Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: J Abnorm Child Psychol. 2019 Nov;47(11):1851–1862. doi: 10.1007/s10802-019-00569-8

Peer Socialization of Non-Suicidal Self-Injury in Adolescents’ Close Friendships

Rebecca A Schwartz-Mette 1, Hannah R Lawrence 1
PMCID: PMC6812616  NIHMSID: NIHMS1532171  PMID: 31209626

Abstract

Objective:

Non-suicidal self-injury (NSSI), or self-harming behavior without intent to die (Nock, 2009), is associated with distress and impairment across domains, including increased risk for suicidality (Kiekens et al., 2018). In adolescence, prevalence of NSSI is high (Swannell, Martin, Page, Hasking, & St John, 2014), and peer influence regarding NSSI is thought to be strong (Brechwald & Prinstein, 2011). Although concern regarding “clusters” of NSSI has long been documented, peer socialization of NSSI in adolescence is understudied. This paper tests peer influence on NSSI frequency within adolescent friendship dyads. Emotion regulation difficulties and friendship quality were evaluated as factors that may influence susceptibility to peer influence effects.

Method:

Adolescents (N = 196, M age = 15.68, 69.9% female, 87.6% White) nested within 93 friendship dyads reported on their own NSSI frequency, difficulties in emotion regulation, and friendship quality at three time points spaced 3 months apart.

Results:

Cross-lagged Actor-Partner Interdependence Models examined peer influence effects over time. Friends’ Time 1 frequency of NSSI uniquely predicted adolescents’ own NSSI frequency over 3 and 6 months, controlling for initial similarity among friends as well as individual risk factors for NSSI. Peer influence effects were strongest in adolescents with higher levels of emotion regulation difficulty but did not vary as a function of friendship quality.

Conclusions:

Friends’ NSSI frequency is a significant and unique predictor of increases in adolescents’ own NSSI frequency over time. Implications for interventions that leverage the important developmental context of peer relationships are discussed.

Keywords: Nonsuicidal self-injury, Peer influence, Socialization, Adolescence, Friendships

Non-suicidal self-injury (NSSI) has received increased attention in recent decades, given high prevalence rates and its association with suicidality (e.g., Kiekens et al., 2018; Muehlenkamp & Gutierrez, 2007). Adolescents are at particular risk, experiencing higher rates of NSSI than at any other period in the lifespan (Swannell, Martin, Page, Hasking, & St John, 2014). There is growing interest in interpersonal factors, such as peer influence, that impact both risk for and exacerbation of NSSI (Nock, 2009). Despite documentation of NSSI clusters (e.g., Taiminen, Kallio-Soukainen, Nokso-Koivisto, Kaljonen, & Helenius, 1998), direct tests of NSSI socialization are lacking (see Jarvi, Jackson, Swenson, & Crawford, 2013). What is more, little is known about how third factors that create risk for NSSI may impact socialization susceptibility.

Addressing limitations of past studies, the current research used a longitudinal design and both adolescents’ and friends’ reports of NSSI to test socialization in friendship dyads over three and six months. Dyadic analyses addressed the nested data and isolated socialization effects above and beyond the impact of selection. Emotion regulation and friendship quality were examined as factors that may help to explain and/or enhance NSSI socialization among friends.

Non-Suicidal Self-Injury in Adolescence

NSSI [self-harming behavior(s) without intent to die; Nock, 2009] can include cutting, scratching, hitting, burning, or inserting objects under the skin. Most individuals who self-injure use multiple methods and self-injure more than once a week (e.g., Klonsky, 2011; Nock & Mendes, 2008; Nock & Prinstein, 2004). Adolescence is a critical developmental period for NSSI, as onset typically occurs during this time (e.g., Gandhi et al., 2018). Community prevalence rates are disproportionately higher in adolescence (17.2%) than in adulthood (5.5%; Swannell et al., 2014), with higher rates in clinical settings (25.7–82%; e.g., Mäkikyrö et al., 2004; Nock & Prinstein, 2004). Adolescents who engage in NSSI have greater internalizing, externalizing, substance use, and personality pathology compared with adolescents who do not (Monto, McRee, & Deryck, 2018; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006). Adolescent NSSI also is associated with greater suicidal ideation and past and future suicide attempts (e.g., Monto et al., 2018; Muehlenkamp & Gutierrez, 2007; Nock et al., 2006).

Current theories posit that NSSI serves both intrapersonal and interpersonal functions (e.g., Nock, 2009; Nock & Prinstein, 2004). Intrapersonally, NSSI may be one way adolescents regulate negative affect through negative reinforcement processes. Interpersonally, NSSI may provide social reinforcement by intensely communicating distress, strengthening affiliations, or aggressing against others (Nock, 2008). Indeed, the most commonly reported reason for NSSI is to regulate negative emotion, and another major function is clearly social (Taylor et al., 2018). Interpersonal factors are understudied as compared to intrapersonal factors. Discussion of social influences began, and has persisted in part, due to observations of NSSI “clusters” in inpatient settings (e.g., Lofthouse & Katz, 2009; Matthews, 1968; Taiminen et al., 1998). Scholars conceptualized these groupings from the perspective of social learning theory (Nock, 2009; Whitlock, Purington, & Gershkovich, 2009; Zelkowitz, Porter, Heiman, & Cole, 2017), suggesting that individuals may observe others’ NSSI and proceed to self-injure themselves.

Studies do indicate that knowing someone who engages in NSSI is a risk factor for NSSI. A majority of adolescents or young adults who self-injure know a peer who does as well (Claes, Houben, Vandereycken, Bijttebier, & Muehlenkamp, 2010; Heath, Ross, Toste, Charlebois, & Nedecheva, 2009). Other studies report that some adolescents get the idea to engage in NSSI from a peer (e.g., Deliberto & Nock, 2008), perhaps because they talk about NSSI, share methods, encourage one another, and/or engage in NSSI together (e.g., Curtis, 2017; Fisher, Fitzgerald, & Tuffin, 2017; Heath et al., 2009; Nock, Prinstein, & Sterba, 2009).

Peer Influence in NSSI

Most studies of social influences in NSSI discuss the existence of contagion, or the “spread” of NSSI behavior from one individual to another. While precise definitions of contagion are lacking, peer influence likely encompasses more than mere exposure to another who self-injures. Peer influence involves both selection and socialization (Kandel, 1978). Selection refers to the fact that individuals tend to befriend others to whom they are similar (e.g., Mercken, Candel, Willems, & deVries, 2011). By contrast, socialization, refers to the notion that friends become more similar to one another over time, which most accurately approximates what has been described as contagion1 (Heilbron & Prinstein, 2008). Directly testing socialization requires a longitudinal design, a large sample with sufficient variability in behavior, both adolescent and peer/friend reports of behavior, and statistics that account for the interdependency of nested data and that simultaneously test selection and socialization. Many studies that reference NSSI socialization are limited in one or more of these regards (see Jarvi et al., 2013).

