Abstract
The current study examined the 3-week prospective associations between cyber-victimization and both depressive symptoms and rumination. In addition, a mediation model was tested, wherein rumination mediated the association between cyber-victimization and depressive symptoms. Participants (N = 565 college-age young adults) completed online surveys at two time points 3 weeks apart. Results indicated that cyber-victimization was associated with increases in both depressive symptoms and rumination over time. Furthermore, results of the path analysis indicated that cyber-victimization was associated with increases in rumination over time, which were then associated with greater depressive symptoms, providing support for the proposed mediation effect for women, but not men. Findings extend previous correlational findings by demonstrating that cyber-victimization is associated with increases in symptomatology over time. Findings also suggest that the negative consequences of cyber-victimization extend beyond mental health problems to maladaptive emotion regulation. In fact, rumination may be a mechanism through which cyber-victimization influences mental health problems, at least for women. Mental health professionals are encouraged to assess cyber-victimization as part of standard victimization assessments and to consider targeting maladaptive emotion regulation in addition to mental health problems in clients who have experienced cyber-victimization.
Recent research on interpersonal victimization has begun to focus on victimization that occurs using the Internet or cell phones (referred to as cyber-victimization 1 ), which can include experiences such as being harassed or threatened online or via text messaging. Given that research on cyber-victimization is in its infancy, studies have conceptualized cyber-victimization in a variety of ways, including victimization that occurs using electronic mail, instant messaging, or text messaging, and using a variety of assessment measures (for a review of cyber-victimization assessment measures, see Berne et al., 2013). Thus, data on the prevalence of cyber-victimization are limited, with estimates ranging from 4% to 39% of youth (Kowalski & Limber, 2007; Mishna, Cook, Gadalla, Daciuk, & Solomon, 2010; Mitchell, Finkelhor, Wolak, Ybarra, & Turner, 2011; Wang, Iannotti, & Nansel, 2009; Ybarra & Mitchell, 2004; Ybarra, Mitchell, & Korchmaros, 2011) and 9% to 43% of college-age young adults (Finn, 2004; Kraft & Wang, 2010; Lindsay & Krysik, 2012; Reyns, Henson, & Fisher, 2012) endorsing cyber-victimization depending on the sample, time frame, and measure. Consistent with research on the association between in-person victimization and mental health problems (Arseneault, Bowes, & Shakoor, 2010; Ttofi, Farrington, Lösel, & Loeber, 2011), the few existing studies on cyber-victimization have found that it is also associated with negative consequences, such as depressive symptoms (Hunt, Peters, & Rapee, 2012; Mitchell, Ybarra, & Finkelhor, 2007; Perren, Dooley, Shaw, & Cross, 2010; Sontag, Clemans, Graber, & Lyndon, 2011; Wang, Tonja, & Iannotti, 2011; Ybarra, 2004).
Despite accumulating evidence supporting associations between cyber-victimization and depressive symptoms, all existing studies are cross-sectional, which precludes understanding of whether cyber-victimization is associated with increases in symptomatology over time. As such, we used a prospective design to test whether these correlational findings translate into significant increases in symptomatology over time. In addition, existing research has focused on psychological consequences of cyber-victimization, but it is likely that consequences extend beyond mental health problems. For instance, research on in-person victimization found that peer victimization is associated with maladaptive emotion regulation, such as rumination (Barchia & Bussey, 2010; Erdur-Baker, 2009), which is the tendency to repetitively focus on distress and its possible causes and consequences (Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). Thus, in addition to examining depressive symptoms as an outcome, we also examined rumination. If cyber-victimization is associated with rumination, then it may be useful for interventions to focus on increasing adaptive emotion regulation as well as reducing symptomatology among individuals who experience cyber-victimization.
