Polyvictimization and Sexual Risk Behaviors in College-Aged Women

Abstract

Current research suggests a link between childhood sexual abuse and risky sexual behaviors (RSBs) in emerging adults. However, previous studies neglect evaluating the influence of high levels of cumulative childhood victimization. The present study examined the relationships among polyvictimization, six aggregate categories of childhood victimization, and RSB in college women. This study first examined the relative contributions of polyvictimization and individual categories of childhood victimization in predicting RSB and then tested whether polyvictimization contributes any unique variance, beyond that accounted for by the combination of all six aggregate categories in a sample of 321 college women in a Southern state. Regression analyses reveal that (a) polyvictimization accounts for a significant proportion of variability in scores for RSB, beyond that accounted for by any of the six categories of childhood victimization alone; (b) the categories of childhood victimization contribute little to no variability beyond that accounted for by polyvictimization; and (c) polyvictimization accounts for a significant proportion of variability in RSB, beyond that already accounted for by the simultaneous entry of all six categories as predictor variables. Results suggest treatment providers working with college students should assess polyvictimization in relation to RSB and inform their prevention efforts given this link.

Keywords

polyvictimization victimization child maltreatment child abuse sexual behavior college students

In the U.S. undergraduate population, risky sexual behavior (RSB), such as engaging in sexual activity without contraceptives or having multiple sexual partners, is common (American College Health Association, 2015). Engaging in RSB results in negative health outcomes for women including infertility, pelvic inflammatory disease, and sexually transmitted diseases (Abajobir, Kisely, Maravilla, Williams, & Najman, 2017). Adverse childhood experiences, including childhood maltreatment, have been linked to RSB, sexually transmitted infections (STIs), and substance use (Dube, Felitti, Dong, Giles, & Anda, 2003; Repetti, Taylor, & Seeman, 2002; Rodgers et al., 2004). Researchers have also linked various forms of violence exposure including sexual victimization, physical victimization, and witnessing community violence, to RSB (Brady & Donenberg, 2006; Senn, Carey, & Vanable, 2008; Voisin & Neilands, 2010; Wilson & Widom, 2008; Wyatt et al., 2002). For example, Brady and Donenberg (2006) found that exposure to violence in childhood was associated with increased substance use across males and females, and furthermore, motivation to cope through escape is related to inconsistent preventative strategies for pregnancy or STIs. Particularly, several studies support an association between childhood sexual abuse and RSB in adulthood (e.g., Cohen et al., 2000; Hamburger et al., 2004; Senn & Carey, 2010; Steel & Herlitz, 2005; Testa, VanZile-Tamsen, & Livingston, 2005). Cohen et al. (2000), for instance, found that a history of childhood sexual abuse puts individuals at risk of future abusive relationships into adulthood and increased risk of HIV through nonconsensual contact. Their findings also suggest that sexual abuse exposure in childhood results in a sequel of risk-taking behavior throughout the life span.

Several theoretical approaches explain the association between childhood maltreatment and RSB. Distorted behavioral responses to beliefs about self-concept or self-schemas centered on childhood sexual abuse that are developed in childhood may lead to RSBs (Abajobir et al., 2017; Neihaus, Jackson, & Davies, 2010). Additionally, exposure to traumatic experiences can lead to RSB. For example, Senn, Carey, and Vanable (2008) found childhood sexual abuse may result in sexualized behaviors in childhood and subsequently engaging in RSB across the life span. Lastly, the syndemic model has been used to connect childhood maltreatment and polyvictimization to long-term health outcomes, as the model explains the link between health problems and social and societal conditions (Ababjobir et al., 2017; González-Guarda, Florom-Smith, & Thomas, 2011).

Studies have suggested an association between sexual victimization history and subsequent RSB (Champion et al., 2004; Parillo, Freeman, Collier, & Young, 2001). This association has been demonstrated in college student populations as well (Combs-Lane & Smith, 2002; Turchik, 2012; Turchik & Hassija, 2014). Gidcyz et al. (2008) found that college women with a history of sexual victimization were 3.3–4.6 times more likely to have engaged in RSB, including engaging in early sexual intercourse and having more sexual partners, compared with women without a history of sexual victimization. In a study of undergraduate women, findings also indicated sexual victimization was associated with increased health risk behavior and difficulties with sexual functioning (Turchik & Hassija, 2014). Another study similarly found college-aged women who experience childhood sexual abuse are more likely to experience subsequent RSB, more sexual problems, and higher negative sexual self-concept than women without victimization (Lacelle, Hébert, Lavoie, Vitaro, & Tremblay, 2012). However, these studies only examined the relationship between childhood sexual abuse and RSB rather than polyvictimization and RSB. Abajobir, Kisely Maravilla, Williams, and Najman (2017) conducted a meta-analysis of eight studies of childhood sexual abuse and RSB. Results indicated that childhood sexual abuse was associated with a 1.59 times greater likelihood of RSB. Abajobir et al. (2017) elucidated the need to explore the association of multiple forms of childhood maltreatment and RSB, as it has been largely absent in research.

Most research has focused on one type of victimization, primarily childhood sexual abuse in isolation, rather than the effects of cumulative victimization in understanding the broad effects of victimization. The cumulative effects of interpersonal victimization during childhood, commonly referred to as polyvictimization, have been predictive of a number of long-term consequences including, psychological symptomatology (Silvern & Griese, 2012; Turner, Finkelhor, Shattuck, & Hamby, 2012), future legal involvement (Stanley & Goddard, 2004), and physical health problems (Lacelle et al., 2012; Scheiderman et al., 2013). Childhood polyvictimization has been found to predict trauma symptoms beyond any single type of victimization, even victimizations that have been experienced repeatedly (Turner, Finklehor, & Ormrod, 2010). For example, depression, suicidal tendencies, lowered self-esteem, drug and alcohol use, and delinquent behaviors are more common among victims of multiple forms of abuse than those who experience fewer forms of abuse (Arata, Langhintichsen-Rolling, Bowers, & O’Farrill-Swails, 2005). In fact, more common and lower severity forms of victimizations (i.e., theft, vandalism, and sibling assault) were more predictive of trauma symptoms than any one particular kind of victimization (Finkelhor, Ormrod, & Turner, 2007a). Researchers posit studies focusing on single forms of victimization likely underestimate the full burden of victimization that children experience and incorrectly specify the risk profile of victims (Arata et al., 2005; Finkelhor et al., 2007a; Turner et al., 2010). For instance, studies have demonstrated polyvictimization accounts for variability above and beyond any one type of victimization. Elliott, Alexander, Pierce, Aspelmeier, and Richmond, (2009) found that undergraduate women who experienced one form of victimization during childhood were more likely to experience multiple forms of victimization throughout their lifetime. Additionally, their results revealed that polyvictimization accounted for more variance in college adjustment domains (i.e., social, emotional, and academic adjustment) than any individual category of victimization resulting in numerous long-term interpersonal difficulties.