Four studies of adolescent NSSI have addressed some of these limitations in testing selection and/or socialization of NSSI behavior. Two examined socialization effects longitudinally but relied on adolescents’ reports of friends’ behavior. Hasking and colleagues (2013) tested whether knowing a friend who engages in NSSI predicted onset and severity of adolescents’ own NSSI over 12 months. Knowing a friend who engaged in NSSI predicted later onset (but not severity) of adolescents’ NSSI, but only under conditions of adolescents’ own high levels of stress (facilitated socialization) or past thoughts about NSSI (mitigated socialization). In Prinstein et al. (2010)’s Study 2 of female adolescent inpatients, girls’ perceptions of their friends’ NSSI behaviors were correlated with their own NSSI behavior and predicted the frequency of their own NSSI behaviors over 18 months. It is not known whether the friendships in these studies were current or reciprocal, or whether friends engaged in NSSI, precluding clear understanding of whether socialization occurred. As Heilbron and Prinstein (2008) discussed, correlations between adolescents’ behavior and perceptions of friends’ behavior can be exponentially greater than correlations between adolescents’ and friends’ actual behaviors (see alsoKandel, 1996).

Two other studies used adolescents’ and friends’ reports of NSSI. You and colleagues (2013) tested selection and socialization within adolescents’ reciprocal, school-based friendships over six months. Although no selection effect was found, friends’ NSSI status predicted adolescents’ later NSSI status (but not frequency), evidencing socialization. Study 1 of Prinstein et al. (2010) assessed NSSI in sixth- to eighth-grade adolescents’ school-based friendships over 12 months, finding support for socialization of NSSI frequency in girls and in sixth graders.

These studies have added to our understanding of NSSI socialization in adolescence, but unanswered questions remain. As adolescents have influential friendships outside of school (Ueno, 2005) and highly-distressed friends may not attend school (Freudenberg & Ruglis, 2007), studying adolescents’ closest friendships whether in or out of school is important. Further, only two studies tested for both selection and socialization (Prinstein et al., 2010 Study 2; You et al., 2013), and no studies employed dyadic data analyses or statistically isolated socialization by controlling for selection. These analytic considerations are important, as socialization estimates may be inflated when selection is not controlled (Kenny, Kashy, & Cook, 2006).

It also is unclear whether other (e.g., “third”) variables could explain peer influence. Such variables are important to include in empirical tests of socialization to ensure that effects remain after controlling for known NSSI predictors. Socialization does persist after controlling for depressive symptoms and impulsive behavior (Prinstein et al., 2010; You et al., 2013), but other risk factors remain untested. Relatedly, third variables may help explain susceptibility to socialization. Although important discussion has taken place on susceptibility (e.g., Heilbron & Prinstein, 2008), few studies have tested moderators of NSSI socialization. As noted, Hasking and colleagues (2013) found that high life stress enhanced socialization but past NSSI cognitions mitigated socialization. Prinstein and colleagues (2010) found that socialization occurred only in girls’ friendships (Study 1 and Study 2) and in younger adolescents’ friendships (Study 1).

Two unexplored areas of potential confound or susceptibility involve emotion regulation difficulties and friendship quality. Theory conceptualizes NSSI as a maladaptive emotion regulation strategy (e.g., Hasking, Whitlock, Voon, & Rose, 2017; Nock & Prinstein, 2004, 2005), and empirical evidence has amassed to support that adolescents who self-injure have high emotional reactivity and significant regulation difficulties (e.g., Nock & Mendes, 2008; Nock, Wedig, Holmberg, & Hooley, 2008; Zelkowitz et al., 2017). Yet no studies have examined whether socialization persists above and beyond risk for NSSI created by adolescents’ emotion regulation deficits. It is possible that socialization may be driven in part by emotion regulation difficulties, or, alternately, these difficulties may render adolescents particularly vulnerable to peer influence and may amplify observed NSSI socialization effects.

Research further suggests that poor quality relationships may create risk for NSSI. Most proximal triggers for NSSI are interpersonal (Whitlock, Powers, & Eckenrode, 2006), and individuals with NSSI more frequently report rejection (Nock et al., 2009), lower quality relationships (Claes et al., 2010), and less social support (Heath et al., 2009; Tatnell, Kelada, Hasking, & Martin, 2014) than individuals who do not self-injure. As such, negative friendship quality may be a risk factor that, if unaccounted for, may drive socialization effects. Negative friendship quality also could exacerbate NSSI socialization effects when they occur.

Interestingly, positive friendship quality has been studied as an amplifier for socialization of adolescent depressive symptoms. Socialization of depressive symptoms is stronger within friendships high in qualities like intimacy and caring (Schwartz-Mette & Smith, 2018; Stevens & Prinstein, 2005). Youth with high-quality friendships spend more time together, potentially offering more opportunity for influence. The idea that particularly close friendships may drive peer influence in NSSI has been used to explain the often observed gender difference in NSSI favoring females (e.g., Bresin & Schoenleber, 2015; Mäkikyrö et al., 2004; Monto et al., 2018; Sornberger, Heath, Toste, & McLouth, 2012), as girls’ friendships tend to be characterized by high levels of intimacy with and emotional reliance upon friends (see Rose & Rudolph, 2006).

The Current Study

The current study addressed limitations of past research in testing the socialization of NSSI frequency. Whether friends’ initial NSSI frequency predicted later increases in adolescents’ own NSSI frequency was considered. In a community-based, longitudinal sample of adolescent friends, both adolescents’ and friends’ reports of NSSI were obtained, and dyadic data analyses simultaneously tested selection and socialization. Adolescents chose their closest friend, allowing for testing of socialization within what they considered their most influential friendship. Additionally, the current study incorporated three assessments, allowing testing of socialization over three months (a shorter time period than has been previously studied) and six months (a timeframe comparable to some past studies of peer influence effects). It was expected that the socialization effect for NSSI frequency would emerge over both 3 and 6 month intervals.

The current study also tested the influence of relevant third variables, emotion regulation difficulties and friendship qualities, to evaluate whether known risk factors for NSSI may drive socialization or, rather, exacerbate socialization risk. It was hypothesized that adolescents’ emotion regulation difficulties would amplify socialization effects should they emerge. Regarding the friendship qualities, two possibilities were considered. On one hand, negative quality may exacerbate socialization of NSSI should it emerge above and beyond the influence of poor relationship functioning. On the other hand, positive qualities may also amplify socialization effects by creating close contexts in which adolescents are at greater vulnerability to peer influence. Gender and age differences in each of the relations also were explored.

Method

Participants

Adolescents aged 13 to 18 were recruited from the rural, surrounding communities of a mid-sized public university in New England. Recruited (target) adolescents (n = 93) participated with a same-gender friend, resulting in a total sample of 186 participants (M age = 15.68, SD = 1.49; 69.9% female) nested in 93 dyads. Participants’ racial and ethnic identities were representative of the community population: 87.6% White, 4.3% Black/African American, 3.2% Asian/Pacific Islander, 1.6% American Indian/Alaskan Native, 3.2% Hispanic or Latino(a)2.

Procedure

All procedures were reviewed and approved by the [institution redacted] Institutional Review Board. The study was advertised to community adolescents as a project aimed at understanding the impacts of friendships on mental health. Invitations were distributed via social media, public posting, and local school and community events. Inclusion criteria were participant age and the ability to identify a same-gender friend within two years of their age. Interested youth or parent(s)/guardian(s) contacted the research team for more information. Parental consent was obtained for each minor participant prior to initiating study procedures. Dyads were then scheduled to attend a lab session. Participants aged 18 years provided consent, and all minor participants provided assent. Adolescents and friends next completed self-report measures of NSSI frequency, emotion regulation difficulties, and friendship quality on computers in separate rooms. Dyads also participated in additional laboratory tasks not relevant to the current study. Three and six months after the lab visit, adolescents and friends completed follow-up self-reports online. Participants received $40 for the lab visit and $10 for each follow-up.

