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Remedial and Special Education
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DOI: 10.1177/0741932514564564
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Article
Bullying is regarded as a significant problem in the United
States among school-aged youth. Between 15% and 23% of
elementary students and 20% and 28% of secondary school
students report being bullied within a 6-month to 1-year
period (Carlyle & Steinman, 2007; National Center for
Education Statistics, 2011; Turner, Finkelhor, Hamby,
Shattuck, & Ormrod, 2011). In a recent study of bully vic-
timization among students with disabilities using the
Special Education Elementary Longitudinal Study and the
National Longitudinal Transition Study–2 data sets revealed
a prevalence rate of 24.5% in elementary school, 34.1% in
middle school, and 26.6% in high school (Blake, Lund,
Zhou, Kwok, & Benz, 2012). Studies have documented that
victims often experience depression, social anxiety, and low
self-esteem, which could then contribute to academic chal-
lenges, with bullies and bully-victims reporting similar aca-
demic, interpersonal, and intrapersonal challenges (Cook,
Williams, Guerra, Kim, & Sadek, 2010).
Bullying, Aggression, and Victimization
Among Students With Disabilities
Students with disabilities are not immune to being involved
in bullying incidents, with many studies suggesting that
they are actually overrepresented within the bullying
dynamic (see Rose, Monda-Amaya, & Espelage, 2011, for
review). In a regional study of middle and high school youth
(n = 21,646), students with disabilities were twice as likely
to be identified as proactive (bully) and reactive (fighting)
perpetrators and victims than students without disabilities
(Rose, Espelage, & Monda-Amaya, 2009). In a similar
study, Rose, Espelage, Aragon, and Elliott (2011) deter-
mined that students with high incidence disabilities engaged
in significantly higher rates of reactive perpetration and
experienced higher levels of victimization than their same-
aged peers without disabilities. Although few scholars have
examined the differences in bullying involvement among
students with disabilities, the preliminary findings suggest
that students with disabilities may be at higher risk of
involvement than their counterparts without disabilities.
564564RSEXXX10.1177/0741932514564564Remedial and Special EducationEspelage et al.
research-article2015
1University of Illinois, Champaign, USA
2University of Missouri, Columbia, USA
3Vanderbilt University, Nashville, TN, USA
Corresponding Author:
Dorothy L. Espelage, University of Illinois, 1310 S. 6th St., Champaign, IL
61820, USA.
Email: espelage@illinois.edu
Social-Emotional Learning Program
to Reduce Bullying, Fighting, and
Victimization Among Middle School
Students With Disabilities
Dorothy L. Espelage, PhD1, Chad A. Rose, PhD2,
and Joshua R. Polanin, PhD3
Abstract
Results of a 3-year randomized clinical trial of Second Step: Student Success Through Prevention (SS-SSTP) Middle School
Program on reducing bullying, physical aggression, and peer victimization among students with disabilities are presented.
Teachers implemented 41 lessons of a sixth- to eighth-grade curriculum that focused on social-emotional learning (SEL)
skills, including empathy, bully prevention, communication skills, and emotion regulation. Two school districts in a larger
clinical trial provided disability information. All sixth-grade students (N = 123) with a disability were included in these
analyses, including intervention (n = 47) and control (n = 76) conditions. Linear growth models indicated a significant
intervention effect for bully perpetration; compared with students in the control condition, intervention students’ bullying
perpetration scale scores significantly decreased across the 3-year study (δ = −.20, 95% confidence interval = [−.38, −.03]).
SEL offers promise in reducing bully perpetration among students with disabilities.
Keywords
management, behavior, life skills, curriculum, evidence-based practice
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2 Remedial and Special Education
To examine subgroup differences, Rose and Espelage
(2012) explored perpetration rates between students with
emotional and behavioral disorders (EBD) and their peers
without disabilities and other disability labels (i.e., other
health impairment, learning disability [LD], and speech and
language impairment). Results indicated that the students
with EBD engaged in higher rates of proactive and reactive
aggression perpetration than any other subgroup of stu-
dents. However, when reactive emotion (i.e., anger) was
included in the model, bully perpetration increased signifi-
cantly more for students with EBD than students with other
disability labels. These findings are consistent with Swearer,
Wang, Maag, Siebecker, and Frerichs’s (2012) findings that
students with behavior-oriented disabilities (e.g., EBD,
attention-deficit/hyperactivity disorder [ADHD]) engaged
in higher levels of perpetration and received more behav-
ioral referrals than any other subgroup of student. Therefore,
it has been argued that these behaviors may be a manifesta-
tion of the students’ disability, which may constitute further
or more intensive special education programming (Rose &
Espelage, 2012).
