Do Party Schools Report Higher Rates of Violence Against Women in Their Clery Data? A Latent Class Analysis

Abstract

The current study examined violent crimes against women among 1,384 four-year private and public college campuses using Clery Act data from 2014-2016 (i.e., rape, domestic and dating violence, stalking, and fondling). Latent class analysis (LCA) was used to identify five types of campuses: smaller (22%), liberal arts (25%), satellite (16%), private (19%), and party schools (18%). Smaller schools reported the lowest rates of violence against women (VAW), whereas private schools had significantly higher reported rapes. These findings have important implications for the types of campuses seem to be abiding by Clery law and reporting crimes that involve VAW.

Keywords

violence against women Clery data party schools Greek life latent class analysis

Violence against women (VAW) on college campuses continues to be a pervasive public health problem, with approximately one in five women experiencing sexual assault and one in nine women experiencing rape while in college (Cantor, Fisher, & Chibnall, 2015; Fisher, Cullen, & Turner, 2000; Koss, Gidycz, & Wisniewski, 1987; Krebs, Lindquist, Warner, Fisher, & Martin, 2007; Marsil & McNamara, 2016; Muehlenhard, Peterson, Humphreys, & Jozkowski, 2017). In addition, research indicates that one in five students has experienced domestic violence with a current partner (Fisher et al., 2000; Koss et al., 1987; Krebs et al., 2007). It is estimated that each year approximately 8-16% of people in college dating relationships experience at least one act of severe physical aggression (e.g., punch, choke, kick by partner), 12-30% experience severe psychological aggression (e.g., threaten to hit partner, destroy personal belonging of partner), and 3-9% experience severe sexual aggression (e.g., force/threats used to obtain sex from partner) (Bell & Naugle, 2007; Hines & Saudino, 2003). In addition, approximately 20% of college women reported some form of stalking victimization (Buhi, Clayton, & Surrency, 2009), and 17% of college men reported that they have engaged in forced sexual contact (e.g., forced fondling; Abbey & McAuslan, 2004). Although college women have experienced high rates of sexual and relationship violence for decades (for a review, see Muehlenhard et al., 2017), recent events seem to have ignited public interest in sexual and relationship violence among college students (e.g., U.S. Department of Education Dear Colleague Letter, 2011; The White House, 2014; Office for Civil Rights [OCR] Title IX investigations; Muehlenhard, Humphreys, Jozkowski, & Peterson, 2016).

Whether one in five women experience sexual assault while in college has been highly debated (Muehlenhard et al., 2017). The prevalence rates of students reporting sexual assault range from 7-53% across campuses (Cantor et al., 2015). Based on this wide range, certain types of universities may have higher rates of campus crime. Palmer and Alda (2016) suggested that many of the sociodemographic factors inherent to each campus may influence the incidence and reporting of sexual assault. Such factors might include the type of institution (e.g., public vs. private, student enrollment, presence of Greek life, National Collegiate Athletic Association [NCAA] member), its diversity (e.g., racial diversity), and its selectivity (e.g., tuition costs). Indeed, Wiersma-Mosley, Jozkowski, and Martinez (2017) found that there are certain institution-level characteristics that are associated with increased reporting of on-campus rape—public institutions, more alcohol violations, and high presence of Greek life and athletics. Each of these institution-level characteristics was individually associated with increased rates of on-campus-reported rape; however, little is known about the synergistic effects of these institution-level factors on campus sexual violence more generally. In other words, these institution-level factors have not been investigated in unison.

Together, the institution-level factors identified by Wiersma-Mosley et al. (2017) are consistent with Weiss’s (2013) description of party schools. Weiss defines party schools as public universities that have large student populations and that emphasize Greek life and athletics. Furthermore, at party schools, alcohol-infused crimes (e.g., sexual assault and domestic violence) are “often seen as a rather normal part of life” (Weiss, 2013, p. 13). Party schools seem to be where men are perpetrating VAW at higher rates than other universities (Lasky, Fisher, Henriksen, & Swan, 2017; Lindo, Siminski, & Swensen, 2015; Weiss, 2013; Weiss & Dilks, 2016). But are party schools reporting higher rates of violent crimes against women compared with other types of universities?

Currently, the most available measure of campus crime reports Clery data. Since 1990, all colleges and universities have been mandated by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act to provide annual reports on campus crime (henceforth referred to as Clery data). Initially, campus crimes included murder, robbery, aggravated assault, burglary, motor vehicle theft, manslaughter, arson, alcohol use violations, drug-related violations, and weapons possessions as reportable offenses. But starting in 2014, Clery data must also include reports of rape, domestic violence, dating violence, and stalking (Marshall, 2014). Clery data would ideally reflect the actual rates of crime that occur on and off campuses, but for a variety of reasons, these data are not considered a reliable or valid measure of the incidence of VAW on college campuses (Marshall, 2014). However, the Clery reports generated by college campuses can provide valuable insights into patterns and environmental factors related to campus sexual assault (Klein et al., 2018). Until campus climate surveys are publicly mandated, Clery data offer the only national data set available to compare reports of campus crime.