In addition to examining the associations between cyber-victimization and its consequences, it is important to understand the mechanisms that may account for these associations. One possible factor that may account for the association between cyber-victimization and depressive symptoms is rumination, given that rumination has consistently been linked to depression (e.g., Aldao, Nolen-Hoeksema, & Schweizer, 2010) and rumination has been found to mediate the associations between other types of victimization and depressive symptoms, including emotional abuse (Raes & Hermans, 2008), sexual abuse (Conway, Mendelson, Giannopoulos, Csank, & Holm, 2004), and peer victimization (Barchia & Bussey, 2010). Although there are no published studies that examine mediators of the association between cyber-victimization and depressive symptoms, research on different aspects of Internet use can shed light on this topic. Recent studies have demonstrated that rumination mediates the associations between negative status updates on Facebook and depressive symptoms (Locatelli, Kluwe, & Bryant, 2012) as well as negative social comparison on Facebook and depressive symptoms (Feinstein et al., 2013). Consistent with the broader victimization literature, these studies provide support for the notion that rumination may be a mechanism through which negative experiences on the Internet influence mental health problems. Given that cyber-victimization may be associated with rumination, and that rumination has consistently been linked to depressive symptoms, it is likely that rumination may act as a mechanism through which cyber-victimization increases depressive symptoms.
In sum, the current study examined the short-term prospective associations between cyber-victimization and both depressive symptoms and rumination. In addition, we examined the extent to which rumination mediated the association between cyber-victimization and depressive symptoms. We hypothesized that cyber-victimization would be associated with increases in depressive symptoms and rumination over time, and that the association between cyber-victimization and depressive symptoms would be mediated by increases in rumination. We also examined potential gender differences, but we did not make specific predictions given the exploratory nature of these analyses. In addition, although most studies on cyber-victimization focused on pre- and early adolescents (Hunt et al., 2012; Mitchell et al., 2007; Perren et al., 2010; Wang et al., 2011; Ybarra, 2004), the current study focused on college-age individuals. As noted, cyber-victimization has been documented among a sizable proportion of young adults (Finn, 2004; Kraft & Wang, 2010; Lindsay & Krysik, 2012; Reyns et al., 2012), underscoring the importance of examining this phenomenon in this population. Furthermore, young adults use social networking sites (Lenhart, Purcell, Smith, & Zickuhr, 2010) and text messaging (Smith, 2011) at high rates and have greater access to these mediums than adolescents due to decreased parental control and increased Internet connectivity via cell phones (Lenhart et al., 2010). Thus, young adults may be particularly likely to experience cyber-victimization and its negative consequences.
Methods
Participants and Procedure
Six hundred twenty undergraduate students (64.7% female) participated in a larger project on technology use and well-being in exchange for course credit. Participants were recruited from introductory and upper-level psychology courses. Data were combined from two samples collected over two semesters at a large, public university in the northeastern United States. Participants had to be at least18 years old and the mean age was 19.55 years (SD = 2.18; range = 18-42). The sample was racially/ethnically diverse (41% Caucasian, 40% Asian/Pacific Islander, 6% Latino/a, 5% African American/Black, 3% Middle Easterner, and 4% Other). The institutional review board at the affiliated university approved this research.
Participants completed an initial online survey (Time 1 [T1]) assessing cyber-victimization, emotion regulation, and mental health, and an identical survey 3 weeks later (Time 2 [T2]). Of the 620 participants in the combined data set who completed the T1 survey, 565 completed the T2 survey (91.1% retention rate). Analyses were run on those who participated at both time points. We compared the two samples at T1 to see if there were significant differences in demographics or any of the variables using chi-square tests (for gender, ethnicity, and cyber-victimization) and independent-samples t tests (for age, depressive symptoms, and rumination); there were no significant differences (smallest p = .14). In the combined data set, we examined whether there were significant differences in demographics or any of the variables between those who participated at T1 only and those who participated at T1 and T2; no significant differences emerged (smallest p = .22).
Measures
Cyber-victimization
Cyber-victimization was assessed using a modified version of the Internet Harassment Experiences questionnaire (Ybarra, 2004). The measure consists of two questions assessing online victimization and we added two modified questions assessing text victimization. Victimization status for the past year was assessed with two yes/no questions each for online and text victimization: (a) whether the participant had felt worried or threatened due to someone bothering or harassing him or her while online (or via text messaging), and (b) whether the participant felt threatened or embarrassed due to someone posting or sending messages for others to see online (or via text messaging). We followed Ybarra’s scoring methodology to create dichotomous variables, such that participants who answered “yes” to any of the four questions were classified as victims and participants who answered “no” to all questions were classified as non-victims. Thus, online and text victimization were combined into a single variable, consistent with previous research (Hunt et al., 2012; Perren et al., 2010; Wang et al., 2011).
