Attitude Toward Sexual Aggression Against Women (ASAW) Scale: Evidence of Discriminant and Incremental Validity

Illinois Rape Myth Acceptance Scale-Short Form

The Illinois Rape Myth Acceptance Scale-Short Form (IRMAS-SF, Payne et al., 1999) is a 20-item self-report scale designed to assess rape myth acceptance. Rape myth acceptance is defined as “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, 1995, p. 134). Example items include “If a woman is raped while she is drunk, she is at least somewhat responsible for letting things get out of control” and “Men don’t usually intend to force sex on a woman, but sometimes they get too sexually carried away”. Participants were asked to rate their agreement with each item on a 4- point Likert-type scale from (1) not at all agree to (4) very much agree. A total score is computed by summing responses to 17 of the 20 items (three items are filler items). Total scores can range from 17 to 68, with higher scores indicating greater endorsement of rape myths. The IRMAS-SF demonstrated excellent internal consistency in the current sample (ω = .93).

RAPE Scale

The RAPE Scale (Bumby, 1996) is a measure of cognitive distortions, which are defined as “learned assumptions, sets of beliefs, and self-statements about deviant sexual behaviors […] which serve to deny, justify, minimize, and rationalize an offender’s actions” (p. 38). It is a self-report scale that asks participants to rate their endorsement of 36 statements on a 4-point Likert-type scale from (1) strongly disagree to (4) strongly agree. Example statements include “A lot of women claim they were raped just because they want attention” and “Men who commit rape are probably responding to a lot of stress in their lives, and raping helps to reduce that stress”. A total score is computed by summing the items (total scores can range from 36 to 144), with higher scores indicating greater endorsement of cognitive distortions. The RAPE Scale demonstrated excellent internal consistency in the current sample (ω = .96).

Attitudes Toward Rape Scale

The Attitudes Toward Rape (ATR) scale (Feild, 1978) was included as another measure of offense-supportive cognition because, although the name implies that it is a measure of attitudes, the items do not appear to reflect attitudes as defined here (i.e., favorable or unfavorable evaluation). Rather, the items appear to assess “people’s beliefs or opinions about rape” (p. 158), as was intended by the author of the measure. Each item is rated on a 4-point Likert-type scale from (1) strongly disagree to (4) strongly agree. Based on a principal components analysis of the ATR with a large community sample, Feild (1978) identified eight subscales: (a) Woman’s Responsibility for Rape Prevention, (b) Victim Precipitation of Rape, (c) Severe Punishment for Rape, (d) Favorable Perception of a Woman After Rape, (e) Resistance as a Woman’s Role During Rape, (f) Sex as Motivation for Rape, (g) Normality of Rapists, and (h) Power as Motivation for Rape. However, this factor structure was not replicated in the current study. As described in more detail in the Results section, ATR items separated onto two factors that appear to reflect pro-rape beliefs and anti-rape beliefs, respectively. Several items did not load onto any of the factors; thus, total scores were computed using only the items that loaded on either the pro-rape or anti-rape factors. Specifically, 19 items were summed to create a pro-rape beliefs total score (ATR Pro-Rape), with higher scores indicating relatively more agreement with rape supportive beliefs, such as “A woman should be responsible for preventing her own rape” and “It would do some women some good to get raped”. Total scores could range from 19 to 76 and internal consistency was excellent (ω = .91). Next, six items were summed to create an anti-rape beliefs total score (ATR Anti-Rape). Scores could range from 6 to 24, with higher scores indicating relatively more agreement with anti-rape beliefs, such as “All rapists are mentally sick” and “A man who has committed rape should be given at least 30 years in prison”. Internal consistency was lower for these items (ω = .68); thus, results involving this sub-scale should be interpreted with caution.

