descriptive research design about cyberbullying

Cyberbullying: relationship with developmental variables and cyber victimization

Affiliations.

• 1 Yenimahalle Science and Art Center, Republic of Turkey Ministry of National Education, Ankara, Turkey.
• 2 Faculty of Educational Sciences, Ankara University, Ankara, Turkey.
• PMID: 33520776
• PMCID: PMC7685499
• DOI: 10.21307/sjcapp-2020-004

Background and objective: Cyberbullying is increasingly turning into a significant problem for children and adolescents due to its adverse psychological and academic outcomes. In the present study, the protective and risk factors for cyberbullying has been investigated. One of the aims of the study was to examine the relationship between peer relations, negative emotion regulation strategies, and cyberbullying. The successful identity development process is thought to influence both cyberbullying behaviors as well as adolescents' peer relations and emotion regulation. Also, cyber victimization is seen as a risk factor for cyberbullying. The second aim of the study is to investigate the causal relationship between cyber victimization and cyberbullying.

Method: The study is a descriptive research in which both cross-sectional and longitudinal data were used. In the cross-sectional part of the study, 1,151 adolescents have participated, and the data of the second wave was obtained from 322 of them four months later. Data were analyzed through structural equation modeling (SEM) and hierarchical regression analyses.

Results and conclusion: According to the results of SEM, good peer relations predicted less cyberbullying. The expressive repression explained the cyberbullying through peer relationships. For identity development, contrary to expectations, commitment dimension of identity seemed to be positively related to more cyberbullying and so did higher reconsideration of commitment. Cross-lagged panel analyses revealed that Time 1 cyber victimization predicted Time 2 cyberbullying. Given the pattern of cross-lagged relationships, it was tentatively inferred that cyber victimization was the temporal precursor to cyberbullying. The results of the study have implications for the prevention of cyberbullying.

Keywords: cross-lagged panel design; cyber victimization; cyberbullying; expressive repression; identity development; peer relations.

© 2020 Authors.

• Open access
• Published: 14 January 2023

Prevalence and related risks of cyberbullying and its effects on adolescent

• Gassem Gohal 1 ,
• Ahmad Alqassim 2 ,
• Ebtihal Eltyeb 1 ,
• Ahmed Rayyani 3 ,
• Bassam Hakami 3 ,
• Abdullah Al Faqih 3 ,
• Abdullah Hakami 3 ,
• Almuhannad Qadri 3 &
• Mohamed Mahfouz 2  

BMC Psychiatry volume  23 , Article number:  39 ( 2023 ) Cite this article

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Cyberbullying is becoming common in inflicting harm on others, especially among adolescents. This study aims to assess the prevalence of cyberbullying, determine the risk factors, and assess the association between cyberbullying and the psychological status of adolescents facing this problem in the Jazan region, Saudi Arabia.

A cross-sectional study was conducted on 355 students, aged between 12–18 years, through a validated online questionnaire to investigate the prevalence and risk factors of cyberbullying and assess psychological effects based on cyberbullying questionnaire and Mental Health Inventory-5 (MHI-5) questions.

The participants in this study numbered 355; 68% of participants were females compared to 32% were males. Approximately 20% of the participants spend more than 12 h daily on the Internet, and the estimated overall prevalence of cyberbullying was 42.8%, with the male prevalence slightly higher than females. In addition, 26.3% of the participants were significantly affected in their academic Performance due to cyberbullying. Approximately 20% of all participants considered leaving their schools, 19.7% considered ceasing their Internet use, and 21.1% considered harming themselves due to the consequences of cyberbullying. There are essential links between the frequency of harassment, the effect on academic Performance, and being a cyber victim.

Conclusions

Cyberbullying showed a high prevalence among adolescents in the Jazan region with significant associated psychological effects. There is an urgency for collaboration between the authorities and the community to protect adolescents from this harmful occurrence.

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Introduction

Cyberbullying is an intentional, repeated act of harm toward others through electronic tools; however, there is no consensus to define it [ 1 , 2 , 3 ]. With the surge in information and data sharing in the emerging digital world, a new era of socialization through digital tools, and the popularization of social media, cyberbullying has become more frequent than ever and occurs when there is inadequate adult supervision [ 4 , 5 ]. A large study that looked at the incidence of cyberbullying among adolescents in England found a prevalence of 17.9%, while one study conducted in Saudi Arabia found a prevalence of 20.97% [ 6 , 7 ]. Cyberbullying can take many forms, including sending angry, rude, or offensive messages; intimidating, cruel, and possibly false information about a person to others; sharing sensitive or private information (outing); and exclusion, which involves purposefully leaving someone out of an online group [ 8 ]. Cyberbullying is influenced by age, sex, parent–child relationships, and time spent on the Internet [ 9 , 10 ]. Although some studies have found that cyberbullying continues to increase in late adolescence, others found that cyberbullying tends to peak at 14 and 15 years old before decreasing through the remaining years of adolescence [ 11 , 12 , 13 ].

The COVID-19 epidemic has impacted the prevalence of cyberbullying since social isolation regulations have reduced face-to-face interaction, leading to a significant rise in the use of social networking sites and online activity. As a result, there was a higher chance of experiencing cyberbullying [ 14 ].

Unlike traditional Bullying, which usually only occurs in school and is mitigated at home, victims of cyberbullying can be contacted anytime and anywhere. Parents and teachers are seen as saviors in cases of traditional Bullying. Simultaneously, in cyberbullying, children tend to be reluctant to tell adults for fear of losing access to their phones and computers, so they usually hide the cyberbullying incident [ 15 ]. Reports show that cyberbullying is a form of harm not easily avoided by the victim. In addition, in the cyber form of Bullying, identification of the victim and the perpetrator is generally challenging compared to traditional Bullying; this makes an accurate estimation of the problem widely contested [ 16 , 17 ].

There is growing evidence that is cyberbullying causes more significant levels of depression, anxiety, and loneliness than traditional forms of Bullying. A meta-analysis examining the association between peer victimization, cyberbullying, and suicide in children and adolescents indicates that cyberbullying is more intensely related to suicidal ideation than traditional Bullying [ 18 ]. Moreover, the significant problem is that cyberbullying impacts adolescent due to its persistence and recurrence. A recent report in Saudi Arabia indicated a growing rise in cyberbullying in secondary schools and higher education, from 18% to approximately 27% [ 19 ]. In primary schools and kindergartens in Saudi Arabia, we were not surprised to find evidence that children were unaware that cyberbullying is illegal. Although the study showed an adequate awareness of the problem in our country, Saudi Arabia, there were relatively significant misconceptions [ 20 ].

Adolescents' emotional responses to cyberbullying vary in severity and quality. However, anger, sadness, concern, anxiety, fear, and depression are most common among adolescent cyber victims [ 21 ]. Moreover, cyberbullying may limit students' academic Performance and cause higher absenteeism rates [ 22 ]. Consequently, this study aims to assess the prevalence of cyberbullying, determine the risk factors, and establish the association between cyberbullying and the psychological status of adolescents. We believe our study will be an extension of and significantly add to the literature regarding the nature and extent of cyberbullying in the Jazan region of Saudi Arabia.

A descriptive cross-sectional study was carried out in the Jazan region, a province of the Kingdom of Saudi Arabia. It is located on the tropical Red Sea coast of southwestern Saudi Arabia.

Design and participants

A descriptive cross-sectional study was carried out in the Jazan region, a province of the Kingdom of Saudi Arabia. It is located on the tropical Red Sea coast of southwestern Saudi Arabia. The study targeted adolescents (12–18 years old) who use the Internet to communicate in the Jazan region. The main inclusion criteria are adolescents between 12–18 years who use the Internet and agree to participate; however, it excludes adolescents not matching the inclusion criteria or those refusing to participate in the study. If participants were under 16, the parent and/or legal guardian should be notified. A sample of participants was estimated for this study, and the ideal sample size was calculated to be 385 using the Cochran formula, n  = (z) 2 p (1 – p) / d 2 . Where: p = prevalence of cyberbullying 50%, z = a 95% confidence interval, d = error of not more than 5%. A convenience sample was used to recruit the study participants. A self-administrated online questionnaire was used to collect the study information from May to December 2021.

The ethical approval for this study was obtained from The Institute Review Board (IRB) of Jazan University (Letter v.1 2019 dated 08/04/2021). Informed consent was acquired from all participants and was attached to the beginning of the form and mandatory to be read and checked before the participant proceeded to the first part of the questionnaire. For the participants under 16, informed consent was obtained from a parent and legal guardian.

Procedure of data collection and study measures

An Arabic self-administrated online questionnaire was used for this research. This anonymous online survey instrument was based on (Google Forms). The study team distributed the questionnaire to the participants through school teachers. The research team prepared the study questionnaire and chose the relevant cyberbullying scale questions from similar studies [ 5 , 6 ]. The questionnaire was translated by two bilingual professionals to ensure the accuracy and appropriateness of the instrument wording. A panel of experts then discussed and assessed the validity and suitability of the instrument for use on adolescents. The panel also added and edited a few questions to accommodate the local culture of Saudi students. It was validated with a pilot study that included 20 participants. The questionnaire was divided into three main sections. The first part of the questionnaire contains the basic participant information, including gender, age, nationality, school grade, residence, and information about family members and the mother's occupation and education. The mother's level of education was considered as it found that mothers' low levels of education specifically had a detrimental impact on the cyberbullying process [ 23 ]. The second section explores the participant's definition of cyberbullying, questions regarding exposure to cyberbullying as a victim or by bullying another person, and questions considering the possible risk factors behind cyberbullying. The last section explores how cyberbullying affects adolescents psychologically based on the standardized questionnaire Mental Health Inventory-5 (MHI-5). MHI-5 is a well-known, valid, reliable, and brief international instrument for assessing mental health in children and adolescents (such as satisfaction, interest in, and enjoyment of life) and negative aspects (such as anxiety and depression) [ 24 ]. It is composed of five questions, as shown in Table 1 . There are six options available for each question, ranging from "all the time" (1 point) to "none of the time" (6 points); therefore, the adolescent's score varies between five and 30. These questions assess both negative and positive qualities of mental health, as well as questions about anxiety and depression. By adding all the item scores and converting this score to a scale ranging from 0 to 100, the final MHI-5 score is determined, with lower scores indicating more severe depressive symptoms. The value for which the sum of sensitivity and specificity was utilized to establish the ideal cut-off score for MHI-5 in many similar studies was reviewed to reach an optimal conclusion. Therefore, we considered all cut-off values with associated sensitivities and specificities of various MHI-5 cut-off points previously employed among adolescents in similar studies and compared them to conclude that MHI-5 = 70 as our cut points. So the presence of depressive symptoms is considered with an MHI-5 cut-off score of ≤ 70 [ 25 ].

The Questionnaires were initially prepared in English and then translated into Arabic. A native speaker with fluency in English (with experience in translation) converted the questionnaire from the initial English version into Arabic. Then, we performed a pilot study among 20 participants to ensure the readability and understandability of the questionnaire questions. We also assessed the internal consistency of the questionnaire based on Cronbach’s alpha, which produced an acceptable value of 0.672. The internal consistency for Mental Health Inventory-5 (MHI-5) was reported at 0.557. In order to assess the factor structure of the Arabic-translated version of the (MHI-5) questionnaire, a factor analysis was conducted. The factor loading of the instrument is shown in Table 1 . Using principal component analysis and the varimax rotation method, we found a one-component solution explaining 56.766% of the total variance. All items loaded on the first factor ranged from (0.688 to 0.824), which confirms that a single factor has explained all the items of the scale. In addition, Bartlett’s test of sphericity was found significant ( p  < 0.001).

Data presentation & statistical analysis

Simple tabulation frequencies were used to give a general overview of the data. The prevalence of cyberbullying was presented using 95% C.I.s, and the Chi-squared test was performed to determine the associations between individual categorical variables and Mental Health. The univariate and multivariate logistic regression model was derived, and unadjusted and adjusted odds ratios (OR) and their 95% confidence intervals (C.I.s) were calculated. A P -value of 0.05 or less was used as the cut-off level for statistical significance. The statistical analysis was completed using SPSS ver. 25.0 (SPSS Inc. Chicago, IL, USA) software.

The distributed survey targeted approximately 385 students, but the precise number of respondents to the questionnaire was 355 (92% response rate), with 68% of female students responding, compared to 32% of male students. More than half of the respondents were secondary school students, with a nearly equal mix of respondents living in cities and rural areas. Table 2 demonstrates that 20% of the participants spend more than 12 h daily on the Internet and electronic gadgets, while only 13% spend less than two hours.

As demonstrated in Table 3 , the total prevalence of cyberbullying was estimated to be 42.8%, with male prevalence somewhat higher than female prevalence. Additional variables, such as the number of hours spent on the Internet, did not affect the prevalence. Table 4 shows the pattern and experience of being cyberbullied across mental health levels, as measured by the MHI-5.

Academic Performance was significantly affected due to cyberbullying in 26.3% of the participants. Furthermore, approximately 20% of all participants considered leaving their schools for this reason. Moreover, 19.7% of the participants thought of stopping using the Internet and electronic devices, while 21.1% considered harming themselves due to the effects of cyberbullying. Regarding associations between various variables and psychological effects using the MHI-5, there are significant associations between whether the participant has been a cyber victim before (cOR 2.8), the frequency of harassment (cOR 1.9), academic Performance (cOR 6.5), and considering leaving school as a result of being a cyber victim (cOR 3.0). In addition, by using univariate logistic regression analysis, there are significant associations between the psychological effects and the participant's thoughts of getting rid of a bully (cOR 2.8), thinking to stop using electronic devices (cOR 3.0), and considering hurting themselves as the result of cyberbullying (cOR 6.4). In addition, the use of the multivariate logistic regression analysis showed that frequency of harassment was the only statistically significant predictor of mental health among adolescents (aOR 2.8). Other variables continue to have higher (aORs) but without statistical significance. All these results are demonstrated in Table 4 .

Cyberbullying prevalence rates among adolescents vary widely worldwide, ranging from 10% to more than 70% in many studies. This variation results from certain factors, specifically gender involvement, as a decisive influencing factor [ 26 , 27 ]. Our study found a prevalence of 42.8% (95% confidence interval (CI): 37.7–48), which is higher than the median reported prevalence of cyberbullying of 23.0% in a scoping review that included 36 studies conducted in the United States in adolescents aged 12 to 18 years old [ 28 ]. A systematic review found that cyberbullying ranged from 6.5% to 35.4% [ 3 ]. These two studies gathered data before the COVID-19 pandemic. When compared to recent studies, it was found that cyberbullying increased dramatically during the COVID-19 era [ 29 , 30 ]. Subsequently, with the massive mandate of world online communication in teaching and learning, young adolescents faced a large amount of cyberspace exposure with all risk-related inquiries. Psychological distress due to COVID-19 and spending far more time on the Internet are vital factors in this problem, which might be a reasonable explanation for our results.

There is insufficient data to compare our findings to the Arab world context, notably Saudi Arabia. Although, according to one study done among Saudi Arabian university students, the prevalence was 17.6%. [ 31 ]. we discovered a considerable discrepancy between this prevalence and our findings, and the decisive explanation is the difference in the target age group studied. Age is a crucial risk factor for cyberbullying, and according to one study, cyberbullying peaks at around 14 and 15 years of age and then declines in late adolescence. Thus, a U-inverted relation exists between prevalence and age [ 11 , 12 , 13 , 32 ].

In our study, males reported being more vulnerable to cyberbullying despite there being more female participants; this inconsistent finding with previous literature requires further investigation. A strong, but not recent, meta-analysis in 2014 reported that, in general, males are likely to cyberbully more than females. Females were more likely to report cyberbullying during early to mid-adolescence than males [ 11 ]. This finding presents a concern for males reporting lower than females’ results in our data and raises some questions about whether cultural or religious conservative values play a role.

Increased Internet hours are another risk factor in this study and were significantly associated with cyberbullying. Specifically, it was likely to be with heavy Internet users (> 12 h/day); a similar result was well documented in one equivalent study [ 3 ]. Notably, while some studies have reported that those living in city areas are more likely to be cyberbullying victims than their counterparts from suburban areas [ 3 ], our observations reported no significant influence of this factor on the prevalence of cyberbullying.

