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The effects of anger management on children’s social and emotional outcomes: A meta-analysis

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Abstract

The occurrence of violent behaviors and bullying in schools continues to be a recognized problem among students and school personnel. The concern caused by these behaviors have led many schools to implement anger management and other impulse control based programs for at-risk students in an effort to prevent many of these incidences. This study was a comprehensive, meta-analytic synthesis of the literature to assess the effectiveness of these interventions, using a total of 60 studies from 1979–2010 for analysis. Results indicated an overall effect size (ES) of −0.27, showing a small to moderate intervention effect in reducing children’s negative emotional and behavioral outcomes including anger, aggression, and loss of self control. Many different types of anger management components were found to be effective in promoting positive outcomes. Implications for school psychologists are discussed.
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DOI: 10.1177/0143034312454360
2012 33: 596 originally published online 23 July 2012School Psychology International
Ashley M. Candelaria, Alicia L. Fedewa and Soyeon Ahn
outcomes: A meta-analysis
The effects of anger management on children's social and emotional
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DOI: 10.1177/0143034312454360
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Article
The effects of anger
management on
children’s social and
emotional outcomes:
A meta-analysis
Ashley M. Candelaria and
Alicia L. Fedewa
University of Kentucky, USA
Soyeon Ahn
University of Miami, USA
Abstract
The occurrence of violent behaviors and bullying in schools continues to be a recog-
nized problem among students and school personnel. The concern caused by these
behaviors have led many schools to implement anger management and other impulse
control based programs for at-risk students in an effort to prevent many of these
incidences. This study was a comprehensive, meta-analytic synthesis of the literature
to assess the effectiveness of these interventions, using a total of 60 studies from 1979–
2010 for analysis. Results indicated an overall effect size (ES) of 0.27, showing a small
to moderate intervention effect in reducing children’s negative emotional and behavioral
outcomes including anger, aggression, and loss of self control. Many different types
of anger management components were found to be effective in promoting positive
outcomes. Implications for school psychologists are discussed.
Keywords
anger-management programs, bullying, school psychologists, social-emotional/behavioral
outcomes
Corresponding author:
Ashley M. Candelaria, University of Kentucky, Department of Educational, School, and Counseling Psychology,
237 Dickey Hall, Lexington, KY 40506, USA.
Email: amca224@uky.edu
at UNIV OF KENTUCKY on May 2, 2013spi.sagepub.comDownloaded from
Most adults are equipped with the emotional understanding and the coping skills
necessary to work through anger. However, coping with anger in childhood can be
particularly challenging, as children and adolescents may not possess the skills to
adequately understand and manage feelings of anger and other emotions. The
confusion and inability to express feelings of anger appropriately may often lead
to behavioral outbursts, bullying, and other externalizing or disruptive behaviors
(Garner & Hinton, 2010). These behaviors are often exhibited during the school
day when children encounter difficulties with peers or academic demands.
The occurrence of violent behaviors in schools continues to be a recognized
problem among students and school personnel. The number of students in
grades 9 through 12 who reported involvement in a fight on school property in
2009 reached nearly 22% while the occurrence of verbal or physical aggression and
bullying for the same grades reached 20% in the same year (Centers for Disease
Control, 2010). The concern caused by these aggressive behaviors have led many
schools to implement anger management and other impulse control based pro-
grams for at-risk students in an effort to prevent these violent behaviors.
While the concept of implementing an anger control intervention is appealing, it
can be difficult to choose an effective, anger-based program for children. Due to the
nature of childhood anger, practitioners have been searching for successful early
interventions for helping children to cope with feelings of anger while effectively
teaching appropriate replacement behaviors. The need for preventive programs of
this nature that align with the USA-based National Association of School
Psychologists (NASP) practice model has also been expressed at the international
level, as the literature continues to reflect a global demand for quality evidence-
based studies dedicated to prevention and intervention of social and emotional
difficulties (Little, Akin-Little, & Lloyd, 2011).
Anger management interventions in schools are often implemented and led by
the school psychologist, and frequently take the form of groups held during lunch-
time or other natural breaks from the academic curriculum (Flanagan, Allen, &
Henry, 2010; Sukhodolsky, Solomon, & Perine, 2000). While many of these prin-
ciples have produced moderate effects for groups overall (Kellner, Bry, & Salvador,
2008; Lochman & Wells, 2003), it is important for school psychologists to be aware
of the reliability of the various types of interventions for anger and to take into
account the various moderating variables that may imply a unique intervention
effect depending on different characteristics of the child (i.e. younger vs. older
children, those from diverse backgrounds, or children previously identified with
an emotional and behavioral disability). Awareness of the most appropriate inter-
ventions dependent upon such moderator variables and manifestations of behavior
is essential to proper implementation of any intervention. Taking these variables
into account will uphold the intervention’s treatment integrity and ensure that the
most appropriate services are delivered (NASP Principles for Professional Ethics
[PPE], 2010).
To date, three meta-analyses have been conducted to measure the effectiveness
of school-based anger management programs. A meta-analysis conducted by
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Gansle (2005) condensed 20 articles of school-based interventions for anger, find-
ing an overall effect size of 0.31. This meta-analysis was limited in that the number
of articles included was relatively small and did not include data from unpublished
articles, suggesting the need for a more extensive analysis of the literature.
A second meta-analysis conducted by Ho, Carter, and Stephenson (2010), evalu-
ated 18 articles and specifically examined outcomes of cognitive-behavioral anger
management interventions for children with special education needs. An overall
moderate effect size of 0.61 was found, although this study was limited to only
students identified for special education. Sukhodolsky, Kassinove, and Gorman
(2004) condensed 40 articles, both published and unpublished, which specifically
examined the effects of cognitive-behavioral therapies on anger management of
children and adolescents. An overall effect size of 0.67 was found. Yet few com-
ponents of particular interventions or participant characteristics were examined.
Each of these meta-analyses indicated that the limited number of articles available
for synthesis at the time of analysis proved to be a limitation to the overall meta-
analysis, suggesting the need for a more updated quantitative review of the
literature.
