Although less numerous, other studies have focused on symptoms of different disorders, especially behavioral problems or learning disabilities. The most significant conclusions stemming from this line of research indicate that behavioral problems predict approaches to learning (Domínguez et al., 2011) and mediate in the relationship between early behavior problems and future academic achievement (Domínguez and Greenfield, 2009). In addition, higher competence motivation may especially lead to reducing the risk of learning disabilities in elementary and secondary education (McDermott et al., 2006).

Most of the research conducted in children with a clinical diagnosis of ADHD has focused on academic achievement, but learning behaviors have not yet been comprehensively studied and understood in this group. However, some studies have used variables that, although not referred to as learning behaviors or enablers, are related to them or form part of them. For example, when comparing children with and without ADHD, children with ADHD demonstrated lower levels of motivation and lower levels of task persistence (Hoza et al., 2001; Carlson et al., 2002). These differences exist in children with high levels of inattention, hyperactivity and impulsivity symptomatology, assessed through the Academic Competence Evaluation Scales (ACES), which measure academic enablers (Demaray and Jenkins, 2011). Given the importance of learning behaviors in competences related to school and general development, it would be essential to understand the explanatory role played by ADHD symptoms and executive functions (EF) in this domain.

Attention and hyperactivity/impulsivity symptoms are negatively related to academic achievement in community and clinical samples, even after controlling for intelligence, comorbidity, and socioeconomic status (Polderman et al., 2010). In children with ADHD, academic impairment is related primarily to inattention symptoms (Langberg et al., 2013; Plamondon and Martinussen, 2015). Furthermore, high levels of hyperactive–impulsive symptoms in childhood have been associated with school dropout and fewer years of attained education (Fredriksen et al., 2014), indicating an increased risk of unfavorable educational outcomes related to these symptoms in childhood. More specifically, the few studies investigating the relationship between ADHD symptoms and learning behaviors show a significant relationship between high levels of inattention, hyperactivity and impulsivity, and lower competence motivation, attention/persistence (Fantuzzo et al., 2005), and academic enabler levels (Volpe et al., 2006; Demaray and Jenkins, 2011). Nonetheless, it is important to highlight that the direction of these relationships is not clear, as one study suggested that learning behavior problems can signal later attention-deficit hyperactivity (McDermott et al., 2016).

Difficulties in learning behaviors could also be a manifestation of deficits in executive functions, which are defined as a set of higher order, self-regulatory, cognitive processes required to direct behavior toward the attainment of a goal (Barkley, 1997). Prior research has found that EF are fundamental to individuals' academic achievement in the general population (Blair and Diamond, 2008; Sasser et al., 2015) and in children with ADHD (Biederman et al., 2004; Miranda et al., 2012; Langberg et al., 2013). EF could be one of the cognitive regulatory processes that underlie and facilitate learning-related behaviors in the classroom, predicting teacher ratings of learning-related behaviors in kindergarteners and elementary school children (Brock et al., 2009; Neuenschwander et al., 2012). Specifically, Brock et al. (2009) found that cool EF predicted learningrelated classroom behaviors in kindergarteners. Along these lines, Vitiello et al. (2011) reported that cognitive flexibility is related to the ability to pay attention and persist in the classroom in preschool children at risk of school failure. Miller et al. (2006) also found an association between observed classroom emotion dysregulation and teacher-rated school adjustment, particularly with motivation. In this context, studies with ADHD samples are needed because EF may have a strong impact on academic variables.

Finally, few studies have analyzed the combined influence of EF and ADHD symptoms on academic variables. Langberg et al. (2013) evaluated the relationship between EF ratings on the Behavior Rating Inventory of Executive Function (BRIEF) and academic functioning, above and beyond the ADHD symptoms in adolescents with ADHD. They developed different prediction models that included combinations of EF and ADHD symptoms depending on the variable to predict (school grades, or homework problems). One of the most complete models was related to the prediction of homework problems, and it included symptoms of inattention and hyperactivity/impulsivity, as well as the EF of planning and organization. However, as mentioned above, only symptoms of inattention and the ability to plan ahead and organize time and materials consistently predicted academic outcomes.

In summary, prior research on learning behaviors has mainly employed community samples. Some studies have been developed in the context of the Head Start program for preschoolers from low-income families (McDermott et al., 2011). As learning behaviors are often associated with higher levels of academic and social achievement, it is important to analyze the similarities and differences observed in children with ADHD compared to TD children. Taking a step forward, it is important to identify the factors that can influence learning behaviors, and determine whether the relationships established among them are different in children with ADHD and TD children, due to the limited existing research on this topic.

The first objective of this study was to compare different components of learning behaviors in children with ADHD and TD children. Our hypothesis is that children with ADHD will have lower scores on learning behaviors than TD children, as occurs with academic achievement. The second objective was to analyze the relationships among learning behaviors, EF, and symptoms of inattention and hyperactivity/impulsivity in both groups and identify which aspects of EF and/or ADHD symptoms have greater relevance in predicting different indicators of learning behaviors in children with ADHD and TD children: Competence Motivation, Attitude Toward Learning Attention/Persistence, and Strategy/Flexibility. Based on theoretical arguments and empirical findings, our hypothesis is that ADHD symptoms, and especially inattention, will be related to learning behaviors in both groups, whereas EF will be more related to learning behaviors in the ADHD group because of their strong influence on the disorder. The results can be relevant in helping students with and without ADHD to achieve academic success, and lead to more effective, targeted intervention strategies.

### MATERIALS AND METHODS

#### Participants

Seventy-two children participated in this study, 35 with a clinical diagnosis of ADHD and 37 TD children, all between the ages of 7 and 11.

Children with ADHD had a previous clinical diagnosis of ADHD by mental health services that was confirmed before their participation in the study: all of them met the strict diagnostic criteria for ADHD from the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), based on information from parents and teachers. Specifically, 77.14% of the participants had an ADHD combined presentation, and 22.86% had an ADHD inattentive presentation. Moreover, 71.4% of the children in the ADHD sample were receiving psychostimulant medication.

Typically developing children were attending school in regular classrooms in the same schools as the clinical sample in the research. They had no history of psychopathology or referral to children's mental health units, according to the information from school records, and they did not meet the DSM-5 criteria for ADHD before beginning the evaluation. The exclusion criteria for all the participants were: an overall IQ below 80, measured with the K-BIT (Kaufman and Kaufman, 2000); neurological or sensorial damage, psychosis, visual, auditory, motor, or sensorial deficits; or autism spectrum disorder, evaluated through an extensive prior anamnesis carried out with the families.

**Table 1** shows the socio-demographic characteristics of the participants and their families. Both groups were matched on age [t(70) = 1.90, p = 0.062], IQ [t(70) = –1.24, p = 0.218] and level of semantic language [t(70) = –1.49, p = 0.140], assessed with the vocabulary subtest of the WISC-IV (Wechsler, 2003). There were statistically significant differences in their symptoms of inattention and hyperactivity/impulsivity, according to parents' and teachers' ratings of DSM-5 criteria (severity of each item from 0 to 3). In addition, 91.42% of the individuals with ADHD and 64.86% of the individuals with TD were male. Regarding the school modality, all children in the TD group were attending school in regular classrooms full time, whereas 94.3% of the children with ADHD attended regular classrooms but received educational support for their specific needs in the school. Regarding the family's socio-cultural status, there were differences between the two groups in the parents' education level [t(70) = –5.39, p < 0.001]. Specifically, the parents of the children in the ADHD sample had significantly less education than the parents of the children in the TD group.

#### Procedure

This study respected the principles outlined in the current legislation on clinical investigation, and it was approved by the Research Ethics Committee of the University of Valencia, which is regulated by Ethical Principles for Medical Research Involving Human Subjects (Declaration of Helsinki 1964, World Medical Association, 2013).

The official and written authorization of the Board of Education and School Management (Consellería de Educación de la Generalitat Valenciana) was obtained to locate children who had received a previous diagnosis of ADHD by professionals in specialized childhood mental health services. A total of 42 schools from the Valencian Community participated in a larger research about neurodevelopmental disorders that included the sample in this study.

Oral permission from the children and written informed consent from their parents and schools were obtained before beginning the evaluation. The intelligence test was administered

Parental education measured as highest level of mother or father (0 = elementary school, 1 = Compulsory secondary school, 2 = Medium level vocational training, 3 = Upper secondary education (Bachiller) or Superior level vocational training, 4 = University degree).

to all the children individually by doctoral-level psychologists or highly trained psychologists in suitable classrooms in the different schools. Teachers and parents filled out questionnaires on ADHD criteria from the DSM-5, and teachers-tutors filled out the questionnaires selected to assess EF and learning behaviors.

#### Measures

#### Learning Behaviors

The Learning Behaviors Scale (McDermott et al., 2001) is a teacher-report questionnaire designed to measure student behaviors related to effective and efficient learning. The Learning Behaviors Scale contains 29 items, six items with positive wording and the remaining items with relatively negative wording in order to reduce response sets. Items are rated on a 3-point Likert scale (0 = Does not apply, 1 = Sometimes applies, 2 = Most often applies). High scores indicate good learning behavior, and low scores are interpreted as faulty learning behavior. Based on the manual, 25 of the 29 items were used to produce a Total score and four subscales: Competence Motivation ("Says task is too hard without making much effort to attempt it"), Attitude Toward Learning ("Shows a lively interest in learning activities"), Attention/Persistence ("Sticks to a task with no more than minor distractions"), and Strategy/Flexibility ("Follows peculiar and inflexible procedures in tackling tasks"). Total and subscale raw scores are converted to normalized T scores (M = 50, SD = 10). In our sample the internal consistency coefficient is high for the total score (α = 0.93) and for the subscales (α = 0.76–0.86). Moreover, studies present supportive psychometric evidence for the Learning behaviors scale scores in different contexts (McDermott, 1999; Canivez and Beran, 2011).

#### ADHD Symptoms

Parents and teachers provided information about the 18 ADHD criteria from the DSM-5, rating the severity of each item from 0 to 3. A score of 2 or 3 on an item was regarded as presence of the symptom. The means of the items assessed by parents and teachers were included in the analyses.

#### Executive Functions

The Behavior Rating Inventory of Executive Function (BRIEF, Gioia et al., 2000). The teacher version of the BRIEF was used in this study to assess the children's EF through the observation of their behavior in the school context. It consists of 86 items rated on a Likert-type scale with three levels (never, sometimes, often). The items are grouped in two indexes: The Behavioral Regulation Index is composed of the following scales: Inhibit, Shift, and Emotional Control scales, assessing the child's capacity to make cognitive changes and adjust his/her emotions and behavior through appropriate inhibitory control. The Metacognition Index is composed of the scales of Initiation, Working Memory, Planning/Organization, Organization of Materials, and Monitor. This index reflects the child's ability to initiate, plan, organize, self-monitor, and maintain information in working memory. It could be interpreted as the ability to self-manage cognitive tasks and supervise their performance. This index is related to the capacity to actively solve problems in a variety of contexts. Direct scores can be transformed into T-scores, with higher scores indicating worse EF. In our sample the internal consistency coefficient is high for the total score (α = 0.98), for the indices (α = 0.96–0.97) and for the subscales (α = 0.78–0.94).The instrument has good psychometric properties in Spanish samples (García Fernández et al., 2014).

#### Statistical Analyses

Data analysis was conducted with the Statistical Package for the Social Sciences (SPSS), version 23. To compare the ADHD and TD learning behaviors, Multivariate Analysis of Covariance (MANCOVA) was used. The parents' educational level and children's gender were included as covariates, as research suggests that girls have higher approaches to learning than boys (Vitiello

et al., 2011). The proportion of total variance accounted for by the independent variables was calculated using partial eta squared (according to Cohen (1988): eta squared, 0.06 = small; 0.06–0.14 = medium, 0.14 = large).

Partial correlations, controlling for parents' educational levels and children's gender, were conducted to examine relationships among EF, ADHD symptomatology, and learning behavior dimensions. Finally, four multiple linear regression analyses were conducted to test the effect of the two EF indices (Behavioral regulation and Metacognition) and the DSM-5 inattention and hyperactivity/impulsivity scores (independent variables – simultaneously entered) on the four learning behavior dimensions (dependent variables).

#### RESULTS

### Differences in Learning Behaviors of Children Diagnosed with ADHD and TD

According to teachers' ratings, 54.3% of the children diagnosed with ADHD exhibited learning behavior problems (T < 35), whereas none of the TD children presented these problems.

