Peer Victimization and Poor Academic Outcomes in Adolescents With ADHD: What Individual Factors Predict Risk?

Abstract

Objective: Examine individual factors associated with peer victimization (PV) in adolescents with attention-deficit/hyperactivity disorder (ADHD) and to examine the association between PV and educational outcomes. Method: Participants were 121 adolescents (M_age = 13.62, SD = 1.03; 89% boys) with diagnosed ADHD. Using path analysis, we tested whether general adolescent factors (ADHD symptoms, comorbid autism spectrum disorder, cognitive and social functioning, and age) were associated with experiences of PV, and associations between PV and academic outcomes. Results: Deficits in working memory (WM) and peer relationship problems were weakly and moderately associated with PV, respectively. PV was in turn was associated with adolescents’ attitudes about school, academic competence, and academic achievement. Conclusion: Adolescents with poor social skills and/or WM difficulties who have ADHD may be particularly vulnerable to being victimized by peers. Failure to identify and manage PV during early adolescence may be connected to poor educational outcomes.

Keywords

peer victimization adolescents ADHD social skills cognitive academic

Youth with attention-deficit/hyperactivity disorder (ADHD) are a high-risk group for peer victimization (PV; Chou et al., 2018; Efron et al., 2021; Holmberg & Hjern, 2008; Taylor et al., 2010). PV is the experience of young people being aggressively targeted by peers which may include teasing, physical threats, violence/property attacks, spreading rumors about the victim, and excluding or socially isolating the victim (Austin & Joseph, 1996; Bond et al., 2007; Mynard et al., 2000; Schäfer et al., 2004). Given that being victimized by peers is associated with increased academic, social, and behavioral difficulties in the general population (Juvonen & Graham, 2014; Taylor et al., 2010), it is essential to examine individual factors that predict or could exacerbate PV among a sample at risk of PV such as adolescents with ADHD. Furthermore, and of relevance to schools, it is important to examine the role of PV in the academic outcomes of these at-risk youth. As such, the present study simultaneously explored multiple risk factors that may predict PV (more frequent and upsetting PV) and the role of PV in student attitudes about school and objective academic outcomes in a sample of adolescents with ADHD.

Experiences of PV in Youth ADHD

As youth with ADHD are known to experience higher rates of PV than typically developing youth (e.g., Aguado-Gracia et al., 2018; Efron et al., 2021; Sciberras et al., 2012; Wiener & Mak, 2009), it is important to consider risk factors for PV and implications of victimization for this population. For example, when victimized, youth with ADHD experience higher emotional and behavioral problems than youth with ADHD who are not victimized, suggesting the PV has a significant negative impact (Taylor et al., 2010). These PV experiences, in turn, may lead to negative academic outcomes in youth with ADHD (e.g., lower grade point average and academic engagement; Juvonen et al., 2011).

An emerging body of research has evaluated individual risk factors as predictors of PV in youth with ADHD (Chou et al., 2018; Efron et al., 2021; Sciberras et al., 2012; Wiener & Mak, 2009). The predictors in these studies include sex, age, ADHD symptomatology, social skills, cognitive/executive function abilities, comorbid symptomatology (oppositional defiant disorder, conduct problems, anxiety, depression, autism spectrum disorder [ASD], tic disorders, language problems), as well as parent mental health, parenting and student–teacher relationships. Although results have been mixed, patterns of relations have emerged: comorbid anxiety, depression, and tic disorders are largely unrelated to PV; younger age, comorbid ASD (Chou et al., 2018), and poorer executive functions (Aguado-Gracia et al., 2018) have been associated with higher rates of PV in one study but not in another (Efron et al., 2021). ADHD symptomatology, sex, intellectual abilities, comorbid oppositional defiant disorder/conduct problems, and social skills have been inconsistently linked with PV, such that some studies suggest more severe ADHD symptoms, female sex, intellectual abilities, conduct problems, and poorer social skills to be associated with greater PV but others fail to find such an association. Part of the inconsistency in findings has been due to differences in how variables are measured; for example, oppositional defiant disorder has been largely unrelated to PV but broader conduct problems have been related to PV. Differences in findings may also reflect the different ages of the samples; for example, sex differences were present in a combined child/adolescent sample (Wiener & Mak, 2009) but not in an adolescent sample (Chou et al., 2018). Furthermore, some studies have focused on one-to-one associations or correlations (Aguado-Gracia et al., 2018), whereas others have examined unique associations while accounting for other relevant variables (Becker et al., 2017). Given these inconsistencies in findings, additional research is required to identify which factors have the strongest association with PV in youth with ADHD.