First, for VAW and other crimes included in Clery data, victims must know where to report. But neither victims nor nonvictims are likely to know the location of their school’s sexual assault center or Title IX coordinator (Palmer & Alda, 2016). Almost two out of three schools (i.e., 63%) incorrectly report campus crime (Karjane, Fisher, & Cullen, 2002; Krebs et al., 2007). Second, the issue of underreporting of campus crime by campus authorities is compounded by the fact that students are underreporting to campus authorities. Studies have consistently shown that less than 10% of student victims report the sexual violence to their institution (Cantalupo, 2014; Fisher et al., 2000; Fisher, Daigle, Cullen, & Turner, 2003; Marshall, 2014; Sabina & Ho, 2014). Thus, universities that rely on student reports are likely providing only a minority of the sexual assaults in their Clery reports. Some of the most frequently identified perceived barriers to reporting sexual victimization (e.g., concerns that others will not believe them and that their information will not be confidential) are issues that reflect a campus climate that discourages reporting (Palmer & Alda, 2016; Zinzow & Thompson, 2011).

Due to these procedural shortcomings, Palmer and Alda (2016) argued that Clery data should be considered a proxy for campus climate (i.e., whether students report campus crime) instead of a true measure of the campus crime rates. They supported this claim by finding that Clery data reports of sexual assault increased by 25% at schools that received funding from the Office on Violence Against Women. This increase in reporting indicated that the more proactive approach to sexual violence during the grant period was actually reducing barriers to reporting perceived by students. If Clery data truly reflect how amenable a given institution is to student victim reporting, then we can examine differences in Clery data to directly investigate whether party schools report higher rates of campus violent crimes against women and to indirectly assess whether these universities are more likely to encourage reporting.

Present Study

Party schools have been qualitatively described in previous literature (e.g., Weiss, 2013); however, to our knowledge, there is no support for the quantitative classification of party schools. Using latent class analysis (LCA), we sought to identify universities that quantitatively fit the label of party school. LCA has been employed to identify mutually exclusive types of people (e.g., Parker, Gielen, Castillo, Webster, & Glass, 2016), types of countries (e.g., Adams et al., 2013), and types of companies (e.g., Cullmann, 2012). However, to our knowledge, no other research has grouped types of universities by sociodemographic factors using LCA. To categorize universities into mutually exclusive groups, we used sociodemographic factors that fit Weiss’s (2013) description of party schools (i.e., public vs. private, student enrollment, alcohol use, presence of Greek life, and athletics) and additional factors that Wiersma-Mosley et al. (2017) identified as predictors of reporting violent crimes against women (i.e., tuition costs and racial makeup). Because party schools are environments where men are more likely to perpetrate VAW (Lasky et al., 2017; Lindo et al., 2015; Weiss, 2013; Weiss & Dilks, 2016), we predicted that universities that are more likely to (a) be public, (b) have large student bodies, (c) have more alcohol use violations, (d) have Greek life, and (e) have prominent athletic programs would report the highest rates of VAW in their Clery data. In other words, we hypothesized the following: The latent class most consistent with Weiss’s (2013) “Party University” would report the most campus crimes compared with universities that do not meet this typology. This prediction is supported by Wiersma-Mosley et al.’s (2017) findings that these institution-level factors are associated with higher reports of on-campus rape. Because the reliability and validity of Clery data have been questioned (Guffey, 2013; Karjane et al., 2002; Krebs et al., 2007; Marshall, 2014; Palmer & Alda, 2016), we wanted to bolster our findings by providing a second source of data—i.e., current and past Title IX investigations—to further indicate the climate on college campuses regarding VAW. A federal Title IX investigation arises independent of campus-related procedures, which means that anyone—complainant, respondent, or an interested third party—can appeal to the OCR, U.S. Department of Health & Human Services (n.d.) to openly investigate a university (Jenkins, 2017). Yung (2015) argues that campuses more accurately report campus crimes when they are under federal government scrutiny (i.e., Title IX investigations). Thus, we predicted that party schools would also have more Title IX investigations.

Method

Procedure

Data for the current study were derived from the Campus Safety and Security Data Analysis Cutting Tool provided by the Office of Postsecondary Education of the U.S. Department of Education (n.d.), required by the Clery Act, the Higher Education Opportunity Act (2008), and the 2013 Violence Against Women Act.¹ The current analyses used 2014, 2015, and 2016 data mandated by the Clery Act, which included 1,384 public and private-nonprofit 4-year institutions with a student body of at least 1,000 (a similar sample was used in Wiersma-Mosley et al., 2017). We used additional archival data collected via multiple online sources to ascertain the study measures for our sample of institutions (see Table 1). These data were combined into a single database (with reliability checks between the primary author and a graduate student), using the institution as the unit of analysis. Approval from the institutional review board was unnecessary as all data were archival.

Table 1.

Item Response Categories of Institution-Level Factors Using Latent Class Analysis.