Sexual Experience Survey-Tactics First: Revised

Past sexually aggressive behavior was assessed using the Sexual Experience Survey-Tactics First: Revised (SES-TFR, Hermann et al., 2018), a modified version of the ‘Tactics First’ version of the Sexual Experience Survey (SES-TF) developed by Abbey et al. (2005). The 36-item SES-TFR asks participants about the frequency with which they have engaged in a number of sexual acts (e.g., sexual touching, oral sex, and vaginal sex) using various sexually aggressive tactics (e.g., arguments and pressure, giving a woman drugs or alcohol, and physical force) since the age of 16. Respondents could report frequencies of up to nine times or more, with scores truncated to three times or more for the calculation of total scores (Abbey et al., 2005). A total score was computed using the separate outcomes and tactics severity weighting scheme developed by Davis et al. (2014), with higher scores indicating more past sexual aggression.

Likelihood of Engaging in Sexually Aggressive Behavior

The Proclivity SES-TFR (Hermann et al., 2018) was used to assess likelihood of engaging in sexual aggression in the future. Participants are asked to rate the likelihood with which they would engage in each of the 36 sexually aggressive behaviors included in the SES-TFR. Each item is rated on a 7-point scale from (1) not at all likely to (7) very likely (anchors only). A total score is computed by averaging responses to the 36 items (ranging from 36 to 252), with higher scores indicating a greater likelihood of engaging in sexually aggressive behavior. The Proclivity SES-TFR has been found to predict subsequent sexually aggressive behavior over a 4-month follow-up period among men from the community (Hermann et al., 2016).

Likelihood to Rape

Likelihood to rape was assessed using the Likelihood to Rape Question (LR; Malamuth, 1981). Participants were asked about the likelihood that they would rape a woman if they could be assured of not being caught or punished on a 5-point scale from (1) not at all likely to (5) very likely. The LR has also been found to predict subsequent sexual aggression during a 4-month follow-up period (Hermann et al., 2016).

Discriminant Validity

Three separate EFAs were conducted to explore the extent to which ASAW items are distinct from the items of the three other measures of offense-supportive cognition (i.e., IRMAS-SF, RAPE Scale, and ATR). We chose to conduct three separate EFAs – each containing the ASAW and one of the other measures of offense-supportive cognition – because we were primarily concerned about the distinctiveness/overlap between the ASAW and the other measures, rather than the distinctiveness/overlap between the latter. ¹ Additionally, we used EFA over confirmatory factor analysis (CFA) because the former allows the examination of cross-loadings, whereas the latter is a more restrictive model in which cross-loadings are fixed to zero. It was important to examine the pattern of cross-loadings in this study to determine whether items loaded primarily onto distinct factors or whether high cross-loadings were present (potentially indicating a lack of discriminant validity). This item-level analysis of cross-loadings was also more conducive to testing whether certain items from the other measures of offense-supportive cognition overlapped with ASAW items, potentially indicating that they are driven by the same underlying construct.

The EFAs were conducted in MPlus version 8.4. We used the same approach for factor extraction and retention as Pedneault et al. (2025). Factors were extracted from a polychoric correlation matrix using robust weighted least square estimation (i.e., WLSMV estimator) and rotated using oblique rotation (Geomin rotation). Three factor retention methods were considered when selecting the number of factors to retain: (a) Kaiser criterion (Kaiser, 1960), (b) parallel analysis (O’Connor, 2000), and (c) minimum average partial (MAP) test (Velicer, 1976). The Kaiser criterion (i.e., factors must have an eigenvalue grater than one) was used to prune factors that explained less variance than a single item, but more weight was given to the results of the parallel analysis and the MAP test when selecting the number of factors to retain (Schmitt, 2011).

When selecting the number of factors to retain, we also examined the pattern of standardized factor loadings on each of the extracted factors. Factor loadings ≥ .40 were considered to load onto a factor, with this threshold being the minimum proposed threshold for retaining an item (Matsunaga, 2010). All extracted models were also evaluated using the following three model fit indices: (a) root mean square error of approximation (RMSEA), (b) comparative fit index (CFI), and (c) standardized root mean square residual (SRMR). RMSEA and SRMR are badness-of-fit measures, with values > .10 indicating poor fit (Kline, 2016). CFI is a goodness-of-fit measure, with values > .95 indicating good fit (Hu & Bentler, 1999). The sample size for this study (N = 647) exceeds published guidelines for EFAs wherein multiple factors are extracted (e.g., Tabachnick & Fidell, 2007).