Social Difficulties in Youth With ADHD

Social relationships/friendships serve many functions for youth; specifically, they increase self-efficacy, provide companionship, provide an opportunity to acquire and refine skills, and serve as a protection from PV (Bukowski et al., 1994). It has been well established that youth with ADHD experience significant difficulties in social functioning. Specifically, research suggests that youth with ADHD are 4 times as likely as typically developing youth to be rejected by peers (Hoza, 2007). Furthermore, youth with ADHD have fewer friends (e.g., Marton et al., 2012) and greater difficulty maintaining friendships (e.g., Normand et al., 2013). Among existing friendships, youth with ADHD display greater conflict and less intimacy, reciprocity, and satisfaction with friendships relative to typically developing peers (e.g., Hoza, 2007; Normand et al., 2013). However, there is research suggesting that youth with ADHD do not typically report social difficulties (e.g., making and keeping friends; Marton et al., 2012), highlighting the importance of using informant report for social behaviors. It is also important to note that although not all youth with ADHD experience social difficulties, youth with ADHD and peer relationship problems are more likely to experience a host of negative outcomes, including conduct problems, depression, substance abuse, eating disorders, and school dropout in adolescence, compared to youth with ADHD without peer relationship problems (Mikami & Hinshaw, 2006). This finding highlights the importance of examining the role of peer relationships in PV and negative academic outcomes in youth with ADHD. Several studies have examined correlations between social problems and PV in youth with ADHD and have found moderate to strong positive correlations (Aguado-Gracia et al., 2018; Humphrey et al., 2007; Sciberras et al., 2012); however, more research is needed to examine whether these relations hold when taking into account other relevant risk factors.

Cognitive Deficits in Youth With ADHD

Youth with ADHD exhibit multiple deficits in cognitive functioning, notably executive functions, such as working memory (WM) and general intellectual ability (intelligence quotient [IQ]; Martinussen et al., 2005). Cognitive abilities may have important risk implications for PV. Specifically, WM is thought to be important for the development of social behaviors, a risk factor for PV, as it permits internal representation of information to guide decision-making and responses to the environment (Kofler et al., 2018). Similarly, individuals with higher IQs, particularly perceptual reasoning abilities, may be better able to take in information and cues from social situations to interact with others, better manage conflict, and as a result be less likely to be victimized by peers (Huepe et al., 2011; Liu et al., 2017). In contrast, youth with lower IQs experience more difficulties in social endeavors, resulting in frustration, low-esteem, and even aggression or bullying perpetration (Huepe et al., 2011; Huesmann et al., 1987). Prior research has found lower executive function and intellectual abilities (Kira et al., 2014) to be associated with PV in community samples. In addition, in typically developing children, IQ has been found to be associated with PV (Huepe et al., 2011; Verlinden et al., 2014) and to be predictive of chronic PV (Bowes et al., 2013). To the best of our knowledge, only three studies (Aguado-Gracia et al., 2018; Efron et al., 2021; Liu et al., 2017) have investigated the role of executive function and intellectual abilities in PV in an ADHD sample. These studies have found better executive functions and certain aspects of IQ (i.e., perceptual reasoning but not full-scale IQ) to be predictive of PV (Aguado-Gracia et al., 2018; Liu et al., 2017), whereas Efron et al. (2021) found that estimated IQ was not independently associated with PV in a community-based sample of children with ADHD (M_age = 8.9 years). Given the mixed results from typically developing youth and limited exploration of these factors within an ADHD sample, research is needed to examine the unique associations between cognitive abilities and PV in youth with ADHD.

Association Between PV and Academic Outcomes

In the general population, research supports an association between PV and worse academic achievement (Juvonen & Graham, 2014; Konishi et al., 2010; Rothon et al., 2011), specifically math and reading achievement and lower grades (Konishi et al., 2010; Strom et al., 2013). In a large diverse community sample of middle school students, PV predicted worse academic performance using both objective (i.e., grade point average) and teacher-reported (i.e., academic engagement) measures at six time points across the middle school years (Juvonen et al., 2011). Prior work has largely been with community samples (i.e., across a school or schools); however, less is known about these relations in youth with ADHD. Efron et al. (2021) found little evidence of an association between PV and teacher-reported academic competence; however, this sample comprised children in the early years of school and we might expect to see more of an association for older children and adolescents. One study examining these relations in a sample of adolescents with ADHD found higher rates than same age peers of PV and social impairments in youth with ADHD but did not find an association between PV and academic outcomes (Taylor et al., 2010). However, this study only used parent-report of academic outcomes; thus, it is necessary to examine these relations using both objective outcomes (e.g., standardized testing) and outcomes from other perspectives (e.g., adolescent, teacher). As such, it is critical for research to examine the association between PV in early adolescence and teacher-reported and objective measures of math, reading, and writing achievement among adolescents with ADHD. Furthermore, previous studies have yet to consider the association between PV and other school variables such as student engagement, which are predictive of school success.