More recently, research has examined the long-term outcomes of childhood polyvictimization. Prior studies have suggested that the trajectory of polyvictimization may occur throughout the life span—from infancy to adolescence to adulthood (Finkelhor, Shattuck, Turner, Ormrod, & Hamby, 2011; Musicaro et al., 2017). Negative effects of polyvictimization have been found to extend past childhood into emerging adulthood, particularly among college-aged adults (Barnes, Howell, & Miller-Graff, 2016; Clemmon et al., 2003; Elliott et al., 2009; Hasselle, Howell, Dormois, & Miller-Graff, 2017; Richmond, Elliott, Pierce, Aspelmeier, & Alexander, 2009; Sabina & Straus, 2008). Therefore, there is a need to examine comprehensive, cumulative forms of childhood maltreatment experiences when examining the link between childhood maltreatment and RSB.

Current Study

Most studies examining the link between childhood victimization and subsequent RSB in college women have only examined a specific form of victimization (e.g., sexual abuse). The presence of varying patterns of childhood victimization and investigating differences in psychological outcomes, such as RSB, across these patterns of victimization is warranted for tailoring preventative and clinical interventions (Charak et al., 2016). The present study examined the association between both the unique and combined effects of polyvictimization and RSB with a sample of college women.

Method

Participants

A total of 321 traditional college female undergraduates aged 19–25 (M = 20.21, SD = 1.15) were recruited from introductory and upper-level psychology courses at a large university in the Southeastern United States during the 2014–2015 academic year. The majority of participants identified as White/European American (86%), followed by Black/African American (6.9%), Hispanic/Latino American (2.5%), Asian American (2.2%), and other racial/ethnic groups (2.5%).

Procedure

Participants were recruited through the university’s study pool and were awarded course credit for their participation. Data were collected using the Qualtrics, 2014 version, online survey software (Qualtrics Labs, 2009) as a part of a larger study examining early childhood experiences, personality characteristics, psychopathy, victimization, and substance use in an undergraduate sample. All participants provided informed consent by endorsing their understanding of the study. All study procedures were approved by the university’s institutional review board.

Measures

Demographic questionnaire

Demographic information was gathered through a comprehensive questionnaire. For instance, participants were asked about their age, race/ethnicity, sex/gender, relationship status, college major, political affiliation, and religious identity.

Polyvictimization

The Juvenile Victimization Questionnaire (JVQ)—Adult Retrospective Version obtains self-reports on 34 types of childhood victimization that cover six general areas: property crime, physical assault, child maltreatment, peer and sibling victimization, sexual victimization, and witness or indirect victimization (Finkelhor, Hamby, Ormrod, & Turner, 2005a, 2005b; Hamby, Finkelhor, Ormrod, & Turner, 2004). Some items assess relatively common but lower severity victimizations (e.g., theft of personal property), while other items assess infrequent and higher severity victimizations (e.g., witnessing an explosion; see Table 1). For the JVQ, participants reported whether or not they experienced each of the 34 different types of victimization from the time they were born until 17 years of age. Sample items include: When you were a child, did you do sexual things with anyone 18 or older, even things you both wanted? and Not including spanking on your bottom, when you were a child, did a grown-up in your life hit, beat, kick, or physically hurt you in any way? The conceptualization and measurement of polyvictimization in the present study was modeled after work of Finkelhor and colleagues, in which higher JVQ total scores reflect higher levels of childhood polyvictimization (e.g., Finkelhor, Ormrod, & Turner, 2007b). Internal consistency is not reported for the JVQ, as victimization experiences on the measure are not expected to intercorrelate (Hasselle et al., 2017). The JVQ has been used in several studies asking college students to retrospectively report childhood victimization experiences (Barnes et al., 2016; Elliott et al., 2009; Howell & Miller-Graff, 2014; Richmond et al., 2009).

Table 1.
Frequency Table for the 34 Types of Childhood Victimization on the JVQ.

Victimization Type N = 321 Frequency (%)

34 Types of childhood victimization (endorsed at least one type) 282 (87.9%)

Property crime aggregate (endorsed at least one type) 204 (63.6%)

Robbery 113 (35.2%)

Theft (steal something from you) 147 (45.8%)

Vandalism (break or ruin something of yours) 157 (48.9%)

Physical assault aggregate (endorsed at least one type) 203 (63.2%)

Assault with a weapon 40 (12.5%)

Assault without a weapon 95 (29.8%)

Attempted assault 40 (12.5%)

Kidnap, attempted, or completed 16 (5.0%)

Bias attack 15 (4.7%)

Physical abuse (not spanking) 41 (12.8%)

Assault by group or gang of peers 10 (3.1%)

Peer or sibling assault 158 (49.2%)

Genital assault 13 (4.0%)

Dating violence 22 (6.9%)

Child maltreatment aggregate (endorsed at least one type) 77 (24.0%)

Physical abuse (not spanking) 41 (12.8%)

Psychological or emotional abuse 54 (16.8%)

Neglect 13 (4.0%)

Custodial interference or family abduction 23 (7.2%)

Peer and sibling victimization aggregate (endorsed at least one type) 202 (68.5%)

Assault by group or gang of peers 10 (3.1%)

Peer or sibling assault 158 (49.2%)

Genital assault 13 (4.0%)

Bullying 132 (41.1%)

Teasing, emotional bullying 139 (43.3%)