Although EFA is useful for conducting item-level analyses and identifying cross-loading items, simulation studies suggest that it can be poor at detecting lack of discriminant validity in some cases (e.g., Henseler et al., 2015). Therefore, we conducted a supplementary test of discriminant validity using the heterotrait-monotrait (HTMT) ratio (Henseler et al., 2015). Specifically, we computed the inter-item polychoric correlation matrices between the ASAW and the IRMAS-SF, the RAPE Scale, and the ATR, respectively. Next, we computed the HTMT ratio for each pair, which is essentially the average of the between measure inter-item correlations divided by the average of the within-measure inter-item correlation. HTMT values below 0.85 are indicative of discriminant validity (Henseler et al., 2015).

Incremental Validity

To test the ASAW’s incremental validity, we examined the extent to which its scores were independently associated with self-reported sexual aggression after accounting for the other measures of offense-supportive cognition. For these analyses, listwise deletion was used for participants with any missing data on the following measures: ASAW (1.1%, n = 7), IRMAS-SF (0.9%, n = 6), RAPE Scale (1.2%, n = 8), ATR Pro-Rape (1.4%, n = 9), ATR Anti-Rape (0.2%, n = 1), and Proclivity SES-TFR (7.6%, n = 49). None of the participants had missing data on the LR question. For the SES-TFR, which is a count variable of past sexually aggressive behavior, participants with more than 15% missing data (i.e., missing data on six or more items) were excluded listwise (1.9%, n = 12). For participants with less than 15% missing data on the SES-TFR, missing data were treated as zeros (never engaged in the sexually aggressive behavior). In total, 77 (11.9%) participants were excluded from the full sample of 647 due to missing data, resulting in a subsample of 570 participants. Participants who were excluded from the subsample did not significantly differ from the rest of the sample in terms of age, d = .04, 95% CI [−.20, .27], race ϕ = .07, p = .847, or education, ϕ = .06, p = .364. However, there was a significant difference in terms of relationship status, ϕ = .15, p = .007. Specifically, fewer participants who were excluded from the subsample were single (25.0% vs. 39.0%, OR = 0.52, 95% CI [0.30, 0.90]) and more were married (61.8% vs. 40.4%, OR = 2.39, 95% CI [1.46, 3.91]).

We conducted separate hierarchical regression models predicting (a) past sexual aggression (SES-TFR), (b) likelihood of engaging in sexual aggression (Proclivity SES-TFR), and (c) likelihood to rape (LR). Each model included one measure of offense-supportive cognition in the first step and the ASAW in the second step. A priori power calculations using G*Power indicated that a sample of approximately 200 participants would be sufficient to detect a small to moderate effect (f² = .08) with power of .80 and alpha = .006 (Bonferroni correction of alpha .05 divided by 9 models); therefore, analyses were adequately powered with the subsample of 570 participants. Analyses were conducted with and without influential residual outliers. Residual outliers were defined as observations with standardized residuals +/− 2.24 and/or Mahalanobis distance greater than critical χ², where the df = number of variables in the model and alpha level = α/n (Aguinis et al., 2013). Influential outliers were identified as residual outliers with DFFITS values ± 2 √ k + 1 n , DFBETAS values ± 2 / √ n , and/or Cook’s Distance values > critical F with df₁ = (k + 1), df₂ = (n – k −1) and alpha level = .50, where k is the number of predictors in the model (Aguinis et al., 2013). Scatter plots of standardized predicted values against standardized residuals were examined to assess normality and homoscedasticity. Regardless of residual outliers, the assumptions of normally distributed and homoscedastic error terms were violated; therefore, we computed bias corrected and accelerated (BCa) 95% bootstrapped confidence intervals to reduce bias in the estimated standard errors. Tolerance and VIF values indicated that multicollinearity was not an issue. These analyses were all conducted in SPSS version 27.