According to a population-based study on cyberbullying and teenage well-being in England, which included 110,000 pupils, traditional Bullying accounted for more significant variability in mental well-being than cyberbullying. It did, however, conclude that both types of Bullying carry a risk of affecting mental health [ 33 ]. We confirmed in this study that multiple occurrences of cyberbullying and the potential for being a victim are risk factors influencing mental health ( P  < 0.001). Moreover, the frequency of harassment also shows a significant, influential effect. The victim's desire to be free from the perpetrator carrying out the cyberbullying is probably an alarming sign and a precursor factor for suicidal ideation; we reported that nearly half of the participants wished they could get rid of the perpetrators. Furthermore, more than 20% of participants considered harming themselves due to cyberbullying; this result is consistent with many studies that linked cyberbullying and self-harm and suicidal thoughts [ 34 , 35 , 36 ].

Adolescence is a particularly vulnerable age for the effects of cyberbullying on mental health. In one Saudi Arabian study, parents felt that cyberbullying is more detrimental than Bullying in the schoolyard and more harmful to their children's mental health. According to them, video games were the most popular social platform for cyberbullying [ 37 ]. Both cross-sectional and longitudinal research shows a significant link between cyberbullying and emotional symptoms, including anxiety and depression [ 38 , 39 ]. Therefore, we employed the MHI-5 to measure the mental impact of cyberbullying on adolescents in this study. Overall, the MHI-5 questionnaire showed relatively high sensitivity in detecting anxiety and depression disorders for general health and quality of life assessments. The questions listed happy times, peacefulness, and sensations of calmness, in addition to episodes of anxiousness, downheartedness, and feelings of depression, as given in Table 1 .

Cyberbullying has been well-documented to affect the academic achievement of the victim adolescents. Therefore, bullied adolescents are likelier to miss school, have higher absence rates, dislike school, and report receiving lower grades. According to one meta-analysis, peer victimization has a significant negative link with academic achievement, as measured by grades, student performance, or instructor ratings of academic achievement [ 40 ]. In our investigation, we reported that up to 20% of participants considered leaving their schools due to the adverse effects of cyberbullying (cOR 3.0) and wished they could stop using the Internet; 26% of participants felt that their school performance was affected due to being cyber victims (cOR 6.5). The results of the univariate analysis showed a high odd ratio related to school performance and a willingness to leave school. This conclusion indicates the likelihood of these impacts specifically with a significant p-value, as shown in Table 5 .

In this study, approximately 88% of the participants were cyber victims compared to only 11% of cyberbullying perpetrators who committed this act on their peers. Mental health affection is well-reported in many studies on cyber victims with higher depression rates than cyberbullying perpetrators [ 41 , 42 ]. However, other studies indicate that cyberbullying victims are not the only ones affected; harm is also extended to involve perpetrators. Cyberbullying perpetrators have high-stress levels, poor school performance, and an increased risk of depression and alcohol misuse. Furthermore, research shows that adolescents who were victims or perpetrators of cyberbullying in their adolescence continue to engage in similar behavior into early adulthood [ 43 , 44 ].

Limitations of the study

Although the current study found a high prevalence and positive connections among variables, it should be emphasized that it was conducted on a determinate sample of respondents, 11 to 18 years old. Therefore, the results could not be generalized for other samples, age groups, and communities from other cultures and contexts. In addition, it was limited to adolescent survey responses, did not include parents' and caretakers' viewpoints, and failed to include other risk factors such as divorce and financial status. We believe future studies should consider parents' perspectives and more analysis of perpetrators' characteristics. Moreover, self-reported tools are susceptible to social desirability bias, which can influence test item responses. As a result, future research should employ a variety of monitoring and evaluation metrics and larger potential populations and age ranges. Another limitation of this analysis is that we cannot make conclusive inferences regarding gender and exact prevalence because male adolescents had a lower response rate than female adolescents, suggesting that males might be more sensitive to disclosing these issues.

Even though experts in the social sciences typically research cyberbullying, it is crucial to investigate it from a clinical perspective because it significantly affects mental health. Adolescents' lives have grown increasingly centered on online communication, which provides several possibilities for psychological outcomes and aggressive actions such as cyberbullying. Stress, anxiety, depressive symptoms, suicidal ideation, and deterioration in school performance are all linked to cyberbullying. Therefore, we emphasize the need for parents and educators to be conscious of these dangers and be the first line of protection for the adolescent by recognizing, addressing, and solving this problem. Furthermore, we urge the responsibility of pediatricians, physicians, and psychiatric consultants to create a comfortable atmosphere for adolescents to disclose and report this problem early and raise awareness of the problem in their communities. Furthermore, practical strategies for dealing with such occurrences involving health, education, and law authorities, should be supported to tackle this problem, which can affect the adolescent mentally and academically. Lastly, to decide how to intervene most effectively, more research must be done on the many methods to assess how schools, communities, and healthcare providers tackle cyberbullying.

Availability of data and materials

The authors ensure that the data supporting the results of this study are available within the article. The raw data for the study will be obtainable from the corresponding author upon reasonable demand.

Krešić Ćorić M, Kaštelan A. Bullying through the Internet - Cyberbullying. Psychiatr Danub. 2020;32(Suppl 2):269–72.

Google Scholar  

Englander E, Donnerstein E, Kowalski R, Lin CA, Parti K. Defining Cyberbullying. Pediatrics. 2017;140(Suppl 2):148–51. https://doi.org/10.1542/peds.2016-1758U .

Article   Google Scholar  

Bottino SM, Bottino CM, Regina CG, Correia AV, Ribeiro WS. Cyberbullying and adolescent mental health: a systematic review. Cad Saude Publica. 2015;31(3):463–75. https://doi.org/10.1590/0102-311x00036114 .

Martín-Criado JM, Casas JA, Ortega-Ruiz R. Parental Supervision: Predictive Variables of Positive Involvement in Cyberbullying Prevention. Int J Environ Res Public Health. 2021;18(4):1562. https://doi.org/10.3390/ijerph18041562 .

Uludasdemir D, Kucuk S. Cyber Bullying Experiences of Adolescents and Parental Awareness: Turkish Example. J Pediatr Nurs. 2019;44:84–90. https://doi.org/10.1016/j.pedn.2018.11.006 .

Jaffer M, Alshehri K, Almutairi M, Aljumaiah A, Alfraiji A, Hakami M, Al-Dossary M, Irfan T. Cyberbullying among young Saudi online gamers and its relation to depression. J Nat Sci Med. 2021;4(2):142–7. https://doi.org/10.4103/JNSM.JNSM_78_20 .

Cyberbullying: An Analysis of Data from the Health Behaviour in School-aged Children (HBSC) Survey for England; 2014. Available from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/621070/Health_behaviour_in_school_age_children_cyberbullying.pdf [Last accessed on 2022 Nov 09].

LI Q. Bullying in the new playground: Research into cyberbullying and cyber victimisation. Australasian J Educ Tech. 2007;23:435–54.

Rao J, Wang H, Pang M, et al. Cyberbullying perpetration and victimisation among junior and senior high school students in Guangzhou. China Inj Prev. 2019;25(1):13–9. https://doi.org/10.1136/injuryprev-2016-042210 .

Samples-Kanyinga H, Lalande K, Colman I. Cyberbullying victimisation and internalising and externalising problems among adolescents: the moderating role of parent-child relationship and child’s sex. Epidemiol Psychiatr Sci. 2018;29:8. https://doi.org/10.1017/S2045796018000653 .

Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth [published correction appears in Psychol Bull. 2014; 140(4): 1073–1137. https://doi.org/10.1037/a0035618

Tokunaga RS. Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Comput Hum Behav. 2010;26(3):277–87.

Pichel R, Foody M, O’Higgins Norman J, Feijóo S, Varela J, Rial A. Bullying, Cyberbullying and the Overlap: What Does Age Have to Do with It? Sustainability. 2021;13(15):8527.

Shin SY, Choi Y-J. Comparison of Cyberbullying before and after the COVID-19 Pandemic in Korea. Int J Environ Res Public Health. 2021;18(19):10085. https://doi.org/10.3390/ijerph181910085 .

Article   CAS   Google Scholar  

Cassidy W, Jackson M, Brown KN. Sticks and stones can break my bones, but how can pixels hurt me?: Students’ experiences with cyberbullying. Sch Psychol Int. 2009;30:383–402. https://doi.org/10.1177/0143034309106948 .

Bonanno RA, Hymel S. Cyberbullying and internalizing difficulties: above and beyond the impact of traditional forms of Bullying. J Youth Adolesc. 2013;42(5):685–97. https://doi.org/10.1007/s10964-013-9937-1 .

Peebles E. Cyberbullying: Hiding behind the screen. Paediatr Child Health. 2014;19(10):527–8. https://doi.org/10.1093/pch/19.10.527 .

Landstedt E, Persson S. Bullying, cyberbullying, and mental health in young people. Scandinavian Journal of Public Health. 2014;42(4):393–9. https://doi.org/10.1177/1403494814525004 .

Al-Zahrani, A. M. . Cyberbullying among Saudi’s Higher-Education Students: Implications for Educators and Policymakers. World Journal of Education.2015; 5(3). https://doi.org/10.5430/WJE.V5N3P15

Allehyani SH. Cyberbullying and It's Impact on The Saudi Kindergarten Children. Journal of Arts, Literature, Humanities, and Social Sciences. 2018;4(22):307-329. https://doi.org/10.33193/1889-000-022-016 .

Ortega R, Elipe P, Mora-Merchan JA, Genta ML, Brighi A, Guarini A, et al. The emotional impact of bullying and cyberbullying on victims: a European Cross-National Study. Aggress Behav. 2012;38:342–56.

Kowalski RM, Limber SP. Psychological, physical, and academic correlates of cyberbullying and traditional Bullying. J Adolesc Health.2013; 53(1Suppl):13–20. doi: https://doi.org/10.1016/j.jadohealth.2012.09.018

Chen Q, Lo CKM, Zhu Y, Cheung A, Chan KL, Ip P. Family poly-victimization and cyberbullying among adolescents in a Chinese school sample. Child Abuse Negl. 2018;77:180–7. https://doi.org/10.1016/j.chiabu.2018.01.015 .

María Rivera-Riquelme, Jose A Piqueras, Pim Cuijpers. The Revised Mental Health Inventory-5 (MHI-5) as an ultra-brief screening measure of bi-dimensional mental health in children and adolescents. Psychiatry Research. 2019; 274:247–253. https://doi.org/10.1016/j.psychres.2019.02.045 .

van den Beukel TO, et al. Comparison of the SF-36 Five-item Mental Health Inventory and Beck Depression Inventory for the screening of depressive symptoms in chronic dialysis patients. Nephrol Dial Transplant. 2012;27(12):4453–7.

Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippet N. Cyberbullying: Its nature and impact in secondary school pupils. J Child Psychol Psychiat. 2008;49:376–85. https://doi.org/10.1111/j.1469-7610.2007.01846.x .

Selkie EM, Fales JL, Moreno MA. Cyberbullying prevalence among United States middle and high school aged adolescents: a systematic review and quality assessment. J Adolesc Health. 2016;58:125–33. https://doi.org/10.1016/j.jadohealth.2015.09.026 .

Hamm MP, Newton AS, Chisholm A, et al. Prevalence and Effect of Cyberbullying on Children and Young People: A Scoping Review of Social Media Studies. JAMA Pediatr. 2015;169(8):770–7. https://doi.org/10.1001/jamapediatrics.2015.0944 .

Zhang Y, Xu C, Dai H, Jia X. Psychological Distress and Adolescents’ Cyberbullying under Floods and the COVID-19 Pandemic: Parent-Child Relationships and Negotiable Fate as Moderators. Int J Environ Res Public Health. 2021;18(23):12279. https://doi.org/10.3390/ijerph182312279 .

Barlett CP, Simmers MM, Roth B, Gentile D. Comparing cyberbullying prevalence and process before and during the COVID-19 pandemic. J Soc Psychol. 2021;161(4):408–18. https://doi.org/10.1080/00224545.2021.1918619 .

Al Qudah MF, Al-Barashdi HS, Hassan EMAH, et al. Psychological Security, Psychological Loneliness, and Age as the Predictors of Cyber-Bullying Among University Students. Community Ment Health J. 2020;56(3):393–403. https://doi.org/10.1007/s10597-019-00455-z .

Hinduja S, Cyberbullying in 2021 by Age, Gender, Sexual Orientation, and Race. https://cyberbullying.org/cyberbullying-statistics-age-gender-sexual-orientation-race .

Przybylski AK, Bowes L. Cyberbullying and adolescent well-being in England: a population-based cross-sectional study. Lancet Child Adolesc Health. 2017;1:19–26. https://doi.org/10.1016/S2352-4642(17)30011-1 .

O’Connor RC, Rasmussen S, Miles J, Hawton K. Self-harm in adolescents: self-report survey in schools in Scotland. Br J Psychiatry. 2009;194(1):68–72.

John A, Glendenning AC, Marchant A, Montgomery P, Stewart A, Wood S, Lloyd K, Hawton K. Self-harm, suicidal Behaviours, and Cyberbullying in children and young people: a systematic review. J Med Internet Res. 2018;20(4): e129.

Nguyen HTL, Nakamura K, Seino K, et al. Relationships among cyberbullying, parental attitudes, self-harm and suicidal behavior among adolescents: results from a school-based survey in Vietnam. BMC Public Health. 2020;20:476. https://doi.org/10.1186/s12889-020-08500-3 .

Alfakeh SA, Alghamdi AA, Kouzaba KA, Altaifi MI, Abu-Alamah SD, Salamah MM. Parents’ perception of cyberbullying of their children in Saudi Arabia. J Family Community Med. 2021;28(2):117–24. https://doi.org/10.4103/jfcm.JFCM_516_20 .

Kim S, Boyle MH, Georgiades K. Cyberbullying victimization and its association with health across the life course: a Canadian population study. Can J Public Health. 2018;108:468–74.

Fahy AE, Stansfeld SA, Smuk M, et al. Longitudinal associations between cyberbullying involvement and adolescent mental health. J Adolesc Health. 2016;59:502–9.

Gardella JH, Fisher BW, Teurbe-Tolon AR. A Systematic Review and Meta-Analysis of Cyber-Victimization and Educational Outcomes for Adolescents. Rev Educ Res. 2017;87(2):283–308. https://doi.org/10.3102/0034654316689136 .

Nansel TR, Craig W, Overpeck MD, Saluja G, Ruan WJ. Cross-national consistency in the relationship between bullying behaviors and psychosocial adjustment. Arch Pediatr Adolesc Med. 2004;158(8):730–6.

Sourander A, Brunstein Klimek A, Ikonen M, et al. Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study. Arch Gen Psychiatry. 2010;67(7):720–8.

Daniela Š, Ivana D, Marija M. Psychological Outcomes of Cyber-Violence on Victims, Perpetrators and Perpetrators/Victims. Hrvat Rev Za Rehabil Istraz. 2017;53:98–110.

Selkie EM, Kota R, Chan Y-F, Moreno M. Cyberbullying, depression, and problem alcohol use in female college students: a multisite study. Cyberpsychology Behav Soc Netw. 2015;18(2):79–86.

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The ethical approval for this study was obtained from The Institute Review Board (IRB) of Jazan University (Letter v.1 2019 dated 08/04/2021). Informed consent was received from all participants, and for participants under age 16, informed consent was obtained from a parent and legal guardian. All methods were carried out under relevant guidelines and regulations.