Dating violence 22 (6.9%)

Witnessing and indirect victimization aggregate (endorsed at least one type) 119 (37.1%)

Witness domestic violence 32 (10.0%)

Witness physical abuse 27 (8.4%)

Witness assault with a weapon 41 (12.8%)

Witness assault without a weapon 92 (28.7%)

Household theft 78 (24.3%)

Someone close murdered 24 (7.5%)

Witness murder 4 (1.2%)

Exposure to shooting, bombs, riots 20 (6.2%)

Exposure to war 3 (0.9%)

Sexual victimization aggregate (endorsed at least one type) 166 (51.7%)

Sexual assault, known adult 8 (2.5%)

Sexual assault, unknown adult 5 (1.6%)

Sexual assault, with peer 43 (13.4%)

Rape, attempted, or completed 33 (10.3%)

Flashing or sexual exposure 59 (18.4%)

Sexual harassment 60 (18.7%)

Note. JVQ = Juvenile Victimization Questionnaire.

RSBs

The Sexual Risk Survey (SRS; Turchik & Garske, 2009) is a 23-item measure used to assess the frequency of sexual risk behaviors in the past 6 months. All items pertain to the participant’s behavior over the past 6 months, and the survey was designed for college students with and without sexual experience. Participants are asked to report the number of times they have engaged in various RSBs, including vaginal, oral, and anal sex without a condom; the number of partners with whom they engaged in such behaviors; and frequency of casual sex. The survey measures a broad range of sexual behavior and each item is score 0–4 based on frequency of the behavior, with a possible scale total range of 0–92, with higher scores indicating greater risk-taking. The SRS is comprised of five subscales (sexual risk-taking with uncommitted partners, risky sex acts, impulsive sexual behaviors, intent to engage in RSBs, and risky anal sex acts) as well as a total score. The present study used the total score as a measure of frequency of RSB. The SRS has evidenced convergent and discriminant validity as well as good internal consistent and test–retest reliability (Turchik & Garske, 2009). In the current study, the internal consistency reliability was .91 for the total SRS score.

The Risky Sex Scale (RSS; O’Hare, 2001) contains 14 items measuring expectancies of enhanced sexual experiences and RSB while intoxicated. Each item was rated on a response scale from strongly disagree (1) to strongly agree (5). The RSS includes three subscales. The risky sex expectancies subscales assessing whether women believe alcohol will make them enjoy sex more, make them a better lover, and make them more romantic. The risky sex behaviors subscale examines whether women would engage in more RSB because of consuming alcohol. Lastly, the gender-based perceptions of risky sex subscale assesses beliefs regarding male and female behaviors associated with drinking alcohol. For the present study, the total score was used to assess RSB. The scale has demonstrated very good internal consistency reliability and good construct and criterion validity (O’Hare, 2005). The RSS has also been shown to provide a reliable and valid indication of RSB and attitudes in university students (O’Hare, 2001, 2005). Internal consistency reliability was .94 for the RSS in the present study.

Data Analysis

The conceptualization and measurement of polyvictimization for the present study was modeled after the theoretical and empirical work by Finkelhor and colleagues (e.g., Finkelhor et al., 2007b). Two separate sets of analyses were conducted. First, a series of regression analyses focused on the relative contributions of measures of polyvictimization and six individual categories of victimization to models predicting RSB. This first set of analyses was conducted for two reasons. First, examination of the unique effects for the six individual categories of victimization in the present study facilitates comparison with the multitude of past studies that have focused exclusively on a single category of victimization in isolation rather than on multiple categories simultaneously. Second, these analyses facilitate comparison with studies by Finkelhor, Ormrod, and Turner (2007b), Richmond, Elliott, Pierce, Aspelmeier, and Alexander (2009), and Elliott et al. (2009) that have conceptualized and measured childhood victimization and polyvictimization in the same manner. Based on these studies, we hypothesized that (a) individual categories of victimization would account for little to no variability in RSB after the variability account for by polyvictimization was controlled and (b) polyvictimization would make a significant unique contribution to regression models predicting RSB after the effects of individual categories of victimization were controlled.

A second set of analyses was then conducted to examine whether polyvictimization represents more than the combined effects of individual types of victimization. In other words, it examines whether polyvictimization is a unique predictor of RSB beyond the combined effects of the individual categories of victimization. In this second set of analyses predicting RSB, we (a) tested the significance of the combined effects of all six individual categories of victimization, (b) examined the unique contributions of each of the six categories of victimization when all six were used simultaneously in a multiple regression equation, and (c) tested the unique contribution of polyvictimization when it was entered as a seventh predictor, in combination with the six individual categories of victimization.

Results

Descriptive Analyses for Entire Sample

As seen in Table 1, approximately 88% of the participants endorsed at least 1 of the 34 questions regarding individual types of childhood victimization on the JVQ. The mean number of victimizations experienced by the women in our sample was 5.6 (range from 0 to 25). When the 34 types of victimization were collapsed into the six aggregate categories, participants’ responses indicated that many had experienced at least one type of peer or sibling abuse (68.5%), physical assault (63.2%), property crime (63.6%), witnessed or indirect victimization (37.1%), sexual victimization (51.7%), and/or childhood maltreatment (24.0%). Because two or more of the 34 individual types of victimization on the JVQ experienced by a single participant could come from the same aggregate category (e.g., they experienced two types of sexual assault), we also examined the percentage of participants who experienced victimization from one or more of the six aggregate categories. A little more than a quarter (27.1%) of the women in our sample reported having experienced abuse in five or six categories. Specifically, 9.7% reported victimization in all six aggregate categories, 17.4% in five, 19% in four, 16.5% in three, 12.8% in two, and 11.8% reported victimization in only one category.

Correlations among the six aggregate categories of victimization were all significant and all but one fell between .28 and .55. The only exception was for peer/sibling abuse and physical assault (r = .70), although the high correlation between these two categories is not surprising given that four of the 6 items that make up the Peer/Sibling Abuse Scale are also included on the broader 10-item Physical Assault Scale. All correlations between polyvictimization and the six categories of victimization also were significant, ranging from .63 for sexual victimization to .84 for the physical assault category.

