Moral disengagement among children and youth review

AGGRESSIVE BEHAVIOR
Volume 40, pages 56–68 (2014)
Moral Disengagement Among Children and Youth:
A Meta‐Analytic Review of Links to Aggressive Behavior
Gianluca Gini1*, Tiziana Pozzoli1, and Shelley Hymel2
1
2
Department of Developmental and Social Psychology, University of Padua, Padova, Italy
Faculty of Education, University of British Columbia, Vancouver, Canada
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A growing body of research has demonstrated consistent links between Bandura’s theory of moral disengagement and aggressive
behavior in adults. The present meta‐analysis was conducted to summarize the existing literature on the relation between moral
disengagement and different types of aggressive behavior among school‐age children and adolescents. Twenty‐seven independent
samples with a total of 17,776 participants (aged 8–18 years) were included in the meta‐analysis. Results indicated a positive
overall effect (r ¼.28, 95% CI [.23, .32]), supporting the hypothesis that moral disengagement is a significant correlate of
aggressive behavior among children and youth. Analyses of a priori moderators revealed that effect sizes were larger for
adolescents as compared to children, for studies that used a revised version of the original Bandura scale, and for studies with
shared method variance. Effect sizes did not vary as a function of type of aggressive behavior, gender, or publication status. Results
are discussed within the extant literature on moral disengagement and future directions are proposed. Aggr. Behav. 40:56–68,
2014. © 2013 Wiley Periodicals, Inc.
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Keywords: aggression; bullying; cyberbullying; moral disengagement; meta‐analysis
INTRODUCTION
Aggressive behavior toward peers during childhood
and adolescence has been studied for decades (Dodge,
Coie, & Lynam, 2006) and has been shown to be a
significant correlate, both concurrently and longitudinally,
of poor health and maladjustment in both perpetrators and
victims (e.g., Card, Stucky, Sawalani, & Little, 2008; Gini
& Pozzoli, 2009, 2013; Ttofi, Farrington, Losel, &
Loeber, 2011). Although the majority of aggressive
children display temporary or desisting aggressive
behavior, about 10% of the general population are
persistently aggressive over the years and can follow a
deviant “career” path (Pepler, Jiang, Craig, & Connolly,
2008). Personal correlates and risk factors for youth
aggressive behavior include positive attitudes toward
(Carney & Merrell, 2001; Rigby & Slee, 1993) and high
self‐efficacy for (Andreou & Metallidou, 2004) the use of
aggression, low empathy (e.g., Gini, Albiero, Benelli, &
Altoè, 2007a; Jolliffe & Farrington, 2011), and high
masculinity (Gini & Pozzoli, 2006). Individual differences in aggression have also been attributed to biases in
morality, with aggressive behavior linked to distorted
moral reasoning that helps to minimize guilt (Arsenio &
Lemerise, 2004; Caravita, Gini, & Pozzoli, 2012;
Hymel, Schonert‐Reichl, Bonanno, Vaillancourt, &
Rocke Henderson, 2010; Malti, Gasser, & Gutzwiller‐
Helfenfinger, 2010; Menesini, Nocentini, & Camodeca,
2013; Tisak, Tisak, & Goldstein, 2006).
The present study focused on one particular type of
moral reasoning, moral disengagement (henceforth MD),
as described in Bandura’s social cognitive theory of
moral agency (1986, 1990). Specifically, this study
reports on the first meta‐analytic synthesis of developmental research on the relation between MD and
aggressive behavior in school‐age children and youth.
We also explored the factors that might moderate the
effect of MD on aggression, including the type of
aggressive behavior, participant characteristics (age,
gender), and methodological features of the studies
conducted to date.
Social Cognitive Theory of Moral Behavior
Bandura (1986, 1990, 1991) focused on moral reasoning and its relation to social behavior in an attempt to
Correspondence to: Gianluca Gini, Department of Developmental and
Social Psychology, University of Padua, via Venezia 8, Padova 35131,
Italy. E‐mail: gianluca.gini@unipd.it
Received 10 November 2012; Accepted 24 July 2013
DOI: 10.1002/ab.21502
Published online 13 September 2013 in Wiley Online Library
(wileyonlinelibrary.com).
© 2013 Wiley Periodicals, Inc.
Moral Disengagement and Aggressive Behavior
explain how “good” people can behave “badly”. With
age, children develop standards of right and wrong that
serve as guides for their conduct. Through this self‐
regulatory process, individuals usually act in ways that
give them satisfaction and a sense of self‐worth, and tend
to avoid behaviors that violate their moral standards
in order to prevent or minimize self‐condemnation.
However, Bandura (2002) argued that the self‐regulation
of behavior involves more than just moral reasoning, and
that moral reasoning is linked to moral behavior through
a series of self‐regulatory mechanisms through which
moral agency is exercised. Moreover, the development of
self‐regulation does not create an invariant control
system within a person, and there are many psychological
and social processes by which self‐sanctions can be
disengaged. Selective activation and disengagement
of internal control permit different types of conduct—
sometimes very negative—with the same moral standards.
Specifically, Bandura described eight mechanisms,
clustered into four broad categories through which moral
control can be disengaged (see Hymel et al., 2010, for a
more detailed discussion). The first, cognitive restructuring, operates by framing the behavior itself in a positive
light, by (i) portraying immoral conduct as warranted
(moral justification); (ii) contrasting a negative act with
worse conduct (advantageous comparison); or (iii) using
language which palliates the condemned act, thus
diminishing its severity (euphemistic labeling). The
second set of disengagement strategies operates by
obscuring or minimizing one’s agentive role in the harm
caused (displacement or diffusion of responsibility). The
third set of strategies operates by minimizing, disregarding or distorting the consequences of one’s action,
allowing individuals to distance themselves from the
harm caused or to emphasize positive rather than
negative outcomes (minimizing or misconstruing consequences). Finally, negative feelings can be avoided by
stripping the recipients of detrimental acts of human
qualities (dehumanization) or considering aggression as
provoked by the victim (attribution of blame). These
mechanisms can lead to aggressive behaviors through a
process of MD, that is a partial gap between the “abstract”
personal idea of moral behavior and the individual’s real
life behavior. In this way, the individual protects him/
herself from negative feelings, such as guilt or shame,
that usually follow immoral conduct (Bandura, 1991).