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Gohal, G., Alqassim, A., Eltyeb, E. et al. Prevalence and related risks of cyberbullying and its effects on adolescent. BMC Psychiatry 23 , 39 (2023). https://doi.org/10.1186/s12888-023-04542-0

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Effects of Bullying in the Academic Performance of Grade 12 ABM StudentsIn Bestlink College of the Philippines School Year 2018-2019

• Mars Vircen Pacite Rodriguez
• Filomeno Teves Enopia Jr
• Roselyn Alarcon Tungol
• Andrea Gatchalian Villaruz
• Jessa Domingo Lagua
• Crystel-Joy S. Tamon

Students’ Academic performance is a very important part in students because it is the key to finish their studies but somehow a lot of factors can affect the academic performance of the students and one of that is bullying. Bullying can affect everyone-those who are bullied, the bully and those who witness bullying. This study aimed to identify the effects of bullying in the academic performance of students in Bestlink College of the Philippines. This study was conducted during School Year 2018-2019.A qualitative method was used in the study. Using descriptive research design, it focused on the assessment of Grade 12 ABM students and the effect of bullying on their academic performance. There were four academic variables identified to be affected by bullying, such as; written works, performance task, self–esteem, and projects. Data were collected by distributing questionnaires to the target respondents using convenience sampling.The results of the study revealed that bullying affect the academic performance of the Grade 12 Business and Accountancy and Management Students in terms of the cited variables; (1) Written Works –Victims of bullying don’t focus their minds in answering quizzes; (2) Performance task –Bullied students are afraid to socialize because of their previous experiences; (3) Self –esteem –Bullied students can’t easily socialize with others because of being afraid that they will be bullied again if they will have a mistake; and (4) Projects -Shows that the emotional and mental stress that cause of bullying leads them to give less of effort in doing projects. The results of this study will help not just the students themselves but also the parents, teachers and the school administration in Bestlink College of the Philippines on to orienting on students on how they will deal with the different circumstances with regard to bullying. A clear and specific seminar to student must be implemented explaining how students can cope with themselves when they encounter bullying and how students can overcome it. Proper counseling and right punishment for those bullied students, a strict implementation and proper monitoring about bullying in school and the consequences they might face when they disobey these rules.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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Towards Descriptive Adequacy of Cyberbullying: Interdisciplinary Studies on Features, Cases and Legislative Concerns of Cyberbullying

1 Guangdong University of Foreign Studies, Guangzhou, People’s Republic of China

Paula Trzaskawka

2 Adam Mickiewicz University, Poznań, Poland

In view of the complexity of cyberbullying, this paper aims to address the linguistic and legal aspects of cyberbullying from an interdisciplinary perspective. Based on authentic data collected from real cases, we will expound on features, defining properties and legal remedies of cyberbullying in the countries that contribute to this special issue, such as Nigeria, France, Poland and China. Firstly, we will present an overview of cyberbullying and its definition, along with cyberbullying’s attributes. Next, we will cover the various forms of cyberbullying, such as hate speech, harassment and trolling. Each of these forms of cyberbullying result in numerous outcomes, many of which are serious and, in the worst case, can result in a victim’s death. A discussion of such consequences and the legal remedies for cyberbullying will be provided. On a final note, the contributors seek to enrich the forthcoming studies on cyberbullying by offering suggestions towards descriptive adequacy of cyberbullying.

Introduction

The last decades have witnessed the remarkable development of the Information Communication Technology (ICT), the prosperity of social media platforms in particular. However, while people harvest the convenience and freedom of online communication, some are haunted by cyberbullying. For example, one of the authors of this editorial introduction was bullied online and flooded with hate speech when she uploaded a video on TikTok in Poland. Even though she received support and comfort from her family and decided not to read and reply to those negative and harmful comments, offensive words, which were like punches in her face, are still remembered. Statistic data show that teenagers are the most vulnerable group of people who tend to experience cyberbullying [ 13 ], but as a matter of fact, even adults can also be reduced to victims of cyberbullying [ 24 ].

In 2019, Wagner postulates a theory for E-communication and E-victimization [ 42 ]. Taking cyberbullying as an example, she explores the triadic relationships among the victim, the perpetrator and the media facilitator, and takes anonymity, exposure, frequency and insecurity as indicators to analyse cyberbullying crimes and assess harm done to victims. It is noticed that “emojis express what words cannot say, as they are universal, save space and time, and most importantly capture the attention of the sender’s audience. The shorter the messages, the more powerful, more visible, and the more attractive the messages are for the recipients” [ 42 ]. In 2020, the first issue devoted to E-discourse aggressiveness was published in the journal Social Semiotics (Volume 30, Issue 3, 2020). It was the first attempt to gather global information about the phenomenon of cybercrimes and cyberbullying. Interesting findings have been found, such as emotional features of emojis [ 40 ], conflicting functions of emojis on gender equality and sexual discrimination, and on forest protection [ 28 ], and aggressiveness of emojis in law [ 6 , 43 ]. These studies are dedicated to the features, functions and consequences of emojis used in cyberbullying. Little attention has yet been paid to language of cyberbullying, the primary carrier of meaning, and to legal remedies of cyberbullying.

Chomsky [ 7 ] once mentioned that linguistic analysis is supposed to be adequate at three levels: observational adequacy, descriptive adequacy and explanatory adequacy. In view of the current research on cyberbullying, particularly the fact that there is no consensus on the definition of cyberbullying, it is vital for linguists to dig deeper and work towards the second level of adequacy. And this is where linguists can contribute the most to this topic. Contributors of this special issue, by drawing on authentic linguistic data and real cases from different parts of the world (such as Nigeria, Poland, France and China), analyse cyberbullying from an interdisciplinary perspective and intend to explore more fundamental issues, including but not limited to: its defining attributes and distinct linguistic patterns. Others focus on legal issues including the necessity to introduce new legislation regulating cyberbullying and available legal remedies for its victims. In the following sections, the defining properties, forms, consequences and legislative concerns of cyberbullying will be discussed, the main ideas and major findings of this Special Issue will be introduced, and suggestions for further research will be put forward.

Definition and Defining Properties of Cyberbullying

There exist many definitions of cyberbullying, but one of the most common states that cyberbullying is “an aggressive, intentional act or behaviour that is carried out by a group or an individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself” [ 30 ]. National Crime Prevention Council claims that “Cyberbullying is similar to other types of bullying, except it takes place online and through text messages sent to cell phones” [ 31 ]. Moreover, cyberbullying is also regarded as “the process of using the Internet, cell phones or other devices to send or post text or images intended to hurt or embarrass another person” [ 12 ]. Below we list definitions of cyberbullying used by contributors in this Special Issue.

In Nigeria, cyberbullying is defined as the “process of using the internet, cell phones or other devices to send or post text or images intended to hurt or embarrass another person”. The word “cyberbullying” is often used interchangeably with “cyber stalking” and in fact as Adediran [ 1 ] observes, the Cybercrimes Act 2015 of Nigeria uses the word ‘cyber stalking’ to refer to any course of conduct directed at a specific person that would cause a reasonable person to feel fear. In Poland, the Polish Criminal Code amended in 2011 recognizes cyberbullying and stalking as a criminal offence. Currently, this offence is punishable under article 190a of Polish Criminal Code [ 29 ]. Pyżalski [ 36 ] further proposes 5 types of electronic aggression based on types of the victim, such as electronic aggression against celebrities and electronic harassment against group members. In France, cyberbullying is defined by Article 222–33-2–2 of the Criminal Code as follows:

The fact of harassing a person by repeated comments or behavior with the purpose or effect of degrading his or her living conditions resulting in an alteration of his or her physical or mental health is punishable by one year’s imprisonment and a fine of €15,000 when these acts have resulted in a total work incapacity of less than or equivalent to eight days or have not led to any work incapacity. (Criminal Code, Article 222-33-2-2 [ 19 ]).

In China, there is no official definition proposed for cyberbullying per se. However, when students are involved, it is regarded as an online form of bullying and is regulated by relevant anti-bullying rules and regulations. In 2017, the Ministry of Education and other 10 competent departments issued the Measures for Strengthening the Comprehensive Treatment of Bullying Among Primary and Secondary School Students in 2017, and defined bullying as follows:

Bullying among primary and secondary school students refers to incidents taking place inside or outside the campus (including primary and secondary schools and secondary vocational schools) between or among students, with one party (an individual or a group of people) deliberately or maliciously conducting insults through physical, language or online methods for one or more times, resulting in the other party (individual or group) suffering from physical injury, property loss or mental distress.

To sum up, cyberbullying is a form of bullying or harassment effected via electronic means and intends to do harm to the victims. Sometimes, the term “cyberbullying” is used interchangeably with “cyber harassment”. Nevertheless, cyberbullying has its distinct features and defining properties. Sadly, it is found that except two shared properties, i.e., intent and repetition, scholars fail to reach a consensus even on the defining properties of cyberbullying, let alone its definition [ 46 ]. For example, Huston [ 22 ] regards electronic form of contact, an aggressive act, and harm of the victim as the other three core features of cyberbullying, while Ira-Katharina and Petermann [ 23 ] take power imbalance, direct and indirect cyberbullying and the victim’s perception as other defining features of cyberbullying.

Forms of Cyberbullying

Cyberbullying behaviour includes posting rumours, threats, sexual remarks, revealing the victim’s personal information, or pejorative labels including hate speech [ 8 ]. However, the distinction between the expressions of offence, hate speech and cyberbullying is often blurred. The problem in defining them unequivocally stems from the fact that cyberbullying is constantly evolving due to new tools and ways of attacking people online. The complexity of cyberbullying can be visible when the victim is not only harassed but also attacked verbally though comments posted on the internet.

One major form of cyberbullying is hate speech. There is a specific reason for the comparison of hate speech with cyberbullying at the same place. Most cyberbullies use hate speech to offend their victims. Hate speech is defined as a “public speech that expresses hate or encourages violence towards a person or group based on something such as race, religion, sex, or sexual orientation” [ 4 ]. Moreover, hate speech is “usually thought to include communications of animosity or disparagement of an individual or a group on account of a group characteristic such as race, colour, national origin, sex, disability, religion, or sexual orientation” [ 32 ].

The second form of cyberbullying is internet trolling [ 29 ]. It is a common form of cyberbullying and aims to evoke a reaction, cause disruption, gain personal amusement or even draw attention of the public [ 9 , 11 ]. Trolling is present in social networking sites when a troll is provoking a response through the use of bad language, or even insults. Internet trolls spend time looking for heated discussion topics where they can attack somebody verbally. In most cases trolls act this way to feel better by making others feel bad.

The third form of cyberbullying may be cyber stalking which is “the transmission of any communication through the means of a computer to bully, threaten or harass another person where such communication places another person in fear of death, violence or bodily harm amounts to cyber stalking” [ 1 ]. Cyber stalking happens when a person is making real threats. Sometimes such threats may change their form from online into real life.

Exclusion, harassment and outing are other forms of cyberbullying. The first one, namely exclusion, is the deliberate act of leaving someone out and it may happen in different ways such as being excluded from friends’ parties or activities, not being tagged in online conversations in group chats or a person may not use social networking sites at all and is being cyberbullied about it online by the peers. The second one, viz harassment, is a sustained, constant and intentional form of sending messages to a victim via a phone or the Internet. The third one, called outing, is a deliberate act of embarrassing or publicly humiliating somebody through the online posting of sensitive, private or embarrassing information without their consent [ 18 ].

There are other forms of cyberbullying which are fraping, creating fake profiles, dissing, trickery or catfishing. Fraping is a dangerous form of cyberbullying in which a bully is impersonating a victim and is posting in his/her name [ 44 ]. Dissing refers to the behaviour that one sends or posts cruel gossip or rumors about the other to damage the other’s reputation or friendships with others [ 18 ]. A cyberbully may post photos, screenshots or videos to put somebody down and to draw one’s attention to the fact that the cyberbullied is not a nice person. In this case, as with the previous one both harasser and the victim probably know each other very well. Another form of cyberbullying is trickery. A perpetrator gains the victim’s trust so that he/she reveals secrets or embarrassing information that then the perpetrator publishes online. Once the trust is gained, the perpetrator sends private information about the victim to a third party. The last form of cyberbullying known to the authors of this editorial is catfishing. Catfishing [ 26 ] is when “another person steals somebody’s online identity, usually photos, and re-creates social networking profiles for deceptive purposes” [ 8 ]. Here, the identity of a cyberbully is also hidden. This person may steal some of the victim’s photos and create an account with fake name and information or it may be other way round. Nevertheless, the good name or image of the person may be destroyed.

To sum up, cyberbullying may exist in various forms, with using hate speech, cyber harassment and cyberstalking as common forms. Due to its aggressive nature, some argues that “cyberbullying is another name for internet aggression” [ 25 , 34 ] which are ‘overt, intentional acts of aggression toward others online’” [ 47 ].

Consequences of Cyberbullying

It appears that consequences of cyberbullying vary a lot. It may be a disturbance to routine life, but can also be extremely harmful or even deadly. The contributors in this Special Issue provide us with many examples of fatal consequencese. In some of the reported cases, victims of cyberbullying have committed suicide as a result of fear or shame [ 1 , 41 ].

According to the statistics provided by IPSOS 2018 online, harassment and bullying are now ubiquitous and widespread in modern societies. It is a worldwide phenomenon but in different regions of the world the numbers of cyberbullied and their bullies differ. For example, Latin America has the highest level of social media bullying, reaching 76%. North America comes second at 67%, followed closely by Europe at 65%, the Middle East/Africa at 61%, and the Asia Pacific at 53%. As for specific countries, Peru is number one with the highest level of social media bullying at 80%. Argentina comes second at 74%, followed by Mexico (73%), Brazil (70%), Malaysia (71%), Great Britain (69%), Canada (68%), and the USA (67%).

It is a well-known fact that when a person is being a target of cyberbullying, he/she may have a higher tendency to abuse drugs or alcohol. Additionally, they will probably suffer emotional trauma and any other physical issues causing low self-esteem, anxiety, depression, delinquency or family problems. In cases of cyberbullying at school, a victim may have problems with grades or he/she may even avoid classes. The consequences to a person who is cyberbullying is not regulated thoroughly. For example, in the USA only 14 states impose criminal penalties. These penalties can include fines (as high as $2500) or jail time (a year). When it comes to cyberbullying at school some school districts must include policies that will fight against such acts to be in compliance with current laws. the bully may be suspended. However, statistics can be terrifying. 1 According to Ditch The Label 2018 the most negative impact on the cyberbullied person may be as follows:

• i. social anxiety (37%),
• ii. depression (36%),
• iii. suicidal thoughts (24%),
• iv. self-harm (23%),
• v. skipping classes (21%),
• vi. developing antisocial behaviours (12%),
• vii. developing eating disorders (10%), and
• viii. running away from home (10%).

It appears that people of different age groups could be victims of cyberbullying. Not only middle and high school children but also adults as it is confirmed by examples given by the authors of this Special Issue [ 1 , 41 , 46 ]. One terrible thing is that cyberbullying seems to have gained normalcy: even though bullies are aware of the criminal connotation of their actions but feel no remorse because of the anonymity provided by the Internet in an unprecedented scale. The very fact that perpetrators do not need to face their victims makes them brazen.

Concerns and Challenges in Legislating Cyberbullying

In view of the severity of cyberbullying, such as the suicide of the American teenager Megan Meier in 2006 and the Canadian teenager Amanda Todd in 2012, many countries have taken a quick action to legislate cyberbullying. New acts are enacted or existing laws are amended. For example, statistics from https://cyberbullying.org/bullying-laws show that by 2015 all states in America have some form of anti-bullying laws, most of which explicitly include cyberbullying in the legal provisions. In France, Article 222–33-2–2 of the Criminal Code was created in 2014 to define cyberbullying and set out the penalty [ 41 ]. In Poland, cyberbullying is punishable under Article 190a of the Polish Criminal Code effective as of 2011 [ 29 ]. In Nigeria, cyberbullying is expressly criminalized by the Cybercrime Act 2015, the Criminal Code Act and Penal Code Act [ 1 ]. In addition to the Cybersecurity Law, China also adds one article to the newly amended Law on the Protection of Minors in 2021 [ 41 ].

The report “Analysis of State Bullying Laws and Policies” released by the U.S. Department of Education in 2011 (“the Report” for short) finds that “Eighteen state laws include specific statutes addressing the rights of bullying victims to seek legal remedies under law” [ 39 ]. In fact, to make it clear that victims have the right to pursue other legal remedies, many state laws include provisions titled “victims’ rights to redress”. For example, the Oregon statute expressly asserts that the state law “may not be interpreted to prevent a victim of harassment, intimidation or bullying or a victim of cyberbullying from seeking redress under any other available law, whether civil or criminal” [ 39 ]. It is thus evident that legislators make great efforts to offer a legal shield to protect the victims of cyberbullying.

Despite these efforts, some scholars still show great concerns about the necessity and effectiveness of legislation to criminalize cyberbullying. For instance, Justin W. Patchin [ 35 ], a co-director of the Cyberbullying Research Center and criminal justice professor, confesses that, “I am not convinced that a state or federal law which criminalizes cyberbullying is necessarily the best approach”. He argues that “The vast majority of all cyberbullying can be effectively handled informally… In the rare event that a cyberbullying incident rises to a level warranting criminal intervention, we already have existing laws which can be utilized (stalking, criminal harassment, felonious assault, etc.).” Apparently, the necessity of legislating cyberbullying is under question. Nevertheless, the assumption may be correct as some researchers point out that bullies are minors [ 27 ] and therefore there are limits to their penalization resulting from the fact that they have not attained the age of majority and may not have full comprehension of their deeds.

