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The Spanish Journal of Psychology (2017), 20, e65, 1–9.
© Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid
doi:10.1017/sjp.2017.59
There is a widespread consensus on recognizing school
discipline as a necessary tool to guarantee the quality of
coexistence and to teach students to respect limits, as
impunity tends to increase transgressions and weakens
the value of rules as references to structure life in society
(Curran 2016). In this sense, adolescence poses a special
difficulty due to the greater tendency to transgress
the rules imposed by adults that occurs at this stage
(Kohlberg, 1980; Power & Power, 1992). In the National
Study of School Coexistence carried out in 301 secondary
education schools in Spain on a large sample of students,
teachers, guidance teams, management teams and fam-
ilies (Díaz-Aguado, Martínez Arias, & Martín Babarro,
2010), 63% of adolescents recognized that students do not
obey school rules. This result may be related to the fact
that 71% of adolescents stated that their opinion was not
taken into account when school rules were elaborated or
changed. The results of this study reflect that school
punishment does not seem to be effective; this fact was
recognized by 67.2% of the students.
Moreover, it was observed that the schools with
higher punishment rates also used worse punish-
ments. It was highlighted as a possible explanation
that the repeated application of punishments may
involve a lot of time and resources and could trigger
an automatic reaction in the teams responsible of
applying such punishments that would hamper their
educational effectiveness.
Research on school discipline in other contexts also
reflects that coercive measures, especially those based
on expulsion, actually increase disruption instead of
reducing it (Baroni, Day, Somers, Crosby, & Pennefather,
2016; Wolf & Kupchik, 2016), making the classroom
environment and the relationship between students
and teachers worse (McGrath & van Bergen, 2015),
increasing the risk of school dropout (Hemphill, Plenty,
Herrenkohl, Toumbourou, & Catalano, 2014), hampering
the development of skills used to positively participate
in society (Kupchik & Catlaw, 2015), increasing the risk
of drug consumption (DuPont et al., 2013) and favoring
the development of violent behaviors and the commis-
sion of crimes (Fabelo et al., 2011; Fernández-Suárez,
Herrero, Pérez, Juarros-Basterretxea, & Rodríguez-Díaz,
Is being Punished at School an Indicator of
Psychosocial Risk?
Ana Isabel Corchado, María José Díaz-Aguado Jalón and Rosario Martínez-Arias
Universidad Complutense (Spain)
Abstract. Research carried out in different cultural contexts shows that the use of exclusively coercive disciplinary mea-
sures does not improve the behavior of those punished, and may even increase the risks underpinning those behaviors.
The aim of this research was to study whether there is a link between repeatedly suffering punishment at school and
psychosocial risks in adolescence. A non-experimental design was implemented with selected groups. The participants
were 507 adolescents from four groups with different risk levels: in social protection (n = 189); subject to court measures
(n = 104); in treatment for drug abuse (n = 25); and comparison group (n = 189). A questionnaire was applied collectively.
The variables measured were school punishments, violence, drug consumption and commission of crimes. The mild
punishments variable predicted and increased the probability of consuming alcohol, tobacco and cannabis by 34% (95% CI
[1.1, 1.5]), and increased the probability of using illegal drugs by 11% (95% CI [1.11, 1.30]). Te severe punishments variable
increased the probability of using illegal drugs by 86% (95% CI [1.41, 2.49]) and increased the probability of committing
crimes by 40% (95% CI [1.13, 1.73]). School punishments, particularly if severe, stand as a visible indicator of psychoso-
cial risk. Behaviors subjected to punishment should alert us to the need to intervene with individuals who manifest them
for which the use of exclusively coercive measures is ineffective. A wider educational intervention is required to help
them find their place in school instead of excluding them from it.
Received 2 November 2016; Revised 5 October 2017; Accepted 16 October 2017
Keywords: adolescents, risk behavior, school punishment.
Correspondence concerning this article should be addressed to Ana
Isabel Corchado. Facultad de Trabajo Social. Universidad Complutense
de Madrid. Madrid (Spain). Phone: +34–913942702. Fax: +34–913942722.
E-mail: aicorcha@ucm.es
This research has been possible thanks to the agreements signed by
the Universidad Complutense de Madrid with the Agencia Madrileña
para la Reeducación y Reinserción del Menor Infractor, Instituto
Madrileño de la Familia y el Menor, and by the agreements signed
between Madrid Salud and Programa de Menores del Proyecto Hombre.