Missing Data and Data Imputation

Of the 186 participants who completed the Time 1 assessment, 140 (75.3%) completed Time 2, and 110 (59.1%) completed Time 3. Retention was similar to rates reported in past longitudinal studies of NSSI (see Boergers & Spirito, 2003). Representative analyses tested whether the 103 youth who completed three assessments differed from the 37 who completed only Time 1 and Time 2, the seven who completed only Time 1 and Time 3, or the 39 who completed only Time 1. One-way ANOVAs indicated that these groups did not differ with regard to Time 1 levels of NSSI [F(3, 182) = 0.57, p = .64], regulation difficulties [F(3, 181) = 1.97, p = .12], positive quality [F(3, 179) = 0.52, p = .67], or negative quality [F(3, 182) = 2.18, p = .09], A chi-square independence test indicated that groups did not differ in Time 1 reported friendship status [χ2(6) = 6.20, p = .40)]. Little’s test indicated data were missing completely at random (MCAR), χ2(117) = 103.75, p = .80. As imputing missing data is preferable to listwise or pairwise deletion when data are MCAR (Widamon, 2006), a multiple imputation procedure was used to impute missing data in Mplus, and the full sample of 186 participants was retained.

Measures

Demographics and friendship status.

At Time 1, participants reported their age, gender identity, racial and ethnic identities, and friendship status (i.e., whether the friend with whom they attended the lab session was a best friend, close friend, just a friend, or not a friend).

NSSI frequency.

Participants completed a seven-item measure assessing frequency of engagement in NSSI adapted from the measure used by Prinstein and colleagues (2008). Items at each time point assessed the past-year frequency at which participants engaged in any type of NSSI [“How often have you harmed or hurt your body on purpose (for example, cutting or burning your skin, hitting yourself, or pulling out your hair) without wanting to die?”] and four specific types of NSSI: cutting, hitting, pulling hair out, and burning one's own skin. Two items allowed participants to describe and rate the past-year frequency of other methods (e.g., “banging head”, “biting myself’, “stabbing self’). Each item was rated on a 5-point Likert scale for frequency over the past year including 0 (Never), 1 (Less than 5 times), 2 (5-10 times), 3 (1-2x times month) 4 (1-2x week) and 5 (Once a day). Participants’ NSSI scores were the highest reported past-year frequency of any type of NSSI at each time point.

Emotion regulation difficulties.

The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) is a 36-item questionnaire assessing the degree to which individuals struggle to regulate emotion (e.g., I experience my emotions as overwhelming and out of control). Although the DERS was originally developed for adults, research has established its psychometric properties for use with adolescents (e.g., Weinberg & Klonsky, 2009). Participants rated each item on a scale ranging from 1 (Almost never) to 5 (Almost always). Item scores were summed to yield a total score with higher scores reflecting greater difficulties (α = .96).

Friendship quality.

Participants completed the revised Friendship Quality Questionnaire (FQQ; Rose, 2002; revision of Parker & Asher, 1993). Twenty five items assessed negative and positive aspects of the friendship with their specific friend. Three items assessed negative friendship quality from the original FQQ. Three FQQ items also assessed each of five positive qualities (validation, help and guidance, conflict resolution, companionship and recreation, intimate exchange), and seven additional items assessed emotional closeness (Bukowski, Hoza, & Boivin, 1994; Camarena et al., 1990). Items were rated on a scale ranging from 1 (Not at all true) to 5 (Really true). A negative friendship quality score was computed by taking the mean of the responses to the three negative items (α = .86), and a positive friendship quality score was computed by taking the mean of the responses to the 22 positive items (α = .92).

Non-Independence of Data and Analytical Approach

Data from each participant were nested within dyads and thus not independent. Intraclass correlation coefficients (ICC) indicated that adolescents and friends were moderately similar with regard to NSSI (Time 1 ICC = .24, p < .0001; Time 2 ICC = .20, p < .01; Time 3 ICC = .33, p < .0001) and regulation difficulties (ICC = .20, p < .01). Friends were highly similar with regard to Time 1 negative quality (ICC = .67, p < .0001) and Time 1 positive friendship quality (ICC = .51, p < .0001). Techniques for handling the dependent data were employed. Specifically, a cross-lagged Actor-Partner Interdependence Model (APIM; Garcia, Kenny, & Ledermann, 2015; Kenny, 1996) was used to estimate influences of the target adolescent (actor effect) and friend (partner or socialization effect) on adolescents’ outcomes, while controlling for initial similarity between friends (selection). A random intercept and fixed main and interaction effects were estimated for each APIM using maximum likelihood estimation with robust standard errors in Mplus (Muthen & Muthen, 2017). All variables were measured at Level 1 (individual level), except gender identity, which was a Level 2 variable. Model fit was assessed using chi-square, root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker-Lewis index (TLI), with smaller values of chi-square and RMSEA and larger values of CFI and TLI indicating better fit (Byrne, 2010; Hu & Bentler, 1999). An adjusted Bonferroni correction of .01, based on number of tests and average correlation among parameters in each model (see Smith & Cribbie, 2013), was applied to all significance tests in the current study.

Results

Descriptive Statistics and Correlations

Adolescents reported that the friend who participated with them was a best (69.9%), close (25.8%), or good (4.3%) friend. See Table 1 for descriptive statistics and bivariate correlations among study variables. Although mean levels of NSSI frequency in the whole sample were low, a sizeable minority reported engaging in NSSI at each time point (Time 1: 27.4%, Time 2: 27%, Time 3: 28%), and moderate stability over time was observed. See Table 2 for information on frequencies by type of NSSI reported. Participants reported moderate levels of emotion regulation difficulties. As is typical in friendship studies (e.g., Chow, Ruhl, & Buhrmester, 2013), levels of negative quality were low and positive friendship quality was high. NSSI frequency at each time point was positively correlated with emotion regulation difficulties but was not correlated with positive or negative quality. Emotion regulation difficulties were associated with lower positive friendship quality but were not correlated with negative quality.

Table 1.

Descriptive Statistics and Correlations

M(SD) 1. 2. 3. 4. 5. 6.
Variable
1. Time 1 NSSI 0.51(1.02) -
2. Time 2 NSSI 0.36(0.82) 0.53**** -
3. Time 3 NSSI 0.42(0.94) 0.54**** 0.74**** -
4. Time 1 Emotion Regulation Difficulties 2.28(0.75) 0.44**** 0.39**** 0.34**** -
5. Time 1 Positive Friendship Quality 4.41(0.57) −0.07 0.00 0.07 −0.16* -
6. Time 1 Negative Friendship Quality 1.62(0.81) 0.10 0.12 0.10 0.05 −0.12 -
Open in a new tab

Notes.

*

p < .05

****

p < .0001.

Table 2.