Besides, the effectiveness of legislation is also challenged. The Report [ 39 ] points out that “[t]he review of state bullying legislation reveals clear differences in the terms used to define bullying and harassment”. As a matter of fact, it is found that the legislative language used in bullying laws are more often than not directly borrowed from harassment statutes, which may blur important legal distinctions between “bullying” and “harassment” [ 15 ]. It is thus not surprising that in the legal context, the use of inconsistent or even contradictory terms “sometimes contributes to confusion concerning how a specific incident should be treated” [ 39 ].

Several reasons may account for the above concerns for legislating cyberbullying. Firstly, a lack of a uniform definition of cyberbullying makes it hard to pin down the exact subject matter of the legislation. Ira-Katharina and Petermann [ 23 ] notice that scholars often tailor the definition of cyberbullying to their own study. They find that there are at least 24 new definitions on cyberbullying from 2012 to 2017, let alone previously proposed ones. Sadly, even the concept analysis of defining attributes of cyberbullying end with different results. As a result, little consensus has been achieved concerning the denotation of cyberbullying. However, there is the more complex reason that may be the root of inconsistency – different nations are sensitive to different types of massages. Thus, the same message may be considered humorous in one country while in another it may be damaging and bullying. The same rule applies to discriminatory actions and other types of harassment (e.g., sexual in the form of allusions or jokes).

Secondly, as an umbrella term of online aggressive behaviour, the connotation of cyberbullying remains quite vague. Doo et al. [ 14 ] find that definitions of cyberbullying are invariably connected either with the place where cyberbullying occurred or with the contents which cyberbullies used to cyberbullying victims. In other words, the “electronic forms of contact” [ 37 ], such as the use of e-mail, instant messaging, chat rooms, social media platforms and cell phones [ 20 ], set the scene for cyberbullying and make the term cyberbullying encompass almost everything that happens online. Apart from differences in various places of occurrence, a vast array of “the aggressive act” [ 22 ] of cyberbullying behaviour complicates the problem. Apart from the typologies presented above we may refer here to the categorization by Willard [ 45 ] who differentiates seven major types of cyberbullying behaviours: flaming, harassment, denigration, impersonation, outing and trickery, exclusion and cyberstalking. As a result, it is hard to pin down the boundaries of cyberbullying.

Thirdly, the tension between the attempt to protect the victims of cyberbullying and the right to free speech poses a further challenge to legislative attempts. On the one hand, people enjoy the fundamental human right to freely express themselves. This right is expressly provided by Article 19 of The Universal Declaration of Human Rights “Everyone has the right to freedom of opinion and expression; this right includes freedom to hold opinions without interference and to seek, receive and impart information and ideas through any media and regardless of frontiers.” On the other hand, victims of cyberbullying are intimated or tormented by online aggressive speech, with some choosing to put an end to their own lives. As a result, cyberbullying cases may draw wider attention from the society than other cases and arouse more heated debates. The problem is the thin red line between the freedom of speech and freedom of expressing one’s opinions and the act of cyberbullying (e.g., by hate speech). It turns out that delineating the borders of the freedom of speech and cyberbullying may be very subjective even in the course of enacting legislation and conducting trials.

Among others, the constitutionality of statutory laws on cyberbullying is one of the central issues to be decided by courts. Current rulings in America reveal that courts attach different degrees of importance to freedom of speech and cyberbullying. For example, the New York Court of Appeals invalidated Albany County’s cyberbullying law in 2014 in People v. Marquan M. By contrast, in 2015, in State v. Bishop, the North Carolina Court of Appeals upheld North Carolina’s cyberbullying statute and rejected Bishop’s First Amendment challenge to the law. Despite disagreements among scholars and practitioners on the intersection between freedom of speech and cyberbullying, it is nevertheless apparent that cyberbullying is a “loaded term” [ 38 ] and problems brought by “the breadth and vagueness of the statutory language” on cyberbullying are to be solved [ 21 ].

To conclude, it has been a trend worldwide to legislate on cyberbullying. However, current challenges and debates on legislating cyberbullying have much to do with terms used to refer to cyberbullying acts and with statutory language defining cyberbullying.

Research Topics and Major Findings of This Special Issue

In this special issue, contributors either focus on theoretical or practical problems of cyberbullying. We are interested in the following questions: How to define cyberbullying? and What can be done to improve research on cyberbullying? The first three papers show examples of cyberbullying through different social media towards different groups of people, such as hunters or foresters. The second group of papers deals with linguistic aspects of cyberbullying in China. The authors carry out their analyses with the usage of corpus linguistics tools.

Wagner points out that “Cyber bullying remains a nebulous concept that can be deciphered in many ways.” [ 42 ] The first perspective is the theoretical perspective with regard to the mechanism of cyberbullying [ 41 ]. In their paper “ Machiavellian Apparatus of Cyberbullying: Its Triggers Igniting Fury with Legal Impacts ”, Anne Wagner and Wei Yu discuss Machiavellian Apparatus which “proves to be sophisticated, given its powerful nature, and results in its victims being ensnared in a cyber net from which they see very little escape”. They divide young netizens who are the most vulnerable to cyberbullying into the silent readers and the active readers, and differentiate three main types of online players, namely, the newbie, the troll and the flamer [ 42 ]. They examine the two triggers that expose the tyrannical mechanisms of such a discourse, which serve as power amplifiers for young netizens. Drawing on real cases, they illustrate how these power amplifiers ignite the fuse that triggers this social networking madness.

The second perspective may be based on laws and regulations unique to a particular country [ 1 ]. In his paper, “ Cyberbullying in Nigeria: Examining the Adequacy of Legal Responses ”, Adediran examines the effectiveness of legal responses to cyberbullying in Nigeria. He finds that cyberbullying, particularly outing, trickery, trolling and roasting, is rampant in Nigeria. He notices that while in theory, most forms of cyberbullying can be prosecuted under the Cybercrimes Act, little notable enforcement of the law to prosecute cyberbullying has been documented. As a result, the author alleges that “the protection of image rights will go a long way to assist in curbing the act of cyberbullying in Nigeria” [ 1 ].

The third perspective focuses on communities which are especially frequently bullied or stereotypically perceived [ 28 , 29 ]. In “ I Would Kill the Director and Teachers in the School” Cyberbullying of Hunters in Poland, Matulewska and Gwiazdowicz [ 28 ] analyze online linguistic aggression towards hunters, in particular, the use of emotion-loaded language in shaping the image of hunters. They find that “people brought up in cities, far away from nature, are easily convinced to attack other groups which they perceive as deviant” [ 3 ]. Due to limited knowledge about nature and its laws, overly idealistic and naïve approach may easily lead to verbal and non-verbal aggression towards the community of hunters.

In another similar topic titled “ Cyberbullying in Polish Debate on the Białowieża National Forest”, Matulewska, Kic-Drgas and Trzaskawka [ 29 ] focus on hate speech concerning the opponents and proponents of the cut out of the Białowieża National Forest due to the attack of the bark beetle in Poland. Their analysis summarizes linguistic patterns of aggressive and vulgar statements. For example, they find out that there are four strategies of staging anger online, including offensive and sometimes vulgar language, irony, rhetorical questions, and using analogies and metaphors. Besides, the authors note that the shorter the comment is, the more vulgar and hateful it is.

Apart from the above mentioned three perspectives on cyberbullying, the second part of this Special Issue aims at analyzing the discourse of cyberbullying and depicting the scenario of cyberbullying in China. In The Invisible Aggressive Fist: Features of Cyberbullying Language in China, Youping Xu [ 46 ] presents lexical and syntactic features of cyberbullying language targeted at adults. Drawing on data from her corpus based on a high-profile defamation case arising out of cyberbullying in China, she argues that if we want to detect and identify cyberbullying through language more efficiently in the future, it is vital to find out: (a) how a group of people target at a victim; (b) how harm is inflicted intentionally to the victim in a repeated way; and (c) how the victim perceives the bullying. She finds that distinct features, such as a high density of the second personal pronoun “you” used to drag the victim into the face-to-face online interaction and an unconventional use of interrogative questions ending with exclamation marks to denounce the behaviour of the victim, will be helpful for further research on parameters in cyberbullying detection.

While Xu [ 46 ] shows interest in the civil dispute of a defamation case arising out of cyberbullying, Jinshi Chen is more concerned about criminal behaviors of cyberbullying. In his paper ‘Y ou are in trouble!’ A Discursive Psychological Analysis of Threatening Language in Chinese Cellphone Fraud Interactions , Chen [ 5 ] analyses 20 pieces of cellphone conversations in the authentic fraud cases from Chinese media (together with the use of Praat 6.1.13). From the discursive psychological perspective, Chen analyzes how cellphone fraudsters construct their fake identities (police officers, procurators, telecom staff or gang leaders) through information gap and information sharing in their turn-taking designs. He finds that fraudsters use such conversational skills in a threatening tone as repetition, interruption, higher pitch and louder speech to trigger victims’ psychological panic based on prepared and designed scripts. The findings of this paper will be conducive to the fight against fraudster’ online threats and bullies.

The last paper From Flaming to Incited Crime: Recognising Cyberbullying on Chinese WeChat Account written by Shaomin Zhang [ 48 ] analyzes twenty-six suspicious Chinese online flaming articles concerning poisoning of dogs posted on the WeChat subscription account. Based on the Speech Act Theory, this quantitative corpus linguistic analysis of Keyness and semantic prosody (AntConc 3.5.7.) intends to find out the lexical, semantic and pragmatic manifestations of cyberbullying, explore how cyberbullying language hurts some readers by the writer’s attitudinal meaning, and discuss typical linguistic items in the cyberbullying article that may incite unlawful action or wrongs. This study helps in penetrating and recognizing online flaming articles in Chinese social media and will provide references for protecting the mass audience from being victims.

By June 20, 2021, the coronavirus (Covid-19) has caused over 3.8 million of deaths worldwide [ 17 ], with over 177 million confirmed cases in over 210 countries and regions. While the coronavirus disrupted the economy of the world, shattered the normal life of numerous families and brought pain to endless individuals, cyberbullying, an invisible “virus” also does great harm to society. According to the statistics from the i-SAFE foundation [ 16 ], a non-profit foundation whose mission is to “educate and empower youth to make their Internet experiences safe and responsible”, over half of adolescents and teens have been bullied online, and about the same number have engaged in cyberbullying. Obviously, the infectious rate of cyberbullying is much higher than coronavirus. Sadly, while we are clear about the structure and properties of coronavirus and have vaccines, little consensus has even been reached as to the definition of cyberbullying. That is why Patchin [ 35 ] urges that “Legislators stop and work to develop a law that is reasonable, practical, constitutional, and informed by research.” To this end, concerted efforts are to be made among a group of experts to identify the real issues going on in cyberbullying.

This Special Issue has been a pioneer to bring together linguists, law professors and forestry experts, addressing the issues of the definition, forms and legislative concerns of cyberbullying based on real cases in four countries through three continents. It covers a wide range of topics and presents the latest findings on cyberbullying. As a complex social phenomenon, we may have just touched a little bit of the corner of the iceberg and filled some research gaps. More joint efforts are expected to be made in the future to address the following issues:

Linguistic Analysis of Interaction Among Various Participants

Though cyberbullying takes on various forms, such as posting a picture or sharing a video, most of the bullying behaviors are executed through language. As a result, language will be the main and sometimes the sole evidence that records cyberbullying. While most of the current studies focus on the interaction between cyber bullies and victims, some have paid close attention to other participants in cyberbullying. According to Olweus [ 33 ], in the Bullying Circle, along with the students who bully and student who is bullied, there are bystanders who play six different roles in bullying. These roles fall on a continuum and are displayed in a U-shape, including Follower, Supporter, Passive Supporter, Disengaged On-looker, Possible Defender and Defender. Thus, to reveal the nature and features of cyberbullying, it is vital to analyze the interaction among these eight roles, including their participation framework, role play and role change.

Construction of Large Corpora of Cyberbullying

Computer sciences scholars who are interested in automatic cyberbullying detection primarily use English data collected from Twitter, MySpace and Formspring [ 46 ]. In this Special Issue, contributors construct their own small-scale corpus in French, English, Polish or Chinese. Their corpus-based study has enlarged the scope of language varieties to the existing literature, showing interesting language-specific and cultural-loaded linguistic patterns of cyberbullying. However, in the era of big data, it is vital to construct large corpora of cyberbullying centering on different languages and to conduct comparative studies. During the construction of such a large corpus, linguistic findings on cyberbullying are urgently needed in data collection, tagging and data analysis. Based on the corpus, the common features of cyberbullying could be found and candidate parameters to distinguish bulling and non-bullying language could be postulated.

Semiotic Analysis of Cyberbullying Through Verbal and Nonverbal (e.g. images) Messages

There is another threat around the corner as such channels of communication (social media) as TikTok, Instagram and others are the most popular platforms nowadays. Why is it a threat? The increasing popularity of such media is going to affect cyberbullying. There is huge possibility of bullying through posting pictures, photos, mems, movies of visual, audiovisual and verbal quality. As it was mentioned in the introduction of this Special Issue it is the fact that even one of the authors experienced this type of online hatred. However, bullying as such may happen also through movies or pictures rather than words. In today’s world we have a tendency to make everything shorter and quicker. Attaching an emoji to a picture or a video can be only one example of this tendency. As it can be seen, the issue may be analyzed through different aspects and semiotic analysis of particular examples may prove that it is not only a one case study.

Interdisciplinary Collaboration in Cyberbullying Detection and Prevention

As a complicated social phenomenon, cyberbullying has aroused interest among sociologists, psychologists, linguists, law experts and computer scientists. While most scholars approach cyberbullying from their own field of expertise, some [ 27 , 41 ] in this Special Issue) have started to explore cyberbullying from an interdisciplinary perspective. As advocated by Janet Ainsworth, President of International Association of Forensic Linguists and law professor of Seattle University, cross-fertilization is needed to nourish the research on language and law [ 2 ]. Hence, the collaboration between law experts and linguists is of great importance to cyberbullying, particularly in the drafting of bullying statutes. Besides, to effectively detect cyberbullying, it is urgent for computer scientists and linguists to cooperate with each other so as to lay down the linguistic conventions in data tagging and the extraction of distinctive linguistic features. Last but not the least, cooperation among educators, psychologists and linguists will undoubtedly be productive in terms of developing manuals and guidelines for cyberbullying prevention.

Cyberbullying is such a complex issue that joint efforts from different walks of life, such as researchers, educators, policy makers, school administrators, parents and Internet users, are urgently needed. This Special Issue is one of the first attempts where the authors provide some examples of cyberbullying towards specific groups from their countries of origin. Global approaches or global databases could broaden our legal and linguistic knowledge in the issue at hand and may allow us to take proper steps to combat cyberbullying. At present, joint efforts are expected to decipher cyberbullying so as to find out its defining features and reach a consensus on its definition. Only in this way can subsequently studies on the automatic detection of cyberbullying and effective prevention of cyberbullying make sense. This urgently call for interdisciplinary efforts made towards descriptive adequacy of cyberbullying.

1 https://comparecamp.com/cyberbullying-statistics/ [accessed on 22.06.2021].

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Youping Xu, Email: moc.nuyila@300gnipuoyux .

Paula Trzaskawka, Email: [email protected] .

Q Methodology as an Innovative Addition to Bullying Researchers’ Methodological Repertoire

• Original Article
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• Published: 11 May 2022
• Volume 4 , pages 209–219, ( 2022 )

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• Adrian Lundberg   ORCID: orcid.org/0000-0001-8555-6398 1 &
• Lisa Hellström   ORCID: orcid.org/0000-0002-9326-1175 1  

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The field of bullying research deals with methodological issues and concerns affecting the comprehension of bullying and how it should be defined. For the purpose of designing relevant and powerful bullying prevention strategies, this article argues that instead of pursuing a universal definition of what constitutes bullying, it may be of greater importance to investigate culturally and contextually bound understandings and definitions of bullying. Inherent to that shift is the transition to a more qualitative research approach in the field and a stronger focus on participants’ subjective views and voices. Challenges in qualitative methods are closely connected to individual barriers of hard-to-reach populations and the lack of a necessary willingness to share on the one hand and the required ability to share subjective viewpoints on the other hand. By reviewing and discussing Q methodology, this paper contributes to bullying researchers’ methodological repertoire of less-intrusive methodologies. Q methodology offers an approach whereby cultural contexts and local definitions of bullying can be put in the front. Furthermore, developmentally appropriate intervention and prevention programs might be created based on exploratory Q research and could later be validated through large-scale investigations. Generally, research results based on Q methodology are expected to be useful for educators and policymakers aiming to create a safe learning environment for all children. With regard to contemporary bullying researchers, Q methodology may open up novel possibilities through its status as an innovative addition to more mainstream approaches.