ASAW vs. IRMAS-SF

Items from the ASAW and the IRMAS-SF were examined in the first EFA. Based on the results of the factor retention analyses, models with one to three factors were extracted (see Figure 1 for the Scree plot of eigenvalues). In the one-factor model, all the items loaded (≥.40) onto a single factor, but fit indices indicated poor fit (RMSEA = .08, 90% CI [.079, .086]; CFI = .91; SRMR = .12). In contrast, the two-factor model fit the data well (RMSEA = .04, 90% CI [.037, .045]; CFI = .98; SRMR = .04). In this model, all the ASAW items loaded highly on one factor and all the IRMAS-SF items loaded highly on a second factor, with these factors showing a strong correlation (r = .64, p < .05). The three-factor model only provided trivial improvement in model fit (RMSEA = .03, 90% CI [.030, .039]; CFI = .99; SRMR = .03) and the third factor consisted primarily of weak cross-loadings (<.40). Thus, the two-factor model was selected (see Table 2).OPEN IN VIEWER

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Table 2.

Rotated Factor Loadings From 2-Factor EFA Solution With the IRMAS-SF

Item	Factor 1	Factor 2
ASAW scale
1. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s refusing to let you kiss or sexually touch her, so you block her from getting away from you (for example, by blocking the doorway) until she lets you kiss or sexually touch her.	.17	.63
2. You’re with a woman who is drunk or high. She’s having sex with you but refuses to let you take sexual pictures of her. You take sexual pictures of her anyway.	.04	.76
3. You’re talking with a woman you just met. You put your hand on her leg, but she pushes it away. You put your hand back on her leg.	−.07	.82
4. You have oral sex with your date/girlfriend/wife when she is too drunk or high to know or stop what is happening.	.01	.78
5. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s giving you oral sex but refuses to have sexual intercourse with you, so you tell her that you’ll spread rumours about her if she doesn’t have sex with you.	−.03	.85
6. Your date/girlfriend/wife agrees to have sex with you, but only if you wear a condom. You put a condom on. While you’re having sex, you purposely break or remove the condom without her knowing and continue to have sex with her without a condom.	.12	.73
7. You’re with a woman who is wearing a sexy dress and has been flirting with you. She refuses to give you oral sex, so you tell her that you’ll make something bad happen to her reputation or employment if she doesn’t give you oral sex.	−.04	.90
8. You’re with a woman you just met who is wearing a sexy dress and has been flirting with you. You grab her butt, but she pushes your hand away. You grab her butt again.	.07	.77
9. Your date/girlfriend/wife refuses to let you kiss or sexually touch her, so you intimidate her by yelling, swearing, or breaking stuff until she lets you kiss or sexually touch her.	.00	.88
10. You have sex with a woman who was flirting with you and wearing a sexy dress when she is too drunk or high to know or stop what is happening.	.14	.74
11. Your girlfriend/wife is refusing to have sex with you, so you tell her that you’ll post sexual pictures of her on the internet if she doesn’t have sex with you.	−.06	.94
12. Your date/girlfriend/wife refuses to give you oral sex, so you physically force her (for example, by holding her down) to give you oral sex.	.20	.74
13. You’re having vaginal sex with your date/girlfriend/wife. You push your penis against her anus to have anal sex with her, but she moves your penis away and says she doesn’t want to have anal sex. You have anal sex with her anyway.	.09	.76
IRMAS-SF
1. If a woman is raped while she is drunk, she is at least somewhat responsible for letting things get out of control.	.79	−.01
2. Although most women wouldn’t admit it, they generally find being physically forced into sex a real “turn-on.”	.77	.03
3. If a woman is willing to “make out” with a guy, then it’s no big deal if he goes a little further and has sex.	.77	.04
4. Many women secretly desire to be raped.	.74	.12
5. If a woman doesn’t physically fight back, you can’t really say that it was rape.	.79	.09
6. Men from nice middle-class homes almost never rape.	.74	−.07
7. Rape accusations are often used as a way of getting back at men.	.75	−.10
8. It is usually only women who dress suggestively that are raped.	.86	−.06
9. If the rapist doesn’t have a weapon, you really can’t call it rape.	.78	.13
10. Rape is unlikely to happen in the woman’s own familiar neighborhood.	.72	.04
11. Women tend to exaggerate how much rape affects them.	.80	.08
12. A lot of women lead a man on and then they cry rape.	.88	−.11
13. A woman who “teases” men deserves anything that might happen.	.85	.03
14. When women are raped, it’s often because the way they said “no” was ambiguous.	.82	.04
15. Men don’t usually intend to force sex on a woman, but sometimes they get too sexually carried away.	.74	−.07
16. A woman who dresses in skimpy clothes should not be surprised if a man tries to force her to have sex.	.86	−.01
17. Rape happens when a man’s sex drive gets out of control.	.69	−.14