Prevalence and Forms of NSSI Reported

% of
Participants
Reporting any
NSSI		 %
Reporting
Cutting		 %
Reporting
Hitting		 %
Reporting
Hair
Pulling		 % Reporting
Burning		 % Reporting
Other Types
Variable
1. Time 1 NSSI 27.4% 28% 32% 12% < 1% 23%
2. Time 2 NSSI 27% 35% 38% 13% < 1% 13%
3. Time 3 NSSI 28% 33% 33% 13% < 1% 7%
Open in a new tab

Notes. Percentages for form represent the proportion of participants reporting any NSSI at each time point. These percentages do not sum to 100 as some participants reported engaging in more than one form of NSSI, and some participants reported the frequency of NSSI without specifying the form.

Mean-Level Gender and Age Differences

A series of multilevel models tested whether mean levels of variables varied by gender or age3. Separate models were tested in which each variable was predicted by gender or age. The gender effect was significant in predicting Time 1 emotion regulation [Standardized Parameter Estimate (SPE) = −.43, p < .01], with girls reporting greater difficulty (M = 2.38, SD = 0.77) than boys (M = 2.07, SD = 0.64). Similarly, girls reported higher Time 1 positive quality (M = 4.26, SD = 0.48) compared to boys (M = 3.87, SD = 0.66), SPE = −.43, p < .0001. No gender differences emerged for NSSI or negative quality; no age differences emerged for any variable.

Socialization of NSSI

A cross-lagged APIM was tested to examine the influence of friends’ NSSI frequency on target adolescents’ own NSSI frequency over six months (see Figure 1). Adolescents’ Time 2 NSSI scores were predicted from their own Time 1 NSSI scores, and their Time 3 NSSI scores were predicted from their Time 2 NSSI scores (within-person stability; actor effects). Adolescents’ Time 2 and Time 3 NSSI scores also were predicted from their friends’ Time 1 and Time 2 NSSI scores, respectively (socialization; partner effects). Initial similarity between friends (covariance between adolescents’ and friends’ Time 1 NSSI; selection effect) was controlled. The model had excellent fit [χ2(14) = 11.80, p = .62, RMSEA = .00, CFI = 1.00, TLI = 1.01]. Adolescents and friends were similar with regard to Time 1 NSSI (COV = .24; p < .01; 95% CI: .13, .36). The actor effects from Time 1 NSSI to Time 2 NSSI (SPE = .45; p < .0001; 95% CI: .39, .51) and from Time 2 NSSI to Time 3 NSSI (SPE = .63; p < .0001; 95% CI: .52, .74) were significant, indicating moderate within-person stability over time. The partner effects were significant from Time 1 to Time 2 (SPE = .29; p < .0001; 95% CI: .22, .36) and from Time 2 to Time 3 (SPE = .20; p ≥ .01; 95% CI: .07, .33) over and above initial similarity and within-person stability, indicating socialization of NSSI frequency within friendship dyads over time.

Figure 1.

Figure 1.

Open in a new tab

Basic NSSI contagion model over six months. The Time 1 to Time 2 and Time 2 to Time 3 intervals were three months. **p < .01. ****p < .0001. Actor and partner effects for adolescents and friends are identical, as per the constraints of the Actor Partner Interdependence Model for indistinguishable dyads (Kenny, Kashy, & Cook, 2006).

Multiple group comparisons next tested whether the model varied by gender or age. An unconstrained model in which all parameters varied by gender or age was compared to a series of increasingly constrained models including the structural weights (structural weights constrained to be equal across gender or age), structural covariances (covariances also constrained), and structural residuals (all parameters constrained) models. The most parsimonious model that did not differ from the unconstrained model for gender was the structural residuals model [Δχ2(17) = 22.80, p = .16], indicating that no aspect of the model differed by gender. The structural weights model best fit the data for age [Δχ2(8) = 7.08, p = .53], indicating that actor and partner effects were equivalent for younger and older adolescents.

Moderation of NSSI Socialization Effects

Analyses further tested whether the socialization of NSSI frequency was driven or moderated by adolescents’ emotion regulation difficulties or by friendship qualities. These models were identical to the initial cross-lagged model except that the main effect of the moderator and the interaction between friends’ Time 1 NSSI scores and the moderator were added as predictors of adolescents’ Time 2 and Time 3 NSSI scores. Significant interactions were explored using a Johnson-Neyman procedure (see Aiken, West, & Reno, 1991). In cases of significant interaction effects, simple slopes were tested, and plots of the values of the adjusted NSSI socialization effect across continuous values of the moderator were created.

A model first tested whether socialization of NSSI frequency was driven or enhanced by adolescents’ difficulties with emotion regulation. Model fit was excellent [χ2(21) = 21.06, p = .46, RMSEA = .00, CFI = 1.00, TLI = 1.00]. Adolescents’ Time 1 NSSI (SPE = .32, p < .0001; 95% CI: .18, .45), friends’ Time 1 NSSI (SPE = .28, p < .0001; 95% CI: .18, .37), and adolescents’ Time 1 emotion regulation difficulties (SPE = .20, p < .01; 95% CI: .09, .32) each uniquely predicted adolescents’ Time 2 NSSI, controlling for friends’ similarity. The interaction of friends’ Time 1 NSSI and adolescents’ Time 1 emotion regulation difficulties also was significant (SPE = .20, p < .01; 95% CI: .08, .32). Simple slopes of the socialization effect were calculated at low (−2 SD) and high (+ 2 SD) levels of difficulty. The socialization effect was not significant at low levels (SPE = .19, p = .20; 95% CI: −.05, .43) but was significant at high levels (SPE = .78, p < .0001; 95% CI: .56, .99). See Figure 2 for the 3-month NSSI socialization effect plotted across continuous values of emotion regulation difficulties.

Figure 2.

Figure 2.

Open in a new tab

NSSI contagion effect from Time 1 to Time 2 plotted against across continuous values of adolescents’ Time 1 emotion regulation difficulties. The x-axis represents centered values of emotion regulation difficulties at Time 1. The y-axis represents the NSSI contagion effect from Time 1 to Time 2 (3 months). Top and bottom (curved) lines represent 95% confidence interval. Non-shaded area includes regions of significance.

Adolescents’ Time 2 NSSI (SPE = .59, p < .0001; 95% CI: .47, .72), friends’ Time 2 NSSI (SPE = .23, p < .01; 95% CI: .11, .35) also uniquely predicted increases in adolescents’ NSSI frequency at Time 3. However, neither the main effect of adolescents’ Time 1 emotion regulation difficulties (SPE = .05, p = .33; 95% CI: −.04, .15) nor the interaction of friends’ Time 1 NSSI and adolescents’ Time 1 emotion regulation difficulties (SPE = .08, p = .17; 95% CI: − .02, .17) was significant in predicting adolescents’ NSSI at six months. Multiple group comparisons tested whether this model varied by gender or age. The structural residuals model best fit the data for gender [Δχ2(29) = 28.48, p = .49], indicating no variability in the model by gender. The structural weights model best fit the data for age [Δχ2(14) = 22.03, p = .08], indicating that actor, partner, and moderation effects did not vary as a function of age grouping.

