Bullying Among Adolescents in North Cyprus and Turkey

Abstract

Peer bullying has been studied since the 1970s. Therefore, a vast literature has accumulated about the various predictors of bullying. However, to date there has been no study which has combined individual-, peer-, parental-, teacher-, and school-related predictors of bullying within a model. In this sense, the main aim of this study was to test a multifactor model of bullying among adolescents in North Cyprus and Turkey. A total of 1,052 adolescents (554 girls, 498 boys) aged between 13 and 18 (M = 14.7, SD = 1.17) were recruited from North Cyprus and Turkey. Before testing the multifactor models, the measurement models were tested according to structural equation modeling propositions. Both models indicated that the psychological climate of the school, teacher attitudes within classroom, peer relationships, parental acceptance-rejection, and individual social competence factors had significant direct effects on bullying behaviors. Goodness-of-fit indexes indicated that the proposed multifactor model fitted both data well. The strongest predictors of bullying were the psychological climate of the school following individual social competence factors and teacher attitudes within classroom in both samples. All of the latent variables explained 44% and 51% of the variance in bullying in North Cyprus and Turkey, respectively.

Keywords

bullying parental factors peer relationships school context teacher attitudes social competence

Bullying may occur in any circumstance in which there are power relationships and a person has an opportunity to abuse his or her power (Smith & Brain, 2000). Peer bullying was first studied by Olweus’s (1973, 1978) in his seminal studies in Norway. After almost 40 years, a huge literature has accumulated about various predictors of bullying behavior. In a recent review study, Monks and colleagues (2009) mentioned that there are numerous theoretical and empirical studies related with bullying. The authors proposed that the step forward after this point was to test accumulated theoretical and emprical knowledge with multifactor models. In this sense, the main aim of this study was to examine individual-, peer-, parental-, teacher-, and school-related predictors of bullying within a model by using two representative adolescent samples from North Cyprus and Turkey. Theoretically, we used Bronfenbrenner’s (1979) ecological model and Masten’s (2006) principles while proposing the model. Following Bronfenbrenner’s (1979) model, individual-, peer-, parental-, and teacher-related factors were analyzed as microsystem variables; school-related factors as the exosystem variable; and the socialization context in North Cyprus and Turkey as the macrosystem variable. We also followed Masten’s (2006) principles to examine developmental problems (principle of systemic approach and principle of multidimensionality). Principle of systemic approach stressed that human development had to be studied by addressing family, peer group, school, and culture. Principle of multidimensionality stressed examining individual, proximal, and distal dimensions to understand developmental outcomes. According to these principles, we examined individual, proximal (parent, peer), and distal (teacher, school) dimensions within a multifactor model. Also, we tried to conceptualize social context as a distal dimension and used the narrow socialization concept to define the social context in North Cyprus and the wide socialization concept to define the social context in Ankara, Turkey. Arnett (1995, 2001) stated that narrow–wide socialization included interpersonal relationships within various systems such as family, peer, school/work, and culture. Specifically he defined narrow socialization as a socialization type which occurred in small settlements where close interpersonal relationships and social networks exist and wide socialization as a socialization type which occurred in large cities and metropolises where the population was more heterogeneous and including more individualistic life styles. According to these definitions, the socialization processes in North Cyprus may be classified as narrow socialization. According to the 2006 data by State Planning Organization, the total population of North Cyprus was 265,100. The most crowded city was Nicosia (85,579) followed by Famagusta (64,269), Kyrenia (62,158), and Morphou (31,116). In addition to the population numbers, the Turkish Cypriot family type can also be an indicator of narrow socialization. Most Turkish Cypriot families live in the same apartment with close relatives or in very close distances. However, Ankara, the capital of Turkey is a typical wide socialization context with a population of 4.5 million. Ankara is the third most crowded city of Turkey and 45th most crowded city of the world according to the data obtained from Turkish Statistical Institute. As a metropolis, Ankara has a high migration rate. Between 2000 and 2011, the population increased 21% because of immigration. Therefore, its population is supposed to be much more heterogeneous than the population of North Cyprus. Moreover, just like other metropolises, the lifestyle in Ankara is supposed to be more individualistic.

A Brief Literature Review Regarding the Variables in the Proposed Model

A brief literature review regarding the variables in the current model—individual factors, parental factors, peer relationships, teacher attitudes, and school factors—has been presented below.

Individual Factors (Social Competency Factors)

Children and adolescents who are socially incompetent may have a greater tendency to experience social adjustment problems including bullying behaviors (Cook, Williams, Guerra, Kim, & Sadek, 2010). Three variables were analyzed as the indicators of social competency: coping ability, social cognitive abilities, and academic competency.

