Traditional Bullying, Cyberbullying, and Subjective Well-being Post-COVID-19 in Indonesia

. Previous research has highlighted bullying as a signiﬁcant issue in Indonesia, with a notable increase in cyberbullying among adolescents during the COVID-19 pandemic, adversely affecting their psychological well-being. However, there has been limited discussion on bullying in the post-COVID-19 era. This study has three aims. The ﬁrst is to examine the Subjective Well-Being (SWB) of children who have either experienced bullying or have not been bullied after the pandemic. The second is to assess the occurrence of both traditional bullying (involving siblings and at school) and cyberbullying in Indonesia following COVID-19. The third aim is to investigate the factors linked to these forms of bullying post-pandemic. The participants were middle school students ( N = 943; 57.2% girls, 45.0% in grade 7). The Children’s Worlds Subjective Well-Being Scale with ﬁve items (CW-SWBS5) was employed to assess children’s SWB. Separate measures were utilized for traditional and cyberbullying. Six factors of family, school climate, personal satisfaction, friendships, safety, and social media usage were analyzed as independent variables using linear regression to determine their impact on bullying forms. The Structural Equation Model (SEM) was applied to evaluate how these bullying types affect SWB. The ﬁndings revealed that sibling bullying and cyberbullying signiﬁcantly inﬂuenced children's SWB, with girls reporting lower SWB scores than boys. Cyberbullying emerged as the most prevalent form of bullying post-pandemic. A positive school climate was found to shield children from bullying. Cummins’ theory of well-being homeostasis was employed to interpret the results. This study also discussed implications for educators and parents.

Borualogo at al. ∥ Subjective Well-being  in Indonesia in correlation with SWB: family, friends, school, neighborhood, self, and time use (Rees et al., 2020), including siblings and school bullying.Cummins (2014) makes clear the homeostasis theory of SWB, which states that while individuals experience undesirable situations, SWB is actively inhibited and sustained similar to the homeostatic feed of body temperature.SWB homeostasis aims to support a decisive sense of well-being, which is considered general and somewhat abstract (Cummins, 2014).When people are asked about their satisfaction with life altogether, their replies reflect the rooted, secure, joyous mood that is the element of SWB.They are based on cognitive and affective appraisal.This general and abstract sense of optimistic perspective is what homeostasis seeks to maintain (Cummins, 2014).Each individual has a homeostatic system that, when projected onto a scale of 100, has a controllable SWB set point range from 60-90, with an average of 75, where 0 means not satisfied at all and 100 means very satisfied (Cummins, 2014).Homeostasis theory also explains that if a person experiences something that inhibits SWB below its set point, the system will restore the level of SWB into the normal range (Cummins, 2014).This mechanism also refers to the process of adaptation.
Although there are number of studies on bullying, little attention has been paid to the correlation between traditional bullying and cyberbullying and the SWB of children (Borualogo & Casas, 2023;Borualogo et al., 2023;Przybylski & Bowes, 2017;Rodriguez-Rivas et al., 2022), let alone research on bullying and SWB post-COVID-19.This study investigates traditional bullying, cyberbullying and SWB of children post-COVID-19 in Indonesia.
There are three objectives of this study.The first objective is to scrutinize the children' SWB who were bullied or never bullied post-COVID-19.The second objective is to analyze the prevalence of traditional bullying (bullying by sibling and school bullying) and cyberbullying post-COVID-19 in Indonesia.The third objective is to explore factors correlated with sibling bullying, school bullying, and cyberbullying post-COVID-19.This study will assist parents, teachers, and policymakers in preventing traditional bullying and cyberbullying, particularly post-COVID-19.The hypothesis of this research are 1) Children reporting higher levels of traditional bullying and cyberbullying victimization post-COVID-19 will be reporting lower levels of SWB.; 2) A positive individual perception of family, friends, school climate, satisfaction, perception of safety, and social media use will be associated with a lower frequency of traditional bullying and cyberbullying.

Participants
The study used cluster random sampling, which included 16 schools in Kota Bandung, West Java Province.Kota Bandung is among the highest bullying incidents in West Java Province (Borualogo & Gumilang, 2019).There were middle school students (N = 943, 57.2% girls, 42.8% boys) grades 7 (n = 424; 45.0%), 8 (n = 306; 32.4%), and 9 (n = 213; 22.6%) with mean age 13.75.Table 1 presents the characteristics of participants.in classes, while ten schools used paper and pencil data collection at school.Data collection used the Google Form.A link to the questionnaires was sent to parents from the schools via WhatsApp.

JURNAL PSIKOLOGI
An introduction to the study and required consent for their children to participate were also sent to parents via WhatsApp.Parents' consent was obtained, then they passed the questionnaire to their children to complete.The study has obtained ethical approval from Nusantara Scientific Psychology Consortium (Konsorsium Psikologi Ilmiah Nusantara, K-PIN) with the letter of approval number 083/2023/Etik/KPIN.

Instruments Bullying Victimization
This study used two items for the frequency of bullying victimization by siblings, three for the frequency of school bullying victimization, and nine for the frequency of cyberbullying.The five sibling and school bullying items were captured from the Children's Worlds international survey (Rees et al., 2020) and reworded into Indonesian (Borualogo & Casas, 2019).Cyberbullying is taken from Patchin and Hinduja (2015) and has been adapted and reworded into Indonesian (Borualogo & Casas, 2023).The cyberbullying scale includes one global item and eight specific behaviors (Patchin & Hinduja, 2015).
Sibling bullying victimization was measured by the frequency of two types of bullying: physical ("How often in the last month have you been hit by your siblings?")and verbal ("How often in the last month have you been called unkind names by your siblings?").School bullying victimization was measured by the frequency of physical ("How often in the last month have you been hit by other children in school?"),verbal ("How often in the last month have you been called unkind names by other children in school?"), and emotional bullying ("How often in the last month have you been left out by other children in your class?") (Borualogo & Casas, 2023).These items are scored on a four-point frequency scale using four response options: 0 = Never, 1 = Once, 2 = Two or three times, and 3 = More than three times (Borualogo & Casas, 2023).Someone spread rumors about me online," (6) Someone threatened to hurt me through a cell phone text message," (7) Someone threatened to hurt me online," and (8) Someone pretended to be me online and acted in a way that was mean or hurtful (Patchin & Hinduja, 2015).These items are scored on a five-point frequency scale using response options: 0 = Never, 1 = Once, 2 = A few times, 3 = Several times, and 4 = Many times.

Family
The relationships with family was measured through five items: (1) "There are people in my family who care about me"; (2) "If I have a problem, people in my family will help me"; (3) "We have a good time together in my family"; (4) "I feel safe at home"; and (5) "My parents listen to me and take what I say into account."These items are scored using a five-point scale where 0 = I do not agree, 1 = I agree a little, 2 = I agree somewhat, 3 = I agree a lot, and 4 = I totally agree (Rees et al., 2020).

Friends
The relationships with friends was measured by four items: (1) "I have enough friends"; (2) "My friends are usually nice to me"; (3) "Me and my friends get along well together"; and (4) "If I have a problem, I have a friend who will support me ."Theseitems are scored using a five-point scale where 0 = I do not agree, 1 = I agree a little, 2 = I agree somewhat, 3 = I agree a lot, and 4 = I totally agree (Rees et al., 2020).

School Climate
School climate was measured through six items: (1) "My teachers care about me"; (2) "If I have a problem at school, my teacher will help me"; (3) "If I have a problem at school, other children will help me"; (4) "There are a lot of arguments between children in my class"; (5) "My teachers listen to me and take what I say into account"; and (6) "At school, I have opportunities to make decisions about things that are important to me ."Theseitems are scored using a five-point scale where 0 = I do not agree; 1 = I agree a little; 2 = I agree somewhat; 3 = I agree a lot; 4 = I totally agree (Rees et al., 2020).

Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia
Satisfaction Items There were different aspects to measuring satisfaction.Family: "How satisfied are you with the people you live with?".Friends: "How satisfied are you with the relationship you have with your friends?".
Being listened to: "How satisfied are you with being listened to by adults?".Student: "How satisfied are you with your life as a student?".Things you have learned at school: "How satisfied are you with things you have learned at school?".Other children: "How satisfied are you with other children in your class?".This item is scored using an 11-point scale where 0 = Not at all satisfied and 10 = Totally satisfied (Rees et al., 2020).

Perception of Safety
The level of safety perceived by participants was measured by three observed variables: (1) "I feel safe when I walk in the area I live in," (2) "I feel safe at home," and (3) "I feel safe at school ."Theseitems are scored using a five-point scale where 0 = I do not agree; 1 = I agree a little; 2 = I agree somewhat; 3 = I agree a lot; 4 = I totally agree (Rees et al., 2020).

Social Media Used
We also used one item to measure social media: "How would you describe yourself as a social media user (Instagram, Facebook, TikTok)?The item is scored using a five-point scale where 0 = I never use social media, 1 = I rarely use social media, 2 = I sometimes use social media, 3 = I often use social media, and 4 =I very often use social media.

Data Analysis
We conducted several analyses to understand the effect of traditional bullying and cyberbullying on SWB, the prevalence of bullying incidents post-COVID-19, and factors associated with bullying incidents.A two-steps depuration procedure was performed.First, based on Casas (2016) recommendation, cases with three or more missing values in the SWB scale should be dropped for further analysis.Twenty-four cases were deleted.SEcond, the remaining missing values in the SWB scales were replaced with multiple imputation regression.
The Structural Equation Model (SEM) was used to analyze the data.SEM represents a set of data analysis techniques comprising Confirmatory Factor Analysis (CFA), multiple regression, and path analysis (Schreiber et al., 2006).Hox and Bechger (1999)  According to Brown (2006), the data can be stated as normal distribution if the appropriate skewness values are between -3 and +3, and kurtosis values between 10 and +10.Kline (2023) stated that data indicated problem when it has skewness values more than 3 and kurtosis values more than 10, therefore it is recommended that the value be less than that.Hair et al (2010) and Byrne (2016) argued if skewness values between -2 and 2 and kurtosis values between -7 and 7, then it can be stated that the data is normally distributed.All objects in this study have a skewness and kurtosis values which is required for performing CFA.
Table 2 shows that gender correlates with SWB (p = .281)and school bullying (p = .068).Grade negatively correlates with school bullying (p = -.066).SWB negatively correlates with all bullying victimization.Sibling bullying correlates with school bullying (p = .365)and cyberbullying (p = 286), and school bullying correlates with cyberbullying (p = .462).This SEM displays an acceptable fit, as presented in Figure 1.Loadings for the CW-SWBS items on its latent variable are between .79 and .93.Field (2005) suggests that a value of at least .6 was excellent regardless of the sample size.
School bullying items correlated with cyberbullying (Figure 1).There are two constructs measured at once.This unmodeled data pattern is due to the effect of the method (Castro-Schilo et al., 2016).The results of correlation errors that occur between measurements also indicate that there are Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia constructs that have not been captured in the model (Kline, 2023).In this case, a correlation was found between items on the school bullying scale and items measuring cyberbullying.We cannot yet determine why this occurs, or why there is a similarity in direction between the two.However, in fact, cyberbullying and school bullying are related (Baldry et al., 2017;Beran & Li, 2008).Therefore, it can be interpreted that children who are bullied in school tend to be bullied in cyber too.Both cyberbullying or school (traditional) bullying behavior predict the same outcomes (Thomas et al., 2014) and the role of traditional bullying, whether as victim or perpetrator, indicated the same role in cyberbullying (Raskauskas & Stoltz, 2007).
The effect of cyberbullying on SWB was -.13 (p = .000),which can be interpreted that when cyberbullying increases by one standard deviation, SWB decreases by .13standard deviations.We examined this model as a multi-group by each grade and gender, and fit statistics were considered satisfactory.The fit statistics do not display any decrease more significant than .01 with each additional constraint.Cheung and Rensvold (2002) stated that the CFI value between compared models is not more than .01; the model can be said to have no difference.Therefore, association, mean scores, and regression are worthy of comparison across groups, and answering styles can be considered equal (Table 5).

Prevalence of Bullying Victimization
Table frequency of bullying incidents post-COVID-19 between gender and grade presented in the supplement.The results are presented here.
Post-COVID-19, children reported experiencing being hit by sibling two or three times (11.7%) and more than three times (10.4%),being called unkind names by sibling two or three times (8.6%) and more than three times (9.9%), being hit by other children at school two or three times (6.7%) and more than three times (6.0%), and being called unkind names by other children at school two or three times (11.7%) and more than three times (15%), being left out two or three times (7.1%) and more than three times (7.6%), and being cyberbullying a few times (16.8%),several times (4.9%) and many times (2.4%).

Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia
More girls reported sibling bullying than did boys.More girls reported being hit by siblings two or three times (6.6%) and more than three times (5.6%) than did boys (5.1% and 4.8%, respectively).
More girls also reported being called unkind names by siblings two or three times (5.1%) and more than three times (6.2%) than did boys (3.5% and 3.7%, respectively).
In contrast, more boys reported school bullying than girls, except for being left out by other children.More boys have been hit by other children two or three times (4.5%) and more than three times (3.4%) and been called unkind names two or three times (6.0%) and more than three times (8.2%).
Girls reported being left out more frequently by other children two or three times (4.9%) and more than three times (5.8%) than boys.
More girls reported being cyberbullied a few times (8.6%), several times (3.6%) and many times (1.6%) than did boys (8.2%, 1.3%, and 0.8%, respectively).ȃMoreparticipants from grade 7 reported sibling bullying, being bullied by children, and cyberbullying than did grade 8 and grade 9. Grade 9 reported fewer bullying experiences than did the two other grades.
Table 6 presents the mean frequency of bullying incidents post-COVID-19.Only significant ones are presented here.There are significant grade differences in being hit by siblings (p = .024),where siblings were more frequently hitting younger children (M = 1.780) than older children (M = 1.712 for grade 8 and M = 1.545 for grade 9).There are significant gender (p = .000)and grade (p = 0.47) differences in being hit by other children in school.Boys (M = 1.594) and younger children (M = 1.512) were more frequently being hit by other children than did girls (M = 1.321), grade 8 (M = 1.398) and grade 9 (M = 1.347).
There are significant gender (p = .000)differences in the other two school bullying incidents.
Boys (M = 2.012) were more frequently being called unkind names than girls (M = 1.699).At the same time, girls were more frequently being left out by other children in the class (M = 1.654) than boys (M = 1.376).For cyberbullying, two bullying incidents are gender differences significance: someone posted a mean or hurtful comment (p = .000),and someone created a mean or hurtful webpage (p = .000).Note: Sibling bullying: F = 4.088; df1 = 21; df2 = 910; Adjusted R square = .082;Sig = .000 School bullying: F = 8.893; df1 = 27; df2 = 9105; Adjusted R square = .185;Sig = .000 Cyberbullying: F = 7.880; df1 = 27; df2 = 910; Adjusted R square = .165;Sig = .000 The models could explain lower to higher percentages of the variability of the dependent variables.The highest percentage was school bullying, which explains 18.5% of the variability of the dependent variables.Cyberbullying explained 16.5% of the variability of the dependent variables.
The lowest percentage was sibling bullying, which explains 8.2% of the variability of the dependent variables.
Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia The positive β scores in linear regression indicated that being a boy is correlated with a higher frequency of school bullying (β = .173)and cyberbullying (β = .072).There are a lot arguments between children in class appear to be correlated with a higher frequency of sibling bullying (β = .095),school bullying (β = .195),and cyberbullying (β = .107).Social media (β = -.106) is correlated with a lower frequency of sibling bullying.Meanwhile, social media used (β = .113)is correlated with a higher frequency of cyberbullying.
Not getting well with friends (β = -.108),not being satisfied with other children (β = -.172), and feeling unsafe at school (β = -.121) are associated with a higher frequency of school bullying.
Not getting well with friends (β = -.103),not satisfied with being listened to (β = -.100),non-satisfied with other children (β = -.188), and not feeling safe at school (β = -.091) are correlated with a higher frequency of cyberbullying.Making important decisions at school (β = .084)and being satisfied with life as a student (β = .097)are correlated with a lower frequency of cyberbullying.

Subjective Well-Being of Bullied Children
This study aimed to investigate the effect of traditional bullying and cyberbullying on students' SWB.
This study found a significant negative effect between being called unkind names by siblings and cyberbullying and students' SWB post-COVID-19.These findings aligned with a study in Indonesia before COVID-19 that showed siblings who called other children unkind names showed significant adverse effects (Borualogo, 2021).Home is supposed to be a place for children that is the safest.
However, this is different.Parents return to offline work post-COVID-19 and leave their children at home unsupervised.Many Indonesian children live at home with their siblings during the day.
Alternatively, the children live at home with a home assistant who never supervises them because they are busy with household chores.This situation increased the number of siblings bullying -particularly called unkind names-and this has affected their SWB.These findings align with a study by Borualogo and Casas (2023).
This current study also revealed that cyberbullying affected students' SWB post-COVID-19.Problematic social media use was most strongly and consistently associated with cyberbullying (Craig et al., 2020).Media reported that children who use social media to socialize with other children become victims of cyberbullying (Jurnal Post, 2021).Children have the possibility of exposure to danger, harm, and hazard.Risk brings the potential for adverse outcomes (Bauman & Rivers, 2023).A study said cyberbullying was lower when peer relationships were more robust (Eden et al., 2023).