Institutional-level variables	Smaller schools	Liberal arts schools	Satellite schools	Private schools	Party schools
Institutional-level variables	22%	25%	16%	19%	18%
Student enrollment (n)^a
<2,503	.48	.49	.22	.38	.00
2,503-6,861	.34	.42	.48	.34	.08
>6,861	.18	.09	.30	.27	.92
University type^b
Public	.47	.15	.69	.00	1.00
Private	.53	.85	.31	1.00	.00
Tuition (US$)^b
<18,594	.52	.12	.60	.02	.50
18,594-28,174	.27	.39	.26	.23	.50
>28,174	.21	.49	.14	.75	.00
White/Caucasian (%)^b
<56%	.48	.18	.57	.26	.24
56-73%	.27	.45	.24	.40	.36
>73%	.25	.37	.19	.34	.40
Alcohol violations (n)^a
<15	.74	.24	.49	.08	.08
15-105	.23	.45	.39	.41	.21
>105	.03	.31	.12	.51	.71
Fraternity men (%)^c,d
0	1.00	1.00	.46	.08	.08
>0	.00	.00	.54	.92	.92
Sorority women (%)^c,d
0	1.00	1.00	.45	.09	.08
>0	.00	.00	.55	.91	.92
Fraternity organizations (n)^d,e,f
0	1.00	1.00	.05	.03	.03
>0	.00	.00	.95	.97	.97
Sorority organizations (n) ^d,e,f
0	1.00	1.00	.04	.05	.03
>0	.00	.00	.96	.95	.97
Athletic net (US$)^b
0	.81	.50	.63	.59	.47
>0	.19	.50	.37	.41	.53
Sports teams (n)^b
<12	.91	.08	.49	.00	.05
12-16	.08	.41	.47	.23	.44
>16	.01	.51	.04	.77	.51
Student athletes (n)^b
<239	.95	.18	.50	.01	.00
239-433	.05	.52	.48	.35	.27
>433	.00	.30	.02	.64	.73
Division^g
DI	.02	.10	.17	.32	.70
DII	.08	.22	.52	.14	.15
DIII	.10	.50	.10	.50	.15
None	.80	.18	.21	.04	.00

Note. Values in bold are the highest probability response. Superscripts refer to the database(s) used to obtain these study variables.

Campus Safety and Security Data Analysis Cutting Tool provided by the Office of Postsecondary Education of the U.S. Department of Education (http://ope.ed.gov/campussafety/#/).

College Factual (http://www.collegefactual.com/).

U.S. News Best Colleges (http://colleges.usnews.rankingsandreviews.com/best-colleges?int=994d08).

Individual university webpages.

Niche College (https://colleges.niche.com).

Greek Rank (http://www.greekrank.com/).

NCAA.org (https://web.archive.org/web/20110807060521/http://www.ncaa.org:80/wps/wcm/connect/public/ncaa/about+the+ncaa/who+we+are/about+the+ncaa+history).

Measures

VAW

Using 2014-2016 data mandated by the Clery Act, the measures regarding VAW included campus-reported rape, domestic violence, dating violence, stalking, and fondling, which we transformed into rates per 1,000 students to account for student enrollment. Three years of data were examined. We summed incidents from on campus, noncampus, student housing on campus, and public property within or adjacent to the campus; consistent with Minton’s (2012) study, we did not distinguish between these four categories. (Refer to descriptive information in Table 2 and the definitions of all VAW crimes and geographic locations in the appendix.)

Table 2.

Campus Clery Reports of Violence Against Women Crimes From 2014-2016 (n = 1,384).

Variable	2014			2015			2016
Variable	M	SD	Range	M	SD	Range	M	SD	Range
Rape	5.68	9.12	0-80	6.45	9.17	0-73	7.51	11.26	0-116
Rates per 1,000 students	1.14	2.20	0-24.81	1.29	2.28	0-22.24	1.50	2.87	0-50.22
Domestic violence	2.51	5.82	0-117	2.93	8.63	0-197	2.95	7.88	0-158
Rates per 1,000 students	0.35	0.87	0-13.41	0.41	1.12	0-19.97	.40	1.00	0-11.67
Dating violence	3.26	5.62	0-48	4.09	7.85	0-170	4.51	7.70	0-80
Rates per 1,000 students	0.61	1.22	0-14.78	0.77	1.46	0-18.15	0.91	2.24	0-53.08
Stalking	3.12	5.80	0-52	4.19	7.30	0-69	4.90	9.39	0-99
Rates per 1,000 students	0.49	1.11	0-21.47	0.67	1.38	0-25.04	0.75	1.76	0-38.46
Fondling	2.29	4.25	0-64	2.91	6.98	0-201	3.07	5.06	0-52
Rates per 1,000 students	0.46	1.07	0-10.76	0.64	1.27	0-14.73	0.64	1.64	0-34.41

Institution-level factors

Using a variety of archival sources (refer to Table 1), we collected data for institution-level factors, which have been used to describe party schools: university type, student enrollment, alcohol use, Greek-life memberships, and athletic prominence. We used these characteristics to create the latent classes, controlling for tuition and racial makeup—factors that previous research has identified as relevant to reporting VAW (Wiersma-Mosley et al., 2017). To accommodate for skewness and to align with standards for LCA, the continuous institution-level measures were recoded into two- and three-level categorical variables to enhance the interpretability of the findings. To create categorical variables for the LCA models, we used cutoff percentiles to make either two equal groups (low reflected <50th percentile, high reflected >50th percentile) or three (low reflected <33rd percentile, moderate reflected between 33rd and 66th percentiles, and high reflected >66th percentile). The categorical variables allow us to assess low, moderate, and high risk factors rather than using a continuum of risk (Wiersma-Mosley et al., 2017).

University type

This factor indicated whether the campus was a 1 = public (n = 602, 43%) or 2 = private (n = 782, 57%) institution.