With regard to friendship qualities, a model next tested whether negative friendship quality drove or moderated socialization of NSSI frequency. Fit was acceptable [χ2(21) = 33.05, p = .05, RMSEA = .06, CFI = 0.96, TLI = 0.94], Actor effects (Time 1 to Time 2 SPE = .44; p < .0001; 95% CI: .34, .54; Time 2 to Time 3 SPE = .62; p < .0001; 95% CI: .50, .74) and partner effects (Time 1 to Time 2 SPE = .27; p < .0001; 95% CI: .18, .36; Time 2 to Time 3 SPE = .24; p < .01; 95% CI: .11, .37) were significant. However neither the main effects of negative friendship quality on Time 2 NSSI (SPE = .04; p = .56; 95% CI: −.07, .13) or Time 3 NSSI (SPE = .01; p = .89; 95% CI: −.09, .10), nor the interactions predicting Time 2 NSSI (SPE = .07; p = .33; 95% CI: −.05, .20) or Time 3 NSSI (SPE = .06; p = .22; 95% CI: −.02, .14) were significant. The structural residuals model best fit the data for gender [Δχ2(29) = 33.09, p = .27], and the structural weights model best fit the data for age [Δχ2(16) = 25.34, p = .06]

A model then tested whether positive quality drove or moderated NSSI socialization. Model fit was acceptable [χ2(21) = 35.48, p = .03, RMSEA = .06, CFI = 0.95, TLI = 0.93]. Similar to the negative quality model, partner effects (Time 1 to Time 2 SPE = .31; p < .0001; 95% CI: .21, .40; Time 2 to Time 3 SPE = .24; p < .01; 95% CI: .11, .36) were significant, controlling for actor effects (Time 1 to Time 2 SPE = .43; p < .0001; 95% CI: .33, .54; Time 2 to Time 3 SPE = .63; p < .0001; 95% CI: .52, .75). However, positive friendship quality did not impact adolescents’ Time 2 NSSI (SPE = −.02; p = .66; 95% CI: −.11, 06) or Time 3 NSSI (SPE = .06; p = .07; 95% CI: −.01, .12), and the interaction was not significant in predicting Time 2 NSSI (SPE = −.02; p = .79; 95% CI: −.13, .10) or Time 3 NSSI (SPE = −.03; p = .41; 95% CI: − .07, .02). Regarding group comparisons, the structural weights model best fit the data for gender [Δχ2(16) = 15.10, p = .52] and age [Δχ2(16) = 17.05, p = .38]. Taken together, results suggest that NSSI socialization persists over and above, but does not differ as a function of, friendship quality, and these socialization effects were not moderated by gender or by age.

Discussion

The current research finds empirical evidence for socialization of NSSI frequency within adolescent friendship dyads. This work contributes to the existing literature on NSSI contagion in adolescence by offering new data from a study designed to extend the efforts of past studies. Specifically, the study utilized a longitudinal design, adolescents’ and friends’ reports of NSSI frequency, and a community sample that included variability in NSSI behaviors and that enabled youths’ participation with their closest same-gender friend. Additionally, the current study employed dyadic data analyses, accounting for the interdependency in the nested data and allowing for isolation of socialization effects above and beyond selection effects.

The frequency of friends’ initial NSSI behaviors significantly predicted increased frequency of adolescents’ own NSSI behavior across time. This effect was moderate in size and held after controlling for the observed selection effect, which suggested that friends are moderately similar to one another with regard to initial levels of NSSI frequency, and the strong within-person stability of adolescents’ NSSI frequency over time. Only one past study of NSSI socialization utilized both adolescents’ and friends’ reports of NSSI behavior (You et al., 2013). Interestingly, the selection and socialization effects were stronger in the current study than in the previous investigation (2013). As youth in the current study were able to select among all friends, as opposed to from only school-based friends, it may be the case that peer influence effects are amplified in youths’ closest relationships. These results suggest that friends’ influence in the exacerbation of NSSI over time is present and an important area of empirical inquiry.

Also noteworthy is that socialization was present after only three months and continued across six months. No past studies had examined socialization in friendships in time periods shorter than six months. That socialization was present over such a relatively short period of time has implications for our understanding of the potency of peer influence, namely that peer influence effects may be quick to onset and may not wane with time. Future research examining the very beginning stages of friendship formation and following dyads over time may allow for more precise isolation of the window of time in which initial socialization occurs and better estimates of the strength of selection effects. In light of findings from inpatient samples in which youth previously unknown to one another were thought to quickly experience socialization (e.g., Ghaziuddin et al., 1992; Taiminen et al., 1998), it may be a concemingly rapid process.

Another major contribution of the current study is the examination of known individual risk factors for NSSI in the context of socialization effects. Few studies to date had examined potential third variables that may drive socialization (Prinstein et al., 2010; You et al., 2013) or, alternately, those that may moderate risk for socialization (Hasking et al., 2013; Prinstein et al., 2010). The current study tested three such variables that had direct theoretical relevance to NSSI risk and that previously not been examined in the context of NSSI contagion: emotion regulation difficulties, negative friendship quality, and positive friendship quality.

Emotion regulation difficulties are known to predict NSSI, with a primary reason for self-injury being to regulate negative affect. The significant bivariate association between emotion regulation deficits and NSSI in the current study underscores this notion. Current data also suggest, however, that taking into account both intrapersonal vulnerabilities, such as difficulties in emotion regulation, and peer influence may better illuminate pathways to NSSI risk. Specifically, the socialization effect persisted significantly above the influence of adolescents’ own deficits in emotion regulation, and effects were amplified for adolescents with higher levels of emotion regulation difficulties. The combination of adolescents’ trouble navigating negative affect and their proximity to friends who engage in NSSI to cope may reflect an especially attractive pathway to increased NSSI behavior. Future research that clarifies the specific nature of adolescents’ emotion regulation difficulties (e.g., Hasking et al., 2017; Madjar, Segal, Eger, & Shoval, 2019) may provide an even more nuanced picture of vulnerability to peer socialization.

With regard to friendship quality, socialization effects persisted over and above the influence of negative friendship quality and did not vary as a function of the level of negative quality within the friendship. Although interpersonal conflict and stress are known NSSI risk factors, it could be that friendships in the current study were not particularly conflictual, given that adolescents voluntarily participated with their closest friends. For youth in very low quality relationships or for youth without close friends, however, interpersonal problems may be stronger predictors of increased NSSI over time and may operate outside of contagion effects.

Socialization also persisted despite the level of positive quality in friendships. Specifically, positive quality, neither on its own nor in concert with friends’ initial NSSI frequency, impacted increases in adolescents’ NSSI frequency over time. As such, having a high quality friendship does not appear to protect adolescents against experiencing socialization or impact the strength of socialization when it occurs, underscoring the notion that NSSI socialization could occur in friendships of high and low quality. This result is in contrast to past studies of depression socialization suggesting that high quality friendships are a context in which peer socialization is more likely to occur (Schwartz-Mette & Smith, 2018). Given that corumination, the excessive discussion of problems and focus on negative affect, is one mechanism of depression socialization (Schwartz-Mette & Rose, 2012), depressive symptoms may be socialized more readily in this highly intimate context. Socialization of NSSI, on the other hand, may not require intense emotional closeness and may more simply reflect adolescents trying new coping behaviors that appear effective for their friends.