Measures of Moral Disengagement
Bandura et al. were the first to develop self‐report scales
to measure proneness to MD (Bandura, Barbaranelli,
Caprara, & Pastorelli, 1996; Caprara, Pastorelli, &
Bandura, 1995). A short version (14 items) of his moral
disengagement scale has been adapted for use with
elementary school children, a 24‐item version has been
57
developed for adolescents, and a longer scale consisting
of 32 items is used with adults (Caprara et al., 1995). The
scale has also been adapted to specific populations (i.e.,
American minority youth; Pelton, Gound, Forehand, &
Brody, 2004) and it is sometimes used in revised versions
including subsets of the original items (e.g., Ando,
Asakura, & Simons‐Morton, 2005; Barchia & Bussey,
2011). The scale has shown good reliability (a ¼ .81;
Bandura et al., 1996), and is by far the most commonly
used measure of MD across countries.
A few studies have used different scales of MD.
Hymel, Rocke‐Henderson, and Bonanno (2005) also
utilized self‐reports to assess MD in children and youth,
although their survey focused specifically on MD
regarding peer bullying. The original 18 items of the
scale were identified “post hoc” from a larger survey
about bullying as reflecting the four broad categories of
MD outlined by Bandura (2002). However, factor
analytic results failed to distinguish the four different
types of MD and instead yielded a single, 13‐item scale
tapping overall MD with regard to bullying (a ¼ .81).
Nevertheless, this measure has been adopted by other
researchers (Almeida, Correia, Marinho, & Garcia, 2012;
Vaillancourt et al., 2006). Although the Bandura and
Hymel et al. scales show a significant degree of
conceptual overlap, a recent study considering a subset
of items from each scale indicated a moderate association
between the two (r ¼ .51; Ribeaud & Eisner, 2010), with
the Bandura scale tapping a broader range of MD beliefs
and the Hymel et al. scale tapping a more restricted set of
MD beliefs about peer bullying.1
Moral Disengagement and Different Types of
Aggressive Behavior
Starting from early age, individuals who morally
disengage may perceive some types of antisocial
behavior as reasonable or justified, at least under some
circumstances, even if they have internalized moral rules
that prohibit such behavior. Indeed, research has shown
that children and youth who endorse these mechanisms
are more likely to engage in both general aggression (e.g.,
Bandura et al., 1996; Caprara et al., 1995) and peer
bullying (e.g., Gini, 2006; Hymel et al., 2005). Importantly, the link between MD and aggressive behavior
remains significant even after other predictors of such
behavior, such as aggression efficacy, rule perception, or
parenting, are controlled (e.g., Barchia & Bussey, 2011;
Caravita & Gini, 2010; Pelton et al., 2004). Interestingly,
1
Menesini et al. (2003) have investigated MD by assessing children’s
attributions of morally engaged emotions (guilt, shame) versus disengaged
emotions (pride, indifference) in response to hypothetical moral transgressions. In this meta‐analysis we did not include that study because its
methodology differs importantly from those considered here.
Aggr. Behav.
58
Gini et al.
MD has been shown to be a significant correlate of these
behaviors in juvenile delinquents samples (Hodgdon,
2010; Kiriakidis, 2008; Shulman, Cauffmann, Piquero,
& Fagan, 2011), representing extremely violent individuals, as well as community samples, thus confirming that
MD mechanisms operate within the “normal” range of
psychological functioning (Bandura, 1986).
Of recent interest is the degree to which MD is
associated with cyberbullying, defined as aggressive
behavior perpetrated via information and communication
technologies, such as the Internet and mobile phones
(Smith et al., 2008). Several authors have suggested that
MD might be less evident with cyberbullying, albeit for
different reasons. Pornari and Wood (2010), for example,
suggest that online aggression may not demand the same
level of rationalization and justification as traditional
aggression because youngsters might consider cyberbullying as less serious than traditional forms of aggression
and “the anonymity, the distance from the victim, and the
consequences of the harmful act do not cause so many
negative feelings (e.g., guilt, shame, self‐condemnation),
and reduce the chance of empathizing with the victim” (p.
89). Others (e.g., Bauman, 2010; Perren & Sticca, 2011)
argue that the inability to observe the immediate reaction
of the victim may allow the aggressor to minimize the
impact of the negative behavior; this would make MD
less necessary. Indeed, the “online disinhibition effect”
(Suler, 2004), which refers to a loosening of social/moral
restrictions and inhibitions during online interaction that
would otherwise be present in face‐to‐face interaction,
itself can represent a variation of MD, allowing the
individuals to behave in ways that are contrary to their
moral code. Drawing upon this literature, our first aim
was to evaluate the strength of the association between
MD and any form of peer‐directed aggressive behavior
among school‐age children and youth. Of additional
interest was whether the magnitude of this effect varied as
a function of the behavior considered (e.g., aggression vs.
bullying vs. cyberbullying).
Testing Potential Moderators
Three categories of potential moderators were hypothesized to influence the relationship between MD and
various forms of aggressive behavior. First considered
are participant characteristics, specifically age and sex.
