Attachment, Self-Esteem, and Psychological Distress: A Multiple-Mediator Model

Fei Shen, Yanhong Liu, Mansi Brat


The present study examined the relationships between childhood attachment, adult attachment, self-esteem, and psychological distress; specifically, it investigated the multiple mediating roles of self-esteem and adult attachment on the association between childhood attachment and psychological distress. Using 1,708 adult participants, a multiple-mediator model analysis following bootstrapping procedures was conducted in order to investigate the mechanisms among childhood and adult attachment, self-esteem, and psychological distress. As hypothesized, childhood attachment was significantly associated with self-esteem, adult attachment, and psychological distress. Self-esteem was found to be a significant mediator for the relationship between childhood attachment and adult attachment. In addition, adult attachment significantly mediated the relationship between self-esteem and psychological distress. The results provide insight on counseling interventions to increase adults’ self-esteem and attachment security, with efforts to decrease the negative impact of insecure childhood attachment on later psychological distress.

Keywords: childhood attachment, adult attachment, self-esteem, psychological distress, mediator


Attachment has been widely documented across disciplines, following Bowlby’s (1973) foundational work known as attachment theory. Attachment, in the context of child–parent interactions, is defined as a child’s behavioral tendency to use the primary caregiver as the secure base when exploring their surroundings (Bowlby, 1969; Sroufe & Waters, 1977). Research has shed light on the significance of childhood attachment in predicting individuals’ intrapersonal qualities such as self-esteem and emotion regulation during adulthood (Brennan & Morris, 1997), interpersonal orientations examined through attachment variation and adaptation across different developmental stages (Sroufe, 2005), and overall psychological well-being (Cassidy & Shaver, 2010; Wright et al., 2014).

Given its clinical significance, attachment has gained increased interest across disciplines. For example, childhood attachment was found to significantly predict coping and life satisfaction in young adulthood (Wright et al., 2017). Relatedly, a 30-year longitudinal study reinforces the vital role of childhood attachment in predicting individuals’ development of “the self and personality” (Sroufe, 2005, p. 352). Sroufe’s (2005) study reinforced the vital role of attachment across the life span. As an outcome variable, attachment is asserted to be associated with empathy (Ruckstaetter et al., 2017) and parenting practice in the adoptive population (Liu & Hazler, 2017). Considering the interplay between individuals’ relationship evolvement and their living contexts (Bowlby, 1973; Sroufe, 2005), attachment is examined at different stages generally labelled as childhood attachment and adult attachment, with the former focusing on the infant/child–parent relationship and the latter on adults’ generalized relationships with intimate others (e.g., romantic partners, close friends). Because of the abstract nature of attachment, it is commonly measured in the form of childhood attachment styles (Ainsworth et al., 1978) or adult attachment orientations (Turan et al., 2016).

Conceptual Framework
The present study is grounded in attachment theory, which is centered around a child’s ability to utilize their primary caregiver as the secure base when exploring surroundings, involving an appropriate balance between physical proximity, curiosity, and wariness (Bowlby, 1973; Sroufe & Waters, 1977). A core theoretical underpinning of attachment theory is the internal working model capturing a child’s self-concept and expectations of others (Bretherton, 1996). Internal working models of self and other are complementary. Namely, a child with strong internal working models is characterized with a perception of self as being worthy and deserving of love and a perception of others as being responsive, reliable, and nurturing (Bowlby, 1973; Sroufe, 2005).

In the context of attachment theory, childhood attachment is considered an outcome of consistent child–caregiver interactions and serves as the foundation for individuals’ later personality development (Bowbly, 1973; Sroufe, 2005). In line with child–caregiver interactions, Ainsworth et al. (1978) came up with three attachment styles based upon Bowlby’s seminal work, including secure, anxious-ambivalent, and anxious-avoidant attachment, following sequential phases of laboratory observations. Attachment theory was subsequently extended beyond the child–parent relationship to include later relationships in adulthood, given the parallels between these relationships (Cassidy & Shaver, 2010). Likewise, four distinct adult attachment styles (i.e., secure, dismissing, preoccupied, and fearful) are referred to based on the two-dimensional models of self and other (Konrath et al., 2014). Adult attachment styles are commonly examined under two orientations: attachment avoidance and attachment anxiety (Turan et al., 2016). Individuals showing low avoidance and low anxiety are considered securely attached, whereas those with high levels of anxiety and avoidance tend to be insecurely attached. Although childhood attachment and adult attachment are broadly considered distinct concepts in the literature, they share a spectrum of behaviors spanning from secure to insecure attachment. The levels of avoidance and anxiety involved in these behaviors are used as parameters to differentiate securely attached individuals from those who are insecurely attached.

Childhood Attachment, Self-Esteem, and Adult Attachment
Despite the conceptual overlaps, childhood attachment to caregivers and adult attachment to intimate others are commonly investigated as two distinct variables associated with individuals’ needs and features of different relationships. Childhood attachment captures a child’s distinct relationship with the primary caregiver (e.g., the mother figure) as well as their ability to differentiate the primary caregiver from other adults (Bowlby, 1969, 1973), whereas adult attachment may involve an individual’s multiple relationships (with parents, a romantic partner, or close friends). Noting the general stability of attachment from childhood to adulthood (Fraley, 2002), previous conceptual work stresses the importance of contexts in individuals’ attachment evolvement, highlighting that “patterns of adaptation” and “new experiences” reinforce each other in a reciprocal way (Sroufe, 2005, p. 349). For instance, an individual may develop secure attachment in adulthood because of healthy interpersonal experiences likely facilitated by trust, support, and nurturing received from significant others or their relationships, despite showing insecure attachment patterns in early childhood. A dynamic view of attachment development is thus warranted.

From a dynamic lens, researchers have generated evidence for the association between childhood attachment and adult attachment (Pascuzzo et al., 2013; Styron & Janoff-Bulman, 1997). For example, in a study of 879 college students (Styron & Janoff-Bulman, 1997), participants’ perception of their childhood attachment to both mother and father significantly predicted 7.9% of the variance in their adult attachment scores. Similarly, Pascuzzo et al. (2013) followed 56 adolescents at age 14 through age 22 and found that attachment insecurity to both parents and peers during adolescence was significantly associated with anxious romantic attachment in adulthood as measured by the Experience in Close Relationships Scale (ECR; Brennan et al., 1998). Studies that rely on retrospective data to assess childhood attachment (e.g., Styron & Janoff-Bulman, 1997) may be limited in validity because of time elapsed and potential compounding variables.

Childhood attachment is well recognized as the foundation for the growth of self-reliance and emotional regulation (Bowlby, 1973). Aligning with self-reliance, self-esteem appears to be frequently studied primarily through self-liking and self-competence (Brennan & Morris, 1997). Brennan and Morris (1997) defined self-liking as general self-evaluation based on perceived positive regard from others, and self-competence as concrete self-evaluation based on personal abilities and attributes. Previous research has suggested that secure attachment (to parents and peers) is significantly associated with higher levels of self-esteem (e.g., Wilkinson, 2004). In contrast, individuals who reported insecure attachment tended to endorse low self-esteem (Gamble & Roberts, 2005).

These results provide theoretical and empirical evidence for links between childhood attachment and adult attachment, but these links are likely to be indirect and mediated by other relevant variables from developmental perspectives. To our knowledge, no study has investigated the effect of self-esteem on the relationship between childhood attachment and adult attachment. The theoretical framework of attachment theory indicates that childhood attachment can have not only direct effects on adult attachment, but also indirect effects on adult attachment via self-esteem. In order to develop effective interventions tackling issues with adult attachment, it is important to examine potential mediators (e.g., self-esteem) between childhood attachment and adult attachment. To address this gap, the present study tests this hypothesized mediation function of self-esteem with a nonclinical sample of adults.