In separate systematic reviews, Rose, Monda-Amaya,
and Espelage (2011) and McLaughlin, Byers, and Vaughn
(2010) determined that two of the most common predictive
factors for involvement of students with disabilities are low
social and communication skills. Therefore, for students
with disabilities who are characterized by or have diagnos-
tic criteria associated with low social skills and low com-
munication skills, there is a higher likelihood of involvement
in bullying incidents (Rose, Espelage, et al., 2011). For
example, autism spectrum disorder (ASD) is characterized
by deficits in social and/or communication skills (American
Psychiatric Association, 2013), where existing research
suggests that students diagnosed with ASD experience high
rates of victimization (L. Little, 2002), and experience
higher levels of repeated victimization than students with
other types of disabilities (Blake et al., 2012). To compound
this issue, students with ASD may struggle with emotional
dysregulation, where increased levels of anger are associ-
ated with increased levels of victimization when compared
with individuals without disabilities (Rieffe, Camodeca,
Pouw, Lange, & Stockman, 2012). As previously stated,
this emotional dysregulation may also hold for individuals
with EBD (Rose & Espelage, 2012) or behavioral-oriented
disabilities (Swearer et al., 2012) and bully perpetration,
where deficits in communication or social skills may mani-
fest in peer-level aggression. Unfortunately, social and
communication skills are necessary to successfully navigate
the social landscape in today’s educational environments,
and students with disabilities are often characterized as hav-
ing lower interpersonal competence (Farmer et al., 2011),
and being ostracized more than their peers without disabili-
ties (Symes & Humphrey, 2010; Twyman et al., 2010). For
example, a meta-analysis of 152 studies found that 8 of 10
children with a LD were peer-rated as rejected, that 8 of 10
were rated as deficient in social competence and social
problem solving, and that students with LD were less often
selected as friends by their peers (Baumeister, Storch, &
Geffken, 2008). Therefore, programs that support the social
and emotional learning (SEL) of individuals with disabili-
ties may increase their social competence and lead to lower
levels of involvement within the bullying dynamic (Farmer,
Lane, Lee, Hamm, & Lambert, 2012; Rose & Monda-
Amaya, 2012).
Efficacy of Bully Prevention Efforts
Despite the number of school-based bully prevention pro-
grams in use, bullying prevention programs in the United
States are producing modest effects (Ttofi & Farrington,
2011). Thus, there remains a troubling chasm between the
scope of the problem, the scale of bullying prevention
efforts and scientifically rigorous research in the United
States that allows for the elucidation of best bullying pre-
vention practices. Furthermore, even less is known about
what are the best bully prevention efforts to reduce bully
perpetration and peer victimization among students with
disabilities (Rose, Monda-Amaya, & Espelage, 2011). To
address this gap, the current study evaluated the impact of
the Second Step SEL program (Committee for Children,
2008) on bullying perpetration, physical aggression, and
peer victimization utilizing a subsample from a large-scale
randomized clinical trial (RCT; Espelage, Low, Polanin, &
Brown, 2013).
SEL Programs to Prevent Bullying,
Aggression, and Victimization
School-based SEL programs developed to prevent school
violence, including bullying, are predicated on the belief
that academic skills are intrinsically linked to youth’s abil-
ity to manage emotions, regulate emotions, and to commu-
nicate and problem-solve challenges and interpersonal
conflicts (Durlak, Weissberg, Dymnicki, Taylor, &
Schellinger, 2011). Within the SEL framework, there are
five interrelated skill areas: self-awareness, social aware-
ness, self-management and organization, responsible prob-
lem solving, and relationship management. Self-regulated
learning is both directly and indirectly targeted in these pro-
grams, with the use of social skill instruction to address
behavior, discipline, safety, and academics and to help
youth become self-aware, manage their emotions, build
social skills (empathy, perspective-taking, respect for diver-
sity), build friendship skills, and make positive decisions
(Zins, Bloodworth, Weissberg, & Walberg, 2004). School-
based violence prevention programs that facilitate SEL
skills, address interpersonal conflict, and teach emotion
management have shown promise in reducing youth
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Espelage et al. 3
violence and disruptive behaviors in classrooms (Wilson &
Lipsey, 2007). Many of these social-emotional and social-
cognitive intervention programs target risk and protective
factors that have consistently been associated with aggres-
sion, bullying, and victimization in cross-sectional and lon-
gitudinal studies (Espelage, Basile, & Hamburger, 2012;
Espelage, Holt, & Henkel, 2003). Given that these risk fac-
tors are particularly relevant to students with disabilities
(Elias, 2004; Rose & Monda-Amaya, 2012), there is reason
to believe that SEL programs hold promise for reducing
bullying and peer victimization for this population.
Research support for SEL programs is growing. A meta-
analysis including 213 SEL-based programs found that if a
school implements a quality SEL curriculum, they can
expect more socially appropriate student behavior and an 11
percentile increase in academic test scores in comparison
with schools without SEL programming (Durlak et al.,
2011). Studies demonstrate that students exposed to SEL
activities feel safer and more connected to school and aca-
demics, build work habits in addition to social skills, and
build stronger relationships with peers and teachers (Zins et
al., 2004). Several RCTs of bullying prevention programs
(based on the SEL framework) have found significant
reductions in teacher-reported physical bullying (Brown,
Low, Smith, & Haggerty, 2011) and self-reported physical
aggression (Espelage et al., 2013); however, no RCT has
been conducted with students with disabilities. Thus, this
study addresses a major failure of prevention science to
attend to the potential impact of SEL programs on aggres-
sion and victimization experienced by students with
disabilities.
Second Step: Student Success
Through Prevention (SS-SSTP) Middle
School Program
The SS-SSTP program (Committee for Children, 2008)
includes direct instruction in risk and protective factors
linked to aggression and violence, including empathy train-
ing, emotion regulation, communication skills, and prob-
lem-solving strategies. There exists a large research base
supporting the inclusion of these risk and protective factors
targeted through the social-emotional framework to reduce
aggression (for a review, see Espelage, Low, Polanin, &
Brown, in press).
