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. Author manuscript; available in PMC: 2017 Jan 1.
Published in final edited form as: Psychol Violence. 2015 Jun 8;6(1):156–162. doi: 10.1037/a0039442

An Examination of the Partner Cyber Abuse Questionnaire in a College Student Sample

Caitlin Wolford-Clevenger 1, Heather Zapor 1, Hope Brasfield 1, Jeniimarie Febres 1, JoAnna Elmquist 1, Meagan Brem 1, Ryan C Shorey 2, Gregory L Stuart 1
PMCID: PMC4801339  NIHMSID: NIHMS692298  PMID: 27014498

Abstract

Objective

To examine the factor structure and convergent validity of a newly developed measure of an understudied form of partner abuse, cyber abuse, and to examine the prevalence of, and gender differences in, victimization by cyber abuse.

Method

College students in a dating relationship (N = 502) completed the Partner Cyber Abuse Questionnaire (Hamby, 2013), as well as measures of partner abuse victimization and depression.

Results

Using exploratory factor analysis, we determined a one-factor solution was the most statistically and conceptually best fitting model. The cyber abuse victimization factor was correlated with depressive symptoms and physical, psychological, and sexual partner abuse victimization, supporting the convergent validity of the measure. The overall prevalence of victimization by cyber abuse was 40%, with victimization by specific acts ranging from 2–31%. Men and women did not differ in their victimization by cyber abuse.

Conclusions

Cyber abuse is prevalent among college students and occurs concurrently with other partner abuse forms and depressive symptoms. Given the interrelated nature of partner abuse forms, prevention and intervention programs should address partner abuse occurring in-person and through technology. Cyber abuse should also be considered in the conceptualization and measurement of partner abuse to more fully understand this social problem.

Keywords: partner abuse, domestic violence, cyber abuse, electronic aggression, college students, prevalence, gender differences, validity

Partner abuse, defined as physical, psychological, and/or sexual violence against an intimate partner, is a significant social problem, affecting up to 28% of men and 35% of women in their lifetime in the United States (Black et al., 2011). Partner abuse victims suffer a number of physical, psychological, and social consequences even after cessation of the abuse (Banyard, Potter, & Turner, 2011; Campbell, 2002; Coker et al., 2002; Coker, Smith, Bethea, King, & McKeown, 2000; Iverson et al., 2012). Despite the high prevalence rates and serious consequences of partner abuse, efforts to improve and expand on the definition and measurement of this social problem have remained relatively stagnant. Only recently have there been efforts to advance our conceptualization of partner abuse by considering additional modalities in which it manifests, such as cyber abuse. The purpose of the current study is to examine the factor structure and convergent validity of a newly developed measure of an understudied form of partner abuse, cyber abuse, and to examine the prevalence of, and gender differences in, victimization by cyber abuse.

Cyber abuse is defined as harassing, threatening, monitoring, impersonating, humiliating, or verbally abusing one’s current partner through the use of technology, such as cell phones, social media, or electronic mail (Belknap, Chu, & DePrince, 2012; Melander, 2010; Southworth, Finn, Dawson, Fraser, & Tucker, 2007; Sugarman & Willoughby, 2013). Cyber abuse has also been referred to as electronic aggression, technological aggression, cyber aggression, cyber harassment, and online harassment. We use the term “cyber abuse” as cyber reflects the breadth of technological means (e.g., cell phones, social media, electronic mail, and online accounts), and abuse reflects the range of harmful actions perpetrated through these means (e.g., threats, surveillance, harassment, and degradation).

The evolution of technology in recent years has increased the modalities in which individuals can abuse their partners (Sugarman & Willoughby, 2013), and this increase has largely outpaced research examining the potential impact of abuse by these new modalities. For example, some evidence suggests that cyber abuse is related to detrimental relationship outcomes such as psychological and physical aggression occurring offline (Brem, Spiller, & Vandehey, 2014; Schnurr, Mahatmya, & Basche, 2013). Despite the potential risks of victimization by cyber abuse, the empirical study of cyber abuse remains limited. Thus, there is a clear need for continued research on cyber abuse, which will broaden our understanding of partner abuse and may identify targetable areas for intervention and prevention programs.