Self-Esteem, Attachment, and Psychological Distress
The extant literature comprises prolific information on the relationship between attachment and psychological well-being (Gnilka et al., 2013; Karreman & Vingerhoets, 2012; M. E. Kenny & Sirin, 2006; Turan et al., 2016; Wright et al., 2014). Existing evidence focuses on the relationship between adult attachment orientations and individuals’ psychological well-being (e.g., Karreman & Vingerhoets, 2012; Lynch, 2013; Roberts et al., 1996; Sowislo & Orth, 2013). Nevertheless, previous research has shed some light on the role of early childhood attachment in predicting psychological distresses in adulthood, including depression and anxiety (Bureau et al., 2009; Lecompte et al., 2014; Styron & Janoff-Bulman, 1997). Lecompte and colleagues (2014) conducted a longitudinal study of a sample of preschoolers (N = 68) with data collected at 4 years and again at 11–12 years; results of the study suggested that children with disorganized attachment at the baseline scored higher in both anxiety and depressive symptoms compared to those classified as securely attached.

Likewise, the effect of self-esteem on psychological distress is well established. A meta-analysis on 80 longitudinal studies published between 1994 and 2010 yielded consistent evidence supporting the relationship between low self-esteem and depressive symptoms (Sowislo & Orth, 2013). More recently, Masselink et al. (2018) examined data collected at four different points of participants’ development from early adolescence to young adulthood, which demonstrated that low self-esteem constitutes a persistent risk factor for participants’ depressive symptoms across developmental stages. Moreover, self-esteem scores in early adolescence significantly predicted the participants’ depressive symptoms at later stages, specifically during late adolescence and young adulthood.

Research has also supported the association between self-esteem, adult attachment, and psychological distress. Lopez and Gormley (2002) followed 207 college students from the beginning to the end of their freshman year and identified adjustment outcomes in association with the participants’ attachment styles and changes of their attachment styles measured by the ECR (e.g., secure-to-insecure attachment, insecure-to-secure attachment). The authors found that participants who remained securely attached scored higher in self-confidence and lower in both psychological distress and reactive coping compared to those who reported consistent insecure attachment. Moreover, participants who maintained secure attachment presented better outcomes in self-confidence and psychological well-being than the comparative group with secure-to-insecure or insecure-to-secure attachment changes (Lopez & Gormley, 2002). Adult attachment (measured by the ECR) was also found to be a mediator for the effects of traumatic events on post-traumatic symptomatology among a sample of female college students (Sandberg et al., 2010). In addition, Roberts et al. (1996) suggested attachment insecurity contributed to negative beliefs about oneself, which in turn activated cognitive structures of psychological distress, such as depression and anxiety, with a sample of 152 undergraduate students.

Taken together, the literature provides consistent support for the significant relationships between childhood attachment and various outcome variables in later adulthood, including adult attachment, self-esteem, and psychological distress. It further reveals a two-fold gap: (a) the variables tended to be investigated separately in previous studies, yet the mechanisms among these variables remained underexplored; and (b) little is known about the role of self-esteem and adult attachment in the association between childhood attachment and psychological distress. Disentangling the mechanisms, including potential mediating roles, involved in the variables will enrich the current knowledge based on attachment and can facilitate counseling interventions surrounding the effects of childhood attachment. In tackling the gap, three hypotheses were posed:

1. Childhood attachment is significantly associated with adult attachment, self-esteem, and psychological distress.
2. Self-esteem mediates the relationship between perceived childhood attachment and adult attachment.
3. Adult attachment mediates the relationship between self-esteem and
psychological distress.


Of the 2,373 voluntary adult participants who took the survey, 1,708 (72%) completed 95% of all the questions and were retained for final analysis. Among the participants, 76.2% (n = 1,302) were female, 22.3% (n = 381) were male, and 1.3% (n = 25) chose not to specify their gender. The mean age of the participants was 29.89, ranging from 18 to 89 years old (SD = 12.44). A total of 66.3% (n = 1,133) of participants described themselves as White/European American, 8.7% (n = 148) as African American, 10.2% (n = 175) as Asian/Pacific Islander, 2.6% (n = 44) as American Indian/Native American, 7.3% (n = 124) as biracial or multiracial, 3.6% (n = 61) as other race, and 1.3% (n = 23) did not specify.

Sampling Procedures
The study was approved by the university’s IRB. We posted the recruitment information on various websites (e.g., Facebook, discussion board, university announcement board, Craigslist) in order to recruit a diverse pool of participants. Individuals who were 18 years old or above and were able to fill out the questionnaire in English were eligible for participating in this project. Participants were directed to an online Qualtrics survey consisting of the measures discussed in the following section. An informed consent form was included at the beginning of the survey outlining the confidentiality, voluntary participation, and anonymity of the study. Participants were prompted to enter their email addresses to win one of ten $15 e-gift cards. Participants’ email addresses were not included in the survey questions and data analysis.

Psychological Distress
Psychological distress was measured using the 10-item Kessler Psychological Distress Scale (K10; Kessler et al., 2003). Participants were asked about their emotional states in the past four weeks (e.g., “How often did you feel nervous?”). Responses were rated on a 5-point scale ranging from 0 (None of the time) to 4 (All of the time). Scores were averaged, with a higher score indicating a higher level of psychological distress. Previous studies using K10 have provided evidence of validity (Andrews & Slade, 2001). The internal consistency for K10 has been well established with a Cronbach’s alpha coefficient ranging from .88 (Easton et al., 2017) to .94 (Donker et al., 2010). In this study, the Cronbach’s alpha coefficient was .94.

Childhood Attachment
Childhood attachment was measured using the Parental Attachment subscale of the Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987). Previous research has demonstrated evidence that this measure has great convergent and concurrent validity (M. E. Kenny & Sirin, 2006). The IPPA has been used to recall childhood attachment in adult populations (Aspelmeier et al., 2007; Cummings-Robeau et al., 2009). This 25-item subscale directs participants to recall their attachment to the parent(s) or caregiver(s) who had the most influence on them during childhood. The subscale consists of three dimensions, including 10 items on trust, nine items on communication, and six items on alienation. Some sample items are: “My parent(s)/primary caregiver(s) accepts me as I am” for trust, “I tell my parent(s)/primary caregiver(s) about my problems and troubles)” for communication, and “I do not get much attention from my parent(s)/primary caregiver(s)” for alienation. Participants rated the items using a 5-point Likert scale ranging from 1 (Almost never or never true) to 5 (Almost always or always true). Items were averaged to form the subscale, with a higher score reflecting more secure childhood attachment. The subscale has demonstrated high internal consistency with a Cronbach’s alpha of .93 (Armsden & Greenberg, 1987). In the present study, Cronbach’s alpha for the subscale was .96.

Adult Attachment
Adult attachment was measured using the ECR (Brennan et al., 1998). The ECR consists of 36 items with 18 items assessing each of the two orientations: attachment anxiety and attachment avoidance. In order to avoid confounding factors, we only assessed adult attachment with close friends or romantic partners, as relationships with parents can confound the childhood attachment outcomes. Responses were rated on a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). Two scores were averaged, with a higher score reflecting a higher level of attachment anxiety or avoidance. In terms of validity, the ECR subscales have been found to be positively associated with psychological distress and intention to seek counseling, and negatively associated with social support (Vogel & Wei, 2005). The ECR has a high internal consistency for both the anxiety (α = .91) and avoidance (α = .94) dimensions (Brennan et al., 1998). For this study, Cronbach’s alphas for attachment anxiety and attachment avoidance were .93 and .92, respectively.

Rosenberg’s Self-Esteem Scale (RSES; Rosenberg, 1965) is a 10-item scale designed to assess an adult’s self-esteem. The scale assesses both self-competency (e.g., “I feel that I have a number of good qualities”) and self-liking (e.g., “I certainly feel useless at times”). Responses were coded using a 4-point Likert scale ranging from 1 (Strongly disagree) to 4 (Strongly agree). Negatively worded statements were reverse-coded. Scores were averaged, with a higher score reflecting a higher level of self-esteem. RSES has been frequently used in various studies with high reliability and validity (Brennan & Morris, 1997; Chen et al., 2017). In this study, the Cronbach’s alpha coefficient was .89.