Bullying Prevention
The SS-SSTP curriculum includes two lessons focused spe-
cifically on bullying, and these lessons are not introduced
until youth are exposed to empathy and communication
training. This allows youth to learn how to work with each
other in dyads and groups to maximize the impact of the les-
sons that focus on recognizing and responding to bullying
and creating class rules. Of note, classroom rules around
bullying were a component of programs in the Ttofi and
Farrington (2011) meta-analysis that produced significant
effect sizes. In the seventh- and eighth-grade curriculum,
youth not only review the components of bullying and how
to respond but are also encouraged to learn ways by which
to intervene to help others as “allies.” Again, a recent meta-
analysis supports this practice of using a direct approach to
address barriers to helping others and then teaching and
role-playing strategies of effective bystander intervention
(Polanin, Espelage, & Pigott, 2012).
Instructional Practices
Successful prevention curricula include a wide range of
instructional practices, from direct instruction, group dis-
cussions, reflection opportunities, and role-plays (Evans &
Bosworth, 1997; Tobler & Stratton, 1997). Thus, the
SS-SSTP lessons are scripted and highly interactive, incor-
porating small group discussions and activities, class dis-
cussions, dyadic exercises, whole class instruction, and
individual work. Lessons are supported through an accom-
panying DVD that contains media-rich content including
topic-focused interviews with students and video demon-
strations of skills. Indeed, video has been found to be one
element of efficacious programs (Ttofi & Farrington, 2011).
Drawing on Bandura’s (1977) social learning theory, les-
sons are skills-based and students receive cueing, coaching,
and suggestions for improvement on their performance.
Lessons are supplemented by homework that reinforces the
instruction, extension activities, academic integration les-
sons, and videos, which are practices that are associated
with greater skill acquisition (Bosworth & Sailes, 1993;
Dusenbury & Weissberg, 1998). The use of group and col-
laborative work also leads to increased skill acquisition by
allowing students to practice new skills in an environment
of positive peer support (Hansen, Nangle, & Kathryn,
1998). Optional “transfer of training” events in which the
teacher connects the lessons to events of the day, reinforces
students for displaying the skills acquired, identifies natural
reinforcement when it occurs, and asks students whether
they used specific skills during the day’s events.
Current Study
Given the lack of systematic evaluations of SEL programs
to address aggression and victimization among students
with disabilities, and the overlap with disability status and
identified risk factors, this study sought to evaluate the
effectiveness of an evidence-based SEL program for reduc-
ing bullying, physical aggression, and victimization among
this population of students. Based on foundational literature
and potential effectiveness of SEL programs, the following
hypotheses were tested:
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4 Remedial and Special Education
Hypothesis 1: Students with disabilities who receive
SEL programming will report lower levels of bullying
over time in comparison with their peers in the control
condition.
Hypothesis 2: Students with disabilities who receive
SEL programming will report lower levels of victimiza-
tion over time in comparison with their peers in the con-
trol condition.
Hypothesis 3: Students with disabilities who receive
SEL programming will report lower levels of physical
fighting over time in comparison with their peers in the
control condition.
Method
Participants
The sample for this study consisted of sixth-grade students
(at baseline) with disabilities in two of five school districts
that were participating in a large-scale RCT of a middle
school SEL curriculum (see Espelage et al., 2013, for more
information). The larger project used a nested longitudinal
cohort design (only sixth-graders enrolled prior to interven-
tion), randomly assigning schools to condition (i.e., inter-
vention or control). The schools were matched on a number
of covariates prior to random assignment (e.g., student
enrollment, percentage of eligible free/reduced lunch, per-
centage of students whose primary language was not
English); we used a random number table to assign schools
to conditions.
Disability data were available for a total of 123 students
across 12 schools in two school districts in the Midwest
United States (see Table 1). Any student labeled with a dis-
ability was selected for inclusion, regardless of disability
type; 47 students were in intervention schools, and 76 were
in control schools. Fifty-three percent of the sample were
female; 65% were 11 years of age, and 35% were 12 years
of age; 31% of sample identified as White, 53% identified
as African American, 6% Hispanic, and 10% as biracial. No
significant differences were found between students in the
intervention versus control conditions on demographic vari-
ables (see Table 1).
Thus, we concluded that the two conditions’ participants
were equivalent prior to the start of the intervention.
Intervention Condition: Second Step Curriculum
The program is composed of 15 lessons at Grade 6 and 13
lessons at Grades 7 and 8. In Grade 6, five lessons focus on
empathy and communication (e.g., working in groups, dis-
agreeing respectfully, being assertive), 2 lessons on bully-
ing, 3 lessons on emotion regulation (e.g., coping with
stress), 2 lessons on problem solving, and 4 lessons on sub-
stance abuse prevention. In Grades 7 and 8, four lessons
focus on empathy and communication, 3 lessons on bullying
(e.g., cyberbullying, sexual harassment), 2 lessons on emo-
tion regulation, 2 lessons on problem solving or goal set-
ting, and 2 lessons on substance abuse prevention. Lessons
are delivered in one 50-min or two 25-min classroom ses-
sions, taught weekly or semi-weekly throughout the school
year. Teachers implemented the lessons in this study.
Teachers completed a 4-hr training session that covered not
only the curriculum and its delivery but also an introduction
to child developmental stages as related to targeted skills
and a background on bullying research.
