Past Year Technology-Involved Peer Harassment Victimization and Recent Depressive Symptoms and Suicide Ideation Among a National Sample of Youth

Abstract

This article aims to better understand the complex role of technology in peer victimization events with recent depressive symptomatology and suicide ideation (SI). Telephone interviews were conducted with a national sample of 791 youth in the United States, aged 10 to 20 years, collected from December 2013 to March 2014. Rates of any peer harassment victimization varied by past month depressive symptomatology and SI —28% of youth with no/low depressive symptomatology reported past year peer harassment as did 43% of youth with high depressive symptomatology without SI, and 66% of youth with SI. When examining the role of technology in peer harassment, youth experiencing any mixed harassment (i.e., those incidents that occurred both in-person and through technology) were almost 4 times more likely to report past month depressive symptoms without SI (RR_adj = 3.9, 95% confidence interval [CI] = [1.5, 10.0], p ≤ .01) and 7.5 times (95% CI = [1.9, 28.9], p ≤ .01) more likely to report past month SI compared with youth who had no past year peer harassment. Given the multilayered relationships among these variables, schools, medical, and mental health professionals might screen youth who are involved in higher risk peer victimization situations, for depressive symptoms and SI to improve their access to appropriate mental health services.

Keywords

peer harassment suicide ideation depression technology cyberbullying youth

Public and professional concern about peer harassment victimization has increased dramatically over the past two decades. Numerous studies have documented its high prevalence and detrimental effects on adolescent social, psychological, and physical health (Blake, Lund, Zhou, Kwok, & Benz, 2012; Rose, Simpson, & Moss, 2015; Son et al., 2014). With the increase in youth communication online (Lenhart, 2015; Lenhart, Smith, Anderson, Duggan, & Perrin, 2015), technology-based harassment victimization or cyberbullying has also become an increasing concern for parents and schools. Research measuring youth experiences with both online harassment and parallel forms of in-person harassment have found that harassment occurs more often in-person than through technology (Robers, Zhang, & Truman, 2010; Wang, Iannotti, & Nansel, 2009; Ybarra, Mitchell, & Espelage, 2012) and that a substantial number of youth experience both kinds of harassment either separately or as part of the same incident (Kowalski & Limber, 2013; Mitchell, Jones, Turner, Shattuck, & Wolak, 2016).

Indeed, there has been particular anxiety around peer victimization that involves technology (often called online or cyberbullying) such that schools, law enforcement, and parents are scrambling to educate youth and establish policies with limited research to guide them. Research suggests that concerns may be warranted; youth reporting harassment that involves technology are more likely to report a range of concurrent problems, including poor caregiver–child relationships (Mitchell, Finkelhor, & Wolak, 2007; Ybarra, Diener-West, & Leaf, 2007; Ybarra, Espelage, & Mitchell, 2007), aggressive behavior (Ybarra & Mitchell, 2007), delinquency (Ybarra & Mitchell, 2004b), substance use (Ybarra, Diener-West, & Leaf, 2007; Ybarra & Mitchell, 2004a, 2004b), poor school indicators (e.g., detention and suspension, skipping school; Ybarra, Diener-West, & Leaf, 2007), and depression (Gámez-Guadix, Orue, Smith, & Calvete, 2013) compared to youth with this experience.