The present study aimed to aggregate both published and unpublished studies
on anger management treatment programs and to explore its effects on children’s
emotional and behavioral outcomes using a meta-analytic method. In the current
meta-analysis, the authors hoped to find the overall effect of anger management
treatments on children’s outcomes and its differential effects by various features of
samples, research methodology, and focus of the anger management intervention.
Implications for school psychologists are discussed.
Method
The search process
The location of relevant studies in this research synthesis included both published
and unpublished literature based on a manual as well as a computerized search of
pertinent databases (e.g. PsychLit, PsychInfo, Dissertation Abstracts, MedLine
and ERIC). Key terms for literature searches included the words anger manage-
ment,anger group,behavior management,anger and emotional awareness,interven-
tion,school anger program,cognitive therapy,social skills,anger groups,students,
and children. As well as database resources, general search engines (e.g. Google)
were employed with the above key terms to capture those studies that had not been
included in the databases. Results yielded over 200 references between 1960–2011.
Studies retrieved from the initial searches were screened using specific criteria:
(1) studies had to investigate the effect of some type of anger management program
on children’s socio-emotional functioning (i.e. the dependent variable was a socio-
emotional outcome of some form); (2) target populations had to be of school-age
(5–18 years); (3) quantitative studies that allowed for the calculation of an effect
size (no qualitative data were included, although possible studies that did not allow
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for the calculation of an effect size were analysed to determine the mean difference
directionality); (4) data that have only been used once in a manuscript to avoid
replication (i.e. studies that had published more than one article on the same par-
ticipants were not included, as were studies that were completed as unpublished
theses and subsequently published); and (5) studies must have been reported in
English.
This selection process identified a total of 81 studies. Out of these 81 studies,
21 studies were excluded due to the following reasons: (1) insufficient information
for calculating effect size (e.g. mean, SD) were provided; (2) solely descriptive data
was provided to assess the program; and (3) studies provided designs of anger
management programs but did not assess the efficacy or outcomes of these
programs.
Coding of studies
Based on a literature review, a systematic coding scheme was developed to identify
salient features of each study. Specifically, variables with regard to study design,
participant characteristics,anger management treatment, and type of children’s emo-
tional and behavioral outcomes were independently coded for all the included stu-
dies by the authors with an inter-rater reliability of 0.92. All coding discrepancies
between two independent coders were resolved upon discussion before statistical
analysis.
Study design characteristics. Sampling method was coded into random, not-random,
and not-informed. The source of data was next coded into primary data obtained
by authors, and secondary data from other researchers. Lastly, other study char-
acteristics such as publication type (i.e. published vs. unpublished) and study loca-
tion (i.e. US vs. non-US) was coded.
Participant characteristics. Participants were coded as children with Attention Deficit
Hyperactivity Disorder (ADHD), children with conduct disorder, children with
bipolar disorder, children with other diagnoses, and not informed. Second, the
source of referral to anger management program was coded as teacher, parent,
discipline, other, and not informed. Other coded participant’s information were
mean age and/or grade level, ethnicity (i.e. White, African American, Hispanic,
Native American, Asian, Mixed, and not informed), and gender (i.e. male, female,
or mixed) were coded.
Anger management program characteristics. The anger management intervention was
characterized into: (1) coping skills training such as the In Control program
designed to teach coping-skills to adolescents; (2) emotional awareness and self-
control such as the strategies used in the SCARE program; (3) problem solving
CBT such as using strategies from the Second Step violence prevention program
designed to teach anger-related problem solving skills; (4) relaxation techniques
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such as deep breathing exercises; and (5) role play or modeling activities involving
acting out scenarios with the professionals implementing the intervention. These
categories were constructed based on the description provided by the authors of the
study, as well as the features of the intervention the children were receiving.
This sorting process was performed by the authors. Other coded information
related to the anger management program was the total length of the intervention
(i.e. number of sessions) and time per session (measured in minutes).
Child outcome characteristics. Children’s outcome measures were categorized into: (1)
Overall behavioral and emotional outcome such as the total behavioral or emo-
tional index of self- and teacher-reported measures; (2) Conduct problem/
Delinquency such as reported number of office referrals and other discipline refer-
rals related to anger; (3) Knowledge of anger management principles such as com-
pletion of a post-quiz assessing knowledge of specific principles taught during the
intervention (i.e. relaxation techniques); (4) Blood pressure; (5) Anger, including
state and trait dimensions, measured by scales such as the State-Trait Anger
Expression Inventory (STAXI-2); (6) Aggression via measures of the Behavior
Assessment System for Children (BASC) and similar behavior rating scales; (7)
Self-control including self-regulation and self-esteem; (8) Forgiveness such as
self-reporting on the Enright Forgiveness Inventory for Children (EFI-C); and (9)
Social skills such as results from the School Social Behavior Scale (SSBS) and
similar rating scales. These nine categories were also constructed based on the
description provided by the authors of the study. Further, the rater of children’s
outcome measures was coded into child, teacher, parent, and others (e.g. peers).
Effect size
Depending on the study design employed by the included studies, different
methods for computing the effect size (gi) were used. First, when studies used the
pre-test–post-test control group design (PPC), the standardized mean change dif-
ference between treatment and control groups (gppci) was computed using the for-
mula (Morris, 2008), gppci¼½ð
Ytrti
XtrtiÞð
Yctli
XctliÞ=Sppc,pooledi, where
Ytrti
and
Yctliare post-test mean scores for treatment (trt) and control (ctl) groups;
Xtrtiand
Xctliare pre-test mean scores for each group; and Spoolediis the pooled
SD of scores from each group. This value was calculated as
Sppc,pooledi¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðntrti1ÞSD2
X,trtiþðnctli1ÞSD2
X,ctliþðntrti1ÞSD2
Y,trtiþðnctli1ÞSD2
Y,ctli
2ðntrtiþnctli2Þ
s,
where ntrtiand nctliare sample sizes for each group; SDX,trt and SDX,ctl are SD of
the pre-tests for each group; and SDY,trt and SDY,ctl are SD of Ytrtiand Yctli
(Morris, 2008). When no descriptive statistics were reported, Hedge’s giwas com-
puted from the reported tor Fstatistics using the formulas outlined in Rosenthal
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(1994). The comparison group without an anger management intervention was
treated as the control group. Second, when only the pre-test and post-test without
a comparison group were provided, the standardized mean change (gchangei) was
computed as
gchangei¼½
Yi
Xi=½Spooledi=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2ð1rXYiÞ
p,
where
Xiand
Yiare pre-test and post-test mean scores; Spoolediis a pooled SD of
pre-test (X) and post-test (Y) scores; and rXYiis the correlation between the pre-test
and post-test score (Lipsey & Wilson, 2001). Because the correlation between the
pre-test and post-test score was not reported in the studies, rXYiof 0.5 was used to
compute gchangeias a default.
Third, from a post-test control group design, the standardized mean difference
(gi) was computed using the formula represented by
gi¼½YtrtiYctli=Spooledi,
where Spoolediis the pooled SD of Ytrtiand Yctli. This mean difference is calculated as
Spooledi¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ððntrti1ÞSD2
trtiþðnctli1ÞSD2
ctliÞ=ððntrti1Þþðnctli1ÞÞ
q,
where SDtrtiand SDctliare SD of Ytrtiand Yctlifor each group (Lipsey & Wilson,
2001).
Effect sizes from the studies using children’s positive outcomes such as self-
esteem, empathy, and anger control were reverse-coded so that the computed
effect sizes held the same meaning. Thus, a negative effect size indicated a beneficial
intervention effect in reducing children’s negative outcomes such as aggression,
anger, and anxiety. Further, the computed effect sizes (gi) were corrected for
small sample size bias using the formula (Hedges, 1994) represented by
di¼gi13=ð4df1Þ½:
In the subsequent analyses, the unbiased effect size (di) was modeled.
Statistical analyses
The statistical analyses were based on the methods described in Cooper, Hedges,
and Valentine (2009). The computed ESs weighted by the inverse of its associated
variance were used to assess the overall homogeneity with Qtotal statistics. When
Qtotal was significant, the fixed-effects assumption cannot be assumed and therefore
the random-effects model which incorporates the estimated additional uncertainty
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was used to estimate the overall intervention effect. Otherwise, the fixed-effects
model was used in assessing homogeneity of variance.
Further, the categorical moderators (i.e. children’s outcomes) or continuous
moderators (i.e. mean age) were explored to explain the variations across effect
sizes. For moderator analyses with significant within-group variations, the mixed-
effects model that incorporates additional variability was employed. Otherwise, the
fixed-effects model with predictors was used (Raudenbush, 2009).
Dependency
A number of studies provided dependent effect sizes from the same participants,
which in turn violates the assumption of independence (Glesser & Olkin, 2009). Of
several ways to handle dependency among effect sizes in meta-analyses (Becker,
2000), the authors used the ‘shifting unit of analysis’ approach suggested by
Cooper (1998). In this approach, the authors first coded all of the studies as if
effect sizes were from an independent sample. For instance, if a study provided
children’s outcomes that were rated by teachers and parents, two effect sizes were
computed. Then, for estimating the overall effect of anger management interven-
tions, these two effect sizes were averaged prior to analysis. Thus, each sample
contributed only one independent effect size for computation. However, in estimat-
ing the differential intervention effect by raters, each effect size was used to compute
the intervention effect separated by raters so that effect sizes within the subcate-
gories of raters were independent. This method was used to retain as many data as
possible from each study, yet continue to hold the assumption of independent effect
sizes for statistical analysis.
Results
Description of studies
From the included 60 studies (s¼38 for published and s¼22 for unpublished stu-
dies), a total of 61 effect sizes were extracted given that Bosworth, Espelage, DuBay,
and Daytner (2000) provided separate effect sizes by gender. Studies were published
between 1979–2010 and most of the studies (s¼52) were conducted in the USA,
while the rest of them were from other countries. Sample sizes used in the included 60
studies ranged from 4 to 287 (M¼55.50, SD ¼60.54), totaling 3,386 participants.
Mean age of participants ranged from 5 to 17.30 years old (M¼12.42, SD ¼2.76).
The majority of samples (s¼44) was based on a mixed-gender group, while 14 sam-
ples were exclusively based on boys and one sample included only girls.
Publication bias
In the current meta-analysis, the authors included both unpublished and published
studies so that publication bias would likely not be problematic. However, when
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publication bias was assessed using Egger’s regression test of intercept (Sutton,
2009), a significant result (t(59) ¼3.87, p<0.01) indicated that the included studies
might pose a slight publication bias.
Overall effect of anger management programs
A total of 61 d-effect sizes examining the intervention effect of anger management
programs on children’s emotional and behavioral outcomes were first analysed.
The significant Q
Total
statistic of 92.13 indicates that the included d-ES were het-
erogeneous, meaning these effect sizes were not from the same population. Further,
the estimated d-ES under the random-effects model was 0.27 with a standard
error (SE) of 0.04, which was statistically significant (z ¼6.09, p<0.01). Such a
significant but negative result indicates that anger management interventions
reduced children’s negative emotional and behavioral outcome measures including
anger, aggression, and loss of self control, when compared to the control group
with no anger management intervention. Table 1 displays these findings.
Table 1. Results from statistical analyses
k
dSE
95% CI
Q
within
LL UL
Overall,Q
total
(60) ¼92.13** 61 0.27** 0.04 0.36 0.19
Outcomes,Q
Between
(8) ¼13.74, p¼0.09 78.39**
Overall behavioral/emotional index 14 0.14** 0.05 0.25 0.04 11.76
Conduct/delinquency 3 0.29* 0.13 0.56 0.03 0.25
Knowledge of anger management principles 1 1.28** 0.50 2.26 0.29 0.00
Blood pressure 1 0.05 0.36 0.76 0.66 0.00
Anger 17 0.33** 0.07 0.47 0.20 24.20
Aggression 16 0.34** 0.08 0.50 0.17 31.08**
Self control 5 0.09 0.16 0.41 0.23 8.05
Forgiveness 1 0.80 0.62 2.01 0.40 0.00
Social skills 3 0.12 0.13 0.37 0.13 3.04
Outcome raters,Q
Between
(6) ¼5.40, p¼0.49 124.54**
Teacher 19 0.27** 0.05 0.37 0.16 29.91*
Parent 8 0.15* 0.08 0.30 0.00 7.23
Child 24 0.15** 0.05 0.25 0.05 51.13**
Observer 2 0.27 0.18 0.63 0.08 0.43
School 1 0.06 0.21 0.35 0.48 0.00
Other 3 0.30* 0.13 0.56 0.04 3.65
Not informed 10 0.13 0.10 0.32 0.06 32.19**
(continued)
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Effects by children’s outcome measures
The effect of anger management programs was evaluated on nine outcomes:
Overall behavioral and emotional index, Conduct problem/ Delinquency,
Knowledge of anger management principles, Blood pressure, Anger, Aggression,
Table 1. Continued
k
dSE
95% CI
Q
within
LL UL
Treatment,Q
Between
(5) ¼9.09, p¼0.11 83.04**
Coping skills training 17 0.19** 0.06 0.31 0.07 14.79
Emotional awareness and self control 12 0.16** 0.06 0.28 0.04 15.64
Problem solving CBT 27 0.33** 0.06 0.45 0.22 49.41**
Relaxation techniques 1 0.69** 0.23 1.15 0.24 0.00
Role play 2 0.10 0.33 0.75 0.55 0.44
Not informed 2 0.15 0.14 0.41 0.12 2.75
Gender,Q
Between
(3) ¼1.70, p¼0.92 90.43**
Male only 14 0.23** 0.08 0.38 0.07 17.79
Female only 1 0.12 0.12 0.36 0.12 0.00
Mixed group 44 0.25** 0.04 0.33 0.17 71.99**
Not informed 2 0.04 0.24 0.51 0.43 0.64
Ethnicity,Q
Between
(3) ¼1.40, p¼0.71 90.73**
White 2 0.35* 0.17 0.69 0.01 0.20
African American 1 0.07 0.37 0.66 0.79 0.00
Mixed 28 0.25** 0.05 0.35 0.15 34.35
Not informed 30 0.21** 0.05 0.31 0.12 56.18**
Diagnosis,Q
Between
(2) ¼1.34, p¼0.51 90.78**
Children with conduct disorder 1 0.14 0.54 0.92 1.20 0.00
Children with other diagnosis 13 0.30** 0.08 0.46 0.14 38.36**
Not informed 47 0.22** 0.04 0.29 0.15 52.42
Publication type,Q
Between
(1) ¼0.72, p¼0.40 89.74**
Published studies 38 0.27** 0.04 0.35 0.19 68.11**
Unpublished studies 23 0.15** 0.06 0.27 0.04 21.63
Sampling,Q
Between
(2) ¼0.80, p¼0.67 91.32**
Random 13 0.20** 0.06 0.31 0.08 20.03
Non-random 46 0.26** 0.04 0.34 0.17 70.10*
Not informed 2 0.20 0.13 0.46 0.06 1.20
Study location,Q
Between
(1) ¼0.94, p¼0.33 91.19**
US 52 0.22** 0.04 0.29 0.15 76.91*
Non-US 9 0.31** 0.08 0.47 0.14 14.27
Note:**p<0.01; *p<0.05; CI ¼Confidence interval; k¼Number of effect sizes; LL ¼Lower level;
UL ¼Upper level.
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Self-control, Forgiveness, and Social skills. The overall ES was not significantly
different depending on types of children’s outcome measures (Q
Between
(8) ¼13.74,
p¼0.09), indicating that the anger management programs were equally effective in
alleviating all of children’s negative outcomes. In particular, of these nine out-
comes, the statistically significant effects of the anger management interventions
were found on the following five outcome measures, including the total index of
behavioral and emotional outcomes (
d¼0.14, SE ¼0.05, k¼14), conduct prob-
lem/ delinquency (
d¼0.29, SE ¼0.13, k¼3), knowledge of anger management
principles (
d¼1.28, SE ¼0.50, k¼1), anger (
d¼0.33, SE ¼0.07, k¼17), and
aggression (
d¼0.34, SE ¼0.08, k¼16).
The effect on these five outcomes was not dependent on how children’s outcomes
were rated (Q
Between
(6) ¼5.40, p¼0.40). In particular, the effect of anger manage-
ment programs was statistically significant when outcome measures were rated by
children (
d¼0.15, SE ¼.05, k¼24), teachers (
d¼0.27, SE ¼0.05, k¼19), par-
ents (
d¼0.15, SE ¼0.08, k¼8), and others, such as peers (
d¼0.30, SE ¼0.13,
k¼3). These findings suggest no significant rater effects when evaluating the out-
comes of anger management programs on children’s behavioral and emotional
functioning.
Effects by anger management focus
No statistically significant mean ES differences were found depending on the type
of anger management program (Q
Between
(5) ¼9.09, p¼0.11), indicating that anger
management interventions were equally effective regardless of its focus, with the
exception of incorporating role-play (modeling). Thus, the anger management pro-
gram was statistically effective when it was focused on coping skills training
(
d¼0.19, SE ¼0.06, k¼17), emotional awareness and self-control (
d¼0.16,
SE ¼0.06, k¼12), problem-solving (
d¼0.33, SE ¼0.06, k¼27), and relaxation
(
d¼0.69, SE ¼0.23, k¼1). Yet no significant intervention effect was found when
the anger management program included role play as the central focus of the
intervention.
In addition, no significant intervention effects due to treatment length or the
time of each intervention session were found. Particularly, slopes for treatment
length (Q
Model
(1) ¼0.80, p¼0.37) and time per session (Q
Model
(1) ¼0.63,
p¼0.43) were 0.005 (SE ¼0.005) and 0.001 (SE ¼0.002), respectively, indicating
that intervention effects were not different depending on either moderator.
Participant characteristics
The effectiveness of anger management interventions was not statistically different
for any of the four moderating participant characteristics: Diagnosis
(Q
Between
(3) ¼1.34, p¼0.64), mean age (Q
model
(1) ¼0.05, p¼0.82), gender
(Q
Between
(3) ¼1.70, p¼0.92) or ethnicity status (Q
Between
(3) ¼1.40, p¼0.71). Yet
there were variations among the gender and ethnicity variables. In terms of gender,
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mean ESs were statistically significant for boys-only treatment groups (
d¼0.23,
SE ¼0.08, k¼14) and mixed-gender groups (
d¼0.25, SE ¼0.04, k¼44), but no
significant intervention effect was found for girls. Moreover, effect sizes were sig-
nificant for all of the ethnic groups but African American children (
d¼0.35 for
White;
d¼0.25 for Mixed ethnic groups;
d¼0.21 for samples with no ethnicity
information provided). However, because of the few number of categorical effect
sizes within the gender and ethnicity moderator variables, the statistical power was
too low to detect the overall mean difference. Thus, low statistical power in effect
nulls the significant differences found in these two moderator variables.
Other moderators
Additional moderator analyses were performed to examine whether the mean ESs
differed by other moderators including publication type (Q
Between
(1) ¼2.39,
p¼0.12), study location (Q
Between
(1) ¼0.94, p¼0.33), and sampling
(Q
Between
(1) ¼0.80, p¼0.67). None of these moderators explained variation
across studies and therefore did not play a significant role in the effect of the
anger management intervention.
Discussion
The effects of anger management programs on children’s outcomes were measured
using 61 independent samples from 60 studies including 38 published and 22
unpublished ones. The results of the meta-analysis yielded a mean effect size of
0.27, indicating a small to moderate intervention effect in reducing children’s
negative emotional and behavioral outcomes. The magnitude of the intervention
effect is similar to the Gansle (2005) meta-analysis, in which 20 published studies
resulted in a mean effect size of 0.31. However, an analysis of 40 studies by
Sukhodolsky et al. (2004) found a medium effect size of 0.67 when assessing the
effects of CBT on children’s anger outcomes. The higher effect size in the
Sukhodolsky analysis is more similar to that of Ho et al. (2010) and the overall
effect size of 0.61. The reason for this discrepancy across effect sizes likely involves
the inclusion of more studies with varying treatment components. The problem
solving component of CBT was found to be one of the most effective in reducing
children’s negative outcomes in the current analysis as well as previous analyses.
However, as the present meta-analysis revealed, when problem-solving using CBT
is compiled with other treatment components such as coping skills and role play,
the overall mean effect was lessened as the individual effect sizes for these treatment
strategies were not as high.
The most salient finding of this meta-analysis involves the apparent positive
effects of school-based anger management programs on children’s social and emo-
tional outcomes. Moderator variables aside, anger management intervention was
found to have a small but positive effect on children’s outcomes. In particular, the
two outcomes that were most positively affected by the anger management
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programs were raters’ reports of the students’ aggressive behaviors and the students’
reported feelings of anger. This finding is important for school psychologists, as
instances of physical aggression and other externalizing behaviors amongst stu-
dents are often commonly reported reasons for initiating special education and
behavioral referrals (Kaufman et al., 2010).
Additionally, with the exception of role play, no statistically significant differ-
ences were found between the various methods of the emphasis or focus of the
anger management programs (i.e. teaching problem solving skills, emotional
awareness), suggesting that treatment was effective regardless of the focus.
These findings are unique, in that the literature base has typically found some
degree of significant variation in the effectiveness of different CBT anger manage-
ment-programs (i.e. problem solving, coping skills training). For example,
Sukholdsky et al. (2004) found skill-development (d¼0.79) and eclectic
approaches (d¼0.74) of anger management training to be more effective than
emotional awareness education programs (d¼0.36). The only variation the cur-
rent study revealed was the relative ineffectiveness of role playing as the sole
focus of anger management interventions. A possible explanation of this may
be that role play alone may not provide students with enough skills to feel con-
fident in resolving situations in which angry feelings are provoked. While no
research to date appears to explore this exclusively, the majority of the anger
management programs in the current literature base consist of a mixture of role
play and supplemental instruction in anger management techniques (Lipman
et al., 2006; Lochman & Wells, 2003). Moreover, Sukholdsky et al. (2004)
showed that modeling contributed to positive outcomes among youth involved
in anger management interventions. It is likely that although modeling may be an
effective component of anger management interventions, other aspects of the
intervention may exert more of an influence on students’ outcomes. More
research is needed to explore the various components of anger management
interventions and to what degree certain aspects of the intervention play a role
in promoting beneficial outcomes for children.
An additional finding of interest was the lack of significance across raters in
measuring the effectiveness of the anger management treatment. There appears to
be stability across different individuals rating the effectiveness of the anger man-
agement programs, suggesting that many raters could be recruited to assist in the
measurement of outcomes and provide consistent and accurate information. This
finding has also been shown to be the case in similar measures of social and emo-
tional well-being, especially in instances requiring the use of multiple informants
(Bulotsky-Shearer & Fantuzzo, 2004). Agreement across informants in regards to
assessing the effectiveness of anger management treatments provides several impli-
cations for school psychologists. Primarily, this finding suggests that school psych-
ologists may be able to rely on the reporting of a variety of individuals regarding
the effectiveness of anger management interventions on a student’s progress and
outcomes. Informants could include parents, teachers, peers, and the student them-
selves. The ability to use multiple raters not only preserves school resources by
Candelaria et al. 607
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eliminating the need for specialized training, but also reduces the amount of time
required by the school psychologist in assessing a program’s effectiveness.
As with any quantitative review, there are limitations inherent in the analyses
due to methodological limitations of the included studies. For the current analysis,
there were few studies that included enough pertinent information regarding the
reporting of means, Standard Deviations, and effect sizes. Many of the pub-
lished articles served as instructional tools as opposed to statistical descriptions
of anger management interventions and outcomes. Moreover, only a handful
of studies reported ethnicity or diagnostic variables, leaving unanswered the ques-
tion as to whether there are differential treatment effects based on these variables.
Information regarding empirically-based interventions in working with culturally
diverse students is a necessary component of delivering culturally competent inter-
ventions and ensuring best practice for school psychologists (Miranda,
2008; Theron & Donald, 2012; Toland & Carrigan, 2011). Further analysis and evi-
dence of the effectiveness of various methods of anger management programs
on the outcomes for students from culturally diverse backgrounds is necessary.
Thus, future research needs to explicitly identify the samples for which the treat-
ment is being provided, as the NASP standards have made clear the need to con-
sider the individual needs of students when providing treatment (NASP PPE,
2010).
Yet, while the current study has several limitations, it also provides a more
comprehensive and current examination of the effectiveness of anger management
on the outcomes of school-age children. The overall findings of this meta-analysis
provide evidence to suggest that anger management intervention—regardless of
participant characteristics or the type and focus of treatment imple-
mented—produces positive effects on students’ behavioral, social, and emotional
outcomes. Of particular importance is the effectiveness of these interventions on the
externalizing behaviors exhibited by many students receiving special education and
office discipline referrals. Students exhibiting externalizing behaviors early in their
educational careers are at later risk for high levels of risk taking behavior, sub-
stance abuse, and delinquency (Thompson et al., 2011). Similarly, research con-
tinues to show that high rates of externalizing behavior, particularly for male
students, leads to an increased likelihood of early school drop-out (Robst &
Weinberg, 2010). Early prevention and intervention of these externalizing behav-
iors, by way of anger management and similarly focused programs, aim to decrease
the prevalence of these and other maladaptive behaviors.
Schools are increasingly viewed as a de facto mental health and behavioral
service provider (Jacob & Coustasse, 2008). Because school aged children and
adolescents spend the majority of their day at school, the importance of having
an accessible anger management program during these hours is significant. The
implications for school psychologists are clear, and continue to emphasize the
importance of providing evidence-based anger management services, similar to
those included in the current analysis, to students experiencing detrimental,
anger-related behaviors.
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Ashley M. Candelaria, MS, is a Doctoral Student in the School Psychology
Graduate Program at the University of Kentucky. Address: 237 Dickey Hall,
Lexington, Kentucky 40506, USA. Email: amca224@uky.edu
Alicia L. Fedewa, PhD, is an Assistant Professor at the University of Kentucky. She
is a Licensed Psychologist in Kentucky and a Nationally Certified School
Psychologist. Address: University of Kentucky, Department of Educational,
School, and Counseling Psychology, 237 Dickey Hall, Lexington, Kentucky
40506, USA. Email: alicia.fedewa@uky.edu
Soyeon Ahn, PhD, is an Assistant Professor of the Research, Measurement,
Evaluation Program in the Department of Educational and Psychological
Studies at the University of Miami. Her research interests include methodological
issues in meta-analysis, particularly related to the synthesis of studies using diverse
measures. Address: PO Box 248065, Coral Gables, FL33124-2040, USA. Email:
s.ahn@miami.edu
614 School Psychology International 33(6)
at UNIV OF KENTUCKY on May 2, 2013spi.sagepub.comDownloaded from
... As a result, the treatments that have developed under the over-arching banner of "anger management" have focused solutions primarily on behaviour management and diverting attention away from the emotional response. These programs, primarily CBT-based, have recorded limited success, with retention rates for group participants dropping as low as 49% in some settings [8]. ...
... A second meta-analysis evaluated 18 articles, finding an overall moderate effect size of 0.61 in cognitive-behavioral anger management intervention outcomes for children in special education. Candelaria et al. reported on additional research, which specifically examined the effects of CBT-based anger management therapies for children and adolescents and finding an overall effect size of 0.67 [8]. ...
... Anger Solutions is currently adapted for schoolaged children (7)(8)(9)(10)(11)(12), youths (13)(14)(15)(16)(17), and adults (18+). Current incident reports could be evaluated to determine the number of aggressive or violent behaviours conducted within the school setting in a time frame of three months. ...
... 16 It has been reported that children and adolescents do not have the skills to understand and manage anger as well as other emotions, and it is difficult to cope with anger in childhood. 17 Poor anger control increases the risks of showing aggression and bullying behavior. [18][19][20] It has been reported that an increase in children's anger control levels is associated with a decrease in bullying behaviors. ...
... 21 It is stated that the development of children's empathy skills and cognitive schemes for bullying will contribute to the reduction of peer bullying and other violent behaviors. 16,17,22 The permanent and destructive effects of bullying on children have led to an increased attention to peer bullying in the world. 23 Studies on peer bullying generally consist of school-31 wide intervention and protection programs. ...
... Anger management is the ability to regulate thoughts, feelings, lust, and anger in the right way and positively and socially acceptable. Studies have shown that anger management only has a mild to medium effect on reducing negative emotions and negative behavior including aggression [5]. Another study explained that anger management training can reduce total aggression which in school teenagers [6]. ...
... This study shows that people are involved in several healthy habits like yoga, meditation, sports, gym, etc. and rely on them for anger management. This is accordance with research that anger management can support children to control the emotions so that maladaptive behavior can be decreased [5]. Another research mentioned that it will be more effective when extra treatment is given to drop the aggression behavior. ...
... Internalizing and externalizing problems are associated with a lack of social-emotional competencies (Ladd et al., 2006). According to meta-analysis studies, SEL interventions provide significant gains in reducing internalizing and externalizing behaviors (Candelaria et al., 2012;Durlak et al., 2011). This result might be because SEL interventions can affect the basic mechanism of internalizing and externalizing problems. ...
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Resumen En este estudio de metanálisis, se han evaluado los efectos de 51 intervenciones de aprendizaje social y emocional (SEL) realizadas en Turquía entre 1997-2021. Los resultados que se discuten se refieren a las intervenciones en tres categorías: habilidades sociales y emocionales, problemas de externalización y problemas de internalización. Se han evaluado los efectos de las intervenciones en términos de ocho variables moderadoras: formato de intervención, tipo de medición, diseño experimental, nivel educativo, edad media, porcentaje de mujeres, número de sesiones y duración de la sesión. Los hallazgos de este metanálisis han mostrado que las intervenciones SEL en Turquía han mejorado las habilidades sociales y emocionales [ES(SE) = 0.81(.10), 95% CI (0.60 - 1.03)] de los estudiantes y han reducido significativamente sus problemas de externalización [ES(SE) = 0.78(.18), 95% CI (0.41 - 1.14)] y problemas de internalización [ES(SE) = 0.95(.19), 95% CI (0.57 - 1.32)].