Depressive symptoms
Depressive symptoms were assessed using the depression subscale of the short version of the Depression, Anxiety, and Stress Scale (DASS; Lovibond & Lovibond, 1995), which includes seven items that assess dysphoric affect. Participants indicated how applicable items were to them during the past week using a 4-point scale (0 = did not apply to me at all, 3 = applied to me very much, or most of the time). Example items include “I felt down-hearted and blue” and “I was unable to become enthusiastic about anything.” Consistent with standard scoring procedures, scores were computed by summing responses to the items and doubling the scores. Total scores could range from 0 to 42, with higher scores indicating greater depressive symptoms. Excellent internal consistency and adequate validity have been reported (e.g., Antony, Bieling, Cox, Enns, & Swinson, 1998; Henry & Crawford, 2005). The current sample’s alpha score was .91 at T1 and .92 at T2.
Rumination
Rumination was assessed using the 22-item Ruminative Responses Scale (RRS; Nolen-Hoeksema & Morrow, 1991), which examines responses to depressed mood that are self-focused, symptom focused, and focused on the possible causes and consequences of depressed mood. Participants were presented with a series of items and asked to indicate how often they think or do each one when they feel down, sad, or depressed. Example items include “Think about how sad I feel” and “Analyze recent events to try to understand why you are depressed.” Participants responded to items using a 4-point scale (1 = almost never, 4 = almost always) and responses were summed to calculate total scores, with higher scores indicating greater rumination. Previous research has demonstrated acceptable validity, internal consistency, and test–retest reliability (e.g., Butler & Nolen-Hoeksema, 1994; Nolen-Hoeksema & Davis, 1999; Nolen-Hoeksema & Morrow, 1991). The current sample’s alpha score was .95 at T1 and .96 at T2.
Results
Means and standard deviations for depressive symptoms and rumination at T1 and T2 for the whole sample and as a function of cyber-victimization status are presented in Table 1. In total, 31.2% of the sample endorsed past-year victimization (16.0% online victimization only, 3.7% text victimization only, and 11.5% both online and text victimization). There were significant associations between depressive symptoms and rumination at both time points (rs = .51 and .52 at T1 and T2, respectively; ps < .001), such that individuals who reported higher depressive symptoms also reported engaging in greater levels of rumination. In addition, depressive symptoms and rumination at T1 were significantly associated with their respective values at T2 (.67 and .65 for depressive symptoms and rumination, respectively; ps < .001). We examined potential gender differences in cyber-victimization, depressive symptoms, and rumination using a chi-square test (for cyber-victimization) and independent-sample t tests (for depressive symptoms and rumination). Women were more significantly more likely than men to endorse cyber-victimization, χ2(1) = 10.87, p< .001 (36.3% and 23.0% for women and men, respectively). In addition, men reported significantly higher depressive symptoms at T2 than women, t(429.04) = −2.20, p = .03 (M = 8.81 and 7.14 for men and women, respectively). In contrast, there were not significant gender differences in depressive symptoms at T1 or rumination at either time point (smallest p = .24).
Means and Standard Deviations for Depressive Symptoms and Rumination at Time 1 and Time 2 for the Entire Sample and as a Function of Cyber-Victimization Status(N = 565).
p< .05. **p< .01. ***p< .001 (two-tailed).
To examine whether there were significant differences in depressive symptoms and rumination between victims and non-victims, we conducted a series of independent-sample t tests (see Table 1). At both time points, victims reported significantly higher depressive symptoms and rumination compared with non-victims. Next, to examine whether cyber-victimization was associated with increases in depressive symptoms and rumination over time, we conducted a series of regression analyses (see Table 2). The two regression analyses included cyber-victimization at T1 predicting either depressive symptoms or rumination at T2 controlling for that variable at T1. Consistent with hypotheses, cyber-victimization was significantly associated with increases in depressive symptoms and rumination at T2.
Linear Regression Analyses Predicting Depressive Symptoms and Rumination at T2 From Cyber-Victimization Status at T1 (N = 565).
Note. T1 = Time 1; T2 = Time 2.
p< .05. **p< .01. ***p< .001 (two-tailed).