Student enrollment

The total number of students enrolled for each campus (in 2014) was recoded as 1 = less than 2,503 students, 2 = 2,503-6,861 students, and 3 = more than 6,861 students. Student enrollment was also used to calculate the rates per 1,000 students on each campus for reported campus crimes.

Alcohol violations

All universities (in 2014) reported the number of alcohol violations on their campuses, such as prohibiting the manufacture, sale, purchase, transportation, possession, or use of alcoholic beverages, not including driving under the influence and drunkenness. This also includes furnishing alcohol to a minor or intemperate person, underage possession, using a vehicle for illegal transportation of alcohol, drinking on a train or public conveyance, and attempts to commit any of the above. These ranged from 0-2,639 violations (M = 128.89, SD = 220.25) and was recoded into 1 = less than 15, 2 = 15-105, and 3 = more than 105 violations. Number of liquor violations is an appropriate proxy for party culture. For example, the university with the highest number of liquor violations was also named the Number 1 party school in 2017 by the Princeton Review. Thus, higher liquor violations may reflect a more active party culture.

Greek-life presence

The number of Greek organizations and the perecentage of students involved—primarily the North-American Interfraternity Conference (IFC) and the National Panhellenic Conference (PHC) for both fraternities and sororities—on campuses were used to assess Greek life. Because the IFC and PHC do not publish statistics regarding the number of members or houses on specific campuses, and universities vary in the extent to which they provide these data, we were forced to ascertain these data from multiple sources (Table 1). Fraternity men on campus ranged from 0-86% (M = 5.13%, SD = 9.46%), with 0-59 organizations (M = 4.73, SD = 7.63), whereas sorority women on campus ranged from 0-80% (M = 5.72%, SD = 10.53%), with 0-36 organizations (M = 3.66, SD = 5.34). Due to the highly skewed data, and to align with LCA standards, Greek-life students and houses were recoded as 1 = none versus 2 = any percentage of students or number of organizations on campus.

Athletics prominence

We assessed campus athletics using four variables: (a) net worth (M = US$318,078.17, SD = US$2,011,525.81), (b) number of sports teams (M = 13.30, SD = 6.51), (c) number of athletes (M = 339.91, SD = 222.35), and (d) athletic division. Net worth was highly skewed and recoded into 1 = zero and 2 = more than zero. The net worth was calculated from the overall revenue and expenses (Equity in Athletics Data Analysis, U.S. Department of Education, n.d.) reported on College Factual. Revenue generation can be a useful indicator of that team’s success and popularity as well as how much the team can spend on athletic resources such as coaches, scholarships, equipment, facilities, and recruitment. Sports was recoded into 1 = less than 12, 2 = 12-16, and 3 = more than 16 sports; and athletes was recoded into 1 = less than 239, 2 = 239-433, and 3 = more than 433 athletes. Each university’s participation in intercollegiate sports was obtained from the NCAA: 1 = Division I (n = 344, 25%), 2 = Division II (n = 288, 21%), 3 = Division III (n = 390, 28%), and 4 = no affiliation (i.e., no athletic programs; n = 362, 26%).

Tuition

Cost of tuition for each campus (M = US$24,069.46, SD = 10,666.37) was recoded as 1 = less than US$18,594, 2 = US$18,558-US$28,174, and 3 = more than US$28,174.

Racial makeup

The percentage of White/Caucasian students on campuses (M = 59.76, SD = 21.68) was recoded as 1 = less than 56%, 2 = 56-73%, and 3 = more than 73%.

Analyses

LCA is a statistical tool used to identify homogeneous, mutually exclusive groups (i.e., classes) that exist within a heterogeneous population. We conducted this analysis using SAS 9.3 with the PROC LCA command procedure to estimate model parameters (Lanza, Collins, Lemmon, & Schafer, 2007). PROC LCA produces maximum likelihood estimates; missing data are handled within the expectation–maximization algorithm and are assumed to be missing at random (Lanza et al., 2007). To identify an optimal model, we fit a sequence of models, ranging from two to seven latent classes, using the likelihood-ratio G² statistic, Akaike information criterion (AIC; Akaike, 1974), and Bayesian information criterion (BIC; Schwarz, 1978). To ensure that the maximum likelihood solution was correctly identified within these models, 100 iterations of each model were run using randomly generated seed values. The resulting G² criterion values were compared across the 100 iterations; the dominant solution (i.e., that which most frequently resulted in the same G² value using the randomly generated seeds) was identified as the maximum likelihood solution. The campuses were assigned to the different classes based on their posterior probabilities for class membership on the institution-level factors. Once the latent classes were determined, MANOVA was used to determine whether the classes differed from each other on campus reports of VAW, using Clery data as our dependent variable. ANOVA was used to determine whether the classes differed from each other on Title IX investigations.

Results

LCA

Overall, VAW rates increased significantly each year, except for fondling from 2015-2016 and domestic violence from 2015-2016. To select the final model solution, we examined the BIC and AIC across all the models (Table 3). In our models, the G² estimates were less than the model’s degrees of freedom, which is typically preferred when identifying a good model fit (Lanza, Flaherty, & Collins, 2003). In addition, we used Lanza et al.’s (2007) suggestion that model interpretability should be considered. For example, each class should be distinguishable from the others on the basis of the item-response probabilities, and it should be possible to assign a meaningful label to each class. Based on both statistical criteria and substantive interpretation, the LCA resulted in a five-class specification of college campuses. Table 1 includes probabilities within each factor and also provides the original sources used to obtain data for the institution-level variables.