The small body of research on cyber abuse has been conducted in adolescent, college student, and help-seeking women samples and has found the prevalence of cyber abuse to be comparable to other forms of partner abuse, ranging from approximately 4–92% (Belknap et al., 2012; Bennett, Guran, Ramos, & Margolin, 2011; Brem et al., 2014; Darvell, Walsh, & White, 2011; Finn, 2004; Jones, Mitchell, & Finkelhor, 2013; Kellerman, Margolin, Borofsky, Baucom, & Iturralde, 2013; Kennedy & Taylor, 2010; Korchmaros, Ybarra, Langhinrichsen-Rohling, Boyd, & Lenhart, 2013; Kraft & Wang, 2010; Spitzberg & Hoobler, 2002; Zweig, Dank, Yahner, & Lachman, 2013). The wide range in cyber abuse rates may be due to limited measure development and sampling differences. That is, cyber abuse may be a multidimensional construct with factors varying in severity and prevalence. For example, Leisring and Giumetti (2014) found that 93% of college students both perpetrated and experienced minor cyber abuse (e.g., swearing at or insulting partner) and 12–13% perpetrated and experienced severe cyber abuse (e.g., threats, public humiliation). Similarly, in a national sample of adolescents, cyber abuse involving verbal abuse (e.g., name-calling) was more commonly experienced (25%) than cyber abuse involving threats (10%) (Picard, 2007). Finally, the wide range in prevalence of cyber abuse may also be due to sampling differences. For example, higher rates have been found among college student and help-seeking samples compared to adolescent samples (Hamby, 2013; Leisring & Giumetti, 2014; Picard, 2007).

The limited research examining gender differences in victimization by cyber abuse has not yielded a clear pattern. In a college student sample, men and women did not differ in victimization by cyber abuse (Finn, 2004). However, this study examined cyber abuse experienced from partners, strangers, and acquaintances, therefore limiting what conclusions can be drawn regarding gender differences in victimization by cyber abuse specific to dating relationships. Among those in current intimate relationships, adolescent girls reported greater victimization by cyber abuse and sexual cyber abuse than boys (Zweig et al., 2013). Additional studies on college students demonstrated that men reported greater electronic aggression victimization than women in four categories: humiliation (e.g., posting embarrassing pictures), hostility (e.g., verbal abuse and threats), intrusiveness (e.g., monitoring behaviors), and exclusion (e.g., blocking their partner) (Bennett et al., 2011; Kellerman et al., 2013). However, the measure used in these studies has not been validated. Using the only known validated measure of cyber abuse among partners, Leisring and Giumetti (2014) found that men reported greater victimization by severe cyber abuse, but not minor cyber abuse, compared to women. Therefore, the equivocal findings on the prevalence and gender differences in cyber abuse may be due to the range in severity and use of unvalidated measures of cyber abuse.

The use of reliable and valid measures is imperative to advance our understanding of the prevalence of, and gender differences in, cyber abuse among partners. The Partner Cyber Abuse Questionnaire (PCAQ; Hamby, 2013) is one of the few available measures developed to assess various cyber abusive behaviors among partners (see Leisring & Giumetti, 2014, for another measure of cyber abuse). The PCAQ assesses cyber abusive acts including harassment, monitoring, humiliation, and verbal abuse through technology, such as cell phones, social media, or electronic mail. The PCAQ, a 9-item self-report measure, was developed using a focus group of college students and has preliminary support for its use in help-seeking women (Hamby, 2013). That is, the PCAQ has demonstrated a one-factor solution, correlations with other forms of partner abuse, partners’ alcohol use, and partners’ infidelity (Hamby, 2013). Although this measure was developed with the help of college students, it has yet to be validated among a college student sample. In addition, the convergent validity of the PCAQ with other constructs expected to correlate with victimization by cyber abuse, such as depressive symptoms, has yet to be examined. Examination of the factor structure and convergent validity of this measure, especially in a college student sample, is needed to support its use and to improve our measurement of cyber abuse.