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Introduction

Bullying, internationally recognized as a problematic and aggressive form of behavior, has negative effects, not only for those directly involved but for anybody and in particular children in the surrounding environment (Modin, 2012 ). However, one of the major concerns among researchers in the field of bullying is the type of research methods employed in the studies on bullying behavior in schools. The appropriateness of using quantitative or qualitative research methods rests on the assumption of the researcher and the nature of the phenomena under investigation (Hong & Espelage, 2012 ). There is a need for adults to widen their understanding and maintain a focus on children’s behaviors to be able to provide assistance and support in reducing the amount of stress and anxiety resulting from online and offline victimization (Hellström & Lundberg, 2020 ). A crucial step for widening this understanding is an increased visibility of children’s own viewpoints. When the voices of children, particularly those of victims and perpetrators, but also those of bystanders are heard in these matters, effective support can be designed based specifically on what children want and need rather than what adults interpret and understand to be supporting the child (O’Brien, 2019 ). However, bullying victims and their perpetrators are hard-to-reach populations (Shaghaghi et al., 2011 ; Sydor, 2013 ) for a range of reasons. To name but a few, researchers perennially face difficulties regarding potential participants’ self-identification, the sensitivity of bullying topics, or the power imbalance between them and their young respondents. Furthermore, limited verbal literacy and/or a lack of cognitive ability of some respondents due to age or disability contribute to common methodological issues in the field. Nevertheless, and despite ethical restrictions around the immediate questioning of younger children or children with disabilities that prohibit researchers to perform the assessments with them directly, it would be ethically indefensible to not study a sensitive topic like bullying among vulnerable groups of children. Hence, the research community is responsible for developing valid and reliable methods to explore bullying among different groups of children, where the children’s own voices are heard and taken into account (Hellström, 2019 ). Consequently, this paper aims to contribute to bullying researchers’ methodological repertoire with an additional less-intrusive methodology, particularly suitable for research with hard-to-reach populations.

Historically, the field of bullying and cyberbullying has been dominated by quantitative research approaches, most often with the aim to examine prevalence rates. However, recent research has seen an increase in the use of more qualitative and multiple data collection approaches on how children and youth explain actions and reactions in bullying situations (e.g., Acquadro Maran & Begotti, 2021 ; Eriksen & Lyng, 2018 ; Patton et al., 2017 ). This may be translated into a need to more clearly understand the phenomenon in different contexts. As acknowledged by many researchers, bullying is considerably influenced by the context in which it occurs and the field is benefitting from studying the phenomenon in the setting where all the contextual variables are operating (see, e.g., Acquadro Maran & Begotti, 2021 ; Scheithauer et al., 2016 ; Torrance, 2000 ). Cultural differences in attitudes regarding violence as well as perceptions, attitudes, and values regarding bullying are likely to exist and have an impact when bullying is being studied. For this reason, listening to the voices of children and adolescents when investigating the nature of bullying in different cultures is essential (Hellström & Lundberg, 2020 ; Scheithauer et al., 2016 ).

In addition to studying outcomes or products, bullying research has also emphasized the importance of studying processes (Acquadro Maran & Begotti, 2021 ). Here, the use of qualitative methods allows scholars to not only explore perceptions and understandings of bullying and its characteristics, but also interpret bullying in light of a specific social context, presented from a specific internal point of view. In other words, qualitative approaches may offer methods to understand how people make sense of their experiences of the bullying phenomenon. The processes implemented by a qualitative approach allow researchers to build hypotheses and theories in an inductive way (Atieno, 2009 ). Thus, a qualitative approach can enrich quantitative knowledge of the bullying phenomenon, paying attention to the significance that individuals attribute to situations and their own experiences. It can allow the research and clinical community to better project and implement bullying assessment and prevention programs (Hutson, 2018 ).

Instead of placing qualitative and quantitative approaches in opposition, they can both be useful and complementary, depending on the purpose of the research (Acquadro Maran & Begotti, 2021 ). In their review of mixed methods research on bullying and peer victimization in school, Hong and Espelage ( 2012 ) underlined that instead of using single methods, mixed methods have the advantage of generating a deeper and more complex understanding of the phenomenon. By combining objective data with information about the personal context within which the phenomenon occurs, mixed methods can generate new insights and new perspectives to the research field (Hong & Espelage, 2012 ; Kulig et al., 2008 ; Pellegrini & Long, 2002 ). However, Hong and Espelage ( 2012 ) also argued that mixed methods can lead to divergence and contradictions in findings that may serve as a challenge to researchers. For example, Cowie and Olafsson ( 2000 ) examined the impact of a peer support program to reduce bullying using both quantitative and qualitative data collection methods. While a quantitative approach collecting pre-test and post-test data showed no effects in decreasing bullying, interviews with peer supporters, students, and potential users of the intervention revealed the strength of the program and its positive impact, in light of students and peer supporters. Thus, rather than rejecting the program, the divergence in findings leads to a new rationale for modifying the program and addressing its limits.

Understandably, no single data collection approach is complete but deals with methodological issues and concerns affecting the research field and the comprehension of bullying. To provide a robust foundation for the introduction of an additional methodological perspective in bullying research, common data collection methods and methodological issues are outlined below.

Methodological Issues in Bullying Research

Large-scale cohort studies generating statistical findings often use R-statistics, descriptive analyses, averages, and correlations to estimate and compare prevalence rates of bullying, to explore personality traits of bullies and victims, and the main correlates and predictors of the phenomenon. Nevertheless, large-scale surveys have a harder time examining why bullying happens (O’Brian, 2019 ) and usually do not give voice to study objects’ own unique understanding and experiences (Acquadro Maran & Begotti, 2021 ; Bosacki et al., 2006 ; Woodhead & Faulkner, 2008 ). Other concerns using large-scale surveys include whether a definition is used or the term bullying is operationalized, which components are included in the definition, what cut-off points for determining involvement are being used, the lack of reliability information, and the absence of validity studies (Swearer et al., 2010 ).

Other issues include the validity in cross-cultural comparisons using large-scale surveys. For example, prevalence rates across Europe are often established using standard questionnaires that have been translated into appropriate languages. Comparing four large-scale surveys, Smith et al. ( 2016 ) found that when prevalence rates by country are compared across surveys, there are some obvious discrepancies, which suggest a need to examine systematically how these surveys compare in measuring cross-national differences. Low external validity rates between these studies raise concerns about using these cross-national data sets to make judgments about which countries are higher or lower in victim rates. The varying definitions and words used in bullying research may make it difficult to compare findings from studies conducted in different countries and cultures (Griffin & Gross, 2004 ). However, some argue that the problem seems to be more about inconsistency in the type of assessments (e.g., self-report, nominations) used to measure bullying rather than the varying definition of bullying (Jia & Mikami, 2018 ). When using a single-item approach (e.g., “How often have you been bullied?”) it is not possible to investigate the equivalency of the constructs between countries, which is a crucial precondition for any statistically valid comparison between them (Scheithauer et al., 2016 ). Smith et al. ( 2016 ) conclude that revising definitions and how bullying is translated and expressed in different languages and contexts would help examine comparability between countries.

Interviews, focus groups and the use of vignettes (usually with younger children) can all be regarded as suitable when examining youths’ perceptions of the bullying phenomenon (Creswell, 2013 ; Hellström et al., 2015 ; Hutson, 2018 ). They all allow an exploration of the bullying phenomenon within a social context taking into consideration the voices of children and might solve some of the methodological concerns linked to large-scale surveys. However, these data collection methods are also challenged by individual barriers of hard-to-reach populations (Ellard-Gray et al., 2015 ) and may include the lack of a necessary willingness to share on the one hand and the required ability to share subjective viewpoints on the other hand.

Willingness to Share

In contrast to large-scale surveys requiring large samples of respondents with reasonable literacy skills, interviews, which may rely even heavier on students’ verbal skills, are less plentiful in bullying research. This might at least partially be based on a noteworthy expectation of respondents to be willing to share something. It must be remembered that asking students to express their own or others’ experiences of emotionally charged situations, for example concerning bullying, is particularly challenging (Khanolainen & Semenova, 2020 ) and can be perceived as intrusive by respondents who have not had the opportunity to build a rapport with the researchers. This constitutes a reason why research in this important area is difficult and complex to design and perform. Ethnographic studies may be considered less intrusive, as observations offer a data collection technique where respondents are not asked to share any verbal information or personal experiences. However, ethnographical studies are often challenging due to the amount of time, resources, and competence that are required by the researchers involved (Queirós et al., 2017 ). In addition, ethnographical studies are often used for other purposes than asking participants to share their views on certain topics.

Vulnerable populations often try to avoid participating in research about a sensitive topic that is related to their vulnerable status, as recalling and retelling painful experiences might be distressing. The stigma surrounding bullying may affect children’s willingness to share their personal experiences in direct approaches using the word bullying (Greif & Furlong, 2006 ). For this reason, a single-item approach, in which no definition of bullying is provided, allows researchers to ask follow-up questions about perceptions and contexts and enables participants to enrich the discussion by adjusting their answers based on the suggestions and opinions of others (Jacobs et al., 2015 ). Generally, data collection methods with depersonalization and distancing effects have proven effective in research studying sensitive issues such as abuse, trauma, stigma and so on (e.g., Cromer & Freyd, 2009 ; Hughes & Huby, 2002 ). An interesting point raised by Jacobs and colleagues ( 2015 ) is that a direct approach that asks adolescents if they have ever experienced cyberbullying may lead to a poorer discussion and an underestimation of the phenomenon. This is because perceptions and contexts often differ between persons and because adolescents do not perceive all behaviors as cyberbullying. The same can be true for bullying taking place offline (Hellström et al., 2015 ).

When planning research with children, it is important to consider the immediate research context as it might affect what children will talk about (Barker & Weller, 2003 ; Hill, 2006 ; Punch, 2002 ). In addition to more material aspects, such as the room or medium for a dialog, the potential power imbalance created in an interview situation between an adult researcher and the child under study adds to a potentially limited willingness to share. Sitting in front of an adult interviewer may create situations where children may find it difficult to express their feelings and responses may be given based on perceived expectations (Punch, 2002 ). This effect is expected to be even stronger when studying a sensitive topic like bullying. Therefore, respondents may provide more honest responses when they are unaware that the construct of bullying is being assessed (Swearer et al., 2010 ). Moreover, in research about sensitive topics, building a strong connection with participants (Lyon & Carabelli, 2016 ), characterized by mutual trust, is vital and might overcome the initial hesitation to participate and share personal accounts. Graphic vignettes have successfully been used as such unique communication bridges to collect detailed accounts of bullying experiences (Khanolainen & Semenova, 2020 ). However, some reluctance to engage has been reported even in art-based methods, usually known to be effective in research with verbally limited participants (Bagnoli, 2009 ; Vacchelli, 2018 ) or otherwise hard-to-reach populations (Goopy & Kassan, 2019 ). Most commonly, participants might not see themselves as creative or artistic enough (Scherer, 2016 ). In sum, the overarching challenging aspect of art-based methods related to a limited willingness to share personal information is an often-required production of some kind.

Ability to Share

Interviews as a data collection method demand adequate verbal literacy skills for participants to take part and to make their voices heard. This may be challenging especially for younger children or children with different types of disabilities. There is a wide research gap in exploring the voices of younger children (de Leeuw et al., 2020 ) and children with disabilities (Hellström, 2019 ) in bullying research. Students’ conceptualization of bullying behavior changes with age, as there are suggestions that younger students tend to focus more on physical forms of bullying (such as fighting), while older students include a wider variety of behaviors in their view of bullying, such as verbal aggression and social exclusion (Hellström & Lundberg, 2020 ; Monks & Smith, 2006 ; Smith et al., 2002 ; Hellström et al., 2015 ). This suggests that cognitive development may allow older students to conceptualize bullying along a number of dimensions (Monks & Smith, 2006 ). Furthermore, the exclusion of the voices of children with disabilities in bullying research is debated. It is discussed that the symptoms and characteristics of disabilities such as Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD), i.e., difficulties understanding the thoughts, emotions, reactions, and behaviors of others, which makes them the ideal target for bullying may also make it hard for them to perceive, verbalize and report bullying and victimization in a reliable and valid manner (Slaughter et al., 2002 ). It may also be difficult for children with ASD to differentiate between playful teasing among friends and hurtful teasing. While many argue that children with ASD are unreliable respondents of victimization, under-reporting using parental and teacher reports has been shown in research on bullying (Waters et al., 2003 ; Bradshaw et al., 2007 ) and child maltreatment (Compier-de Block et al., 2017 ).

This Paper’s Contribution

The present paper contributes to this special issue about qualitative school bullying and cyberbullying research by reviewing and discussing Q methodology as an innovative addition to more mainstream approaches in the field. Despite the fact that Q methodology had been proclaimed as “especially valuable […] in educational psychology” (Stephenson, 1935 , p. 297) nearly 90 years ago, the approach has only relatively recently been described as an up-and-coming methodological choice of educational researchers interested in participants’ subjective views (Lundberg et al., 2020 ). Even though, Q enables researchers to investigate and uncover first-person accounts, characterized by a high level of qualitative detail in its narrative description, only few educational studies have applied Q methodology to investigate the subject of bullying (see Camodeca & Coppola, 2016 ; Ey & Spears, 2020 ; Hellström & Lundberg, 2020 ; Wester & Trepal, 2004 ). Within the wider field of bullying, Q methodology has also been used to investigate workplace bullying in hospitals (Benmore et al., 2018 ) and nursing units (Choi & Lee, 2019 ). By responding to common methodological issues outlined earlier, the potential Q methodology might have for bullying research is exemplified. A particular focus is thereby put on capturing respondents’ subjective viewpoints through its less-intrusive data collection technique. The present paper closes by discussing implications for practice and suggesting future directions for Q methodological bullying and cyberbullying research, in particular with hard-to-reach populations.

An Introduction to Q Methodology

Q as a methodology represents a larger conceptual and philosophical framework, which is by no means novel. However, the methodology has largely been marginalized since its invention in the 1930s by William Stephenson (Brown, 2006 ). As a research technique, it broadly consists of three stages that each can be split into a set of steps (see Fig.  1 ); (1) carefully constructing a data collection instrument, (2) collecting data, and (3) analyzing and interpreting data. The central, and therefore also best-known feature of Q methodology is Q sorting to collect data in the form of individual Q sorts. Participants thereby rank order a sample of self-referent stimuli along a continuum and in accordance with a central condition of instruction; for example, children might be asked to what extent particular scenarios describe bullying situations (Hellström & Lundberg, 2020 ) or they might be instructed to sort illustrated ways to resolve social exclusion according to the single face-valid dimension of “least preferred to most preferred” (de Leeuw et al., 2019 ). As soon as all items are placed on a most often bell-shaped distribution grid (see Fig.  2 ), participants might be asked to elaborate on their item placement to add a further layer of qualitative data. Such so-called post-sorting activities might include written annotations of items placed at the ends of the continuum or form the structure for interviews (Shemmings & Ellingsen, 2012 ).

Three stages and six steps of a Q methodological research process (adapted from Lundberg et al., 2020 )

A vertical distribution grid with two examples of face-valid dimensions. This rather small distribution is designed for a 16-item Q sample and therefore contains 16 slots to be filled

For participants to provide their subjective viewpoint toward a specific topic in the form of a Q sort, researchers need to construct the data collection instrument, called Q sample. Such a set of stimulus items is a representative sample from all possible items concerning the topic, which in the technical language in Q methodology is called concourse (Brown, 1980 ). The development of such a concourse about the topic at hand might stem from a wide range of sources, including academic literature, policy documents, informal discussions, or media (Watts & Stenner, 2012 ). Moreover, in a participatory research fashion, participants’ statements can be used verbatim to populate the concourse. This way, children’s own words and voices are part of the data collection instrument. A sophisticated structuring process then guides the researchers in selecting a Q sample from all initial statements in the concourse (Brown et al., 2019 ). In Hellström & Lundberg ( 2020 ), a literature review on findings and definitions of bullying, stemming from qualitative and quantitative research, provided the initial concourse. A matrix consisting of different modes, types, and contexts of bullying supported the construction of the final Q sample.