Table 3.

Criteria to Assess Model Fit for Latent Class Analysis Models.

	2 Classes	3 Classes	4 Classes	5 Classes^a	6 Classes	7 Classes
AIC	10,036.34	8,875.56	8,096.64	7,276.00	6,936.63	6,663.36
BIC	10,261.35	9,215.69	8,725.89	7,846.37	7,622.12	7,463.97
CAIC	10,304.35	9,280.69	8,812.89	7,955.37	7,753.99	7,616.97
G ²	9,950.34	8,745.56	8,096.64	7,058.00	6,674.63	6,357.36
df	186,580	186,558	186,536	186,514	186,492	186,470

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion, CAIC = consistent Akaike information criterion; G² = goodness-of-fit test statistic.

Class number chosen.

Latent Class 1

The first latent class (22%, n = 300 schools) included schools that were characterized by a high probability of few alcohol violations, no Greek students or Greek organizations, an athletic net of zero, and no athletics in terms of sports, athletes, or being affiliated with the NCAA. These campuses were both public and private and were likely to have smaller student populations, fewer White/Caucasian students, and lower tuition, and we labeled this class smaller schools. According to Best Value Schools (2019), small colleges tend to have smaller class sizes and overall smaller student enrollment (less than 4,000 students). Students who attend schools of this type are often looking to benefit from a more intimate, less intimidating campus environment.

Latent Class 2

The second latent class (25%, n = 344 schools) was characterized by a high probability of private institutions, no Greek life, and mixed probabilities in sports and athletes—the highest probability was for Division III athletic programs. These campuses were likely to have high tuition costs and to be near the median in racial makeup and alcohol violations and were labeled liberal arts schools. These schools are typically smaller, privately run, and emphasize undergraduate education with more attention from faculty (Bridgestock, 2015).

Latent Class 3

The third latent class (16%, n = 219) was characterized by public institutions, low tuition, diverse in terms of lower percentage of White/Caucasian students on campus, and student populations near the median in terms of size. These campuses had high Greek-affiliated students and houses, but were less likely to have alcohol violations. These schools were likely to have zero athletic net, few sports and athletes and were primarily Division II programs; they were labeled as satellite schools. A satellite campus is physically at a distance from the flagship campus, typically located in a different city, and is often smaller than the main flagship campus. Satellite campuses may be under the same accreditation and share resources, or they may share administrations but maintain separate budgets and resources from the flagship campus. In many cases, satellite campuses are intended to serve students who cannot travel far from home for college because of family responsibilities, their jobs, financial limitations, or other factors; thus, they include more nontraditional students (Wilkins & Huisman, 2012).

Latent Class 4

The fourth latent class (19%, n = 266) was characterized by private institutions, expensive tuition, and high numbers of Greek-affiiliated students and houses. These schools had zero athletic net, but a high number of sports and athletes in primarily Division III programs. These schools were also likely to have high alcohol violations and be near the median in terms of racial makeup, and included campuses with small, medium, and large student enrollment, and were labeled private schools. Private universities are more selective about whom they admit and often have more stringent admission standards than public universities, which cater to a larger, wider range of students. Private universities are considered elite institutions with lower acceptance rates and higher tuition prices.

Latent Class 5

The final latent class was consistent with Weiss’s (2013) party school (18%, n = 255) and was labeled accordingly; this class was characterized by public institutions, high student enrollment, high alcohol violations, high Greek-life membership, and high athletics prominence (i.e., high probability of increased athletic net, number of sports, athletes, and Division I programs). This class also had a higher percentage of White/Caucasian students. Party schools are typically public state universities founded and operated by state government entities, meaning they receive some level of funding from the state government, making them, on average, more affordable (Bridgestock, 2015), and mostly comprise main or flagship campuses.

MANOVA

With these five latent classes, we examined the association of campus classification and the reported VAW using Clery data from each campus. Using LCA, we examined how latent classes associated with different types of Clery-reported crimes from 2014-2016. Using MANOVA, there was an overall significant effect across classes (Wilks’s λ = .78, p < .001), indicating that specific latent classes had increased reporting of VAW. As shown in Table 4, small schools reported the lowest rates of VAW per 1,000 students. Private schools reported significantly higher reported rapes compared with all the other types of classes from 2014-2016. There were similar patterns for liberal arts and private schools, in that both classes had significantly higher reports of dating violence, stalking, and fondling as compared with the other classes, whereas satellite schools had higher reports of domestic violence compared with other classes (from 2015-2016).

Table 4.

Campus-Reported VAW as a Function of Latent Classes.