It was somewhat surprising that few gender differences emerged. Similar to past research, females reported higher levels of emotion regulation problems (Neumann, van Lier, Gratz, & Koot, 2011) and positive friendship quality (see Rose & Rudolph, 2006) than did males. Although some past studies found a gender difference in NSSI favoring females, this finding does not always emerge (see Bresin & Schoenleber, 2015 for meta-analysis) and also was not observed here. Context may play a role, as observed gender differences in NSSI tend to be larger in clinical settings than in community settings (Bresin & Schoenleber, 2015). Future research should test whether gender differences may depend, in part, on the context of investigation and population studied. What is more, no gender or age differences in NSSI socialization or moderation of socialization were found. Given that only one past study examined gender and age differences in NSSI socialization (Prinstein et al., 2010) and that the current sample size was smaller, this is an important direction for further inquiry. Research with larger samples may illuminate gender- or age-specific vulnerabilities to socialization, if they exist.

Although the current study adds to our understanding of NSSI socialization within adolescents’ friendships, its limitations point to important future directions. The NSSI measure used in the current study was a brief snapshot of NSSI frequency. Use of more extensive measures of NSSI (e.g., SITBI; Nock, Holmberg, Photos, & Michel, 2007) could enable testing of whether NSSI socialization processes differ as a function of nuanced characteristics of the behavior. The current sample also was not diverse with regard to gender, racial, or ethnic identities. It is not known whether adolescent NSSI differs as a function of these identities, and future studies with more diverse samples would be better able to address these questions.

The current study did not directly test mechanisms of NSSI contagion, and it will be important for future research to elucidate the mechanisms by which NSSI is socialized within adolescent friendships. Previous studies suggest that individuals may hear or talk about NSSI with friends and even may engage in NSSI together with friends (Glenn & Klonsky, 2009; Heath et al., 2009; Zelkowitz et al., 2017), but no studies to date speak directly to the nature of specific mechanisms. Clarifying mechanisms of NSSI socialization will enable the confirmation and/or extension of existing recommendations (e.g., Richardson, Surmitis, & Hyldahl, 2012) for impeding socialization in those in close contact with self-injuring adolescents.

It may be that existing intrapersonal vulnerability creates a fertile context in which the idea of NSSI is planted. Like any shared idea or activity among friends, NSSI may bring friends together in a non-judgmental context in which adolescents feel less shame or guilt regarding their NSSI behavior, which then may reinforce NSSI in both individuals over time. Given its short-term effectiveness as an emotion regulation strategy (Nock, 2009), it may not take much more than the aforementioned sequence of events to socialize NSSI. Yet it remains to be seen whether more active interpersonal processes characteristic of dyadic friendships, such as co-rumination (Rose, 2002), which is implicated in depression socialization (Schwartz-Mette & Rose, 2012; Schwartz-Mette & Smith, 2018), may reflect other ways in which NSSI is socialized. What is more, it is not known whether socialization of NSSI occurs outside of dyadic friendships and across adolescents’ broader peer groups. Studies assessing youths’ larger social networks may illuminate differential pathways for peer socialization depending on the context.

Future research with larger samples could allow for more in-depth analysis of subgroups, such as how peer influence impacts the onset of NSSI behavior in those without NSSI history or history of other mental illness. Such studies could further employ latent class analysis to study peer influence processes in those adolescents who experience onset, increase in, decrease in, or remittance of NSSI over time. Additionally, existing research on peer socialization has tested only linear trajectories, and studies with larger samples and more frequent assessment could test for the possibility of nonlinear trajectories in peer influence (e.g., using growth curve modeling). Future research also could assess whether adolescents sought treatment to identify sensitive periods for peer influence and, if treatment was received, the impact of intervention.

Despite the need for further inquiry, the current research has implications for intervening with youth who engage in NSSI. The observed prevalence and stability of NSSI suggest that a sizeable proportion of community adolescents are struggling. In contrast to youth in clinical samples, many youth presumably were not receiving treatment, despite these clear concerns. Moreover, results underscore that friends of youth who self-injure are at risk. This stresses the importance of attending not only to youth who engage in NSSI but also to their friends. This is in contrast to a suggestion from Hasking and colleagues (2013) that having a close circle of friends may protect against NSSI, even if the friends engage in NSSI. Of course, the companionship and support afforded youth by close friendships is generally positive for adjustment (Parker & Asher, 1993). On the other hand, friendships may exacerbate NSSI, should contagion be at play.

Adolescents who self-injure clearly need intervention to equip them with adaptive coping skills with which they can regulate difficult emotions and manage social stress. Friends of adolescents who self-injure warrant clinical attention as well, particularly if they exhibit emotion regulation vulnerabilities. What is more, there exists the optimistic possibility that friendships could be a context in which positive behaviors could be socialized. If friends see one another effectively managing stress, they may be more likely to do so as well. Along with indicated interventions, school-based prevention (e.g., Gillham et al., 2012) focused on acquisition of adaptive regulation tools may represent a convenient approach to addressing risk for NSSI at a community level. Such programs not only promote the use of adaptive techniques to regulate mood and behavior, they directly target some factors known to enhance vulnerability to the development of NSSI as well as peer socialization, such as difficulties in emotion regulation.

Footnotes

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

1

In line with current recommendations for language around peer influence, the term contagion is used here only in historical context, and the term socialization is used throughout to reflect efforts to destigmatize individuals struggling with NSSI (see Hasking & Boyes, 2018).

2

Percentages do not sum to 100 as some participants identified as more than one race and/or ethnicity or did not respond to this item.

3

Three participants selected a non-binary gender identity. In each case, they were the friend of a target adolescent who reported a gender identity of either female or male. Given that target adolescents recruited a friend of their same gender, gender was treated as a Level 2 variable. The pattern of results was identical including these dyads (and using the gender identity reported by the target adolescent in each case as the Level 2 gender variable) and excluding these dyads. As such, all three dyads were retained for study analyses. The sample was stratified by age into two groups: younger adolescents (13-15 years, n = 80) and older adolescents (16-19 years, n = 106).