How to cite this article:
Corchado, A. I., Díaz-Aguado Jalón, M. J., & Martínez-Arias, R. (2017).
Is being punished at school an indicator of psychosocial risk? The
Spanish Journal of Psychology, 20. e65. Doi:10.1017/sjp.2017.59
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2 A. I. Corchado et al.
2016; Jaggers, Robison, Rhodes, Guan, & Church II,
2016).
In this sense, the conclusions drawn by the American
Psychological Association Zero Tolerance Task Force (2008),
which was created to assess the effectiveness of the
basically coercive discipline policy developed in the
United States after the strong social alarm arisen from
a number of serious cases of school violence that had
taken place, become especially relevant. These conclu-
sions emphasized that the results obtained across five
decades should lead to a lot of skepticism about the
effectiveness of punishment, as the punished student
tends to respond by escaping from the context and the
agent who is punishing him/her, developing very neg-
ative attitudes of fear and hostility towards the agent.
The fact that the student seems immune to coercion
often triggers a reactive escalation of increasingly
severe punishments, causing the school to devote more
time and resources to the application of these coercive
measures with little or no efficacy (Skiba, 2014).
Fortunately, there are effective alternatives based on
intervention programs at different levels, with: 1) school-
wide primary prevention, involving families and
training teachers in proactive classroom management
and care of difficult students; 2) specific prevention
programs with students at risk of violence, helping
them to develop other alternatives; and 3) discipline
programs that can be applied when transgressions occur
that include collaboration between the school and
other contexts (American Psychological Association
Zero Tolerance Task Force, 2008; Boccanfuso & Kuhfield,
2011; Cerezo & Méndez, 2012; Díaz-Aguado, 2005;
Petras, Masyn, Buckley, Ialongo, & Kellam, 2011; Skiba,
2014; Sugai & Horner, 2006; Trianes, 2000). Furthermore,
the American Academic of Pediatrics (2003) has also
warned about the adverse effects of strictly coercive
measures, which deteriorate the school environment
and increase the risk of delinquency and drug con-
sumption, which become considerably worse when
the offenders are out of school. These authors pro-
posed as an alternative the reduction of the causes
that lead to punished behaviors, among which are:
family problems, abuse and emotional or behavioral
disorders.
A possible explanation for the adverse effects of the
exclusively coercive measures can be found in social
learning (Bandura, Caprara, Barbaranelli, Pastorelli, &
Regalia, 2001; Garrido, Herrero, & Masip, 2002) and
social development (Catalano, Haggerty, Oesterle,
Fleming, & Hawkins 2004) theories, which show that
maladjustment and exclusion from the school system
can generate a perception of lack of self-efficacy in aca-
demic performance and positive relationships within
the school context, producing a disengagement from
school values, a desire for revenge, and links to
violence and drug abuse contexts (Catalano et al.,
2004; Díaz-Aguado & Martínez Arias, 2013); These
behaviours, once initiated, tend to repeat themselves as
they provide a perception of self-efficacy to transgress
that the individual does not otherwise achieve
(Bandura et al., 2001; Garrido et al., 2002).
The exclusion from standardized environments as
the only procedure has also shown little effectiveness
in the re-education of juvenile offenders. In this sense,
the studies carried out in Spain show that the severity
of the first crimes and the harshness of the internment
sanctions with which they are associated are two of the
main predictors of criminal recidivism. Accordingly, it
has been concluded that extending the severity and
duration of punishments, as is sometimes proposed,
does not favor the prevention of recidivism, warrant-
ing broader interventions with both, the sanctioned
individual and the context with which he/she must
interact in order to overcome delinquency (Bravo,
Sierra, & del Valle, 2009; Rose, 2002).
To our knowledge, no research has been carried out
in Spain regarding the relationship between the pun-
ishments suffered throughout life at school and the
psychosocial risk in adolescence. Most of the studies
performed in recent decades have been conducted in
the United States in order to delve into the conse-
quences of the so-called Zero Tolerance policy devel-
oped in response to gun violence. The main objective
of this present study carried out in Spain is to find out
whether there is a relationship between school punish-
ments and psychosocial risk in adolescence within a
different sociocultural context, where it is perceived
from different perspectives that the current punish-
ments are not effective in improving the behavior of
the punished student. The present study aims to con-
trast the following hypotheses:
1. The adolescents of the three groups at risk (those
that have lived through family abandonment, those
who have committed crimes or those suffering from
drug addiction) will have experienced more school
punishments, both mild and severe, than the adoles-
cents in the comparison group.