	Smaller schools	Liberal arts schools	Satellite schools	Private schools	Party schools	F	R ²
	22% (n = 300)	25% (n = 344)	16% (n = 219)	19%(n = 266)	18%(n = 255)	F	R ²
2014
Rape	0.43^abc	1.50^adef	0.57^dg	2.40^begh	0.66^cfh	43.31*	.11
Domestic violence	0.27	0.37	0.43	0.33	0.40	1.41ns	.00
Dating violence	0.24^abc	0.81^ad	0.69^be	0.86^cf	0.44^def	13.95*	.04
Stalking	0.37^ab	0.62^acd	0.32^ce	0.69^bef	0.41^df	6.05*	.02
Fondling	0.22^ab	0.60^acde	0.25^cf	0.91^bdfg	0.26^eg	22.82*	.06
2015
Rape	0.50^ab	1.79^acde	0.64^cf	2.54^bdfg	0.83^eg	44.90*	.12
Domestic violence	0.29^a	0.35^b	0.69^abcd	0.43^c	0.39^d	4.55*	.01
Dating violence	0.39^abc	0.88^ad	0.84^be	1.11^cef	0.63^df	10.19*	.03
Stalking	0.41^ab	0.85^acd	0.55^ce	0.98^bef	0.54^df	8.80*	.03
Fondling	0.32^ab	0.80^acde	0.28^cf	1.05^bdfg	0.34^eg	20.94*	.06
2016
Rape	0.60^ab	1.83^acde	0.75^cf	3.25b^dfg	0.95^eg	44.11*	.11
Domestic violence	0.16^abcd	0.36^ae	0.61^bef	0.52^c	0.41^df	8.00*	.02
Dating violence	0.56^ab	1.02^acd	0.95^e	1.40^bcef	0.63^df	6.36*	.02
Stalking	0.55^ab	0.82^ac	0.55^d	1.19^bcde	0.62^e	6.45*	.02
Fondling	0.28^ab	0.83^acde	0.33^cf	1.36^bdfg	0.37^eg	23.00*	.06

Note. Reports average campus-reported VAW rates per 1,000 students using Clery data (n = 1,348). VAW = violence against women. Means with matching superscripts differ across classes significantly at p < .05 by Fishers LSD test.

p < .001.

ANOVA

These results were surprising given the low reports from party schools, with research suggesting that these campuses would be the highest in VAW (Wiersma-Mosley et al., 2017); thus, post hoc analyses further examined the number of Title IX investigations using the Title IX tracker (Chronicle of Higher Education, 2017). There have been 397 campus investigations for possibly mishandling reports of sexual violence, and the current study found these investigations occurred at 230 campuses. There were significant differences between the classes using ANOVA, F(4, 1,379) = 28.00, p < .001, indicating that there were Title IX investigations in 5% of small schools, 12% of liberal arts schools, 9% of satellite schools, 28% of private schools, and 31% of party schools. Schools that were categorized as private and party schools had significantly more Title IX investigations compared with all the other classes.

Discussion

Our quantitative findings supported Weiss’s (2013) qualitative account of party schools. Using LCA, we identified an independent class of universities that is more likely to (a) be public, (b) have large student bodies, (c) have more alcohol use violations, (d) have Greek life, and (e) have prominent athletic programs. Extant literature suggests that women at party schools are more likely to report violent victimization (Lasky et al., 2017; Lewis et al., 1997; Lindo et al., 2015; Weiss, 2013; Weiss & Dilks, 2016). Using Clery data, our study extends these previous findings by examining the associations between types of institutions and reports of violent victimization at the national level. Contrary to our prediction, the latent class identified as party schools did not report more rapes, domestic violence, dating violence, stalking, or fondling than other classes of universities. This finding was particularly surprising, because Wiersma-Mosley et al. (2017) found that each of the factors that compose party schools was associated with increased reports of on-campus crime.

Nevertheless, our findings suggest that there are meaningful institution-level patterns that are associated with increased reporting of campus violent crimes against women. Our study aligns with the implication that not all campuses are the same when it comes to reporting VAW (e.g., Cantor et al., 2015). For example, some universities (i.e., private schools) report more campus violent crimes than others. Unexpectedly, party schools actually had relatively low reports of campus violent crimes against women in their Clery data.

Perhaps students in larger campus settings are not reporting sexual violence at party schools due to not knowing the proper channels to report; this is commonly cited as a reason for underreporting in general (Planty, Langton, Krebs, Berzofsky, & Smiley-McDonald, 2013; Wolitzky-Taylor et al., 2011). Reasons for not reporting may also include not identifying the experience as sexual assault, wanting to keep the experience private, or feeling ashamed (Wood & Stichman, 2016). However, the OCR has opened Title IX investigations at 31% of the party schools, so it may be that victims are trying to report but are unsuccessful in their attempt due to institutional barriers. Or, it may have to do with civil-rights complaints or the lack of campus compliance (Chronicle of Higher Education, 2017). At present, the Clery Act requires only that schools gather campus crime data in situations when victims report to school officials; however, these “officials” do not necessarily have to include faculty members or on-campus counselors and physicians (Marshall, 2014). Rather, these rules vary from campus to campus. For example, law enforcement officials on some campuses have given exemption status to on-campus counseling centers to avoid larger campus crime statistics (Guffey, 2013). Unfortunately, this type of closed-door policy may result in a campus climate where many of the sexual assault cases reported to institutions are regularly excluded from Clery data (Guffey, 2013). Alternatively, institutions may exclude employees such as counselors from the list of mandatory reporters to provide an on-campus resource to students who do not want to report their assault.