Previous longitudinal research by Paciello, Fida, Tramontano, Lupinetti, and Caprara (2008) examined
stability and change in MD and its relation to aggressive
behavior among 366 Italian adolescents, followed at four
time points from 14 to 20 years. Although generally MD
appeared to decline with age, especially between ages 14
and 16, four distinct trajectories were identified: (i) non‐
disengaged adolescents (37.9% of the sample) who
initially showed low levels of MD followed by a
Aggr. Behav.
significant decline, (ii) a normative group (44.5%) with
initially moderate levels that later declined, (iii) a “later
desistent” group (6.9%) that started with initially high‐
medium levels followed by a significant increase from
ages 14 to 16 and an even steeper decline from ages 16 to
20, and (iv) a “chronic” group (10.7%) that maintained
constant medium‐high levels of MD. Importantly, youth
who maintained high levels of MD were more likely to
engage in aggressive acts in later adolescence. At least
one study has reported age differences in mean levels of
MD favoring older students (e.g., Barchia & Bussey,
2011), whereas others reported no significant age differences (e.g., Bandura et al., 1996; Pornari & Wood, 2010).
However, these studies did not assess directly whether
age‐groups differed in the link between MD and
aggressive behavior. This meta‐analysis tested whether
the relation between MD and aggression varied as a
function of age, comparing studies of children versus
adolescents. Consistent with Bandura’s idea that moral
disengagement develops over time as a result from
behaving in contrast to internal moral values, it was
expected that the relation between MD and aggression
would be stronger in adolescence as compared to
childhood.
Regarding sex differences, higher levels of self‐
exonerating mechanisms have consistently been found
in male as compared to female samples from different
cultural contexts, even after controlling for other
demographic variables, such as ethnicity or socio‐
economic status (Bandura et al., 1996; Obermann,
2011; Yadava, Sharma, & Gandhi, 2001). Less clear is
whether the magnitude of the relation between MD and
aggression varies across boys and girls. Some authors
have suggested stronger links for boys (Bussman, 2007;
Paciello et al., 2008), others report the reverse (Yadava
et al., 2001), and still others find no moderating effect of
gender (Gini, 2006; Obermann, 2011). Accordingly, the
present meta‐analysis tested directly whether sex
moderated the link between MD and aggressive behavior.
Discrepancies in findings across studies as a function
of methodological differences were also considered.
First, because different scales to measure MD exist, we
tested whether effect sizes varied as a function of the type
of instrument, by comparing studies that used the original
scale devised by Bandura, studies that used a revised
shortened version of that scale, and studies that employed
other scales. Second, although MD is always assessed
through self‐reports, studies differ in their assessments of
children’s aggressive behavior, with self, peers, and
adults (teachers, parents) used as sources of information.
As demonstrated in a previous meta‐analysis by Hawker
and Boulton (2000) on the relations between peer
victimization and psychosocial adjustment, use of the
same informant for both constructs can inflate the
Moral Disengagement and Aggressive Behavior
magnitude of the measured effects due to shared method
variance. Accordingly, this meta‐analysis tested whether
this feature accounted for differences in effect sizes
across studies.
Finally, publication bias is a threat to any meta‐analytic
review, with concern that unpublished studies are more
likely to have smaller or non‐significant results and less
likely to be included in a meta‐analysis than published
studies, yielding estimated effect sizes larger than those
that actually exist. To reduce publication bias, efforts
were made to include as many unpublished studies as
possible (e.g., dissertations, conference papers). To check
whether a significant difference existed between published and unpublished studies in the reported effect
sizes, we also tested for the moderating effect of
publication status.
METHODS
Literature Search
Multiple methods were used to identify potentially
eligible studies. First, computer literature searches from
the year each database started until March 2012 were
conducted using PsychInfo, Educational Research
Information Center, Scopus and Google Scholar with
“moral disengagement,” “aggressive behavior,” “aggression,” “bullying,” “school violence,” “antisocial behavior” used as keywords. Second, recent review articles and
book chapters on aggressive behavior, bullying, or
morality in children were reviewed for relevant citations.
Third, reference sections of the collected articles were
searched for relevant earlier references (i.e., “backward
search” procedure). Finally, authors were contacted
directly to obtain other relevant studies. With unpublished studies (conference papers, dissertations), principal investigators were contacted to ask for ad hoc analysis
(if no response was received, a second e‐mail was sent
2–3 months after the first). A total of 70 potentially
relevant journal articles, chapters, conference and
dissertation abstracts were reviewed.
Inclusion Criteria
The most basic requirement for inclusion in the
present meta‐analysis was consideration of measures of
Bandura’s MD mechanisms and any form of aggressive
behavior, or bullying, or cyberaggression/cyberbullying,
including self‐report questionnaires, as well as peer‐,
parent‐, or teacher‐reports. Studies were excluded if the
aggression items were part of a wider measure (e.g., a
scale measuring externalizing problems) and a separate
effect size was not available. In one case (Hyde, Shaw, &
Moilanen, 2010), the original author was able to
calculate, upon request, the effect size for aggressive
59
behavior from a broader measure of externalizing
problems at the age of 15 and this study was then
included in the present meta‐analysis. Second, eligible
studies were required to have enough quantitative
information to calculate effect sizes. Therefore, studies
based on interviews or open‐ended questions were
excluded. Third, study participants were school‐age
children or adolescents from the community, with studies
involving clinical samples or incarcerated offenders, and
studies of adults excluded. Finally, both published reports
(i.e., journal articles) and unpublished studies (e.g.,
conference papers, doctoral theses) were considered. In
the latter case, data were obtained from the principal
investigator or his/her supervisor. When multiple reports
(e.g., a conference paper or dissertation and a published
article) presented results from the same sample, only one
effect size was used in the meta‐analysis. Using these
inclusion criteria, the final sample of the current meta‐
analysis included 27 studies; 12 examined the relation
between MD and general aggression, 11 considered MD
and bullying and four considered MD and cyberbullying
(see Table I).