Present Study

This study addressed several gaps in the literature by simultaneously examining the relation between several individual risk factors that have previously been associated with PV (social and cognitive functioning, age, comorbid ASD, ADHD symptom domains), PV, and academic outcomes in a sample of adolescents with ADHD. The first aim was to examine the unique relations between (a) ADHD symptom domains (inattention and hyperactivity/impulsivity), (b) self-, parent-, and teacher-reported peer relationship problems, (c) cognitive abilities (estimated IQ and WM), (d) comorbid ASD, and (e) age with PV, to further elucidate which youth with ADHD may be at risk of PV. Given prior research examining associations for these factors, it was predicted that inattention symptoms, social functioning, cognitive functioning, and comorbid ASD would be uniquely associated with PV. Given the narrow age range, we did not anticipate that age would be related to PV in the present sample. The second aim was to examine the role of PV in self-reported attitudes about school and more objective measures of academic functioning (teacher-reported academic competence and standardized academic testing). It was predicted that youth with ADHD who experienced greater PV would have poorer attitudes about school and poorer academic functioning.

Method

Participants

Participants were 130 (89% boys) English-speaking adolescents with ADHD in Grades 7 and 9 (M_age = 13.62, SD = 1.03, range = 12.0–15.7 years) from the adolescent follow-up of the Attention to Sleep study. Sample characteristics are shown in Table 1.

Table 1.

Sample Characteristics (N = 121).

Variable	M (SD, range)
Child
Age	13.6 (1.0, 12–15.7)
Male, n (%)	108 (89)
ADHD medication use, n (%)	84 (69)
IQ, FISQ-2^a	100.3 (15.8, 64–143)
ADHD total symptoms^b (pr)
Inattention	17.7 (5.3, 2–27)
Hyperactivity/impulsive	13.3 (5.7, 1–25)
Autism spectrum disorder,^c n (%)	38 (31)
Victimized ever in last 6 months,^d n (%)	54 (45)
Peer victimization^d	1.01 (1.23, 1–4)
PR peer problems^e	3.8 (2.3, 0–10)
TR peer problems^e	2.6 (2.4, 0–10)
SR peer problems^e	2.3 (1.9, 0–10)
Academic achievement & attitudes
Academic competence^f	84.8 (15.5, 55–124)
Reading^g	537.6 (80.8, 384–890)
Writing^g	465.8 (86.1, 194–640)
Math^g	529.4 (82.0, 321–700)
School connectedness^h	4.0 (0.79, 1–5)
Peer connectedness^h	3.9 (0.79, 1–5)
Student motivation^h	4.1 (0.77, 1–5)
Parent/guardian
Mother responded, n (%)	117 (97)
Completed high school or above, n (%)	85 (70)
SES (ICSEA)ⁱ	1009.1 (68.0, 816–1,114)
School
Teacher surveyed, n (%)	102 (84)
School settings, n (%)
Government	73 (60)
Catholic	24 (20)
Independent	17 (14)
Government other (special, alternate)	8 (6)
Metropolitan location, n (%)	89 (73)

Note. ADHD = attention-deficit/hyperactivity disorder; ICSEA = Index of Community Socio-Educational Advantage; IQ = intelligence quotient; WASI-II = Wechsler Abbreviated Scale of Intelligence–Second Edition SES = socioeconomic status; PR = parent report; SR = self-report; TR = teacher report.

FSIQ-2, full-scale IQ on two subsets (vocabulary and matrix reasoning) of WASI-II. ^bParent report of symptoms on the Inattention and Hyperactivity/Impulsivity subscales of the ADHD Rating Scale-IV. ^cParent report of prior diagnosis of autism spectrum disorder. ^dMeasured on the Gatehouse Bullying Scale. ^eStrengths and Difficulties Questionnaire. ^f Teacher report of academic competence on Social Skills Improvement System (SSIS) Rating Scales (normative M = 100; SD = 15). ^gReading, writing, and math standardized scores on the National Assessment Program–Literacy and Numeracy (NAPLAN) tests—(Average year 7 & 9 state performance: reading (M = 568.7, SD = 64.9), writing (M = 5,541.4, SD = 71.1), math (M = 572.2, SD = 66.6). ^hSelf-rated attitudes about school scales; and ⁱIndex of Community Socio-Educational Advantage (M = 1,000; SD = 100), higher scores reflect greater education advantage.