Regression Analyses Using Polyvictimization and Individual Categories of Childhood Victimization as Predictor of RSB

Data analysis and organization

Regression analyses were conducted to examine the relative contributions of (a) polyvictimization and (b) each of the six aggregate categories of childhood victimization in predicting RSB (Table 2). These multiple regression analyses were conducted in two parts. In Part 1, we examined whether the continuous measure of polyvictimization would significantly contribute to a multiple regression model predicting RSB after the individual childhood victimization category had already been entered separately as a predictor in the first block of analysis. In Part 2, we separately examined whether each category of childhood victimization significantly contributed to a multiple regression model predicting RSB when polyvictimization had already been entered as a predictor in the first block of the analysis. No problems with multicollinearity were detected for any multiple regression analysis.

Table 2.
Regression Analyses Examining the Relative Contributions of the JVQ Aggregates and Polyvictimization in Predicting Sexual Risk-Taking.

Criterion Variable Start Model: Aggregate R² Add: Polyvictimization R² Change Start Model: Polyvictimization R² Add: Aggregate R² Change Total Variance^a

RSS total score

Child maltx .03 .04 .06 .00 .06

Property crime .02 .04 .06 .00 .06

Physical assault .01 .06 .06 .00 .07**

Peer/sibling .02* .05 .06 .00 .07

Sexual victimization .03 .03 .06 .00 .07**

Witness/indirect .01* .05 .06 .00 .07

SRS total score

Child maltx .05 .06 .11 .00 .11**

Property crime .01* .10 .10 .01 .11

Physical assault .03 .08 .11 .00 .11

Peer/sibling .01 .12 .11** .01* .12

Sexual victimization .05 .06 .11 .00 .11

Witness/indirect .04 .06 .11 .00 .11**

Note. JVQ = Juvenile Victimization Questionnaire; RSS = Risky Sex Scale; SRS = Sexual Risk Survey.

^aThe proportions of variability accounted for in Steps 1 and 2 of each set of regression analyses should sum to the value reported in the total variance column. Minor differences from this expected pattern in the table are due to the rounding of values to two decimal places.

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

Unique contribution of polyvictimization in predicting RSB

Part 1 of the regression analyses revealed that, when entered into the multiple regression model first, the percentages of variability accounted for by each of the six categories of victimization were significant for the both the RSS and SRS with a few exceptions. Overall, the percentage of variability accounted for across the six categories ranged from 0% to 5% (see Column 1). As predicted, when polyvictimization accounted for a significant proportion of variability on the RSS and SRS. The proportion of variability accounted for by polyvictimization ranged from 4% to 12% (see Column 2), with the highest percentage occurring when predicting SRS with the peer or sibling aggregate.

Unique contributions of six individual childhood victimization categories in predicting RSB

Part 2 of the regression analyses revealed that when polyvictimization was entered in the regression model by itself, it accounted for a significant percentage of variability for the RSS and SRS (ranging from 6% to 11%; see Column 3). In addition, as predicted, the pattern of results revealed that the six categories of childhood victimization contributed little to no variability beyond that accounted for by polyvictimization for the RSS and SRS (ranging from 0% to 1%; see Column 4).

Total variance in sexual risk-taking accounted for by polyvictimization and six categories of childhood victimization

The total variance accounted for by the combination of polyvictimization and each individual category of childhood victimization was significant across all both measures of RSB (ranging from 6% to 12%; see Column 5).

Regression Analyses Predicting RSB From Polyvictimization and the Combined Effects of All Six Aggregate Categories

Given that individuals often experience multiple categories of victimization rather than a single category, a separate set of regression analyses was conducted in which all six childhood victimization aggregates were entered simultaneously as the first block of predictors, and the unique contributions for each predictor was examined. The second step of this analysis examined whether polyvictimization accounted for any unique variance in RSB, beyond that accounted for by the initial block of six predictors. A summary of results is provided in Table 3. Regression models using all six aggregate categories of victimization as predictors accounted for significant proportions of variability in the RSS and SRS. As seen in Step 1, the proportion of the variability accounted for was .06 for the RSS and .11 for the SRS. Squared semipartial correlations used to identify the unique contribution for each of the six individual predictor variables indicated that none of the predictor variables were significant for the RSS, and child maltreatment, peer and sibling victimization, sexual victimization, and witnessing violence aggregates made a significant unique contribution to regression model predicting the SRS.

Table 3.
Regression Analyses Examining the Unique Contribution of Polyvictimization in Predicting RSB After Simultaneously Entering All Six Aggregate Categories of Victimization as Predictor Variables.

R² for Six Aggregate Combined Significant Squared Semipartial Correlation for Six Aggregate Combined Unique Predictor

Step 1

RSS total score .06 None None

SRS total score 11 .22 .23* .32** .23* Child maltx Peer/sibling Sex victimization Witness

R²-Change with PV added in block 2 R² for six aggregate plus PV Significant squared semipartial correlation For Six aggregates and PV Unique predictor

Step 2

RSS total score .01* 0.7** .23* Polyvictimization

SRS total score .03 .14 .34 .29 Polyvictimization Peer/sibling

Note. PV = polyvictimization; RSS = Risky Sex Scale; SRS = Sexual Risk Survey. **indicates p < .01. indicates p < .05.

In Step 2, polyvictimization was added as a seventh predictor to each regression model. These results are present in the bottom half of Table 3. As seen in Column 1, R² change values indicated polyvictimization accounted for a significant proportion of variability beyond that previously accounted for by the combined six categories for the RSS and SRS. These unique contributions were .01 for the RSS and .03 for the SRS. Examination of the R² values in Column 2 reveals that when polyvictimization was included as a prediction with all six aggregate categories, the total proportion of variability accounted for was .07 for the RSS and .14 for the SRS. These predictor variables account for significant proportions of variability for the SRS and RSS.

Finally, squared semipartial correlations were used to identify the unique contribution to the regression models for each of the six individual predictor variables and for polyvictimization. Results indicated that polyvictimization was the only predictor variable making a significant contribution for the RSS (see Column 4). Both polyvictimization and peer or sibling aggregate made significant unique contributions for the SRS. Unique contributions for polyvictimization ranged from .23 to .34 for the RSS and SRS (see Column 3).