All studies were coded independently by the first and
the second author, using an a priori coding scheme,
recording authors and year of publication, the type and
form of MD and aggression measures used (self‐report
vs. peer/adult reports), sample size, national setting, and
demographic characteristics of participants (age, gender).
Inter‐rater agreement was found to be very good; all
Cohen’s kappas exceeded .92. Discrepancies were
resolved by discussion.
Data Analysis
Pearson’s r was used as the effect size metric, because
almost all studies provided zero‐order correlation
coefficients between the constructs of interest. In three
cases (Bacchini, Amodeo, Ciardi, Valerio, & Vitelli,
1998; Del Bove, Caprara, Pastorelli, & Paciello, 2008;
Hymel et al., 2005), the effect size was calculated from
the comparison between a group of aggressive children
and a control (non‐aggressive) group, by first calculating
the standardized mean difference and then converting it
into r (for details see Borenstein, Hedges, Higgins, &
Rothstein, 2009, p. 48; Card, 2011, p. 100–101). Most
studies did not report data separately for boys and girls.
Given that one aim of the study was to test the possible
moderating role of gender, authors were contacted and
asked for ad‐hoc analyses. This resulted in 45 correlation
coefficients disaggregated by gender (Hyde et al.’s study
only provided boys’ effect size). In four cases for a given
study we had two independent effect sizes for each
gender (e.g., for primary school boys/girls and for middle
school boys/girls). However, the very small numbers of
these subgroups did not allow for a more detailed
Aggr. Behav.
60
Gini et al.
TABLE I. Summary of Studies Included in the Meta‐Analysis
Authors (Year)
Almeida, Correia, Marinho,
and Garcia (in press)
Ando et al., (2005)
Bacchini et al. (1998)
Bandura et al. (1996)
Barchia and Bussey (2011)
Bauman (2010)
Bussey and Quinn (2012)
Bussman (2008, study 2)
Caprara et al. (1995)
Caravita and Gini (2010)
Caravita, Gini, and Pozzoli, (2011)
Del Bove et al. (2008)
Fitzpatrick and Bussey (2012)
Gini (2006)
Gini et al. (2007b)
Gini, Pozzoli, and Hauser (2011)
Hyde et al. (2010)
Hymel et al. (2005)
Menesini, Fonzi, and Vannucci (1999)
Obermann (2011)
Paciello et al. (2008)
Pelton et al. (2004)
Perren and Sticca (2011)
Pornari and Wood (2010)
Qingquan, Zongkui, Fan, and Lei (2009)
Stevens and Hardy (in press)
Yadava et al. (2001)
Sample Size
(% of Girls)
499 (47.1%)
2,301
169
799
1,285
190
1,152
136
706
538
879
475
708
581
1,084
719
257
468
652
677
349
245
480
359
1,578
290
200
(49.8%)
(46.9%)
(45.2%)
(53.8%)
(54.2%)
(37.2%)
(52.9%)
(43.6%)
(46.6%)
(47.4%)
(45.1%)
(57.1%)
(49.2%)
(50.9%)
(48.5%)
(0%)
(43%)
(48.2%)
(47.6%)
(53.3%)
(49.4%)
(48.9%)
(53%)
(48%)
(60.3%)
(50%)
Behavior Measure
Shared
Method
Variance
Effect
Size: r
Spain
Cyberbullying, SR
Yes
.28
Japan
Italy
Italy
Australia
United States
Australia
United States
Italy
Italy
Italy
Italy
Australia
Italy
Italy
Italy
United States
Canada
Italy
Denmark
Italy
United States
Switzerland
UK
China
Samoa
India
Bullying, SR
Bullying, SR
Aggression, SR, PN, TR, PR
Aggression, SR
Cyberbullying, SR
Aggression
Aggression, PN
Aggression, SR, PN, TR
Bullying, PN
Bullying, PN
Aggression, SR
Bullying, SR
Bullying, PN
Bullying, PN
Bullying, PN
Aggression, PR
Bullying, SR
Bullying, PN
Bullying, SR, PN
Aggression, PN
Aggression, SR, TR, PR
Cyber/Bullying, SR
Cyber/Aggression, SR
Aggression
Aggression
Aggression
Yes
Yes
Mixed
Yes
Yes
Yes
No
Mixed
No
No
Yes
Yes
No
No
No
No
Yes
No
Mixed
No
Mixed
Yes
Yes
No
Yes
Yes
.25
.16
.27
.27
.32
.47
.16
.20
.18
.20
.22
.31
.22
.27
.13
.20
.59
.14
.22
.17
.13
.42
.40
.23
.54
.30
Age
Range
National
Setting
11–18
12–15
9–14
10–15
12–15
10–14
12–17
9–12
8–14
9–15
8–15
11–18
12–16
8–11
15–17
9–13
15
13–16
8–14
11–14
12–14
9–14
12–18
12–14
9–11
13–18
15–17
Note. Measures of moral disengagement were all self‐reports. SR, self‐report; PN, peer nominations, TR, teacher‐report; PR, parent‐report.
analyses and effect sizes were thus pooled by gender
group. In order to avoid violation of the assumption of
independence, mean effect sizes for the total sample were
calculated for those studies reporting multiple effect sizes
(e.g., two or more informants for the same behavior)
(Becker, 2000; Borenstein et al., 2009).
Outlying effect sizes and sample sizes were identified
on the basis of standardized z values larger than 3.29 or
smaller than 3.29 (e.g., Tabachnick & Fidell, 2001). No
outliers were detected for effect size, but there was one
study with an outlying sample size (Ando et al., 2005).
We winsorized this number of participants (i.e., reduced
to the next largest sample size, following Barnett &
Lewis, 1994; Lipsey & Wilson, 2000), resulting in an
N of 1,578.