Data Analysis
Descriptive statistics were computed using SPSS version 23 followed by a multiple-mediator model analysis using Mplus version 7.4 (Muthén & Muthén, 2012). Missing data were treated with the full information maximum likelihood estimation in Mplus, which was one of the most pragmatic approaches in producing unbiased parameter estimates (Acock, 2005). The multiple-mediator model includes childhood attachment as the predictor, self-esteem and adult attachment anxiety and avoidance as mediators, and psychological distress as the outcome variable (see Figure 1). The mediation analysis was conducted using bootstrapping procedures (J = 2,000), which was a resampling method to construct a confidence interval for the indirect effect (Preacher & Hayes, 2008). Several model fit indices based on Kline’s (2010) guidelines were employed, including the ratio of chi-square to degree of freedom (χ2/df), root-mean-square error of approximation (RMSEA), Tucker-Lewis index (TLI), comparative fit index (CFI), and standardized root-mean-square residual (SRMR). Indicators of good model fit are a nonsignificant chi-square value, a CFI and TLI of .90 or greater, RMSEA of .08 or less, and an SRMR of .05 or less (Hooper et al., 2008).

Figure 1

Multiple-Mediator Model: Self-Esteem, Anxious Adult Attachment, and Avoidant Adult Attachment as Multiple Mediators Between Childhood Attachment and Psychological Distress



Descriptive Statistics and Correlations
The descriptive statistics of each variable are reported in Table 1.


Table 1

Descriptive Statistics for Variables (N = 1,708)


Pearson’s correlations between variables were computed. All bivariate statistics are presented in Table 2 and provided full support for our Hypothesis 1. For instance, childhood attachment was positively associated with self-esteem (r = .38, p < .001) and negatively correlated with adult attachment anxiety (r = -.26, p < .001) and avoidance (r = -.45, p < .001), as well as with psychological distress (r = -.35, p < .001). Significant negative correlations were found between self-esteem and adult attachment anxiety (r = -.49, p < .001) and avoidance (r = -.46, p < .001), and between self-esteem and psychological distress (r = -.63, p < .001). Both adult attachment anxiety (r = .57, p < .001) and avoidance (r = .42, p < .001) were positively associated with psychological distress. Significant correlation was found between adult attachment anxiety and avoidance (r = .31, p < .001).


Table 2

Correlation Matrix of Variables (N = 1,708)

*p < .05. **p < .01. ***p < .001 (two-tailed).


The Multiple-Mediator Model
     The multiple-mediator model involving self-esteem and adult attachment as mediators, with bootstrapping procedures, yielded satisfactory fit indices: χ2(1) = 12.24, p < .001, CFI = 1.00, TLI = 0.96, SRMR = .01. However, the index of RMSEA = .08, 90% CI [0.05, 0.12] indicated a mediocre fit, with the upper value of 90% CI larger than the suggested cutoff score of 0.08. D. A. Kenny et al. (2015) suggested that the models with small degrees of freedom had the average width of the 90% CI above 0.10, unless the sample size was extremely large. The nonsignificant χ2 value was interpreted as a good fit index.

The present study further revealed that secure childhood attachment was associated with high self-esteem (β = .25, p < .001) and low levels of anxiety (β = -.12, p < .001) and avoidance (β = -.41, p < .001) of adult attachment. Meanwhile, high self-esteem was associated with low anxiety (β = -.95, p < .001) and low avoidance (β = -.64, p < .001) of adult attachment. In addition, high self-esteem (β = -.68, p < .001) and low adult attachment anxiety (β = .26, p < .001) and avoidance (β = .11, p < .001) were significantly associated with low psychological distress. The results supported both Hypotheses 2 and 3 in that self-esteem mediated the relationship between childhood attachment and adult attachment, and adult attachment mediated the relationship between self-esteem and psychological distress.

The mediating role of self-esteem was examined using bootstrapping procedures. Results demonstrated that self-esteem significantly mediated the association between childhood attachment and adult attachment anxiety (b = -.24, 95% CI [-0.27, -0.21]) and avoidance (b = -.16, 95% CI [-0.19, -0.14]).

The present study further supported the mediating role of adult attachment (i.e., anxiety and avoidance). The association between self-esteem and psychological distress was significantly mediated by both adult attachment anxiety (b = -.24, 95% CI [-0.29, -0.21]) and avoidance (b = -.07, 95% CI [-0.10, -0.05]). Mediation effects are denoted in Table 3.


Table 3

Mediation Analysis With Bootstrapping: Unstandardized and Standardized Estimates and Confidence Intervals for Mediation Effects

Note. Bootstrap J = 2,000, CI = confidence interval; IV = independent variable; DV = dependent variable; CA = Childhood Attachment; SE = Self-Esteem; AnA = Anxious Adult Attachment; AvA = Avoidant Adult Attachment; PD = Psychological Distress. Direct effect of path direction, IV® Mediator, Mediator ® DV, IV ® DV. Statistical significance was evaluated based on whether 95% bias corrected bootstrap CIs include zero or not. If zero was included in the CI, then it was not a significant indirect effect. Model fit: χ2(1) = 12.24, p < .001, CFI = 1.00, TLI = 0.96, SRMR = .01, RMSEA = .08 (90% CI [0.05, 0.12]).



The present study highlights the significance of childhood attachment and its associations with self-esteem and psychological distress in adulthood. Participants who reported secure childhood attachment scored higher on self-esteem and lower on psychological distress. Secure childhood attachment was also found to be associated with low adult attachment anxiety and avoidance. Our study builds upon previous research (e.g., Sroufe, 2005) to capture the complexity of key variables related to attachment and its evolvement from childhood to adulthood. The results shed further light on the mechanisms among childhood attachment, self-esteem, adult attachment, and psychological distress. Self-esteem was found to be a significant mediator between childhood attachment and adult attachment; meanwhile, adult attachment was found to be a mediator between self-esteem and psychological distress.

The findings support Hypothesis 1 in that individuals with more secure childhood attachment reported higher levels of self-esteem, lower levels of adult attachment anxiety and avoidance, and less psychological distress. The results echo attachment theory (Bowlby, 1973), positing childhood attachment as a predictor of later adjustment as well as self-esteem, indicating that the quality of attachment appears to be intimately related to how to cope with stress and how to perceive oneself (Wilkinson, 2004). The results are also consistent with previous research that highlighted secure childhood attachment as a protective factor against anxiety, depression, and later emotional and relational distress (e.g., Karreman & Vingerhoets, 2012).

Results also lend support to Hypothesis 2 in that self-esteem mediated the relationship between childhood attachment and adult attachment. Self-esteem as a mediator echoed previous research that indicated the influence of childhood attachment on one’s self-esteem may be mitigated by expanded social networks in adulthood (Steiger et al., 2014). For instance, it is likely that improving self-esteem through peer connections (e.g., friendship; romantic relationships) may contribute to individuals’ adaptation to close relationships and enhance attachment security in adulthood, despite their insecure attachment with primary caregivers in childhood (Fraley, 2002; Sroufe, 2005).

Congruent with Hypothesis 3, adult attachment was a mediator for the relationship between self-esteem and psychological distress. Previous research provided evidence that low self-esteem increases the risk of developing psychological distress such as depressive and anxious symptoms (Li et al., 2014); nevertheless, individuals may experience less psychological impact with secure attachment manifested through their close relationships. Little is known about the relationship between insecure adult attachment (i.e., anxious and avoidant attachment) and psychological distress, and the mediating role of adult attachment has rarely been addressed. In a sample of 154 women in a community context, Bifulco et al. (2006) found that fearful and angry-dismissive attachment partially mediated the relationship between childhood adversity and depression or anxiety. The present study extends the Bifulco et al. study to include a larger, gender-inclusive, and racially diverse population that captures a wider age range. Further, using continuous measurements, the present study counteracts the limitations of dichotomous measures used in Bifulco et al.’s study, thus reflecting the spectrum and complexity of attachment.

Implications for Counseling Practice
The present study sheds light on interventions for clients’ psychological distress. The results corroborated positive associations between psychological distress and insecure childhood attachment and attachment anxiety and avoidance during adulthood. Although adults can no longer change their childhood experiences, including their attachment-related adversities, interventions that target improving adult attachment may still mitigate the negative effect of childhood attachment on psychological distress later during adulthood. Considering the reciprocal influence noted between self-esteem and adult attachment (Foster et al., 2007), counseling strategies encompassing both self-esteem and adult attachment are thus desirable.