Peer Victimization and Thoughts of Suicide

Between 2007 and 2014, suicide moved from the fourth to second leading cause of death among 10- to 14-year-olds and from the third to second leading cause of death among 15- to 24-year-olds, where it currently remains (National Center for Health Statistics & National Vital Statistics System, 2005-2014). Suicidal ideation (SI) is a precursor to suicidal behavior, making it an important intervention target (Birmaher et al., 2004; Herba, Ferdinand, van der Ende, & Verhulst, 2007; Kerr, 2008; Lewinsohn, Rohde, & Seeley, 1994; Reinherz, Tanner, Berger, Beardslee, & Fitzmaurice, 2006). Adding to this public health problem are media stories focusing on teens who have committed suicide with cyberbullying victimization histories. Researchers have sought to understand more about the relationships between the two events with emergent literature suggesting peer victimization, and cyberbullying in particular, to be one in a series of complex stressors which interacts to increase one’s likelihood of SI and attempts (Bauman, Toomey, & Walker, 2013; Hinduja & Patchin, 2010; Litwiller & Brausch, 2013; Schneider, O’Donnell, Stueve, & Coulter, 2012; van Geel, Vedder, & Tanilon, 2014). A limitation of this research is that only limited data have explored the nature of technology involvement in the incident (i.e., did the cyberbullying occur only through technology or was it a component of victimization that was also occurring in-person). This distinction is important given evidence that online harassment often overlaps with traditional face-to-face victimizations (Cassidy, Faucher, & Jackson, 2013; Mitchell, Finkelhor, Wolak, Ybarra, & Turner, 2011) and that peer harassment incidents that occur only online, only in-person, and those that include both online and in-person components (“mixed” episodes) each differ in characteristics and outcomes (Mitchell et al., 2016).

The current study examines the role of technology in peer harassment events to determine whether harassment that involves technology is related to an increased risk for SI and depressive symptomatology compared with harassment that only occurs in-person as well as non-harassed youth.

Method

Participants

The Technology Harassment and Victimization Survey (THV) used telephone survey methodology, drawing the sample from a subset of households that completed a previous survey, the Second National Survey of Children’s Exposure to Violence (NatSCEV 2) conducted in 2011-2012 (Finkelhor, Turner, Shattuck, & Hamby, 2013). The eligible sample pool consisted of 2,127 youth between the ages of 10 and 20 at the time of the THV recruitment. The THV survey data were collected from December 2013 to March 2014. A total of 791 youth interviews for the THV were completed (36% response rate), acceptable for national random-digit dial surveys (Babbie, 2012; Keeter, Kennedy, Dimock, Best, & Craighill, 2006; Kohut, Keeter, Doherty, Dimock, & Christian, 2012) and for a methodology that involved interviewing youth. The average time for a completed survey was 58 min. Youth respondents who completed the survey were sent a US$25 check.

Procedure

After a brief caregiver survey, interviewers obtained consent from the caregiver and assent from the focal child to proceed to the child portion of the interview. All procedures were authorized by the Institutional Review Board of the University of New Hampshire and complied with the confidentiality guidelines set forth by the U.S. Department of Justice. More details on the study methodology are published elsewhere (Mitchell et al., 2016).

Measures

Suicide Ideation and Depression

Suicide ideation (SI) and depression were assessed using the Trauma Symptom Checklist (Briere, 1996) for youth 10 and older. Youth were asked to indicate how often they have experienced each symptom within the last month. Depression was calculated by summing the depression items minus the one SI item. Given high skewness of the data in favor of lower rates of depressive symptomatology, high depression was indicated for those youth with scores one standard deviation above the mean or higher. One item captured SI: “In the last month how often have you been wanting to kill yourself?” Any positive response was coded as yes (1) versus never (0).

Peer Harassment

Youth were asked whether they had any past year experience of harassment committed by any nonfamily member. Specific types of harassment that the youth were questioned about included (a) kids calling them mean names, making fun of them, or teasing them in a hurtful way, (b) kids excluding or ignoring them or getting others to turn against them, (c) kids spreading false rumors about them or sharing something that was meant to be private (such as something they wrote or a private picture or video of them), and (d) kids hitting, kicking, pushing, shoving, or threatening to hurt them.

When a youth had experienced any such harassment in the past year, the interviewer followed a protocol to have the youth identify up to two unique incidents for detailed follow-up questioning. Follow-up questions gathered detailed information about the perpetrator, involvement of technology, and impact of a particular incident. The following hierarchy for selecting incidents was used: (a) At least two unrelated technology-involved harassment events: details were gathered about both; (b) One technology-involved harassment event and one nontechnology-involved harassment event: details were gathered on both; (c) No technology-involved events but one or more unrelated harassment events that did not involve technology: details were gathered on up to two of those events.