Procedures

Participants were recruited as part of a larger study focusing on sleep in children with ADHD recruited via pediatric practices in the community (see Lycett, Sciberras, Mensah, Gulenc, & Hiscock, 2014, for details). Schooling in Australia encompasses primary school (Grades prep to 6), followed by secondary education (high school; Grades 7–12). Participants attended government (60%), Catholic (24%), independent (17%) and special education (6%) operated schools, ranging in socioeducational advantage (M_{ICSEA scores} = 1,009.0, SD = 68, range = 816–1,114; population M = 1,000, SD = 100) (Australian Curriculum Assessment and Reporting Authority [ACARA], 2018).

Ethics approval was obtained from the Human Research Ethics Committee of the Human Research Ethics Committees of The Royal Children's Hospital (#33206) and the Department of Education and Training (DET) (#2013_002202) and the Catholic Education Office (#1958), Victoria; both adolescent and parent written consent were obtained. Inclusion criteria for the current study included aged 12 years or older, transitioned to secondary school, and agreed to be approached about future research (n = 200), of which 130 (65%) enrolled. Participants were required to have a diagnosis of ADHD, confirmed by researchers using the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000) criteria via parent-reported symptoms on the ADHD Rating Scale-IV (Dupaul et al., 1998) and ratings of impairment, setting, and symptom duration. At baseline, children were excluded if they had an intellectual disability (IQ < 70), a severe medical condition (e.g., cerebral palsy), or sleep apnea, or parents had insufficient English to complete the questionnaires.

Assessment and interviews were conducted with a parent and adolescent in the family home. During the home visits, a trained researcher conducted cognitive assessments, and the adolescent completed questionnaires using a tablet device. All home visits were conducted by PhD candidates who were supervised by a clinical psychologist. Parents also completed online questionnaires. With consent, homeroom teachers (82% response rate) completed measures on adolescent school functioning, classroom behavior, and academic competence (68%).¹

Measures

Peer relationship problems

Peer relationship problems were assessed on the adolescent and teacher-rated Strengths and Difficulties Questionnaires (SDQ; Goodman, 1997). The SDQ is a well-validated and reliable instrument to measure psychosocial adjustment in children and adolescents (Goodman, 2001). The peer problems subscale includes items about friendship and social interactions (e.g., “I have one good friend or more”). Items are rated based on behavior over the previous 6 months, on a 3-point scale from 0 = not true to 2 = certainly true. The present study used mean scores from the five-item peer relationship problems subscale of the SDQ by the teacher (α = .77), parent (α = .67), and self-report (α = .61).² Higher scores indicate greater difficulties.

Intelligence quotient

Estimated IQ was obtained using the two subtest form of the Wechsler Abbreviated Scale of Intelligence, Second Edition (Wechsler, 2011) consisting of Vocabulary and Matrix Reasoning. Participants scores ranged from extremely low (<69) to very superior (>130) (M = 100.42, SD = 15.81). This is an abbreviated IQ measure, which provides an estimate of verbal and nonverbal IQ and identifies individuals who may benefit from a full cognitive assessment.

WM

WM was assessed using the Digit Span subtest of the Wechsler Intelligence Scale for Children, Fourth Edition (Wechsler, 2003), valid for use with children between the ages of 6 and 16 years. Digit span comprised digits forward and digits backward. The digits backward subscale was used in the present study as it has been shown to be more sensitive to deficits in WM (Hale et al., 2002). Standard scores normed for age were used.

ADHD symptoms

The ADHD Rating Scale-IV by parent report was used to assess ADHD symptoms (Dupaul et al., 1998). It is an 18-item validated scale measuring two subscales: inattention (nine items; α = .88) and hyperactivity/impulsivity (nine items; α = .90) symptoms. Items are rated on a 4-point scale from 0 = never or rarely to 3 = very often. To calculate the inattention and hyperactivity-impulsivity subscale raw scores, the items within each subscale were summed. Both subscales were then summed to calculate the total score. Higher scores on each scale reflect more severe symptoms.

ASD diagnosis

Parents reported on whether their child had previously been diagnosed with ASD. A dichotomous variable indicating diagnostic status was used in the present study. Consistent with clinical samples of adolescents with ADHD (Becker & Fogleman, 2020), 31% (n = 40) of the current sample had a prior professional diagnosis of ASD.

Age

Participant age at the time of the assessment was used in the present study; participants ranged in age from 12.07 to 15.88 years.