... We know that the profile of adolescents who commit aggressions is characterized by low social adjustment (Gómez-Ortiz et al., 2017) and by developing hostile behavior toward their peers (Walters & Espelage, 2018). In line with expectations, problems in managing social relationships and one's own emotions, including anger, cause some adolescents to repeatedly and intentionally bully others regardless of the harm caused (Candelaria et al., 2012). ...
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There is ample evidence of bullying victimization-perpetration association. However, little is known about the role of socioemotional variables in reinforcing or mitigating it. There is a need to delve deeper into what victims feel and do that leads them to bully. This study aimed to analyze the moderating role of peer relationship management and anger regulation in the victimization-perpetration association, controlling gender and age. This cross-sectional study involved 874 Spanish middle school students (47.8% girls) aged 11 to 18 years ( M = 13.92, SD = 1.45). The results revealed that the perpetration was positively related to victimization and negatively related to peer relationship management and anger regulation. In addition, peer relationship management can moderate the victimization-perpetration association, acting as a protective factor, when the ability of victims to regulate anger is sufficiently trained. These findings highlight the importance of designing effective prevention based on socio-emotional development to fully curb bullying.
... Internalizing and externalizing problems are associated with a lack of social-emotional competencies (Ladd et al., 2006). According to meta-analysis studies, SEL interventions provide significant gains in reducing internalizing and externalizing behaviors (Candelaria et al., 2012;Durlak et al., 2011). This result might be because SEL interventions can affect the basic mechanism of internalizing and externalizing problems. ...
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This meta-analysis study aimed to examine the effects of 51 social and emotional learning (SEL) interventions conducted in Turkey between 1997 and 2021. The results were discussed under three categories: social-emotional skills, externalizing problems, and internalizing problems. The effects of the interventions were investigated in terms of eight moderator variables: intervention format, types of measurement, experimental design, educational level, average age, percentage of females, number of sessions, and duration of sessions. The findings showed that SEL interventions in Turkey improved students' social-emotional skills [ES(SE)=0.81(.10), 95%CI (0.60-1.03)] and significantly reduced their externalizing [ES(SE)=0.78(.18), 95%CI (0.41-1.14)] and internalizing problems [ES(SE)=0.95(.19), 95%CI (0.57-1.32)].
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Background This study was carried out to evaluate the effect of a solution-focused approach on anger management and violent behavior in adolescents. Method The study was an experimental and randomized controlled trial including pre-test and post-test measurements and control group. The sample of experimental (n = 24) and control groups (n = 24) of the study consisted of 48 students. A 7-session program of anger management and prevention of violent behavior based on a solution focused approach was applied to the experimental group. Results There were no statistically significant differences between trait anger, anger in, anger out, anger control sub-dimensions and VTS scores of the experimental and control groups before the intervention (p > 0.05). In post-test and follow-up test, trait anger (23.32 ± 2.83) (21.10 ± 3.51), anger-out (14.95 ± 4.16) (15.50 ± 3.49) and the mean scores of VTS (30.77 ± 4.01) (32.80 ± 4.53) of the individuals in the experimental group were significantly lower, whereas their mean score of anger control (24.86 ± 3.62) (23.65 ± 4.44) were significantly higher than the control group. Conclusion Solution-oriented approach programs should be used by psychiatric nurses to provide anger management and reduce violence tendencies in adolescents.
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Challenging classroom behaviors can interfere with student social and academic functioning and may be harmful to everyone in schools. Self‐management interventions within schools can address these concerns by helping students develop necessary social, emotional, and behavioral skills. Thus, the current systematic review synthesized and analyzed school‐based self‐management interventions used to address challenging classroom behaviors. The current study aimed to inform practice and policy by (a) evaluating the effectiveness of self‐management interventions at improving classroom behaviors and academic outcomes and (b) examining the state of research for self‐management interventions based on existing literature. Comprehensive search procedures included electronically searching online databases (e.g., EBSCO Academic Search Premier, MEDLINE, ERIC, PsycINFO), hand‐searching 19 relevant journals (e.g., School Mental Health, Journal of School Psychology), reference‐list searching 21 relevant reviews, and searching gray literature (e.g., contacting authors, searching online dissertation/theses databases and national government clearinghouses/websites). Searches were completed through December of 2020. Included studies employed either a multiple group‐design (i.e., experimental or quasi‐experimental) or single‐case experimental research design and met the following criteria: (a) utilized a self‐management intervention, (b) conducted in a school setting, (c) included school‐aged students, and (d) assessed classroom behaviors. Standard data collection procedures expected by the Campbell Collaboration were used in the current study. Analyses for single‐case design studies incorporated three‐level hierarchical models to synthesize main effects, and meta‐regression for moderation. Further, robust variance estimation was applied to both single‐case design and group‐design studies to account for dependency issues. Our final single‐case design sample included 75 studies, 236 participants, and 456 effects (i.e., 351 behavioral outcomes and 105 academic outcomes). Our final group‐design sample included 4 studies, 422 participants, and 11 total behavioral effects. Most studies occurred in the United States, in urban communities, in public schools, and in elementary settings. Single‐case design results indicated that self‐management interventions significantly and positively impacted both student classroom behaviors (LRRi = 0.69, 95% confidence interval [CI] [0.59, 0.78]) and academic outcomes (LRRi = 0.58, 95% CI [0.41, 0.76]). Single‐case results were found to be moderated by student race and special education status, whereas intervention effects were more pronounced for African American students (F = 5.56, p = 0.02) and students receiving special education services (F = 6.87, p = 0.01). Single‐case results were not found to be moderated by intervention characteristics (i.e., intervention duration, fidelity assessment, fidelity method, or training). Despite positive findings for single‐case design studies, risk of bias assessment indicated methodological shortcomings that should be considered when interpreting findings. A significant main effect of self‐management interventions for improving classroom behaviors was also revealed for group‐design studies (g = 0.63, 95% CI [0.08, 1.17]). However, these results should be interpreted with caution given the small number of included group‐design studies. The current study, conducted using comprehensive search/screening procedures and advanced meta‐analytic techniques, adds to the large amount of evidence indicating that self‐management interventions can be successfully used to address student behaviors and academic outcomes. In particular, the use specific self‐management elements (i.e., self‐determining a performance goal, self‐observing and recording progress, reflecting on a target behavior, and administering primary reinforcers) should be considered within current interventions as well as in the development of future interventions. Future research should aim to assess the implementation and effects of self‐management at the group or classroom‐level within randomized controlled trials.