2. Punishments suffered at school will predict drug
consumption in adolescence, with the expectation
that the higher the incidence of punishments, the
greater the consumption.
3. Punishments suffered at school will predict the vio-
lence that occurs in adolescence. It is expected that
the higher the incidence of punishments is, the more
frequent this violence will be.
4. Punishments suffered at school will predict crimes
committed during adolescence. It is expected that
the higher the incidence of punishments is, the
greater the number of offenses will be.
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School Punishments and Psychosocial Risks 3
Method
Participants
The sample was composed of 507 participants, consist-
ing of two groups, 318 in the study group (62.7%) and
189 in the comparison group (37.3%), of which 62.9%
were male and 37.1% were female. The mean age of
the sample was 16 years (SD = 1.89; minimum = 12 and
maximum = 19).
The at-risk group was composed of three subgroups
(1) adolescents who live in protection centers as a
result of a situation of abadonment, generally due to
abuse or neglect suffered in their family environment
and that usually lead to behavior problems, i.e. under
social protection (González, Fernández, & Secades, 2004),
(n = 189, 37.2%); 2) adolescents in compliance with cus-
todial measures under a closed regime, i.e. those under
court orders (n = 104, 20.5%); 3) adolescents under
treatment for drug consumption (n = 25, 4.9%).
The members of the comparison group were selected
to pair up to the participants from the other groups,
following the inclusion criteria of age, sex, attending a
public school and being of medium-low socio-economic
level. (n = 190, 37.4%). To this end, Secondary Education,
(professional) training and employment centers were
visited in order to recruit the sample. Table 1 shows the
distribution of each group according to age and sex.
The greater percentage of male participants in the
groups of juvenile offenders and drug users corre-
sponded to their greater presence within the popula-
tion belonging to the chosen groups, which prevented
analysing the results according to the sex of the partici-
pants (Instituto Nacional de Estadística (INE) a,b, 2013).
Procedure
A cross-sectional, comparative study was carried out,
combining qualitative and quantitative approaches. In
the first qualitative stage, sixteen in-depth interviews
were carried out, for which a theoretical, flexible, itera-
tive and continuous sampling was formulated, taking
into account the criterion of typological representation
and saturation point (Valles, 2014). Those selected
responded to the typology: adolescents under treat-
ment for drug consumption; adolescents in compliance
of court orders and adolescents in social protection. The
content of the collected experiences was systematized
and the analytical categories were constructed: vio-
lence, drug consumption and commission of crimes.
These results were then used to develop a second
quantitative phase in which a questionnaire was con-
structed in order to test the hypotheses of the study on
an incidental sample.
The at-risk group was selected through collabora-
tion agreements between the Universidad Complutense
and the institutions responsible for the care and cus-
tody of the participants in the Comunidad Autónoma
de Madrid. The selection of the centers and the total
number of subjects in each group depended on the
decision of each entity. These centers were distributed
as follows: 11 juvenile protection centers, 6 custodial
enforcement centers and 8 outpatient treatment cen-
ters for drug consumption.
The questionnaire was applied collectively, with the
presence of the researcher. The size of the groups
ranged between 5 and 25. The instructions were pro-
vided by the researcher, who resolved any issues that
arose during the application.
An information letter was drawn up describing the
terms of the study which also included an informed
consent form for the guardians or parents of the ado-
lescent. In the case of protected minors, the consent
was granted by the responsible entity.
Variables and assessment instruments
Sociodemographic variables: Sex, age and group.
School punishments: The punishment scale was inspired
by the sanctioning regulation, Decree 15/2007, which
establishes the regulatory framework for coexistence
in schools of the Community of Madrid, set out in the
colloquial form in which adolescents express them-
selves. Due to the lack of a theoretical model, it could
be considered a formative variable. For its study, a
Table 1. Age and Sex Distribution for Each Group
n
Age Sex
Min. Max. Mean SD
Male Female
nPercentages nPercentages
1. Social Protection 189 12 18 15.4 1.47 101 53.4 88 46.6
2. Court measures 104 14 19 17.4 1.13 93 89.4 11 10.6
3. Drug abuse treatment 25 15 19 17.6 1.32 20 80 5 20
4. Comparison 189 12 19 15.8 2.08 105 55.3 85 44.7
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4 A. I. Corchado et al.
Likert-type scale was elaborated with a response range
of 0 (never) to 4 (many times) for the six types of punish-
ment included in the sanctioning regulation (Decree
15/2007): to notify the family; to be sent to the Principal’s
office; to repair the damage caused; a temporary removal
from the center; a disciplinary record; and final expulsion.
In order to reduce dimensionality, an unweighted least
squares factor analysis of the indicators, followed by a
Promin rotation were performed. The Kaiser-Meyer-
Olkin (KMO) statistic reached an acceptable value (.65).
Several solutions were examined, opting for the two
factor solution that was the one informed by previous
research and recommended by Horn’s parallel analysis
(1965).
The factors extracted explained 66% of the total var-
iance and can be interpreted as light punishments,
formed by four items: to notify the family; to be sent to the
Principal’s office; to repair the damage caused; and a tempo-
rary removal from the center, which obtained reliability
coefficient of (α = .88) 95% CI [.86, .89]; and severe pun-
ishments, consisting of two items: a disciplinary record,
and final expulsion, with a coefficient (α = .63) 95% CI
[.57, .68].
Violence: An ad-hoc, 10-item Likert-type scale was cre-
ated from the content analysis of the interviews using a
response range of 0 (never) to 4 (almost always/always).
A confirmatory factor analysis was performed to show
the internal structure. A well-defined solution and an
adequate fit of three first-order latent variables and
one second-order varible were obtained. The first vari-
able could be defined as a disposition towards violence,
and consisted of five items, such as “when someone is
looking for a fight, he will find me” (R2 = .47), “I`ll join
my friends in a fight even if I don’t know what caused
it” (R2 = .56); the second variable was defined as Intra-
family violence, consisting of two items: “I have caused
physical violence situations with my family” (R2 = .80)
and “I have caused situations of non-physical violence
with my family” (R2 = .49); and the third variable was
defined as Group violence, consisting of three items: “I
have taken part in violent actions against homeless
people” (R2 = .48), “I have taken part in violent actions
against ethnic minorities” (R2 = .63) and “I have taken
part in violent actions against gangs” (R2 = .57). The
second-order latent variable was defined as violence
(Model fit indexes: χ2(32) = 146.85, p < .001; IFI = .96;
RMSEA = .072; RMR = .041; CFI = .96; GFI = .96). The
reliability coefficient for the scale was .86; 95% CI [.84,
.87]; M = 17.68 and SD = 7.21. All items had a high level
of significance p < .001; the minimum value of the
t-statistic was 11.11.
Drug Consumption. It refers to the consumption or
abstinence, during the last 30 days, of legal (a checklist
with three drugs) and illegal (a checklist with 10 drugs)
substances included in the questionnaire with the
following order and nomenclature: tobacco, alcohol,
cannabis, glue, cocaine, pills, magic mushrooms, meth,
acid, ketamine, GHB, heroin and others. This selec-
tion coincides with most epidemiological studies
(e.g., European Monitoring Centre for Drugs and Drug
Addiction, 2014; Observatorio Español de las Drogas y
Toxicomanías, 2013). In order to facilitate analysis,
three variables were constructed by adding the corre-
sponding indicators: alcohol, tobacco and cannabis consump-
tion, (starting drugs) consumption of illegal drugs (except
cannabis) and drug consumption, which included all
substances.
Commission of Crimes. For the construction of this
variable, a checklist with 22 types of crimes was created
to evaluate the commission or not of the most fre-
quent crimes among the study groups1 (Agencia de
la Comunidad de Madrid para la Reeducación y
Reinserción del Menor Infractor (ARRMI), 2014).
Data analysis
Descriptive analyzes were carried out to determine the
characteristics of the participants, the risk index for the
analysis of categorical variables, and the differences
among interest groups; an exploratory factor analysis
was used to reduce dimensionality in the variable
punishments and a confirmatory factor analysis was
performed to establish the latent dimensions of the
Violence variable, the reliability of the dimension scores.
A logistic regression analysis was used to assess the
relationship between school punishments, drug con-
sumption and commission of crimes, and a linear regres-
sion analysis to assess the relationship between such
punishments and violent behavior.