To examine the proposed mediation model (see Figure 1), we used path analysis with measured variables in IBM SPSS Amos Version 20, which has the advantage of simultaneously testing the associations among multiple predictor and outcome variables. The proposed model hypothesized that cyber-victimization at T1 would lead to increases in depressive symptoms at T2 through increases in rumination at T2. To account for the prospective design, depressive symptoms and rumination at T1 were controlled for. Bootstrapping analyses were used to estimate bias-corrected confidence intervals to test the significance of the hypothesized indirect effect of cyber-victimization on depressive symptoms mediated through rumination (cf. MacKinnon, Lockwood, & Williams, 2004). Missing data were handled by imputing a participant’s mean score on a measure in place of a missing value on one of the measure’s items; less than 1% of the data from the final sample was missing. Fit criteria are not applicable in this analysis, because the proposed model is saturated and saturated models demonstrate perfect fit.
Path model depicting the significant indirect effect of cyber-victimization on increases in depressive symptoms through increases in rumination.
The model demonstrated that cyber-victimization was significantly associated with increases in rumination, which, in turn, were significantly associated with depressive symptoms (see Figure 1). Bootstrapping analyses supported the significance of the indirect effect of cyber-victimization on increases in depressive symptoms through increases in rumination, β = .02, bias-corrected 90% confidence interval (CI) = [.01, .04], SE = .01. The direct effect of cyber-victimization on increases in depressive symptoms was not significant in the path analysis. However, as noted, cyber-victimization was significantly associated with increases in depressive symptoms over time in the prospective analysis when the mediator (rumination) was not included. Together, these findings provide support for the hypothesized mediation effect.
In addition, we conducted the path analysis separately for women and men to test potential gender differences in the proposed model. Results for women were consistent with results for the entire sample. Cyber-victimization was significantly associated with increases in rumination (standardized path coefficient = .08, p = .04), which were significantly association with depressive symptoms (standardized path coefficient = .36, p< .001). The indirect effect of cyber-victimization on increases in depressive symptoms through increases in rumination was also significant for women, β = .03, bias-corrected 90% CI = [.01, .06], SE = .02. In contrast, cyber-victimization was not significantly associated with increases in rumination for men (standardized path coefficient = .04, p = .46), although increases in rumination were still associated with increases in depressive symptoms (standardized path coefficient = .22, p< .001). The indirect effect was not significant for men, β = .01, bias-corrected 90% CI = [−.01, .04], SE = .01. In sum, although we found support for the hypothesized mediation effect in the entire sample, additional analyses revealed that this effect was significant for women, but not men.
Discussion
The current study extends previous research on cyber-victimization by using a short-term prospective design to examine the extent to which cyber-victimization was associated with changes in depressive symptoms and rumination in a sample of college-age young adults. Furthermore, to better understand the mechanism underlying the association between cyber-victimization and depressive symptoms, we tested the hypothesis that rumination would mediate the association. We found that women were more likely to endorse cyber-victimization than men, which is consistent with some previous research that demonstrated a higher rate of cyber-victimization among girls compared with boys (e.g., Kowalski & Limber, 2007). However, other research has demonstrated that the gender difference in cyber-victimization depends on the type of cyber-victimization (e.g., Mishna et al., 2010). Consistent with previous research (Hunt et al., 2012; Mitchell et al., 2007; Perren et al., 2010; Sontag et al., 2011; Wang et al., 2011; Ybarra, 2004), correlational analyses indicated that cyber-victimization was associated with greater depressive symptoms. In addition, cyber-victimization was associated with increases in depressive symptoms over time. Given that this is the first prospective study to examine the consequences of cyber-victimization, it will be important for future research to replicate these findings. We also found that cyber-victimization was associated with greater rumination in cross-sectional analyses as well as increases in rumination over time, suggesting that the association between in-person victimization and rumination (Barchia & Bussey, 2010; Erdur-Baker, 2009) extends to cyber-victimization. Cyber-victimization appears to increase the extent to which individuals respond to depressed moods with repetitive focus on their distress and its possible causes and consequences, which is significant given that rumination can maintain and exacerbate distress (e.g., Mennin, Holoway, Fresco, Moore, & Heimberg, 2007; Nolen-Hoeksema et al., 2008).