Discussion

Exposure to childhood cumulative trauma, or polyvictimization, is a risk factor for subsequent psychopathology and behavioral problems throughout the life span (Musicaro et al., 2017). Researchers have emphasized the need to examine cumulative forms of childhood maltreatment in women when examining behavioral outcomes (Scott-Storey, 2011; Thibodeau, Lavoie, Hébert, & Blais, 2017). The aim of the present study was to extend the breath of research on the development of RSB as a sequel of polyvictimization in college women. While childhood sexual abuse has been established to be unique risk factor for engaging in RSB through the life span (Abajobir et al., 2017; Senn & Carey, 2010), examining the cumulative effects elucidated the unique contribution of polyvictimization on RSB. Previous studies have primarily focused on link between childhood sexual abuse and RSB, and the results help to address a gap in evaluating the impact of cumulative victimization on RSB.

First, college women in the present study reported high rates of childhood victimization, with approximately 87.9% reporting exposure to at least one category of victimization (i.e., property crime, physical assault, peer or sibling abuse, child maltreatment, witness or indirect victimization, and sexual victimization) assessed using the JVQ, which is lower than previous literature reporting rates of 98% for college women (e.g., Richmond et al., 2009). The results suggest that individuals who have experienced one category of childhood victimization are likely to have experienced other categories as well (e.g., Arata et al., 2005; Finkelhor et al., 2005a).

Overall, the results of the present study are consistent with the study hypotheses, and polyvictimization in childhood was associated with RSB in college in women. The results are consistent with research linking childhood victimization with health risk behaviors (Turchik & Hassija, 2014). None of the predictor variables in isolation were significant for the RSS, and polyvictimization was found to be the only significant contribution for the RSS. This measure accounts for RSB that occurs while under the influence of alcohol or a person’s expectations about sexual behaviors while under the influence of alcohol. A recent study of college men and women found that alcohol use mediated the relationship between sexual victimization and RSB (Silverstein, Fix, & Alexander, 2017). Thus, future research should investigate this relationship and consider alcohol use as a mediating variable between polyvictimization and RSB.

Polyvictimization was also a better predictor of RSB than were any of the individual categories alone. In most cases, although each individual category of victimization accounted for a significant proportion of the variability in measures of RSB when examined in isolation, the effect sizes tended to be relatively small. In addition, in most cases, the categories of victimization accounted for little to no variability beyond that already accounted for by polyvictimization. Overall, the results of the current study suggest that although categories of childhood victimization are associated with RSB, exposure to high cumulative levels of victimization may be a particularly strong risk factor for engaging in RSB. This finding is consistent with past studies that have suggested that exposure to multiple types of victimization is at least as important as exposure to any individual category of victimization for a variety of outcomes measures (e.g., Arata et al., 2005; Arata, Langhinrichsen-Rohling, Bowers, & O’Brien, 2007; Finkelhor et al., 2007a, 2007b).

Limitations

The present study has several limitations. First, given that the majority of participants in our sample were European American female college students between the ages of 19 and 25, the generalizability to other populations (e.g., males, non-European Americans, older students, and community samples) is limited. The present study did not control for these demographic variables, which may highlight group differences in behavior. Another limitation of the current study is that it relied on retrospective self-report data and, therefore, is subject to all potential limitations and biases associated with such data. Retrospective reports in adulthood of adverse childhood experiences yield biases in reporting (Hardt & Rutter, 2004). Therefore, it is possible that participants either over- or underreported their victimization experiences. Similarly, participants may have underreported their RSB. A third limitation of the present study is that cause and effect conclusions cannot be drawn from a correlational design, as the study did not allow for the examination of variables over time. It is possible that other factors not assessed in the present study might have influenced RSB in college women. Finally, the large number of tests conducted raises the possibility of an inflated family-wise risk for Type I error. As a result, the focus of our interpretation and discussion of the results has emphasized the overall pattern of findings, across combinations of predictor and criterion variables rather than specific individual effects.

Implications for Future Research and Practice

These results have implications for both further research and prevention programming on college campuses. First, results suggest individuals who meet criteria for polyvictimization might be at an increased risk of RSB. Adjustment to college for college-aged women is influenced by childhood polyvictimization (Elliott et al., 2009). Further, Bigras, Daspe, Godbut, Briere, and Sabourin (2017) found sexual behaviors and difficulties/expectancies not just linked to sexual trauma but affected by the impact of childhood polyvictimization. Outreach efforts can be made to inform students about the potential mental health and behavioral consequences and encourage them to seek assistance from victim service departments on campus. Programming aimed to support college women during this transitional period is essential in mitigating the negative outcomes associated with their victimization histories. Additionally, these findings help encourage mental health providers on college campuses to screen for polyvictimization given the potential negative behavioral trajectory.

Future studies assessing polyvictimization, childhood maltreatment, and RSB are needed are essential to replicate these findings and extend them to a more diverse sample of participants, as these data were collected on predominately European American college women in a Southern state. Additional research is also needed to further explore how to best define and measure exposure to multiple categories of victimization (e.g., advantages and disadvantages of classifying participants based on the occurrence of any* victimization within a given category vs. counting multiple instances of the same event or weighting events by their severity). Further, future research should examine other sexual health outcomes outside of RSB. Finally, given that a variety of other factors might have influenced our results (i.e., race, socioeconomic status, negative family characteristics, other nonvictimization adverse life experiences, and recent stressors), future studies that control for these variables are needed.

Conclusion

Research has shown the negative effects of polyvictimization have been found to extend past childhood into emerging adulthood, particularly among college-aged adults (Barnes et al., 2016; Clemmon et al., 2003; Elliott et al., 2009; Hasselle et al., 2017; Richmond et al., 2009; Sabina & Straus, 2008). RSB is a common problem among college students and prevention and psychoeducation strategies are needed to address these behaviors. The present study addresses an important need to evaluate polyvictimization in research on long-term consequences of childhood victimization on RSB in college women. The results of the present study also underscores how adverse childhood experiences remain impactful in emerging adulthood. Furthermore, the results help to encourage clinicians view childhood victimization holistically in their inquiries rather than limiting their assessment of childhood maltreatment to a single predominant type of victimization (e.g., childhood sexual abuse). This comprehensive approach provides understanding of the cumulative effective of childhood maltreatment on RSB. Campus outreach efforts and treatment approaches created to inform students about the potential mental health and behavioral consequences of polyvictimization in childhood can be critical in reducing negative consequences such as RSB.