Specifically, counselors could conceptualize self-esteem in a relational context in which they may incorporate clients’ support systems (e.g., partner, close friends, parents) into the treatment. A key treatment goal may be utilizing close relationships to boost self-esteem. On the contrary, counselors may engage clients with low self-esteem in communicating their attachment needs while involving significant others (e.g., partners) to enact positive responses, such as attentive listening and validation of mutual needs. Counselors are encouraged to assess how childhood attachment experiences may have influenced the client’s adult attachment, as insecure attachment may lead to challenges with perceived trustworthiness of self and others, which could hinder growth in the interpersonal relationships. Clients may further benefit from reflecting over specific attachment behaviors and interactional patterns within close relationships (e.g., how they manage proximity to an attachment figure when they experience distress) in order to restructure and enhance their attachment security internally and externally (Cassidy et al., 2013).

The finding of self-esteem as a significant mediator supports the proposition that self-esteem is responsive to life events and that these can influence one’s perception and evaluation of self. Previous research indicated that individuals with low self-esteem may be easily triggered by stressful life events and consequently respond irrationally and negatively (Taylor & Montgomery, 2007). Counselors may consider adapting Fennell’s (1997) Cognitive Behavioral Therapy model comprising early experience, bottom line, and rules for living to help clients enhance self-esteem. Fennell’s model suggests that clients’ early experiences (e.g., childhood attachment, traumatic experience, cultural context) may have an influence on the development of a fundamental bottom line about themselves (e.g., “I am not good enough,” “I am worthless”). Counselors may further assist clients with mapping out the rules for living (e.g., dysfunctional assumptions) related to distorted thoughts on what they should do in order to cultivate their core beliefs (as being loved or accepted or vice versa). For example, if clients have formed insecure attachment during childhood (early experience), they may develop a bottom line that “I am not good enough.” In making efforts to feel accepted in the family, they may have the rules for living that “I have to receive all As in all my classes.” If clients fail to achieve the rules for living, they likely would develop anxious and depressive symptoms, which may activate the confirmation of the bottom line. To counteract the negative patterns, counselors may work with clients to process the impact of early experience (e.g., early insecure attachment) on their bottom line and revise the rules of living to develop healthier coping strategies. When clients develop alternative rules of living, counselors may further help them to re-evaluate the bottom line and enhance self-acceptance.

Limitations and Future Research Directions
     Although the results supported all three hypotheses, the present study was subject to a few limitations. First, the self-report measures may have been subject to biases, especially for the memory of childhood attachment. Another limitation pertains to a retrospective assessment of perceptions of childhood attachment that may be changed over time because of life experiences (e.g., death, parental divorce). Relatedly, the cross-sectional study could not capture the changes over a period of time. Not knowing the types of childhood attachment (i.e., anxious attachment, avoidant attachment) presented as another limitation for researchers’ understanding of the variations of attachment and how each type might impact long-term outcomes. In the future, researchers may consider longitudinal studies to explore the variations and changes in attachment over the life span and examine what other mechanisms contribute to the changes to protect against the negative impact. Future research may also incorporate other-report data filled out by significant others (e.g., parents, romantic partners) to minimize social desirability and provide multiple perspectives.


Attachment theory provides a strong theoretical framework in understanding individuals’ psychological well-being over the life span (DeKlyen & Greenberg, 2008). Informed by attachment theory, the present study investigated the mediating roles of self-esteem and adult attachment (measured through the levels of anxiety and avoidance) on the relations between childhood attachment and psychological distress, and between self-esteem and psychological distress, respectively. The multiple-mediator analysis with bootstrapping supports both self-esteem and adult attachment as significant mediators. Our results also support the associations between childhood attachment with self-esteem, adult attachment, and psychological distress. The study contributes to the gap pertaining to adult attachment and provides practical implications for counselors working in various settings in their work with clients surrounding attachment security, self-esteem, and psychological well-being.


Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.



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Fei Shen, PhD, is a staff therapist at Syracuse University. Yanhong Liu, PhD, NCC, is an assistant professor at Syracuse University. Mansi Brat, PhD, LPC, is a staff therapist at Syracuse University. Correspondence may be addressed to Fei Shen, 150 Sims Drive, Syracuse, NY 13210,

Psychosocial Prediction of Self-Injurious Behavior: A Comparison of Two Populations

Melissa J. Sitton, Tina Du Rocher Schudlich, Christina Byrne


A psychosocial approach to predicting self-injurious behavior (SIB) may allow for more accurate predictions and enhance intervention for individuals who engage in SIB. We examined psychosocial predictors of SIB within and between two populations: individuals with traits of borderline personality disorder (BPD; N = 60) and college students (N = 116). All participants met the inclusion criteria of engaging in SIB at least once in the past year. All participants completed measures of psychological distress, social functioning, and SIB. Methods of SIB did not vary across samples, but SIB rates did. Psychological distress and population type (BPD or student) predicted SIB, whereas social factors did not. Additionally, we found a significant interaction wherein psychological distress was more related to SIB in individuals with traits of BPD. Accordingly, we recommend that counselors consider population and psychological distress when assessing SIB risk in clients.

Keywords: self-injurious behavior, borderline personality disorder, college students, psychological distress, social functioning


Self-injurious behavior (SIB), the deliberate act of self-inflicted bodily harm, is of growing concern to counselors and clinicians. According to Nock (2010), SIB is a broad concept encompassing self-injury completed with suicidal intent (i.e., suicide attempts), without suicidal intent (i.e., nonsuicidal self-injury), or with ambivalence toward life (i.e., ambivalent, meaning neither strictly suicidal nor nonsuicidal). In other words, an individual can engage in SIB with differing goals that vary in intent from harming themselves to dying. The American Psychiatric Association (2013) considers suicide behavior disorder and nonsuicidal self-injury to be “conditions for further study” (p. 801). Individuals who engage in SIB over time are likely to do so with greater frequency, more methods, and increasing lethality (Andrews et al., 2013). Therefore, there is a great need for counselors and clinicians to assess their clients for SIB.

Although there are differing theories of the development and maintenance of SIB based on intent, particularly regarding the development of suicidal and nonsuicidal SIB, there are similar intrapersonal and interpersonal themes across theories. For instance, in their four-function model of nonsuicidal SIB, Nock and Prinstein (2004, 2005) proposed that intrapersonal (e.g., affective) and interpersonal (e.g., help-seeking) factors act as positive and negative reinforcers of nonsuicidal SIB. Similarly, in their renowned interpersonal–psychological theory of suicide, Joiner and colleagues (Joiner, 2005; Van Orden et al., 2010) proposed that individuals who attempt suicide are characterized both by a desire to die (i.e., interpersonal factors of perceived burdensomeness and thwarted belongingness) and the acquired capability to attempt (i.e., intrapersonal factors such as past SIB).

Notably, there is no specific theory to date regarding ambivalent SIB. Researchers and clinicians often differentiate SIB into two categories (Nock, 2010). In the first category, there is no explicit intent to die, and therefore it is considered nonsuicidal SIB. In the second category, there is no clear lack of suicidal intent, and therefore it is considered suicidal SIB. Thus, ambivalent SIB is often categorized as suicidal SIB, rather than as a unique experience. Regardless of how ambivalent SIB is classified, it is likely that both intrapersonal and interpersonal factors relate to ambivalent SIB given that both are relevant to suicidal and nonsuicidal SIB. Furthermore, individuals who engage in SIB often report multiple intents behind their past SIB (Andover et al., 2012; Klonsky & Olino, 2008). Because of these similarities and the clinical significance of each, we examined intrapersonal (i.e., psychological distress) and interpersonal (i.e., social functioning) predictors of SIB in the current study.