PV

The Gatehouse Bullying Scale (GBS) was used to assess PV (Bond et al., 2007). GBS is a 12-item measure that asks whether the adolescent has been subject to recent teasing, rumors, social exclusion, and threats of physical harm or actual violence from other students over the previous 3 months. For each aspect of PV that the adolescent endorsed, they answered how often it occurred (1 = most days, 2 = about once a week, 3 = less than once a week) and how upset the child was by each type of victimization behavior (1 = not at all, 2 = a bit, 3 = I was quite upset). As per scoring instructions (Bond et al., 2007), a categorical variable using the mean item score across the four types of PV was used: 0 = does not experience PV; 1 = PV but not frequently and not upset; 2 = PV, either frequently or upset, but not both; and 3 = PV frequently and upset; thus, higher scores on PV indicate worse impact or experience of PV. Internal consistency in the current study is high (α = .83).

Attitudes about school

Adolescent attitudes about school were measured on three subscales reproduced from the annual Attitudes to School Survey for Victorian school students (Department of Education & Training, 2015): school connectedness (5 items), connectedness to peers (4 items), and motivation (4 items). The school connectedness scale measures the extent to which students feel they belong and enjoy attending school. The connectedness to peers scale measures the extent to which students feel socially connected and get along with their peers. The motivation scale measures the extent to which students are motivated to learn. Items consisted of 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree) with higher scores indicating greater engagement. Internal consistency in this study was good (αs = .85–.89).

Academic achievement

Standardized reading, writing, and math test scores were obtained directly from the national assessments (National Assessment Program–Literacy and Numeracy [NAPLAN] tests scores). The NAPLAN testing program is administered under similar test conditions in May to all Australian students in Grades 7 and 9 (ACARA, 2015). Scores were obtained by linking to centralized records held by the Victorian Curriculum Assessment Authority. Scaled scores between 0 and 1,000 are provided for each academic domain.

Academic competence

The Academic Competence Scale (ACS), a subscale of the Social Skills Improvement System, was used to assess teacher-rated academic competence (Elliott & Gresham, 2013). The ACS is a seven-item teacher-rated measure of a student’s overall academic performance, motivation, reading, and mathematical ability compared with classmates. Internal consistency in the present study was high (α = .94).

Analytic Plan

For preliminary analyses, descriptive statistics were calculated for all variables. Correlations were also examined to check for extreme collinearity among variables. Given that a substantial portion of participants were on ADHD medication, examination of the relation between medication status and all study variables was examined to determine whether it should be included as a control variable. A structural equation multiple regression analysis was conducted to evaluate associations between general adolescent factors (ADHD symptom domains, comorbid ASD, and age), adolescent cognitive functioning (WM, estimated IQ), social functioning (teacher-, parent-, and self-report peer relationship problems), and experiences with PV (frequent and upsetting PV), and associations between PV and academic outcomes (adolescent-rated attitudes about school, including school connectedness, connectedness to peers, and student motivation; academic achievement on standardized tests of reading, writing, and math; and teacher-rated academic competence). Covariances between related dependent variables (e.g., academic competence and attitudes about school; academic competence and academic achievement) and independent variables (e.g., estimated IQ and WM; ASD and hyperactivity-impulsivity symptoms) were included in the models. A latent variable was created for peer relationship problems (teacher-, parent-, and self-report peer relationship problems), student attitudes about school (school connectedness, connectedness to peers, and student motivation), and academic achievement (reading, writing, and math).

After testing the initial conceptual models with all above-noted pathways specified, a model trimming procedure was implemented to achieve optimal model fit. Specifically, the least-significant path was trimmed from the model, and the model was reevaluated until a significant chi-square difference indicated that the model had been overtrimmed or all remaining pathways exhibited p values ≤ .20. Once the optimal model for each set of outcomes was identified, standardized coefficients were examined for all regression pathways to compare the relative contributions of each path toward explaining the full model variance. These coefficients can be interpreted as r values (Durlak, 2009): values > .10 indicate a small effect, values > .30 indicate a medium effect, and values > .50 indicate a large effect (Cohen, 1988). All analyses were conducted using Mplus version 7 (Muthén & Muthén, 1998–2011). Full-information maximum likelihood was used to address missing data, which uses all observed information to estimate parameters.

Results

Sample characteristics are shown in Table 1. Forty-five percent of the sample reported experiences of PV, of which 41% reported being moderately impacted (either frequently or upset, but not both) and 43% reported greater impact (frequent and upsetting PV). Correlations between study variables are presented in Table 2; as medication status was not significantly related to any study variable (rs = −.10 to .19; ps > .05), it was not included as a control variable in subsequent analyses. Moderate to strong correlations were found between teacher-, parent-, and self-report of peer relationship problems, supporting the use of a latent variable to capture this construct. ADHD symptom domains (positive correlations) and cognitive abilities (estimated IQ and WM; negative correlations) were weakly related to PV; age and ASD were unrelated to PV. Adolescent attitudes about school subscales displayed strong correlations with each other, and objective measures of academic achievement displayed moderate to strong correlations with one another, also supporting the use of latent variables for these constructs. No variables exhibited severe collinearity (all r values < .80).