Control Condition: Stories of Us
Curriculum
Control schools were provided with one copy of the P3:
Stories of Us—Bullying program (Faull, Swearer, Jimerson,
& Espelage, 2008). P3 is composed of two films and educa-
tional resources for supporting students, educators, and the
broader community in addressing the problem of bullying
in schools. We selected this program for the control schools
to offer them something as they waited for 3 years to receive
the Second Step curriculum. This middle school classroom
resource is designed to help students and teachers develop
effective strategies to enhance awareness, understanding,
and reduce bullying behaviors among students. None of the
control schools in the subsample analyzed here adopted the
P3R curriculum.
Procedure
Parental consent. A waiver of active (passive) parental con-
sent was approved by the university institutional review
board for the 12 schools. Parents of all sixth grade students
enrolled in all participating schools were sent letters inform-
ing them about the purpose of the study. Several meetings
were held to inform parents of the study in each community.
In the early fall 2009, investigators attended Parent–Teacher
conference meetings and staff meetings, and the study was
announced in school newsletters and emails from the prin-
cipals. Parents were asked to sign the form and return it
only if they were unwilling to have their child participate in
the investigation. At the beginning of each survey adminis-
tration, teachers removed students from the room if they
were not allowed to participate, and researchers also
reminded students that they should not complete the survey
if their parents had returned the form. An 86% participation
rate was achieved in schools in the analyses reported here.
Students were asked to consent to participate in the study
through an assent procedure included on the coversheet of
the survey.
Survey administration. At each wave of data collection, six
trained research assistants, the primary researcher, and a
faculty member collected the data. At least two of these
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Espelage et al. 5
individuals administered surveys to classes ranging in size
from 10 to 25 students. The research assistants first informed
students about the general nature of the investigation. Stu-
dents were then given survey packets and the survey was
read aloud to them. It took students approximately 40 min
to complete the survey: Fall 2010 (T1), Spring 2011 (T2),
Spring 2012 (T3), and Spring 2013 (T4). T1 represented the
baseline survey prior to implementation of the program.
Measures
The survey included four pertinent sections to this project:
demographics, verbal/relational bullying perpetration, peer
victimization, and physical aggression. The demographic
section collected student information on age, gender, eth-
nicity, grades, and mother’s education. Disability data were
obtained from the school districts, where the diagnoses
were based on the legally identified disability category in
accordance to the Individuals With Disabilities Education
Improvement Act (2004) and state regulations, and, there-
fore, was not assessed on the student surveys.
Bullying perpetration. The nine-item Illinois Bully Scale
(Espelage & Holt, 2001) assesses the frequency of bullying at
school. Students are asked how often in the past 30 days they
did the following to other students at school: teased other stu-
dents, upset other students for the fun of it, excluded others
from their group of friends, helped harass other students, and
threatened to hit or hurt another student. Response options
include “Never,” “1 or 2 times,” “3 or 4 times,” “5 or 6
times,” and “7 or more times.” The construct validity of this
scale has been supported via exploratory and confirmatory
Table 1. Descriptive Statistics (Percentages).
Variable Intervention Control χ2(p value)
n47 76
Gender 0.71 (.39)
Male 61.7 53.9
Female 38.3 46.1
Age 0.04 (.95)
11 65.2 65.8
12 34.8 34.2
Race 7.78 (.10)
African American 53.2 52.6
Asian 4.3 0
Biracial 2.1 14.5
Hispanic 2.1 6.6
White 38.3 26.3
Mother’s education 3.84 (.57)
Less than high school 14.6 9.5
High school graduate 31.7 39.2
Some college 19.5 20.3
College graduate 17.1 20.3
Graduate school+ 17.1 10.9
Type of disability 9.43 (.09)
Cognitive disability 15.6 6.6
Emotional disability 6.2 2.6
Health impairment 12.5 6.6
Multiple disabilities 3.1 0
Specific learning disability 46.9 47.4
Speech/language impairment 15.6 36.8
Grades 2.50 (.87)
Mostly As 31.3 21.0
Most As and Bs 34.4 48.4
Most Bs 3.1 3.2
Most Bs and Cs 15.6 14.5
Mostly Cs 3.1 4.8
Mostly Cs and Ds 9.4 6.5
Mostly Ds and Fs 3.1 1.6
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6 Remedial and Special Education
factor analysis (Espelage & Holt, 2001). Factor loadings in
the development sample for these items ranged from .52 to
.75, and this factor accounted for 31% of the variance in the
factor analysis (Espelage & Holt, 2001). Higher scores indi-
cated more self-reported bullying behaviors. The scale cor-
related moderately with the Youth Self-Report Aggression
Scale (r = .65; Achenbach, 1991), suggesting that it was
somewhat unique from general aggression. Concurrent
validity of this scale was established with significant cor-
relations with peer nominations of bullying (Espelage et al.,
2003). More specifically, students who reported the highest
level of bully perpetration on the scale received signifi-
cantly more bullying nominations (M = 3.50, SD = 6.50)
from their peers than students who did not self-report high
levels of bullying perpetration (M = .98; SD = 1.10; Espel-
age et al., 2003). This scale was not significantly correlated
with the Illinois Victimization Scale (r = .12), and thus pro-
vided evidence of discriminant validity (Espelage et al.,
2003). Cronbach’s alpha coefficients were .76, .77, .78, and
.84 for each of the four waves of data collection in this
study.