The IBM-SPSS (v.21) statistical package was used to
perform the statistical analyses of the data, the Factor
(v.10.3) program was used for the exploratory factor
analysis and the Lisrel (v.9.2) program for the confir-
matory factor analyzes.
Results
Differences between groups and school punishments
A 29.2% of the total sample had suffered at least one
serious punishment at school and 63.1% had had
minor sanctions. Contingency tables were created
between the severe and mild punishments and the
1Misdemeanors may be summarized as follows: Violent theft, Theft,
Injury / Aggression, Robbery with force, Robbery with Intidimation,
Abuse / Family Abuse / Gender Violence, Attack on Authority /
Public Order, Threats / Intimidation / Coercion / Attack against
moral integrity / Injury / Insults, Against road safety, Against public
health / Drug Trafficking, Sexual offenses, Usurpation (housing, iden-
tity …), Deterioration of property / Fire, Breaking and entering, Fraud,
Illegal possession of weapons, Breach of measure, Homicide / Murder
(included in attempted degree).
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School Punishments and Psychosocial Risks 5
study and comparison groups. The probability of
receiving mild punishments was 3 times higher if the
adolescent belonged to the study group (or = 2.90); the
relationship between the two variables was statisti-
cally significant N = 507; χ2(1) = 36.01; V = .248; p < .001.
The risk increased in the case of severe punishments: 11
times greater if the adolescent belonged to the study
group (or = 11.08); the relationship between both vari-
ables was statistically significant N = 507; χ2(1) = 76.06;
V =.38; p < .001.
Table 2 presents the descriptive statistics of each of
the interest groups for the three variables.
The non-parametric Kruskal-Wallis test was per-
formed, as the severe and mild punishment variables
did not fulfill the normality assumption. It was not
necessary to eliminate the effect of age since the corre-
lations were very low between mild punishments and
age .11 p = .01, and between severe punishments and
age .16, p < .01. Significant differences were found
between groups for the two variables: severe punish-
ments χ2(2) KW = 38.05 p < .001 and mild punishments
χ2(2) KW = 24.87, p < .001. For a posteriori contrast, the
Mann-Whitney U-statistic with Bonferroni correction
and Rosenthal’s statistic were used for the calculation
of effect sizes. As can be observed in Table 3, these sta-
tistics described small and medium effect sizes for the
differences between interest groups (Cohen, 1988).
Tables 2 and 3 show that the groups at risk have
received more punishments than the control group,
as can be observed from the means of each group.
The most punished group was the one under court
orders, difference due to the severe punishments. The
second most punished group was the one being treated
for drug consumption, due to the mild punishments,
and the third most punished group was the one in
social protection. The largest effect sizes correspond to
the differences in the severe punishments and the mild
punishments between the groups of custodial mea-
sures (2), the drug consumption group (3), and the social
protection group (1) with the comparison group (4).
Predictive analyses based on punishments
A linear regression analysis was performed to evaluate
the effect of school punishments on violent behavior.
The severe and mild punishment variables were intro-
duced into a single block. School punishments are con-
sidered predictors of the violence variable; the model
explains 24% of the criterion variable (adjusted R2 = .24;
SE = 6.29; F(2, 504) = 80.39, p < .001). See table 4.
To evaluate the relationship between school punish-
ments and the probability of drug consumption and
the commission of crimes, a logistic regression analysis
was performed. The strong positive asymmetry of
both dependent variables advised their dichotomiza-
tion. The severe and mild punishments variables were
defined as 1 = punished and 0 = not punished and
were introduced into a single block. The consumption of
alcohol, tobacco and cannabis, and illegal drugs variables
were analyzed for the whole sample and the commis-
sion of crimes variable was chosen for those over 13 years
old (minimum age to demand sanctioning responsi-
bility according to LO 5/2000 on 12th January). The
results are presented in Tables 5, 6 and 7.