In summary, to understand and ultimately reduce partner abuse, a wider range of abusive acts, such as cyber abuse, needs to be accurately measured and understood. Therefore, the purpose of this study was to advance the literature on the prevalence, gender differences, and measure development of cyber abuse. The specific aims and hypotheses of the current study were as follows:

  1. To explore the factor structure of a newly developed measure of cyber abuse, the PCAQ, in a college student sample. We made no a priori hypotheses about the underlying factor structure of the PCAQ, as the factor structure has only been examined in a shelter-seeking sample of women (Hamby, 2013).

  2. To examine the convergent validity of the PCAQ. We hypothesized that the PCAQ would exhibit positive correlations with depressive symptoms and partner abuse victimization (physical assault, psychological aggression, sexual coercion, and injury). Regarding the strength of these correlations, we expected cyber abuse to exhibit moderate correlations with depressive symptoms and high correlations with other forms of partner abuse, with the highest correlation being with psychological aggression.

  3. To examine the prevalence of, and gender differences in, victimization by cyber abuse. We made no a priori predictions about the prevalence of or gender differences in cyber abuse due to the inconsistent findings reported in the literature.

Method

Participants

Data were collected from 502 undergraduate students who voluntarily participated in an online study in exchange for research credit in their psychology course. Only students who were in a current romantic relationship were recruited for the study. The mean age of the sample was 18.80 years (SD = 1.93) and the majority was female (65.7%). The academic classifications of the participants were as follows: freshmen (76.1%), sophomores (14.1%), juniors (6.6%), seniors (3.0%), and post-baccalaureate students (0.2%). The racial/ethnic composition of the sample was White/Caucasian (81.2%), Black/African American (9.1%), Asian American (3.5%), Hispanic/Latino (2.1%), Indian/Middle Eastern (1.7%), Native American (0.8%), and “Other” (1.6%). The participants’ family income was as follows: less than $50,000 (23.4%), $50,000–$100,000 (32.3%), $100,000–$150,000 (20.6%), $150,000–$200,000 (12.4%), and greater than $200,000 (11.2%). A majority of the sample reported being in a relationship with an opposite-sex partner (95.6%). Most of the sample reported that they were in a dating relationship (93.5%) and not living with their intimate partner (93.6%). The average relationship length was 14.37 months (SD = 14.55).

Procedures

The study procedures were approved by the Institutional Review Board of the first author. Participants completed informed consent and a battery of self-report measures through the university’s online survey system and were given partial course credit for their participation. Due to the sensitive nature of the items related to abuse within the survey, the last page of the survey displayed mental health resources including contact information for the first author in the event that distress resulted from the study.

Measures

Demographic Questionnaire

Demographic characteristics including age, gender, racial/ethnic identity, academic level, and relationship characteristics were collected.

Partner Cyber Abuse Questionnaire (PCAQ; Hamby, 2013)

The PCAQ was used to measure victimization of partner abuse perpetrated via technology over the past year. The PCAQ contains nine items scored on a 6-point Likert scale ranging from 0 (“never”) to 5 (“five or more times”). The total score is computed from the sum of the participants’ responses to all nine items. Participants were instructed to answer each question in regard to their current romantic relationship. Example items include “my partner sent angry or insulting text messages to me” and “my partner checked or read my emails or texts without my permission.” The internal consistency of the PCAQ in the current sample was acceptable (α = .72).

Revised Conflict Tactics Scales (CTS2; Straus, Hamby, Boney-McCoy, & Sugarman, 1996; Straus, Hamby, & Warren, 2003)

The CTS2 was used to measure partner abuse victimization experienced by the participant in the past twelve months within their current relationship. The CTS2 is a 78-item measure that assesses the frequency 0 (“never”) to 7 (“more than twenty times”) of use and experience of negotiation, physical assault, psychological abuse, sexual coercion, and injury. The score of each subscale is computed as the sum of the midpoints of the participants’ scores on each subscale item (Straus et al., 1996). The current study utilized the victimization subscales of physical assault, psychological abuse, sexual coercion, and injury. Past research using samples of court-mandated males and undergraduates supported the CTS2 as having good internal consistency, test-retest reliability, and construct and discriminant validity (Straus, 2004; Straus et al., 1996; Vega & O’Leary, 2007). The internal consistencies for each victimization subscale in the current study were as follows: physical assault victimization (α = .79), psychological abuse victimization (α = .63), sexual coercion (α = .60), and injury (α = .61). These coefficients are consistent with past work using the CTS2 in college students (e.g., Shorey et al., 2011).