As a student and assistant of Charles Spearman, Q’s inventor Stephenson was well-informed about R-methodological factor analysis based on correlating traits. The British physicist-psychologist however inverted the procedure and thereby suggested correlating persons to study human behavior (Stephenson, 1935 , 1953 ). A detailed description of the statistical procedure of Q factor analysis is outside the scope of this article, especially as the focus of this special issue is put on qualitative research methods. In addition, with its focus on producing quantifiable data from highly subjective viewpoints (Duncan & Owens, 2011 ), it is safe to say that Q methodology is more often treated as a qualitative methodology with quantitative features than the other way around. Nevertheless, it is important to note that through factor analysis, individual viewpoints are clustered into so-called factors, representing shared viewpoints if they sufficiently correlate (see Fig.  3 ). In that sense, no outside criterion is applied to respondents’ subjective views and groups of similar sorts (factors/viewpoints) are not logically constructed by researchers. Instead, they inductively emerge through quantitative analysis, which helps “in learning how the subject, not the observer, understands and reacts to items” (Brown, 1980 , p. 191). This procedure allowed Hellström & Lundberg ( 2020 ) to describe two age-related definitions of bullying. Older students in particular perceived offline bullying as more severe than online bullying and their younger peers were mostly concerned about bullying situations taking place in a private setting.

A simplified illustration of Q factor analysis (step 5). Arrow A represents the statistical correlation of all collected individual viewpoints. Arrow B represents inverted factor analysis as the data condensation technique resulting in a manageable number of shared viewpoints

Despite its quantitative analysis, participant selection in Q methodology is largely in line with purposive sampling with small numbers. It, therefore, represents a major difference to R methodological research, where larger opportunity samples are desired. In Q methodology, participants are selected strategically in line with those who might likely “express a particularly interesting or pivotal point of view” (Watts & Stenner, 2012 , p. 71). Investigating a large number of similar respondents might therefore simply lead to more participants correlating with the same shared viewpoint and not necessarily add new viewpoints. In recent educational Q research, the average number of participants is 37 (Lundberg et al., 2020 ). Many studies have however been successfully conducted with considerably fewer, as for example illustrated by Benmore et al. ( 2018 ), who described three distinctive groups within their sample of 12 participants.

To illustrate Q methodology in bullying research, our small scale and exploratory study published in Educational Research (Hellström & Lundberg, 2020 ) serves as a practical example. The purpose of that study was to investigate definitions of bullying from young people’s perspectives and was guided by the following research question: What are students’ subjective viewpoints on bullying behavior? . In Table 1 , we describe the methodological steps introduced in Fig.  1 .

Q Methodology’s Response to the Methodological Issues Outlined Above

Above, methodological issues have been structured according to participants’ willingness and ability to share their subjective viewpoints and lived experiences. In order to respond to those, the present section focuses on Q methodology’s built-in features. A particularly important component is Q sorting as the central data collection technique that facilitates participants’ communicability of their subjectivity.

Engaging participants in a card sorting activity encourages students to express their viewpoints and thereby making their voices heard in a less-intrusive way, despite being cognitively engaging. Because they are asked to rank-order a predetermined sample of items, ideally in accordance with a carefully selected condition of instruction, they do not need to report or disclose their own personal experiences and are not obliged to actively create anything, as criticized in arts-based research. In that sense, Q methodology can be seen as a method to collect sensitive data in a more depersonalized way. This provides the basis to find a vital “balance between protecting the child and at the same time allowing access to important information” (Thorsen & Størksen, 2010 , p. 9), which is of particular importance for research about emotionally charged situations or sensitive topics as it is often the case with bullying (Ellingsen et al., 2014 ). Sharing their view through a fixed collection of items certainly makes participation in research for young children or otherwise hard-to-reach respondents less intimidating and results can be expected to be more truthful.

In comparison to researchers applying ethnographical approaches, who immerse themselves into the studied context to understand and document patterns of social behavior and interaction in a less intrusive way, Q methodologists are not expected to observe their participants. Even though the purpose of these approaches is different, being part of the culture under investigation or at least involving community partners in Q methodological research can still be useful for at least two reasons. As mentioned in Table 1 featuring the study by Hellström & Lundberg ( 2020 ), the pupils’ physical education and health teacher guided an exploratory and informal discussion and thereby provided valuable insights into the participants’ lifeworld that informed the Q sample. In addition to better tailoring the sample to the participants and making them feel seen and heard, the community partner could help build a positive rapport between participants and researchers, which otherwise requires much work. During the actual data collection exercise, participants were already familiar with the topic, well-informed about the research project, and perceived the sorting activity as an integral part of their lesson.

The play-like character of Q sorting has as well been reported as a positive influence on respondents’ motivation to participate (de Leeuw et al., 2019 ) and Wright ( 2013 ) mentions the engaging atmosphere created between the sorter and the researcher. The combination of these features allows assuming that obtaining participants’ viewpoint through Q methodology is less threatening than for example sitting in front of an interviewer and providing on-spot oral responses about a sensitive topic.

Q sorting as a data collection instrument represents a major advantage for Q methodological research with participants that do not (yet) possess sufficient verbal literacy and/or cognitive ability to process receptive or expressive language. To illustrate, two features are outlined here: first the flexibility of the Q sample, say the set of stimuli and second the fact that primary data collection in Q methodology is based on a silent activity.

Written statements are undoubtedly the most common type of items used in Q methodology and the number of such in a Q sample greatly varies. In recent research reporting from compulsory education settings, the average Q sample consists of about 40 items (Lundberg et al., 2020 ). In addition to applying a smaller set of items, their complexity can easily be adapted in line with participants’ receptive literacy skills and their developmental stage to facilitate understanding. Statements can for example be shortened or they can start identically to make the activity less taxing (Watts & Stenner, 2012 ). A different approach to cater to limited verbal literacy is the use of images instead of written statements. Constructing a visual Q sample might be more challenging for the researcher, in particular, if images are carefully selected and culturally tailored, meaning that they are clear, appealing and without too many details (Thorsen & Størksen, 2010 ). It might nevertheless be worth it, as such items provide a powerful tool to elicit viewpoints from otherwise marginalized or hard-to-reach research participants. Combes and colleagues ( 2004 ) for example, created a 37-item-Q sample with intellectually disabled participants’ own pictures to evaluate the planning of activities and de Leeuw et al. ( 2019 ) have used 15 images of hypothetical scenarios of social exclusion in a study with primary school pupils. Furthermore, as illustrated by Allgood and Svennungsen ( 2008 ) who photographed their participant’s own sculptures, Q samples consisting of objects (e.g., toys) or symbols (emojis) might be other options to investigate issues about bullying and cyberbullying without using text.

In addition to adaptations to the data collection instrument, the sorting process is usually carefully introduced and illustrated. Researchers might want to go through the entire Q sample to ensure the participants are able to discriminate each item (Combes et al., 2004 ). Even with adult participants without any cognitive impairments, it is suggested to pre-sort items into three provisional categories (Watts & Stenner, 2012 ). Two categories represent the respective ends of the continuum in the distribution grid and might be labeled and. Any items the sorter feels insecure or neutral about, are moved to the third category, which receives a question mark (?) for the sake of this exercise. During the actual rank-ordering process, the participants start to allocate items to one of the ends of the continuum (the top of the distribution grid in Fig.  2 ) with cards from the ☺ category and work themselves toward the center of the distribution grid. The process continues with items in the ☹ category, which are placed from the opposite end of the continuum toward the center. Any free spots are then filled with the remaining items in the (?) category. The graphic display of their viewpoint has been experienced as enabling for self-reflection (Combes et al., 2004 ) and might be utilized for a further discussion about the topic, for example as part of teacher workshops (Ey & Spears, 2020 ).

Meeting children at an appropriate cognitive level through adaptations of the data collection instrument and procedure, is not only a promising and important ethical decision in order to show young participants the respect they deserve (Thorsen & Størsken, 2010 ), but makes the sorting procedure a pleasant experience for the participants (John et al., 2014 ). Unsurprisingly, Q methodology has been described as a respectful, person-centered, and therefore child-friendly approach (Hughes, 2016 ).

Limitations

Despite its potential for bullying research, Q methodology has its limitations. The approach is still relatively unknown in the field of bullying research and academic editors’ and reviewers’ limited familiarity with it can make publishing Q methodological research challenging. Notwithstanding the limitation of not being based on a worked example, the contribution of the present paper hopefully fulfills some of the needed spadework toward greater acceptability within and beyond a field, which has only seen a limited number of Q methodological research studies. Because the careful construction of a well-balanced Q sample is time-consuming and prevents spontaneous research activities, a core set of items could be created to shorten the research process and support the investigation of what bullying means to particular groups of people. Such a Q sample would then have to be culturally tailored to fit local characteristics. Finally, the present paper is limited in our non-comprehensive selection of data collection methods as points of comparison when arguing for a more intensive focus on Q methodology for bullying research.

Future Research Directions

The results of Q methodological studies based on culturally tailored core Q samples would allow the emergence of local definitions connected to the needs of the immediate society or school context. As illustrated by Hellström & Lundberg ( 2020 ), even within the same school context, and with the same data collection instrument (Q sample), Q methodology yielded different, age-related definitions of bullying. Or in Wester and Trepal ( 2004 ), Q methodological analysis revealed more perceptions and opinions about bullying than researchers usually mention. Hence, Q methodology offers a robust and strategic approach that can foreground cultural contexts and local definitions of bullying. If desired, exploratory small-scale Q research might later be validated through large-scale investigations. A further direction for future research in the field of bullying research is connected to the great potential of visual Q samples to further minimize research participation restrictions for respondents with limited verbal or cognitive abilities.

Implications for Practice

When designing future bullying prevention strategies, Q methodology presents a range of benefits to take into consideration. The approach offers a robust way to collect viewpoints about bullying without asking participants to report their own experiences. The highly flexible sorting activity further represents a method to investigate bullying among groups that are underrepresented in bullying research, such as preschool children (Camodeca & Coppola, 2016 ). This is of great importance, as tackling bullying at an early age can prevent its escalation (Alsaker & Valkanover, 2001 ; Storey & Slaby, 2013 ). Making the voices of the hard-to-reach heard in an unrestricted way and doing research with them instead of about them (de Leeuw et al., 2019 ; Goopy & Kassan, 2019 ) is expected to enable them to be part of discussions about their own well-being. By incorporating social media platforms, computer games, or other contextually important activities when designing a Q sample, the sorting of statements in Hellström & Lundberg, ( 2020 ) turned into a highly relevant activity, clearly connected to the reality of the students. As a consequence, resulting policy creation processes based on such exploratory studies should lead to more effective interventions and bullying prevention programs confirming the conclusion by Ey and Spears ( 2020 ) that Q methodology served as a great model to develop and implement context-specific programs. Due to the enhanced accountability and involvement of children’s own voices, we foresee a considerable increase in implementation and success rates of such programs. Moreover, Q methodology has been suggested as an effective technique to evaluate expensive anti-bullying interventions (Benmore et al., 2018 ). Generally, research results based on exploratory Q methodology that quantitatively condensates rich data and makes commonalities and diversities among participants emerge through inverted factor analysis are expected to be useful for educators and policymakers aiming to create a safe learning environment for all children. At the same time, Q methodology does not only provide an excellent ground for participatory research, but is also highly cost-efficient due to its status as a small-sample approach. This might be particularly attractive, when neither time nor resources for other less-intrusive methodological approaches, such as for example ethnography, are available. Due to its highly engaging aspect and great potential for critical personal reflection, Q sorting might be applied in classes regardless of representing a part of a research study or simply as a learning tool (Duncan & Owens, 2011 ). Emerging discussions are expected to facilitate and mediate crucial dialogs and lead toward collective problem-solving among children.

The use of many different terminologies and different cultural understandings, including meaning, comprehension, and operationalization, indicates that bullying is a concept that is difficult to define and subject to cultural influences. For the purpose of designing relevant and powerful bullying prevention strategies, this paper argues that instead of pursuing a universal definition of what constitutes bullying, it may be of greater importance to investigate culturally and contextually bound understandings and definitions of bullying. Although the quest for cultural and contextual bound definitions is not new in bullying research, this paper offers an additional method, Q methodology, to capture participants’ subjective views and voices. Since particularly the marginalized and vulnerable participants, for example, bullying victims, are usually hard to reach, bullying researchers might benefit from a methodological repertoire enriched with a robust approach that is consistent with changes in methodological and epistemological thinking in the field. In this paper, we have argued that built-in features of Q methodology respond to perennial challenges in bullying research connected to a lack of willingness and limited ability to share among participants as well as studying bullying as a culturally sensitive topic. In summary, we showcased how Q methodology allows a thorough and less-intrusive investigation of what children perceive to be bullying and believe that Q methodology may open up novel possibilities for contemporary bullying researchers through its status as an innovative addition to more mainstream approaches.

Availability of Data and Material

Not applicable.

Code Availability

Change history, 18 july 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s42380-022-00135-9

Acquadro Maran, D., & Begotti, T. (2021). Measurement ideas relevant to qualitative studies. In P. K. Smith & J. O’Higgins Norman (Editors). Handbook of bullying (no pages assigned yet). John Wiley & Sons Limited.

Allgood, E., & Svennungsen, H. O. (2008). Toward an articulation of trauma using the creative arts and Q-methodology: A single-case study. Journal of Human Subjectivity, 6 (1), 5–24.

Google Scholar  

Alsaker, F. D., & Valkanover, S. (2001). Early diagnosis and prevention of victimization in kindergarten. In J. Juvonen & S. Graham (Eds.), Peer harassment in school (pp. 175–195). Guilford Press.

Atieno, O. P. (2009). An analysis of the strengths and limitation of qualitative and quantitative research paradigms.  Problems of Education in the 21st Century ,  13 (1), 13–38.

Bagnoli, A. (2009). Beyond the standard interview: The use of graphic elicitation and arts-based methods. Qualitative Research, 9 (5), 547–570. https://doi.org/10.1177/1468794109343625

Article   Google Scholar  

Barker, J., & Weller, S. (2003). “Is it fun?” Developing children centred research methods. International Journal of Sociology and Social Policy, 23 (1/2), 33–58. https://doi.org/10.1108/01443330310790435

Benmore, G., Henderson, S., Mountfield, J., & Wink, B. (2018). The Stopit! programme to reduce bullying and undermining behaviour in hospitals: Contexts, mechanisms and outcomes. Journal of Health Organization and Management, 32 (3), 428–443. https://doi.org/10.1108/JHOM-02-2018-0047

Article   PubMed   Google Scholar  

Bosacki, S. L., Marini, Z. A., & Dane, A. V. (2006). Voices from the classroom: Pictorial and narrative representations of children’s bullying experiences. Journal of Moral Education, 35 , 231–245. https://doi.org/10.1080/03057240600681769

Bradshaw, C. P., Sawyer, A. L., & O’Brennan, L. M. (2007). Bullying and peer victimization at school: Perceptual differences between students and school staff. School Psychology Review, 36 , 361–382. https://doi.org/10.1080/02796015.2007.12087929

Brown, S. (1980). Political subjectivity: Applications of Q methodology in political science . Yale University Press.

Brown, S. (2006). A match made in heaven: A marginalized methodology for studying the marginalized. Quality and Quantity, 40 (3), 361–382. https://doi.org/10.1007/s11135-005-8828-2

Brown, S., Baltrinic, E., & Jencius, M. (2019) From concourse to Q sample to testing theory. Operant Subjectivity, 41 , 1–17. https://doi.org/10.15133/j.os.2019.002

Camodeca, M., & Coppola, G. (2016). Bullying, empathic concern, and internalization of rules among preschool children: The role of emotion understanding. International Journal of Behavioral Development, 40 , 459–465.

Choi, J. K., & Lee, B. S. (2019). Response patterns of nursing unit managers regarding workplace bullying: A Q methodology approach. Journal of Korean Academy of Nursing, 49 , 562–574.

Combes, H., Hardy, G., & Buchan, L. (2004). Using Q-methodology to involve people with intellectual disability in evaluating person-centred planning. Journal of Applied Research in Intellectual Disabilities, 17 , 149–159. https://doi.org/10.1111/j.1468-3148.2004.00191.x

Compier-de Block, L. H., Alink, L. R., Linting, M., van den Berg, L. J., Elzinga, B. M., Voorthuis, A., Tollenaar, M. S., & Bakermans-Kranenburg, M. J. (2017). Parent-child agreement on parent-to-child maltreatment. Journal of Family Violence, 32 , 207–217. https://doi.org/10.1007/s10896-016-9902-3

Cowie, H., & Olafsson, R. (2000). The role of peer support in helping the victims of bullying in a school with high levels of aggression. School Psychology International, 21 , 79–95.

Creswell, J. (2013). Qualitative inquiry and research design: Choosing among five approaches (3 rd ed.). Sage.