Predicting SIB With Psychosocial Functioning
     The relations between psychological distress and SIB are well established in the literature. Researchers have found positive associations between SIB and depression (Andover et al., 2005; Kirkcaldy et al., 2007), anxiety (Andover et al., 2005; Klonsky & Olino, 2008), obsessive-compulsion (Kirkcaldy et al., 2007), and interpersonal sensitivity (Kim et al., 2015; Kirkcaldy et al., 2007). These studies and others examined specific experiences of psychological distress as it relates to SIB in adults and adolescents and in community and inpatient samples.

Previous studies have also demonstrated relations between social functioning and SIB. For instance, SIB is associated with less social support from family and friends (Rotolone & Martin, 2012; Tuisku et al., 2014). Similarly, SIB is related to more negative interactions or negative relational dynamics with family (Halstead et al., 2014; Van Orden et al., 2010) and friends (Adrian et al., 2011).

Predicting SIB in Different Populations
     Some individuals may be at greater risk for developing SIB. In particular, SIB is especially prevalent in individuals with borderline personality disorder (BPD). According to the American Psychiatric Association (2013), BPD is characterized by “marked impulsivity” along with “a pervasive pattern of instability of interpersonal relationships, self-image, and affects” (p. 663). Notably, one diagnostic criterion of BPD is “recurrent suicidal behavior, gestures, threats, or self-mutilating behavior” (p. 663). Additionally, some risk factors for developing BPD (e.g., high emotion dysregulation, trauma exposure, etc.; Crowell et al., 2009) are also risk factors for engaging in SIB (Nock, 2009, 2010). Although lifetime rates of SIB in individuals with BPD vary, one study found that 92.2% of individuals who sought outpatient treatment for symptoms of BPD had engaged in nonsuicidal SIB within the past 2 months (Andión et al., 2012). Additionally, up to 75% of individuals with BPD reported at least one instance of suicidal SIB (Black et al., 2004). Furthermore, there appear to be differences in SIB engagement when comparing individuals with BPD to a community sample. For example, adults with BPD reported engaging in nonsuicidal SIB more recently and frequently, using more varied methods, and causing more physically severe injuries that require medical attention, compared to individuals without BPD who engaged in nonsuicidal SIB (Turner et al., 2015).

Although the rates and severity of SIB are higher in individuals with BPD than in the general population (Bentley et al., 2015), SIB is considered relatively common in other populations, including nonsuicidal SIB among college students (e.g., Whitlock et al., 2006, 2013). College students are thought to engage in SIB more than the general population (as suggested by Wilcox et al., 2012) with approximately 17%–41% of college students participating in nonsuicidal SIB (Whitlock et al., 2006) compared to 5.9% of adults in the general population (Klonsky, 2011). Most college students are also in the highest risk age group for nonsuicidal SIB (Rodham & Hawton, 2009), and suicide is the second leading cause of death during this period (18–25 years old; Centers for Disease Control and Prevention, 2017). Notably, college students and non–college students of the same age (i.e., 16–24 years old) do not appear to differ in rates of SIB (McManus & Gunnell, 2020).

Current Study
     A wealth of research has identified important psychological and social factors that may be associated with the occurrence of SIB. However, it remains unclear how these factors intersect to predict SIB. Additionally, as Turner et al. (2015) suggested, most research on SIB has considered either individuals with BPD or nonclinical samples (e.g., college students) without considering potential differences in predictors between these populations.

The current study used a comprehensive psychosocial approach to examine psychological distress and social functioning in two samples: a high-risk, treatment-seeking sample of individuals with traits of BPD and a sample of college students. This allowed us to characterize how key factors may intersect in predicting SIB. Our objectives were to (a) examine SIB within and between the two populations, (b) evaluate which psychosocial factors predicted total lifetime SIB for both populations, and (c) determine whether the predictors of total lifetime SIB varied by population (i.e., test for an interaction between psychosocial predictors and sample).


Participants and Procedure
     This study included a sample of individuals with BPD traits and a college student sample. For both samples, our inclusion criteria required that participants have a history of SIB with at least one self-reported episode of SIB (i.e., SIB of any intent) in the past year. We required recent SIB so that the measures of current psychological and social functioning would be appropriate predictors, rather than examining current functioning with a retrospective report of SIB after several years.

Sample 1: Individuals With Traits of BPD
     The first sample consisted of data from a larger study on dialectical behavior therapy (DBT) in teens and adults (Sitton et al., 2020). Participants sought treatment for BPD symptoms from community-based counselors, although not all participants had formal diagnoses of BPD. The counselors obtained informed consent from participants and collaborated with a local university for this larger IRB-approved study. Of the 62 participants in this larger study, 96.8% (n = 60) reported engaging in SIB in the past year and constituted the BPD-Tx sample.

BPD-Tx participants (n = 60) were mostly young adults (M = 23.53 years, SD = 6.85 years, range = 18–48 years old). Based on self-reports, there were 49 females (81.7%), eight males (13.3%), and three participants who identified as non-binary or androgynous (5%). This sample was mostly White/European American (83.1%), followed by multiracial (10.2%), Asian American (1.7%), and Hispanic/Latinx (1.7%), with an additional 3.4% identifying as “other” or not reporting. Most (80%) reported no counseling experience prior to receiving DBT from the community counselors (i.e., at the time of recruitment). Data on sexual orientation was not available for this sample.

Sample 2: Undergraduate College Students
     The second sample consisted of undergraduate students in introductory psychology courses at a university in the Pacific Northwest. We recruited students to participate in a study called “Emotional and Behavioral Responses to Stress” and informed all participants that they might experience distress as part of the study. After giving their informed consent, participants completed the measures online in a campus computer lab so any questions or concerns could be immediately addressed by a research assistant trained in suicide prevention. Debriefing included an extensive form that included on- and off-campus mental health resources.

Of the 536 students who completed the survey, 43.8% reported engaging in SIB during their lifetime, and 116 students (21.6%) met the inclusion criteria of engaging in SIB in the past year. This proportion of students is high compared to some student samples (e.g., Whitlock et al., 2006; Wilcox et al., 2012), but it is comparable to the lifetime rate from at least one other university sample (Gratz et al., 2002).

Student participants included in this study (n = 116) were mostly young adults (M = 19.62 years, SD = 1.58 years, range = 18–27 years old). Based on self-reports, there were 89 females (78.4%), 23 males (19.8%), and four participants who identified as non-binary or androgynous (4%). This sample was mostly White/European American (69%), followed by multiracial (19.8%), Asian American (6%), and Hispanic/Latinx (4.3%). Participants’ sexual orientations were as follows: 60.3% heterosexual, 18.1% bisexual, 7.8% pansexual, 6.9% homosexual, 1.7% asexual, and 1.8% who identified as “other.” Most (77.6%) reported previous counseling experiences, with about one-fifth currently seeing a counselor (22.4%). Other studies have found rates of prior experience with counseling services to be closer to 55% in college students (e.g., Niegocki & Ægisdóttir, 2019). Most student participants reported seeking counseling services for stress- and mood-related symptoms, and none reported seeking treatment specifically for BPD. 

Self-Injurious Behavior (SIB)
     We used the Lifetime Suicide Attempt Self-Injury Interview (LSASI; Linehan & Comtois, 1996) to assess participants’ history of SIB, including frequency, method, and intent. This 20-item measure asks about the dates of the most recent and most severe SIB act (e.g., “When was the most recent time that you intentionally injured yourself?”) and assesses the total lifetime frequency for 11 methods of SIB, as well as the separate intent(s) of each SIB act (suicidal, nonsuicidal, or ambivalent). Higher scores indicate more SIB acts.

Internal consistency was adequate for both samples (BPD-Tx sample, Cronbach’s α = .75; student sample, Cronbach’s α = .73). Notably, the LSASI was created for clinical use rather than research use; therefore, there are no known studies of its reliability or validity. However, the LSASI was already in use by the counselors in the larger study of DBT described, and they chose to use it to assess SIB in the BPD-Tx sample. We used it for the student sample to be consistent with the existing sample data. Following Linehan and Comtois’s (1996) scoring instructions, we calculated a total lifetime frequency for each participant by summing all SIB of any intent.