References

  1. Aiken LS, West SG, & Reno RR (1991). Multiple regression: Testing and interpreting interactions. New York: Sage. [Google Scholar]
  2. Boergers J & Spirito A (2003). Follow-up studies of child and adolescent suicide attempters In King RA & Apter A (Eds.), Cambridge child and adolescent psychiatry. Suicide in children and adolescents (pp. 271–293). New York, NY, US: Cambridge University Press. [Google Scholar]
  3. Brechwald WA & Prinstein MJ (2011). Beyond homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21, 166–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bresin K & Schoenleber M (2015). Gender differences in the prevalence of nonsuicidal self-injury: A meta-analysis. Clinical Psychology Review, 38, 55–64. [DOI] [PubMed] [Google Scholar]
  5. Bukowski WM, Hoza B, & Boivin M (1994). Measuring friendship quality during pre- and early adolescence: The development and psychometric properties of the friendship qualities scale. Journal of Social and Personal Relationships, 11, 471–484. [Google Scholar]
  6. Byrne BM (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge Academic. [Google Scholar]
  7. Camarena PM, Sarigiani PA, & Petersen AC (1990). Gender specific pathways to intimacy in early adolescence. Journal of Youth and Adolescence, 19, 19–32. [DOI] [PubMed] [Google Scholar]
  8. Chow CM, Ruhl H, & Buhrmester D (2013). The mediating role of interpersonal competence between adolescents' empathy and friendship quality: A dyadic approach. Journal of Adolescence, 36, 191–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Claes L, Houben A, Vandereycken W, Bijttebier P, & Muehlenkamp J (2010). Brief report: The association between non-suicidal self-injury, self-concept and acquaintance with self-injurious peers in a sample of adolescents. Journal of Adolescence, 33, 775–778. [DOI] [PubMed] [Google Scholar]
  10. Curtis C (2017). Non-suicidal self-injury: Suicide risk or social activity? New Zealand Journal of Psychology, 46, 106–14. [Google Scholar]
  11. Deliberto TL & Nock MK (2008). An exploratory study of correlates, onset, and offset of non-suicidal self-injury. Archives of Suicide Research, 12, 219–231. [DOI] [PubMed] [Google Scholar]
  12. Fisher K, Fitzgerald J, & Tuffin K (2017). Peer responses to non-suicidal self-injury: Young women speak about the complexity of the support-provider role. New Zealand Journal of Psychology, 46, 146–55. [Google Scholar]
  13. Freudenberg N & Ruglis J (2007). Peer reviewed: Reframing school dropout as a public health issue. Preventing Chronic Disease, 4. [PMC free article] [PubMed] [Google Scholar]
  14. Gandhi A, Luyckx K, Baetens I, Kiekens G, Sleuwaegen E, & Claes L (2018). Age of onset of non-suicidal self-injury in Dutch-speaking adolescents and emerging adults: An event history analysis of pooled data. Comprehensive Psychiatry, 80, 170–178. [DOI] [PubMed] [Google Scholar]
  15. Garcia RL, Kenny DA, & Ledermann T (2015). Moderation in the actor–partner interdependence model. Personal Relationships, 22, 8–29. [Google Scholar]
  16. Ghaziuddin M, Tsai L, Naylor M, & Ghaziuddin N (1992). Mood disorder in a group of self-cutting adolescents. Acta Paedopsychiatrica, 55, 103–5. [PubMed] [Google Scholar]
  17. Gillham C, Reivich KJ, Brunwasser SM, Freres DR, Chajon ND, Kash-Macdonald VM..& Seligman ME (2012). Evaluation of a group cognitive-behavioral depression prevention program for young adolescents: a randomized effectiveness trial. Journal of Clinical Child and Adolescent Psychology, 41, 621–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Glenn CR & Klonsky ED (2009). Social context during non-suicidal self-injury indicates suicide risk. Personality and Individual Differences, 46, 25–9. [Google Scholar]
  19. Gratz KL & Roemer L (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the Difficulties in Emotion Regulation Scale. Journal of Psychopathology and Behavioral Assessment 26, 41–54. [Google Scholar]
  20. Hasking P, Andrews T, & Martin G (2013). The role of exposure to self-injury among peers in predicting later self-injury. Journal of Youth and Adolescence, 42, 1543–56. [DOI] [PubMed] [Google Scholar]
  21. Hasking P & Boyes M (2018). Cutting words: A commentary on language and stigma in the context of non-suicidal self-injury. Journal of Nervous and Mental Disease, 206, 829–833. [DOI] [PubMed] [Google Scholar]
  22. Hasking P, Whitlock J, Voon D, & Rose A (2017). A cognitive-emotional model of NSSI: Using emotion regulation and cognitive processes to explain why people self-injure. Cognition and Emotion, 31, 1543–56. [DOI] [PubMed] [Google Scholar]
  23. Heath NL, Ross S, Toste JR, Charlebois A, & Nedecheva T (2009). Retrospective analysis of social factors and nonsuicidal self-injury among young adults. Canadian Journal of Behavioural Science, 41, 180–6. [Google Scholar]
  24. Heilbron N & Prinstein MJ (2008). Peer influence and adolescent nonsuicidal self-injury: A theoretical review of mechanisms and moderators. Applied and Preventive Psychology, 12, 169–177. [Google Scholar]
  25. Hu L & Bentler PM (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55 [Google Scholar]
  26. Jarvi S, Jackson B, Swenson L, & Crawford H (2013). The impact of social contagion on non-suicidal self-injury: A review of the literature. Archives of Suicide Research, 17, 1–19. [DOI] [PubMed] [Google Scholar]
  27. Kandel DB (1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84, 427–36. [Google Scholar]
  28. Kandel DB (1996). The parental and peer contexts of adolescent deviance: An algebra of interpersonal influences. Journal of Drug Issues, 26, 289–315. [Google Scholar]
  29. Kenny DA (1996). Models of non-independence in dyadic research. Journal of Social and Personal Relationships, 13, 279–294 [Google Scholar]
  30. Kenny DA, Kashy DA, & Cook WL (2006). Methodology in the social sciences (Kenny DA, Series Editor). Dyadic data analysis. New York: Guilford. [Google Scholar]
  31. Kiekens G, Hasking P, Boyes M, Claes L, Mortier P, Auerbach RP, … & Myin-Germeys I (2018). The associations between non-suicidal self-injury and first onset suicidal thoughts and behaviors. Journal of Affective Disorders, 239, 171–179. [DOI] [PubMed] [Google Scholar]
  32. Klonsky ED (2011). Non-suicidal self-injury in United States adults: prevalence, sociodemographics, topography and functions. Psychological Medicine, 41, 1981–6. [DOI] [PubMed] [Google Scholar]
  33. Lofthouse N & Katz L (2009). Adolescent nonsuicidal self-injury in an inpatient setting In Nixon MK & Heath NL (Eds.), Self-injury in youth: The essential guide to assessment and intervention (pp. 258–274). New York, NY: Routledge. [Google Scholar]
  34. Madjar N, Segal N, Eger G, & Shoval G (2019). Exploring particular facets of cognitive emotion regulation and their relationships with nonsuicidal self-injury among adolescents. Crisis, 1–7. [DOI] [PubMed] [Google Scholar]
  35. Mäkikyrö TH, Hakko HH, Timonen MJ, Lappalainen JA, Ilomäki RS, Marttunen MJ, … & Räsänen PK (2004). Smoking and suicidality among adolescent psychiatric patients. Journal of Adolescent Health, 34, 250–3. [DOI] [PubMed] [Google Scholar]
  36. Matthews PC (1968). Epidemic self-injury in an adolescent unit. International Journal of Social Psychiatry, 14, 125–33. [DOI] [PubMed] [Google Scholar]
  37. Mercken L, Candel M, Willems P, & deVries H (2011). Disentangling social selection and social influence effects on adolescent smoking: The importance of reciprocity in friendships. Addiction, 102, 1482–92. [DOI] [PubMed] [Google Scholar]