Cromer, L. D., & Freyd, J. J. (2009). Hear no evil, see no evil? Associations of gender, trauma history, and values with believing trauma vignettes. Analyses of Social Issues and Public Policy, 9 (1), 85–96. https://doi.org/10.1111/j.1530-2415.2009.01185.x

De Leeuw, R. R., de Boer, A. A., Beckmann, E. J., van Exel, J., & Minnaert, A. E. M. G. (2019). Young children’s perspectives on resolving social exclusion within inclusive classrooms. International Journal of Educational Research, 98 , 324–335. https://doi.org/10.1016/j.ijer.2019.09.009

De Leeuw, R. R., Little, C., & Rix, J. (2020). Something needs to be said – Some thoughts on the possibilities and limitations of ‘voice.’ International Journal of Educational Research, 104 , 101694. https://doi.org/10.1016/j.ijer.2020.101694

Duncan, N., & Owens, L. (2011). Bullying, social power and heteronormativity: Girls’ constructions of popularity. Children & Society, 25 , 306–316. https://doi.org/10.1111/j.1099-0860.2011.00378.x

Ellard-Gray, A., Jeffrey, N. K., Choubak, M., Crann, S. E. (2015). Finding the hidden participant: Solutions for recruiting hidden, hard-to-reach, and vulnerable populations.  International Journal of Qualitative Methods, 1–10.  https://doi.org/10.1177/1609406915621420

Ellingsen, I. T., Thorsen, A. A., Størksen, I. (2014). Revealing children’s experiences and emotions through.  Q Methodology Child Development Research, 1–9. https://doi.org/10.1155/2014/910529

Eriksen, M. I., & Lyng, S. T. (2018). Relational aggression among boys: Blind spots and hidden dramas. Gender and Education, 30 (3), 396–409. https://doi.org/10.1080/09540253.2016.1214691

Ey, L., & Spears, B. (2020). Engaging early childhood teachers in participatory co-design workshops to educate young children about bullying. Pastoral Care in Education, 38 , 230–253. https://doi.org/10.1080/02643944.2020.1788129

Greif, J. L., & Furlong, M. J. (2006). The assessment of school bullying. Journal of School Violence, 5 , 33–50. https://doi.org/10.1300/J202v05n03_04

Griffin, R. S., & Gross, A. M. (2004). Childhood bullying: Current empirical findings and future directions for research. Aggression and Violent Behavior, 9 , 379–400. https://doi.org/10.1016/S1359-1789(03)00033-8

Goopy, S., & Kassan, A. (2019). Arts-based engagement ethnography: An approach for making research engaging and knowledge transferable when working with harder-to-reach communities. International Journal of Qualitative Methods, 18 , 1–10. https://doi.org/10.1177/1609406918820424

Hellström, L. (2019). A systematic review of polyvictimization among children with Attention Deficit Hyperactivity or Autism Spectrum Disorder. International Journal of Environmental Research and Public Health, 16 (13), 2280. https://doi.org/10.3390/ijerph16132280

Hellström, L. & Lundberg, A. (2020). Understanding bullying from young people’s perspectives: An exploratory study. Educational Research, 62 (4), 414-433. https://doi.org/10.1080/00131881.2020.1821388

Hellström, L., Persson, L. & Hagquist, C. (2015). Understanding and defining bullying– adolescents’ own views. Archives of Public Health 73 (4), 1-9. https://doi.org/10.1186/2049-3258-73-4

Hill, M. (2006). Children’s voices on ways of having a voice: Children’s and young people’s perspectives on methods used in research and consultation. Childhood, 13 (1), 69–89. https://doi.org/10.1177/0907568206059972

Hong, J. S., & Espelage, D. L. (2012). A review of mixed methods research on bullying and peer victimization in school. Educational Review, 64 (1), 115–126. https://doi.org/10.1080/00131911.2011.598917

Hughes, M. (2016). Critical, respectful, person-centred: Q methodology for educational psychologists. Educational and Child Psychology, 33 (1), 63–75.

Hughes, R., & Huby, M. (2002). The application of vignettes in social and nursing research. Journal of Advanced Nursing, 37 (4), 382–386. https://doi.org/10.1046/j.1365-2648.2002.02100.x

Hutson, E. (2018). Integrative review of qualitative research on the emotional experience of bullying victimization in youth. Journal of School Nursing, 34 (1), 51–59. https://doi.org/10.1177/1059840517740192

Jacobs, N., Goossens, L., Dehue, F., Völlink, T., & Lechner, L. (2015). Dutch cyberbullying victims’ experiences, perceptions, attitudes and motivations related to (coping with) cyberbullying: Focus group interviews. Societies, 5 (1), 43–64. https://doi.org/10.3390/soc5010043

Jia, M., & Mikami, A. (2018). Issues in the assessment of bullying: Implications for conceptualizations and future directions. Aggression and Violent Behavior, 41 , 108–118. https://doi.org/10.1016/j.avb.2018.05.004

John, A., Montgomery, D., & Halliburton Tate, A. (2014). Using Q methodology in conducting research with young children. In O. Saracho (Editor): Handbook of Research Methods in Early Childhood Education , Volume 1, (pp. 147–173), University of Maryland.

Khanolainen, D., & Semenova, E. (2020). School bullying through graphic vignettes: Developing a new arts-based method to study a sensitive topic. International Journal of Qualitative Methods, 19 , 1–15. https://doi.org/10.1177/1609406920922765

Kulig, J. C., Hall, B., & Grant Kalischuk, R. (2008). Bullying perspectives among rural youth: A mixed methods approach. Rural and Remote Health, 8 , 1–11.

Lyon, D., & Carabelli, G. (2016). Researching young people’s orientations to the future: The methodological challenges of using arts practice. Qualitative Research, 16 (4), 430–445. https://doi.org/10.1177/1468794115587393

Lundberg, A., de Leeuw, R., & Aliani R. (2020). Using Q methodology: sorting out subjectivity in educational research. Educational Research Review, 31 , 100361. https://doi.org/10.1016/j.edurev.2020.100361

Modin, B. (2012). Beteendeproblem bidrar till utsatthet för mobbning och psykisk ohälsa. I: Skolans betydelse för barns och ungas psykiska hälsa - en studie baserad på den nationella totalundersökningen i årskurs 6 och 9 hösten 2009 behavior problems contribute to victimization from bullying and poor self-related mental health. in: The role of the school for young people’s mental health. A study based on the national Swedish survey of students in grades 6 and 9 in 2009. ( No. 2012–5–15). National Board of Health and Welfare and Centre for Health Equity Studies.

Monks, C. P., & Smith, P. K. (2006). Definitions of bullying: Age differences in understanding of the term, and the role of experience. British Journal of Developmental Psychology, 24 (4), 801–821. https://doi.org/10.1348/026151005X82352

O’Brien, N. (2019). Understanding alternative bullying perspectives through research engagement with young people. Frontiers in Psychology, 10 , 1984. https://doi.org/10.3389/fpsyg.2019.01984

Article   PubMed   PubMed Central   Google Scholar  

Patton, D. U., Hong, J. S., Patel, S., & Kral, M. J. (2017). A systematic review of research strategies used in qualitative studies on school bullying and victimization. Trauma, Violence & Abuse, 18 (1), 3–16. https://doi.org/10.1177/1524838015588502

Pellegrini, A. D., & Long, J. D. (2002). A longitudinal study of bullying, dominance, and victimization during the transition from primary school through secondary school. British Journal of Developmental Psychology, 20 , 259–280. https://doi.org/10.1348/026151002166442

Punch, S. (2002). Research with children: The same or different from research with adults? Childhood, 9 (3), 321–341. https://doi.org/10.1177/0907568202009003005

Queirós, A., Faria, D., & Almeida, R. (2017). Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies, 3 (9), 369–387. https://doi.org/10.46827/ejes.v0i0.1017

Scheithauer, H., Smith, P. K., & Samara, M. (2016). Cultural issues in bullying and cyberbullying among children and adolescents: Methodological approaches for comparative research. International Journal of Developmental Science 10 , 3–8.  https://doi.org/10.3233/DEV-16000085

Scherer, L. (2016). Children’s engagements with visual methods through qualitative research in the primary school as “Art that didn’t work.” Sociological Research Online, 21 (1), 1–16. https://doi.org/10.5153/sro.3805

Shaghaghi, A., Bhopal, R. S., & Sheikh, A. (2011). Approaches to recruiting ‘hard to reach’ populations into research: A review of the literature. Health Promotion Perspectives, 1 , 86–94. https://doi.org/10.5681/hpp.2011.009

Shemmings, D., & Ellingsen, I. T. (2012). Using Q methodology in qualitative interviews. In J.F. Gubrium, J.A. Holstein, A.B. Marvasti, & K.D. McKinney (Eds.), The SAGE handbook of interview research: The complexity of the craft (2 nd ed), (pp. 415–426). Sage.

Slaughter, V., Dennis, M. J., & Pritchard, M. (2002). Theory of mind and peer acceptance in preschool children. British Journal of Developmental Psychology, 20 , 545–564.

Smith, P. K., Cowie, H., Olafsson, R. F., & Liefooghe, A. P. D. (2002). Definitions of bullying: A comparison of terms used, and age and gender differences, in a fourteen–country international comparison. Child Development 73 (4): 1119–1133.  https://doi.org/10.1111/1467-8624.00461

Smith, P. K., Robinson, S., & Marchi, B. (2016). Cross-national data on victims of bullying: What is really being measured? International Journal of Developmental Science, 10 (1–2), 9–19. https://doi.org/10.3233/DEV-150174

Stephenson, W. (1935). Technique of factor analysis. Nature, 136 , 297.

Stephenson, W. (1953). The study of behavior: Q technique and its methodology. Chicago University Press.

Storey, K., & Slaby, R. (2013). Eyes on bullying in early childhood. Education Development Center. Retrieved from www.eyesonbullying.org

Swearer, S. M., Siebecker, A. B., Johnsen-Frerichs, L. A., & Wang, C. (2010). Assessment of bullying/victimization. The problem of comparability across studies and across methodologies. In S.R. Jimerson, S.M. Swearer, & D.L. Espelage (Eds.), Handbook of bullying in schools: An international perspective (pp 305- 328). Routledge

Sydor, A. (2013). Conducting research into hidden or hard-to-reach populations. Nurse Researcher, 20 , 33–37.

Thorsen, A. A., & Størksen, I. (2010). Ethical, methodological, and practical reflections when using Q methodology in research with young children. Operant Subjectivity, 33 (1–2), 3–25.

Torrance, D. A. (2000). Qualitative studies into bullying within special schools. British Journal of Special Education, 27 , 16–21.

Vacchelli, E. (2018). Embodiment in qualitative research: Collage making with migrant, refugee and asylum seeking women. Qualitative Research, 18 (2), 171–190. https://doi.org/10.1332/policypress/9781447339069.003.0004

Waters, E., Stewart-Brown, S., & Fitzpatrick, R. (2003). Agreement between adolescent self-report and parent reports of health and well-being: Results of an epidemiological study. Child Care Health Development, 29 , 501–509. https://doi.org/10.1046/j.1365-2214.2003.00370.x

Watts, S., & Stenner, P. (2012). Doing Q methodological research: Theory, method and interpretation . Sage.

Book   Google Scholar  

Wester, K. L., & Trepal, H. C. (2004). Youth perceptions of bullying: Thinking outside the box. Operant Subjectivity, 27 , 68–83.

Woodhead, M., & Faulkner, D. (2008). Subjects, objects or participants? Dilemmas of psychological research. In P. Christensen & A. James (Eds.), Research with children (2nd ed., pp. 10–39). Routledge.

Wright, P. N. (2013). Is Q for you?: Using Q methodology within geographical and pedagogical research. Journal of Geography in Higher Education, 37 (2), 152–163. https://doi.org/10.1080/03098265.2012.729814

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Lundberg, A., Hellström, L. Q Methodology as an Innovative Addition to Bullying Researchers’ Methodological Repertoire. Int Journal of Bullying Prevention 4 , 209–219 (2022). https://doi.org/10.1007/s42380-022-00127-9

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ORIGINAL RESEARCH article

Exposure to workplace bullying: the incremental effect of gelotophobia beyond the big five.

• 1 Department of Psychology, Palacký University Olomouc, Olomouc, Czechia
• 2 School of Psychology, University of Sunderland, Sunderland, United Kingdom

The role of Big Five personality traits in exposure to workplace bullying has been a focus of numerous studies. Yet less is known about the incremental validity of narrower personality constructs. The aim of the present study was to investigate the incremental effect of gelotophobia (the fear of being laughed at) in predicting exposure to workplace bullying beyond the Big Five personality domains. The sample comprised 328 employees (77% females) from different regions of the Czech Republic. Correlational analysis showed that negative emotionality and gelotophobia were related to workplace bullying in theoretically expected ways. Results from a multiple regression indicated that gelotophobia had an incremental effect in predicting exposure to workplace bullying over and above the personality domains. Overall, this study provides new insights and extends previous investigations concerning the role of gelotophobia in workplace bullying. We also discuss the limitations of our study and provide suggestions for future research.

Introduction

Two main perspectives, namely the work environment hypothesis and the individual dispositions hypothesis, are typically used to explain the antecedents of workplace bullying [ Nielsen and Knardahl, 2015 ; also see Leymann (1996) , Zapf and Einarsen (2001) , and Balducci et al. (2021) ]. From the work environment perspective, factors such as job design, organizational climate and culture, and leadership are usually seen as crucial for the occurrence of workplace bullying ( Gamian-Wilk et al., 2022 ), while on the other hand, the individual dispositions hypothesis (also known as the vulnerability hypothesis) focuses on individual characteristics which might increase the risk of being a target or a victim of bullying [ Nielsen and Knardahl, 2015 , see also Gamian-Wilk et al. (2022) ].

In the present study, we follow the vulnerability hypothesis – keeping in mind that the perspectives should be seen as complementary, but not opposite – and investigate the role of the Big Five domains ( Soto and John, 2017a ) and the incremental validity of gelotophobia ( Ruch and Proyer, 2008a ) in predicting workplace bullying, understood as “the persistent exposure to interpersonal aggression and mistreatment from colleagues, superiors or subordinates” ( Einarsen et al., 2009 , p. 24). In particular, we consider workplace bullying as operationalized with the Negative Acts Questionnaire-Revised (NAQ-R) ( Einarsen et al., 2009 ), which includes three different forms of exposure to bullying (work-related, person-related, and physically intimidating). Moreover, we introduce a dimension labeled as humor-related bullying. The role of the personality dimensions and gelotophobia in predicting self-labeled victimization from bullying is one of the interests of this study as well.

Personality and workplace bullying

In a meta-analysis, investigating the relationship between the Five-Factor Model of personality ( McCrae and Costa, 1987 ) and exposure to harassment (a higher-ordered construct including different forms of psychological mistreatment), Nielsen et al. (2017) reported that being exposed to harassment was positively associated with neuroticism, and negatively related to extraversion, agreeableness, and conscientiousness. The theoretical relations between the personality dimensions and harassment were built upon the four mechanisms explaining the relationship between bullying and individual dispositions ( Nielsen and Knardahl, 2015 ).

In sum, Nielsen and Knardahl (2015) propose that bullying and individual dispositions relationship can be explained by (1) the no-relationship mechanism; indicating no association, (2) the target-behavior mechanism; implying that employees with certain dispositions not only fail to meet expectations but also irritate others, possibly by violating the usual norms of polite and friendly interaction, which can lead to others responding with aggressive behaviors, (3) the negative perceptions mechanism; suggesting that specific individual dispositions are related with a lowered threshold for interpreting behaviors as harassing, and therefore, employees with such dispositions have a higher risk than others for labeling negative events at the workplace as bullying, and (4) the reverse causality mechanism; individual dispositions are viewed as outcomes rather than precursors of workplace bullying.

Nielsen et al. (2017) highlight that the findings regarding the association between bullying and personality should not be used to conclude whether dispositional characteristics among those harassed are causes or consequences of harassment, as their study was cross-sectional [see Bowling et al. (2010) , Nielsen and Knardahl (2015) , and Podsiadly and Gamian-Wilk (2017) for longitudinal research findings]. As our study is also cross-sectional in nature, we firstly focus on replicating the above findings in a Czech cultural setting. Moreover, we utilize a more recent operationalization of the Big Five dimensions [see Soto and John (2017a , b) ]. Soto and John (2017a) used the following labels for the Big Five personality dimensions: Extraversion, Agreeableness, Conscientiousness, Negative Emotionality (also known as Neuroticism; see, e.g., McCrae and Costa, 2008 ), and Open-Mindedness [also known as Openness to Experience, Intellect, or Imagination; see Goldberg (1993) , John et al. (2008) , and McCrae and Costa (2008) ]. The Big Five Inventory-2 (BFI-2) operationalizes these domains using a total of 60 items, and it was shown that it has a robust hierarchical structure, controls for individual differences in acquiescence, and has conceptual breadth, specificity, and predictive power ( Soto and John, 2017a ). In the present study, we employ the abbreviated 30 items version of the scale, i.e., the BFI-2-S ( Soto and John, 2017b ).