Peer victimization. The four-item University of Illinois Vic-
timization Scale (Espelage & Holt, 2001) assesses victim-
ization from peers. Students are asked how often the
following have happened to them in the past 30 days: “Other
students called me names”; “Other students made fun of
me”; “Other students picked on me”; and “I got hit and
pushed by other students.” Response options are “Never,”
“1 or 2 times,” “3 or 4 times,” “5 or 6 times,” and “7 or more
times.” The construct validity of this scale has been sup-
ported and scores have converged with peer nominations of
victimization (Espelage & Holt, 2001). Higher scores indi-
cate more self-reported victimization. Cronbach’s alpha
coefficients were .87, .92, .93, and .91 for each of the four
waves of data collection in this study.
Fighting perpetration. The four-item, University of Illinois
Fighting Scale (UIFS; Espelage & Holt, 2001) assesses
physical fighting behavior (e.g., I got in a physical fight; I
fought students I could easily beat) the respondent engaged
in over the past 30 days. Response options include “Never,”
“1 or 2 times,” “3 or 4 times,” “5 or 6 times,” and “7 or more
times.” The Fighting Scale had a low correlation with the
Victimization Scale (r = .21) and was only moderately cor-
related with the Bullying Scale (r = .58), providing evidence
of discriminant validity (Espelage & Holt, 2001). Cron-
bach’s alpha coefficients were .81, .75, .74, and .71 for each
of the four waves of data collection in this study.
Analysis
Missing data analysis. We used a multiple imputation proce-
dure to avoid biases from missing data. Any student with a
survey completed at T1 was eligible for analysis. The impu-
tation procedures were completed using SPSS Version 21
(IBM Corp., 2013), using the fully conditional specification
Markov chain Monte Carlo (MCMC) maximum likelihood
procedure. Enders (2010) recommended the replication and
use of 10 complete data sets. The average, imputed means
and standard deviations for each time point were provided
in Table 2. In addition, we followed an intent-to-treat design
where students were analyzed by their condition assign-
ment instead of treatment actually received (R. J. A. Little
& Rubin, 1987). This procedure provides “practical utility”
of the intervention (R. J. A. Little & Yau, 1996, p. 1324)
while allowing for the use of all individuals included in the
intervention, so long as they are measured at T1.
Statistical analysis. We estimated a linear mixed growth
model where students’ survey scores were nested within the
individual students. Due to sample size restrictions, we
were unable to fit the original, three-level analytical model
and, instead, estimated a two-level model. Following the
logic described by Raudenbush and Bryk (2002), we esti-
mate the Level-1 model:
Ye
ti ii ti ti
=+×+ Time
1
ππ
0,
where Yti represented the outcome scale score at time t for
person i, π0i represented the intercept of person i, π1i × Timeti
was the relationship of the time variable (coded 0–3) to the
outcome, and eti was the independent and normally distrib-
uted error term. Both the intercept and time variables were
allowed to vary across individuals as a function of the
Level-2 model, namely,
Table 2. Means and Standard Deviations of Outcomes for Intervention and Control Conditions.
Intervention Control
Variable T1 T2 T3 T4 T1 T2 T3 T4
Bully perpetration 0.45 (0.60) 0.32 (0.54) 0.20 (0.64) 0.36 (0.62) 0.59 (0.70) 0.42 (0.62) 0.63 (0.81) 0.81 (0.96)
Bully victimization 1.08 (1.13) 1.19 (1.27) 0.92 (1.53) 1.02 (1.46) 1.15 (1.26) 1.06 (1.23) 1.14 (1.53) 1.29 (1.64)
Physical aggression 0.69 (0.82) 0.40 (0.69) 0.54 (0.77) 0.54 (0.80) 0.97 (1.05) 0.91 (0.94) 1.06 (1.12) 1.10 (1.20)
Note. Intervention n = 32, Control n = 76; T1–T4 = Time 1–Time 4; Number in parentheses is the standard deviation.
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Espelage et al. 7
πβββ β
β
0i=+×+×+×
+×
Male White Hispanic
Asi
00 01 02 03
04 aan Biracial Age
Intervention
05 06
07
+× +× +
×
ββ
β 0
+ri,
πβββ β
β
110111213
1
i= + ×× ×Male + White + Hispanic +
441516
17
×× ×
×
Asian +Biracial + Age +
Interventio
ββ
βnn +ri1,
where female, African American, and control condition rep-
resented the reference groups. Age was grand mean-
centered. The error term r1i was allowed to be estimated at
each time point, whereas a common variance was assumed
for r0i. The β17 × Intervention coefficient was our primary
interest, testing the difference between the intervention and
control group slopes. To test for appropriate model fit, we
estimated the deviance statistic across an alternative ran-
dom effects covariance structures, the identity structure. A
likelihood-ratio test was used for the comparison procedure.
A measure of the R2 was also provided (Snijders & Bosker,
2012). We calculated an effect size for the difference in lin-
ear growth slopes (i.e., δ) following Raudenbush and Xiao-
Feng (2001). All analyses were conducted using SPSS
Version 21 (IBM Corp., 2013). The plot was created using
the R package ggplot2 (Wickham, 2009).
Results
Bully Perpetration
The results of the linear growth model indicated a signifi-
cant intervention effect (β17 = −.15, SE = .07, p < .05; see
Table 3). Compared with students in the control condition,
intervention students’ bullying perpetration scale scores
significantly decreased across the four waves (δ = −.20,
95% confidence interval [CI] = [−.38, −.03]). To visualize
this trend, a line plot depicting the conditions is shown in
Figure 1. None of the other Time × Student characteristic
interactions were significant. One other significant variable
was found for this outcome: Compared with African
American students, White students were significantly more
likely to endorse bullying perpetration (β02 = .33, SE = .13,
p < .01).