Coping abilities can be active-adaptive or passive-maladaptive (Lazarus & Folkman, 1984). Individuals who use active strategies to cope with problems are more tended to focus on the solution of the problem. Studies have shown that bullies (especially the ones who use overt strategies) frequently use self-focused destructive (maladaptive) coping strategies (Baldry & Farrington, 2005; Olafsen & Viemeroe, 2000).

According to the social-cognitive model of aggression (Arsenio & Lemerise, 2004; Crick & Dodge, 1999), aggressive people have a social-cognitive deficit which prevents them from focusing on hints in a social context. However, this model had some limitations in explaining some forms of bullying. The direction of the correlation between social-cognitive ability and bullying may change according to the type of bullying. In other words, overt-physical bullying may be negatively related to social-cognitive abilities. However, covert-relational bullying may be positively related to these abilities (Kaukiainen, Bjorkqvist, Osteman, & Lagerspetz, 1996; Kaukiainen et al., 1999).

Another controversial subject is whether bullies have academic competence. A limited number of studies (e.g., Andreou & Metallidou, 2004) have indicated that bullies who use more overt strategies have weaker academic efficacy. However, bullies who used covert strategies were found to be more successful in school because of their high social-cognitive abilities (Wolke, Woods, Bloomfield, & Karstadt, 2000). Briefly, academic competence /incompetence can be related to the type of bullying.

Parental Factors (Parental Rejection vs. Parental Acceptance)

The role of parental factors in relation to bullying have been studied since the beginning of research conducted to investigate bullying. For example, Olweus (1980) mentioned three parental factors related to bullying in children: (a) lack of warmth and support, (b) insensitivity to child’s aggressive behaviors, and (c) using physical and/or verbal aggression as a disciplinary method. These three factors were supposed to be related to parental rejection/acceptance. A number of studies have indicated that bullies perceived less acceptance and more rejection from their parents (Demaray & Malecki, 2003; Georgiou, 2008). Therefore, a series of variables which have been hypothesized to be related to parental rejection/acceptance has been chosen for the current model. Parental monitoring, closeness, and self-disclosure to parents have been analyzed as the indicators of parental acceptance. However, parental psychological control and conflict with parent have been analyzed as the indicators of parental rejection.

There is a well-known literature about the association between parental monitoring, closeness, and bullying. Olweus (1993) and Vail (2000) have found negative correlations between parental monitoring and bullying. Similarly, parental closeness was found to be negatively correlated with bullying (Atik, 2006; Turgut, 2005). Self-disclosure is an associated psychological construct to parental acceptance. Crouter and Head (2002) have stated that parents’ knowledge about their children includes not only parental monitoring but also self-disclosure of the children. A couple of studies have shown that children who disclose themselves less were more vulnerable to victimization (Charach, Pepler, & Zeigler, 1995; Harris, Petrie, & Willoughby, 2002). This association might be valid for bullying and self-disclosure because it is well known that bullies and victims might share similar familial backgrounds (Haynie, Nansel, & Eitel, 2001). Also, just as mature peer relationships need self-disclosure (Buhrmester, 1996), warm parent–adolescent relationships must need self-disclosure of adolescents to their parents. However, to our knowledge, there was no study which has examined this relationship.

Another well-known association is conflict with parents and bullying. A number of studies have indicated that children who experience more conflict with their parents had a greater tendency to bully others (Pepler, Jiang, Craig, & Connolly, 2008; Stevens, Bourdeaudhuij, & Oost, 2000). However, the association between parental psychological control which was supposed to be related to parental conflict and bullying has not yet been examined sufficiently. Barber (1996) defined psychological control as restricting, neglecting, and/or manipulating the child’s psychological and emotional experiences. Psychological control can be expressed by the parent as trivializing, humiliating, and neglecting all of which are related to parental rejection. Therefore, we may assume a significant relationship between parental psychological control and bullying. This hypothesis has been supported by one of our previous studies which indicated that high levels of maternal/paternal psychological control lead to bullying (Bayraktar, Ozdikmenli-Demir, & Sayil, 2008).

Peer Relationships

Bullying in schools is also known as peer bullying. Therefore, there is a concrete link between peer relationships and bullying. In our model, we analyzed friendship quality and peer attachment as the indicators of positive peer relationships and peer conflict as the indicator of negative peer relationships. Previous studies have indicated that bullies feel lower levels of trust toward their peers (King & Terrance, 2006) and feel more rejected by their peers (Chang et al., 2005). These results show that some bullies may have insecure and low-quality relationships with their peers. However, some other bullies with high social-cognitive abilities who use relational strategies may have high quality and securely attached friendships (Hawley, 2003; Xie, Swift, Cairns, & Cairns, 2002). In other words, the quality of friendship and security or insecurity of attachment to peers may be related to the type of bullying. A similar association can be valid for peer conflict and bullying. Bullies who use relational and indirect aggression were found to be more inclined to use their peer relationships as social sources to harass their victims (Pellegrini & Bartini, 2001). This result can show that these bullies experience less conflict with their peers to hold the social source. However, other bullies with less social-cognitive abilities were found to be more tended to use overt and direct aggressive tactics, more tended to experience conflict with their peers, and more vulnerable to be rejected (Arsenio & Lemerise, 2004; Coie & Dodge, 1998).