The results of the logistic regression show that the
mild punishments variable predicted in a statistically
significant way the consumption of drugs χ2(2) = 63.16,
p ≤ .001. The mild punishments variable increased the
probability of consuming alcohol, tobacco and cannabis
by 34% (95% CI [1.1, 1.5]). The likelihood ratio indicated
the superiority of the final model over the null model
χ2(1) = 48.82, p ≤ .001. The overall percentage of correct
classifications was 79.3%, and the correct classification
of cases of those that consume these substances was
100%. The effect size was small (or = 2.48). The pseudo-R2
were also low, between .12 (Cox & Snell R2) and .18
Table 2. Descriptive Statistics of the Punishments According to
Group
N
Severe Mild
Mean SD Mean SD
1. Social Protection 189 .69 1.27 3.46 3.75
2. Court measures 104 1.73 1.68 5.53 3.82
3. Drug abuse treatment 25 .84 .85 5.64 3.49
4. Comparison 189 .10 .44 1.20 1.73
Table 3. Diferences between Groups in Punishments and Effect Sizes
Severe Mild
U r U R
2 > 1 –.21 2 > 1 –.27
3 > 1 –.12 3 > 1 –.10
1 > 4 –.24 1 > 4 –.26
2 > 4 –.42 2 > 4 –.48
3 > 4 –.27 3 > 4 –.33
1: Protecction; 2: Court measures; 3: Drugs; 4: Control.
Table 4. Prediction of Violent Behavior from the Severity of School
Punishments
B SE βt p
Constants 15.85 .37 39.77 .001
Severe Punishments 1.38 .29 .25 4.68 .001
Mild Punishments .59 .10 .29 5.59 .001
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6 A. I. Corchado et al.
Table 5. Logistic Reggression for the Prediction of Alcohol, Tobacco and Cannabis Consumption according to the Severity of School
Punishments
B SE Wald df Sig. Exp(B)
95% CI for EXP(B)
Inferior Superior
Severe .31 .22 1.88 1 .17 1.36 .877 2.11
Mild .39 .06 19.44 1 .00 1.34 1.17 1.52
Constant .64 .14 22.39 1 .00 1.90
Table 6. Logistic Reggression for the Prediction of Illegal Drug Consumption according to the Severity of School Punishments
B SE Wald df Sig. Exp(B)
95% CI for EXP(B)
Inferior Superior
Severe .629 .14 18.84 1 .00 1.86 1.41 2.49
Mild .185 .04 22.20 1 .00 1.20 1.11 1.30
Constant –.951 .13 51.65 1 .00 .86
(Negelkerke R2). The severe punishments variable was
not significant for prediction.
It was also shown that the punishments significantly
predicted the consumption of illegal drugs χ2(2) =
131.85, p ≤ .001. The mild punishments variable increased
the probability of using illegal drugs by 11% (95% CI
[1.11, 1.30]), and the severe punishments variable by 86%
(95% CI [1.41, 2.49]). The likelihood ratio indicated the
superiority of the final model over the null model χ2(1) =
101.16, p ≤ .001. The overall percentage of correct
classifications was 73.6%, correctly classifying 60% of
the cases that use these substances and 86.7% that do
not consume them. The effect size was mild (or = 9.60
for those who had severe punishments and 4.48 for
those who had mild punishments). The pseudo-R2
were large, between .23 (Cox & Snell R2) and .31
(Negelkerke R2).
Similar results were obtained for the commission of
crimes variable. Punishments were significantly related
to the probability of committing offenses χ2(2) = 57.84.
p ≤ .001. The analysis showed that having been sub-
jected to severe punishments increased the probability of
committing crimes by 40% (95% CI [1.13, 1.73]) and
14% (95% CI [1.05, 1.24]) if they had been mild. The
likelihood ratio indicated the superiority of the final
model over the null model χ2(1) = 62.85, p ≤ .001. The
overall percentage of correct classifications was 82.2%,
correctly classifying 14% of the cases that committed
crimes and 96.2% of cases that did not. The effect sizes
were medium (or = 6.69 for severe punishments and
small (or = 4.41 for mild punishments), as well as the
pseudo-R2 that were between .11 (Cox & Snell R2) and
.18 (Negelkerke R2).
Discussion
The results obtained confirm the hypothesis about the
relationship between the frequency with which school
punishment has been experienced and the membership
of a psychosocial risk group, noting that the relation-
ship was much greater when considering severe pun-
ishments (disciplinary report and final expulsion from
the center) than when considering mild punishments
(notifying the family, being sent to the Principal’s office,
repairing the damage caused and temporary removal).