For the purposes of this study, we were interested in child-level experiences so we created three unique variables so we could examine the effects of differing levels of technology involvement in the peer harassment incident: (a) any in-person-only harassment (i.e., no technology involved), b) any technology-only harassment (i.e., no in-person element), and c) any mixed harassment (i.e., involved both in-person and technology elements). These classifications were developed and published in a prior article from the same study (Mitchell et al., 2016). Of the 791 youth who participated in the THV survey, 230 (unweighted) or 34% (weighted) had experienced at least one incident in the past year (Mitchell et al., 2016). Data were collected on 311 unique incidents for these 230 youth.

Statistical Analysis

First we compare youth characteristics across three groups, youth with (a) no/low depressive symptomatology, (b) high depressive symptomatology but no SI, and (c) any SI using cross-tabulations reporting overall Design-based F statistics (see Table 1). Table 2 depicts the percentage of youth reporting any peer harassment and peer harassment type (mixed, technology only, in-person only) by report of depressive symptomatology and thoughts of suicide. This is followed with a multinomial logistic regression depicting the relative odds of high depressive symptoms (vs. none/low depressive symptoms) and SI (vs. none/low depressive symptoms) given peer harassment experience while adjusting for youth demographic characteristics. Sample weights adjusted for differential attrition for the THV survey. Data were analyzed in 2016.

Table 1.

Child Demographic Characteristics by Depressive Symptomatology and SI.

Characteristic	No/Low Depressive Symptoms^a (n = 528)% (n)	Youth With High Depression Symptoms^a (no SI)(n = 219)% (n)	Youth With Thoughts of Suicide(n = 44)% (n)	Design-BasedF
Youth age (years)
10-12	35 (127)	19 (28)	11 (3)	2.5*
13-15	25 (147)	25 (62)	15 (11)
16-17	21 (117)	29 (72)	42 (20)
18-20	19 (137)	27 (57)	32 (10)
Youth sex**
Boy	55 (304)	36 (75)	46 (18)	4.3**
Girl	45 (224)	64 (144)	54 (26)
Race/ethnicity
White, non-Hispanic	58 (393)	59 (165)	69 (36)	1.0
Black, non-Hispanic	13 (59)	12 (24)	15 (5)
Other race, non-Hispanic	6 (32)	12 (12)	12 (2)
Hispanic, any race	23 (44)	17 (18)	4 (1)
Family structure
Two biological parents	53 (362)	54 (154)	47 (28)	0.6
Single parent	33 (98)	34 (37)	49 (13)
Parent and step-parent	9 (42)	7 (20)	2 (2)
Other adult	4 (26)	4 (8)	1 (1)
Socioeconomic status
Low	22 (88)	24 (32)	28 (7)	1.5
Medium	64 (323)	53 (132)	64 (30)
High	13 (117)	23 (55)	8 (7)

Note. Socioeconomic status is a composite based on the sum of the standardized household income and standardized parental education (highest) scores, which was then restandardized. SI = suicide ideation.

Calculated minus thoughts of suicide.

p ≤ .05. **p ≤ .01.

Table 2.

Rates and Relative Odds of Past Month SI and High Depressive Symptomatology Given Past Year Peer Harassment (N = 791).

Construct	% (n)			RR_adj (95% CI)^a
Construct	No/Low Depressive Symptoms^b (n = 528)	Youth With High Depression Symptoms^b (no SI)(n = 219)	Youth With Thoughts of Suicide(n = 44)	High Depressive Symptoms^b (no SI)	SI
Any peer harassment	28 (113)	43 (90)	66 (27)	2.3 [1.3, 4.2]**	5.7 [2.1, 15.8]***
By technology involvement
None (Reference)	72 (415)	57 (129)	34 (17)	1.0	1.0
Any mixed	7 (38)	22 (43)	30 (15)	3.9 [1.5, 10.0]**	7.5 [1.9, 28.9]**
Any technology only	3 (15)	3 (12)	9 (3)	0.9 [0.3, 2.6]	4.2 [0.7, 23.5]^†
In-person only	17 (60)	17 (35)	27 (9)	1.9 [0.9, 4.0]^†	5.2 [1.3, 20.8]*

Note. SI = suicide ideation; RR_adj = adjusted relative risk; CI = confidence interval.