Psychological Distress
     The Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1975) is a broad-spectrum psychiatric symptom checklist. Participants rate their distress level in the past week on a Likert-type scale from 0 (not at all) to 4 (extremely) for each of 90 items (e.g., “How much were you distressed by feeling critical of others?”). This measure assesses nine factors of psychological distress. For this study, we were interested in the factors of Depression, Anxiety, Obsessive-Compulsion, and Interpersonal Sensitivity. The internal consistency of this measure was very high in the BPD-Tx sample (α = .97).

To reduce participant burden, we used the Brief Symptom Inventory (BSI; Derogatis & Spencer, 1982), a 53-item version of the SCL-90-R, for student participants. The internal consistency was very high in the student sample (α = .96).

To assess the comparability of the SCL-90-R and the BSI for subsequent analysis, we separately averaged all items for the factors of Anxiety, Depression, Obsessive-Compulsion, and Interpersonal Sensitivity to determine BPD-Tx participants’ scores of psychological distress using these two measures. We found strong correlations between the SCL-90-R factors and the BSI factors (Depression: r = .92, p < .001; Anxiety: r = .97, p < .001; Obsessive-Compulsion: r = .95, p < .001; Interpersonal Sensitivity: r = .90, p < .001; and Average Psychological Distress: r = .98, p < .001). Following Derogatis (1993), who found no significant difference in validity between the SCL-90-R and the BSI, we used only the BSI items to create symptom factors for both samples. The internal consistency of the BSI items for the
BPD-Tx sample was very high (α = .95).

Social Functioning
     The Network of Relationships Inventory-Behavioral Systems Version (NRI; Furman & Buhrmester, 2009) is a 33-item self-report measure of social support and negative interactions in various relationships (i.e., one’s mother, father, friends, and romantic partner). Participants rate the frequency of positive support or negative interactions on a Likert-scale from 1 (little or none) to 5 (the most). The Positive Support scale includes five subscales: Seeks Secure Base, Provides Secure Base, Seeks Safe Haven, Provides Safe Haven, and Companionship. The Negative Interactions scale includes three subscales: Conflict, Antagonism, and Criticism. Higher scores indicate more of each factor. The internal consistency was high for both samples (BPD-Tx, α = .93; student sample, α = .94).

We calculated the mean score of the Positive Support subscales, including Seeks Secure Base, Seeks Safe Haven, and Companionship. We did not include Provides Secure Base or Provides Safe Haven because Furman and Buhrmester (2009) described these as “caretaking” factors rather than “attachment” or “affiliation” factors. We also calculated the mean score of all three Negative Interactions subscales.

Data Analysis Plan
     To begin, we tested for the assumptions of analysis, following guidelines proposed by Tabachnick and Fidell (2019). We defined outliers as data points beyond three standard deviations from the mean. We evaluated outliers within each group and replaced them with the value that was three standard deviations above the group mean. We chose this more liberal approach to outliers to maximize variability in the data. It was especially important to maintain variability in the outcome variable of total SIB given that higher levels of SIB have great clinical significance. For skewness and kurtosis of the composite variables, we used ±2 as our acceptable range of values. We transformed variables that did not meet our criteria for normality. We also utilized the missing completely at random test and found no systematic patterns to missing data, and thus used the group means to replace missing values for analysis.

To assess SIB in the two samples, we examined the intent of SIB acts separately for each sample and analyzed if SIB rates differed based on demographic information. To examine psychosocial predictors of SIB, we conducted a multiple linear regression. We used total SIB (including suicidal, nonsuicidal, and ambivalent SIB) as the outcome variable. We also examined differences in predictors of total SIB between the BPD-Tx and student samples by including interaction terms (e.g., psychological distress x sample). Statistically significant interactions were graphed to aid interpretation (Howell, 2013).

For the multiple linear regression analysis, we used effect coding for sample type (Daly et al., 2016), which allows comparison of a sample mean to the overall mean instead of using one sample as a reference group. Additionally, we centered the predictor variables around the grand mean for the whole sample to reduce the risk of multicollinearity. We inspected the tolerance and variance inflation factors, and used multiple sources (e.g., correlations between variables, p-values, and the standard error of the regression coefficients) to determine if multicollinearity was an issue.


We used SPSS 24.0 to analyze the data. Using one-way analysis of variance (ANOVA), we found no differences between the samples based on gender or ethnicity (all p values > .05). However, using an independent samples t-test, we found that the BPD-Tx sample (M = 23.53, SD = 6.85) was older on average than the student sample: M = 19.62, SD = 1.58, t(173) = 5.85, p < .001. Additionally, the BPD-Tx sample (13.3%) reported prior experience with counseling (dichotomous variable) less often than the student sample (77.6%) on average: χ2(1) = 59.39, p < .001.

Sample Differences in SIB
     We conducted descriptive analyses for all SIB variables. See Table 1 for descriptive statistics of the different intents of SIB (nonsuicidal, ambivalent, and suicidal), total SIB (including the untransformed total score), and the reported number of SIB methods. Table 1 also includes difference scores of SIB acts based on independent sample t-tests in consideration of the two samples. Individuals in the BPD-Tx sample engaged in more nonsuicidal, ambivalent, and total SIB in their lifetime compared to the student sample. Although there appeared to be no difference between samples in suicidal SIB, it is worth noting that this variable did not meet our criteria for normality in either sample even after transformation.


Table 1

Means (With Standard Deviations) and Difference Scores for Self-Injurious Behavior (SIB) by Sample

      Variable BPD-Tx

(N = 60)


(N = 116)

t(df) p
Nonsuicidal SIB     3.13 (1.81)   2.34 (1.55)  t(174) = 3.01  .003
Ambivalent SIB     1.92 (2.02)   1.07 (1.33)  t(86.25) = 2.94    .004
Suicidal SIB     0.66 (0.90)   0.45 (0.81)  t(174) = 1.61    .110
Total SIB     3.87 (1.84)   2.86 (1.43)  t(96.56) = 3.73 < .001
Total SIB (untransformed) 166.31 (268.69) 44.10 (75.60)  t(63.88) = 3.45    .001
Number of SIB methods     3.28 (1.53)   3.28 (2.11)  t(174) = -0.004  .997

 Note. BPD-Tx = participants with traits of borderline personality disorder; Total SIB (untransformed) =
untransformed values after adjusting the outliers in the raw reported values. Significant p values are in bold.
Although the normality of suicidal SIB was improved using a transformation, we were unable to meet our
acceptable range of ±2 for kurtosis (BPD-Tx kurtosis = 4.22; student kurtosis = 2.71).

In the BPD-Tx sample, we found no differences in SIB frequency based on gender, age, ethnicity, or counseling experience using one-way ANOVA. In the student sample, we found no differences in SIB frequency based on age, ethnicity, living situation, or counseling experience using one-way ANOVA. However, SIB frequency differed by gender such that those who identified as non-binary (M = 4.64, SD = 1.35) reported significantly higher rates of SIB than both males (M = 2.80, SD = 1.31) and females (M = 2.95, SD = 1.20). There were no differences in SIB frequency or severity based on sexual orientation in the student sample.

Psychosocial Predictors of SIB
     We compared the two samples on the predictor variables first by using independent sample t-tests. We found that BPD-Tx participants reported less psychological distress (M = 2.21, SD = 0.78) than student participants: M = 2.78, SD = 0.89, t(174) = −4.16, p < .001. The BPD-Tx participants (M = 3.25, SD = 0.49) also reported less positive social support than student participants: M = 3.44, SD = 0.54, t(174) = −2.26, p = .025. Lastly, BPD-Tx participants (M = 1.22, SD = 0.43) reported more negative interactions than student participants: M = 1.07, SD = 0.43, t(174) = 2.15, p = .033.

We conducted bivariate correlations between all predictor variables and the outcome variable for each sample. In the BPD-Tx sample, total SIB was positively correlated with average psychological distress (r = .37, p = .004). In the student sample, total SIB was negatively correlated with positive social support (r = −.18, p = .049). In both samples, average psychological distress was positively associated with negative interactions (BPD-Tx: r = .36, p = .005; student: r = .24, p = .008). No other variables were significantly correlated in either sample.