Beyond social cognitive abilities, peer-group dynamics may be related with bullying behaviors. Research in the past 25 years repeatedly showed that the aggression and/or bullying levels of people in the same peer group resembled each other (Cairns, Cairns, Neckerman, Gest, & Gariepy, 1988; Salmivalli, Huttunen, & Lagerspetz, 1997; Witvliet et al., 2010). In other words, bullies and/or aggressive children/adolescents with similar bullying/aggressive behavior levels may affiliate with each other. However, this affiliation does not mean that these children/adolescents are popular and liked within their peer relationships. Just like affiliation of children/adolescents with similar behaviors, the studies showed that children/adolescents with similar popularity levels affiliate each other (e.g., Ray, Cohen, Secrist, & Duncan, 1997). In other words, less popular children/adolescents tend to affiliate with others who are also less popular. Therefore, peer attachment, peer conflict, and friendship quality scores of the members of these less popular peer groups may be similar to each other.

Teacher Attitudes Within Classroom

Teachers have important social roles in the lives of children and adolescents as secondary attachment figures. Therefore, similar arguments to parent–child interactions may also be valid for teacher–student interactions. For example, similar to associations between parental monitoring and conflict with parent and bullying, monitoring by the teacher and conflict with teacher was also found to be related to bullying (Parault, Davis, & Pellegrini, 2007; Rigby & Bagshaw, 2003).

Just like parents, teachers may abuse their power. Paul and Smith (2000) defined six areas where teachers are more likely to abuse their power relationships with students: (a) discipline techniques, (b) academic evaluation, (c) dividing students into groups, (d) forcing students to obey rules within the classroom and school, (e) instruction techniques within the classroom, and (f) misuse of the social status. In line with these, similarly to the parents, abuse of power by teachers (for example, psychological maltreatment or control) can be related with negative behavioral outcomes including bullying. However, proper use of power (supporting students, promoting performance goals, interaction, and mutual respect within classroom) may decrease bullying.

Previous studies showed that perceived teacher support was negatively associated with bullying (Demaray & Malecki, 2003). Creating learning objectives and targeting students to these objectives (performance goals) is a well-known antibullying policy and the empirical results showed that this policy decreased bullying behaviors in the classroom (Stevens et al., 2000). Promoting social-cognitive skills which have the potential to enhance interpersonal relationships (i.e., interactions) and mutual respect was also found to be negatively correlated with bullying-victimization within the classroom (Hirschstein, Edstrom, Frey, Snell, & MacKenzie, 2007). However, psychological maltreatment (a part of psychological control), such as humiliating, name-calling, or defaming the students, was found to be related with increased aggressive behaviors among students in the classroom (Hyman & Perrone, 1998). Parallel to these empirical findings, we analyzed teacher support, promoting interaction, mutual respect, and performance goals within the classroom by the teacher as the indicators of positive teacher attitudes within classroom and teacher psychological control as the indicator of negative teacher attitude.

School Factors

We analyzed school factor as psychological climate of the school. Psychological climate includes the quality of interpersonal relationships and application of and obedience to the rules in the school (Welsh, 2000). Olweus (1992, 1994) stressed four factors related to psychological climate of the school as decreasing bullying: (a) care and warmth by adults in schools, (b) strict rules against undesirable behaviors including bullying, (c) monitoring students, and (d) discipline techniques which do not include physical or psychological violence. We analyzed school climate and adult (teacher and headmaster) disciplinary techniques as indicators of psychological climate of the school. Another indicator which we analyzed was school bonding. Weak bonding to the school has been found to be related to antisocial behaviors which include bullying (Hawkins et al., 1998; Sprott, Jenkins, & Doob, 2005).

Hypothesized Multifactor Model

We used structural equation modeling (SEM) to test the hypothesized model. We analyzed 23 indicators (or observed variables) and these variables created eight latent variables: individual social competence factors, parental acceptance and rejection factors, positive and negative peer relationships, positive and negative teacher attitudes within classroom, and psychological context of the school (see Table 1). SEM gave us the chance to analyze all these variables simultaneously, and hence we could analyze the independent and combined effects of each dimension of bullying. The indicator variables in the model were selected to represent proximal and distal factors which have been shown as potential predictors of bullying in previous studies or which have not yet been studied but are theoretically acceptable as indicator variable.

Table 1.

Indicator and Latent Variables in the Hypothesized Model

Indicator Variables	Latent Variables
Coping ability	Individual social competency factors
Academic efficacy
Social cognition
Parental monitoring	Parental acceptance factors
Self-disclosure to parent
Parental closeness
Parental psychological control	Parental rejection factors
Conflict with parent
Friendship quality	Positive peer relationship
Peer attachment
Conflict with peer	Negative peer relationship
Promoting mutual respect	Positive teacher attitudes within classroom
Promoting interaction
Promoting performance goals
Teacher support
Teacher psychological control	Negative teacher attitudes within classroom
School bonding	Psychological context of the school
School psychological context
Discipline style of teachers
Discipline style of school administration

As mentioned before, Bronfenbrenner (1979) stated that human development was the result of interaction between individual and contextual factors. In the same lines, Garbarino (1999) ranked the risk factors related with aggressive behaviors as individual, familial-parental, peer, school, and social factors from the strongest to the weakest predictor. When bullying literature was reviewed, Soutter and McKenzie (2000) supposed that the basic factor related with bullying was the context where bullying occurred. Lee (2000) proposed a relatively more holistic approach and stated that beyond contextual factors, individual variables must be taken into account.

A limited number of studies showed that individual factors were the strongest predictors of aggressive/violent behaviors. For example Welsh, Greene, and Jenkins (1999) showed that within a representative sample from Philadelphia, United States, 94% of the variance in aggressive behaviors at school was explained by individual factors and 5% by contextual factors. In another study, Khoury-Kassabri, Benbenishty, Avi Astor, and Zeira (2004) compared the individual, familial, school-related, and cultural factors related with violence in Israel and found that individual factors were the strongest predictors following school context and familial factors.

Although these studies did not examine bullying directly, following their implications we hypothesized that individual factors would be one of the strongest predictors of bullying as a proximal factor. However, we identified school psychological environment as the strongest predictor following Olweus’s (2005) statement that school context was the central to any bullying behavior among students. Similarly, teacher attitudes were expected to be strongly related with bullying. The rank of the other predictors has been hypothesized to vary depending on their proximal versus distal status (i.e., parental factors and peer relationships) although both were accepted as mainly proximal factors.

Method

Participants

North Cyprus sample

A total of 544 adolescents (284 girls, 259 boys) aged between 13 and 18 (M = 14.7, SD = 1.17) were recruited from various schools in four major settlements in North Cyprus: Nicosia, Famagusta, Kyrenia, and Morphou. We used an eight-level Likert-type scale for maternal and paternal education (1 = illiteracy, 8 = PhD holder). According to the reports of adolescents themselves, the mean education level for mothers was 3.39 (SD = 1.29) which indicated secondary school level; and the mean education level for fathers was 3.61 (SD = 1.48) which indicated high school level. Also, according to self-reports, most of the adolescents perceived themselves to be in the middle class.

Turkey sample

A total of 509 adolescents (270 girls, 239 boys) aged between 13 and 18 (M = 14.7, SD = 1.29) were recruited from various schools in Ankara. Ankara is the capital city of Turkey and one of the major migration destinations. According to Turkish Statistical Institute, the major immigration to Ankara is from proximal cities (i.e., Yozgat, Cankiri, Sivas, Kirikkale, Corum, Kirsehir), and these cities are consisting of a Turkish majority and Kurdish, Kirghiz, Uzbek, Balkar, Osset, Arab, and Jewish minorities. Therefore, it was supposed that our sample could represent various ethnic and cultural backgrounds in Turkey. Also, 9 schools from various regions of Ankara were selected to collect the data which represent different socioeconomic levels (low, middle, high). According to the reports of adolescents, the mean education level for mothers was 3.30 (SD = 1.46) which indicated secondary school level, and the mean education level for fathers was 3.91 (SD = 1.48) which indicated high school level. Also, according to self-reports, most of the adolescents perceived themselves to be in the middle class. Shortly, we could suppose that the North Cyprus and Turkey samples were comparable with each other according to the criteria we used for socioeconomic status.

Measures

The summary of measures was presented as a table (see Table 2) because of the limitation of space. The means, standard deviations and Cronbach’s alphas of the measures in North Cyprus and Turkey was given. The measures have been divided into the following sections; 1- Measures for bullying, 2- Measures for individual social competence factors, 3- Measures for parental factors, 4- Measures for peer relations, 5- Measures for teacher attitudes and 6- Measures for school related factors. Additional information about the measures of bullying are presented below. The details of other measures used can be requested from the corresponding author.

Table 2.

Names, Authors, Number of Items, Means, Standard Deviations, and Cronbach Alphas of Measurement Tools Used in North Cyprus and Turkey

			North Cyprus		Turkey
Name of the Measurement Tool	Authors	Number of Items	M (SD)	α	M (SD)	α
Peer Bullying Questionnaire	Gultekin and Sayil (2005)	36	42.15 (9.47)	.92	42.31 (8.57)	.90
Peer nomination for relational bullying	Hawley, Little, and Card (2007)	2	0.07 (0.93)	.71	0.06 (1.07)	.80
Peer nomination for overt bullying	Hawley, Little, and Card (2007)	2	0.09 (1.05)	.85	0.09 (94)	.86
Approach Oriented Coping Sub-Scale of Coping Across Situations Questionnaire	Seiffge-Krenke (1995)	10	27.36 (5.34)	.70	27.08 (5.23)	.75
Theory of mind vignettes	Bosacki (2000)	9	11.19 (4.20)	.80	11.69 (4.23)	.77
Academic Efficacy sub-scale of perceptions of the school psychological environment	Roeser, Midgley, and Urdan (1996)	5	17.54 (4.83)	.82	16.39 (4.88)	.80
The Adolescent Family Process Measure subscales	Vazsonyi, Hibbert, and Snider (2003)
Conflict		5	11.95 (5.05)	.86	11.84 (4.79)	.80
Monitoring		5	18.61 (4.52)	.79	15.83 (4.02)	.80
Closeness		5	16.31(4.41)	.81	18.68 (4.19)	.84
Psychological Control Scale	Barber (1996)	8	9.59 (2.79)	.84	9.67 (5.38)	.84
Self-Disclosure Scale	Stattin and Kerr (2000)	5	12.17 (2.29)	.73	15.03 (4.97)	.83
Parental and Peer Attachment Scale	Armsden and Greenberg (1987)	12	30.71 (6.76)	.74	29.38 (6.24)	.74
Friendship Qualities Scale	Bukowski, Hoza, and Boivin (1994)
Positive friendship		18	69.65 (14.83)	.87	67.59 (14.20)	.89
Conflict with friends		5	19.44 (4.12)	.84	17.56 (4.05)	.85
Students’ Perceptions of the Classroom Environment	Ryan and Patrick (2001)
Promoting interaction within the classroom		6	26.06 (8.38)	.84	26.74 (7.35)	.87
Mutual respect		6	19.88 (5.56)	.85	20.38 (5.07)	.88
Performance goals		6	21.93 (6.04)	.73	22.23 (6.00)	.74
Teacher support		6	13.66 (4.77)	.85	13.79 (4.65)	.88
Teacher Psychological Control Scale	Adopted from Barber (1996)	7	10.00 (3.18)	.85	9.91 (3.15)	.89
School Bonding Scale	McFall (2005)	16	55.69 (10.62)	.83	55.07 (10.08)	.83
School Climate Scale	Coker and Borders (2001)	5	22.30 (5.05)	.79	23.33 (4.78)	.84

Measures of Bullying

Peer Bullying Questionnaire

We used an adapted version of the multidimensional Peer Victimization Questionnaire (Mynard & Joseph, 2000) by rewording the items (e.g., “I hit the others” instead of “The others hit me.” The adapted version was shown as a reliable and valid measurement tool by a previous study done in Turkey (Gultekin & Sayil, 2005). It was a 3-level Likert-type scale (response options: 1 = never, 2 = once, 3 = more than once). There was a total of 36 items. These items were loaded under three dimensions (relational bullying, α = .88; physical bullying, α = .89; teasing, α = .75) and Cronbach’s alpha was .92 for the whole questionnaire.

Peer nomination for bullying

In line with Hawley, Little, and Card’s (2007) questions, we used standard within class restricted peer-nomination procedures. The students were asked to nominate at least 1 and at most 3 peers showing overt and relational aggression within the class. A total of four questions were used: two questions for overt aggression (“Who starts fights to get what they want?” “Who pushes, kicks, or punches others because they’ve been angered by them?” α = .85) and two questions for relational aggression (“Who tells their friends to stop liking someone in order to get what they want?” “Who gossips or spreads rumors about others if they’re mad at them?” α = .79). Total nominations were converted to z scores to decrease the potential effect of class size over total scores.

Other than these measures, four categories were created to evaluate perceived teacher and school administration disciplinary methods (two indicators of school psychological environment): harsh and insensitive, sensitive but irregular, insensitive and neglecting, and sensitive and normative. We combined three disciplinary methods (i.e., harsh and insensitive, sensitive but irregular, insensitive and neglecting) as nondemocratic disciplinary methods, and we identified sensitive and normative as democratic disciplinary methods. These two variables were used to observe the psychological environment of the school.

Procedure

To conduct the study, informed consent was obtained from the Ministry of Education and school administrations in North Cyprus. Voluntary participants were required to complete a number of questionnaires which approximately took 1 hr and 30 min. For this reason, the completion of the questionnaires took place in two sessions. The questionnaires which measured the perceptions about the attitudes of mothers and fathers were applied in different sessions to prevent probable overlapping responses. On completion of the questionnaires, participants were given a pencil each.

Class rosters were collected from class teachers for peer-nomination measures. A paper with the names of students and their student numbers within the class was given to each student, and they were asked to nominate 1 to 3 peers from the list. Two researchers were present in the sessions to give the instructions and to answer any questions.

Results

Incidence of Bullying in North Cyprus and Turkey

When we used “bullying at least once in the past 1 month period” as a criterion for bullying, 76% of the participants in North Cyprus and 80% of the participants in Turkey reported that they bullied someone. When we used “1 standard deviation over the mean score” as a criterion, 10% and 13% of the participants were bullies in North Cyprus and Turkey, respectively.

Testing the Hypothesized Model

Covariance matrixes and maximum likelihood estimation were used in testing the model. The fitness of the model to the data was analyzed by depending on chi-square goodness of fit, goodness-of-fit indexes (GFIs), and comparative fit indexes (CFIs). The scores from the aforementioned questionnaires and scales were used as the observed variables (indicators) of the measurement model and SEM.

Before conducting SEM, measurement model was tested to see which latent variables were represented best by which observed variables. In this sense, measurement model can be evaluated as a confirmatory factor analyses which is used to see the relationship between observed variables and latent variables. In the cases when the latent variable was represented by only one observed variable, the error variance of the observed variable was stabilized to zero or the value resulting from ([1 – variance of the variable] × [alpha of the variable]) equation (i.e., negative peer relationships was represented only by peer conflict and negative teacher attitudes was represented only by teacher psychological control).

Measurement models fitted the data very well: χ²(362, N = 544) = 314.35, p = .97, root mean square error of approximation (RMSEA) = .00, GFI = .96, adjusted goodness-of-fit index (AGFI) = .95, CFI = 1.00, non-normed fit ındex (NNFI) = 1.00 for North Cyprus and χ²( 362, N = 509) = 16.32, p = .99, RMSEA = .00, GFI = .99, AGFI = .98, CFI = 1.00, NNFI = 1.00 for Turkey. Modification indexes indicated that there was no need for any modification of the measurement model of the North Cyprus data. However, the model indicated that physical environment of the school explained 1% of the variance in bullying and negative peer relationships explained 3%. Because of these low values, it was decided not to take these latent variables in SEM. Modification indexes indicated that there was no need for any modification for the measurement model of the Turkey data. However, the model indicated that negative peer relationships and negative teacher attitudes within the classroom explained 0.8% and 0.3% of the variance in bullying, respectively, and physical environment of the school explained .05%. Because of these low values, it was decided not to take these latent variables in SEM. Moreover, correlations between latent variables showed that these variables had no significant correlation with bullying. This result also indicated that these latent variables should not be placed in the hypothesized model (see Table 3). Bullying latent variable was represented by self-reported relational bullying, physical bullying, teasing, and peer-reported overt bullying. Peer-reported covert bullying was omitted from both models because of the low standardized coefficients (.07 and .05, respectively, in North Cyprus and Turkey).

Table 3.

Correlations Between Latent Variables

	1	2	3	4	5	6	7	8	9	10
1	—	.67**	−.32**	.29**	.05	.59**	−.05	.72**	.03	−.38**
2	.67**	—	−.42**	.54**	−.03	.50**	−.06	.59**	.07	−.31**
3	−.26**	−.43**	—	−.12*	.07	−.31**	.08	−.47**	−.14*	.39**
4	.80**	.49**	−.15**	—	−.03	.19*	−.01	.24**	.02	−.06
5	−.05	−.09	.24	−.06		−.04	.02	−.05	.01	.09
6	.69**	.51**	−.19**	.55**	−.02	—	−.14*	.87**	.03	−.37**
7	.22**	.05	.29**	−.08	.09	−.01	—	−.16*	−.05	.10
8	.87**	.71**	−.37**	.64**	−.13*	.83**	−.10	—	−.17*	−.42**
9	.23**	.05	−.02	.21**	−.05	.13*	.00	.13*	—	.24**
10	−.23**	−.30**	.49**	−.29**	.09	−.24**	.43**	−.34**	.10	—

Note: 1 = Individual social competence factors; 2 = Parental rejection factors; 3 = Parental acceptance factors; 4 = Positive peer relationships; 5 = Negative peer relationships; 6 = Positive teacher attitudes; 7 = Negative teacher attitudes; 8 = Psychological environment of school; 9 = Physical environment of school; 10 = Bullying. Shaded area includes the values for Turkey sample.

p < .05. **p < .001.

The hypothesized models for the North Cyprus and Turkey samples were exactly the same except for negative teacher attitudes within the classroom latent varible in North Cyprus model. This can be caused by the indicator (i.e., teacher psychological control) of the latent variable. In other words, students in Turkey might perceive their teachers not to use psychological control techniques within the classroom or they might be unwilling to report these attitutes. This could result in a more homogeneous variance in indicator variable and an insignificant standardized coefficient between negative teacher attitudes within the classroom latent varible and bullying.

GFIs indicated that hypothesized multifactor model fitted the data well both in North Cyprus and Turkey: χ²(246, N = 544) = 456.48, p < .001, RMSEA = .04, GFI = .93, AGFI = .92, CFI = .91, NNFI = .90 for North Cyprus and χ²(246, N = 509) = 270.84, p < .01, RMSEA = .08, GFI = .91, AGFI = .88, CFI = .90, NNFI = .90 for Turkey. The order of the predictors was the same both in North Cyprus and Turkey. The strongest predictor of bullying was psychological environment of school (standardized coefficients = −.88 and −.87 in North Cyprus and Turkey, respectively). This was followed by individual social competence factors (standardized coefficients = −.79 and −.74 in North Cyprus and Turkey, respectively), positive teacher attitudes (standardized coefficient = −.77 and −.73 in North Cyprus and Turkey, respectively), parental acceptance factors (standardized coefficient = −.73 and -.72 in North Cyprus and Turkey, respectively), positive peer relationships (standardized coefficient = −.69 and −.63 in North Cyprus and Turkey, respectively), and parental rejection factors (standardized coefficient = .43 and .54 in North Cyprus and Turkey, respectively). Negative teacher factors had no significant effect on Bullying in North Cyprus model. Except for this, all standardized coefficients were significant in both models. All latent variables explained 44% and 51% of the variance in bullying in North Cyprus and Turkey, respectively (see Figure 1).

Figure 1.

Hypothesized multifactor model for bullying

Discussion

As mentioned before, the main aim of this study was to test a hypothesized multifactor model of bullying among adolescents in North Cyprus and Turkey. The results revealed that the strongest predictor of bullying was psychological environment of school followed by individual social competence factors, positive teacher attitudes, parental acceptance factors, positive peer relationships, and parental rejection factors in both North Cyprus and Turkey. Therefore, the hypothesis that narrow versus wide socialization would be related to bullying was partially fulfilled. The prevalence rates showed that bullying rates were higher in Turkey as a wide socialization place, but the predictors of bullying (even the order of the predictors from most strong to least strong) were the same in both samples. Therefore, the hypothesis related to the rank of the predictors was met. In other words, both distal and proximal factors have to be taken into account while conducting research on bullying. Also, according to the results, to understand the underlying mechanisms of bullying, we have to analyze the micro- and exosystem variables rather than macrosytem following Bronfenbrenner’s (1979) model.

It was supposed that restructuring the schools as democratic systems, developing students’ bonding to the school, and encouraging teachers and administrative staff of the school to use authoritative discipline techniques would be the most critical points in antibullying intervention and prevention programs both in North Cyprus and Turkey. Therefore, this result along with those of Olweus (2005) indicated that the school must be central to any antibullying program. In other words, to prevent bullying it is critical to enrich the psychological environment of the schools. Moreover, according to this result, we suppose that there might be some common features of the school psychological environment in North Cyprus and Turkey. Previous studies indicated that teachers and administrative staff in North Cyprus and Turkey generally follow authoritarian rather than authoritative discipline techniques (Gursoy & Aksoy, 2007; Mahiroglu & Buluc, 2003). A nondemocratic environment might decrease students’ bonding to the school because it is well known that participation in the school’s administrative decisions would increase people’s commitment to the school (e.g., Libbey, 2004).

Individual social competence factors which were represented by social cognition, academic efficacy, and coping abilities were the second strongest predictor of bullying both in North Cyprus and Turkey. It was supposed that the deficiency of these variables was related with bullying behaviors among adolescents in both regions. As mentioned above, the direction of relationships between bullying and these individual factors might change according to the type of bullying. In other words, bullying behaviors were related to the deficiency of social cognition, academic efficacy, and adaptive coping abilities if bullying was dominantly represented by overt bullying. Results indicated that bullying latent variable was represented strongly by self-reported physical bullying (standardized coefficient was higher than self-reported relational bullying and teasing) and peer-reported overt bullying. According to these results, we might suppose that bullying in both samples was represented by overt, physical bullying rather than covert bullying. Therefore, these results were comparable with Crick and Dodge’s (1999) deficiency model. Of course, the results could be read from a positive viewpoint: developing social cognitive abilities, academic efficacy, and coping abilities would decrease bullying.

Positive teacher attitudes within classroom were the third most powerful predictor of bullying in both samples. This result showed that teachers were important figures in preventing bullying by using positive attitudes and methods within their classroom. In other words, when students perceived support from their teachers and perceived that their teachers promote mutual respect and interaction between students and try to develop performance goals in the class, the risk to bully others might decrease. Three conclusions can be drawn from this result. First, if a teacher as a role model does not abuse his or her power, the students within the classroom will learn that there are other ways to show power (e.g., supporting the weak ones). Second, if peer support as a well-known protective factor against victimization (Kumpulainen, Rasanen, & Henttonen, 1999; Pellegrini, Bartini, & Brooks, 1999) was promoted by teachers within classrooms, this can decrease bullying as well. Third, according to this result it was supposed that creating a democratic environment in the school might start in the classroom by encouraging teachers to behave authoritatively. In other words, creating a bullying-free environment in the school can be induced by preventing bullying within classrooms and then generalizing this to the whole school.

Another powerful predictor both in North Cyprus and Turkey was parental acceptance factors. This result indicated that when parents monitored their adolescents, when there was a close relationship with parents and adolescents, and when the adolescents disclosed themselves to their parents, bullying decreased. This was an expected result and parallel to the previous studies (i.e., Atik, 2006; Crouter, & Head, 2002; Olweus, 1993; Vail, 2000). However, when parents reject their children by using psychological control techniques and by engaging in conflict, bullying increased. This too was an expected result and was parallel to the previous studies (Demaray & Malecki, 2003; Georgiou, 2008). Therefore, an antibullying program in North Cyprus or Turkey should definitely aim to develop parental acceptance factors and to reduce parental rejection factors by adding the families to the program. However, we have to remind that the associations between bullying and some indicators (i.e., self-disclosure and parental psychological control) were studied for the first time, and these relationships must be reanalyzed by future studies with different samples.

The results revealed that positive peer relationships were negatively related with bullying in both samples. In other words, when the participants perceived a high-quality friendship and secure attachment to their peers, the risk to bully decreased. This result was comparable with previous studies (Chang et al., 2005; King & Terrace, 2006) and showed again that the bullies in both our samples were using overt strategies dominantly because, as we mentioned before, bullies who use more covert strategies might have high-quality and securely attached friendships (Hawley, 2003; Xie et al., 2002).

The standardized coefficients for positive teacher attitudes, parental acceptance factors, and positive peer relationships were very close to each other in both samples (positive teacher attitudes: standardized coefficient = −.77 and −.73 in North Cyprus and Turkey, respectively; parental acceptance factors: standardized coefficient = −.73 and −.72 in North Cyprus and Turkey, respectively; positive peer relationships: standardized coefficient= -.69 and -.63 in North Cyprus and Turkey, respectively). According to these values, we might suppose that teachers, parents, and peers had very similar influences over adolescents’ bullying behaviors in North Cyprus and Turkey. This result was parallel to Masten’s (2006) principle of multidimensionality which suggested that proximal and distal factors should be handled at the same time. This result also indicated that a multidimensional approach which handle all stakeholders should be applied in antibullying programs in both regions.

As with all hypothetical models, the proposed model was not free from limitations. First of all, the model depended on cross-sectional data. This made it difficult to discuss cause-and-effect relationships within the model. Therefore, retesting this model in a longitudinal research (especially including the school shifting times) would strengthen the revealed results.

In our models, gender and age differences were omitted for practical reasons. In other words, retesting the proposed model for boys and girls and for early, middle, and late adolescents separately would exceed the main aims of the study. However, because of different socialization processes, the model could yield various results for boys and girls. Moreover, because of rapid changes in adolescence, the relationships between bullying and its predictors could change in different age groups (i.e., early, middle, and late adolescence). Therefore, the model has to be retested for gender and age groups in the following studies.

Latent variable of bullying was represented by four indicators (i.e., teasing, relational bullying, physical bullying, and peer-reported overt bullying). In the following studies, retesting the model with single indicators for bullying might give valuable results. It is well known that single indicators can decrease the GFIs in SEM. However, a method called parceling which randomly divides the questionnaire items into two or three and creates multiple indicators from a single indicator can be used. In some conditions, this method can reveal better GFIs (Bandalos, 2002).

Of the variance in bullying, 56% and 49% stayed unexplained in both samples. The models can be improved by adding various other variables (e.g., marital conflict for negative parental factors, self-esteem for individual social competence factors) or dimensions (e.g., biological-hereditary factors). These added variables and dimensions could increase the explained variance.

In conclusion, despite all the limitations mentioned above, our results revealed that theoretically based multifactor models might be tested by using the advantages of advanced statistical techniques. Testing these kinds of models seem to be so critical because these models would be the basis of multidimensional antibullying programs.

Footnotes

Acknowledgements

This study is dedicated to the author’s mentor Prof. Dr. Melike Sayil (Hacettepe University-Turkey).

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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