In addition, campus authorities may not even refer incidents to law enforcement, hoping to retain control of the proceedings by not reporting and to avoid public and media scrutiny (American Association of University Professors [AAUP], 2013). Even universities that properly report campus crime indicate that their efforts may have the adverse effect of making their institution seem less safe for students compared with institutions that file fewer campus crimes (Karjane et al., 2002). When schools retain this negative view of accurate reporting and they are simultaneously not held accountable for reporting campus crimes properly, the accuracy of Clery data suffers (Guffey, 2013). However, it seems when universities are investigated, either by not reporting honestly (i.e., Clery reports) or for compliance issues (i.e., Title IX investigations), they may be forced to be honest in their reports (Yung, 2015). Thus, all campuses need to be held responsible for accurately reporting campus crimes. Certain schools may worry that increased and honest reporting of campus violent crimes against women may be perceived as the result of their biggest draws (i.e., large campuses with Greek organizations and prominent athletics). Party schools may feel particularly pressured to maintain a positive image because these universities are more prominent, generate more revenue, have larger party cultures, and have more to lose if incidents of sexual violence are made public (Lindo et al., 2015; Weiss, 2013).

Underreporting of campus violent crimes against women was ubiquitous; Clery data from all classes of universities were remarkably lower than estimates of VAW based on self-reported data. For example, rates of rape on college campuses according to Clery data are remarkably lower than the estimate that one in nine women are raped in college (Cantor et al., 2015; Marsil & McNamara, 2016). This estimate of campus rape rates based on self-report data (i.e., 110 rapes per 1,000 students) is approximately 7,000-9,000% higher than the campus rape rates reported to campus officials from 2014-2016 (i.e., 1.14-1.50 rate per 1,000 students). Therefore, our data provide further evidence that Clery data do not accurately reflect the prevalence of campus crime.

It is possible that students on certain campuses (i.e., smaller schools) are not reporting crimes because they are at smaller campuses where there is less anonymity when reporting. Therefore, we cannot be sure that campus crime reports accurately reflect the prevalence of any crime on a particular campus. Furthermore, small schools may also have tighter communities in which people are more likely to watch out for one another, such as intervening in bystander situations (Banyard, Plante, & Moynihan, 2004). It may also be that this class of universities is experiencing less campus violent crimes against women because sexual assault is connected to Greek life and athletics (Armstrong, Hamilton, & Sweeney, 2006; Jozkowski & Wiersma-Mosley, 2017; Wiersma-Mosley et al., 2017).

At the individual level, research suggests that male university athletes and fraternity men are more likely to perpetrate sexual assault compared with college men in general due to belonging to all-male peer groups (e.g., Adams-Curtis & Forbes, 2004; DeKeseredy & Schwartz, 2013; Foubert, Newberry, & Tatum, 2007; Godenzi, Schwartz, & DeKeseredy, 2001; Humphrey & Kahn, 2000; Kimble, Russo, Bergman, & Galindo, 2010; Minow & Einolf, 2009; O’Sullivan, 1991; Safai, 2002; Sanday, 1990; Schwartz & DeKeseredy, 1997). According to the male peer support model, VAW can be explained by peer groups that reinforce such behaviors. Male peer support is defined as attachment to peers who sexually assault women and typically consist of male college peers who influence other men to victimize women through informational support, social learning, and motivation (i.e., encouragement, support by other males; DeKeseredy & Schwartz, 2013; Schwartz & DeKeseredy, 1997). In fact, one study found that men who had attachments to abusive peers and who drank more often were 9 times more likely to report committing sexual abuse (Schwartz, DeKeseredy, Tait, & Alvi, 2001). Thus, it seems that all-male peer groups (i.e., fraternities, athletics), which are more likely to be at party schools, are (a) part of patriarchal, homosocial networks, and (b) motivated to abuse women who are “suitable targets” (Schwartz et al., 2001, p. 629).

Multiple studies have found that reports of rape were higher on campuses with Division I athletic programs (party schools) compared with Divisions II and III athletic programs and campuses with no athletics (Minton, 2012; Stotzer & MacCartney, 2015; Wiersma-Mosley & Jozkowski, 2019). The special status of athletes at party schools might foster a type of protection in that administrators may not be reporting to law enforcement, hoping to avoid public and media scrutiny (AAUP, 2013) and to avoid developing a negative reputation and subsequently risking difficulty in attracting students and potential donors (Yung, 2015). Indeed, there have been situations in which university officials seemed to cover up or avoid reporting violent crimes when talented athletes came from high-profile programs at well-known universities (e.g., Baylor University; Stader & Williams-Cunningham, 2017). Given some of the recent news stories, it seems as though there is a systematic pattern of widespread suppression by top leaders. Perhaps certain party schools are only becoming more honest in their Clery reports due to media scrutiny and public awareness surrounding abuse scandals. Unfortunately, campuses have not been held accountable for reporting their crimes dishonestly.

Limitations and Future Directions

Although this study begins to elucidate institution-level factors related to Clery reports of campus violent crimes against women, findings should be approached with caution as there are important limitations to note. First, because many of the variables of interest (e.g., Greek life, athletics) are not collected by the Department of Education, we had to find these data elsewhere as shown in Table 1. We also note that our LCA model was based on categorical indicators of the variables. We recommend that alternative models (e.g., latent profile analysis) based on continuous measures be estimated in future studies. Also, our latent classes are probabilistic. They artificially make homogeneous groups within heterogeneous populations. As such, not every school in a particular class fits the exact description that the class is assigned. We used LCA to define party schools and to examine whether these institutions report higher rates of campus violent crimes against women. Future research could use these latent classes to assess whether self-reported rates of VAW differ across universities using campus climate data. We also did not distinguish between on- and off-campus VAW, even though research suggests that college women are victimized at higher rates off campus compared with on campus (Fisher et al., 2000). More clarification is needed regarding what constitutes “off campus” for Clery locations and to what extent these off-campus venues are really an extension of the university. Future research should use clear and reasonable definitions to capture important nuances of college geographical location and associations (e.g., Greek houses). Finally, it will be important for researchers to investigate reporting practices in the wake of the U.S. Education Secretary’s retraction of the Obama administration’s Dear Colleague Letter. This event may affect how universities approach campus VAW going forward.