Victimization Type	N = 321 Frequency (%)
34 Types of childhood victimization (endorsed at least one type)	282 (87.9%)
Property crime aggregate (endorsed at least one type)	204 (63.6%)
Robbery	113 (35.2%)
Theft (steal something from you)	147 (45.8%)
Vandalism (break or ruin something of yours)	157 (48.9%)
Physical assault aggregate (endorsed at least one type)	203 (63.2%)
Assault with a weapon	40 (12.5%)
Assault without a weapon	95 (29.8%)
Attempted assault	40 (12.5%)
Kidnap, attempted, or completed	16 (5.0%)
Bias attack	15 (4.7%)
Physical abuse (not spanking)	41 (12.8%)
Assault by group or gang of peers	10 (3.1%)
Peer or sibling assault	158 (49.2%)
Genital assault	13 (4.0%)
Dating violence	22 (6.9%)
Child maltreatment aggregate (endorsed at least one type)	77 (24.0%)
Physical abuse (not spanking)	41 (12.8%)
Psychological or emotional abuse	54 (16.8%)
Neglect	13 (4.0%)
Custodial interference or family abduction	23 (7.2%)
Peer and sibling victimization aggregate (endorsed at least one type)	202 (68.5%)
Assault by group or gang of peers	10 (3.1%)
Peer or sibling assault	158 (49.2%)
Genital assault	13 (4.0%)
Bullying	132 (41.1%)
Teasing, emotional bullying	139 (43.3%)
Dating violence	22 (6.9%)
Witnessing and indirect victimization aggregate (endorsed at least one type)	119 (37.1%)
Witness domestic violence	32 (10.0%)
Witness physical abuse	27 (8.4%)
Witness assault with a weapon	41 (12.8%)
Witness assault without a weapon	92 (28.7%)
Household theft	78 (24.3%)
Someone close murdered	24 (7.5%)
Witness murder	4 (1.2%)
Exposure to shooting, bombs, riots	20 (6.2%)
Exposure to war	3 (0.9%)
Sexual victimization aggregate (endorsed at least one type)	166 (51.7%)
Sexual assault, known adult	8 (2.5%)
Sexual assault, unknown adult	5 (1.6%)
Sexual assault, with peer	43 (13.4%)
Rape, attempted, or completed	33 (10.3%)
Flashing or sexual exposure	59 (18.4%)
Sexual harassment	60 (18.7%)

Criterion Variable	Start Model: Aggregate R²	Add: Polyvictimization R² Change	Start Model: Polyvictimization R²	Add: Aggregate R² Change	Total Variance^a
RSS total score
Child maltx	.03**	.04**	.06**	.00	.06**
Property crime	.02**	.04**	.06**	.00	.06**
Physical assault	.01	.06**	.06**	.00	.07**
Peer/sibling	.02*	.05**	.06**	.00	.07**
Sexual victimization	.03**	.03**	.06**	.00	.07**
Witness/indirect	.01*	.05**	.06**	.00	.07**
SRS total score
Child maltx	.05**	.06**	.11**	.00	.11**
Property crime	.01*	.10**	.10**	.01	.11**
Physical assault	.03**	.08**	.11**	.00	.11**
Peer/sibling	.01	.12**	.11**	.01*	.12**
Sexual victimization	.05**	.06**	.11**	.00	.11**
Witness/indirect	.04**	.06**	.11**	.00	.11**

	R² for Six Aggregate Combined	Significant Squared Semipartial Correlation for Six Aggregate Combined	Unique Predictor
Step 1
RSS total score	.06**	None	None
SRS total score	11**	.22* .23* .32** .23*	Child maltx Peer/sibling Sex victimization Witness
	R²-Change with PV added in block 2	R² for six aggregate plus PV	Significant squared semipartial correlation For Six aggregates and PV	Unique predictor
Step 2
RSS total score	.01*	0.7**	.23*	Polyvictimization
SRS total score	.03**	.14**	.34 .29	Polyvictimization Peer/sibling

Footnotes

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.

References

Abajobir

A. A.

Kisely

Maravilla

J. C.

Williams

Najman

J. M.

(2017). Gender differences in the association between childhood sexual abuse and risky sexual behaviours: A systematic review and meta-analysis. Child Abuse & Neglect, 63, 249–260. doi:10.1016/j.chi.abu.2016.11.023

American College Health Association. (2015). American college health association national college health assessment II: Reference group executive summary. American college health association. http://www.acha-ncha.org/docs/NCHA-II_WEB_SPRING_2015_REFERENCE_GROUP_EXECUTIVE_SUMMARY.pdf

Arata

C. M.

Langhinrichsen-Rohling

Bowers

O’Brien

(2007). Differential correlates of multi-type maltreatment among urban youth. Child Abuse & Neglect, 31, 393–415. doi:10.1016/j.chiabu.2006.09.006

Arata

C. M.

Langhinrichsen-Rohling

Bowers

O’Farrill-Swails

. (2005). Single versus multi-type maltreatment: An examination of the long-term effects of child abuse. Journal of Aggression, Maltreatment, & Trauma, 11, 29–52. doi:10.1300/J146v11n04_02

Barnes

S. E.

Howell

K. H.

Miller-Graff

L. E.

(2016). The relationships between polyvictimization, emotion dysregulation, and social support among emerging adults victimized during childhood. Journal of Aggression, Maltreatment, and Trauma, 25, 470–486. doi:10.1080/10926771.2015.1133749

Bigras

Daspe

Godbout

Briere

Sabourin

(2017). Cumulative childhood trauma and adult sexual satisfaction: Mediation by affect dysregulation and sexual anxiety in men and women. Journal of Sex & Marital Therapy, 43, 377–396. doi:10.1080/0092623X.2016.1176609

Brady

S. S.

Donenberg

G. R.

(2006). Mechanisms linking violence exposure to health risk behavior in adolescence: Motivation to cope and sensation seeking. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 673–680. doi:10.1097/01.chi.0000215328.35928.a9

Champion

H. L. O.

Foley

K. L.

Durant

R. H.

Hensberry

Altman

Wolfson

(2004). Adolescent sexual victimization, use of alcohol and other substances, and other health risk behaviors. Journal of Adolescent Health, 35, 321–328. doi:10.1016/j.jadohealth.2003.09.023

Charak

Byllesby

B. M.

Roley

M. E.

Claycomb

M. A.

Durham

T. A.

Ross

… Elhai

J. D

(2016). Latent classes of childhood poly-victimization and associations with suicidal behavior among adult trauma victims: Moderating role of anger. Child Abuse & Neglect, 62, 19–28. doi:10.1016/j.chiabu.2016.10.010

10.