  38. Monto MA, McRee N, & Deryck FS (2018). Nonsuicidal self-injury among a representative sample of US adolescents, 2015. American Journal of Public Health, e1–e7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Muehlenkamp JJ & Gutierrez PM (2007). Risk for suicide attempts among adolescents who engage in non-suicidal self-injury. Archives of Suicide Research, 11 69–82. [DOI] [PubMed] [Google Scholar]
  40. Neumann A, van Lier PAC, Gratz KL, & Koot HM (2011). Multidimensional assessment of emotion regulation difficulties in adolescents using the Difficulties in Emotion Regulation Scale. Assessment, 17, 138–149. [DOI] [PubMed] [Google Scholar]
  41. Nock MK (2008). Actions speak louder than words: An elaborated theoretical model of the social functions of self-injury and other harmful behaviors. Applied and Preventive Psychology, 12, 159–168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Nock MK (2009). Why do people hurt themselves? New insights into the nature and functions of self-injury. Current Directions in Psychological Science, 18, 78–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Nock MK, Holmberg EB, Photos VI , & Michel BD (2007). The Self-Injurious Thoughts and Behaviors Interview: Development, reliability, and validity in an adolescent sample. Psychological Assessment, 19, 309–317. [DOI] [PubMed] [Google Scholar]
  44. Nock MK, Joiner TE Jr, Gordon KH, Lloyd-Richardson E, & Prinstein MJ (2006). Non-suicidal self-injury among adolescents: Diagnostic correlates and relation to suicide attempts. Psychiatry Research, 144, 65–72. [DOI] [PubMed] [Google Scholar]
  45. Nock MK & Mendes WB (2008). Physiological arousal, distress tolerance, and social problem-solving deficits among adolescent self-injurers. Journal of Consulting and Clinical Psychology, 76, 28–38. [DOI] [PubMed] [Google Scholar]
  46. Nock MK & Prinstein MJ (2004). A functional approach to the assessment of self-mutilative behavior. Journal of Consulting and Clinical Psychology, 72, 885–90. [DOI] [PubMed] [Google Scholar]
  47. Nock MK & Prinstein MJ (2005). Clinical features and behavioral functions of adolescent self-mutilation. Journal of Abnormal Psychology, 114, 140–6. [DOI] [PubMed] [Google Scholar]
  48. Nock MK, Prinstein MJ, & Sterba SK (2009). Revealing the form and function of self-injurious thoughts and behaviors: A real-time ecological assessment study among adolescents and young adults. Journal of Abnormal Psychology, 118, 816–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Nock MK, Wedig MM, Holmberg EB, & Hooley JM (2008). The Emotion Reactivity Scale: Development, evaluation, and relation to self-injurious thoughts and behaviors. Behavior Therapy, 39, 107–16. [DOI] [PubMed] [Google Scholar]
  50. Parker JG & Asher SR (1993). Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology, 29, 611–621. [Google Scholar]
  51. Prinstein MJ, Heilbron N, Guerry JD, Franklin JC, Rancourt D, Simon V, & Spirito A (2010). Peer influence and nonsuicidal self injury: Longitudinal results in community and clinically-referred adolescent samples. Journal of Abnormal Child Psychology, 38, 669–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Prinstein MJ, Nock MK, Simon V, Aikins JW, Cheah CSL, & Spirito A (2008). Longitudinal trajectories and predictors of adolescent suicidal ideation and attempts following inpatient hospitalization. Journal of Consulting and Clinical Psychology, 76, 92–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Richardson B, Surmitis K, & Hyldahl R (2012). Minimizing social contagion in adolescents who self-injure: Considerations for group work, residential treatment, and the internet. Journal of Mental Health Counseling, 34, 121–132. [Google Scholar]
  54. Rose AJ (2002). Co-rumination in the friendships of girls and boys. Child Development, 73, 1830–1843. [DOI] [PubMed] [Google Scholar]
  55. Rose AJ & Rudolph KD (2006). A review of sex differences in peer relationship processes: Potential trade-offs for the emotional and behavioral development of girls and boys. Psychological Bulletin, 132, 98–131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Schwartz-Mette RA & Rose AJ (2012). Co-rumination mediates contagion of internalizing symptoms within youths’ friendships. Developmental Psychology, 48, 1355–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schwartz-Mette RA & Smith RL (2018). When does co-rumination mediate depression contagion in adolescent friendships? Investigating intrapersonal and interpersonal factors. Journal of Clinical Child and Adolescent Psychology, 47, 912–924. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Sornberger MJ, Heath NL, Toste JR, & McLouth R (2012). Nonsuicidal self- injury and gender: Patterns of prevalence, methods, and locations among adolescents. Suicide and Life-Threatening Behavior, 42, 266–78. [DOI] [PubMed] [Google Scholar]
  59. Smith CE & Cribbie RA (2013). Multiplicity control in structural equation modeling: Incorporating parameter dependencies. Structural Equation Modeling, 20, 79–85. [Google Scholar]
  60. Stevens EA & Prinstein MJ (2005). Peer contagion of depressogenic attributional styles among adolescents: A longitudinal study. Journal of Abnormal Child Psychology, 33, 25–37. [DOI] [PubMed] [Google Scholar]
  61. Swannell SV, Martin GE, Page A, Hasking P, & St John NJ (2014). Prevalence of nonsuicidal self-injury in nonclinical samples: Systematic review, meta-analysis and meta-regression. Suicide and Life-Threatening Behavior, 44, 273–303. [DOI] [PubMed] [Google Scholar]
  62. Taiminen TJ, Kallio-Soukainen K, Nokso-Koivisto H, Kaljonen A, & Helenius H (1998). Contagion of deliberate self-harm among adolescent inpatients. Journal of the American Academy of Child & Adolescent Psychiatry, 37,211–7. [DOI] [PubMed] [Google Scholar]
  63. Tatnell R, Kelada L, Hasking P, & Martin G (2014). Longitudinal analysis of adolescent NSSI: The role of intrapersonal and interpersonal factors. Journal of Abnormal Child Psychology, 42, 885–896. [DOI] [PubMed] [Google Scholar]
  64. Taylor PJ, Jomar K, Dhingra K, Forrester R, Shahmalak U, & Dickson JM (2018). A meta-analysis of the prevalence of different functions of non-suicidal self-injury. Journal of Affective Disorders, 227, 759–769. [DOI] [PubMed] [Google Scholar]
  65. Ueno K (2005). The effects of friendship networks on adolescent depressive symptoms. Social Science Research, 34, 484–510. [Google Scholar]
  66. Weinberg A & Klonsky ED (2009). Measurement of emotion dysregulation in adolescents. Psychological Assessment, 21, 616–21. [DOI] [PubMed] [Google Scholar]
  67. Whitlock JL, Powers JL, & Eckenrode J (2006). The virtual cutting edge: the internet and adolescent self-injury. Developmental Psychology, 42, 407–17. [DOI] [PubMed] [Google Scholar]
  68. Whitlock J, Purington A, & Gershkovich M (2009). Media, the internet, and nonsuicidal self-injury In Nock MK (Ed.), Understanding nonsuicidal self-injury: Origins, assessment, and treatment (pp. 139–155). Washington, DG: American Psychological Association. [Google Scholar]
  69. Widaman KF (2006). Best practices in quantitative methods for developmentalists: III. Missing data: What to do with or without them. Monographs of the Society for Research in Child Development, 71, 42–64 [DOI] [PubMed] [Google Scholar]
  70. You J, Lin MP, Fu K, & Leung F (2013). The best friend and friendship group influence on adolescent nonsuicidal self-injury. Journal of Abnormal Child Psychology, 41, 993–1004. [DOI] [PubMed] [Google Scholar]
  71. Zelkowitz RL, Porter AC, Heiman ER, & Cole DA (2017). Social exposure and emotion dysregulation: Main effects in relation to nonsuicidal self-injury. Journal of Adolescence, 60, 94–103. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES