Patterns of physical and relational aggression in a school-based sample of boys and girls.
ABSTRACT The current study investigated the patterns of aggressive behavior displayed in a sample of 282 students in the 4th through 7th grades (M age = 11.28; SD = 1.82). Using cluster analyses, two distinct patterns of physical aggression emerged for both boys and girls with one aggressive cluster showing mild levels of reactive aggression and one group showing high levels of both reactive and proactive aggression. Both aggressive clusters showed problems with anger dysregulation, impulsivity, thrill and adventure seeking, positive outcome expectancies for aggression, and higher rates of bullying. However, the combined cluster was most severe on all of these variables and only the combined aggressive group differed from non-aggressive students on their level of callous-unemotional traits. Similar patterns of findings emerged for relational aggression but only for girls.
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ABSTRACT: Unfortunately, very little research has examined the link between antisocial personality traits in childhood and adult psychopathy. This study used data from a clinic-referred sample of 177 boys, assessed annually from recruitment (ages 7 to 12) through age 19. Parent and teacher ratings of interpersonal callousness (IC) were tested at predictors of psychopathy ratings at 18 and 19. In regression models, conduct disorder (CD) and teacher-rated IC both predicted both Factor 1 (interpersonal and affective items) and Factor 2 (impulsivity and antisocial behavior items) of the Psychopathy Checklist-Revised, as did child IQ. Prenatal tobacco exposure and cortisol measured in adolescence predicted only Factor 1. When each factor was included in the prediction of the other, CD and IC no longer predicted Factor 1 but remained significant predictors of Factor 2.Journal of Clinical Child & Adolescent Psychology 01/2007; 36(3):334-46. · 1.92 Impact Factor
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ABSTRACT: This article highlights how a developmental approach to understanding the causes of conduct disorder (CD) could be critical for understanding and preventing delinquent and violent behavior in youth. The approach recognizes that there may be multiple causal pathways leading to CD. It also integrates developmental research and theory with research on antisocial and aggressive behavior to explain how normal developmental mechanisms may be disrupted to place a child at risk for showing problem behavior. The article reviews research outlining several such pathways that differ in the age at which serious conduct problems develop and that differ on the presence or absence of callous-unemotional traits. The presence of these different developmental pathways has important implications for how research is conducted to study CD and interventions are implemented to prevent or treat children who have CD.Child and Adolescent Psychiatric Clinics of North America 05/2006; 15(2):311-31, vii. · 2.60 Impact Factor
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ABSTRACT: A teacher rating scale of reactive aggression, proactive aggression, and covert antisocial behavior was evaluated in a normative sample of third- to fifth-grade predominantly white lower middle class boys (N= 186). Factor analysis revealed independent and internally consistent Reactive Aggression (six reactive items), and Proactive Aggression (five proactive items, five covert items) factors. Although the factors were intercorrelated (r= .67), and each factor was significantly correlated with negative peer social status (r= .26 for each, controlling for grade), the independence of the factors was supported by the unique relation of Reactive Aggression with in-school detentions (r= .31), controlling for Proactive Aggression and grade. These results supported the reliability and validity of Reactive and Proactive Aggression as rated by teachers, which should facilitate further research of these constructs.Journal of Abnormal Child Psychology 07/1996; 24(4):473-480. · 3.09 Impact Factor
Patterns of Physical and Relational Aggression
in a School-Based Sample of Boys and Girls
Ann Marie Crapanzano & Paul J. Frick &
Andrew M. Terranova
Published online: 15 December 2009
# Springer Science+Business Media, LLC 2009
Abstract The current study investigated the patterns of
aggressive behavior displayed in a sample of 282 students in
the 4th through 7th grades (M age=11.28; SD=1.82). Using
cluster analyses, two distinct patterns of physical aggression
emerged for both boys and girls with one aggressive cluster
showing mild levels of reactive aggression and one group
Both aggressive clusters showed problems with anger
dysregulation, impulsivity, thrill and adventure seeking,
positive outcome expectancies for aggression, and higher
rates of bullying. However, the combined cluster was most
severe on all of these variables and only the combined
aggressive group differed from non-aggressive students on
their level of callous-unemotional traits. Similar patterns of
findings emerged for relational aggression but only for girls.
Aggression has long been viewed as an important construct to
study because, by definition, it involves behaviors that are
intendedtohurtorharmothers (Berkowitz 1993). One critical
issue that has been the focus of a great deal of recent research
is whether or not there are important distinctions between
different types of aggression. Specifically, research has
frequently distinguished between reactive and proactive
forms of aggression (Card & Little 2006). Reactive aggres-
sion is generally defined as aggression that is impulsive and
occurs as an angry response to a perceived provocation or
threat. In contrast, proactive aggression is generally defined
as more planned and premeditated aggressive acts that are for
instrumental gain or dominance over others.
In support of this distinction, separate dimensions have
consistently emerged in factor analyses across many different
types of samples and using various assessment formats (Little
et al. 2003; Poulin & Boivin 2000; Salmivalli & Nieminen
2002). In addition, research has consistently documented
differences in the emotional and cognitive correlates to the
two types of aggression. For example, reactive aggression has
been consistently linked to low frustration tolerance, poorly
regulated emotional responses to provocation, impulsivity,
and a tendency to misinterpret ambiguous behaviors as hostile
provocation (Atkins et al. 2001; Hubbard et al. 2001; Munoz
et al. 2008; Phillips & Lochman 2003). In contrast, proactive
aggression has been associated with the tendency to have
more positive views of aggression as an effective means to
reach goals (i.e., positive outcome expectancies), a reduced
emotional responsiveness to negative emotional stimuli, and a
callous-unemotional interpersonal style (i.e., lacking guilt and
empathy; a callous manipulation of others) (Crick & Dodge
1996; Frick et al. 2003; Hubbard et al. 2002). Based on this
research, any adequate causal theory of aggressive behavior
needs to explain these different correlates to the two types of
There are several critical issues that are important for
interpreting this research. The first issue involves the high
correlation between these two types of aggression, which
ranges from 0.40 to 0.90 across samples of youth and with
the typical estimate being about 0.70 (Little et al. 2003;
Poulin & Boivin 2000). This high degree of correlation
A. M. Crapanzano:P. J. Frick (*)
Department of Psychology, University of New Orleans,
2001 Geology & Psychology Bldg.,
New Orleans, LA 70148, USA
A. M. Terranova
Department of Psychology, Stephen F. Austin State University,
J Abnorm Child Psychol (2010) 38:433–445
leads to several methodological and theoretical consider-
ations. One methodological consideration is whether this
high correlation appropriately reflects the expression of
aggressive behavior in youth or whether it is an artifact of
limitations in the methods used to assess it (Card and Little
2006). Further, if it does appropriately reflect the expres-
sion of aggressive behavior, it presents problems in the
choice of the most appropriate methods for data analysis.
That is, it is important to use some method for controlling
for the co-occurrence of the two patterns of aggression
when studying differential correlates (Little et al. 2003;
Marsee and Frick 2007).
Importantly, research has consistently shown an asymme-
try in the overlap between the two types of aggression.
Specifically, there appears to be a significant number of
most children who show high levels of proactive aggression
also show high rates of reactive aggression (Brown et al.
1996; Dodge and Coie 1987; Frick et al. 2003; Munoz et al.
2008; Pitts 1997). Therefore, there appears to be two groups
of aggressive children, one which is highly aggressive
showing both types of aggressive behavior and the other
being less aggressive overall and typically only showing
reactive types of aggression. As a result, methods for
studying the different patterns of aggression must account
for this specific typology in aggression scores.
Unfortunately, the degree and pattern of overlap between
the two types of aggression makes studying the differential
correlates difficult because it violates the assumption of
bivariate normality required for typical variable centered
and correlational approaches (e.g., partial correlations,
multiple regression) to testing independent effects (Cohen
1983). That is, because bothgroups of aggressive youth show
high rates of reactive aggression, correlates specific to the
purely reactive aggressive group may not be apparent or
appear weak in simple correlations with measures of reactive
aggression (Raine et al. 2006). Also, linear interactions
between reactive and proactive aggression measures using
multiple regression procedures may not emerge as significant
or may be misleading due to the absence of a group high on
proactive aggression but low on reactive aggression. Alter-
natively, interactions may not be significant and suppressor
effects may emerge (e.g., reactive aggression being more
strongly related to measures of emotional dysregulation
when controlling for proactive aggression) which are
difficult to unambiguously interpret (Lynam et al. 2006).
As a result, simply identifying distinct correlates to reactive
and proactive aggression does not clearly translate into
potential differences in characteristics in children within the
various aggressive typologies. This has led many researchers
to advocate for the use of person-centered approaches to
analyses when studying correlates to the different types of
aggression (Barker et al. 2006; Frick 2006).
The high correlation between the two types of aggres-
sion and the fact that the combined aggressive group is
typically more aggressive overall also have important
theoretical implications. Specifically, some researchers have
questioned the importance of distinguishing between
reactive and proactive aggression (Bushman and Anderson
2001; Walters 2005). That is, an alternative way of
interpreting these findings is that proactive aggression is
simply a marker of a more severe pattern of aggression, and
not a different type of aggression. Thus, research needs to
consider whether children who only show purely reactive
aggression show qualitative differences on important
emotional and cognitive variables from those in the
combined proactive/reactive group rather than being simply
less impaired. Again, such tests are more directly tested
through person-centered group comparisons, rather than
testing differences in correlations with the two types of
aggression. Also, such tests are important to conduct in
non-referred samples, to minimize potential referral biases
resulting from the most impaired children (i.e., those high
on both types of aggression) being over-represented in
clinic-referred or forensic samples (Costello and Angold
Another issue in the study of aggression subtypes is
whether the distinct patterns of aggression are found for
both boys and girls. That is, most of the studies on physical
aggression have been conducted primarily on male samples
(Card and Little 2006). The few studies of girls do suggest
that physical aggression can be problematic for girls and
that it can be divided into both proactive and reactive
aggression forms in girls (Marsee and Frick 2007).
However, recent research has also suggested that, when
girls behave aggressively, they are more likely to choose
relational forms of aggression rather than physical aggres-
sion (Crick 1996; Crick et al. 1997; Crick and Grotpeter
1995; Lagerspetz et al. 1988; Ostrov and Keating 2004).
Relational aggression focuses on harming others by harm-
ing their social relationships (e.g., gossiping or telling lies
about them; excluding them from groups).1
As a result, it is important to determine whether there are
subtypes of relational aggression in girls that are similar to
those found for physical aggression. In support of this
possibility, Little et al. (2003) studied a large normative
sample of German youth (grades 5 through 10) and found
1The label relational aggression is sometimes used interchangeably
with the terms “social aggression” and “indirect aggression (see Card
et al., 2008 for a more extended discussion of the similarities and
differences in the use of these terms). We chose to use the term
“relational aggression” because the conceptualization that guided this
study and the measure used to assess this construct in the current study
focused on behaviors related to harming others through hurting their
social relationships (e.g., spreading rumors and lies about another
person; excluding others from a friendship group).
434J Abnorm Child Psychol (2010) 38:433–445
that both physical aggression and relational aggression
could be divided into proactive and reactive subtypes. In a
sample of detained adolescent girls, Marsee and Frick
(2007) further demonstrated that these subtypes of relation-
al aggression showed divergent emotional and cognitive
correlates, similar to the findings on physical aggression.
That is, reactive relational aggression was uniquely associ-
ated with poorly regulated emotion and anger to perceived
provocation, whereas proactive relational aggression was
uniquely associated with callous-unemotional (CU) traits
and positive outcome expectations for aggression.
As is the case between reactive and proactive forms of
aggression, physical and relational aggression are also highly
correlated, with a recent meta-analysis showing an overall
correlation of 0.76 across 148 studies (Card et al. 2008).
However, despite this high level of associations, factor
analyses of teacher (Crick 1996; Rhys and Bear 1997), self
(Marsee et al. 2006; Prinstein et al. 2001), and peer ratings
(Crick and Grotpeter 1995) provide some support for the
distinctiveness of relational and physical aggression. Further,
while many studies have shown that both boys and girls can
act both relationally and physically aggressive (e.g., David
and Kistner 2000; Putallaz et al. 2007), relational aggression
predicts social-psychological maladjustment above and
beyond the variance accounted for by physical aggression
for girls but not boys (e.g., Crick and Grotpeter 1995;
Marsee and Frick 2007; Prinstein et al. 2001).
Thus, these studies suggest that relational and physical
aggression share about 50% of their variance but show
incremental prediction of important outcomes at least for
girls. An important issue that has not been addressed in past
research is documenting how many children, especially girls,
who show significant levels of relational aggression would
not score high on measures of physical aggression and
whether these girls show problems in adjustment that may
warrant intervention. This is an important question to test in a
non-referred sample because relational aggression may not
lead a child to be referred for treatment as frequently as
physically aggressive behavior, even though relational ag-
such behavior (Prinstein et al. 2001; Putallaz et al. 2007).
Thus, the incremental effects of relational aggression in
predicting problems in adjustment may not be as apparent in
clinic-referred samples in which those high on both types of
aggression may be over-represented.
A final important issue when studying the subtypes of
aggression in samples of youth is to clarify the association
between patterns of aggression and bullying. Bullying has
been defined as aggression specifically against a person
who is perceived to be weaker and less able to defend him
or herself than the aggressor (Qlweus 1991). It has recently
received significant attention in schools because it has been
shown to lead to significant and long-term problems for its
victims (Storch et al. 2005). Unlike the distinction between
reactive and proactive aggression, which focuses on the
function and motivation of the aggressive act (e.g., in
response to perceive provocation; to obtain dominance), the
definition of bullying focuses largely on the characteristics
of the victim. Thus, it is unclear how this form of
aggression would relate to the reactive and proactive
typologies. Further, in two studies that did investigate the
associations between bullying and types of aggression,
there did not appear to be clear associations between
bullying and specific types of aggression (Camodeca et al.
2002; Unever 2005). Instead, like the study of aggression
overall, both studies suggested that some youth who bullied
were high on both proactive aggression and reactive
aggression and others were high largely on reactive forms
Based on this background literature, we tested whether
the profiles documented in past samples (i.e., low aggres-
sion, high on reactive aggression, high on both proactive
and reactive aggression) could be found in a school-based
sample of both boys and girls. Further, we tested whether
these patterns could be identified with both physical and
relational aggression. Second, we compared the resulting
aggression groups on theoretically important cognitive and
emotional variables that have shown to differentiate
aggressive subtypes in past research (i.e., measures of
emotional dysregulation, impulsivity, CU traits, thrill and
adventure seeking, positive outcome expectances for
aggression). Importantly, we tested whether group differ-
ences supported a distinct typology model (i.e., qualitative
differences among aggressive groups) or a severity model
in which the differences were mainly due to differences in
the level of risk. Again, we tested whether any group
differences were similar when studying physical and
relational forms aggression. Third, we compared subgroups
of aggressive youth on their level of bullying, using both
self-report and peer-report of bullying, to provide a direct
test of whether or not bullying behaviors are associated
with specific patterns of aggressive behavior. Fourth, we
compared subgroups of aggressive youth, formed based on
physical and relational aggression measures, to determine
the differences in which youth, especially girls, would be
classified based on the different types of aggression.
Participants were recruited from the 4th through 7th grades
at four schools in a semi-rural public school system in the
southeastern United States. All of the schools were Title I
schools, meaning a substantial proportion of students (at
J Abnorm Child Psychol (2010) 38:433–445435
least 66%) received free or reduced lunches due to low
family incomes. Boys and girls in special education classes
were excluded from the study. Participants were all between
the ages of 9 and 14, with a mean age of 11.28 (SD=1.82).
Of the participants, 37% were 4th graders, 32% were 5th
graders, 24% were 6th graders, and 7% were 7th graders.
Girls made up 54.2% of the sample and nearly half of
the sample reported being Caucasian (49.3%) as their
ethnicity and 38.4% as African-American, 3% as
Hispanic-American, 1% as Asian-American, and the rest
self-reported other ethnicities. The gender and ethnic
composition of the sample was representative of the
participating public schools based on data published by
the school system. Specifically, data published by the
school system indicated that for all school age children
in their population (K-12), 51.4% were Caucasian, 46.1%
were African-American, 1.8% were Hispanic-American,
and 0.5% were Asian-American.
Institutional Review Board approval was obtained prior to
data collection. Students were contacted for the study via
letters with consent forms sent home to parents. Once
consent was obtained from parents, the questionnaires were
administered to small groups of students during portions of
the school day that minimized disruptions to instructional
time (e.g. study period, guidance counseling time). Students
were asked to sign an assent form before participating. Any
students who did not wish to participate in the study or
whose parents did not sign a consent form were asked to do
an alternative activity while the questionnaire was admin-
istered. To control for differences in reading ability, the
questionnaires were read out loud. During the questionnaire
administration, participants were spaced far enough apart to
make it difficult to determine other participants’ responses.
Additionally, participants were provided with a cover sheet
to hide their responses.
Parental consent was returned for 349 (70%) of
approximately 500 eligible students. Of this 349, 53
students did not participate in data collection, either due
to absences or other activities on data collection days or due
to unwillingness to provide assent. Another 14 students did
not complete forms or did not complete forms correctly,
leading to the final sample of 282. No data are available on
those that did not participate in data collection. However, of
the 14 students who were partial completers, 68% were
African-American and 50% were boys.