Adjusted for youth age, sex, race and ethnicity, socioeconomic status, and family structure.

Calculated minus thoughts of suicide.

†

p ≤ .10. *p ≤ .05. **p ≤ .01. ***p ≤ .001.

Results

Five percent of youth (n = 44) reported past month thoughts of suicide, 27% (n = 219) reported high depressive symptoms without SI, and 68% (n = 528) reported neither. Among the youth reporting thoughts of suicide, age differences were noted: 11% were aged 10 to 12 years, 15% were aged 13 to 15 years, 42% were aged 16 to 17 years, and 32% were aged 18 to 20 years (Table 1). Differences were also noted by sex: among youth with high depression without SI, there were more girls (64%) compared with boys (36%); 54% of youth who had thoughts of suicide were girls and 46% were boys. No differences were noted based on race and ethnicity, family structure, or socioeconomic status (SES).

Rates of any peer harassment victimization varied by past month depressive symptomatology and SI—28% of youth with no/low depressive symptomatology reported past year peer harassment as did 43% of youth with high depressive symptomatology without SI, and 66% of youth with SI (Table 2). Adjusting for demographic characteristics, youth with peer harassment victimization were over 2 times more likely to report past month depressive symptomatology without SI compared with youth with no/low depressive symptoms and almost 6 times more likely to report past month SI (Table 2). When examining the role of technology in peer harassment, youth experiencing any mixed harassment (i.e., those incidents that occurred both in-person and through technology) were almost 4 times more likely to report past month depressive symptoms without SI and 7.5 times more likely to report past month SI compared with youth who had no past year peer harassment. Youth with any past year in-person-only harassment were over 5 times more likely to report SI compared with youth reporting no past year peer harassment. No significant differences were noted for youth who experienced technology-only peer harassment.

Discussion

Findings reveal that the relationship between technology-involved harassment and SI and depression symptoms is not straightforward. Results extend findings described in a previous article by the authors that found these mixed technology and in-person incidents were more predictive of incident-level emotional distress, even controlling for a range of other aggravating factors (e.g., injury, bias language; Mitchell et al., 2016). Indeed, such mixed episodes were also related to increased odds of SI and high depressive symptoms in the current analysis. Overall, our data indicate that mixed harassment incidents appear to be representative of higher risk situations, both in terms of the youth who are involved as victims or perpetrators, and the impact of the incidents on youth. However, no direct relationship was found between harassment that only occurred through technology and high depressive symptoms or SI. This suggests that the studies examined in the meta-analysis (van Geel et al., 2014) finding a stronger relationship between cyberbullying and SI than for in-person bullying did not differentiate between more (i.e., involving in-person aggression as well) and less complex negative online harassment behaviors across environments. Our data indicate this is a critical distinction. Any academic or media-based discussion or commentary about the link between SI and online bullying should not suggest that the link between peer victimization and SI is simplistic. Indeed, as has been noted in previous studies (Beautrais, 2000; Cash & Bridge, 2009; Centers for Disease Control and Prevention, 2012; Foley, Goldston, Costello, & Angold, 2006; Kloos, Collins, Weller, & Weller, 2007), SI is a complex issue that is predicted by a multitude of factors. Our study finds that there is a unique influence of some more intensive types of peer victimization involving both in-person and technological elements that are related to higher rates of depressive symptoms and SI.

As expected, differences in reports of high depressive symptoms and thoughts of suicide were noted for sex and age in the current study. Indeed, age and sex are known to be critical factors in suicide: deaths by suicide increase dramatically in the late teens (Gould, Greenberg, Velting, & Shaffer, 2003). Suicide attempts also increase in frequency through adolescence, peaking between 16 to 18 years of age (Kessler, Borges, & Walters, 1999). Rates of SI and attempts are higher among girls, while rates of suicide are higher among boys (Child Trends Databank, 2016; Curtin, Warner, & Hedegaard, 2016; Mulye et al., 2009). Our findings confirm these data and also highlight the critical importance of conducting research about suicide attempts and thoughts of suicide directly with adolescents to capture a time when these behaviors are generally emerging. This, combined with the knowledge that adolescents are more likely to seek and receive help from their friends rather than other sources, suggests that talking to adolescents directly might be an opportunity for future suicide prevention and intervention efforts as well (Berger, Hasking, & Martin, 2017; Evans, Hawton, & Rodham, 2005; Fortune, Sinclair, & Hawton, 2008; Michelmore & Hindley, 2012).