Conclusion
The objective of this study was to examine the impact of traditional bullying, cyberbullying, and Subjective Well-Being (SWB) among Indonesian children following the COVID-19 pandemic.The research revealed that traditional forms of bullying, especially being called unkind names by siblings, along with cyberbullying, significantly diminished children's SWB post-pandemic.Although children often endure bullying, they frequently conceal their struggles from parents and teachers, potentially leading to severe issues.Cyberbullying emerged as the predominant form of bullying in the post-pandemic period.While both boys and girls experienced bullying, they faced different forms, with girls reporting lower SWB scores than boys, indicating a degree of forced adaptation to these adverse situations.Thus, it is crucial for parents and teachers to recognize the potential severity of these issues.
The study also emphasizes the need for school authorities to foster a positive school climate to mitigate bullying and cyberbullying.Additionally, the role of social media in cyberbullying was highlighted as a critical area for intervention.

Recommendation
However, the study has several limitations.Notably, the Structural Equation Modeling (SEM) analysis used only one general item to measure cyberbullying, leading to a poor model fit when combined with eight specific behavioral items, which likely measured overlapping aspects of cyberbullying.This methodological issue underscores the need for future research to employ multiple items to better capture the nuances of cyberbullying in the Indonesian context, as suggested by similar research by Patchin and Hinduja (2022).Moreover, the cross-sectional nature of the study and the timing of data collection post-COVID-19 limit the generalizability of the findings.Lastly, the study did not

Declaration
Cyberbullying victimization was measured by one global cyberbullying question (I have been cyberbullied) and eight specific behaviors: (1) Someone posted mean or hurtful comments about me online," (2) Someone posted a mean or hurtful picture online of me online," (3) Someone posted a mean or hurtful video online of me online," (4) Someone created a mean or hurtful web page about me," (5) Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia include children under 13, pointing to a gap that future research should address by exploring younger Borualogo at al. ∥ Subjective Well-being Post-COVID-19 in Indonesia children's perceptions of their SWB post-pandemic.

Table 1
Characteristic Participants Data collected on Mei 2023.It has been eight months since students went back to school.Students have already been back to learning face-to-face since September 2022.Six schools administered online Hooper et al. (2007)ysis can estimate the parameters of the correlation between variables and evaluate the fit structure of models about the data.Hooper et al. (2007)recommended while using SEM to use more than one suitable index to Subjective Well-beingCorrelation between variables, cross-tabulation, mean scores, and frequencies were computed separately using SPSS 25.ANOVA was used to test the mean differences between gender and age groups.The contribution of independent variables (family relationships, school climate, relationships with friends, satisfaction, safety, internet use, and learning changes) on each bullying incident (sibling bullying, school bullying, and cyberbullying) with gender and age groups as control variables was analyzed by linear regression.The scores for CW-SWBS5 were converted into a 0−100 scale; therefore, they were visually comparable in the tables.SPSS version 25 and AMOS 23 were used for all analyses.
Marsh et al. (2010)re considered tolerable errors of approximation, and according toMarsh et al. (2010), CFI of above .90wasconsidered to reflect tolerable fit to the data.Borualogo at al. ∥

Table 3
presents the mean scores of CW-SWBS by grade and gender.Girls (M = 69.19)were significantly lower than boys (M = 81.39).There are no significant differences between grades.

Table 4
Mean Scores of CW-SWBS by Grade and Gender Patchin and Hinduja (2022)itionalBullying,Cyberbullying,andSWB of Bullied ChildrenTo analyze the consequence of all bullying types here discussed on SWB, an SEM was constructed linking two sibling bullying items, three school bullying items, and one cyberbullying item, gender, and age, with the latent variable CW-SWBS, which was considered an endogenous variable in the model.We only used one general cyberbullying item (I have been cyberbullied) post-COVID-19, asPatchin and Hinduja (2022)did in a study before and during COVID-19.

Table 7 (
Continued) Zhao et al. (2021)021)023)indicated the increased cyberbullying incidents during COVID-19 because students use online platforms more frequently.This current study revealed that students who experience cyberbullying most likely experienced school bullying by the same person who perpetrated them.According toCarvalho et al. (2021), cyberbullying is related to school bullying.Children who reported being victims of cyberbullying also reported being victims of school bullying.Baldry et al.Subjective Well-being Post-COVID-19 in Indonesia things to children is correlated with a lower frequency of being cyberbullied.Children who have the opportunity to be heard at school in making important decisions for themselves feel that teachers understand their needs, particularly about bullying experiences.A positive school climate is an important factor to protect children from bullying.childrenfrombullying.This current study is aligned with research byZhao et al. (2021)that revealed that a positive school climate was correlated with bullying victimization with a sample of Chinese adolescents, mediated by self-esteem.Social media use is correlated with a greater frequency of being cyberbullied.During COVID-19, children communicated with friends using social media.It has become an innate part of adolescent life.