The two groups were compared on their learning behaviors, using parental education and children's gender as covariates. The results of the first MANCOVA showed a statistically significant difference between the two groups on learning behaviors (Wilks Lambda = 0.54, F5,64 = 10.81, p < 0.001, η 2 p = 0.458). The differences were statistically significant on all the subscales: Competence Motivation (F1,68 = 31.62; p < 0.001; η 2 p = 0.317), Attitude Toward Learning (F1,68 = 28.39; p < 0.001; η 2 p = 0.295), Attention/Persistence (F1,68 = 37.68; p < 0.001; η 2 p = 0.357), Strategy/Flexibility (F1,68 = 29.39; p < 0.001; η 2 p = 0.302), and Total Score (F1,68 = 35.82; p < 0.001; η 2 p = 0.345). In all cases, the ADHD group presented significantly higher scores than the TD group (**Table 2**).

### Relationships Among EF, Inattention, Hyperactivity/Impulsivity, and Learning Behaviors in ADHD and TD Groups

**Table 3** presents the correlations among EF, ADHD symptoms, and learning behaviors in children with ADHD and children with TD. In the TD group, correlation analyses showed that both EF indexes and the inattention symptoms were significantly correlated with all the learning behaviors (p < 0.05), and correlations were moderate to large in magnitude, ranging from r = –0.400 to r = –0.799. However, in this group, hyperactivity/impulsivity symptoms presented significantly low correlations only with Competence/motivation, Strategy/ Flexibility, and the Total Score.

In the ADHD group, both EF indexes were significantly correlated with Attitude toward learning, Strategy/flexibility, and the Total score, with moderate correlations ranging from r = –0.427 to r = –0.677. The Metacognition index additionally presented a moderate to high correlation with Competence/motivation (r = –0.664). Inattention symptoms presented significant correlations with all the learning behaviors, except Strategy/Flexibility (r = –0.428 to r = –0.518), whereas the Hyperactivity/impulsivity symptoms were significantly correlated with Attention/persistence (r = –0.353).

### Predictors of Learning Behaviors in ADHD and TD groups

Four separate multiple regressions for each group were conducted to study whether EF and ADHD symptoms are differentially related to learning behaviors (Competence/ Motivation, Attitude Toward Learning, Attention/Persistence and Strategy/Flexibility) in the ADHD and TD groups. All the regressions models were significant (See **Table 4**).

The regressions conducted with the ADHD group indicated that the Metacognition index was a significant individual predictor of Competence/motivation (β = –0.66, t = –3.76, p = 0.001) and Attitude Toward Learning (β = –0.46, t = – 2.46, p = 0.020). All the predictors collectively explained 50 and 44% of the variance of Competence/ motivation and Attitude Toward Learning, respectively. The Behavioral regulation index was an individual predictor (β = –0.42, t = –2.30, p = 0.029) of Strategy/Flexibility, with 44% of the variance explained by all the predictor variables. There was no unique individual predictor of Attention/Persistence, but collectively, EF and ADHD symptoms, explained 27% of its variance.

Regarding the TD group analyses, Inattention was a significant individual predictor of Attitude Toward Learning (β = –0.61, t = –3.63, p = 0.001) and Attention/Persistence (β = –0.46, t = –2.44, p = 0.020). All the predictors collectively explained 51 and 40% of their variance, respectively. Inattention (β = – 0.56, t = –4.77, p < 0.001) and the Metacognition index (β = –0.48, t = –3.93, p < 0.001) were significant predictors of Competence/Motivation and, along with the other predictor variables, explained 77% of its variance. Finally, the Behavioral regulation index (β = –0.61, t = –3.64, p = 0.001) was the only significant predictor of Strategy/Flexibility, with 44% of the variance explained by all the predictor variables.

#### DISCUSSION

This study examined learning behaviors in children diagnosed with ADHD. To date, research related to school failure has primarily focused on academic achievement using mainly standardized test scores or school grades. However, this study expands on previous work demonstrating that children with ADHD have poorer learning behaviors than TD children. The results of our first objective showed differences between the two groups in all the learning behaviors measured, even after controlling for gender and parents' education. Thus, children with ADHD had less competence motivation, that is, less tendency to engage in challenging tasks or work independently at tasks; less attention/persistence, that is, less ability to focus on tasks, resist distractions, and persist appropriately; less attitude toward learning, that is, less ability to tolerate frustration, cooperate, and accept help when needed; and less strategy/flexibility, for example, by following peculiar and inflexible procedures in tackling tasks.

#### TABLE 2 | Comparisons of learning behaviors in ADHD and TD.

∗p < 0.001.

#### TABLE 3 | Partial Correlations between executive functions (EF), ADHD symptoms, and learning behaviors in ADHD and TD groups.

∗p < 0.05, ∗∗p < 0.01. Controlling for parental education and gender. BRI, Behavioral Regulation Index; MI, Metacognition Index; Inatt, Inattention symptoms; H/I, Hyperactivity/Impulsivity symptoms.

#### TABLE 4 | Multiple regression analysis of EF and ADHD symptoms predicting learning behaviors in ADHD and TD groups.

∗p < 0.001; ∗∗p < 0.005. MI, Metacognition Index; BRI, Behavioral Regulation Index; H/I, Hyperactivity/Impulsivity.

Our results are similar to other studies that compared motivation and task persistence or academic enablers in children with ADHD and TD, finding lower performance in children with ADHD (Hoza et al., 2001; Carlson et al., 2002; Demaray and Jenkins, 2011). Moreover, the motivation deficits are in line with explanatory models of ADHD that point to disturbances in motivational processes, involving frontoventral striatal reward circuits and mesolimbic branches (Sonuga-Barke, 2005), and structural and functional neuroimaging studies that suggest dysfunctions in motivational neural networks and systems that mediate the control of cognition and motivation (Cubillo et al., 2012).

These findings highlight the importance of examining a range of indicators that can be related to poor learning behaviors. Consequently, the second objective of this study was to examine the relationships between ratings of EF and ADHD symptoms and learning behaviors. Correlation analyses revealed significant moderate to large correlations between most of the learning behaviors and both EF and ADHD symptoms, although the patterns of these relationships were slightly different in the TD group and the ADHD group. In the TD group, all the learning behaviors presented a negative correlation with the metacognition index, the behavioral regulation index, and inattention symptoms, whereas hyperactivity/impulsivity symptoms presented lower correlations that were only significant with Competence/motivation and Strategy/flexibility. The ADHD group presented a less uniform pattern: Attention/persistence significantly correlated with ADHD symptoms (inattention and hyperactivity/impulsivity); Strategy/flexibility significantly correlated with EF indicators and Competence/motivation; and Attitude toward learning mainly correlated with inattention and the metacognition index. Consistent with previous research conducted on achievement (Papaioannou et al., 2016), results demonstrated a close link between EFs, inattention symptoms, and learning behaviors. Inattention symptoms were more related to learning behaviors than hyperactivity symptoms were in both groups, supporting the results of other studies on academic impairments (Langberg et al., 2013). Moreover, our results confirmed the high correlation between motivation and inattention (r = –0.80), with a similar value found in previous research (Volpe et al., 2006; Plamondon and Martinussen, 2015).

To further explore the relations among the constructs, we conducted multiple regressions to determine whether deficits in learning behaviors are mainly driven by EF or by the effects of the ADHD symptoms. The results showed some similarities between children with ADHD and TD children. The model with the highest percentage of explained variance in both cases was Competence/Motivation, so that EF and ADHD symptoms are the best predictors of engagement on learning tasks, even reaching 77% in the case of the TD group. Behavioral regulation, defined as the capacity to make cognitive changes and adjust emotions and behaviors through appropriate inhibitory control, has an important weight in the flexible use of strategies, that is, the capacity to modify task execution procedures in both children with ADHD and TD children. However, the models also present some differences between the groups. In the ADHD group, metacognition (which encompasses the EF of initiation, working memory, planning/organization, organization of materials, and monitor), was the only significant predictor of Competence/motivation and Attitudes toward learning, whereas in the TD group, inattention was the main predictor of Competence/motivation, Attitude toward learning and Attention/Persistence, suggesting that attention plays an important role in the learning behaviors of children without ADHD.

One aspect that should be highlighted is that EF deficits, which are frequently related to ADHD (Barkley, 1997), appear to play a role in the learning behaviors of children with ADHD, predicting them beyond the typical symptoms of inattention and hyperactivity/impulsivity, whereas inattentive symptoms (alone or with EF) were the main predictor of most of the learning behaviors in the TD group. There are similarities between our study using learning behaviors and other studies using other academic functioning outcomes. Langberg et al. (2013) found that, among different facets of executive functions and ADHD symptoms, inattention symptoms and two main subscales of the Metacognition index (Organization of materials and Planning organization), rated by both parents and teachers, were the main predictors of school grades and homework problems. As in our results, it seems that the metacognition index plays a more important role than the behavioral regulation index in academic-related areas. Moreover, Langberg et al. (2013) found a greater relationship between inattention symptoms and academic outcomes. There can be various reasons for this. First, their main objective was to evaluate associations between EF and multiple academic outcomes, using ADHD symptoms to determine whether their predictions are verified above and beyond the role of these symptoms. For this reason, their regression method was different from ours. Langberg et al. (2013) controlled for the ADHD symptoms in the first block of the hierarchical regression, whereas in our analyses all the predictor variables were simultaneously introduced (however, main results do not change using their methodology). The differences can also be due to the ages of the participants in the two studies (children and adolescents), as well as the use of performance tasks instead of learning behaviors, where EF may be more influential.

Moreover, there seems to be a unique contribution of specific aspects of EF in predicting different learning behaviors, with behavioral regulation being more related to Strategy/Flexibility and metacognition being more related to Competence/motivation and Attitudes toward learning. Our results suggest that EF may be involved in selecting and activating positive, appropriate approaches to learning (Vitiello et al., 2011), so that a child with strong EF (especially related to metacognition) may be better able to select and activate motivated responses to a specific learning situation, such as voluntarily engaging in an activity that was previously found to be challenging.

#### Implications

According to our results, children with ADHD present poor learning behaviors, which are modifiable risk factors that have been consistently associated with educational deficits in the

general population. The first practical implication of our results refers to the importance of early identification of learning behaviors within the classroom context. These variables can be assessed with rating scales or direct observations that will help to identify a student's strengths and weaknesses. Specific evaluations may be useful in providing insight into where to focus additional support or in recommending learning-related interventions. As learning behaviors are potentially teachable through modeling or programmed instruction (McDermott et al., 2016), a key factor for children with ADHD would be to strengthen the way they engage in learning and their enthusiasm for learning. Some effective teaching practices that promote learning behaviors use modeling to foster positive learning attitudes and behaviors by elaborating and expanding children's ideas and actions, praising children's effort, or scaffolding learning (Hyson, 2008). According to DiPerna (2006), many different intervention strategies can be employed to promote the development of the academic enablers, including modeling, coaching, behavioral rehearsal, and reinforcement.

This study also provides evidence that EF strongly predicts learning behaviors of elementary school children with ADHD and TD children, suggesting that professionals attempting to improve learning behaviors should also focus on children's EF and inattention symptoms. It is important to consider programs that focus on developing strategies to improve real world aspects of EF or school-based interventions that target executive function to improve academic achievement (Jacob and Parkinson, 2015). Moreover, motivation has a direct connection with persistence on learning goals. Even though more research is needed on programs designed to set realistic learning goals and self-monitoring their achievement, executive processes should be taught and scaffolded in programs that put students in charge of their own motivation.

#### Limitations and Future Directions

The current study has a number of limitations that need to be addressed. First, the small sample size may have limited the power of the analyses, hiding some possible effects that may have been evident with a larger sample and more complex statistical analyses. Moreover, most of the ADHD participants were male, so studies with female participants should be conducted. Second,

### REFERENCES

EF have been defined broadly; in the future, a more specific approach using tasks related to single constructs, such as inhibition and working memory, should be used. Furthermore, this study used teacher ratings of EF, and future studies should be conducted using neuropsychological measures. Fourth, there may be a degree of overlap between ratings of EF, and ratings of ADHD symptoms and learning behaviors; therefore, the relationships with more direct assessments of these skills should be examined in future studies. Fifth, other factors that may influence learning behaviors can be behavioral problems, social relationships, or more contextual factors such as teaching styles. Future studies should include these factors in order to better understand learning behaviors in children with ADHD. Lastly, the relationships among ratings of EF, ADHD symptoms, and learning behaviors may change throughout development. Longitudinal studies are needed to address the importance of these relationships over time because approaches to learning develop as the grade level increases in ways that are consistent with school socialization effects (Chen et al., 2011).