Next, we conducted a multiple linear regression using total SIB as the outcome variable for both samples together. We entered seven predictors simultaneously: psychological distress, positive social support, negative interactions, sample type, and the interactions between sample type and the three other predictors. Together, these seven variables significantly predicted total SIB: F(7,168) = 5.01, p < .001, MSE = 2.33, r2 = .17. As shown in Table 2, psychological distress (sr2 = .06), sample type (sr2 = .12), and the interaction between psychological distress and sample type (sr2 = .03) were each significant unique predictors of total SIB. Specifically, based on the positive β weights, more psychological distress and being in the BPD-Tx sample were both associated with higher lifetime rates of SIB. Notably, multicollinearity did not appear to be an issue in this regression given the moderate to low correlations between factors, sufficiently high tolerance values, acceptable variance inflation factor values (ranging from 1.25–1.55), and the low standard error of regression coefficients relative to their scale.


Table 2

Multiple Regression Analysis Predicting Total Self-Injurious Behavior for the Whole Sample (N = 176)

Variable    B SE B  β    t     p  sr2 Lower Upper
Psych. distress  0.57 0.16  .31  3.55    .001 .06  0.25 0.89
Pos. social support −0.48 0.25 −.16 −1.96    .052 .02 −0.97 0.00
Neg. interactions −0.26 0.30 −.07 −0.85    .399   .003 −0.85 0.34
Sample type  0.68 0.14  .39  4.87 < .001 .12  0.40 0.95
Psych. distress x sample  0.40 0.16  .21  2.46    .015 .03  0.08 0.71
Pos. social support x sample  0.00 0.25  .00  0.00    .997   .001 −0.49 0.49
Neg. interactions x sample −0.08 0.30 −.02 −0.25    .801   .001 −0.67 0.52

Note. Psych. = psychological; Pos. = positive; Neg. = negative; sr2 = squared semipartial correlation. Sample type was
coded so that BPD-Tx sample = 1, student sample = -1. Significant p values are in bold.


Sample Differences in SIB Predictors

To further probe the statistically significant interaction, we plotted the regression paths for psychological distress predicting total SIB by sample type. As shown in Figure 1, more psychological distress was related to higher lifetime rates of total SIB in both samples, which supports the main effect of psychological distress found in the multiple regression analysis. However, the relation between psychological distress and total SIB was stronger in the BPD-Tx sample than in the student sample (as evidenced by the steeper slope of the regression line representing the BPD-Tx sample compared to that of the student sample).


Figure 1

Regression Lines of Average Psychological Distress Predicting Total Self-Injurious Behavior (SIB) by Sample Type


The primary goals of the current study were to establish a more comprehensive set of predictors of SIB and to better understand how the experience of SIB varied by population (BPD-Tx vs. college students). This study was unique in its psychosocial approach to predictors. Additionally, we tested for interactions between sample type and the psychosocial predictors of SIB. This singular examination of interacting predictors has seldom been conducted in the literature, and thus is an important strength of this study.

SIB Engagement and Psychosocial Functioning
     The results demonstrate a very high lifetime frequency of SIB in both samples. Although most studies do not report the lifetime frequency rates of SIB of their participants, the frequency of SIB in our student sample was comparable to that found in another study of students using the same SIB methods with nonsuicidal intent (Croyle & Waltz, 2007). The frequency rate of SIB in the BPD-Tx sample appeared to be lower than found in some other studies with individuals with BPD (e.g., Turner et al., 2015).

Additionally, we found that the lifetime frequency rates of SIB were higher in the BPD-Tx sample than in the student sample, which aligns with previous studies (e.g., Klonsky & Olino, 2008; Turner et al., 2015). This makes sense given the maladaptive behaviors often seen in individuals with BPD. Additionally, given that the BPD-Tx sample was older than student participants on average, it is also possible that their increased lifetime rates of SIB reflected a greater number of years to engage in it. Alternatively, the higher SIB frequency reported by the BPD-Tx participants may serve an interpersonal function. According to Linehan (1993), nonsuicidal SIB is commonly used by individuals with BPD to communicate with and gain attention from others.

Interestingly, despite higher rates of total SIB, BPD-Tx participants reported less psychological distress than did student participants. This was contrary to many other studies showing a strong association between psychological distress and engagement in nonsuicidal SIB for individuals with BPD (e.g., Sadeh et al., 2014; Turner et al., 2015). One possible explanation for the lower rates of psychological distress reported by BPD-Tx participants is that their baseline level of psychological distress was higher, leading these negative emotions to be considered normal and therefore not “distressing.” Additionally, given that fewer BPD-Tx participants reported prior experience with counseling than student participants, it could be that BPD-Tx participants reported less psychological distress because of a lack of emotional self-awareness. This aligns with Turner et al.’s (2015) finding that participants with BPD who engage in nonsuicidal SIB reported less awareness of their emotional states. Another explanation is that the BPD-Tx participants were recruited from a community-based clinic wherein they were preparing to start DBT. Although the data used in the current study represents pretest data gathered prior to treatment, it is possible that the BPD-Tx participants were experiencing lowered distress at the time of data collection because of the hope and positive expectations that are often associated with starting a new treatment (Dew & Bickman, 2005).

Socially, the BPD-Tx participants reported less positive support than student participants. This finding aligns with the biosocial theory of BPD (Linehan, 1993), which suggests that individuals with BPD may experience or perceive an invalidating environment. Alternatively, BPD-Tx participants may be more likely to interpret interactions with others as negative, which aligns with Peters et al.’s (2015) finding that individuals with traits of BPD often demonstrated maladaptive responses to emotional experiences, leading them to interact negatively with others.

Psychosocial Predictors of SIB
     An important finding of the current study is that psychological distress predicted total SIB with a small to moderate effect size. This suggests that psychological distress (including experiences of anxiety, depression, obsessive-compulsion, and interpersonal sensitivity) is an important component of SIB of various intents. Specifically, psychological distress may act as a catalyst for SIB, wherein individuals engage in SIB to decrease their psychological distress. This explanation aligns with Nock and Prinstein’s (2004, 2005) theory of the intrapersonal negative reinforcement function of nonsuicidal SIB. Namely, that one might engage in SIB in order to reduce tension or psychological distress, particularly anxiety.

Contrary to the majority of extant literature (e.g., Wilcox et al., 2012), neither positive social support nor negative interactions predicted total SIB in the current study. We also did not find an interaction between either social variable and sample type, suggesting that social functioning might not be a direct, distinct predictor of total SIB for either population. However, it is possible that social functioning is indirectly related to total SIB. For example, we found a significant positive correlation between negative interactions and psychological distress in both samples. Given these correlations, negative interactions may contribute to experiences of psychological distress, which then predict total SIB. This proposed indirect relation is supported by Adrian et al.’s (2011) study, which found that emotion dysregulation partially mediated the relation between interpersonal problems (i.e., problems with one’s family and peers) and nonsuicidal SIB.

Another possible explanation for the lack of significant social predictors of SIB in the current study is the variability in the data that stems from inconsistent timing of social support. Specifically, it is unclear if positive support preceded SIB engagement, followed the SIB act, or both. Turner et al. (2016) found that perceived social support increased after participants disclosed their nonsuicidal SIB acts to others. However, they also found that this increased support was associated with increased nonsuicidal SIB urges and acts the following day, presumably because the SIB had achieved the desired interpersonal function. Thus, similar to Turner et al.’s (2016) study, the lack of a clear, linear relation between SIB and social support may have contributed to nonsignificant findings of social predictors in the current study.

Notably, the strongest single predictor of total SIB was sample type, with BPD-Tx participants showing greater frequency of total lifetime SIB than student participants. This aligns with Turner et al. (2015), who found that individuals with BPD traits engage in nonsuicidal SIB more often than do those without BPD traits.