Notes. N = 647. ASAW = Attitude toward Sexual Aggression against Women. IRMAS -SF = Illinois Rape Myth Acceptance Scale – Short Form. Bolded values indicate rotated factor loadings ≥ .40.

ASAW vs. RAPE Scale

A second EFA was conducted with the ASAW and RAPE Scale items. Factor retention analyses suggested extracting models with one to six factors (see Figure 2 for the Scree plot of eigenvalues). As with the IRMAS-SF, the two-factor model fit the data well (RMSEA = .04, 90% CI [.039, .044]; CFI = .97; SRMR = .04) and provided improved fit over the one-factor model (RMSEA = .07, 90% CI [.064, .068]; CFI = .92; SRMR = .09). For the two-factor model, all ASAW items loaded highly onto one factor, and all but one RAPE Scale item loaded highly onto a separate factor. Both factors were strongly correlated (r = .65, p < .05). Models with three factors (RMSEA = .04, 90% CI [.034, .039]; CFI = .98; SRMR = .04), four factors (RMSEA = .03, 90% CI [.031, .036]; CFI = .98; SRMR = .03), and more factors provided trivial improvements in model fit and additional factors consisted mostly of cross-loadings. Thus, the two-factor model was selected (see Table 3).OPEN IN VIEWER

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Table 3.

Rotated Factor Loadings From 2-Factor EFA Solution With the RAPE Scale

Item	Factor 1	Factor 2
ASAW scale
1. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s refusing to let you kiss or sexually touch her, so you block her from getting away from you (for example, by blocking the doorway) until she lets you kiss or sexually touch her.	.13	.65
2. You’re with a woman who is drunk or high. She’s having sex with you but refuses to let you take sexual pictures of her. You take sexual pictures of her anyway.	.11	.71
3. You’re talking with a woman you just met. You put your hand on her leg, but she pushes it away. You put your hand back on her leg.	−.01	.77
4. You have oral sex with your date/girlfriend/wife when she is too drunk or high to know or stop what is happening.	.04	.75
5. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s giving you oral sex but refuses to have sexual intercourse with you, so you tell her that you’ll spread rumours about her if she doesn’t have sex with you.	.00	.82
6. Your date/girlfriend/wife agrees to have sex with you, but only if you wear a condom. You put a condom on. While you’re having sex, you purposely break or remove the condom without her knowing and continue to have sex with her without a condom.	.16	.70
7. You’re with a woman who is wearing a sexy dress and has been flirting with you. She refuses to give you oral sex, so you tell her that you’ll make something bad happen to her reputation or employment if she doesn’t give you oral sex.	−.04	.90
8. You’re with a woman you just met who is wearing a sexy dress and has been flirting with you. You grab her butt, but she pushes your hand away. You grab her butt again.	.06	.77
9. Your date/girlfriend/wife refuses to let you kiss or sexually touch her, so you intimidate her by yelling, swearing, or breaking stuff until she lets you kiss or sexually touch her.	.03	.86
10. You have sex with a woman who was flirting with you and wearing a sexy dress when she is too drunk or high to know or stop what is happening.	.13	.74
11. Your girlfriend/wife is refusing to have sex with you, so you tell her that you’ll post sexual pictures of her on the internet if she doesn’t have sex with you.	−.09	.97
12. Your date/girlfriend/wife refuses to give you oral sex, so you physically force her (for example, by holding her down) to give you oral sex.	.24	.71
13. You’re having vaginal sex with your date/girlfriend/wife. You push your penis against her anus to have anal sex with her, but she moves your penis away and says she doesn’t want to have anal sex. You have anal sex with her anyway.	.14	.71
RAPE scale
1. Men who commit rape are probably responding to a lot of stress in their lives, and raping helps to reduce that stress.	.65	−.10
2. Women who get raped probably deserved it.	.71	.10
3. Women generally want sex no matter how they can get it.	.65	.19
4. Since prostitutes sell their bodies for sexual purposes anyway, it is not as bad if someone forces them into sex.	.68	.23
5. If a woman does not resist strongly to sexual advances, she is probably willing to have sex.	.73	.12
6. Women often falsely accuse men of rape.	.87	−.33
7. A lot of women who get raped had “bad reputations” in the first place.	.70	.11
8. If women did not sleep around so much, they would be less likely to get raped.	.75	.09
9. If a woman gets drunk at a party, it is really her own fault if someone takes advantage of her sexually.	.86	−.06
10. When women wear tight clothes, short skirts, and no bra or underwear, they are asking for sex.	.80	.01
11. A lot of women claim they were raped just because they want attention.	.91	−.24
12. Victims of rape are usually a little bit to blame for what happens.	.79	.02
13. If a man has had sex with a woman before, then he should be able to have sex with her any time he wants.	.72	.23
14. Just fantasizing about forcing someone to have sex isn’t all that bad since no one is really being hurt.	.38	.08
15. Women who go to bars a lot are mainly looking to have sex.	.76	−.01
16. A lot of times when women say “no” they are just playing hard to get, and really mean “yes.”	.74	.16
17. Part of a wife’s duty is to satisfy her husband sexually whenever he wants it, whether or not she is in the mood.	.71	.15
18. Often a woman reports rape long after the fact because she gets mad at the man she had sex with and is just trying to get back at him.	.89	−.16
19. As long as a man does not slap or punch a woman in the process, forcing her to have sex is not as bad.	.70	.24
20. When a woman gets raped more than once, she is probably doing something to cause it.	.80	.06
21. Women who get raped will eventually forget about it and get on with their lives.	.69	.12
22. On a date, when a man spends a lot of money on a woman, the woman ought to at least give the man something in return sexually.	.77	.15
23. I believe that if a woman lets a man kiss her and touch her sexually, she should be willing to go all the way.	.76	.10
24. When women act like they are too good for men, most men probably think about raping the women to put them in their place.	.68	.09
25. I believe that society and the courts are too tough on rapists.	.64	.08
26. Most women are sluts and get what they deserve.	.72	.17
27. Before the police investigate a woman’s claim of rape, it is a good idea to find out what she was wearing, if she had been drinking, and what kind of person she is.	.86	−.11
28. Generally, rape is not planned--a lot of times it just happens.	.70	−.21
29. If a person tells himself that he will never rape again, then he probably won’t.	.58	.06
30. A lot of men who rape do so because they are deprived of sex.	.84	−.30
31. The reason a lot of women say “no” to sex is because they don’t want to seem loose.	.71	.02
32. If a woman goes to the home of a man on the first date, she probably wants to have sex with him.	.74	−.07
33. Many women have a secret desire to be forced into having sex.	.78	.003
34. Most of the men who rape have stronger sexual urges than other men.	.82	−.21
35. I believe that any woman can prevent herself from being raped if she really wants to.	.78	−.07
36. Most of the time, the only reason a man commits rape is because he was sexually assaulted as a child.	.73	−.06

Notes. N = 647. ASAW = Attitude toward Sexual Aggression against Women. Bolded values indicate rotated factor loadings ≥ .40. Although Rape Scale item 14 did not load onto either factor (i.e., factor loadings < .40), it was included in the calculation of the RAPE Scale total score in subsequent analyses.