### AUTHOR CONTRIBUTIONS

CC, CB, BR, IB, and AA each made substantial contributions to the conception or design of the work, or the acquisition, analysis, or interpretation of data for the work, drafting the work or revising it critically for important intellectual content, final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

#### FUNDING

This work is supported by the Spanish State Research Agency (Agencia Española de Investigación, AEI) and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional, FEDER) through the Project PSI2016-78109 (AEI/FEDER, UE) and by the University of Valencia (UV-INV-PREDOC15-265889).

Yen, C., Konold, T. R., and McDermott, P. A. (2004). Does learning behavior augment cognitive ability as an indicator of academic achievement? J. Sch. Psychol. 42, 157–169. doi: 10.1016/j.jsp.2003.12.001

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Colomer, Berenguer, Roselló, Baixauli and Miranda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Understanding Rejection between First-and-Second-Grade Elementary Students through Reasons Expressed by Rejecters

Francisco J. García Bacete1,2 \*, Virginia E. Carrero Planes1 , Ghislaine Marande Perrin1,2 and Gonzalo Musitu Ochoa3

1 Department of Developmental, Educational and Social Psychology and Methodology, Universitat Jaume I, Castellón de la Plana, Spain, 2 GREI Interuniversity Research Group, Universitat Jaume I, Castellón de la Plana, Spain, 3 Department of Education and Social Psychology, Pablo de Olavide Universit, Sevilla, Spain

Objective: The aim of this research was to obtain the views of young children regarding their reasons for rejecting a peer.

Method: To achieve this goal, we conducted a qualitative study in the context of theory building research using an analysis methodology based on Grounded Theory. The collected information was extracted through semi-structured individual interviews from a sample of 853 children aged 6 from 13 urban public schools in Spain.

#### Edited by:

José Carlos Núñez, Universidad de Oviedo Mieres, Spain

#### Reviewed by:

Eva M. Romera, Universidad de Córdoba, Spain Fuensanta Cerezo, University of Murcia, Spain Jose-Maria Roman, University of Valladolid, Spain Isabel Hombrados-Mendieta, University of Málaga, Spain

> \*Correspondence: Francisco J. García Bacete

#### Specialty section:

<EMAIL_ADDRESS>
This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received: 02 January 2017 Accepted: 13 March 2017 Published: 04 April 2017

#### Citation:

García Bacete FJ, Carrero Planes VE, Marande Perrin G and Musitu Ochoa G (2017) Understanding Rejection between First-and-Second-Grade Elementary Students through Reasons Expressed by Rejecters. Front. Psychol. 8:462. doi: 10.3389/fpsyg.2017.00462 Results: The children provided 3,009 rejection nominations and 2,934 reasons for disliking the rejected peers. Seven reason categories emerged from the analysis. Four categories refer to behaviors of the rejected children that have a cost for individual peers or peer group such as: direct aggression, disturbance of wellbeing, problematic social and school behaviors and dominance behaviors. A further two categories refer to the identities arising from the preferences and choices of rejected and rejecter children and their peers: personal identity expressed through preferences and disliking, and social identity expressed through outgroup prejudices. The "no-behavior or no-choice" reasons were covered by one category, unfamiliarity. In addition, three context categories were found indicating the participants (interpersonal–group), the impact (low–high), and the subjectivity (subjective–objective) of the reason.

Conclusion: This study provides researchers and practitioners with a comprehensive taxonomy of reasons for rejection that contributes to enrich the theoretical knowledge and improve interventions for preventing and reducing peer rejection.

Keywords: reasons for peer rejection, grounded theory, norms, group, preferences, identity, unfamiliarity, early elementary education

### INTRODUCTION

Maintaining a minimum number of meaningful, positive and lasting interpersonal relationships (Baumeister and Leary, 1995), and belonging to groups (Nesdale, 2007) are persistent motivations for people. During early elementary school children make new relationships and start being actively involved in various peer networks (Ladd, 2005). However, whereas some children are accepted and included in different groups, other children are rejected and excluded by their peers (Gifford-Smith and Brownell, 2003; Abrams et al., 2005; Asher and McDonald, 2009; Killen et al., 2009). Peer rejection is a common peer experience that predicts maladjustment

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outcomes, mood disorders and victimization in childhood and adulthood (Leary, 2001; McDougall et al., 2001; Saarento et al., 2013). Knowing what drives some children to reject/dislike others is therefore a question of interest (Mikami et al., 2010).

This study aims to understand the reasons given by firstand-second-grade children for rejecting some of their peers. Most studies using peer assessment, attributional approach or social exclusion have been descriptive of children aged 8 or older, as younger children's assessments of their classmates have been considered little reliable (Nesdale and Duffy, 2011). In contrast, we think that, instead of asking the reasons why one thinks that others are rejected or oneself is rejected, which is the information collected by the aforementioned techniques, it is more suitable to ask the rejecters themselves the reasons why they dislike some peers. Selman (2003), in his interpersonal coordination theory, stated that most children aged 6–8 years are cognitively aware of their own thoughts, motives and feelings in social interactions. Besides, the sociometric methods have shown to be useful to assess the "attractions" and "repulsions" between children as young as three (Cillessen, 2009). We used open-ended questions to collect the reasons given by the rejecters. Then, in order to analyze these reasons we chose a qualitative theory building approach to seize the whole richness of the responses, avoiding pre-established categories. Working with 6-and-7-year old children will provide valuable information to implement specific actions designed to prevent and reduce peer rejection at early ages (García Bacete et al., 2014).

McDougall et al. (2001, p. 214) referred to peer rejection as "dislike on the part of one's peers which may or may not be accompanied by varying degree of victimization, exclusion, or intentional isolation from peer activities." This definition includes the three characteristics of peer rejection: (1) Rejection is based on dislikes, which are attitudes and not necessarily behaviors (Leary, 2005). In this study we use indistinctly dislike and rejection. (2) Rejection occurs between peers who know each other in the context of a group, which indicates a common history (Doosje et al., 2002). The often private character of disliking requires focusing on who rejects, and the fact that disliking occurs between known peers requires conducting the study in the ecological settings where the peer interactions take place, like classrooms. (3) When rejection is perpetrated by a significant number of peers, it is conceptualized as peer rejection.

However, for many years the predominant conceptualization in rejection research has been based on the deficits of the rejected child that contribute to her/his social difficulties (Asher, 1990; Bierman, 2004). Thus, most studies have focused on the rejected person, largely by examining the correlates and consequences of rejection (Coie et al., 1990; Bierman, 2004), and other questions such as perceptions of being rejected (Guerra et al., 2004), reactions to rejection (Sandstrom and Zakriski, 2004), or problematic social situations for peer-rejected students (Martín-Antón et al., 2016). When attention is given to the rejecter, the focus has generally been on explicit rejection behaviors (Asher et al., 2001; Lev-Wiesel et al., 2013). It is therefore unsurprising that research into the reasons for rejection has also focused on the rejected person and on behavioral correlates of rejection as the causes of rejection (Rubin et al., 2006). In contrast, few studies directly asked rejecters to give their reasons for rejecting known peers in real situations (Monjas et al., 2008).

### Correlates of Rejection and Other Reasons for Rejection

For many years scholars have been keen to learn about which behavioral characteristics lead children and adolescents to be accepted or rejected by their peers (Asher and McDonald, 2009). This is what is known as the correlates of rejection (Coie et al., 1990; Bierman, 2004; Rubin et al., 2006). Newcomb et al. (1993) found that high levels of aggression and withdrawal and low levels of sociability and cognitive abilities are associated with rejected peer status. Bierman (2004) summarized this set of reasons as she stated that rejected children can be more argumentative, disruptive, and aggressive, more socially awkward and insensitive, less skillful in engaging in prosocial play, and/or have more negative interactions with teachers than their peers. In general, the rejected person's characteristics and behaviors are considered to invite rejection from others (Asher, 1990; Newcomb et al., 1993; McDougall et al., 2001; Mikami et al., 2010). However, correlates cannot be interpreted so readily as causes (McDougall et al., 2001), and can frequently be better understood as consequences of the rejection itself (Orue and Calvete, 2011). Chang (2004) found that some rejected children are popular and central members of their group, which suggests that rejection is a function of those who reject rather than of the rejected person's behaviors. Thus, rejection is not a property of the rejected child or a characteristic of her/his behavior (Dirks et al., 2007).

Alongside, other studies presented children with hypothetical situations or experimental designs and asked them else to select or rate causes of social failure or peer rejection, as did social attribution studies (Elig and Frieze, 1975; Goetz and Dweck, 1980; Earn and Sobol, 1984), or to reason the legitimacy of social exclusion behaviors in manipulated hypothetical situations, as did studies on social exclusion based in the socialcognitive domains (Abrams et al., 2005; Smetana, 2006; Killen, 2007; Abrams and Rutland, 2008; Killen et al., 2009). In the attributional studies, children's responses are usually interpreted in accordance with the three dimensions proposed by Weiner, locus, stability, and control (e.g., "She played better," external, unstable, controllable). But above all the attributional approach emphasizes the success/failure of the rejected child's behavior in a specific social situation (e.g., managing to "play with," "get on well with"), whereas peer rejection cannot be interpreted in terms of the failure neither of the rejected child, because "not being liked" does not depend on the child her/himself but on the other, nor of the rejecter because nobody can accept everyone (Leary, 2001). In the social exclusion studies, the reasons for exclusion are classified according to moral reasons such as treating the ingroup and the outgroup members in the same fair and equitable way, social-conventional reasons such as identification with the group and the way it functions, and personal reasons such as preferences and attributions of intentions to others (Horn, 2006;

Killen et al., 2009). These contributions are valuable; however, the focus of these studies is the behavior of rejection or exclusion, and not the rejection as an attitude. In addition, in this type of studies, the reasons are derived from interpretation of situations and evaluation of behaviors (Turiel, 1998; Smetana, 2006). Both approaches use strongly structured designs in which the children are asked to explain why a certain event and behavior occur and the presented situations and reasons may have little meaning for the children. Moreover, the analysis of children's responses is based on pre-established dimensions or categories.

### Evaluation by the Rejecters and Group Context

So far, little is known about the reasons that may explain the process of rejection as a result of an interpersonal interaction in a social group, as proposed by Bierman (2004). This perspective implies two relevant issues: First, research on the reasons for rejection must focus on the rejecter child; and second, the social context in which peer interactions occur influences rejection.

The first question is related to the principle of relational evaluation put forward by Leary (2001, 2005). According to this principle, any form of rejection, regardless of the behaviors or traits of the rejecters or rejected child, is a state of relatively low relational evaluation in which a person does not consider his/her relationship with another individual as valuable. Many of our decisions to reject reflect personal preferences based on our attitudes, interests, abilities, goals, and previous social experiences (Leary, 2001; Nucci, 2001; Scandroglio et al., 2008). Similarly, Asher and McDonald (2009) stated that the basis for peer rejection is not the behavior of the rejected child but rather the others' interpretation that the relationship with that child does not properly satisfy their needs (e.g., "Does this child seek to have influence in ways that are not acceptable for me?").

As for the second issue, it is important to recognize that neither the rejecter nor the rejected student are on their own, but they are members of groups (class group, class subgroups, social categorization groups as gender, birth place, etc. . .) (Bourdieu, 1985; Nesdale, 2011). Several authors have concluded that a complete model of peer rejection could be obtained only through the understanding of social context influences (García Bacete et al., 2014; Mulvey et al., 2014). Killen et al. (2013) suggested that what seems to be interpersonal rejection might actually reflect group rejection.

Mikami et al. (2010) described the processes by which groups influence peer rejection: cognitive biases held by the accepted peer group, deviation from peer group norms; and social dominance hierarchy in the peer group. Abrams and Rutland (2008) found that individuals who do not fit well into their own group are highly likely to be rejected. Poor adaptation to a group can occur either because the person does not maintain typical relationships with her peers (e.g., "being shy," see Rubin et al., 2006), or because she contributes little or nothing, or harms the group (e.g., "violate the natural tendency to cooperate within the group," see Levine and Moreland, 1994). In turn, Nesdale (2007, 2011) found that belonging to other groups entailed greater probability of rejection, either due to preference for one's own group, or due to prejudice toward other groups (gender, ethnic group etc.) (Brown and Bigler, 2005; Scandroglio et al., 2008; Mulvey et al., 2010).