Table 7. Logistic Reggression for the Prediction of Commission of Crimes according to the Severity of School Punishments
B SE Wald df Sig. Exp(B)
C.I.95% for EXP(B)
Inferior Superior
Severe .337 .10 9.71 1 .00 1.40 1.13 1.73
Mild .132 .04 9.39 1 .00 1.14 1.05 1.24
Constant –2.45 .20 147.22 1 .00 .09
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School Punishments and Psychosocial Risks 7
In addition, when comparing each risk group with the
control, the differences were larger, especially in the
case of adolescents under court measures, both in
severe and mild punishments and, secondly, with drug
consumption.
The results of the regression analyzes also confirm
the hypotheses about the relationship of violence, drug
consumption and the commission of crimes with the
punishments experienced at school, resulting in this
sense specially significant the severe punishments
with respect to the consumption of illegal drugs other
than cannabis and also with the commission of crimes.
These results lead us to emphasize that having suf-
fered serious punishments at school, based on expul-
sion from the center and the disciplinary records that
preceded it, are a very visible indicator of psychosocial
risk, a cry for help that should alert us to the need to
intervene with whom expresses it in order to help
them avoid that risk. Although the results cannot con-
firm that punishments are the source of the risk, since
frequently punished students, especially with severe
punishments, have often experienced serious prob-
lems, which are at the origin of the behavior for which
they are sanctioned, as noted by Bravo et al., (2009),
they do confirm that only with punishment (and espe-
cially with the expulsion from the center), the behavior
that originated it cannot be changed. In fact, it can even
be aggravated, as with the expulsion important risk
conditions can be increased: time out of school (as the
American Academy of Pediatrics, 2003, warns), disen-
gagement from the values and rules that the school
tries to teach as well as the linkage with values and
behaviors of antisocial groups with which they become
related to when they are expelled (as Catalano et al.
2004 confirm) and the opportunities to feel effective
with behaviors, such as violence, drug consumption or
crime (as found by Bandura et al., 2001). It should be
borne in mind that the repetition of mild punishments,
although to a lesser extent, is also a risk indicator, so
the alert should start from this indicator.
The results suggest that traditional school punish-
ments that continue to be used in schools are not effec-
tive in changing the behavior of the punished adolescent,
as is recognized in Spain by families, students, teachers
as well as by management and guidance teams (Díaz-
Aguado et al., 2010). In the direction proposed by the
American Psychological Association Zero Tolerance
Task Force (2008), it is also necessary to sensitize
society and those responsible for education, about the
need to adopt a preventive perspective, working with
the entire school, training teachers in proactive conflict
management, articulating a much closer collaboration
with families, detecting as early as possible the at-risk
students to help develop competences that allow them
to succeed, integrate themselves into positive peer
groups and form links with the values that the school
tries to promote. Coercive measures must be accompa-
nied when they are unavoidable with an educational
intervention that helps the punished adolescent to
understand why what he did is wrong, repent and
initiate behaviors that repair the damage caused.
Although absent from most of the analyzes on school
discipline cited above, it is important to bear in mind
that another important line of intervention to improve
discipline is to involve all members of the school, in-
cluding students, in the development and application
of School rules, so that their fulfillment will be much
more than mere obedience to authority, becoming loyal
to a group to which the adolescent feels attached to and
loves, as has been proven for decades in both schools
and correctional facilities (Kohlberg, 1980; Power &
Power, 1992).
Most of the previous research carried out on this
subject has been performed in the United States, in
response to the extreme violence with firearms pro-
duced in that context. The main contribution of this
study is to have been carried out in Spain, a very
different context, but in which students, teachers and
management teams recognize the ineffectiveness of the
punishments to improve the behavior of the punished.
The present research delves further into this topic, its
results suggesting that students who receive severe
punishments often appear to be initiating antisocial
behaviors, possibly related to behavior problems and
family difficulties, which should be diagnosed accu-
rately as soon as possible to help the adolescent out of
a path that can lead to delinquency and illegal drug
consumption. This goal is not only achieved with the
type of discipline they are currently receiving.
Among the main limitations of this study, which
should be overcome in future research, are that the
characteristics of the sample should be noted, which
should be extended to other Autonomous Communities,
and that the need to carry out a longitudinal follow-up
that allows to relate the type of school discipline suf-
fered with later development should be taken into
account, as well as the convenience of completing the
evaluation of the discipline with qualitative measures
based on the account of those who receive it.
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