Sample Differences in SIB Predictors
     The relation between psychological distress and total SIB was stronger for the BPD-Tx sample than for the student sample. This finding is somewhat supported by previous literature; for example, Klonsky and Olino’s (2008) latent class analysis revealed that the group with the most nonsuicidal SIB also reported more symptoms of BPD and psychological distress and reported regularly engaging in nonsuicidal SIB to help regulate their emotions. In comparison, individuals with BPD traits in the current study reported engaging in more total SIB (as well as nonsuicidal SIB) but did not report greater levels of psychological distress than did the student participants. However, if our BPD-Tx participants used SIB for emotion regulation, too, then perhaps this strategy allowed them to experience lower levels of psychological distress day-to-day than student participants. This aligns with Sadeh et al.’s (2014) finding that BPD symptoms related to the affect-regulating function of SIB, especially nonsuicidal SIB.

Additionally, the significant interaction we found between psychological distress and sample type resembles Andover et al.’s (2005) finding that BPD symptoms accounted for the relation between anxiety and nonsuicidal SIB. However, in our study, psychological distress was a significant unique predictor of total SIB (in addition to the significant interaction between psychological distress and sample type). In other words, sample type seems to be a moderator between psychological distress and SIB in our study, as opposed to a mediator.

Counseling Implications
     Our findings have several treatment implications. Many counselors will not be surprised by the high rates of SIB found in our BPD-Tx sample. However, we also found a clinically important high rate of SIB in college students. Given that past engagement in SIB is one of the strongest predictors of future SIB (including nonsuicidal and suicidal SIB; Tuisku et al., 2014), the high lifetime rates of SIB found in both samples in the current study are noteworthy for service providers. Specifically, our results suggest that universities and other institutions concerned with mental health in college students should consider utilizing SIB screening tools. Additionally, the high prevalence of students with a lifetime history of any SIB suggests the need for widespread intervention programs for student populations. For example, some research (e.g., Kannan et al., 2021) has examined the implementation of DBT skills groups in college counseling centers for students with a variety of presenting issues, including SIB. Such intervention programs could benefit a wider range of students and help improve quality of life for many, especially those struggling with SIB.

Given that psychological distress predicted total SIB, it may be beneficial for counselors to regularly assess the level of psychological distress in all clients, including those with BPD and college students. Clients with high psychological distress, including anxiety, depression, obsessive-compulsion, and interpersonal sensitivity, will likely engage in more SIB than those with low psychological distress, and thus the counselor may be able to intervene before the client escalates to a high frequency of SIB. Assessing and tracking affective distress levels may be common with suicide assessment and safety planning, but there may be less awareness about the need for this with SIB. Treatment protocols could also focus on lowering psychological distress to see if that will decrease SIB. For example, DBT, which emphasizes psychological distress tolerance, has been increasingly implemented in college campus counseling centers (see Chugani, 2015). However, given that the current study’s findings are not causal, we cannot definitively conclude that lowering psychological distress will affect SIB.

Importantly, the interaction between psychological distress and sample type is noteworthy given that it contributes to the small extant evidence of divergence between populations of individuals with symptoms of BPD and other, more community-based populations like college students. Specifically, we found differences in SIB prevalence, in lifetime frequency, and in one predictor (i.e., psychological distress) in our two samples. This aligns with Turner et al.’s (2015) findings that individuals who engaged in SIB with and without BPD differed in SIB frequency, severity, and comorbid affective symptomology.

It is also worth noting that the correlational analysis revealed a difference between these two samples in social functioning. In particular, there was a statistically significant negative correlation between total SIB and positive social support in the student sample, but not in the BPD-Tx sample. Because of this, although we only found one statistically significant interaction between psychosocial predictors and sample type, it is plausible that there are other notable differences in SIB risk factors between these two populations. Thus, when treating SIB, it may be worth assessing for other symptoms of BPD to form a more accurate representation of a client’s experience and to help form a specific treatment plan.

Limitations and Future Studies
     One potential limitation of the current study is that we included only individuals who reported engaging in SIB in the past year because we wanted to examine current predictors of current SIB. However, it is possible that psychological distress and social support are more effective predictors of future SIB acts. In other words, the current study examined predictors of the frequency of SIB using current psychosocial functioning, yet the psychosocial variables might have been better at predicting whether or not an individual will engage in SIB in the future. This theory aligns with Heath et al.’s (2009) interpretation of their lack of results linking social support to lifetime rates of nonsuicidal SIB. Specifically, that social support may better relate to differences between those who will engage in SIB compared to those who will not, as opposed to the degree (i.e., frequency) of SIB. It is unclear how the results may have differed if we included a comparison group of individuals who do not engage in SIB or have never engaged in SIB.

A second limitation was the need to use specific measures to compare the student sample to the existing BPD-Tx sample data. Although the LSASI measure has the advantage of thoroughly examining SIB methods and intent, it was intended for clinical use rather than research. Additionally, the LSASI is a lifetime measure of SIB as opposed to assessing recent SIB; although our inclusion criteria required participants to have engaged in SIB at least once in the past year, it is unknown how recent or severe the SIB was in the past year relative to one’s lifetime. Because of this, a dichotomous measure of past-year engagement in SIB may have better suited our need for recent SIB assessment. Nonetheless, the LSASI provided a great depth and variability in the data that was not only valuable in the current research study, but also clinically important to the counselors with whom we collaborated in the larger DBT study.

A third limitation is that there may be other variables involved in predicting SIB that were not assessed, such as emotion regulation skills or trauma exposure. For example, SIB frequency might be more strongly related to one’s ability to regulate distress rather than the presence of distress itself. Given that emotional reactivity and trauma exposure are both risk factors for SIB (Nock, 2009, 2010) and for the development of BPD (Crowell et al., 2009), future studies may want to further explicate these relations.

It is also worth noting that the samples in the current study may include theoretically overlapping populations. Specifically, we did not screen the BPD-Tx group for current academic status, and therefore it is possible that some participants in the BPD-Tx group were also college students. We decided not to exclude BPD-Tx participants based on academic status in order to reduce barriers to study participation and so that the BPD-Tx sample would represent people who seek treatment for BPD in the community, not just those who are not college students. Additionally, although we screened the student sample for the explicit endorsement of BPD diagnosis, it is possible that some participants in the student sample had subthreshold symptoms of BPD (especially considering that SIB itself is a symptom of BPD) or simply had not received a diagnosis of BPD at the time of this study.

Future studies should continue to examine psychosocial predictors of SIB with larger and more diverse samples in order to explore the relations between psychological and social predictors. Additionally, future studies should explore other relevant factors with the psychosocial predictors (e.g., emotion regulation, trauma exposure) to determine if other factors may better explain (or mediate the relations with) SIB. Moreover, longitudinal and experience-sampling designs would allow researchers to gain better understanding of how changes in psychosocial functioning relate to decisions to engage in SIB as well as the exact sequence of events for SIB acts. Although some studies have recently begun using these techniques, a more psychosocial approach to predictors and consequences of SIB (also considering various intents) may provide more prudent information for intervention and treatment of individuals who engage in SIB.

     The current study sought to identify psychosocial predictors of SIB in two clinically different populations and to compare predictors between these populations. We found high lifetime frequency rates of SIB in both samples, suggesting a need for more widespread assessment of SIB in young adults from different populations. We also found that population type itself was the strongest predictor of SIB—individuals with traits of BPD engaged in more SIB in their lifetimes than did college students. Additionally, psychological distress predicted SIB; however, we also found a significant interaction between population and psychological distress, which suggests that psychological distress may be more related to SIB in individuals with traits of BPD than in more community-based populations like college students. Consequently, counselors should consider population and psychological distress when assessing SIB risk in clients.

Conflict of Interest and Funding Disclosure
Data from the existing BPD sample was partially
funded by an internal grant awarded to coauthor
Christina Byrne. The authors reported no conflict
of interest or other funding for the development
of this manuscript.



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Melissa J. Sitton, MS, is a doctoral student at Southern Methodist University. Tina Du Rocher Schudlich, PhD, MHP, is a professor at Western Washington University. Christina Byrne, PhD, is an associate professor at Western Washington University. Correspondence may be addressed to Melissa J. Sitton, Department of Psychology, Southern Methodist University, P.O. Box 750442, Dallas, TX 75275-0442,