To test model fit, we estimated the model using an alter-
native random effects covariance structure. The hypothe-
sized model allowed for a variance component to be
Table 3. Multilevel Modeling Results for Outcomes (N = 123).
Bully perpetration Bully victimization Physical aggression
Fixed effects β (SE) 95% CI β (SE) 95% CI β (SE) 95% CI
Intercept −0.42 (0.76) [−1.92, 1.08] −0.61 (1.39) [−3.33, 2.10] −1.75 (1.10) [−3.9, 0.41]
Time 0.17 (0.37) [−0.58, 0.92] 0.72 (0.75) [−0.79, 2.23] 0.59 (0.52) [−0.46, 1.64]
Gender −0.02 (0.12) [−0.26, 0.22] −0.03 (0.22) [−0.45, 0.39] 0.17 (0.18) [−0.19, 0.53]
White 0.33 (0.13)* [0.07, 0.59] 0.10 (0.24) [−0.37, 0.57] 0.79 (.19)* [0.42, 1.15]
Hispanic 0.22 (0.26) [−0.29, 0.74] 0.97 (0.46)* [0.05, 1.88] 0.52 (0.36) [−0.19, 1.23]
Asian 0.46 (0.66) [−0.84, 1.76] 0.71 (1.19) [−1.63, 3.05] 1.36 (0.94) [−0.50, 3.21]
Biracial 0.05 (0.21) [−0.37, 0.47] 0.14 (0.38) [−0.61, 0.88] 0.35 (0.31) [−0.26, 0.95]
Age −0.06 (0.13) [−0.31, 0.19] −0.09 (0.23) [−0.54, 0.36] −0.20 (0.18) [−0.55, 0.15]
Condition 0.02 (0.13) [−0.22, 0.27] 0.05 (0.22) [−0.39, 0.49] −0.14 (0.18) [−0.49, 0.21]
Time × Male 0.02 (0.05) [−0.09, 0.13] −0.17 (0.11) [−0.40, 0.06] −0.03 (0.07) [−0.17, 0.12]
Time × White 0.02 (0.06) [−0.11, 0.14] −0.07 (0.12) [−0.30, 0.17] −0.01 (0.07) [−0.14, 0.13]
Time × Hispanic −0.02 (0.13) [−0.26, 0.26] −0.39 (0.23) [−0.84, 0.06] −0.07 (.17) [−0.40, 0.27]
Time × Asian −0.09 (0.3) [−0.69, 0.5] −0.08 (0.57) [−1.21, 1.04] −0.38 (0.39) [−1.17, 0.41]
Time × Biracial −0.01 (0.14) [−0.29, 0.28] −0.06 (0.25) [−0.58, 0.46] −0.09 (0.18) [−0.47, 0.29]
Time × Age −0.02 (0.05) [−0.12, 0.09] −0.08 (0.10) [−0.28, 0.11] 0.02 (0.07) [−0.12, 0.16]
Time × Condition −0.15 (0.07)* [−0.28, −0.02] −0.04 (0.11) [−0.27, 0.18] −0.13 (0.07) [−0.28, 0.02]
Random effects Variance (SE) 95% CI Variance (SE) 95% CI Variance (SE) 95% CI
Time 1 0.27 (0.08) [0.11, 0.42] 0.62 (0.17) [0.27, 0.97] 0.47 (0.12) [0.22, 0.73]
Time 2 0.28 (0.08) [0.11, 0.44] 0.92 (0.26) [0.39, 1.45] 0.41 (0.09) [0.24, 0.58]
Time 3 0.35 (0.14) [0.06, 0.64] 1.66 (0.49) [0.62, 2.70] 0.32 (0.10) [0.12, 0.53]
Time 4 0.40 (0.15) [0.09, 0.72] 1.81 (0.70) [0.30, 3.32] 0.45 (0.14) [0.15, 0.74]
Intercept 0.27 (0.08) [0.07, 0.36] 0.83 (0.19) [0.45, 1.20] 0.47 (0.11) [0.26, 0.70]
Note. Time (0 = Time 1, 1 = Time 2, 2 = Time 3, 3 = Time 4); Gender (0 = Female, 1 = Male); Race, African American is reference group; Age (0 = 12,
1 = 11); Condition (0 = Control, 1 = Intervention). CI = confidence interval.
*p < .05.
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8 Remedial and Special Education
estimated at each time point. The constrained model, where
the variance component was equivalent at each time point,
yielded significantly worse model fit (χ2 = 23.43, df = 3, p <
.01). The final model accounted for 9.5% of the variance at
Level 2.
Bully Victimization
The results of the model revealed a non-significant inter-
vention effect (β17 = −.04, SE = .11, p > .05; see Table 3).
The intervention and control students failed to show signifi-
cant differences in slopes (δ = −.03, 95% CI = [−.19, .13]).
The results revealed only one other significant effect for
this model. Hispanic students, relative to African American
students, endorsed bullying victimization at a significantly
greater rate (β03 = 1.04, SE = .45, p < .05). None of the other
variables were statistically significant.
We again tested model fit by imposing an alternative ran-
dom effects covariance structure. The results of this test
revealed a significantly worse fitting model (χ2 = 33.35,
df = 3, p < .01). This model explained, not surprisingly, only
1.4% of the variance in the outcome at Level 2.