ASAW vs. ATR

Next, ASAW and ATR items were included in a third EFA. Models with one to seven factors were extracted (see Figure 3 for the Scree plot of eigenvalues). The one-factor model demonstrated poor fit (RMSEA = .07, 90% CI [.072, .076]; CFI = .86; SRMR = .10), with several factor loadings < .40. The two-factor model showed improvement (RMSEA = .06, 90% CI [.056, .061]; CFI = .92; SRMR = .08), but still indicated poor fit. In this model, ASAW items clustered together onto a single factor and most ATR items loaded onto a separate factor; however, eight ATR items cross- loaded or had primary loadings on the same factor as the ASAW items. The three-factor model fit the data well (RMSEA = .04, 90% CI [.039, .044]; CFI = .96; SRMR = .05) and the factor loadings were more interpretable as few cross-loadings remained. All the ASAW items loaded highly onto a single factor, whereas most of the ATR items separated onto two distinct factors. These two factors appeared to reflect pro-rape beliefs (e.g., In most cases when a woman was raped, she was asking for it) and anti-rape beliefs (e.g., A convicted rapist should be castrated), respectively. Five of the ATR items (e.g., A woman can be raped against her will) did not load on any of the three factors. The factor on which the ASAW items loaded was highly correlated with the ATR pro-rape beliefs factor (r = .66, p < .05), but showed a small negative correlation with the ATR anti-rape beliefs factor (r = −.24, p < .05). Similarly, the pro-rape beliefs factor showed a small negative correlation with the anti-rape beliefs factor (r = −.20, p < .05). Models with four (RMSEA = .036, 90% CI [.033, .039]; CFI = .97; SRMR = .04), five (RMSEA = .03, 90% CI [.027, .033]; CFI = .98; SRMR = .04), and more factors did not show meaningful improvements in model fit and generally consisted of cross-loadings and trivial factors (i.e., factors with fewer than three loadings > .40). Therefore, the three-factor model was selected (see Table 4).OPEN IN VIEWER

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Table 4.