Combs-Lane

A. M.

Smith

D. W.

(2002). Risk of sexual victimization in college women: The role of behavioral intentions and risk-taking behaviors. Journal of Interpersonal Violence, 17, 165–183.

11.

Cohen

Deamant

Barkan

Richardson

Young

Holman

… Melnick

(2000). Domestic violence and childhood sexual abuse in HIV-infected women and women at risk for HIV. American Journal of Public Health, 90, 560–565. doi:10.2105/AJPH.90.4.560

12.

Dube

S. R.

Felitti

V. J.

Dong

Giles

W. H.

Anda

R. F.

(2003). The impact of adverse childhood experience on health problems: Evidence from four birth cohorts dating back to 1900. Preventive Medicine, 37, 268–277. doi:10.1016/S0091-7435(03)00123-3

13.

Elliott

A. E.

Alexander

A. A.

Pierce

T. W.

Aspelmeier

J. E.

Richmond

J. M.

(2009). Childhood victimization, poly-victimization, and adjustment to college in women. Child Maltreatment, 14, 330–343. doi:10.1177/1077559509332262

14.

Finkelhor

Hamby

S. L.

Ormrod

Turner

(2005). The Juvenile Victimization Questionnaire: Reliability, validity, and national norms. Child Abuse & Neglect, 29, 383–412. doi:10.1016/j.chiabu.2004.11.001

15.

Finkelhor

Hamby

S. L.

Ormrod

Turner

(2005). Measuring polyvictimization using the JVQ. Child Abuse & Neglect, 29, 1297–1312. doi:10.1016/j.chiabu.2005.06.005

16.

Finkelhor

Ormrod

Turner

(2007a). Poly-victimization: A neglected component in child victimization. Child Abuse & Neglect, 31, 7–26. doi:10.1016/j.chiabu.2006.06.008

17.

Finkelhor

Ormrod

R. K.

Turner

H. A.

(2007b). Polyvictimization and trauma in a national longitudinal cohort. Development and Psychopathology, 19, 149–166. doi:10.1017/S0954579407070083

18.

Finkelhor

Shattuck

Turner

H. A.

Ormrod

Hamby

S. L.

(2011). Polyvictimization in developmental context. Journal of Child & Adolescent Trauma, 4, 291–300. doi:10.1080/19361521.2011.610432

19.

González-Guarda

R. M.

Florom-Smith

A. L.

Thomas

(2011). A syndemic model of substance abuse, intimate partner violence, HIV infection, and mental health among hispanics. Public Health Nursing, 28, 366–378.

20.

Hamburger

M. E.

Moore

Koenig

L. J.

Vlahov

Schoenbaum

E. E.

Schuman

.,… the HIV Epidemiology Research Study Group. (2004). Persistence of inconsistent condom use: Relation to abuse history and HIV serostatus. AIDS and Behavior, 8, 333–344. doi:10.1023/B:AIBE.0000044080.04397.97

21.

Hamby

S. L.

Finkelor

Ormrod

R. K.

Turner

H. A.

(2004). The Juvenile Victimization Questionnaire (JVQ): Administration and scoring manual. Durham, NH: Crimes Against Children Research Center.

22.

Hardt

Rutter

(2004). Validity of adult retrospective reports of adverse childhood experiences: Review of the evidence. Journal of Child Psychology and Psychiatry, 45, 260–273. doi:10.1111/j.1469-7610.2004.00218.x

23.

Hasselle

A. J.

Howell

K. H.

Dormois

Miller-Graf

L. E.

(2017). The influence of childhood victimization on disordered eating symptoms in emerging adulthood. Child Abuse & Neglect, 68, 55–64. doi:10.1016/j.chiabu.2017.04.003

24.

Howell

K. H.

Miller-Graff

L. E.

(2014). Protective factors associated with resilient functioning in young adulthood after childhood exposure to violence. Child Abuse & Neglect, 38, 1985–1994. doi:10.1016/j.chiabu.2014.10.010

25.

Lacelle

Hébert

Lavoie

Vitaro

Tremblay

R. E.

(2012). Child sexual abuse and women’s sexual health: The contribution of CSA severity and exposure to multiple forms of childhood victimization. Journal of Child Sexual Abuse, 21, 571–592. doi:10.1080/10538712.2012.688932

26.

Musicaro

R. M.

Spinazzola

Arvidson

Swaroop

S. R.

Grace

L. G.

Yarrow

… Ford

J. D

. (2017). The complexity of adapatation to childhood polyvictimization in youth and young adults: Recommendations for multidisciplinary responders. Trauma, Violence, & Abuse (Advance online publication). doi:10.1177/1524838017692365

27.

Neihaus

A. F.

Jackson

Davies

(2010). Sexual self-schemas of female child sexual abuse survivors: Relationships with risky sexual behavior and sexual assault in adolescence. Archives of Sexual Behavior, 39, 1359–1374. doi:10.1007/s10508-010-9600-9

28.

O’Hare

(2001). Substance abuse and risky sex in young people: The development and validation of the Risky Sex Scale. Journal of Primary Prevention, 22, 89–101. doi:10.1023/A:1012653717412

29.

O’Hare

(2005). Risky sex and drinking contexts in freshman first offenders. Addictive Behaviors, 30, 585–588. doi:10.1016/j.addbeh.2004.07.002

30.