Peer Conflict Scale (PCS; Marsee and Frick 2007; Munoz
et al. 2008) The PCS is a 40-item measure developed to
assess the various types of aggressive behaviors and is
available at http://fs.uno.edu/pfrick/. It includes four 10-
item scales. The two reactive subscales, Reactive-Physical
(e.g., “If others make me mad, I hurt them” ; “I have gotten
into fights, even over small insults from others”) and
Reactive-Relational (e.g., “If others make me mad, I tell
their secrets” “When others make me mad, I write mean
notes about them and pass them around”) have items
worded such that there is a clear provocation, and the
reaction is either to hurt or fight the other person (physical)
or to harm them socially (relational). In contrast the
Proactive-Physical subscale (e.g., “I carefully plan out
how to hurt others” , “I start fights to get what I want”)
also involves hurting others or fighting, but in a way that is
clearly planned or for gain. Similarly, the Proactive-
Relational subscale (e.g, “ I deliberately exclude others
from my group, even if they haven’t done anything to me”,
“I gossip about others to become popular”) involves hurting
others socially but again in a way that is clearly not in
reaction to a perceived provocation or for gain. Each item
was scored either 0 (Not at all true), 1 (Somewhat true), 2
(Very true), or 3 (Definitely true). In the current sample, the
internal consistency of the four aggression scales was
adequate: reactive relational aggression alpha=0.85; reac-
tive physical aggression alpha=0.88; proactive relational
aggression alpha=0.85; proactive physical aggression
The factor structure of the PCS was tested in a sample of
juvenile justice involved adolescents (N=470; age range=
12–18) (Marsee et al. 2006). Confirmatory factor analysis
(CFA) showed that a hierarchical four-factor model fit the
data better than a one factor model (i.e., general aggression
factor), a two-factor model (i.e., physical and relational
factors), and a four-uncorrelated factor model.2In the same
sample, the reactive and proactive physical aggression
scales were positively associated with a self-report of the
number of violent acts (Kimonis et al. 2008). In another
study of detained boys, the aggression scales were also
correlated with a laboratory measure of aggressive and the
reactive and proactive dimensions showed different
responses to provocation (e.g., reactive aggression being
associated with aggressive responses to low provocation)
(Munoz et al. 2008). In a detained sample of girls, the
reactive and proactive subscales for both relational and
physical aggression showed differential correlations with
2Given the relatively small sample size for the analyses, the results
have to be interpreted with extreme caution. However, a confirmatory
factor analysis was performed on the PCS in the current sample and
the results were similar to past factor analyses. That is, the four factor
structure showed adequate fit (e.g., CFI=.934; RMSEA=.063) and
showed significantly better fit than one factor (i.e., general aggression)
or two factor (i.e., relational and physical aggression factors) models
using a chi-square difference test.
436J Abnorm Child Psychol (2010) 38:433–445
important external criteria (i.e., reactive being correlated
with measures of emotional dysregulation and proactive
being correlated with measures of CU traits and positive
outcome expectations for aggression) (Marsee and Frick
2007). Finally, in an ethnically diverse community sample
similar in age (range 6–17; Mn=11.09; SD=3.38) to the
current sample, the reactive and proactive subscales for
relational aggression again showed differential correlations
with anxiety and cognitive errors (Marsee et al. 2008).
Antisocial Process Screening Device (APSD, Frick and
Hare 2001) The APSD is a self-report behavior rating scale
with each item scored either 0 (Not at all true), 1
(Sometimes true), or 2 (Definitely true). This scale
measures three factors including Impulsivity, Narcissism,
and Callous-Unemotional traits. Only the 5-item Impulsiv-
ity (e.g., “I act without thinking of the consequences”) and
6-item Callous-Unemotional subscales (e.g., “I feel guilty
or bad when I do something wrong”, which is reversed
score) were used in this study. Scores from the self-report
version of the APSD have been shown to be relatively
stable over 3 years in a non-referred sample (Munoz and
Frick 2007) and have been associated with greater
aggression and violence (Kruh et al. 2005) and with
laboratory measures of deficient affective experiences
(Loney et al. 2004). The internal consistency of the two
scales in the current sample were modest but consistent
with findings from past samples (impulsivity alpha=0.51;
callous-unemotional alpha=0.60) (Munoz and Frick 2007).
Children’s Emotion Management Scale (Zeman et al.
2001) This questionnaire is a 23-item self-report instrument
measuring 6 subscales of anger and sadness. For the
purposes of this study, an anger dysregulation scale was
formed combining the 3-item anger dysregulation (i.e. I
attack whatever it is that makes me mad) and the reverse
scored 4-item anger inhibition (i.e. I get mad inside but
don’t show it) subscales, as suggested by a factor analysis
in a community sample of 227 4th and 5th graders (Zeman
et al. 2001). The internal consistency of this scale in the
current sample was alpha=0.58.
Thrill and Adventure Seeking Scale (TAS, Frick et al.
2003) The TAS is a 12-item subscale of the modified
Sensation Seeking Scale for Children (Russo et al. 1993)
that measures self-reported preferences for novel and
dangerous activities. The participant chooses between a
pair of statements to indicate which one was more true of
him or her. For each item one statement (e.g., “I enjoy the
feeling of riding my bike fast down a big hill”) describes
sensation seeking behaviors. The other statement (e.g.,
“Riding my bike fast down a big hill is scary for me”)
describes a preference for avoiding sensation seeking
behaviors. To increase the variance in scores, the scale
was modified to include a question regarding how well the
chosen behavior described the child by selecting either sort
of true for me or really true for me. This modification
created a four-point scale for each item. Both the original
(Frick et al. 1999) and revised (Frick et al. 2003) version of
the TAS subscale have been associated with conduct
problems, including aggression. In the current sample, the
internal consistency of the TAS scale was alpha=0.78.
Attitudes and Beliefs Toward Aggression (Vernberg et al.
1999) This self-report measure assesses social-cognitive
styles that have been related to aggressive behavior. Two
subscales were combined and used in the current study: a 7
item Aggression Legitimate subscale indicating the belief
that it is okay to be aggressive or that the victims deserve it
and a 4 item Aggression Pays subscale indicating the belief
that aggression gets you what you want (Vernberg et al.
