hugo Lopez

(2015, June 15). Is being Hispanic a matter of race, ethnicity or both? Retrieved from http://www.pewresearch.org/fact-tank/2015/06/15/is-being-hispanic-a-matter-of-race-ethnicity-or-both/

20.

Hair

J. F.

Jr. Anderson

R. E.

Tatham

R. L.

Black

W. C.

(1995). Multivariate data analysis (3rd ed.). New York, NY: Macmillan.

21.

Heath

N. M.

Lynch

S. M.

Fritch

A. M.

Wong

M. M.

(2013). Rape myth acceptance impacts the reporting of rape to the police: A study of incarcerated women. Violence Against Women, 19, 1065-1078. doi:10.1177/1077801213501841

22.

Kassing

L. R.

Beesley

Frey

L. L.

(2005). Gender role conflict, homophobia, age, and education as predictors of male rape myth acceptance. Journal of Mental Health Counseling, 27, 311-328. doi:10.17744/mehc.27.4.9wfm24f52kqgav37

23.

Kassing

L. R.

Prieto

L. R.

(2003). The rape myth and blame-based beliefs of counselors-in-training toward male victims of rape. Journal of Counseling & Development, 81, 455-461. doi:10.1002/j.1556-6678.2003.tb00272.x

24.

Katz

Hass

R. G.

(1988). Racial ambivalence and American value conflict: Correlational and priming studies of dual cognitive structures. Journal of Personality and Social Psychology, 55, 893-905. doi:10.1037/0022-3514.55.6.893

25.

Katz

Wackenhut

Hass

R. G.

(1986). Racial ambivalence, value duality, and behavior. In Dovidio

J. F.

Gaertner

S. L.

(Eds.), Prejudice, discrimination, and racism (pp. 35-59). San Diego, CA: Academic Press.

26.

King

Tomz

Wittenberg

(2000). Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science, 44, 341-355. doi:10.2307/2669316

27.

Lammers

W. J.

Badia

(2005). Fundamentals of behavioral research. Belmont, CA: Thomson/Wadsworth.

28.

Lemaire

K. L.

Oswald

D. L.

Russell

B. L.

(2016). Labeling sexual victimization experiences: The role of sexism, rape myth acceptance, and tolerance for sexual harassment. Violence and Victims, 31, 332-346. doi:10.1891/0886-6708.VV-D-13-00148

29.

Lonsway

K. A.

Fitzgerald

L. F.

(1994). Rape myths: In review. Psychology of Women Quarterly, 18, 133-164. doi:10.1111/j.1471-6402.1994.tb00448.x

30.

McMahon

(2010). Rape myth beliefs and bystander attitudes among incoming college students. Journal of American College Health, 59, 3-11. doi:10.1080/07448481.2010.483715

31.

Menard

(1995). Applied logistic regression analysis: Sage university series on quantitative applications in the social sciences. Thousand Oaks, CA: Sage.

32.

Pan

Jackson

R. T.

(2008). Ethnic difference in the relationship between acute inflammation and serum ferritin in US adult males. Epidemiology & Infection, 136, 421-431. doi:10.1017/S095026880700831X

33.

Payne

D. L.

Lonsway

K. A.

Fitzgerald

L. F.

(1999). Rape myth acceptance: Exploration of its structure and its measurement using the Illinois Rape Myth Acceptance Scale. Journal of Research in Personality, 33, 27-68. doi:10.1006/jrpe.1998.2238

34.

Peterson

Z. D.

Muehlenhard

C. L.

(2004). Was it rape? The functions of women’s rape myth acceptance and definitions of sex in labeling their own experiences. Sex Roles, 51, 129-144. doi:10.1023/B:SERS.0000037758.95376.00

35.

Remick

L. A.

(1993). Read her lips: An argument for a verbal consent standard in rape. University of Pennsylvania Law Review, 141, 1103-1151.

36.

Rogerson

P. A.

(2001). Statistical methods for geography. London, England: SAGE.

37.

Schur

(1988). The Americanization of sex. Philadelphia, PA: Temple University Press.

38.

Sivakumaran

(2005). Male/male rape and the “taint” of homosexuality. Human Rights Quarterly, 27, 1274-1306. doi:10.1353/hrq.2005.0053

39.

Ståhl

Eek

Kazemi

(2010). Rape victim blaming as system justification: The role of gender and activation of complementary stereotypes. Social Justice Research, 23, 239-258. doi:10.1007/s11211-010-0117-0

40.

Stoll

L. C.

(2013). Race and gender in the classroom: Teachers, privilege, and enduring social inequalities. Lanham, MD: Lexington Books.

41.

Stoll

L. C.

Lilley

T. G.

Pinter

(2017). Gender-blind sexism and rape myth acceptance. Violence Against Women, 23, 28-45. doi:10.1177/1077801216636239

42.

Suarez

Gadalla

T. M.

(2010). Stop blaming the victim: A meta-analysis on rape myths. Journal of Interpersonal Violence, 25, 2010-2035. doi:10.1177/0886260509354503

43.

Swim

J. K.

Aikin

K. J.

Hall

W. S.

Hunter

B. A.

(1995). Sexism and racism: Old-fashioned and modern prejudices. Journal of Personality and Social Psychology, 68, 199-214. doi:10.1037/0022-3514.68.2.199

44.

Tomz

Wittenberg

King

(2001). CLARIFY: Software for interpreting and presenting statistical results (v. 2.0). Retrieved from https://gking.harvard.edu/files/clarify.pdf

45.

Tougas

Brown

Beaton

A. M.

Joly

S. S.

(1995). Neosexism: Plus ca change, plus c’est pareil. Personality and Social Psychology Bulletin, 21, 842-849. doi:10.1177/0146167295218007

46.

U.S. Census Bureau. (2011). Overview of Race and Hispanic Origin: 2010. Retrieved from https://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf

47.

White

B. H.

Robinson Kurpius

S. E.

(2002). Effects of victim sex and sexual orientation on perceptions of rape. Sex Roles, 46, 191-200. doi:10.1023/A:1019617920155

48.

Yodanis

C. L.

(2004). Gender inequality, violence against women, and fear: A cross-national test of the feminist theory of violence against women. Journal of Interpersonal Violence, 19, 655-675. doi:10.1177/0886260504263868