Center for Epidemiologic Studies-Depression Scale (CES-D; Radloff, 1977)

The CES-D is a 20-item measure that assesses depressive symptoms over the past week on a 4-point Likert scale ranging from 0 (“rarely or none of the time”) to 3 (“most or all of the time”). After reverse-scored items are recoded, the total score is computed as the sum of the scores on the twenty items. The CES-D has been shown to have test-retest reliability, internal consistency, and construct validity (Radloff, 1977). The internal consistency of the CES-D has been shown to be good in prior college student samples (e.g., Herman et al., 2011), as well as in the current sample (α = .89).

Results

Exploratory Factor Analysis of the Partner Cyber Abuse Questionnaire

The first step in our data analysis was to explore the factor structure of the PCAQ. We chose to utilize exploratory factor analysis (EFA), as there is not substantial prior empirical investigation or theoretical articulation of this construct to warrant a confirmatory approach (Fabrigar, Wegener, MacCallum, & Strahan, 1999). Prior to conducting the EFA, we identified how many factors to extract by employing comparison data (Ruscio & Roche, 2012). Analysis of comparison data determines how many factors to extract in EFAs by comparing the observed data to several generated datasets with known factor structures (Ruscio & Roche, 2012). Comparison data has been found to be superior to traditional methods such as the Kaiser criterion and scree test, as well as parallel analysis, in determining the number of factors (Courtney, 2013). The comparison data approach improves upon parallel analysis by accounting for both sampling error and the multivariate distribution of the data (Courtney, 2013). Comparison data analysis was conducted using a custom dialogue, R-Menu 2.2.1, of SPSS. As recommended by Ruscio & Roche (2012), the analysis was conducted with the following settings: the analysis was performed until nonsignificant improvement; the population sizes for comparison data were set to 10,000; 500 samples were drawn from each population; and an alpha level of .30 was set for determining improvement with the addition of a factor. Subsequent to determining the number of factors to extract, we conducted the EFA in Mplus version 6.12, using maximum likelihood estimation as the model fitting procedure and geomin (oblique) as the rotation method (Muthén & Muthén, 2007).

Results from comparison data specified a one-factor solution (Root Mean Square Residual [RMSR] Eigenvalue = .13). The one-factor model also provided conceptual and parsimonious fit. Therefore, one factor was specified to be extracted in the EFA. Each item met the factor loading threshold (.30), except for the second item, which had a factor loading of .27 (See Table 2, for factor loadings). However, given that the factor loading closely approached the cut-off and that the removal of the second item did not improve the reliability of the factor (a change from α = .72 to .71), the item was retained in the factor.

Table 2.

Factor Loadings and Prevalence of the Cyber Abuse Questionnaire Items by Gender

Factor
Loading		 Total
Prevalence		 Female Prevalence Male
Prevalence
1. My partner sent messages from my Facebook profile without my permission. .50 2.7%
n = 12		 2.4%
n = 7		 3.3%
n = 5
2. My partner wrote something negative about me on social media such as Facebook or Twitter when he was angry. .27 5.5%
n = 24		 5.2%
n = 15		 6.0%
n = 9
3. My partner sent angry or insulting text messages to me. .53 30.7%
n = 135		 29.0%
n = 84		 34%
n = 51
4. My partner forwarded embarrassing online or text messages or pictures about me. .42 3.0%
n = 13		 2.8%
n = 8		 3.4%
n = 5
5. My partner changed my password so I couldn’t access my (or our) accounts, such as bank or credit card accounts. .35 1.1%
n = 5		 1.7%
n = 5		 0%
n = 0
6. My partner checked or read my emails or texts without my permission. .66 19.7%
n = 87		 19.6%
n = 57		 19.9%
n = 30
7. My partner monitored my profile or used phone applications as a way to keep tabs on me. .56 12.4%
n = 55		 10.7%
n = 31		 15.9%
n = 24
8. My partner sent me frequent emails or texts when he knew I didn’t want them. .52 8.8%
n = 39		 10.7%
n = 31		 5.3%
n = 8
9. My partner checked up on my location by getting me to send cell phone pictures of where I was. .60 4.8%
n = 21		 5.2%
n = 15		 4.0%
n = 6
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Convergent Validity