Data from individual studies were pooled (with
comprehensive meta‐analysis program—v.2.2) using a
random effects model. To account for variations in
sample size, which influences precision with larger
samples yielding more precise estimates than smaller
samples, each study was weighted by the inverse of its
variance (Hedges & Olkin, 1985). Moreover, because the
Aggr. Behav.
use of correlation coefficients can result in problematic
error formulation, the correlation coefficient for each
study was converted to the Fisher’s z scale, and all
analyses were performed using the transformed values
(Lipsey & Wilson, 2000; Rosenthal, 1991). Then, the
resulting summary effect and its confidence interval were
converted back to correlations for ease of interpretation.
A 95% confidence interval (CI) was computed around
each mean effect size. Confidence intervals not including
zero were interpreted as indicating a statistically
detectable result favoring the association between MD
and aggressive behavior.
Heterogeneity was assessed using the Q statistic
(which is distributed as x2 with df ¼ k 1, where k
represents the number of effect sizes; Lipsey &
Wilson, 2000), evaluating whether the pooled studies
represented a homogeneous distribution of effect sizes.
Significant heterogeneity indicates that variations in
effect sizes are likely due to sources other than sampling
error (e.g., study characteristics). Also reported is the I2
statistic, indicating the proportion of observed variance
that reflects real differences in effect size (Higgins,
Moral Disengagement and Aggressive Behavior
Thompson, Deeks, & Altman, 2003). Moderator analyses were conducted to examine this variability.
Even though we were able to include several
unpublished studies, we evaluated the potential “publication bias” in different ways. We computed the “fail‐safe
N” (Nfs) according to the method proposed by Orwin
(1983), which is more conservative than the traditional
Rosenthal’s Nfs (Rosenthal, 1978, 1979). Orwin’s Nfs
determines the number of additional studies yielding null
results that would be needed to reduce meta‐analytic
results to a negligible result of .05 (Durlak &
Lipsey, 1991). We also inspected the funnel plot, which
displays effect sizes plotted against the sample size,
standard error, or some other measure of the precision of
the estimate. An unbiased sample of studies would
ideally show a cloud of data points that is symmetric
around the population effect size (Field & Gillett, 2010).
Moreover, the association between the effect sizes and
the variances of these effects was analyzed by rank
correlation with use of the Kendall’s t method. If small
studies with negative results were less likely to be
published, the correlation between variance and effect
size would be high. Conversely, lack of significant
Study
correlation can be interpreted as absence of publication
bias (Begg & Mazumdar, 1994).
RESULTS
Association Between MD and Aggressive
Behavior
The 27 studies examining the association between
MD and aggressive behavior reported data on 17,776
participants aged 8–18. The distribution of effect sizes is
presented in Figure 1. Under the random effects model,
the mean effect size for the association between MD and
aggressive behavior was r ¼ .28, which was significantly
different from zero (Z ¼ 11.06, P <.001), with a 95% CI
ranging from .23 to .32. The Nfs of null results needed
to overturn this significant result suggested that there
would need to be at least 127 studies with a null effect
size added to the analysis before the cumulative effect
would become negligible. In order to evaluate the
existence of publication bias, a funnel plot of standard
errors plotted against effect sizes was developed. The
funnel plot indicated no systematic publication bias, with
Statistics for each study
Correlation
Almeida et al. (in press)
Ando et al. (2005)
Bacchini et al. (1998)
Bandura et al. (1996)
Barchia & Bussey (2011)
Bauman (2010)
Bussey & Quinn (2012)
Bussman (2007, study 2)
Caprara et al. (1995)
Caravita & Gini (2010)
Caravita et al. (2011)
Del Bove et al. (2008)
Fitzpatrick & Bussey (2012)
Gini (2006)
Gini et al. (2007b)
Gini, Pozzoli, Hauser (2011)
Hyde et al. (2010)
Hymel et al. (2005)
Menesini et al. (1999)
Obermann (2011)
Paciello et al. (2008)
Pelton et al. (2004)
Perren & Sticca (2011)
Pornari & Wood (2010)
Qingquan et al. (2009)
Stevens & Hardy (in press)
Yadava et al. (2001)
0,28
0,25
0,16
0,27
0,27
0,32
0,48
0,16
0,20
0,18
0,20
0,22
0,31
0,22
0,27
0,13
0,20
0,59
0,14
0,22
0,17
0,13
0,42
0,40
0,23
0,54
0,30
0,28
Lower
limit
0,20
0,21
0,01
0,21
0,22
0,19
0,43
-0,01
0,13
0,10
0,11
0,14
0,25
0,15
0,22
0,05
0,08
0,53
0,06
0,15
0,06
0,00
0,34
0,31
0,18
0,46
0,16
0,26
Upper
limit
0,36
0,28
0,30
0,34
0,32
0,44
0,52
0,32
0,27
0,26
0,29
0,30
0,38
0,30
0,33
0,21
0,31
0,64
0,21
0,30
0,27
0,25
0,49
0,49
0,28
0,62
0,42
0,29
61
Correlation and 95% CI
Z-Value
p-Value
6,41
12,09
2,09
7,90
9,99
4,54
17,51
1,84
5,30
4,21
4,13
4,97
8,64
5,48
9,17
3,32
3,23
16,06
3,49
5,86
3,14
2,03
9,70
7,87
9,29
10,64
4,28
37,45
0,00
0,00
0,04
0,00
0,00
0,00
0,00
0,07
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,04
0,00
0,00
0,00
0,00
0,00
0,00
-1,00
-0,50
Favours A
0,00
0,50
1,00
Favours B
Fig. 1. Forest plot for random‐effects meta‐analysis of the association between moral disengagement and aggressive behavior. Note. Studies are
represented by symbols whose area is proportional to the study’s weight in the analysis.