1999). Both subscales and their combination have shown to
have strong internal consistency in samples of 3rd through
9th grade students (Biggs et al. 2008; Dill et al. 2008;
Vernberg et al. 1999). Additionally, these subscales have
been associated in expected directions with aggressive
behaviors, negative affect, and response to intervention
(Biggs et al. 2008, Dill et al. 2008; Vernberg et al. 1999). In
this study the combined Aggression Legitimate and the
Aggression Pays scales had an internal consistency of
Bullying The bullying scale and the definition of bullying
were always presented after the PCS, so that the definition
of bullying would not influence student’s responses to the
PCS. Both self and peer reports of bullying behaviors were
assessed. First, students were read the definition of bullying
based on the one provided by Olweus (2001): “Bullying is
when a student is mean to another student over and over
again. The student who is being bullied is usually at a
disadvantage, such as being smaller, outnumbered, or
having fewer friends. Bullying includes hitting, calling
people names, telling stories about people, and ignoring
people.” After the definition was read, the students were
asked to rate each of the classmates on a class roster that
included all those who were in the student’s homeroom and
who were also participating in the study. The rating was
made on a scale of 1 (never) to 3 (often) to the question,
“How often does this classmate bully others?” The children
in this study were also asked to rate themselves on a scale
of 1 (never) to 5 (always) on the question “How often do
you bully classmates?”.
These methods for assessing bullying behavior have
been reliably used in several studies (Nansel et al. 2001;
Solberg et al. 2007; Sourander et al. 2007). Also, research
suggests that bullying behavior using these procedures has
J Abnorm Child Psychol (2010) 38:433–445437
been associated with several indices of poor psychosocial
adjustment (Nansel et al. 2001; Sourander et al. 2007).
Another study using similar peer and self-report methods
found individuals who were reported to frequently engage
in bullying behaviors were more likely to be both
proactively and reactively aggressive (Salmivalli and
Nieminen 2002). In the current sample, the peer and self-
report of bullying were correlated r=0.40 (p<0.001).
The distributions of all study variables are described in
Table 1. The distributions indicate that most variables were
relatively normally distributed in this sample, with the
exception of the proactive aggression scales. Both the
relational and physical proactive aggression scales showed
strong positive skewness. As expected from previous
studies, the proactive and reactive subscales of the PCS
were highly correlated for both physical (0.71) and
relational (0.82) aggression. Also, the relational and
physical aggression subscales were also highly correlated
for both reactive (0.68) and proactive (0.84) aggression.
Defining Aggression Clusters
The first set of analyses tested whether or not the three
aggression profiles that have been identified in past
research could be identified in the current sample. This
was tested by first converting the four aggression subscales
from the PCS to standardized z-scores and then using a K-
means cluster analysis. Separate cluster analyses were
conducted for the reactive and proactive types of physical
aggression and the reactive and proactive types of relational
aggression. Also, these cluster analyses were conducted for
the full sample and for boys and girls separately. In all
analyses, 2, 3, 4, and 5 clusters were compared.
Cluster Analyses of Physical Aggression Subscales In the
full sample using the two physical aggression subscales of
the PCS, a 2 cluster solution resulted in clusters differen-
tiating children into high and low aggression clusters.
The 3 cluster solution showed aggressive groupings
corresponding with predictions, and the clusters descrip-
tions are provided in Table 2. This cluster solution resulted
in a large group of children low on aggression (n=176),
another cluster that was relatively high on reactive
aggression only (n=84), and a third cluster high on both
types of aggression (n=18). When 4 and 5 clusters
solutions were inspected, in no case did a pure proactive
group emerge, consistent with expectations. Also, in
support of the 3 cluster solution, the R2showed a dramatic
jump in variance explained from the 2 (R2=0.38) to 3 (R2=
0.67) cluster solution with only modest increases when 4th
(R2=0.76) and 5th clusters (R2=0.81) were isolated.
Further, the pseudo F statistic increased significantly from
the 2 (331.08) to 3 (474.25) cluster solution but decreased
with the 4 cluster solution (455.38). Importantly, the cluster
analyses were all run using the child’s ID number to
determine the initial starting value but the same cluster
solutions emerged after randomly resorting the data set and
repeating the cluster analysis twice. Further, a very similar
cluster solution emerged when the cluster analyses were
repeated for girls and boys separately.
Thrill and adventure seeking
Peer report bullying
Self report bullying
Table 1 Distributions of All
438J Abnorm Child Psychol (2010) 38:433–445
Cluster Analyses of the Relational Aggression Subscales
Similar cluster analyses were conducted using relational
aggression subscales of the PCS. However, unlike for
physical aggression, these analyses showed very different
patterns of aggression for boys and girls. Thus, the cluster
analysis in the full sample was not interpreted. For girls, a
pattern very similar to the results found for physical
aggression emerged. Specifically, the 2 cluster solution
differentiated girls into high and low aggression clusters.
The 3 cluster solution showed aggressive groupings similar
to the physical aggression clusters. This cluster solution
resulted in a large group of girls low on aggression (n=105),
another cluster that was relatively high on reactive
aggression only (n=38), and another group consisting of
individuals high on both reactive and proactive relational
aggression (n=10), as describe in Table 2. When the 4 and
5 cluster solutions were inspected, again no purely
proactively aggressive group emerged. Also in support of
the 3 cluster solution, the pseudo F statistic increased from
the 2 (343.22) to 3 (476.37) cluster solutions, while the 4
cluster solution resulted in a decrease in the pseudo F
statistic (422.10). Similarly, the R2 showed a dramatic jump
in variance explained from the 2 (R2=0.41) to the 3 (R2=
0.68) cluster solution but only a modest increase when a 4
cluster solution was isolated (R2=0.76). Importantly, the
cluster analyses were all run using the child’s ID number to
determine the initial starting value but the same results were
found after randomly resorting variables in the data set and
repeating the cluster analyses twice.