The second step in the data analysis was to test the convergent validity of the PCAQ. To test our hypothesis that the PCAQ would exhibit moderate, positive correlations with depressive symptoms and high, positive correlations with partner abuse victimization, we computed correlation coefficients between the PCAQ score and the scores of the CES-D and the psychological aggression, physical assault, sexual coercion, and injury subscales of the CTS2. Given the positive skew of the abuse variables, the coefficients were computed using log transformed variables, as well as the original variables, which did not differ; therefore, the correlation coefficients for the original variables are presented. Our hypotheses were partially supported with the cyber abuse scale exhibiting positive correlations with the CTS2 abuse victimization and depressive symptom scales. However, cyber abuse exhibited only low correlations with depressive symptoms, physical assault, sexual coercion, and injuries and moderate correlation with psychological aggression (See Table 1 for the r values and p-values).

Table 1.

Bivariate Correlations and Descriptive Statistics for the PCAQ and Study Measures

1 2 3 4 5 6
1. Cyber Abuse Victimization - - - - - -
2. Physical Victimization .20*** - - - - -
3. Psychological Victimization .45*** .42*** - - - -
4. Sexual Coercion .11* .29*** .35*** - - -
5. Injury Victimization .12** .44*** .19*** .19*** - -
6. Depressive Symptoms .13** .15** .21** .13* .08 -
Mean (total sample) 10.85 2.13 7.18 2.88 .51 29.43
SD (total sample) 3.52 9.32 13.13 9.34 3.20 8.57
Mean (men) 11.04 2.71 6.76 3.34 .49 28.84
SD (men) 3.90 10.96 12.69 9.66 2.92 8.51
Mean (women) 10.75 1.83 7.39 2.64 .52 29.74
SD (women) 3.30 8.34 13.37 9.17 3.34 8.60
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Note:

*

p < .05,

**

p < .01,

***

p < .001 (two-tailed).

Prevalence of and Gender Differences in Cyber Abuse

The third step in the data analysis examined the prevalence of and gender differences in victimization of cyber abuse. To examine the prevalence of cyber abusive tactics, we dichotomized participants’ responses to each of the PCAQ items and the total score of the PCAQ resulting from the factor analysis into 1 (“present”) or 0 (“absent”) and computed frequencies for each of these items. Next, we examined mean differences in total PCAQ scores and individual PCAQ items for men and women using t-tests. T-tests were conducted with log transformed variables, as well as the original variables, which did not differ; therefore, the results using the original variables are presented. Second, we tested differences in total cyber abuse prevalence and individual item prevalence between men and women using Chi Square tests.

Table 1 displays the correlations, means, and standard deviations of the PCAQ, partner abuse subscales, and depressive symptoms. Forty percent of the sample reported experiencing at least one act of partner cyber abuse over the past year. The least frequently reported act was having their partner change their password to restrict their access to online accounts (1.8%). The most frequently reported acts were their partner sent angry or insulting text messages to them (30.6%) and their partner checked their email or text messages without their permission (20.4%) (See Table 2 for prevalence of each item). T-tests indicated that men and women did not differ in the overall frequency of victimization by cyber abuse, t(483) = .87, p = .38, nor in frequency of the individual acts (ps > .05). Chi square tests revealed that men and women did not differ in overall prevalence of victimization by cyber abuse, χ2(1, N = 485) = 0.12, p = .73, nor in any of the prevalence of any of the individual acts (ps > .05).