Study Limitations

The main focus of the study was on describing technology-involved harassment so such incidents are slightly overrepresented; the limits on follow-up questions led to some undercounting of nontechnology-involved harassment incidents. Conservative estimates suggest that this only impacted a minority of incidents however; a total of 3.5% of youth (n = 22) reported two incidents that involved technology and at least one harassment incident that did not involve technology; therefore, these nontechnology incidents were not captured in our estimates. Youth responses may have been influenced by social desirability. Some findings may be influenced by unmeasured dimensions, such as a greater willingness among some respondents to disclose personal experiences. Finally, the data are crosssectional so directionality of the relationships between peer harassment and thoughts of suicide cannot be determined.

Implications

Certainly, as our understanding of suicide continues to grow, the influence that technology may be having on behavior needs to also continue to evolve. Literature suggests that websites, chat groups, and other online spaces that encourage and educate youth about how to injure and kill themselves (Alao, Soderberg, Pohl, & Alao, 2006; Fortune & Hawton, 2005; Murray & Fox, 2006; Tam, Tang, & Fernando, 2007; Whitlock, Powers, & Eckenrode, 2006) are easily accessible. At the same time, others have noted the potential for the Internet to provide heath information and social support for at-risk youth (Becker & Schmidt, 2005; Hoffmann, 2006; Murray & Fox, 2006; Prasad & Owens, 2001; Whitlock et al., 2006). Even within the context of existing literature, there are significant gaps in our understanding of how places and social experiences online affect suicide as well as intersect with more traditional, offline social influences. For example, more information is needed about whether exposure to websites which encourage suicide increases the probability of suicidal behavior before we can consider their risks in more than just an exploratory way. As such, the risks and opportunities of this technological environment are of urgent need for exploration to help develop prevention and intervention strategies.

Conclusion

Findings reinforce the complexity of the relationship between thoughts of suicide, peer victimization, and technology. How the online social community fits into the larger public health perspective on suicide has yet to be determined but crucial to our understanding of this phenomenon given the large numbers of youth who have integrated new technology into their daily lives.

Footnotes

Authors’ Note

Points of view or opinions in this presentation are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice or Digital Trust Foundation.

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 project was supported by Grant No. 2012-IJ-CX-0024 awarded by the National Institute of Justice with supplemental funding by the Digital Trust Foundation for all authors. No authors have any financial conflicts of interest.

ORCID iDs

Kimberly J. Mitchell

Heather A. Turner

Author Biographies

Kimberly Mitchell, PhD, is a research associate professor of psychology at the Crimes Against Children Research Center. She has specific expertise in research methodology and children’s exposure to violence. In her 17 years of experience, she has conducted numerous national youth surveys on topics such as victimization online and in-person and the health impact of such experiences. Her work has resulted in over 85 peer-reviewed publications which have made significant contributions to policy and practice.

Lisa M. Jones, PhD, is a research associate professor of psychology at the Crimes Against Children Research Center. She has over 15 years of experience conducting research on child victimization, including epidemiological studies, measurement development, and evaluations of prevention and intervention efforts. She has published and presented extensively on child victimization. She served as a coinvestigator on the Technology-Based Harassment Victimization Study and is currently principal investigator on a study of youth bias victimization.

Heather A. Turner, PhD is a professor of sociology at the University of New Hampshire. Her research has concentrated on the effects of violence, victimization, and other forms of adversity on the mental health of children and adolescents. She has over 15 years of research experience on childhood exposure to violence, has conducted numerous national surveys, and has published over 80 articles. She is principal investigator for National Surveys of Children’s Exposure to Violence.

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