Aggr. Behav.
62
Gini et al.
TABLE II. Tests of Categorical Moderators
95% Confidence Interval
Study Characteristics
Type of behavior
Aggression
Bullying
Cyberbullying
Gender
Boys
Girls
Age group
Children (8–11 years)
Adolescents (12–18 years)
Type of MD scale
Bandura’s original
Bandura‐revised
Others
Shared method variance
Yes
No
Mixed
Publication status
Published
Not published
Between‐Group Effect (Qb)
Effect Size (r)
Lower Limit
Upper Limit
k
N
.27
.25
.31
.20
.17
.27
.34
.32
.36
12
11
4
7,472
8,776
1,528
.26
.26
.21
.21
.31
.31
23
22
5,704
5,267
.18
.31
.14
.26
.22
.36
10
22
4,201
12,326
.24
.31
.36
.19
.23
.07
.28
.38
.60
16
8
3
8,734
7,939
1,103
.35
.20
.24
.28
.17
.15
.42
.23
.33
13
10
4
8,576
6,773
2,427
.27
.29
.21
.20
.33
.37
19
8
11,516
6,260
2.63
0.001
13.47
62.80
14.89
0.14
Note. k indicates the number of independent subsamples; N indicates the number of participants.
P <.05.
P <.01.
P <.001.
studies distributed symmetrically about the mean effect
size. This was confirmed by the Egger’s test, which
yielded a statistically non‐significant P‐value of 0.48
(one‐tailed), and by the rank correlation Kendall’s
t ¼ .06, P ¼.66.
Moderator Effects
The test of homogeneity of variance revealed significant heterogeneity across studies, Q ¼ 271.05, P <.001,
I2 ¼ 90.41%. Therefore, mixed effects moderator analyses were conducted to examine the association between
study characteristics and their effect sizes. The mixed
effects model assumes that the variability in effect sizes
consists of systematic variance (that can be statistically
modeled), sampling error, and an additional, unexplainable random component. Using the random effects model
to combine studies within subgroups in the moderator
analyses, a mixed effects model typically allows for
population parameters to vary across studies, reducing
the probability of Type I error, and is usually regarded as
a more rigorous meta‐analytical model than a fixed effect
model only (Borenstein et al., 2009; Hedges & Vevea,
1998). The results of the moderator analyses are
summarized in Table II. Furthermore, to enhance
Aggr. Behav.
interpretability of moderation results, especially in cases
of multiple categories, we report contrasts of correlations
(with their 95% CIs) between different study categories
(Bonett, 2008).2
First, we analyzed the existence of any difference in
effect size as a function of the type of behavior measured.
Only four of the studies reported data for cyberbullying
and two of them (Perren & Sticca, 2011; Pornari &
Wood, 2010) reported data for both traditional
aggression/bullying and cyberbullying from the same
sample. To avoid dependence of data, we first ran a
moderation analysis excluding the effect sizes about
cyberbullying and comparing the 12 studies that
measured aggression with the 11 studies that measured
bullying. The mean effect size in these two subgroups
was very similar (r ¼ .27 and .25, respectively).
Subsequently, the subgroup of studies assessing cyberbullying was included in the analysis (with the exclusion
of relative MD‐aggression/bullying effect size), yielding
a non‐significant between‐group difference: Q(2) ¼ 2.63,
P ¼.27. The contrast between bullying and cyberbullying effect sizes yielded a value of .06, 95% CI
2
We thank an anonymous reviewer for this suggestion.
Moral Disengagement and Aggressive Behavior
[.15, .02]. The contrast between aggression and
cyberbullying yielded a value of .04 [.13, .04].
A second moderator analysis compared the effect sizes
observed for boys versus girls. Interestingly, the effect of
sex was not significant, with the effect sizes from the two
sex groups being identical. Potential age differences
were tested by comparing effect sizes observed for
children (i.e., 8‐ to 11‐year olds) versus adolescents (i.e.,
12‐ to 18‐year olds). Four studies provided two
independent effect sizes each, one for children and one
for adolescents. Two studies (Bacchini et al., 1998;
Bauman, 2010) were excluded because separate effect
sizes for the two age‐groups were not available. The
analysis revealed a significant difference between the two
age‐groups favoring adolescents: Q(1) ¼ 13.47, P <.001
(contrast: .13, 95% CI [.19, .06]). Because some
studies included a wider range of adolescent ages (until
18 years) compared to others, and Paciello et al. (2008)
have reported changes in MD particularly during middle‐
adolescence (14–16‐years), a sensitivity analysis was
performed. Exclusion of the samples with participants
older than 15 (Bussey & Quinn, 2012; Del Bove
et al., 2008; Fitzpatrick & Bussey, 2012; Gini, Albiero,
Benelli, Matricardi, & Pozzoli, 2007b; Perren &
Sticca, 2011; Stevens & Hardy, 2013; Yadava et al.,
2001) did not change this result (Q(1) ¼ 4.48, P ¼.03,
with adolescents’ effect size r ¼ .27). In sum, the
associations observed between aggression/bullying and
MD did not vary as a function of gender but were stronger
among adolescents, as compared to children.
Moderation analysis by type of MD scale yielded a
significant between‐group difference: Q(2) ¼ 62.80,
P <.001. The contrast between studies that used the
original scale devised by Bandura and those that used a
revised version of that scale yielded a value of .07, 95%
CI [.16, .02], indicating that the effect tended to be
slightly higher in the latter studies. The contrast between
studies that used the original scale and those that used a
different MD scale yielded a value of .12 [.36, .17].
Finally, the contrast between studies that used a revised
version of Bandura’s scale and those that used a different
scale yielded a value of .05 [.30, .25].