In summary neither the rejection nor the reasons for rejection can be understood as a characteristic of a person or of a behavior, or as an attribution or cause, or as an evaluation of behaviors, social situations, or social failures. Peer rejection is an attitude or a feeling, a negative or low relational evaluation, and the reasons are an attempt to explain this relational evaluation in the group context. We agree with Asher and McDonald (2009, p. 235) in that "scholars who study the behavioral correlates of acceptance and rejection rarely discuss the ways that the behaviors they study are powerful because they speak to peoples' fundamental needs,. . . that it might suggest other characteristics that are relevant to acceptance and rejection but have not yet been studied."

### Open–Ended Question and Qualitative Method

It is therefore important to include open–ended questions to study reasons for rejection since, as pointed out by Elig and Frieze (1979), they are more meaningful and allow for greater spontaneity than the aforementioned approaches, and they do not hint at any reasons that children have not even thought about.

This strategy has been used in a few studies. Smith (1950), a teacher, asked her fourth-grade class open-ended questions about the reasons for their positive and negative sociometric choices. The children's responses showed that verbal abusiveness, rule violations, and bullying were associated with negative nominations. Monjas et al. (2008), using the responses of fifth-and-sixth-grade children to the questions "Which of your classmates do you not like and why," developed a taxonomy of 15 reasons for peer rejection ranging from unfriendliness and lack of companionship to aggressive, dominating and antisocial behaviors. Feinberg et al. (1958) asked sixth-andseventh-grade students to describe the classmates with whom they felt uncomfortable or annoyed, found that the traits fighting, disruptive, conceited, and silly were associated with negative status. These studies represented a contribution since they analyzed the reasons in situations where rejected children and rejecters interacted on a daily basis, which were highly meaningful and very different from the hypothetical situations and anonymous protagonists with no background history nor future exposed in the aforementioned more structured methods (Doosje et al., 2002). However, the reasons for rejection in these studies are still presented in a descriptive way without going deeper into the interconnections between categories, they are largely interpreted in terms of the behavior of the rejected child without taking into consideration the rejecter and rejected children's group context.

A qualitative approach is needed to understand the reasons that prompt children to reject or dislike some peers. Grounded Methodology developed by Glaser and Strauss (1967), has proved capable of generating basic conceptual categories from data that explain the processes that occur in complex social situations (Carrero et al., 2012). From this perspective neither a predetermined taxonomy of possible reasons nor an explanatory

framework to interpret them is necessary. Instead of that, the Grounded Theory starts from the data provided by the social participants and, by applying the constant comparison of the incidents found (the reasons given by the rejecters), makes emerge substantive categories which are compared to new incidents. These successive comparisons continue until achieving the theoretical saturation of the data given by the participants. This process yields an underlying structure that explains the variability of the elicited reasons. Data collection, coding process, integration of categories, and construction of theory are thus guided by methodology as it emerges.

This study aims to understand the reasons given by firstand-second-grade children for rejecting some of their peers. In order to analyze these reasons the conducted study focuses on: (1) Defining peer rejection as an attitude or a feeling, a negative or low relational evaluation. (2) Asking the rejecters themselves the reasons why they dislike some peers. (3) Including the influence of social context in which peer interactions occur. (4) Applying a qualitative theory building approach to seize the whole richness of the responses and avoid pre-established categories.

### MATERIALS AND METHODS

#### Design and Participants

In this study we used a theory building qualitative approach with a methodology of analysis based on Grounded Theory (Glaser and Strauss, 1967) consisting of analyzing the information provided by a sample of children through the codification, comparison, and conceptualization of data coming from their speech to form categories and establish relationships between them (Strauss and Corbin, 2007). The sample has been selected by applying master selection criteria (age, sociometric type) and heterogeneity-homogeneity criteria (sex, education level, place of residence, and socioeconomic status) according to the objectives of the research (Valles, 2000; Suárez et al., 2013, 2016) (**Table 1**). An incidental sampling was used to select public elementary schools situated in urban districts with average socioeconomic level close to the four universities where the researchers were conducting a broader study that included a large intervention later on. These criteria and the extend of the sample was designed taking into account the need to ensure the point of redundancy (Lincoln and Guba, 1985) where the new information analyzed is redundant with the previous data and could be integrated into the existing categories.

Finally, to guarantee the validity of the results and the consistency of the emergent categories several procedures, based on the recommendations claimed by Glaser (1978, 1992) and Thomas (2006), were applied in this study: first, to find a way to avoid bias in the researcher during the process of coding without losing his theoretical sensitivity and, second, to verify the consistency of the final categories. To achieve the first goal, the substantive coding phase (Strauss and Corbin, 2007) was developed by a researcher who was trained previously in coding technique but who was not an expert in peer rejection (third author) while for the selective and theoretical coding phases, three experts in peer rejection and qualitative methodology joined the analysis work (first, second, and fourth author). Secondly, to verify the consistency of the final categories we applied a coding consistency check (Valles, 2000; Thomas, 2006) by consulting two experts in rejection and bullying and then calculated the interrater agreement (Dubé, 2008) using Cohen's kappa statistic (κ) in accordance with the guidelines proposed by Landis and Koch (1977).

Participants were 939 pupils of both sexes, aged 5–7 years, who were studying in 40 first-and-second-grade classrooms of 13 schools in four Spanish cities: Valencia, Palma de Mallorca, Sevilla and Valladolid.

Of the 939 initial subjects 86 pupils (9.2%) did not respond. Forty-six of them because they did not have the parental permission, were not at school during the assessment, or left the school during the study (although they continued to be nominated by their classmates). The other 40 children gave nominations of acceptation but not of rejection. The final

TABLE 1 | Description of the sample of respondents (n = 853) according to place of residence, education level, age, gender, sociometric type, number of classrooms, number of schools, and socioeconomic status.

an = sample (%). bSES = Socioeconomic status: M1 = Medium-low; M2 = Medium; M3 = Medium-high.

sample of respondents consisted of 853 pupils (49.6% girls) (Mage = 6.76 years, SD = 0.67).

#### Data Collection

fpsyg-08-00462 March 31, 2017 Time: 17:58 # 5

In each city, two trained evaluators (research collaborators graduate in educational psychology) gathered the data by means of 20-min individual interviews in which they asked two questions extracted from the sociometric questionnaire (García Bacete and González, 2010): (1) Who in your class do you like least? (2) Why don't you like (classmate's name)? To answer the first question, the child identified all the classmates he or she did not like on the class photographs. After that, the researcher asked the student the second question for each of the classmates nominated negatively and wrote down verbatim the child's reasons. The information gathered in the interviews allowed compiling a list of reasons verbalized by the pupils to explain the rejection toward their peers. To ensure that all students provide a comparative number of reasons, only a maximum of five reasons were used per student.

Each school left a private room at disposal for carrying out the interviews. The present study was conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards, with the approval of the management board of schools, the educational inspection services, the Department of Education of the Regional Government of Valencia (Spain), the Childhood Observatory of the Regional Government of Andalusia (Spain), the Socio-Educational Institute Foundation s'Estel of the Government of the Balearic Islands (Spain); and the Observatory School Coexistence of the Autonomous Government of Castilla y León (Spain). Review and approval from an ethics committee was not required as per the institutional and national requirements. Participation in the study was voluntary. All subjects gave written informed consent. The required authorizations from the education authorities, the schools, and the children's families were obtained.

#### Analytic Strategy

First, the list of reasons given by the participants was read in detail by the researchers to familiarize themselves with the content and to approach a first understanding of the "themes" and details of the subject. After that, in the first step of the analysis we used the substantive coding. The substantive codes break down (fracture the data) and then conceptually grouping it into codes that then become the theory which explains what is happening in the data (Glaser, 1978). During substantive coding the data are examined, and compared for similarities and differences. The researcher compares incident to incident with the purpose of establishing the underlying uniformity and its varying conditions (Glaser, 1978). In our study we grouping under the same label the reasons exposed by the children that use common key words used by the participants (e.g., friend, "She doesn't want to be my friend," "He is Rob's friend"), or that had a certain similarity (e.g., lack of hygiene, "He's always got a runny nose"; "she doesn't clean her teeth"). This process yielded 10 groups of reasons or categories (**Table 2**).

Applying the constant comparison method (Glaser, 1978; Carrero et al., 2012), we then created subgroups within the same category. Depending on the frequency and distinctive meaning of the reasons that appeared under the same label, they form a subcategory within the existent category or a new independent category. After this first categorization the resulting categories still needed to be refined, therefore we continue applying the constant comparative technique to the data (**Table 3**).

As the reader can see on **Table 3**, during this continuous process of open coding, the configuration and definition of the list of categories suffered constant changes. For example, in the second categorization, the bad behavior/disruptive conduct category divided into two categories: Bad ("stupid," "He's a cheat," "She steals") and disruptive behavior in class ("He doesn't keep quiet in classroom," "He gets punished"). In the third categorization, these two new categories combined with the category poor academic skills and gave way to two categories: bad pupil and antisocial behavior.

#### TABLE 2 | Initial grouping of reasons for rejection.

TABLE 3

categorization.

∗∗it coincides with Cat 7 of Third categorization;

 ∗∗∗it comes from Cat 1 of Fourth categorization.

 =

With each new category configuration or change to labels or definitions, we revised each reason one by one, and the number of unclassified reasons gradually disappeared. By this point (third categorization), practically all the main reasons were present: aggression, dominance, annoyance, antisocial behavior, bad pupil, no friendship/relationship/play, appearance.

At this point of the analysis, relationships between concepts were analyzed, identifying the position of the actors (rejecter or rejected child) as regard their disliking or liking referring to friendship, relationship or play, and defining the links between categories and subcategories hierarchically, establishing a series of main categories or supracategories (selective coding). In this way, we first distinguished between preference and aversion and then gradually differentiated two principal sets of reasons for rejection: behavior (supracategory of rejection because of what the rejected child does, says, or tries to do), preference (supracategory of rejection because of what the rejected child s/he likes).

The last step in the analysis consisted in validating the final theoretical scheme, confirming the relationships between categories, removing data and filling in categories that still needed further refinement and development (theoretical coding). For example, from the preference category we decided to remove reasons that expressed little or no contact between rejecter and rejected child because above all they revealed an absence of preference (choice) or of shared activity (behavior), for which they could not be in the preference nor in the behavior supracategories, thus forming a third independent supracategory, unfamiliarity. Simultaneously it became clear that preference is a positive choice and dislike is a negative choice and that both would underlie personal identity, whereas prejudices against people belonging to or representing certain social categories would account for the base of social identity, which led to the categories personal identity and social identity.

In this theoretical phase another important aspect had gained relevance. It is observed that all the reasons exposed contain information concerning the children who participate ("She hits me," "He hits us"), the different times or intensity of the event ("Sometimes she does not play with me," "He never plays with me"), and the different levels of subjectivity-objectivity of what is said ("I don't like him," "She didn't invite me to her birthday party"). From these observations, three context categories emerged: participants, impact, and subjectivity–objectivity. The context categories can be represented as transversal axes to each reason category.

Finally, to calculate the interrater agreement we sent the definition of each category to the two expert raters and asked them to assign a list of 500 reasons randomly to the final set of categories.

### RESULTS

The pupils provided 3,009 rejection nominations, of which 2.5% contained no reasons for rejection, or the respondent claimed not to know the reasons for the rejection. The analysis of the remaining 2,934 reasons produced seven reason categories, arranged in three supracategories, and three context categories. The interrater agreement reached with the two expert raters was strong (κ = 0.90 with rater 1, and κ = 0.81 with rater 2).

#### Reason Supracategories and Categories Supracategory: Behavior (Cost) (74.1%)

The reasons of this category express rejection of the other because of what s/he does, says, or tries to do and these behaviors represent threats to or attacks on social and school expectations and norms ("She takes things away," "He's bad at reading"), personal and group wellbeing ("He bothers", She speaks when we're working"), autonomy ("He bosses people about," "She pushes me around"), and physical and emotional safety ("He hits," "She makes fun of everybody"). Only when these behaviors are interpreted as costs for the personal and groups norms and functioning, or do not contribute to satisfy the individual and group needs, the child becomes the object of rejection.

#### **Problematic social and school behaviors category (17.4%)**

This category highlights simultaneously antinormative and abnormal social and school behaviors of the rejected child. On one hand, such behaviors go against what is considered appropriate and desirable, against general or specific social and school expectations and norms, which reflect the breach of social and school rules norms ("She steals things," "She makes the teacher angry"). On the other hand, they refer to deficits in the social and school skills necessary for relational and educational success or a deficiency in fulfilling expectations and achieving goals ("He doesn't leave me anything," "He reads really badly").

#### **Disturbance of wellbeing category (18.4%)**

This category refers to the rejected child's behaviors that make people feel uncomfortable and angry and obstruct people to achieve their objectives ("He says silly things," "She is always interrupting"). The reasons in this category are characterized by frequent behaviors of low intensity that interfere with what one wants, when and how one wants, and in the end cause personal or group discomfort.

#### **Dominance category (4.5%)**

Dominance is understood as behaviors of the rejected child that aim to impose what is to be done, influence others for one's own advantage or strengthen one's own ego at the expense of others ("He bosses people about," "He acts cocky with me").

#### **Aggressive category (33.8%)**

This category is defined by direct behaviors of the rejected child that cause personal or physical harm, or insecurity. They may be verbal and gestural aggressive aimed to humiliate others or damage their reputation (4.7%) ("He insults," "She shouts at me"), physical aggression aimed to cause physical damage (23.1%) ("He hits," "He spits"), or intimidation aimed to frighten the person through threats or abuse (6.0%) ("She treats me badly," "He threatens"). All these behaviors share the purpose of harming, but in the case of intimidation, it is the fear of what may occur that makes feel the harm beforehand.

#### Supracategory: Preference (Identity) (18.6%)

This supracategory refers to what the rejecter or the rejected child likes and is. It consists of two categories, personal identity and social identity, which are based on two different attraction processes: personal attraction, which derives from the idiosyncratic preferences arising in interpersonal relationships; and social attraction, which derives from the degree to which an individual represents the prototype of his or her group.

In this supracategory the reason are firstly situated in the affective frame of likings/dislikes of the rejecter or of the rejected child (in fact, the rejecter's perception of the rejected child's preferences). In a second moment, the preferences become cognitive or/and behavioral choices, which can involve direct rejection ("I don't want to be his friend") or indirect ("I play with my friends"). Finally, when the preferences and choices are systematically used and based on exclusion norms the personal identity becomes reinforced. Moreover, when these systematical preferences are based on negative reputations and social prejudices, then the social identity becomes strengthened. This identity process may lead to situations where differences strongly clash, people forget about ethics and egalitarian treatment, and rejection settles down. Here too the influence of the peer context or the group is present.

#### **Personal identity category (14.5%)**

In this category the subject shows that her/his likings are different from those of the people s/he rejects. This category has two subcategories: preference and dislike.

Preference subcategory (3.7%). This subcategory expresses the rejecter's likings, enjoyments or choices (I like it/him/her) or those of the rejected child (s/he likes it/him/her) referring to a person, relationship, friendship, activity or play, that extends to the group (we like it/him/her, I like them, they like me, s/he likes us). In the reasons of this category, the rejecter affirms her/his preferences or those of others ("I like playing football," "He plays with his friends").

Dislike subcategory (10.8%). This category expresses the rejecter's volition (I don't want/like) or the rejected child's volition as perceived by the rejecter (s/he doesn't want/like) not to establish a relationship or share friendship and play. This intention can also be extended to their groups (we don't want, they don't want). In this subcategory the rejecter shows her/his own dislikes or those of his/her group, or reacts to the dislikes of other or others ("I don't like their games," "She doesn't let me play," "He doesn't want to play with me").

#### **Social identity category (4.1%)**

In this category the dislike of other children is based on their belonging to a social group or category ("She's a girl") or on their doing activities typical of those same groups ("He plays girls' games"), in the absence of other more specific reasons. These reasons actually express stereotypes and prejudices against those who are not like me or us, or belong to other group ("She's Romanian," "He's new").

#### Supracategory: Unfamiliarity (Inertia/Self-protection) (7.4%)

This supracategory is a set of reasons that express little or absence of choices-sharing and of activities-sharing (We aren't. . ., we don't go. . ., we don't play. . .), for which they cannot be in the preference nor can they be in the behavior supracategories. This supracategory is formed by a unique category, unfamiliarity. It includes reasons that reflect low interest in little known others, or hesitation to make new relationships, which are manifested in not searching for contacts and shared activities ("I don't know him/her," "We don't play together").

**Figure 1** describes the final map of categories where three paths to the motivation for peer rejection can be observed: first path to rejection lies in the rejected children's deviant behavior (What s/he does, says, tries) in the context of their personal and group relationships (costs of the behavior); second path to rejection is built on the preferences and choices of the rejecters and rejected children, defining what they are (identities). The third path to rejection consists of the absence of behavioral interactions and choices (unfamiliarity).

### Context Categories

The context categories are referred to conditions in which rejection occurs. These categories modify as well as increase the diversity of reasons given by the rejecters. The context must be considered to deeply understand the reasons given by the interviewed children about why they reject or dislike some peers.

The participants context distinguishes between interpersonal rejection and group rejection. The interpersonal rejection occurs when the participants are the rejecter–rejected pair (33.7%) ("He hits me") or trios in which in the rejection between rejecter and rejected a third pupil is included (4.1%) ("She hits my friend"). The group rejection occurs when the rejecter or the rejected child or both are in a group. The groups can be number-limited or formed by known peers (5.7%) ("He hits my friends"), or be larger collectives, or relate to general statements (56.5%) ("He hits everybody").

The impact context includes frequency and intensity of the event and distinguishes between low, medium and high impact. Low impact (8.6%) refers to single or occasional cases ("He pushed me once"), or low intensity ("He's a little bad"). Medium impact occurs when there are no intensity or frequency indicators, or is expressed in indefinite or third person (66.1%) ("She doesn't let others play"). High impact refers to maximum frequency or intensity (25.2%) ("He doesn't lend things to anybody").

The third context category relates to the subjectivity– objectivity feature of the reason, whether the meaning of the reason is a function of the subject who rejects or a function of the event or object for which rejection occurs. We use the term subjectivity when the rejecter's feeling and thinking are part of the reason and the rejecter acts as a judge or interpreter. In this way, the valence of the reason can be different (positive, negative, or neutral), depending on the evaluator. Subjective reasons may be: total when referring to the whole rejected person (8.7%) ("I don't like her") or partial when referring to only one aspect (21.9%) ("He's annoying"). On the contrary, other reasons focus

on the rejected child as something external, and the rejecter only describes the reason as a narrator or observer of an event in which the rejected child participated. The descriptive reasons can be: general and imprecise (45.8%) ("She calls names") or specific or precise (23.7%) ("She plays with Danny").

## DISCUSSION

From the above said several contributions of this study can be deduced. First of all, it major part of reasons refers to what the rejected does, in line with the studies of the correlates of rejection and the social attribution (Earn and Sobol, 1984; Coie et al., 1990; Bierman, 2004). However, in contrast to those studies, rejection here does not appear to be the direct result of what the rejected does, but of the relational evaluation of this behavior done by the rejecters (Leary, 2001), of how they interpret that this behavior affects their needs and the group functioning (Levine and Moreland, 1994; Asher and McDonald, 2009), and its degree of typicality in comparison to the behavior of the own group or of other groups (Nesdale, 2007; Abrams and Rutland, 2008). This path of rejection highlights the power of the behavioral interactions and the deviation from the norms to provoke peer rejection, since the classmates interpret them as costs to the interpersonal relationships and the group functioning (Levine and Moreland, 1994; Ladd, 2005). The information provided by the context categories strengthens the utility of using the rejecter and the group approaches in the study of peer rejection.

Secondly, the rejecters provided two types of reasons that do not usually appear in the traditional studies on rejection (e.g., Bierman, 2004), -namely the reasons for rejection due to preference and to unfamiliarity-, because the classic studies focus mainly on the rejected children's behavior (what they do, say or try to do). The second path highlights the power of the preferences both in the personal domain and in the social categorization to provoke peer rejection (Smetana, 2006; Nesdale, 2007; Abrams and Rutland, 2008; Mikami et al., 2010), since the children interpret them as likings and choices that crystallize in personal and group identities (Scandroglio et al., 2008; Nesdale, 2011). The third path highlights that the social inertia toward choosing and doing what has already been preferred and done (Bourdieu, 1985), or the fear and mistrust to what is unknown or unfamiliar (Gifford-Smith and Brownell, 2003), may also lead to rejection (Allport, 1954). The reasons included in those two supracategories are external to the rejected children's behavior since they focus on the rejecters' attractions and choices, (or those

that the rejecters attribute to the rejected), and on what does not befall (not sharing activities, not making choices). These new reasons could appear owing to having put the interest on the rejecter, and because rejecters and rejected children are known peers, members of the same classroom. The rejecters' answers "I don't know" or "no response" are other examples of reasons external to the rejected child's behavior.

Thirdly, the fact that children at this age, in their explaining their reasons for peer rejection, turn to arguments simultaneously referring to the self, the group, and stereotypes, confirm the results of the theory of social-cognitive domain to explain the exclusion (Turiel, 1998; Smetana, 2006; Killen, 2007), and strengthen the idea that the social judgments are not a characteristic of one stage but emerge simultaneously in development, unlike to Kohlberg's (1984) model. In fact, the reasons for peer rejection show a parallelism with the three socialcognitive domains: The reasons based on personal identity match with personal domain (Nucci, 2001; Gifford-Smith and Brownell, 2003), the reasons based on the rejected child's behavior with the social normative domain (Abrams et al., 2005; Ladd, 2005), the reasons based on social identity with the moral domain (Brown and Bigler, 2005; Mulvey et al., 2010; Hatfield and Rapson, 2011), and the reasons based on unfamiliarity with both personal and social domains.

Fourthly, the content and the form of the reasons reflect the three characteristics of peer rejection: private evaluation, group influence, context of known peers.

Private evaluation: Rejection is above all a private and attitudinal evaluation (Leary, 2001, 2005). The presence of reasons that make it difficult for the observer to identify certain situations as rejection puts in evidence this private character. This is the case of reasons under the heading do not know/no response; of reasons expressed in past tense that refer to a memory and not a present reality; and of reasons included in the unfamiliarity category. The private evaluative nature is also present in the reasons where the rejecter's opinion is an inseparable part of the content, where the rejection is not so much due to the behavior or the event but rather to the interpretation the rejecter makes of it, since the same behavior ("he's always singing") may be evaluated as positive, negative, or neutral by different evaluators. In other reasons, like those in the social identity category, the content itself of the reason includes the evaluation, in this case discriminatory or prejudiced. Thus, the rejecter's perspective becomes indispensable to know the reasons for peer rejection, because since rejection is essentially a private evaluation, the reasons are arguments for rejection only if the rejecter feels and thinks like it.

Group influence: Although the methodology used for the data collecting had an interpersonal basis, the analysis methodology allowed the emergence of clear indicators of the influence of the others and of the group in the reasons, showing that reasons can also be based on the group and/or norms (Horn, 2006). The influence of the group in the interpersonal system mentioned by Mikami et al. (2010) is observed in the transversal and majority presence in all the categories of group reasons, reasons in which groups act as spokespersons or recipients (62.2% of the participants context). Even though interpersonal and group reasons overlap in all categories and permit tracking this group basis (Killen et al., 2013), some categories reflect the group influence with more clarity, as in the cases of the problematic social and school behaviors category that reports on ingroup dynamics (Abrams and Rutland, 2008) and the social identity category on outgoup dynamics (Nesdale, 2007, 2011).

Context of known peers: Finally, the fact that peer rejection occurs between known peers who share identity or history is corroborated by the constant references in the reasons to particular classmates or relatives, terms such as 'friends' or 'us,' and known situations and norms. This condition of known peers in peer rejection has two implications. Precisely the fact that the children share a classroom and will probably continue to do so for some time makes rejection a highly socially significant situation, strongly stable and with negative outcomes (McDougall et al., 2001; García Bacete et al., 2014). Simultaneously this condition requires the reasons for peer rejection to be studied in their context (García Bacete et al., 2014; Mulvey et al., 2014).

### Limitations and Future Directions

Some limitations of this study would refer to the studied populations, other to the use of data collecting techniques and other to group influence. Differential analyses need to be performed on the reason and context categories according to gender (Sureda et al., 2009), broadening the study to other ages (8–10, 11–13, and older), examining the differences in the reasons for rejection between minority and majority groups (e.g., children from minority/majority ethnic group, with/without special educational needs, rejected/average).