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Aggression, defined as a behavior intended to inflict harm on another person, causes misery and suffering to people and creates high costs for societies. This article presents the major psychological theories seeking to explain aggressive behavior, which focus on the cognitive and affective processes involved in aggressive behavior. Differences in aggressive behavior as a function of the person (personality traits and gender) and the situation (alcohol use, aggressive cues, exposure to violent media, hot temperatures) are discussed, followed by an analysis of approaches for preventing aggression.
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The purpose of this study was to investigate the possibility that cognitive and affective variables form a reciprocal relationship when it comes to predicting future bullying perpetration. To this end, the bidirectional relationship between cognitive impulsivity and anger was evaluated in an effort to determine whether both cross-lagged pathways contributed to a rise in bullying behavior. The reciprocal hypothesis was tested in a sample of 1,160 early adolescents (567 boys, 593 girls) from the Illinois Study of Bullying and Sexual Violence (ISBSV). Cognitive impulsivity and anger were cross-lagged at Waves 1 and 2 of the ISBSV, after which they were correlated with bullying perpetration at Wave 3 in a three-wave longitudinal path analysis. Results from the path analysis identified the presence of a significant bidirectional association between Cognitive Impulsivity-1 and Anger-2 and between Anger-1 and Cognitive Impulsivity-2, with both cross-lags effectively predicting future bullying behavior.
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Formal analysis of research publications serves as one indicator of the current status of a profession or a journal. Content analyses provide both practitioners and academicians with information on the status of research in the profession. These types of analyses can also provide information on the concordance between published research and what professional organizations consider key areas of practice. The current study examined articles published in one journal, School Psychology International, for a 22-year period from 1990 through 2011 (n = 671 articles). All articles were coded for demographics of the author, including country of origin and level of international content, and the content matching categories found in the NASP Model for Comprehensive and Integrated School Psychological Services. Results indicated that the journal is very much international with authors coming from 57 countries. With regard to gender, a trend was noticed for increased participation by women. Finally, the analysis of content found a pattern that was not highly consistent with the categories identified by NASP. Factors contributing to the lack of consistency between research and areas of practice are discussed.
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Evaluated the effectiveness of a school-based, preventive 10-wk group program for 4th–6th grade children of divorce. The program, a modified version of the children's support group used by A. L. Stolberg et al (1982) and Stolberg and P. M. Cullen (1983), creates a group atmosphere in which children can share divorce-related feelings, clarify common misconceptions, and reduce feelings of isolation and build competence. Participants are taught problem-solving, communication, and anger-control skills to help them cope adaptively with challenges posed by parental divorce. 72 3rd–6th graders who were children of divorce, in demographically matched groups, were randomly assigned to an immediate intervention (experimental) or a delayed intervention (control) group. The experimental group improved significantly more on teacher ratings of problem behaviors and competence and parent ratings of adjustment and self-reported anxiety. Group leaders also rated experimental Ss as having improved significantly. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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This volume considers the problem of quantitatively summarizing results from a stream of studies, each testing a common hypothesis. In the simplest case, each study yields a single estimate of the impact of some intervention. Such an estimate will deviate from the true effect size as a function of random error because each study uses a finite sample size. What is distinctive about this chapter is that the true effect size itself is regarded as a random variable taking on different values in different studies, based on the belief that differences between the studies generate differences in the true effect sizes. This approach is useful in quantifying the heterogeneity of effects across studies, incorporating such variation into confidence intervals, testing the adequacy of models that explain this variation, and producing accurate estimates of effect size in individual studies. After discussing the conceptual rationale for the random effects model, this chapter provides a general strategy for answering a series of questions that commonly arise in research synthesis: 1. Does a stream of research produce heterogeneous results? That is, do the true effect sizes vary? 2. If so, how large is this variation? 3. How can we make valid inferences about the average effect size when the true effect sizes vary? 4. Why do study effects vary? Specifically do observable differences between studies in their target populations, measurement approaches, definitions of the treatment, or historical contexts systematically predict the effect sizes? 5. How effective are such models in accounting for effect size variation? Specifically, how much variation in the true effect sizes does each model explain? 6. Given that the effect sizes do indeed vary, what is the best estimate of the effect in each study? I illustrate how to address these questions by re-analyzing data from a series of experiments on teacher expectancy effects on pupil's cognitive skill. My aim is to illustrate, in a comparatively simple setting, to a broad audience with a minimal background in applied statistics, the conceptual framework that guides analyses using random effects models and the practical steps typically needed to implement that framework. Although the conceptual framework guiding the analysis is straightforward, a number of technical issues must be addressed satisfactorily to ensure the validity the inferences. To review these issues and recent progress in solving them requires a somewhat more technical presentation. Appendix 16A considers alternative approaches to estimation theory, and appendix 16B considers alternative approaches to uncertainty estimation, that is, the estimation of standard errors, confidence intervals, and hypothesis tests. These appendices together provide re-analyses of the illustrative data under alternative approaches, knowledge of which is essential to those who give technical advice to analysts.
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Youth in alternative schools often engage in high rates of risk behaviors for AIDS and violence. This study included 42 youth, ages 13 to 18, from alternative schools in San Diego, CA, and tested the effectiveness of behavioral skills training based on the Behavioral-Ecological Model. Two interventions were evaluated: one teaching condom use skills and the other teaching anger management skills. Changes in most skills were significant at postintervention but were not maintained at 6 months. Few risk-related attitudes or behaviors improved at 6 months for either group.