In order to consider the possible effect of shared
method variance, we distinguished studies with shared
method variance, studies with no shared method
variance, and studies that employed both self‐reports
and other informants to measure aggression (“mixed”
studies). The analysis revealed a significant between‐
group heterogeneity: Q(2) ¼ 14.89, P ¼.001. Studies
with shared method variance reported significantly
higher effect sizes relative to those that employed
different informants (Q(1) ¼ 14.73, P <.001). The
contrast between studies with shared method variance
and studies without this problem yielded a value of .15,
63
95% CI [.07, .23], whereas the difference between
the former and studies with mixed method was .11
[.004, .22]. Finally, mean effect sizes did not vary as a
function of publication status (Q(1) ¼ 0.14, P ¼.71;
contrast: .02, [.12, .09]).
DISCUSSION
A meta‐analytic review of 27 independent studies was
conducted to summarize current research on the link
between MD and aggressive behavior in children and
adolescents and to test whether differences in the reported
effects can be explained by the type of aggressive
behavior considered, characteristics of the participants,
and/or methodological features of the studies. Of interest
was computation and interpretation of effect sizes in
order to determine whether the magnitude of the effect
represents something psychologically important. In
doing so, one possibility is to compare a computed
effect size with “standard” cut‐off criteria. For example,
Cohen (1992) proposed conventional values as benchmarks for what are considered to be “small,” “medium,”
and “large” effects (r: .1, .3, and .5, respectively). More
recently, based on empirical findings, Hemphill (2003)
recommended a reconceptualization of effect sizes in
psychological research, in which r ¼ .1 is “small,” r ¼ .2
is “medium,” and r ¼ .3 is “large” (see also Huang, 2011).
These benchmarks, however, have been criticized
because they are purely conventional, and somewhat
arbitrary, whereas practical and clinical importance
depends on the situation researchers are dealing with
(e.g., Kline, 2004; Thompson, 2002). A preferable
solution is to put one effect size into a meaningful
context, comparing it to other effects that have been
reported within the same literature and are commonly
considered important. Such an approach is especially
needed when dealing with a multi‐causal phenomenon
such as aggressive behavior, where one should not expect
any single factor to explain much of the variance
(Anderson et al., 2010).
The composite effect size yielded by the present meta‐
analysis was small‐to‐medium according to Cohen’s
criteria, and medium‐to‐large according to Hemphill’s
criteria. A qualitative comparison of our findings with
available meta‐analyses on individual risk‐factors and
correlates of aggressive behavior in children and
adolescents indicates that the MD‐aggressive behavior
link is equal—in absolute value—to the association
between other‐related cognitions (i.e., children’s
thoughts, beliefs, or attitudes about others, including
normative beliefs about others, empathy, and perspective
taking) and bullying (r ¼ .27; Cook, Williams, Guerra,
Kim, & Sadek, 2010). Moreover, the current result is
larger than the associations reported between hostile
Aggr. Behav.
64
Gini et al.
attributions and aggression (r ¼ .17; Orobio de Castro,
Veerman, Koops, Bosch, & Monshouwer, 2002), between emotion knowledge and externalizing problems
(r ¼ .17; Trentacosta & Fine, 2010), and larger than
effect sizes previously reported for other individual
predictors of bullying (Cook et al., 2010), such as social
competence (r ¼ .15), self‐related cognitions (e.g., self‐
esteem, self‐efficacy, r ¼ .09), social problem‐solving
(r ¼ .18), and academic performance (r ¼ .18).
In sum, the link between MD and aggressive behavior
is both statistically significant and practically
meaningful.
As expected, significant heterogeneity across effect
sizes was also observed, and some significant a priori
moderators were identified. Estimated effect size was
higher for older participants than for children, indicating
the existence of a developmental change in the link
between MD and aggressive behavior. As noted in the
introduction, Paciello et al. (2008) documented different
developmental trajectories of MD during adolescence
and found that higher levels of MD increased risk for
youth aggression, although such changes were not
observed for children. The stronger relation observed
between MD and aggression among adolescents relative
to children is also consistent with Bandura’s description
of MD as a gradual process: “disengagement practices
will not instantly transform considerate people into cruel
ones. Rather, the change is achieved by progressive
disengagement of self‐censure. Initially, individuals
perform mildly harmful acts they can tolerate with
some discomfort. […] The continuing interplay between
moral thought, affect, action and its social reception is
personally transformative” (Bandura, 2002, p. 110). In
other words, one would expect disengaged justifications
and moral transgressions to reinforce each other over
time (Bandura et al., 1996; Gibbs, Potter, & Goldstein,
1995). For chronically disengaged adolescents, however,
MD could represent a “strategy of adaptation that is
embedded into a system of beliefs about the self and
others and leads to perceive aggression and violence as
appropriate means to pursue one’s own goals” (Paciello
et al., 2008, p. 1302). Understanding the nature and
mechanisms underlying this developmental shift is an
important focus in future research. To this end, it
becomes important to develop techniques to assess MD
in children below 8 years of age, in order to study when
such distortions emerge and their relation to moral
development (e.g., the emergence of the distinction
between moral and social‐conventional rules) and with
environmental factors, such as parenting or early
experiences with peers. Finally, future research would
benefit from comparisons of developmental changes as
documented in cross‐sectional versus longitudinal research. The present meta‐analysis included three longiAggr. Behav.
tudinal studies (Barchia & Bussey, 2011; Hyde et al.,
2010; Paciello et al., 2008) that differed considerably in
design. As a result, we did not feel that the studies were
sufficiently comparable to include analyses comparing
longitudinal and cross‐sectional studies. Efforts to tease
apart variations in moral development and aggression as
a function of age, time, or cohort may be a particularly
fruitful focus in future research.