Conclusion

Our findings indicate that party schools, as well as other universities, need cultural shifts to encourage more comprehensive reporting of campus crimes, to stop ignoring VAW, or to halt cover-ups that seem to occur at some universities. When perpetrators receive encouragement, or no punishment, from peers and campus administrators, then the lack of effective guardianship leads to a rape-supportive culture, whereby men are not only motivated to be violent, but women are also willing to take blame and never report sexual assault (Schwartz et al., 2001). Thus, the student culture on college campuses needs to change, especially at the high-risk party schools. A way that seems to be working, albeit slowly, is focusing on interventions that dismantle male dominance and patriarchy, which could help shift cultural climates conducive to rape. The majority of men do not sexually assault women (e.g., Strang & Peterson, 2016); however, many men also do nothing to stop it and thus perpetuate its existence (Kimmel & Kimmel, 2008). Fraternity members and athletes could be powerful spokesmen and allies for the prevention of VAW, especially with their high social status on campus. Multiple programs, including the Men’s Program (Foubert et al., 2007), Bringing in the Bystander (Banyard et al., 2004), and the Mentors in Violence Prevention program (Katz, 1995) have all been shown to be appealing to men, as the approach views men as allies in preventing violence versus viewing men as solely perpetrators or potential perpetrators. These programs are currently used by many colleges and universities. Indeed, according to the SaVE Act (i.e., Campus Sexual Violence Elimination Act; Violence Against Women Reauthorization Act [VAWA], 2013), universities are mandated to do some form of bystander intervention programming. Specific efforts should empower party schools and champions within high-risk men’s groups (e.g., athletics, fraternities) to speak out and engage as bystanders to help shift the hegemonic culture of some of these groups toward a more approachable egalitarian culture (e.g., Jozkowski & Wiersma-Mosley, 2017; Murnen, Wright, & Kaluzny, 2002).

In general, we need to better hold universities accountable, and efforts should be made to encourage reporting and more accurately measure the prevalence of campus crime; Clery data have been chastised for not adequately doing so (Guffey, 2013; Karjane et al., 2002; Krebs et al., 2007; Marshall, 2014; Palmer & Alda, 2016). One way universities might address this measurement barrier is by converting to a data collection method that sends anonymous surveys to all students, such as a campus climate survey (Marshall, 2014), with some type of modest incentive to encourage participation. Institutions that have started assessing the incidence of campus crime via such surveys are able to more accurately depict the extent of sexual violence experienced by their students (Cantalupo, 2014). Unfortunately, only 16% of campuses in 2014 had conducted an annual campus climate survey (McCaskill, 2014), and many use different measures making cross-campus comparisons challenging. However, in 2017, the majority (57%) of campuses reported that they had completed a campus climate survey, whereas 28% were in the planning stages, and 15% had not completed a survey (Wiersma-Mosley & DiLoreto, 2018). Schools that rely strictly on victim reporting such as Clery data, as some schools do, likely get relatively few reports in comparison with the sexual violence actually occurring (Cantalupo, 2014). Victims’ reasons for not reporting are dominated by beliefs that others, especially those in positions of authority, will not believe them (Fisher et al., 2000). Going forward, campuses should encourage those in positions of authority to welcome reporting and to take reports seriously; they should also conduct campus climate surveys, and make these data available to the public, so that it might increase awareness for students and parents.

In conclusion, these findings suggest a unique way to define party schools and assess reports of VAW on college campuses. All types of universities inadequately report violent crimes against women, not just those that are associated with higher rates of perpetration (i.e., party schools). Thus, future research must identify better measures of VAW to provide more meaningful comparisons beyond Clery data. Future policies should encourage administrators to create campus resources to encourage better reporting measures, as well as supporting their student victims.

Footnotes

Appendix

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

Jacquelyn D. Wiersma-Mosley, PhD, is an associate professor of human development and family sciences in the School of Human Environmental Sciences at the University of Arkansas. She studies alcohol use, romantic relationships, and violence among young adults.

Malachi Willis, MA, is a doctoral student in community health promotion in the Department of Health, Human Performance, and Recreation at the University of Arkansas. He works in the Sexuality Education and Consent Studies Lab and studies how college students conceptualize and communicate consent.

Kristen N. Jozkowski, PhD, is an associate professor of public health in the Department of Health, Human Performance, and Recreation, affiliate faculty in gender studies, director of the Sexual Health Research Lab at the University of Arkansas, and research fellow with the Kinsey Institute for Research in Sex, Gender, and Reproduction at Indiana University. She studies sexual consent and sexual violence prevention.

Michael J. Cleveland, PhD, is an associate professor in the Department of Human Development at Washington State University. He studies social contextual factors and health risk behaviors.

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