Physical Aggression
A non-significant intervention effect was found for the
physical aggression outcome (β17 = −.13, SE = .07, p > .05).
Students in the intervention condition did not differ from
students in the control condition with regard to slope value
(δ = −.13, 95% CI = [−.29, .03]). Again, only one other sig-
nificant variable was yielded from the model. White stu-
dents were significantly more likely to endorse physical
aggression compared with African American students (β02 =
.79, SE = .18, p < .01). The other variables in the model
failed to indicate statistical significance. Finally, model fit
was tested by imposing an alternative covariance structure.
The likelihood-ratio test results yielded a significantly
worse fitting model (χ2 = 9.25, df = 3, p < .05). The model
accounted for 16.1% of the total variance in the outcome at
Level 2.
Discussion
Bullying involvement has become a notable concern for
American youth. However, research suggests that students
with disabilities are overrepresented within the bullying
dynamic (Rose, Monda-Amaya, & Espelage, 2011).
Evidence suggests that this overrepresentation may be
attributed to social and communication skills deficits
(McLaughlin et al., 2010), which are foundational skills
taught in SEL program. Therefore, in this study, it was
hypothesized that direct instruction in the areas of self-
awareness, social awareness, self-management, problem
solving, and relationship management would serve as a
vehicle to reduce bullying, victimization, and fighting over
time for students with disabilities.
Figure 1. Bully perpetration across time points for intervention and control conditions.
Note. Shaded lines represent the 95% confidence interval.
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Espelage et al. 9
Although no RCT has been conducted to assess the
impact of SEL on bullying involvement for students with
disabilities, existing literature in the area of social compe-
tence development supports the promise of SEL programs
in reducing bullying among this population. For example,
in the self-determination literature, students with disabili-
ties who receive direct and systematic instruction in goal
setting, self-advocacy, and responsible decision making
report higher levels of self-determination than students with
disabilities who do not receive direct instruction (Wehmeyer,
Palmer, Shogren, Williams-Diehm, & Soukup, 2013).
These findings extend to decades of research on self-man-
agement and students with disabilities (McDougall, 1998),
where it has been established that the ability to effectively
manage one’s own behavior has been linked to increased
academic completion and achievement (Falkenberg &
Barbetta, 2013; Joseph et al., 2012), decreased behavioral
problems (Briesch & Chafouleas, 2009), and increased
social interactions (Koegel, Park, & Koegel, 2014).
Therefore, our hypotheses that a SEL program would reduce
bullying and aggression were grounded in the social foun-
dation of bullying involvement, and decades of research on
social development of students with disabilities.
Bullying
The significant reduction in bullying perpetration among
students with disabilities over this 3-year study is a notable
finding because much of the existing literature suggests that
students with disabilities are overrepresented as perpetra-
tors within the bullying dynamic (McLaughlin et al., 2010;
Rose, Espelage, et al., 2011). For example, Estell and col-
leagues (2009) determined that students with mild disabili-
ties were more likely to be identified as perpetrators by their
peers and teachers when compared with students without
disabilities and students classified as academically gifted.
However, perpetration is often separated by disability iden-
tification, where students with behavioral-oriented disabili-
ties tend to engage in higher levels of peer aggression, or
bullying, than their classmates without disabilities or other
disability diagnoses (Rose & Espelage, 2012).
Although assessing the predictive and protective factors
associated with bullying involvement among students with
disabilities were beyond the scope of this study, it is con-
ceivable that an interaction between disability identification
and placement of services exists. More specifically, and as
previously stated, students with behavioral-oriented dis-
abilities (e.g., EBD, ADHD) engage in significantly more
perpetration than their peers (Rose & Espelage, 2012). Rose
and Espelage (2012) also argued that the proactive aggres-
sion may be a function or manifestation of the students’ dis-
abilities because higher levels of reactive emotion (i.e.,
anger) predicted higher levels of proactive aggression (i.e.,
bullying) for students with EBD. Therefore, the bullying
may be an aggressive reaction to social stimuli, where stu-
dents with behavioral-oriented disabilities must be provided
with skills to effectively regulate these emotions (Ho,
Carter, & Stephenson, 2010; Kim & Deater-Deckard, 2011).
Unfortunately, the simple manifestation of behaviors
may not encompass the entire explanation. More specifi-
cally, Rose and colleagues (2009) determined that students
with disabilities who receive a majority of their educational
services in a self-contained environment are twice as likely
to engage in bullying behaviors when compared with their
peers without disabilities and 1.3 times as likely to engage
in bullying behaviors when compared with their peers in
more inclusive environments. Consequently, this is a nota-
ble issue for students with behavioral-oriented disabilities,
where 39.3% of students with behavioral disorders receive
their educational services in restrictive environments (U.S.
Department of Education, 2012). Although the function of
restrictive environments is to allow for an intensive
approach to providing academic and/or behavioral accom-
modations (Maggin, Wehby, Partin, Robertson, & Oliver,
2009), it is conceivable that a systematized homopholic
structure (i.e., homophily hypothesis) is being established,
where peer groups are constructed based on similarities,
and these peer group structures play an integral role within
the bullying dynamic (Hong & Espelage, 2012). For exam-
ple, Estell and colleagues (2009) determined that student
associations were important to the perception of roles,
where students who associate with other students who are
perceived as bullies, are also perceived as bullies. Given the
potential interaction between disability label and placement
of educational services, it is important to provide explicit
instruction regarding SEL to reduce bullying among stu-
dents with disabilities.