As seen above, many of the reasons are expressed briefly and imprecisely, with no type of indicator. It may be possible that children do not need more precise or detailed arguments; however, it would be interesting to carry out in-depth interviews to allow the children to explain what they do not like about their classmates in a broader and more precise way. It may be a potentially useful method for finding out whether they think the rejected child knows s/he is not liked, whether they do or say anything to make him or her know that they dislike him/her, whether children have criteria that they use consistently when thinking about how they feel about another child, and other similar questions.

Finally, we need to progress in the study of the influence of the classroom and the others. As discussed, our study has provided indicators, but it remains far from systematically undertaking this goal. Two alternatives: first, through the realization of multilevel studies in which being a member of a classroom represents the higher level, this study will help to know whether the motivational structure of rejection in a given classroom differs from the one in another classroom. Secondly, by examining if there is consensus across children on what they say about a particular child.

### CONCLUSION

The present study reinforces that rejection is part of children's daily life. Only 4.5% of the participants did not name negatively

any children. Moreover, 94% of the rejecters express reasons for rejection. The richness of reasons for rejection as well as the subsequent comprehensive taxonomy could have been obtained only through the conceptual and methodological decisions adopted in this study, namely: understanding rejection as a relational evaluation, focusing on the rejecters as informants, studying rejection in its ecological context, and using qualitative methodology in the data collection and analysis.

In brief, main contributions of this study are: (a) Peer rejection is external to the rejected child; that is, what the rejected child does or says does not lead directly or inevitably to rejection. (b) Rejection occurs during the exchange of activities/behaviors and preferences/choices in a group context, or even in the absence of exchange, between a rejecter or group of rejecters and a rejected child or group of rejected children. (c) The sources and pathways that lead to rejection are the rejecters' interpretations of these exchanges in terms of interpersonal and group costs, negative personal and social identities, personal or group inertia or self-protection, evidencing a great interdependence between the interpersonal and the group levels. (d) Through the study of the reasons we could observe that peer rejection is a heterogeneous social reality, in the number of participants and the link between them, the frequency or intensity of it, and the degree of objectivity/subjectivity with which the rejecter refers to the exchanges. (e) The development of children at age 6 already displays a rich knowledge of the socio-cognitive domains used to explain the peer relationships, so that our findings revealed personal reasons, normative or socio-conventional reasons and moral reasons.

In summary, peer rejection at this age can be understood as a negative relational evaluation, expressed by individual or group rejecters, toward individually or group rejected children, in the form of a specific or general description, or a partial or

#### REFERENCES

total judgment, both unidirectionally and bidirectionally, and with a variable impact. Such evaluation operates simultaneously in the personal, social and moral domains (Mulvey et al., 2014), as an exercise of personal autonomy (Nucci, 2001), or in response to attacks or threats (Ladd, 2005), or as a prejudicial aversion (Mulvey et al., 2010), or even in the absence of interactions and arguments (Williams and Zadro, 2001). In definitive, the study provides researchers and practitioners with a comprehensive taxonomy of reasons for rejection that contributes to the theoretical construct of peer rejection and the design of interventions for preventing and reducing peer rejection.

### AUTHOR CONTRIBUTIONS

FG: Led and designed the study, coordinated data collection, performed the analysis and interpretation of the data, and drafted the manuscript. VC: Revised the analytic strategies, participated in the analysis and interpretation of the data, and helped to draft the manuscript. GMD: Participated in data collection, participated in the analysis and interpretation of the data, and helped to draft the manuscript. GMO: Contributed to the analysis and interpretation of the data, revised critically the study, and participated in drafting the manuscript. All authors approved the final manuscript as submitted.

#### ACKNOWLEDGMENTS

This work was supported by grant EDU2012-35930 from the Spanish Ministry of Education and grant P1-1A2012-04 from the Jaume I University. We thank all the students, teachers, and families for their participation.

actual changes in the intergroup status hierarchy. Br. J. Soc. Psychol. 41, 57–76. doi: 10.1348/014466602165054

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 García Bacete, Carrero Planes, Marande Perrin and Musitu Ochoa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Risk and Protective Factors Associated to Peer School Victimization

#### Inmaculada Méndez1 \*, Cecilia Ruiz-Esteban1 and J. J. López-García2

1 Department of Developmental Psychology and Education, University of Murcia, Murcia, Spain, 2 Department of Basic Psychology and Methodology, University of Murcia, Murcia, Spain

The main objective of this study is to analyze the relationship between peer school victimization and some risk and protection factors and to compare the differences by role in victimization with those of non-involved bystanders. Our participants were 1,264 secondary students (M = 14.41, SD = 1.43) who participated voluntarily, although an informed consent was requested. A logistic regression model (LR) was used in order to identify the victim's potential risks and protective factors related to non-involved bystanders. A multiple LR and a forward stepwise LR (Wald) were used. The results showed the variables related to the victim profile were: individual features (to be male, to be at the first cycle of compulsory Secondary Education and a few challenging behaviors), school environments (i.e., school adjustment), family environment (parental styles like authoritarianism) and social environment (i.e., friends who occasionally show a positive attitude toward drug consumption and easy access to drugs, access to drugs perceived as easy, rejection by peers or lack of social acceptance and social maladjustment). The results of the study will allow tackling prevention and intervention actions in schools, families, and social environment in order to improve coexistence at school and to assist the victimized students in the classroom.

#### Edited by:

José Jesús Gázquez, University of Almería, Spain

#### Reviewed by:

Eva M. Romera, Universidad de Córdoba, Spain Thomas James Lundy, Cuttlefish Arts, USA Esther Maria Secanilla, Universitat Autònoma de Barcelona, Spain

> \*Correspondence: Inmaculada Méndez<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received: 09 November 2016 Accepted: 09 March 2017 Published: 22 March 2017

#### Citation:

Méndez I, Ruiz-Esteban C and López-García JJ (2017) Risk and Protective Factors Associated to Peer School Victimization. Front. Psychol. 8:441. doi: 10.3389/fpsyg.2017.00441 Keywords: bullying, secondary education, adolescence, drug consumption, peers, family

## INTRODUCTION

Among the problems that arise at school ages, there may appear situations of harassment or bullying (Ortega-Ruiz, 2015), that is, an aggressive and intentional attack carried out repeatedly and overtime by a group or an individual against a victim who cannot easily fight back; or in other words, a power imbalance (Olweus, 1993, 2013). This problem makes no distinctions between geographic location, social status, public or private schools, etc. The report issued by Save the Children in Spain (Sastre, 2016) reveals that 9.3% of students have ever been bullying victims. Moreover, 5.4% of them admitted to have been bullied. There are three groups of key actors involved in bullying: aggressor, victim, provocative victim (Olweus, 2013). Literature also reinforces the key role of non-involved bystanders in bullying dynamics. Bystanders not involved in the action can take on different roles (Sullivan et al., 2005): accomplices, boosters, non-involved bystanders and defenders). Out of fear of clashing with the aggressor, some students become morally involved in false rules of silence (Ortega, 2000; Armas, 2007). Thus, the main objective of this study is to analyze the relationship between peer school victimization and some risk and protection factors and the differences by role in victimization comparing them with those of the non-involved bystanders in the action. There are different

risks or protective factors, both personal and contextual or environmental, that accelerate victimization or make it more likely to happen.

With regards to social environment, examples of risk factors that can be associated with peers include at interpersonal level: peer group as pattern of submission and need of acceptance (Sullivan et al., 2005); modeling (Sánchez et al., 2007; Alfonso et al., 2009; Pérez-Fuentes and Gázquez, 2010; Delegación del Gobierno para el Plan Nacional sobre Drogas [DGPNSD], 2014), especially through the best friend (Espada et al., 2008); promoting access to drug use (Cerezo et al., 2013); the existence of drugs in the social environment which implies their accessibility, their visibility and their availability together with the attitude of society toward drugs (Delegación del Gobierno para el Plan Nacional sobre Drogas [DGPNSD], 2007).

Several studies have found that victimization is related to multiple variables related to individual features too. For instance, it has been observed that the possibility of becoming a victim depends on some variables. Some personal features are: to be younger than the aggressors and the average classmate (Astor et al., 2001; Cerezo, 2009; Dinkes et al., 2009), to be at the first cycle of secondary studies (Serrano and Iborra, 2005); males as more likely to become victims, shyness, little self-control, low self-esteem, high anxiety, etc. (Cerezo, 2009); provocative victim involved in other risky behaviors such as drugs/consumption (Kaltiala-Heino et al., 2000; Cerezo and Méndez, 2009; Tharp-Taylor et al., 2009).

As family protective factors against victimization, some studies underline that adolescents may perceive an over protective family environment, organization and control. Such an over protection would imply a great difficulty to face arrogant or abuse attitudes (Ortega, 2000; Samper-García et al., 2015). Studies have shown that sibling relationships are considered a source of risk or of protection against violence or victimization depending on the sibling size (Piñero-Ruiz et al., 2012).

Traditionally, literature on bullying points out that it is school adaptation what predicts the role of victim. Some studies have evidenced that failure to adapt to school promotes aggressive behaviors as opposed to victimization (Cerezo, 2009; Méndez and Cerezo, in press). As far as the child's interaction with the peer group diminishes, the child may become more and more isolated and socially rejected (Armas, 2007; Cerezo, 2009; Cerezo and Ato, 2010). Even peer acceptance is recognized as a protection factor against peer victimization (Demaray and Malecki, 2003; Schmidt and Bagwell, 2007).

Thus, the main objective of this study is to analyze the relationship between peer victimization and some risk and protection factors and to identify the differences by role in victimization and compare them with the ones of noninvolved bystanders. Some risk and protection factors (personal and environmental) as well as the level of comprehensive maladjustment (personal, at school, in society and with family), often favor or prevent other risk behaviors (consumption of legal drugs and challenging behavior) that shape the victim profile involved in bullying. To this purpose, this research identifies victims' potential risk and protective factors and compares them with those of non-involved bystanders.

### MATERIALS AND METHODS

### Participants

Participants in this study were 1.264 students (50.8% female). Age range: 11–18 years old, M = 14.41, SD = 1.43 (0.2% 11 years old, 11.3% 12 years old,15.7% 13 years old, 22.3% 14 years old, 27.9% 15 years old, 15.7% 16 years old, 5.9% 17 years old and 0.9% 18 years old) in 13 compulsory secondary education institutions. The participants attended public (66.2%) and private/semi-private (33.8%) secondary schools in different geographical areas of the Region of Murcia (72.8% urban and 27.2% rural areas). 83.5% of them were Spanish and 16.5% were foreigners. Distribution by grade: 45.1% (n = 557) at first level and 54.9% (n = 679) at second level.

### Design and Procedure

This research work is transversal and descriptive. The selection of the participant schools was determined by their acceptance to take part in the study. The participant students were selected from secondary schools in the Region of Murcia, Spain. After obtaining the corresponding permission, students were approached in their own classrooms at school. Researchers explained the objectives of the study and the instruments that would be used. Participation was voluntary and anonymous. The inclusion criteria used were: students in compulsory secondary education, aged between 11 and 18 years. They were requested to attend the school and sit a test that classified them as victims or non-involved bystanders by their own classmates, according to the test Bull-S (Cerezo, 2012). On the other hand, the exclusion criteria were: nonattendance the day the test was passed out, language problems to fully understand the instruments, to be considered an aggressor or a provocative victim by their peers according to the test Bull-S (description in instrument). After obtaining the sample, the selection of individuals was based on the inclusion criteria mentioned above, as it was necessary to focus on the roles of victim and non-involved bystander.

This study was carried out in accordance with the recommendations of the Oviedo Agreement and it was reviewed and approved by the Ethic Committee for clinic research of the University of Murcia. All participants were requested a written informed consent. Parents also gave written informed consent in accordance with the Declaration of Helsinki.

Two sessions of 50 min were used to complete the tests (20 min the Bull-S Test, 20–25 min the second scale, 15–20 min the FRIDA and 30–40 min the TAMAI).