Rotated Factor Loadings From 3-Factor EFA Solution With the ATR

Item	Factor 1	Factor 2	Factor 3
ASAW scale
1. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s refusing to let you kiss or sexually touch her, so you block her from getting away from you (for example, by blocking the doorway) until she lets you kiss or sexually touch her.	.68	.11	.03
2. You’re with a woman who is drunk or high. She’s having sex with you but refuses to let you take sexual pictures of her. You take sexual pictures of her anyway.	.76	.05	.02
3. You’re talking with a woman you just met. You put your hand on her leg, but she pushes it away. You put your hand back on her leg.	.78	−.07	−.13
4. You have oral sex with your date/girlfriend/wife when she is too drunk or high to know or stop what is happening.	.83	−.07	.02
5. You’re with a woman who is wearing a sexy dress and has been flirting with you. She’s giving you oral sex but refuses to have sexual intercourse with you, so you tell her that you’ll spread rumours about her if she doesn’t have sex with you.	.76	.02	−.18
6. Your date/girlfriend/wife agrees to have sex with you, but only if you wear a condom. You put a condom on. While you’re having sex, you purposely break or remove the condom without her knowing and continue to have sex with her without a condom.	.70	.14	−.04
7. You’re with a woman who is wearing a sexy dress and has been flirting with you. She refuses to give you oral sex, so you tell her that you’ll make something bad happen to her reputation or employment if she doesn’t give you oral sex.	.85	.01	−.08
8. You’re with a woman you just met who is wearing a sexy dress and has been flirting with you. You grab her butt, but she pushes your hand away. You grab her butt again.	.78	.06	−.002
9. Your date/girlfriend/wife refuses to let you kiss or sexually touch her, so you intimidate her by yelling, swearing, or breaking stuff until she lets you kiss or sexually touch her.	.78	.10	−.11
10. You have sex with a woman who was flirting with you and wearing a sexy dress when she is too drunk or high to know or stop what is happening.	.80	.07	.04
11. Your girlfriend/wife is refusing to have sex with you, so you tell her that you’ll post sexual pictures of her on the internet if she doesn’t have sex with you.	.88	−.03	−.15
12. Your date/girlfriend/wife refuses to give you oral sex, so you physically force her (for example, by holding her down) to give you oral sex.	.72	.21	−.01
13. You’re having vaginal sex with your date/girlfriend/wife. You push your penis against her anus to have anal sex with her, but she moves your penis away and says she doesn’t want to have anal sex. You have anal sex with her anyway.	.76	.10	.03
ATR
1. A woman can be raped against her will. (R)	.20	.12	.05
2. The reason most rapists commit rape is for the thrill of physical violence.	.04	.24	.18
3. Rapists are “normal” men.	−.05	.35	−.12
4. In forcible rape, the victim never causes the crime. (R)	.06	.32	−.13
5. All rapists are mentally sick. (AR)	.06	−.20	.44
6. A charge of rape two days after the act has occurred is probably not rape. (PR)	.19	.63	.01
7. A woman should be responsible for preventing her own rape. (PR)	.09	.67	.04
8. A man who has committed rape should be given at least 30 years in prison. (AR)	.28	−.27	.75
9. Women are trained by society to be rape victims. (PR)	−.02	.62	−.01
10. A raped woman is a less desirable woman. (PR)	.02	.67	.05
11. If a woman is going to be raped, she might as well relax and enjoy it. (PR)	.08	.74	−.16
12. Rape provides the opportunity for many rapists to show their manhood. (PR)	.01	.58	−.04
13. Most women secretly desire to be raped. (PR)	.07	.76	−.07
14. It would do some women some good to get raped. (PR)	.09	.84	−.04
15. Women provoke rape by their appearance or behavior. (PR)	.01	.80	.06
16. “Nice” women do not get raped. (PR)	.18	.68	.03
17. Most charges of rape are unfounded. (PR)	−.10	.64	.16
18. In order to protect the male, it should be difficult to prove that a rape has occurred. (PR)	−.11	.77	−.04
19. Rape is the expression of an uncontrollable desire for sex. (PR)	−.05	.67	.34
20. Rape is the worst crime that can be committed. (AR)	.25	.02	.55
21. Rape is a sex crime. (AR)	−.01	−.17	.53
22. All rape is a male exercise in power over women.	−.03	.32	.14
23. During a rape, a woman should do everything she can do to resist. (AR)	.12	.02	.51
24. Rapists are sexually frustrated individuals.	−.04	.45	.43
25. In most cases when a woman was raped, she was asking for it. (PR)	.04	.82	−.07
26. The reason most rapists commit rape is for sex.	−.04	.54	.42
27. Rape of a woman by a man she knows can be defined as a “woman who changed her mind afterward”. (PR)	.02	.75	−.04
28. A convicted rapist should be castrated. (AR)	.28	−.01	.67
29. A woman should feel guilty following a rape. (PR)	.05	.74	.001
30. The degree of a woman’s resistance should be the major factor in determining if a rape has occurred. (PR)	.10	.69	.23
31. A raped woman is a responsible victim, not an innocent one. (PR)	.03	.73	.01
32. Rape serves as a way to put or keep women in their “place”. (PR)	.10	.61	−.10

Notes. N = 647. ASAW = Attitude toward Sexual Aggression against Women. ATR = Attitudes toward Rape. R = Reverse coded. PR = Included in ATR Pro-Rape total score. AR = Included in ATR Anti-Rape total score. Bolded values indicate rotated factor loadings ≥ .40.

Heterotrait-Monotrait Ratio

We used the heterotrait-monotrait ratio (HTMT; Henseler et al., 2015) as a supplementary test of discriminant validity. Specifically, the HTMT ratio was computed from the inter-item polychoric correlation matrixes between the ASAW and the IRMAS-SF, the RAPE Scale, and the ATR, respectively. The HTMT ratio was below 0.85 when comparing the ASAW to each of the other measures of offense-supportive cognition, suggesting that the ASAW measures a distinct construct from the IRMAS-SF (HTMT ratio = 0.61), RAPE Scale (HTMT ratio = 0.65), and ATR (HTMT ratio = 0.65).