For boys, the relational aggression clusters were very
different. The 2 cluster solution, like the previous versions,
divided the groups into a high relational aggression group
and a low relational aggression group. However, the 3
cluster solution resulted in one large group low on both
types of aggression (n=76), one group that was moderate
on both types of relational aggression (n=44) and a third
group high on both types of aggression (n=5). Thus, for
relational aggression in boys, there was no evidence for
distinct patterns of reactive and proactive aggression in this
sample but simply clusters based on the level of aggressive
Validating the Aggression Clusters
In order to validate the clusters, we compared the aggression
clusters using constructs that have been differentially
associated with reactive and proactive aggression in past
research. Three MANCOVA’s were conducted to compare
the clusters on 1) variables predicted to be most strongly
associated with reactive aggression, 2) variables predicted to
bullying behavior from both self and peer report, while
controlling for important demographic covariates. Thus, a p-
value of p<0.01 was used in these overall tests to adjust for
the multiple omnibus tests. Further, given that the results
were very similar for boys and girls on the physical
aggression cluster, only results from the full sample are
reported. Also, given that the relational aggression clusters
for boys were only based on level of aggression and not
type of aggression, only the comparison of relational
aggression clusters in girls are reported.
Differences Among Physical Aggression Clusters: Full
Sample The physical aggression cluster was first compared
on age, gender, and race. The groups differed significantly
on age (F (2,264)=4.12, p<0.01) with the combined
proactive and reactive aggression group being significantly
older (Mn=12.00; SD=1.18) than the low aggression
cluster (Mn=11.06; SD=1.85) but not the high reactive
cluster (Mn=11.64; SD=1.80). Also, the groups differed in
the percentage of Caucasian students (X2(df=2)=12.35,
p<0.01) and percentage of girls (X2(df=2)=12.55, p<
0.01). Specifically, the low aggression cluster had a greater
percentage of Caucasian students (58%) and girls (63%)
Table 2 Description of Aggression Clusters Formed Using the Peer Conflict Scale
Low (n=176) Hi reactive (n=84) Combined(n=18) Cluster effectEta2
Full sample physical Wilk’s Lambda F(4, 548)=303.00***
Low (n=105)Hi reactive (n=38) Combined (n=10) Cluster effect
Wilk’s Lambda F(4, 298)=141.51***
Girls only relational
***p<0.001; **p<0.01; *p<0.05; Means with different superscripts differ significantly in pairwise comparisons
J Abnorm Child Psychol (2010) 38:433–445439
than the cluster high on reactive aggression (36% and 42%,
respectively) and the cluster high on both forms of
aggression (35% and 39%, respectively). Thus, all analyses
of group differences were conducted controlling for age,
gender, and ethnicity.
Using the more conservative level to determine signifi-
cance, the overall MANCOVA was significant for the
variables predicted to be most associated with reactive
aggression, namely, anger dysregulation and impulsivity
(Wilk’s Lambda; F(4, 512)=23.31, p<0.001, Eta2=0.15).
The follow up ANCOVA’s for both variables were also
significant (Eta20.24 and 0.12, for anger dysregulation and
impulsivity, respectively). Results of these analyses are
presented in Table 3 and this table includes the means and
standard deviations for all dependent variables for each
cluster. Pairwise comparisons indicated that the three clusters
were significantly different from each other on the measure
of anger dysregulation. The combined proactive and reactive
group had the highest level of anger dysregulation, followed
by the high reactive group which was significantly higher
than the low aggression group. For impulsivity, pairwise
comparisons showed the same pattern of results.
For the variables predicted to be associated with
proactive aggression (e.g., CU traits, positive outcome
expectations for aggressive behavior, thrill and adventure
seeking), the overall MANCOVA was also significant
(Wilk’s Lambda; F(6, 508)=14.04, p<0.001, Eta2=0.14).
All three ANCOVA’s were also significant (Eta20.05–
0.21). Results of these analyses are presented in Table 3.
Follow up pairwise comparisons indicated that the com-
bined group had the highest level of CU traits. Both the
reactive only group and the low aggression group showed
significantly lower levels of these traits and did not
significantly differ from each other. In contrast, pairwise
comparisons for positive expectations for aggression indi-
cated that all three groups differed significantly, with the
combined group showing the highest level followed by the
high reactive and the low aggressive groups. Finally, for
the thrill seeking measure, the high reactive cluster and the
combined cluster did not differ significantly from each
other, although they each differed significantly from the
low aggression group.
The third MANCOVA examined differences among the
physical aggression clusters on peer reported and self
reported bullying behaviors and this also resulted in a
significant overall effect (Wilk’s Lambda; F(4, 514)=19.57,
p<0.001, Eta2=0.13). Results of these analyses are also
presented in Table 3. Follow up ANCOVA’s revealed
significant differences for both variables (Eta2of 0.14 and
0.21 peer-report and self-report, respectively). Pairwise
comparisons indicated that both peer and self-reports of
bullying were highest in the combined aggressive group,
followed by the reactive only cluster, and the low
Differences Among Relational Aggression Clusters: Girls
Only Using similar analyses, we examined differences
among the relational aggression clusters that emerged in
Table 3 Comparison of Physicalc Aggression Clusters in Full Sample
Low (n=168)Hi Reactive (n=81)Combined (n=15)Cluster Effect Eta2
Reactive correlates Wilk’s Lambda F(4, 512)=23.31***
Proactive correlatesWilk’s Lambda F(6,508)=14.04***
BullyingWilk’s Lambda F(4, 514)=19.57***
Peer report bullying
Self report bullying
***p<0.001, **p<0.01, *p<0.05
CU Traits Callous-Unemotional Traits. Effects are the between cluster effects from a one-way MANCOVA followed by individual ANCOVAwith
age, sex, and ethnicity as the covariates. Means reported are least square means adjusted for the covariates. Means with different superscripts
differ significantly in pairwise comparisons
440J Abnorm Child Psychol (2010) 38:433–445
girls. The relational aggression clusters did not differ
significantly on age (F (2,146)=8.10, p = n.s.) but did
differ on the percentage of youth who were Caucasian (X 2
(df=2)=5.70, p<0.05) with the non-aggression cluster
having a higher percentage of Caucasian students (53%)
than the reactive relational aggression (37%) and combined
relational aggression (20%) clusters. Thus, ethnicity was
included as a covariate in analyses.