Victimization
In contrast to our original hypothesis, the intervention group
did not report lower levels of victimization when compared
with their peers in the control condition. Although this find-
ing was unexpected, the explanation may be grounded in
the inclusive practice literature. The majority of special
education literature suggests that students with disabilities
are overrepresented as victims (McLaughlin et al., 2010;
Rose, Monda-Amaya, & Espelage, 2011). The prevalence
rates range depending on measurement, identification of
disability status, and definition of bullying (Blake et al.,
2012); however, many studies report prevalence rates of
victimization in excess of 50% for students with disabilities
(Rose, Monda-Amaya, & Espelage, 2011). To compound
this issue, there is a national push for inclusive practices,
where more students are being educated in the general edu-
cation environment, which may pose a risk for students who
are not skilled in avoiding victimization (Rose & Monda-
Amaya, 2012). Although inclusive practices are, in part,
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10 Remedial and Special Education
designed to increase socialization between students with
and without disabilities, if students are not fully integrated
into a peer group, inclusive settings may exacerbate the vic-
timization (Martlew & Hodson, 1991). In other words, even
if bullying behaviors were reduced among this population
of students, the reciprocal relationship between bullies and
victims is not exclusive to students with disabilities, where
this population may be reporting victimization from indi-
viduals without disabilities.
Fighting
Similar to victimization, fighting was also found to be non-
significant between the intervention and control groups.
This finding was unexpected given that significant reduc-
tions in fighting behaviors for the treatment group were
found for fighting in the larger RCT from which this sample
was drawn (Espelage et al., 2013). However, the differential
treatment effect for bullying and fighting represents the dif-
ference between proactive and reactive aggression. More
specifically, SEL programming allowed students with dis-
abilities to be more reflective on proactive types of behav-
iors, while actively managing their own behaviors. This is
consistent with previous research that suggests that with
direct instruction, students with disabilities can successfully
manage their own behaviors (Briesch & Chafouleas, 2009).
However, fighting is typically a reactive behavior, where
individuals may not have the immediate cognitive process-
ing to avoid the immediate reaction without direct instruc-
tion (Rose, Espelage, Monda-Amaya, Shogren, & Aragon,
2013). More specifically, the reactive aggression, or fight-
ing, may be a result of social information processing defi-
cits, where students with disabilities may act too aggressively
to non-threatening or non-aggressive stimuli (Burks, Laird,
& Dodge, 1999; Sabornie, 1994), and may have greater dif-
ficulty with intrapersonal factors such as impulsivity, asser-
tion, and self-control (Mayer & Leone, 2007). Therefore,
the reactive physical aggression may be a manifestation of
the individual’s disability, which requires specific individu-
alization on the students’ Individualized Education Program
(IEP; Rose & Espelage, 2012) to develop specific, function-
based interventions through the use of a functional analyses
(Rose & Monda-Amaya, 2012). For example, high levels of
reactive aggression may be maintained by external reinforc-
ers that extend beyond a universal SEL program. More spe-
cifically, aggressive behaviors for individuals with
disabilities may serve as a positive reinforcer if used to gain
access to attention, activities, or tangibles; or as a negative
reinforcer if used to escape or avoid attention, activities, or
tangibles (May, 2011). In a systematic review of functional
analyses, Beavers, Iwata, and Lerman (2013) determined
that a majority of the studies that used functional analyses
for aggressive behaviors found that aggression is main-
tained by social consequences. Therefore, to address high
levels of aggressive behaviors among individuals with
disabilities, function-based interventions, above and beyond
the universal SEL programming, should be implemented to
address the antecedent events, removal of reinforcement,
and/or differential reinforcement (Iwata & Worsdell, 2005).
Limitations and Future Directions
This study is not without limitations. First, the study sample
of students with disabilities was relatively small and was
drawn from a much larger RCT; however, securing disabil-
ity data from the school districts was particularly challeng-
ing. Thus, the findings generalize to mid-sized urban
districts in the Midwest. Second, the district did not provide
data indicating the extent to which the students with dis-
abilities received the SEL curriculum in self-contained
classrooms or were exposed to the curriculum with other
students without disabilities. It would be important in future
clinical trials to assess where the students are provided the
SEL instruction. Third, only self-report student data were
collected given that the larger RCT was conducted with 36
middle schools comprising over 3,600 students. Budget
constraints precluded the use of teacher report or the collec-
tion of observational data. Future research should develop
unobtrusive, efficient, and cost-effective methods of col-
lecting data beyond self-report. Peer nominations are often
proposed as an additional form of data to track changes in
bullying and aggression, however, the middle schools in
this study were very large and peer nominations become a
less viable option as the peer networks extend beyond an
individual classroom. Finally, because of the small sample
size, analyses were not conducted at the school level. It
should be noted, however, that this study did demonstrate a
reduction in bully perpetration through the use of SEL pro-
gramming, which is extremely promising, and should
prompt future clinical trials to be replicated and extend the
findings.
Authors’ Note
Opinions expressed herein do not necessarily reflect those of the
Centers for Disease Control and Prevention, or related offices
within.
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:
Research for the current study was supported by the Centers for
Disease Control and Prevention (1U01/CE001677) to
Dorothy Espelage (PI) at the University of Illinois at Urbana-
Champaign. Polanin was funded by the Institute of Educational
Sciences Postdoctoral Training Grant R305B100016.
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Espelage et al. 11
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