Findings from the path analysis indicated that cyber-victimization predicted increases in rumination, which, in turn, were associated with greater depressive symptoms. The significant indirect effect of cyber-victimization on depressive symptoms through rumination coupled with the non-significant direct effect with the mediator in the model provides preliminary support for the possibility that rumination does indeed mediate the association in question. This is consistent with two recent studies which found that rumination also mediated the associations between negative status updates on Facebook and depressive symptoms (Locatelli et al., 2012) and negative social comparison on Facebook and depressive symptoms (Feinstein et al., 2013). These findings suggest that rumination about one’s cyber-victimization experiences may maintain and exacerbate distress. This notion is consistent with theories that suggest that rumination may maintain and exacerbate depression by decreasing the extent to which individuals engage in more adaptive emotion regulation strategies (e.g., problem solving, behavioral activation; Nolen-Hoeksema et al., 2008). Together, these findings support the notion that rumination may play a mechanistic role in the associations between a variety of negative Internet experiences and depressive symptoms, as similar mediation effects have been demonstrated across studies with different methodological features.
However, tests of gender differences revealed that the aforementioned mediation effect was significant for women, but not men. The non-significant mediation for men was driven by cyber-victimization not being significantly associated with increases in rumination over time for them. Although speculative, it is possible that cyber-victimization is associated with increases in rumination for women, but not men, because women tend to place more emphasis on interpersonal experiences than men. Mezulis, Abramson, and Hyde (2002) examined gender differences in rumination in response to different types of events (e.g., achievement events, interpersonal events) and found that although women reported higher rumination in all domains, effects were larger for interpersonal events compared with achievement events. Thus, in the current study, women may have been more likely than men to ruminate in response to cyber-victimization. This possibility is consistent with findings that women experience heightened reactivity to interpersonal stress compared to men (e.g., Cyranowski, Frank, Young, & Shear, 2000). However, given that we did not directly assess rumination in response to cyber-victimization, it will be important for future research to examine this possibility.
These findings have important implications for cyber-victimization prevention and intervention efforts, which tend to focus on pre- and early adolescents. It is important to acknowledge that this problem occurs among young adults as well, as approximately 31% of our young-adult sample endorsed cyber-victimization within the past year. This may be an underestimate of the true prevalence rate, given that cyber-victimization was assessed with four yes/no questions; future research, particularly epidemiological studies, should continue to examine the prevalence of cyber-victimization among young adults. The lack of attention to cyber-victimization among young adults may lead victims to feel alone and ashamed, as though there is something wrong with them that would lead them to be victimized at this age. Simply acknowledging the fact that cyber-victimization can and does occur among young adults may decrease feelings of isolation and shame as well as increase support seeking in this population. In addition, given that cyber-victimization is associated with increases in depressive symptoms over time, mental health professionals should consider including cyber-victimization as part of standard victimization assessments. Furthermore, research has demonstrated that youth who endorsed cyber-victimization were 1.9 times more likely to have attempted suicide than non-victims (Hinduja & Patchin, 2010), underscoring the importance of assessing for this type of victimization. The present findings also suggest that individuals who experience cyber-victimization may be more likely than non-victims to engage in maladaptive emotion regulation strategies, which may then maintain and exacerbate their distress. Victims of cyber-victimization may benefit from therapeutic techniques that focus on increasing adaptive emotion regulation in addition to decreasing symptomatology.
Although this study had several strengths, there are limitations that should be considered. All measures relied on self-report, rendering findings vulnerable to method variance problems. In addition, cyber-victimization was assessed with four questions that specified the past year as the time frame, limiting our understanding of the specific details of the victimization. Future research should assess cyber-victimization more thoroughly, including its recency, frequency, and severity, as well as who the perpetrator was, the victim’s emotional responses and perceptions of its causes, and whether or not others intervened. Finally, the time interval between assessments was relatively short, so it will be important for findings to be replicated in studies that use longer term prospective designs. Despite limitations, the current findings provide support for the negative mental health and emotion regulation consequences of cyber-victimization among young adults.
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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is based upon work supported by a National Science Foundation Graduate Research Fellowship awarded to Brian A. Feinstein (Grant No. 1315232). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.