Parillo

K. M.

Freeman

R. C.

Collier

Young

(2001). Association between early sexual abuse and adult HIV-risky sexual beahviors among community-recruited women. Child Abuse & Neglect, 25, 335–346. doi:10.1016/S0145-2134(00)00253-2

31.

Qualtrics [Software]. (2009). Provo, UT: Qualtrics Labs. Retrieved from https://www.qualtrics.com

32.

Repetti

R. L.

Taylor

S. E.

Seeman

T. E.

(2002). Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin, 128, 330–366. doi:10.1037/0033-2909.128.2.330

33.

Richmond

J. M.

Elliott

A. N.

Pierce

T. W.

Aspelmeier

J. E.

Alexander

A. A.

(2009). Polyvictimization, childhood victimization, and psychological distress in college women. Child Maltreatment, 14, 127–147. doi:10.1177/1077559508326357

34.

Rodgers

C. S.

Lang

A. J.

Laffate

Satz

L. E.

Dresselhaus

Stein

M. B.

(2004). The impact of individual forms of childhood maltreatment on health behavior. Child Abuse and Neglect, 28, 575–586. doi:10.1016/j.chiabu.2004.01.002

35.

Sabina

Straus

M. A.

(2008). Polyvictimization by dating partners and mental health among U.S. college students. Violence and Victims, 23, 667–682. doi:10.1891/0886-6708.23.6.667

36.

Scheiderman

J. U.

Smith

Arnold-Clark

J. S.

Fuentes

Duan

Palinkas

L. A.

(2013). Overweight and obesity among Hispanic children entering foster care: A preliminary examination of polyvictimization. Child Maltreatment, 18, 264–273. doi:10.1177/1077559513508236

37.

Scott-Storey

(2011). Cumulative abuse: Do things add up? An evaluation of the conceptualization, operationalization, and methodological approaches in the study of the phenomenon of cumulative abuse. Trauma, Violence, & Abuse, 12, 135–150. doi:10.1177/1624838011404253

38.

Senn

T. E.

Carey

M. P.

(2010). Child maltreatment and women’s adult sexual risk behavior: Childhood sexual abuse as a unique risk factor. Child Maltreatment, 15, 324–335.

39.

Senn

T. E.

Carey

M. P.

Vanable

P. A.

(2008). Childhood and adolescent sexual abuse and subsequent sexual risk behavior: Evidence from controlled studies, methodological critique, and suggestions for research. Clinical Psychology Review, 28, 711–735. doi:10.1016/j.cpr.2007.10.002

40.

Silvern

Griese

(2012). Multiple types of child maltreatment, posttraumatic stress, dissociative symptoms, and reactive aggression among adolescent criminal offenders. Journal of Child & Adolescent Trauma, 5, 88–101. doi:10.1080/19361521.2012.671799

41.

Silverstein

Fix

R. L.

Alexander

A. A.

(2017). Risky sex: Testing moderated mediation among college students. Journal of Aggression, Conflict, and Peace Research, 9, 246–256. doi:10.1108/JACPR-05-2017-0296

42.

Stanley

Goodard

(2004). Multiple forms of violence and other criminal activities as an indicator of severe child maltreatment. Child Abuse Review, 13, 246–262. doi: 10.1002/car.855

43.

Steel

J. L.

Herlitz

C. A.

(2005). The association between childhood and adolescent sexual abuse and proxies for sexual risk behavior: A random sample of the general population of Sweden. Child Abuse & Neglect, 29, 1141–1153. doi:10.1016/j.chiabu.2004.10.015

44.

Testa

VanZile-Tamsen

Livingston

J. A.

(2005). Childhood sexual abuse, relationship satisfaction, and sexual risk taking in a community sample of women. Journal of Consulting and Clinical Psychology, 73, 1116–1124. doi:10.1037/0022-006X.73.6.1116

45.

Thibodeau

Lavoie

Hébert

Blais

(2017). Childhood maltreatment and adolescent sexual risk behaviors: Unique, cumulative and interactive effects. Child Abuse & Neglect, 72, 411–420. doi:10.1016/j.chiabu.2017.09.002

46.

Turchik

J. A.

(2012). Sexual victimization among male college students: Assault severity, sexual functioning, and health risk behaviors. Psychology of Men & Masculinity, 13, 243–255. doi:10.1037/a0024605

47.

Turchik

J. A.

Garske

J. P.

(2009). Measurement of sexual risk taking among college students. Archives of Sexual Behavior, 38, 936–948. doi:10.1007/s10508-008-9388-z

48.

Turchik

J. A.

Hassija

C. M.

(2014). Female sexual victimization among college students: Assault severity, health risk behaviors, and sexual functioning. Journal of Interpersonal Violence, 29, 2439–2457. doi:10.1177/0886260513520230

49.

Turner

H. A.

Finklehor

Ormrod

(2010). Poly-victimization in a national sample of children and youth. American Journal of Preventive Medicine, 38, 323–330. doi:10.1016/j.amepre.2009.11.012

50.

Turner

H. A.

Finkelhor

Shattuck

Hamby

(2012). Recent victimization exposure and suicidal ideation in adolescents. Archives of Pediatric and Adolescent Medicine, 166, 1149–1154. doi:10.1001/archpediatrics.2012.1549

51.

Voisin

D. R.

Neilands

T. B.

(2010). Community violence and health risk factors among adolescents among adolescents on Chicago’s Southside: Does gender matter? Journal of Adolescent Health, 46, 600–602. doi:10.1016/j.jadohealth.2009.11.213

52.

Wilson

H. W.

Widom

C. S.

(2008). An examination of risky sexual behavior and HIV among victims of child abuse and neglect: A thirty-year follow-up. Health Psychology, 27, 49–158. doi:10.1037/0278-6133.27.2.149

53.

Wyatt

G. E.

Myers

H. F.

Williams

J. K.

Kitchen

C. R.

Loeb

Carmona

J. V.

… Presley

(2002). Does a history of trauma contribute to HIV risk for women of color? Implications for prevention and policy. American Journal of Public Health, 92, 660–665. doi:10.2105/AJPH.92.4.660