### Data Analysis

In this paper we used a logistic regression (LR) procedure to relate a dichotomous variable (bullying victim/non-involved bystanders) to a set of categorical and continuous variables, which enabled us to identify potential risks and protective factors. In order to analyze the effect of each variable separately, a simple LR (crude odds ratio) was performed. In addition, with the purpose of identifying the variables related to the victim role, a multiple LR and a forward stepwise LR (Wald) were applied. The Odds Ratio (OR) and the 95% confidence interval were calculated

in each case. In these multiple models, we have weighted the fit to the model (Hosmer-Lemeshow Test), the significance of coefficients (Omnibus test) as well as an estimation of the (pseudo) determination coefficient (CoxSnell and Nagelkerke). All analyses were run with SPSS 19.0.

#### Instruments

Students were requested to fill in the following instruments:

First of all, the Bull-S test (version 3.3) Assessment Test of Aggressiveness was used (Cerezo, 2012). It consisted of 15 direct choice Likert items and was addressed to all individuals in the group-class. The test had three dimensions:

The test included socio-demographic variables too. Gender (male/female), age, grade, origin (Spanish/foreigner), course repetition (yes/no), nature of the school (public/private/semiprivate) and geographical location (urban/rural) were also collected as variables. Cronbach's alpha coefficient was 0.68 for total scale scores (73 for aggressors and 0.84 for victims) (Cerezo, 2012). In this study, the coefficient was 0.68 for total scale scores (0.83 for aggressors and 0.84 for victims). Example of items: Whom would you choose as a classmate in the classroom?

The second scale we applied (Méndez et al., unpublished) was based on the "National Survey on Drug Consumption in Secondary School Students" (ESTUDES), issued by the Government Delegation for the National Drug Plan –Delegación del Gobierno para el Plan Nacional sobre Drogas [DGPNSD] (2008) to detect substance use among adolescents in educative contexts. It included 19 dichotomous items about drug consumption and other behaviors. The scale consisted of two factors. Factor I – "Substance Abuse and Health Consequences" – was based on the use of illegal drugs; a higher score indicated a greater possibility of health risk behaviors (have you either participated in any fighting or suffered or initiated any physical attack?, have you been arrested by the police, expelled from school for one or more full days or carry out activities that put your health at risk?) and illegal drug consumption. And Factor II – "Legal Drug Consumption and Challenging Behavior" –, where a higher score indicated a greater possibility of challenging behaviors (Have you had a major conflict or argument with parents or siblings? have you run away from home for more than a day?) and legal drug consumption. Cronbach's alpha reliability coefficient for total scale scores was 0.64 (0.63 for Factor I and 0.64 for Factor II). The Bartlett statistics were good indicators that a matrix of tetrachoric correlations could be subject to EFA Bartlett (190) = 4269.1, p < 0.001, and KMO index, KMO = 0.82. Each factor consisted of different items with a factorial loading > 0.30. The total variance explained by two factors was 58.3%. Example of items: Have you ever smoked a cigarette? Yes/No.

In the third place, we used FRIDA – Interpersonal Risk Factors for Drug Consumption in Adolescence (Secades et al., 2006). It consisted of 90 items in a Likert scale (3 or 5 points), providing a global index of vulnerability or risk and measuring seven factors. Factor 1 (α = 0.88) – "Family Reaction against Drug Consumption" – higher values indicate lower family reaction; for example, the family does not get annoyed if the child is discovered to be smoking. Factor 2 (α = 0.86) – "Peers" – it evaluates friends' attitude toward drug consumption, friends' drug consumption and risk activities; higher levels indicate friends have a higher permissive attitude toward drugs and may even be drug consumers. Factor 3 (α = 0.89) – "Access to drugs" – it evaluates how easily adolescents access drugs; the higher the value, the easier the access; Factor 4 (α = 0.64) – "Family Risks" – it inquiries into family relationships, drug consumption and family conflicts; higher values indicate more family conflicts and drug consumption. Factor 5 (α = 0.85) – "Family Education about Drugs" – evaluates the amount of information adolescents receive from their families about drugs; high values indicate a lack of rules about drug consumption. Factor 6 (α = 0.74) – "Family Protective Activities" – includes leisure and sport activities and measures the quality of relationships and academic achievement; higher values indicate less protective activities. Factor 7 (α = 0.70) – "Parental Educational Styles" – it reports on how authoritarian or permissive the parenting style is (higher scores indicate more permissiveness, while lower scores indicate a democratic style and moderate scores an authoritative one). Cronbach's alpha reliability coefficient was 0.925 for total scale scores (Secades et al., 2006). In this study, the reliability coefficient was 0.81 and in each dimension: Factor 1 (α = 0.88), Factor 2 (α = 0.80), Factor 3 (α = 0.90), Factor 4 (α = 0.79),

Factor 5 (α = 0.71), Factor 6 (α = 0.85), and Factor 7 (α = 0.83). Example of item: My best friend smokes. A Not at all, B Occasionally, C Sometimes, D Often.

The fourth scale we used was the Multifactorial Self-evaluation Child Adaptation Test -TAMAI- (Hernández- Guanir, 2015); it consists of 175 dichotomous items that measure five factors. Factor P (α = 0.85) – "Personal Maladjustment," a high score reports a lack of self-acceptance. Factor E (α = 0.86) – "School Maladjustment," a high score indicates a lack of satisfaction at school, the appearance of disruptive behavior in the classroom and negative attitudes toward learning. Factor S (α = 0.75) – "Social Maladjustment," a high score means poor social abilities showing apprehension or distrust. Factor F (α = 0.75) – "Family Maladjustment" – a high score implies a lack of satisfaction with home environment and parents relationship. Factor IH (α = 0.70) – "Sibling Maladjustment" – a high score indicates a lack of satisfaction with sibling interaction. Cronbach's alpha reliability coefficient was 0.92 for the total scale scores (Hernández- Guanir, 2015). In this study, the reliability coefficient was 0.80 and in each dimension: Factor P (α = 0.71), Factor E (α = 0.83), Factor S (α = 0.70), Factor F (α = 0.70) Factor IH (α = 0.64). Example of item: I have few friends (A) YES (B) NO.

### RESULTS

The distribution of roles in bullying issues among the 1,264 adolescents we studied was as follows: 125 (9.9%) victims, 109 (8.6%) aggressors 7 (0.6%) provocative victim and 1,023 (80.9%) non-involved bystanders. In order to identify possible risk and protection factors in the victim role, a LR analysis was conducted. We compared 125 young victims with 1023 non-involved bystanders. **Table 1** shows the categorical and quantitative variables used for the LR procedure. Firstly, a simple LR analysis (crude) enabled us to detect the individual effect of each variable in the role of victim. The variables in the table proved to be significant in the simple LR (crude). The following risk factors resulted statistically significant: (1) Perceived attitude in friends toward access to drugs (OR = 1.879): students who perceive that their friends would have a moderate easy access to drugs are more likely to be victims than those who perceive little facility; (2) a difficult access to drugs (OR = 2.667): students with easy access to drugs are more likely to fit the profile than those who perceive it as difficult; (3) Parenting style (OR: 2.995): an authoritarian education style can result in three times more risk than a democratic style; (4) Compulsory Secondary Education level (OR = 1.531): undergraduate students (youth) are more likely to become victims than students in the second cycle; (5) Sex (OR = 4.066): being male multiplied the risk by 4 when compared to females; (6) Student social status (OR = 8.280): students 'rejected' by their peers have eight times more risk of becoming a victim than other students; (7) Social maladjustment (OR = 1.062): students with higher social maladjustment show a higher risk of becoming victims; (8) Challenging behaviors (OR = 0.848): lower challenging behavior increases the possibilities of becoming a victim; (9) School maladjustment (OR = 0.968): students with higher school adjustment are more likely to become victims. On the other hand, there were significant protection factors: (1) Being popular among peers (OR = 0.218), compared to average students; (2) Age (OR = 0.848): the older peers take a lower risk; and (3) Social Preference (OR = 0.872): those elected by fellow students show a lower risk.

Subsequently, a multiple LR analysis (Adjusted) was accomplished, aiming to identify risk/no protection or non-redundant factors. With this procedure, the following simultaneous risk factors to become a victim were identified: being male, school adapted, socially maladjusted and slightly preferred by their peers.

Last, aiming at the exclusion of irrelevant or redundant factors, some variables were selected with a Forward LR (Wald statistics) procedure (Forward selection), which confirmed the aforementioned factors, including an additional one: Perception of the friends' attitude toward access to drugs. Students who perceive that their friends would have a moderate or easy access to drugs are more likely to become victims.

These results are similar if analyzed separately for boys and girls.

#### DISCUSSION

Understanding the factors that predict peer victimization at school requires a close examination of the complex interrelationships between the individual and his/her environment. In this study, a number of factors related to victimization in secondary education adolescents have been identified.

Concerning social environment, the results of our study show that adolescents who have less drug-friendly friends are more likely to be potential victims than those who show a higher tolerance. The results obtained in relation to social environment are consistent with other research works that show adolescents can be influenced by their group of friends on drugs consumption (Sánchez et al., 2007; Alfonso et al., 2009; Pérez-Fuentes and Gázquez, 2010; Delegación del Gobierno para el Plan Nacional sobre Drogas [DGPNSD], 2014), especially through best friend (Espada et al., 2008), and even promote the perception of easy access to its consumption (Cerezo et al., 2013).

In this sense, our findings point out that those adolescents who perceive easier access to drugs might be at greater risk of becoming victims. Nevertheless, the victim profile is not usually involved in challenging behaviors (i.e., legal drug consumption, have you had a major conflict or argument with parents or siblings, run away from home for more than a day?), unlike studies on the aggressor profile, provocative victim or noninvolved bystanders (Kaltiala-Heino et al., 2000; Cerezo and Méndez, 2013). Probably, this difference between group values and individual's behavior makes perception become a risk factor to become a victim.

In relation to family environment, the results obtained regarding the parenting style support revealed that children exposed to peer victimization have a different home environment than those who are not. Children whose parents show an

#### TABLE 1 | Logistic regression for victim role.

fpsyg-08-00441 March 20, 2017 Time: 15:8 # 5

N: Total cases. %: Victim percent. B/OR (95% CI): Regression coefficient/Odds Ratio (95% confidence interval). aMean (standard deviation).

∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001. HLT, Hosmer Lemeshow Test. OT, Omnibus test. Ref, Reference value. Ref∗ , Significance level of categorical variable.

authoritarian style run a greater risk to become a victim than those coming from permissive and democratic family environments. In contrast, some studies showed that permissive parental style predicts the experience of victimization while the authoritarian parental style best predicts bullying behavior (Baldry and Farrington, 2000; Georgiou and Stavrinides, 2013). Therefore, it will promote victimization and inhibit the attachment to peers (Ortega, 2000; Samper-García et al., 2015).

Regarding personal features, our data show that students at the first cycle of secondary school are more likely to be at risk than those at the second, which also confirms the decreasing risk associated to the variable age. Older students are more likely to experience bullying than younger school students and perceive school as unsafe as a number of studies have shown (Astor et al., 2001; Cerezo, 2009; Dinkes et al., 2009). Serrano and Iborra (2005) consider the probability to become a victim is more likely to happen during the 1st years of secondary studies while it tends to decrease in the following years. These studies are coherent with our data that point out age as a protective factor. The protection

and social skills that adolescents develop with age explained data presented in this study.

Researchers are careful about conclusions on gender differences in bullying behavior (Hong and Espelage, 2012). Previous findings indicated that boys are usually either victims or authors of direct forms of bullying while girls experience indirect bullying (Olweus, 1993; Varjas et al., 2009). Cerezo (2009) points out males as more likely to become victims. Our study indicates that males are four times more likely than females to become victims.

Regarding student social status, other studies prove that noninvolved children are better placed in their social networks than those involved in bullying dynamics (García-Bacete et al., 2010). According to our results, adolescents rejected by their peers are at higher risk, up to 45 times higher, than the average student is. Among involved children, aggressors get more support and are more accepted by their peers than victims (Estévez et al., 2007; Salmivalli, 2010; Van der Schoot et al., 2010). Victims are rejected, when not ignored, by most of the group members

(Cerezo and Ato, 2010) which certainly contributes to their helplessness (Ortega, 2000). This finding supports the results obtained in this study.

Cerezo and Ato (2010) point out that victim were worse placed than aggressors in the network of interpersonal relationships. That is, both victims and aggressors are rejected but victims are also considered cowards. Regarding social perception, victims reported to be lonely, nobody caring about them, because the rest is not concerned about the seriousness of the situation, and that could encourage the persistence of bullying. In this line, this research consistently shows that students rejected by their peers run 8 times more risk of becoming a victim than average students.