The results of these analyses for relational aggression
clusters are reported in Table 4 which includes the means
and standard deviations across groups for each dependent
variable. Overall, the results of these analyses were very
similar to what was found for physical aggression in the full
sample. Specifically, the MANCOVA for the variables
predicted to be associated with reactive relational aggres-
sion was significant (Wilk’s Lambda: F(4, 288)=15.18, p<
0.001, Eta2=0.17). Follow up ANCOVA’s on each of the
variables were also significant (Eta2of 0.20 for both) and
these results are also reported in Table 4. Pairwise
comparisons indicated that for anger dysregulation, both
relational aggressive clusters differed from the non-
aggressive cluster. For impulsivity, the combined cluster
had the highest scores followed by the high reactive and the
low aggressive clusters.
For the variables predicted to be associated with
proactive aggression, the overall MANCOVA was again
significant (Wilk’s Lambda: F(6, 288)=15.18, p<0.001,
Eta2=17) but only two of the three follow-up ANCOVA’s
reached significance. For CU traits, the ANCOVA was also
significant (Eta2=0.06) and indicated that only the com-
bined group differed from the low aggression group on
these traits. The follow-up ANCOVA for positive expect-
ations for aggression was also significant (Eta2=0.13) and
revealed that that each cluster differed significantly, with
the combined cluster showing the most positive expect-
ations for aggression, followed by the high reactive and the
low aggression clusters. The follow-up ANCOVA did not
indicate significant differences across relational aggression
clusters on thrill seeking.
When comparing the relationally aggressive clusters on
bullying behaviors, the MANCOVA was again significant
(Wilk’s Lambda: F(2, 290)=12.32, p<0.001, Eta2=0.15)
and the follow up ANCOVA’s (see Table 4) conducted for
both peer- (Eta2=0.22) and self-reports (Eta2=0.16) were
also significant. Pairwise comparisons indicated that all of
the clusters differed on both self and peer reported bullying,
with the most bullying behaviors being displayed by the
combined group, followed by the high reactive group, and
low aggressive groups.
Overlap Between Physical Aggressive and Relational
Aggressive Clusters-Girls Only
A chi-square analysis was conducted to examine the
overlap between the two aggressive clusters in girls. The
results of this analysis are reported in Table 5 and it resulted
in a significant chi-square (X2 (df=4)=76.01; p<0.001).
Several interesting patterns emerged from these analyses.
Table 4 Comparison of Relational Aggression Clusters-girls Only
Low (n=105) Hi Reactive(n=38) Combined (n=10) Cluster Effect Eta2
Reactive correlatesWilk’s Lambda F(4, 288)=15.18***
Proactive correlatesWilk’s Lambda F(6, 288)=5.30***
BullyingWilk’s Lambda F(4, 290)=12.32***
Peer report bullying
Self report bullying
***p<0.001; **p<0.01; *p<0.05
CU Traits Callous-Unemotional Traits
Effects are the between cluster effects from a one-way MANCOVA followed by individual ANCOVA with ethnicity as the covariate. Means
reported are least squares means adjusted for the covariates. Means with different superscripts differ significantly in pairwise comparisons
J Abnorm Child Psychol (2010) 38:433–445 441
First, all of the girls in the combined proactive and reactive
relational aggression clusters fell into one of the two
physically aggressive clusters. Second, there were 15 girls
who were in the low relational aggression cluster but who
were classified in the reactive physical aggressive cluster
(n=13) or combined physical aggression cluster (n=2).
Third, over half (n=21, 55%) of the girls who were in the
high reactive relational cluster fell in the low physical
The results of our cluster analysis supports past research
suggesting that there are two distinct groups of children
with aggressive behavior; one group that shows moderate
levels of reactive aggression only and one that shows high
rates of both reactive and proactive forms of aggression.
Such findings have been found in clinic-referred (Dodge
and Coie 1987), non-referred (Brown et al. 1996; Frick et
al. 2003) and adjudicated (Munoz et al. 2008; Pitts 1997)
samples. Given the consistency of these findings, the
significant correlation between these two forms of aggres-
sion needs to be interpreted in light of this clustering of
individuals. Specifically, studies showing different corre-
lates to reactive and proactive aggression need to be
interpreted in light of the fact that children with proactive
aggression also typically show significant levels of reactive
aggression as well (Frick et al. 2003; Hubbard et al. 2002).
We tested predictions from two methods for explaining
these clusters of youths. One explanation suggests that the
combined aggressive group is largely a more severely
disturbed group. The second explanation suggests that there
may be qualitatively distinct emotional and cognitive
characteristics for the two aggressive groups. Our results
were largely consistent with the first model. That is, the
combined group consistently showed the highest rates of
emotional and cognitive risk factors, including those
typically associated with reactive aggression (e.g., emo-
tional dysregulation, impulsivity) and those typically
associated with proactive aggression (e.g., positive outcome
expectancies for aggressive behavior). Further, this group
also showed the highest rates of bullying behaviors, based
on both self-report and peer-report. Thus, this cluster, which
accounted for only 7% of this school-based sample, is a
group that shows a number of characteristics to suggest that
they should be important targets of school-based interven-
tion programs (Lynn et al. 2003).
In the analyses of physical aggression, there was a
much larger of group of children with moderate levels of
reactive aggression (31%). Although this group was
clearly less severe on most measures than the combined
group, they also showed several characteristics that could
indicate the need for services. Specifically, they showed
more problems regulating anger, higher levels of impul-
sivity, more positive expectancies for aggression, and
stronger preferences for thrill seeking behaviors than
non-aggressive students. Most importantly, however, they
also showed higher rates of bullying. Thus, programs
targeting a reduction in bullying in school settings need
to consider these children with problems regulating their
emotions and integrate treatment components that direct-
ly target the emotional and cognitive deficits displayed
by these youths (Lochman et al. 2008).