Notably, effect sizes did not significantly vary as a
function of type of aggressive behavior considered
(aggression vs. bullying vs. cyberbullying). In the case of
cyberbullying, however, the estimated correlation with
MD was slightly higher than that observed for traditional
aggression/bullying. The contrasts of correlations suggest that cyberbullying may have slightly stronger links
with MD, given the CI just hugs zero. However, this
analysis was limited by the small number of studies that
examined the relation between MD and cyberbullying,
which were limited to adolescent samples and were
characterized by low precision that resulted in a quite
wide confidence interval. Future studies are certainly
needed to further explore the issue of moral justifications
in online/virtual aggressive relationships.
Results of the present meta‐analysis also revealed that
the correlation between MD and aggressive behavior did
not differ significantly across boys and girls. Even though
absolute levels of moral disengagement and aggressive
behavior are often higher in boys, the relation between
the two variables is identical. These results should be
viewed with caution, however, given that the small
number of studies in each cell did not allow for a more
thorough analysis and we cannot rule out the possibility
that gender differences do not exist at different age‐
levels. Results of the present meta‐analysis are important
in guiding the design of future studies testing specific
hypotheses regarding sex differences.
Regarding the possible effect of methodological
differences, the link between MD and aggression was
moderated by type of MD scale, with the mean effect
being slightly larger when MD was measured through a
revised version of Bandura’s scale as compared to the
original scale. This difference may be due to the fact that,
at least in some cases, the revised scale was designed to
retain items more explicitly related to disengagement for
aggressive acts. In addition, as expected, shared method
variance was found to be a significant moderator, with
somewhat larger effect sizes observed when the same
informant (participating child/youth) evaluated both MD
and aggressive behavior. Unfortunately, the very small
number of studies using adult informants precluded any
further comparisons. Finally, effect sizes did not differ for
published versus unpublished studies, supporting the
validity of the current meta‐analytic estimations, which
were not inflated by publication bias.
Moral Disengagement and Aggressive Behavior
Limitations and Future Directions
Despite the strengths of the present meta‐analysis, it
also presents limitations due to the characteristics and
quality of the primary studies. One limitation deals with
the limited capacity to measure single mechanisms in a
reliable and valid manner (e.g., the four broad categories
of MD strategies). Indeed, studies to date have treated
MD as a unidimensional construct (see Pozzoli, Gini, &
Vieno, 2012, for an exception) and we know nothing
about whether different MD mechanisms act differently
in aggressive behavior nor whether their relative
importance varies as a function of age. Conceptually,
this may suggest that the psychological function of the
MD process—to free the individual from self‐censure
and potential guilt—is more important than the specific
strategy used to achieve the individual’s self‐serving goal
(e.g., justifying the negative behavior enacted vs.
blaming the victim). Still, a better understanding of
whether and under what circumstances these mechanisms can be differently activated may have both
theoretical and practical implications. In this regard,
future research may benefit from consideration of new
research paradigms and methodologies, in both laboratory and field studies. Future research would also benefit
from further consideration of other ways to conceptualize
and measure MD. Particularly promising here are studies
examining how morally disengaged emotions, such as
pride or indifference (instead of guilt or shame) following
an aggressive act influence subsequent behavior. Efforts
to expand and integrate the two approaches into a
coherent theoretical model of MD would be welcomed.
Another limitation is that the directionality of the
association between MD and aggressive behavior is not
clear, and bidirectionality might be the rule instead of the
exception. Indeed, MD mechanisms are likely to
influence aggressive behavior over time (Hyde et al.,
2010; Paciello et al., 2008), but their activation may
also be made easier by repeated immoral acts (e.g.,
Bandura, 1990) and frequent exposure to an aggressive
environment can alter children’s evaluation of moral
transgression (Ardila‐Rey, Killen, & Brenick, 2009).
Unfortunately, this domain of research is still largely
dominated by correlational studies that preclude definite
conclusions about direction of effects and causality in
general. Intervention studies investigating whether
changing proneness to MD also changes children’s
use of aggression could help to clarify the issue of
directionality.
Our analyses were limited by the methodological
quality of the studies available and we were not able to
account for the reliability of the measures used in
the studies because such information was not available
in many cases. Because score reliability influences
65
observed effect sizes and should be used to interpret
those effects, it is important that measurement studies as
well as substantive studies systematically report reliability coefficients for their samples. A lack of data also
prevented the consideration of additional potential
moderators. For example, the samples generally included
participants from a variety of SES and ethnic/racial
groups, yet no studies reported the association separately
for the different groups. Moreover, although the positive
association between MD and aggressive behavior is
established, in reviewing studies for this meta‐analysis,
the lack of research investigating moderators of the
association between MD and aggression was readily
apparent. Although some studies have shown that the
link between MD and aggressive behavior is significant
even after the role of other variables are accounted for
(e.g., Barchia & Bussey, 2011; Caravita & Gini, 2010;
Pelton et al., 2004), little is known about how moral
disengagement interacts with other individual risk
factors, particularly personality characteristics that
make some youth more likely to engage in antisocial
conduct, such as aggression. Overall, the individual and
contextual factors that may buffer or exacerbate the
relation of MD and aggression remains unclear. Given
the current findings, it is time to move from “main effect”
studies, aimed at establishing a relation between MD
and aggressive behavior, to “interaction effect” studies,
testing specific hypotheses and more complex patterns of
relations.
In conclusion, this study presents the first meta‐
analytic synthesis of the research on the relation between
MD and aggressive behavior in school‐age children and
adolescents. Our results showed that MD can be
considered one major correlate of aggressive behavior,
and that this relation is moderated by age and shared
method variance. It is clear that additional methodologically strong studies are needed to have a more complete
understanding of these factors, especially in relation to
developmental processes and contextual influences on
MD, that will provide useful information for prevention
and intervention efforts.
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