Discussion

The current study is the first to our knowledge to examine the factor structure and convergent validity of a newly developed measure of cyber abuse, the Partner Cyber Abuse Questionnaire, in a college student sample. Additionally, this study is among the first to examine the prevalence of, and gender differences in, victimization by cyber abuse among college students. Results from the current study support the PCAQ to have a one-factor solution, as well as convergent validity. Findings also suggested that, although the prevalence of each act of cyber abuse varied greatly (2–31%), the overall prevalence of cyber abuse among this college student sample was relatively high (40%). Men and women did not differ in victimization by cyber abuse in this sample.

The one-factor solution resulting from the factor analysis is not entirely consistent with the multifaceted conceptualization of cyber abuse, including acts of harassment, threats, surveillance, impersonation, and verbal and emotional aggression via technological mediums (Belknap et al., 2012; Bennett et al., 2011; Kellerman et al., 2013; Melander, 2010; Southworth et al., 2007; Sugarman & Willoughby, 2013). This is further surprising given that a factor analysis of a similar cyber abuse measure, the Cyber Psychological Abuse Scale, resulted in a two-factor model of minor and severe cyber abuse (Leisring & Giumetti, 2014). Although the PCAQ and the Cyber Psychological Abuse Scale assess similar abusive acts (e.g., surveillance, verbal abuse, humiliation, and threats), the PCAQ has fewer items, especially pertaining to minor verbal abuse. It may be that the PCAQ assesses more controlling violence perpetrated via technology, whereas the Cyber Psychological Abuse Scale taps both minor verbal abuse and severe controlling abuse in its two-factor measure. For example, the PCAQ includes items not measured by the Cyber Psychological Abuse Scale that assess financial control and surveillance of victims’ location. Moreover, the Cyber Psychological Abuse Scale contains multiple items assessing minor verbal abuse (e.g., calling names, insulting, swearing, abruptly ending communication) across several technological mediums, whereas the PCAQ includes only one item regarding anger and insults through text messages. Given these differences, future work should expand cyber abuse measurement to include both minor and severe abusive acts across all technological mediums and determine whether cyber abuse is best conceptualized and measured as a unitary or multidimensional construct.

The current study also supports that the PCAQ measures an important construct related to, but not captured by, traditional measures of partner abuse. The PCAQ exhibited convergent validity through positive correlations with other forms of partner abuse and depressive symptoms. This finding is consistent with past work demonstrating correlations between cyber abuse and other forms of partner abuse (Leisring & Giumetti, 2014). However, the PCAQ exhibited only low to moderate correlations with depressive symptoms and other forms of partner abuse, with the strongest correlation being with psychological aggression. This suggests that cyber abuse is related to, but also unique from, other forms of IPV, including psychological aggression. Further, these results suggest that cyber abuse is a form of partner abuse that likely occurs within a pattern of multiple forms of abuse and is associated with mental health problems among victims.

The findings also demonstrated that the overall prevalence of cyber abuse (40%) was toward the lower end of the wide range of previously reported prevalence rates (4–92%; e.g., Belknap et al., 2012; Bennett et al., 2011; Darvell et al., 2011; Finn, 2004; Zweig et al., 2013). The prevalence rates of the individual acts of the PCAQ (2–31%) were more similar to the prevalence rate of severe cyber abuse (12%) than that of minor cyber abuse (93%) in Leisring & Giumetti’s study. As discussed prior, this may be due to the PCAQ assessing more severe cyber abuse acts compared to previously used measures of cyber abuse. Continual refinement of cyber abuse measures is critical to increase their generalizability to this population.

Finally, no gender differences were found in victimization by cyber abuse in the current study. This finding is consistent with some past research (Finn, 2004) but is not consistent with other research supporting gender differences in cyber abuse (Bennett et al., 2011; Kellerman et al., 2013; Leisring & Giumetti, 2014; Zweig et al., 2013). In particular, the current finding is descrepant with Leisring and Giumetti’s (2014) finding that men experience greater severe cyber abuse than women, as the PCAQ appears to asssess more severe acts of cyber abuse. This discrepant finding may be due to the PCAQ assessing financial control and surveillance behaviors that are not measured by scales used in previous studies (e.g., Bennett et al., 2011; Kellerman et al., 2013; Leisring & Giumetti, 2014). However, this lack of gender differences is consistent with studies demonstrating gender symmetry in rates of psychological and physical partner abuse victimization among college men and women. Replication is imperative before maintaining confidence in this conclusion (Archer, 2000; Langhinrichsen-Rohling, Misra, Selwyn, & Rohling, 2012; Shorey, Cornelius, & Bell, 2008).