Table 2.

Correlations for Study Variables.

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1. Peer victimization	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
2. TR peer problems	.28**	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
3. SR peer problems	.36***	.52***	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
4. PR peer problems	.39***	.40***	.42***	—	—	—	—	—	—	—	—	—	—	—	—	—	—
5. Estimated IQ	.21*	.04	.17	.20*	—	—	—	—	—	—	—	—	—	—	—	—	—
6. WM	.25**	.03	.14	.18	.47***	—	—	—	—	—	—	—	—	—	—	—	—
7. IA symptoms	.22*	.21*	.33***	.29**	.05	.04	—	—	—	—	—	—	—	—	—	—	—
8. HI symptoms	.21*	.38***	.44***	.35***	.07	.13	.54***	—	—	—	—	—	—	—	—	—	—
9. ASD	.05	.16	.26**	.25**	.07	.19*	.18*	.28**	—	—	—	—	—	—	—	—	—
10. Age	.04	.06	.03	.09	.03	.21*	.05	.17	.15	—	—	—	—	—	—	—	—
11. SR school connected	.22*	.17	.21*	.34***	.12	.07	.31**	.21*	.02	.08	—	—	—	—	—	—	—
12. SR peer connected	.32***	.33**	.23*	.55***	.24*	.19*	.27**	.23*	.24*	.01	.51***	—	—	—	—	—	—
13. SR student mot	.15	.07	.10	.27**	.16	.17	.26**	.11	.04	.05	.62***	.52***	—	—	—	—	—
14. TR academic comp	.32**	.07	.11	.24*	.54***	.48***	.21	.11	.12	.00	.30**	.25*	.37**	—	—	—	—
15. Reading ach	.13	.07	.18	.18	.65***	.44***	.22*	.16	.01	.00	.15	.21*	.15	.58***	—	—	—
16. Writing ach	.25*	.22*	.09	.16	.49***	.34**	.16	.09	.04	.00	.07	.13	.15	.44***	.58***	—	—
17. Math ach	.29**	.00	.13	.08	.71***	.47***	.04	.08	.16	.02	.09	.23*	.09	.43***	.70***	.53***	—
n	121	107	129	119	111	111	129	129	130	130	120	120	120	80	102	100	102

Note. SR = self-report; PR = parent report; TR = teacher report; Ach = achievement; ASD = autism spectrum disorder; comp = competence; connected = connectedness; HI = hyperactive/impulsive; IA = inattention; IQ = intelligence quotient; mot = motivation; WM = working memory.

p < .05. **p < .01.***p < .001.

The trimmed regression model for the association between the individual risk factors, PV, adolescent attitudes about school, academic achievement, and academic competence is presented in Figure 1; this model had acceptable fit, root mean square error approximation (RMSEA) = .08, comparative fit index (CFI) = .97, standardized root mean square residual (SRMR) = .08. All three reports of peer relationship problems strongly loaded on the latent peer relationship variables (βs = 0.62–0.71, ps < .001). Similarly, all three measures of academic achievement (βs = 0.37–0.90, ps < .001) and attitudes about school (βs = 0.67–0.79, ps < .001) displayed strong loadings. Only peer relationship problems and WM remained as significant predictors of PV in the trimmed model. The relation between peer relationship problems and PV was a moderate, almost strong relation, suggesting that students with more peer relationship problems were more likely to be victimized at higher rates and to be more distressed by the victimization. WM was also significantly, though weakly, associated with PV, such that students with lower WM ability were more likely to be victimized and to be more distressed by the victimization. In turn, PV was weakly to moderately associated with lower attitudes about school, academic competence, and academic achievement.

Figure 1.

Path Model for the Associations Between Peer Relationships, ADHD Symptoms, ASD, Cognitive Abilities, Age, Peer Victimazation, and Academic Functioning.

Discussion

This study examined PV in adolescents with ADHD. Forty-five percent of adolescents reported PV, of which 37% encountered frequent and/or upsetting PV. As hypothesized, peer relationship problems accounted for the most variance in PV. While WM was also uniquely associated with PV, we did not find evidence of an independent association between inattention or hyperactive/impulsive symptoms, estimated IQ, comorbid ASD, or age and PV. In relation to school outcomes, PV was associated with worse academic achievement, poorer attitudes about school, and poorer academic competence.