The data we have obtained show that social maladjustment increases the risk of victimization while school maladjustment reduces it (therefore, adolescents with a higher level of school adjustment are also at higher risk). High achievement is usually linked to school adjustment. Students with the highest achievement are more rejected than average students. This may help to interpret our data. In addition, at school environments, the victim role shows a higher academic achievement than the aggressor role, being similar to the average achievement of the peer group/classroom (non-involved bystanders) (Cerezo, 2009; Méndez and Cerezo, in press).

The last related variable is social preference. The risk adolescents run diminishes as they are more socially accepted. Preference by peers, popularity, and friendship are very important for adolescents (Espelage, 2002). Besides our findings, other studies found friendship to be a protection against victimization (Demaray and Malecki, 2003; Schmidt and Bagwell, 2007).

Both adjusted LR and forward selection procedures identify the same subgroup of significant variables in relation to the victimization and can define the test type features in the sample under study: to be male, to perceive that friends are not at high risk of drug consumption, low school maladjustment, high social maladjustment and low social acceptance by peers. The identified variables, however, explain only 34%, at the most, of the variation on victimization. This moderated percentage underlines the complexity of the issue. Other studies point to different risk factors that were not studied but should be considered in future studies. Some personal factors are shyness, little self-control, low self-esteem, high anxiety, depression, race or ethnicity, handicaps, learning disabilities (Cerezo, 2009), challenging victim involvement in other risky behaviors (Kaltiala-Heino et al., 2000; Cerezo and Méndez, 2009; Tharp-Taylor et al., 2009); some contextual factors as: (a) over protective family environment (Ortega, 2000), hierarchical relationships among siblings (Piñero-Ruiz et al., 2012); negative peer relationships (Salmivalli, 2010); (b) school environment features, such as the lack of resources or little experienced teachers (Serrano and Iborra, 2005), inter-parental violence (Corvo and deLara, 2010); and (c) social environment, for instance exposure to violence in the media (David-Ferdon and Hertz, 2007).

These results should have consequences for educational policy and practice. It is necessary to promote inclusion (Llorent et al., 2016). It is also necessary to strengthen emotional education and acquisition of social skills (Sastre, 2016). At school level, it is recommended to provide teachers with resources (Serrano and Iborra, 2005). It is important that society as a whole breaks the law of silence or helplessness (Ortega, 2000), giving an active role to non-involved bystanders. Kärnä et al. (2010) suggest that noninvolved behaviors in bullying situations moderate the effects of individual and interpersonal risk factors for victimization. Influence on these behaviors might be an effective way to protect vulnerable children from victimization.

It is convenient to take into account teachers and family's perspectives and even gather information on other behaviors that may be influencing victimization, such as personality, selfesteem, or self-concept, and to collaborate with specialists when dealing with medical problems or psychological consequences of victimization.

There have been several meta-analyses and studies on bullying prevention and intervention programs. Results indicated moderate effect sizes on self-reported victimization that students experienced from aggressors (Smith et al., 2004). Hong and Espelage's (2012, p. 2012) social-ecological approach considered that responses to aggressors, rather than rely on traditional punitive measures, should approach both aggressors and victims patterns of behavior, with particular attention to non-involved bystanders at school, as well as the classroom-social climate and other influences such as family, community and society. Maybe, intervention programs have been too focused on aggressors and rarely on victims and non-involved bystanders.

Researchers also noted that anti-bullying programs were more efficient when implemented with older students (i.e., 11 and older) (Smith et al., 2004). In spite of the large number of prevention programs implemented in our country: "Educating in harmonious coexistence in order to prevent violence" (Ortega, 2000); "Aid between peers Program" (Cowie and Fernández, 2006); KiVa antibullying program (Kärnä et al., 2011); "System to detect racial-based Bullying through Gamification" (Álvarez-Bermejo et al., 2016), "Using a 3D simulation Instrument in educational settings" (Cangas et al., 2016). We must insist in prevention programs based on ecological approach that take into account risk and protection factors. Furthermore, intervention programs should address victims and non-involved bystanders instead of only aggressors. In addition, it should include risky behaviors related to bullying dynamics, like the consumption of drugs.

Like many of the existing studies on the topic of bullying and peer victimization, the present study used a standard cross-sectional methodology. Even though this is an established method in social sciences, it also shows limitations, such as significant constraints in unfolding cause and effect relationships. Our conclusions are limited because they are based on correlational relationships. Additional research on these variables with longitudinal data is needed.

### AUTHOR CONTRIBUTIONS

IM: Fieldwork, theoretical development and writing. CR: Theoretical development and writing. JL: Fieldwork and methodological treatment.

### REFERENCES

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[Influence of social models on alcohol use among adolescents]. Psicothema 20, 531–537.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Méndez, Ruiz-Esteban and López-García. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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# Verbal Emotional Disclosure of Traumatic Experiences in Adolescents: The Role of Social Risk Factors

#### Silvia Pérez, Wenceslao Peñate, Juan M. Bethencourt and Ascensión Fumero\*

Psicología Clínica, Psicobiología y Metodología, Universidad de La Laguna, La Laguna, Spain

It is well-known that traumatic events and adverse life situations are very important in both physical and psychological health. Prevalence studies suggested that adolescents experience at least one potentially traumatic event before reaching age 18. The paradigm of research centered on expressive writing has evidenced the beneficial effects that the emotional disclosure of previous traumas produces on physical health and psychological adjustment. The aims of the study are threefold: determining the prevalence of adverse or traumatic events; examining the extent to which psychopathological symptoms developed in those exposed to traumatic events; and exploring an verbal emotional disclosure (VED) paradigm in which variations on time spent talking about traumatic experiences to others resulted in a reduction of the psychological impact of trauma in a sample of Spanish adolescents. 422 volunteer adolescents participated, 226 boys and 192 girls, from 10 to 19 years old, all of them living in Tenerife. The mean age was 14.8 years (SD = 1.83). All of them completed the instruments used to assess the psychological impact of traumatic experiences and VED. The main results indicated that 77% of the participants had suffered a traumatic situation. The participants who have been exposed to traumatic events scored significantly higher in measures of post-traumatic stress, disorder, intrusive thoughts, avoidance behaviors, anxiety and depression, compared to those without trauma. Furthermore, results show a decrease in symptomatology scores as a function of time spent disclosing emotional experiences to others, particularly when disclosure occurred several times. In conclusion, stressful events or traumatic experiences and their concomitant emotional effects are highly prevalent in adolescents, and repeated VED to others appears to ameliorate their impact. VED shows greater therapeutic benefits when adolescents narrate the experience on several occasions and in an extensive way.

Keywords: verbal emotional disclosure, traumatic experiences, psychopathological symptoms, adolescents at risk, social exclusion

## INTRODUCTION

The experiencing of traumatic, painful, or stressing situations in a person's life is not harmless, and may cause serious difficulties in psychological adjustment (Walter and Bates, 2012). When these experiences become part of everyday life, the probability of suffering a psychological disorder is very high (Campo-Arias et al., 2014). The impact of the traumatic situations or adverse life events

#### Edited by:

José Jesús Gázquez, University of Almería, Spain

#### Reviewed by:

Juan Preciado, York College – The City University of New York, USA Sonia Zambrano Hernandez, Universidad Catolica de Colombia, Colombia

> \*Correspondence: Ascensión Fumero<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received: 06 December 2016 Accepted: 27 February 2017 Published: 14 March 2017

#### Citation:

Pérez S, Peñate W, Bethencourt JM and Fumero A (2017) Verbal Emotional Disclosure of Traumatic Experiences in Adolescents: The Role of Social Risk Factors. Front. Psychol. 8:372. doi: 10.3389/fpsyg.2017.00372

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during childhood and adolescence may be much more significant. Adolescence is a potentially significant period in developing stress and coping processes (Hollenstein and Lougheed, 2013). It is well-documented that exposure to multiple traumatic events, as well as other risk behavior, such as gambling or substance use, are common among juvenile offenders (Lee et al., 2012). The importance of the adverse events in adolescence may be due to several reasons. First, change is viewed as an inherent component of stress, and adolescence is characterized by changes in biological functioning, cognitive development, social roles, and social environments. Second, because adolescence is a period of transition, change and adaptation. Third, cognitive and social development during adolescence may make it an optimal time for learning new coping skills to reduce the adverse effects of stressful events (Compas et al., 1985).

Adolescents at risk of social exclusion constitute a population especially sensitive to traumatic experiences. They lacked the necessary moral or material assistance which must be provided by those who are supposed to take care of them; they suffered abandonment, physical and/or psychological abuse, regular alcoholism or drug addiction in members of the family, sexual abuse, serious violent behavior or attitudes by relatives or third parties in the family unit, incitement to mendicancy, delinquency, prostitution, or to any other form of economic or sexual exploitation.

Studies about prevalence of exposure to trauma suggest that many children and adolescents experience at least a potentially traumatic event before reaching 18 years of age (Alisic et al., 2014). It is estimated that approximately 1 in 4 youths will experience some type of substantive trauma, such as physical abuse, sexual abuse or domestic violence during his or her developmental years (Duke et al., 2010). Adolescents are considered an at-risk population due to the serious negative consequences associated with exposure to trauma, from PTSD (post-traumatic stress disorder) to other anxiety disorders, behavior issues, depression, substance abuse, and risk behavior for health (Shi et al., 2016; Zhen et al., 2016). It has been demonstrated that a greater number of adverse events during childhood result in worse health in adults (Barboza Solis et al., 2015). With four or more adverse events in childhood, the risk of suffering several medical conditions increased exponentially (Perry, 2014).

The importance of language in the processing of trauma and subsequent health outcomes has been recognized. There is empirical support for a relationship between emotional disclosure about traumatic events and health outcomes (Pennebaker et al., 1988; Pennebaker, 1989). Specifically, verbal disclosure in comparison with writing achieved the greatest improvements in cognitive change, self-esteem, and adaptive coping strategies (Esterling et al., 1994). Also, most forms of psychotherapy include trauma disclosure. The reason should be in using language to label an emotion, and an experience creates a structure, which facilitates the assimilation and understanding of the event, and thus the reduction of emotional arousal that is detrimental to physical and psychological health (Pennebaker et al., 1997).

There is evidence of significant benefits in emotional health, physical health, positive changes in the immune system, and in the general psychological functioning after emotional selfdisclosure or communication to others (Frattaroli, 2006; Lumley et al., 2014). However, results are less consistent for participants with psychological difficulties (Baikie et al., 2012; Travagin et al., 2015). While some studies have supported written emotional disclosure in students with a history of trauma (Ironson et al., 2013), benefits appear to be limited in samples of participants with a negative image of their own body (O'Connor et al., 2011; Lafont and Oberle, 2014), adults who suffered child abuse, exhibit symptoms of depression and post-traumatic stress, or had lost a relative (Baikie et al., 2012; Meston et al., 2013; Unterhitzenberger and Rosner, 2014). There have been significant effects on positive affect, negative affect, and level of depression, after an intervention based on expressive writing. However, anxiety, intrusive thoughts, and avoidance behaviors did not change (Del Pino et al., 2016).

In spite of the promising results with the adult population, there is scant evidence about the effectiveness of emotional disclosure in the adolescents. Findings indicate the beneficial effects of the use of emotional disclosure in children and adolescents on the symptoms of internalizing and externalizing behaviors (Zajac et al., 2015), a significant increase in optimism, a decrease in negative affect, the development of better coping strategies, such as positive reframing, and optimistic thinking (Margola et al., 2010; Graham-Bermann et al., 2011), and the strengthening of the academic self-concept (Facchin et al., 2014). Travagin et al. (2015) carried out a meta-analysis that evaluated the effects of expressive writing on adolescents; results suggest that emotional disclosure may help reduce internalization problems, behavior issues, somatic complaints, as well as improve social adjustment, school participation and performance. Briefly, emotional disclosure tends to produce significant improvements in the well-being of adolescents.

 Results suggest that emotional disclosure was an effective way of helping students deal with the stress factors they experienced, with those adolescents most exposed to violence being the most benefited. Furthermore, the adolescents who reported body dissatisfaction, and disclosed their emotional experiences through expressive writing showed a reduction in thin ideal internalization, personal dissatisfaction, and psychosocial deterioration (Graham-Bermann et al., 2011). Besides, adolescents who regarded problems with classmates as a stress factor improved their personal well-being and social adjustment in the long term, after emotional disclosure (Travagin et al., 2015).