One notable exception to the trend for the two aggressive
clusters to differ largely in severity, rather than in type of
risk factor, was the finding for callous-unemotional (CU)
traits. Specifically, only the group high on both proactive
and reactive aggression differed from non-aggressive
children on these traits. Although this was only one
variable, this finding is important given that CU traits are
associated with a severe and stable pattern of antisocial
behavior (see Frick and Dickens 2006 for a review) and
they tend to be associated with distinct temperament and
emotional correlates, such as being associated with a lack
of responsiveness to distress cues in others (see Frick and
White 2008 for a review). Thus, the presence of CU traits
was one of the few indicators in the current study that this
group may have unique affective risk factors compared to
other aggressive youth. Further, this finding supports the
contention that children with only reactive forms of
aggression may be distressed by the effect of their behavior
on others but have difficulty regulating their emotions
sufficiently to inhibit their aggressive behavior (Frick and
Importantly, our results showed that the different
aggression clusters emerged for both boys and girls for
physical aggression but only for girls in analyses using
relational aggressive behaviors. Further, within girls, the
pattern of cognitive and emotional correlates was very
similar to what was found for physical aggression.
Importantly, this was the case for bullying as well, with
both clusters of relationally aggressive girls showing more
bullying behaviors by both peer and self-report. These
Table 5 Overlap Between the Relational and Physical Aggression
Clusters using the Peer Conflict scale- Girls Only
442 J Abnorm Child Psychol (2010) 38:433–445
findings support past research suggesting that relational
aggression may be particularly important for understanding
aggressive behavior in girls (Archer and Coyne 2005; Crick
1996; Lagerspetz et al. 1988; Ostrov and Keating 2004;
Underwood et al. 2001). Also, our results suggest that,
although boys may also show relational aggression, the
distinct profiles (e.g., reactive aggression only; combined
reactive/proactive aggression) may not be apparent. Given
that such gender specific patterns have not been studied in
previous research, such findings should be replicated before
conclusive statements can be made. However, these find-
ings could suggest that the causes of relational aggression
in boys may be different from the causes of physical
aggression, whereas in girls the two forms of aggression
may be related to similar causal factors.
We also tested how many aggressive girls would be
missed, if only measures of physical aggression or only
measures relational aggression had been used (see Table 5).
These analyses indicated that there is fairly strong corre-
spondence between the two methods of clustering the
sample, especially for those viewed as non-aggressive and
those that fall into the high combined reactive/proactive
cluster. The differences between the two approaches largely
were due to differences in the moderate reactive aggression
cluster. That is, 13 girls (12%) who were low in
relational aggression showed moderate levels of reactive
physical aggression and 21 girls (19%) in the low
physical aggression cluster showed moderate levels of
reactive relational aggression. Given the evidence from
our analyses that these clusters showed risk factors for
problem behavior (e.g., elevated levels of emotional
dysregulation and impulsivity) and they showed higher
levels of bullying behaviors, it suggests that adequate
assessments of aggression in girls should include
measures of both relational and physical aggression.
All of these findings need to be interpreted in light of
several limitations inthe study.First,withthe exception ofthe
peer-report of bullying, all other measures were self-report.
Thus, the associations with cluster membership could be
inflated due to shared method variance among the measures.
self-report measures. Also, past studies using the self-report
measure of aggression have shown that it is correlated with
laboratory measures of aggression and psychophysiological
correlates to aggression (Munoz et al. 2008). Further, the
differential associations across clusters (all of which were
formed by self-report) for the callous-unemotional measure
cannot be fully explained by method variance. Second,
several of the measures had a relatively low number of items
that resulted in poor internal consistency estimates. Thus,
improved measurement of these constructs may have
resulted in more power to detect significant differences
across groups. Third, the measure of aggression in this study
has primarily been used in older samples. Further, some of
the items related to physical aggression describe fighting
with other children, whereas other items describe “hurting
others”, which could be interpreted as including acts other
than physical harm. Fourth, although the sample was
representative of the participating schools and was ethnically
diverse, the schools were from a semi-rural area and, as a
result, it is not clear how well the findings would generalize
to schools from urban areas. Fifth, the modest participation
rate may have influenced the findings, in that the most
aggressive children may have been less likely to participate.
We feel that even with this level of participation, the range of
aggressive behavior in this sample is more typical to that
found in middle schools than if a clinic or forensic sample
was studied. Further, this participation rate is consistent with
the rate of active parental consent found in research
conducted in other Title I schools characterized by a high
rate of poverty (Esbesnsen et al. 2008). Finally, a large study
of 13,195 students from 143 high schools did not find that
participation rates differed based on the students’ aggressive
behavior (i.e., carried a weapon during the past 30 days;
been in a physical fight during the past 12 months) (Eaton et
With these cautions in mind, our results have several
important implications. First, our findings support previous
research to suggest that there is a group of school children
who show high levels of both reactive and proactive
aggression and they appear to be important targets of
intervention. Most importantly, they show high rates of CU
traits and these traits in childhood have been linked to a
severe and chronic patterns of antisocial behavior that last
into adolescence and adulthood (e.g., Burke et al. 2007;
Frick et al. 2005; Lynam et al. 2007). In addition, this
combined aggression group showed the highest rate of
bullying behaviors in this school-based sample. This is a
critical finding in light of research showing that the
experience of bullying can lead to significant and long-
term problems for its victims (Storch et al. 2005). Second,
our results suggest that there is a second group of
aggressive youth who show less severe problems of
reactive aggression. Although not at the level of the
combine group, children in the reactive group showed
problems in their ability to regulate their emotions (e.g.,
anger dysregulation) and they showed higher rates of
bullying than non-aggressive youth. Thus, they also appear
to be in need of services, albeit potentially at a lower level
of intensity than the combined group. Third, our results
suggest that utilizing either measures of relational aggres-
sion or physical aggression alone would not identify a
significant number of girls with mild levels of reactive
aggression who show a number of problems in adjustment
that may benefit from intervention.
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