PV and Individual Adolescent Factors

Our findings of moderate to strong relations between peer relationship problems and PV are consistent with previous research suggesting that social problems and PV are linked (Aguado-Gracia et al., 2018). Having more friends appears to be a protective factor against PV for children with poor social skills (Fox & Boulton, 2006). However, given that youth with ADHD have fewer friends than their non-ADHD peers, it is especially important to consider other protective factors for this population that may mitigate the link between peer relationship problems and victimization (Marton et al., 2012). These factors may include strong student–teacher and student–parent relationships, supportive classrooms and family environments, and participation in extracurricular activities (Konishi et al., 2010; Rothon et al., 2011). Identifying contextual factors within the school and family which may be protective remains an important area for future research.

The hypothesized association between cognitive functioning and PV was only partially confirmed. Deficits in WM were weakly associated with PV, which supports findings from research in typically developing youth (Kira et al., 2014; Verlinden et al., 2014). Our finding that WM was a unique predictor of PV provides some evidence that perhaps cognitive abilities such as nonverbal reasoning skills and other executive planning skills (e.g., problem-solving skills) may help explain variation in PV among youth with ADHD. In contrast, and consistent with Liu et al. (2017) and Efron et al. (2021), there was little evidence of an association between IQ and PV in youth with ADHD. It has been suggested that full-scale IQ is not related to PV, but that certain aspects of IQ, namely, nonverbal intellectual abilities such as perceptual reasoning, are protective factors (Liu et al., 2017). Future research examining these additional factors may help to elucidate the role of cognitive abilities in PV.

In the present study, inattentive and hyperactive/impulsive symptoms, comorbid ASD, and age were not associated with PV in the path model, and only ADHD symptoms were weakly associated with PV at the bivariate level. Although these factors have previously been linked to PV in youth with ADHD (Aguado-Gracia et al., 2018; Chou et al., 2018; Sciberras et al., 2012; Wiener & Mak, 2009), there have been inconsistent findings within these studies. For example, ADHD symptoms have been related to PV in school children 6 to 18 years with ADHD (Aguado-Gracia et al., 2018) and adolescent girls with ADHD (Sciberras et al., 2012), but this relation was not evident in a clinical sample of middle school students with ADHD (Chou et al., 2018) and a community-based sample of children with ADHD (Efron et al., 2021). Furthermore, Chou et al. found that ASD and age were associated with PV, whereas Aguado-Gracia et al. (2018) did not.

PV and Academic Outcomes

Consistent with the hypothesis and prior research in a general population of adolescent students (Juvonen et al., 2011), PV displayed a moderate negative relation with teacher-rated academic competence and a weak negative relation with adolescent-rated attitudes about school (less school and peer connectedness, less motivation) and academic achievement in youth with ADHD. Our finding that PV was associated with teacher-reported academic competence is inconsistent with prior findings (Efron et al., 2021; Taylor et al., 2010) who did not find a link between PV and teacher-reported academic competence and parent-rated academic outcomes. The strength of the present study is that we used a multi-informant approach (self-report and teacher report of academic outcomes) and obtained individual results on national standardized tests, which may explain these differential findings.

Adolescence is a period when individuals form a sense of self and identity, and when peer relationships and peer approval become increasingly important. Negative experiences during early adolescence can have immediate and lasting effects on mental health and well-being (Menesini & Salmivalli, 2017). This study highlights that PV is a substantial problem for youth with ADHD and may have important implications for student outcomes. Youth with ADHD and peer relationship problems or deficits in WM are at increased risk of PV by peers. Furthermore, youth who experience PV are more likely to be low academic achievers, which is linked with school dropout and a host of negative consequences over time (Henry et al., 2012). We also found PV evoked a strong emotional response for almost a third of the sample, and that youth who experienced PV reported poorer attitudes about school, had lower academic achievement, and displayed lower teacher-reported academic competence. These findings are unsurprising given we know that PV can lead to significant emotional distress, and victims of PV can feel sad and isolated (Juvonen & Graham, 2014). Increasingly, PV is linked to higher anxiety, depression, and in extreme cases, self-harm (e.g., Menesini & Salmivalli, 2017; Schäfer et al., 2004). If emotional problems escalate because of PV over time, there may be long-term implications to adolescent mental health and consequently school outcomes. Longitudinal research is needed to explore this possibility.

Limitations

The findings of the present study should be interpreted with several limitations in mind. First, this study is cross-sectional in nature, which prevents conclusions from being drawn regarding the directionality or causality of the effects of individual risk factors on PV or of PV on academic outcomes (see Reijntjes et al., 2011, for review). Longitudinal data are needed to confirm the direction of these associations. Second, this study used a categorical measure for PV behaviors, which limits the variability of the measure in our analyses. This limited variability may have contributed to the weak to moderate effects; however, this also gives greater confidence in the significant associations that were found. Similarly, missing data on outcome variables, that is, teacher rating of academic competence, reduce the power of a trial. We did not control for factors such as the presence of externalizing comorbidities (e.g., conduct disorder) that may be associated with PV. Including greater details on comorbidities would likely strengthen findings. Finally, the current sample consisted of English-speaking families in Australia, with an overrepresentation of males. As a result, we were not able to examine sex as a predictor of PV. However, past work with adolescent samples did not find this to be a relevant predictor (Chou et al., 2018). It will be important for the findings to be replicated to determine whether these results would generalize to all adolescents with ADHD.