Limitations

The current study carried several limitations. First, the cross-sectional design of this study precluded any tests of predictive validity of the PCAQ. Additionally, longitudinal data would enable tests of whether cyber abuse progresses to severe controlling behaviors, physical assault, or sexual coercion occurring offline. Second, the current study only assessed convergent validity of the PCAQ with the CTS2 and the CES-D; future work should examine its associations with additional assessments of partner abuse and related outcomes to further test its construct validity. Third, the current study did not assess participants’ perpetration of cyber abuse, which is important to examine in future work given that research suggests partner abuse is often bidirectional (Langhinrichsen-Rohling et al., 2012). Relatedly, we also did not gather collateral cyber abuse data from participants’ partners, which would test whether there is low level of agreement about the occurrence of cyber abuse similar, a problem demonstrated in other forms of partner abuse (Langhinrichsen-Rohling & Vivian, 1994). Finally, a majority of the sample identified as Caucasian; research using samples with greater racial/ethnic diversity will be informative.

Research Implications

In addition to improving upon the limitations of the current study, additional empirical and theoretical work in the cyber abuse literature is sorely needed. Researchers should conduct more qualitative research on college students’ use of technology to abuse their partners to inform what potential acts of cyber abuse current measures are missing. Relatedly, a theoretical model that attempts to explain how cyber abuse fits into the development and maintenance of partner abuse in relationships is critical to gain an understanding of this problem. For example, future work could examine the potential varying functions cyber abuse may serve in relationships. In a relationship in which abuse serves as a means to control, the monitoring and surveillance aspects of cyber abuse may be most functionally relevant. In contexts in which abuse serves as a means of emotional regulation, the verbally abusive aspects of cyber abuse may be most relevant. Kellerman et al., (2013) found that jealousy and insecurity were the most common motivations behind cyber abuse perpetration; additional work is needed to clarify the varying functions of cyber abuse. Expanding conceptual models of partner abuse to account for cyber abuse will not only facilitate theoretical coherence in our research, but will also inform what facets of cyber abuse our measurements may miss. In addition, research should expand on the impact cyber abuse has on its victims. In particular, it may be useful to identify what specific cyber abusive acts invoke the most fear or other negative emotions. Similarly, identifying cyber abusive acts that are more strongly predictive of physical injury or potentially lethal physical violence will critically inform assessment and intervention efforts.

Clinical and Policy Implications

Additional research on the measurement of, prevalence of, and gender differences in cyber abuse is needed before making clinical or policy recommendations based on these data. However, taken together with prior research, the current findings highlight the importance of cyber abuse as a facet of partner abuse that should be assessed in clinical settings. Given the present findings, clinicians may consider observations of abuse occurring via technology as suggestive of the presence of other forms of partner abuse and depressive symptoms in both college men and women. For individuals experiencing cyber abuse, clinicians should educate them about safety and privacy in the use of technology, for example, by utilizing The Technology Safety Project of the Washington State Coalition Against Domestic Violence as a model (Finn & Atkinson, 2009). The Technology Safety Project aims to bring awareness and education about technology safety to victims of partner abuse and the providers that treat them by teaching safe, private use of technology. Clinicians should also encourage individuals experiencing cyber abuse from their partners to document the abuse (e.g., screenshot a threatening text) to have concrete evidence in the event that they seek legal aid and protection from future abuse. Educational institutions should include cyber abuse in policies regarding interpersonal violence, which will help ensure that students experiencing cyber abuse will be protected and provided with resources. Moreover, programs aimed to prevent or reduce partner abuse on college campuses may wish to consider educating students on the role of technology in partner abuse.

Acknowledgments

This work was supported, in part, by grant K24AA019707 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) awarded to the last author. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.

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