Clinical Implications and Conclusions

PV must not be overlooked if we wish to improve school outcomes for students with ADHD in early adolescence. Friendships and peer group acceptance have been shown to help protect youth from becoming victims of PV (Juvonen & Graham, 2014; Menesini & Salmivalli, 2017). However, to date, there is little evidence that social skills training programs for children and adolescents with ADHD are effective (Morris et al., 2020; Storebo et al., 2019). Youth with ADHD and peer problems may need extra support from their teachers and parents to help them to develop stable relationships with one or two peers.

In addition, helping students to build their self-confidence and to be more assertive may help minimize the adverse effects for those who are victimized (Salmivalli, 2010). Teaching students to recognize social situations that may lead to PV and to develop strategies that they can use in these situations may also be helpful. Explicitly teaching cognitive skills to adolescents with ADHD, such as organizational and planning skills, in the school context, has been shown to strengthen skills and improve academic functioning (Langberg et al., 2012). Future research needs to explore whether adolescent social skills can be strengthened using a similar approach and whether this reduces risk for PV and negative academic outcomes. In addition, regularly screening students for PV, using brief and easy-to-administer measures or tests of peer relationship problems and WM deficits, could help identify students at risk of PV. For now, creating safe places within schools that limit opportunities for repeated victimization for at-risk students would be an important next step. Raising students’ and teachers’ awareness of the effects of PV on the victim and teaching students and teachers strategies to intervene may be useful.

Footnotes

Acknowledgements

We would like to acknowledge the research assistants and interns who contributed to data collection for this study. We would also like to thank the many families, teachers, and schools for their participation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a philanthropic grant awarded to N. Z. from the Cripps Family.

ORCID iDs

Nardia Zendarski

Melissa Mulraney

Emma Sciberras

Notes

Author Biographies

Nardia Zendarski is a research fellow at the Department of Pediatrics, the University of Melbourne and is an honorary researcher at the Murdoch Children’s Research Institute. She is involved in research investigating ways to improve outcomes for children with ADHD.

Rosanna Breaux was a postdoctoral fellow at Virginia Commonwealth University at the time of manuscript preparation. At the time of manuscript submission, Dr. Breaux is an assistant professor at Virginia Tech. Dr. Breaux's research focuses on the emotional and social functioning of children and adolescents with ADHD, with a focus on emotion regulation.

Hana-May Eadeh, BS, is a graduate student in the Clinical Psychology PhD program at the University of Iowa. Her research examines the development of comorbid diagnoses within adolescent populations diagnosed with ADHD. In particular, she focuses on how executive functioning and emotion regulation skills impact the progression of symptoms.

Zoe R. Smith, MS, is a doctoral candidate at Virginia Commonwealth University and is completing her clinical internship at University of Chicago Medicine. Zoe's research focuses on sluggish cognitive tempo and motivation in adolescents with ADHD.

Stephen J. Molitor was a graduate student at Virginia Commonwealth University at the time of manuscript preparation. He is currently a postdoctoral fellow in the Department of Pediatrics at the University of Minnesota Medical School. His research and professional interests are focused on improving the evidence-based psychological assessment of children and adolescents, and particularly in how cognitive factors may affect academic and social functioning.

Melissa Mulraney is a research fellow in Health Services Research, Murdoch Children’s Research Institute and is an honorary research fellow at the Department of Pediatrics, the University of Melbourne. Her research interests include child and adolescent mental health.

Elizaveta Bourchtein, MS, is a doctoral candidate in Clinical Psychology (child/adolescent concentration) at Virginia Commonwealth University. She is completing her clinical internship at Penn State Hershey Medical Center. Elizaveta's research focuses on predictors and outcomes of sleep behaviors in youth with and without ADHD.

Cathrin D. Green, MS, is a doctoral student at Virginia Commonwealth University. Her research focuses on the influence of sociodemographic and contextual factors on the academic and social outcomes of youth with Attention-Deficit/Hyperactivity disorder.

Emma Sciberras is an associate professor and clinical psychologist at Deakin University. She is also an honorary research fellow at the Murdoch Children’s Research Institute and the University of Melbourne. She leads a research program focused on understanding developmental trajectories for children with ADHD and developing interventions that can improve outcomes for children with ADHD.

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