The role of gelotophobia

Gelotophobia is defined as the pathological fear of being an object of laughter or appearing to others as a ridiculous object ( Ruch and Proyer, 2008b ; Titze, 2009 ). Individuals scoring high on gelotophobia do not perceive humor and laughter as relaxing and joyful social experiences ( Titze, 2009 ) and they fail to discriminate between ridicule and good-humored teasing ( Platt, 2008 ). Clinically observed behaviors described by Titze (1997 , 2009) was developed into a model of the causes and consequences of gelotophobia ( Ruch and Proyer, 2008a ), which later was updated to include putative causes and the moderating factors ( Ruch et al., 2014 ). Ruch and Stahlmann (2020) advanced the framework to a dynamic model of gelotophobia, providing an updated definition that is anchored in the construct of vulnerability. Gelotophobia is understood “as a distinguishable pattern of lacking resources (i.e., misinterpretation of joy and laughter) that can result in negative consequences (e.g., reduced well-being and performance) if individuals have no access to further resources (e.g., social support) or are exposed to severe stressors (e.g., workplace bullying)” ( Ruch and Stahlmann, 2020 , p. 16,369).

By far, a few studies have investigated the association between gelotophobia and (workplace) bullying. Some of these previous investigations focused on children and/or adolescents ( Führ, 2010 ; Proyer et al., 2012 , 2013 ) and reported that higher gelotophobia was related to feelings of being a victim of bullying. Therefore, the importance of gelotophobia in school therapy practice has been already put forward ( Bledsoe and Baskin, 2014 ; Platt et al., 2016 ). Considering workplace bullying – although the role of gelotophobia in workplace bullying has been theoretically discussed (e.g., Hofmann et al., 2017 ) – to our knowledge there are the only three empirical studies to date.

In a sample of adults, Platt (2008) found that participants who reported that were victims of bullying had higher gelotophobia scores (in comparison to individuals who did not disclose such an experience). It should be noted that it was not considered whether being a victim of bullying was specifically in the workplace. Gelotophobes [for cut-off points indicating slight pronounced, and extreme expression of gelotophobia, see Ruch and Proyer (2008b) ] also did not discriminate between scenarios of ridicule (a form of bullying) and friendly teasing; individuals with extreme gelotophobia had same emotional reactions (i.e., disgust, surprise, and shame) to both types of interactions. Platt et al. (2009) confirmed and extended these findings, in particular by showing that being a victim of bullying was best predicted by high gelotophobia scores and by low happiness scores concerning playful teasing situations. Although some of the participants were recruited from an anti-workplace bullying support network group, the victim of bullying status was more broadly defined as in the previous study. The most recent panel study by Ruch and Stahlmann (2020) focused, among other things, on the relationship between gelotophobia and workplace bullying (workplace bullying was operationalized with the four-item Workplace Incivility Scale; Cortina et al., 2001 ), and found that there was a positive correlation between gelotophobia and workplace bullying in all of the six measurement intervals (waves) in their research.

Finally, it should be stressed that previous investigations have related gelotophobia to personality dimensions ( Ruch et al., 2008 , 2013 ; Hřebíčková et al., 2009 ; Ruch and Proyer, 2009 ; Proyer and Ruch, 2010 ; Ďurka and Ruch, 2015 ). Utilizing a Czech sample, Hřebíčková et al. (2009) reported that gelotophobia was associated with higher neuroticism, and lower extraversion, agreeableness, and openness to experience (neuroticism and extraversion showed the most robust relations); and concluded that personality dimensions play a significant role in whether individuals cope with ridicule easily, or whether they find it difficult. In general, gelotophobes can be described as introverted neurotics with a lower inclination to openness ( Ruch et al., 2013 ). While personality characteristics have been studied in relation to gelotophobia, there is no previous research taking into account the joint consideration of personality and gelotophobia, and their relation to workplace bullying.

Aim of the present study

The current study aimed to investigate the relationship between personality dimensions, gelotophobia, and exposure to workplace bullying. Furthermore, our specific aim was to explore whether gelotophobia has an incremental validity in predicting exposure to workplace bullying beyond the Big Five dimensions of personality. We expect that gelotophobia will have an incremental effect over and above personality dimensions in predicting exposure to workplace bullying (and especially humor-related bullying).

Two research gaps are considered in this study. First, the joint investigation of the Big Five dimensions and gelotophobia – and their relation to workplace bullying – has not been undertaken yet. Secondly, previous studies (relating gelotophobia with bullying) have either focused on children and/or adolescents ( Führ, 2010 ; Proyer et al., 2012 , 2013 ) or used only self-labeled victimization from bullying ( Platt, 2008 ; Platt et al., 2009 ; cf., Ruch and Stahlmann, 2020 ). In general, we aim to extend previous findings by including a well-established behavioral type measure of workplace bullying (the Negative Acts Questionnaire-Revised – NAQ-R; Einarsen et al., 2009 ) and consider a humor-related bullying dimension (a specific sub-factor of person-related bullying) as well.

Materials and methods

Participants and procedure.

Data was gathered online using software for online assessment (MindMap Diagnostic Methods). On the first page of the study link, participants were informed about the purpose of the study and it was stated that by continuing to fill in the questionnaires they provide an informed consent. In addition, it was stated that they should participate in the research if they are more than 6 months employed. The data collection was conducted from January 2023 to June 2023, and participants were recruited by social media posts on Facebook and LinkedIn. No monetary incentives were offered for participation. An email from one of the authors of this study was provided in case of further questions regarding the research. However, no participant utilized this option.

The study sample size was a priori estimated with power analysis on previous correlations of gelotophobia and workplace bullying (from Ruch and Stahlmann, 2020 ), where the mean zero-order correlation was r  = 0.145. Using 85% power, a standard α  = 0.05, and a one-sided test, the needed total sample would be 339 participants (performed with package pwr, Champely et al., 2020 ). We maximized our collection possibilities; in total, 482 participants opened the test battery, with 137 not completing all of the questionnaires. Therefore, only 345 individuals completed the test battery. The final sample consisted of 328 participants (77% females) as 17 participants were flagged as outliers (and removed from the dataset) using the Mahalanobis Distance procedure in the careless package [v1.1.3; Yentes and Wilhelm, 2018 ; see also Meade and Craig (2012) for identifying careless responding by using Mahalanobis Distances]. Data cleaning was conducted on the answers provided on the separate scales on the Big Five Inventory-2 (BFI-2-S; see Instruments section). Participants were from the Czech Republic and their age was between 20 and 66 years ( M  = 37.95, SD  = 9.66). The sociodemographic characteristics of the sample are provided in the Supplementary Table S1 .

Instruments

Workplace bullying.

Exposure to workplace bullying was assessed with the Czech version ( Cakirpaloglu et al., 2017 ) of the NAQ-R ( Einarsen et al., 2009 ). The questionnaire is comprised of 22 behavioral items measuring three factors (work-related bullying, person-related bullying, and physically intimidating bullying). For the purpose of this study, a humor-related bullying factor comprised of three items involving behaviors related to humor was calculated. The included items were “Being humiliated or ridiculed in connection with your work” (item 2); “Practical jokes carried out by people you do not get along with” (item 15) and, “Being the subject of excessive teasing and sarcasm” (item 20). The NAQ-R also includes self-labeled victimization from bullying during the last 6 months assessed with a single-item measure (item 23: Have you ever been bullied at work?) following a definition of workplace bullying ( Einarsen and Skogstad, 1996 ; see also Supplementary Table S3 ). Participants choose one of the five alternatives on the behavioral items (“Never,” “Now and then,” “Monthly,” “Weekly,” and “Daily”) and the single-item measure [“no,” “yes, sometimes (rarely),” “yes, several times per month,” “yes, several times per week” and “yes, almost daily”]. Internal consistency in the present sample was high for all of the bullying dimensions as well as the total NAQ-R score, namely McDonald’s Omegas were 0.86 (work-related bullying), 0.95 (person-related bullying), 0.78 (physically intimidating bullying), 0.79 (humor-related bullying), and 0.96 (NAQ-R total score).

Personality domains

To assess the Big Five personality domains, we used the short form of the Big Five Inventory-2 (BFI-2-S) ( Soto and John, 2017b ; Hřebíčková et al., 2020 ). The scale is composed of 30 items, which have a common item stem (“I am someone who…”) and short descriptive phrases (e.g., “Tends to be quiet,” “Is temperamental, gets emotional easily”). A 5-point scale (ranging from disagree strongly to agree strongly) is utilized for the rating of each item by the participants. In this study, McDonald’s Omegas were 0.74 (extraversion), 0.74 (agreeableness), 0.77 (conscientiousness), 0.84 (negative emotionality), and 0.74 (open-mindedness).

Gelotophobia

Gelotophobia was measured using the Czech language version of the GELOPH<15> ( Ruch and Proyer, 2008a ; Hřebíčková et al., 2009 ), which is the psychometrically valid 15-item Czech language self-report instrument used for assessment of gelotophobia (e.g., “When others make joking remarks about me I feel being paralyzed”). Participants answer each item on a four-point scale (1 = “strongly disagree,” 4 = “strongly agree”). McDonald’s Omega was 0.93.

Descriptive statistics

The descriptive statistics (mean, standard deviation, skewness, and kurtosis) of the measures are depicted in Table 1 .

Table 1 . Descriptive statistics of the measures used.

Table 1 shows that skewness and kurtosis indicated substantial non-normality for the NAQ-R total score and the bullying dimensions (i.e., values for the skewness and/or kurtosis were greater than +2). For the rest of the measures, the values of the skewness and kurtosis could be considered acceptable [see Hair et al. (2022) ].

Considering the prevalence of workplace bullying, in our sample it was found that 27.1% of the participants were exposed to workplace bullying when Leymann’s criterion ( Leymann, 1996 ) was applied (i.e., facing at least one of the 22 negative acts on a weekly/daily basis during a minimum of 6 months). When a more strict criterion was used, or two acts on a week/daily basis ( Mikkelsen and Einarsen, 2001 ), 12% of the participants could be classified as victims of workplace bullying. The prevalence of self-reported bullying was 9%. The percentage of endorsed behavioral items and the self-labeled victimization from bullying are given in the Supplementary Tables S2, S3 .

Intercorrelations

Pearson product–moment correlations between the Big Five domains, gelotophobia and exposure to workplace bullying are given in Table 2 .

Table 2 . Pearson product–moment correlations between the study variables.

Table 2 shows that negative emotionality was positively related to the NAQ-R total score, and to each of the bullying dimensions, as expected. However, there was not a statistically significant relation between negative emotionality and self-labeled victimization from bullying. The rest of the Big Five traits were not related to any of the bullying measures, with an exception of the significant negative association between extraversion and humor-related bullying. In line with the expectations, gelotophobia correlated positively with the NAQ-R total score, the separate bullying dimensions, and the self-labeled victimization from bullying. The relation between personality and gelotophobia also corroborated previous findings, as gelotophobia was related to each of the personality domains, and the most robust correlations were found with extraversion and negative emotionality. Finally, self-reported victimization from bullying was strongly related to the NAQ-R total score and its dimensions.

In order to correct for the substantial non-normality of the exposure to bullying measures, we also calculated Spearman rank order correlations (see Supplementary Table S4 ). Each of the above mentioned findings were replicated (except for the extraversion relation to humor-related bullying, which cease to be significant), albeit the correlations were generally weaker.

Regression analysis

We conducted two hierarchical multiple regression models and used the NAQ-R total score as a dependent variable. Model 1 included the personality domains (Big Five), whereas Model 2 incorporated both personality domains and gelotophobia to assess its incremental effect on bullying. A summary of both models can be found in Table 3 . Assumptions of linear regression were mostly met, however residuals had a minor deviation from normal distribution.

Table 3 . Linear regression models for exposure to workplace bullying (NAQ-R total score).

In the first model, negative emotionality was the only statistically significant predictor of the NAQ-R total score; beta = 0.16 [95% CI (0.03, 0.29), t (322) = 2.43, p  = 0.016]. However, in the second model, gelotophobia had the only significant effect [beta = 0.37, 95% CI (0.24, 0.50), t (321) = 5.61, p  < 0.001], and the difference in R 2 between Models 1 and 2 is Δ R 2 = 0.086.

Next, humor-related bullying was used as a dependent variable. Similar results occurred (see Table 4 ), where only gelotophobia was statistically significant in the second model [beta = 0.39, 95% CI (0.26, 0.52), t (321) = 5.98, p  < 0.001].

Table 4 . Linear regression models for exposure to humor-related bullying.

The current study aimed to explore and advance knowledge pertaining to the relation between personality dimensions, gelotophobia, and exposure to workplace bullying. The specific goal was to investigate the incremental validity of gelotophobia in predicting workplace bullying beyond the personality dimensions. We firstly focus on the prevalence of workplace bullying in our sample, and afterwards discuss the main findings of our study.

The percentage of individuals who were classified as victims of workplace bullying (using both the looser and stricter criterion) via the behavioral items are comparable to previous studies. For example, the study by Cakirpaloglu et al. (2017) , which was also done in a Czech context utilizing a sample of 7,103 employees, reported that 24.78% (using the looser criterion) and 14.84% (using the stricter criterion) of the participants in their study could be classified as victims of bullying. Furthermore, we have also found a drop of the percentage when the self-labeling approach was applied, and the percentage of individuals who self-labeled as victims of bullying in our study is similar to previous investigations (e.g., Einarsen et al., 2009 ; Tsuno et al., 2010 ; Dujo López et al., 2020 ).

The correlational findings indicated that negative emotionality was related to exposure to workplace bullying (i.e., experienced negative acts). This is in line with the meta-analytic investigation by Nielsen et al. (2017) which concluded that neuroticism is the strongest and most consistent correlate of exposure to harassment. It should be noted that each of the three mechanisms indicating an association between individual dispositions and bullying ( Nielsen and Knardahl, 2015 ) can be applied to explain this relation [see Nielsen et al. (2017) ; see also Djurkovic et al. (2006) and Bowling et al. (2010) ]. The present results confirmed previous investigations and extend to a different cultural context. Moreover, we have utilized a more recent operationalization of the neuroticism (i.e., negative emotionality) construct – which was not considered in Nielsen et al. (2017) – comprised of the facets of anxiety, depression, and emotional volatility ( Soto and John, 2017b ).

It is interesting to note that we have not confirmed the results considering the relation between the other personality dimensions (i.e., extraversion, agreeableness, and conscientiousness) and workplace bullying. However, this can be explained by the fact that one of the moderators for this association was the geographical region [see Nielsen et al. (2017) ]. In particular, the relation between agreeableness and conscientiousness, and exposure to workplace harassment was moderated by the geographical region, that is higher estimates were found in studies from the United States compared to Europe (for agreeableness), and studies from the USA and Asia/Oceania compared to Europe (for conscientiousness). As the Czech Republic represents a central European context, our findings confirm this trend. However, the non-significant association between extraversion and workplace bullying cannot be explained by the moderator analyses reported by Nielsen et al. (2017) , and needs further consideration.

Gelotophobia can be placed on introverted neurotic personality dimensions with a lower inclination to openness ( Ruch et al., 2013 ). While personality characteristics have been studied in relation to gelotophobia, this is the first study that accounts for the joint consideration of personality and gelotophobia, and their relation to workplace bullying. Focusing specifically on the gelotophobia and exposure to workplace bullying relation, the results indicated that gelotophobia was positively related to both the behavioral measures of bullying and the self-labeled victimization. Furthermore, it was a stronger correlate to workplace bullying than neuroticism, and in line with theoretical considerations, the most robust relation was found with humor-related bullying. In general, the association between gelotophobia and workplace bullying can also be theoretically explained using the mechanisms proposed by Nielsen and Knardahl (2015) , as analogous to the personality dimensions – bullying relation.

Gelotophobia overlaps with neuroticism/negative emotionality in regard to behaviors related to nervousness and insecurity (e.g., indicators of gelotophobia are difficulty to hold eye contact and stiffness) and these behaviors could be seen by others as annoying. Thus, making gelotophobes both provocative and easy targets of bullying. This is consistent with the explanation applying the target behavior mechanism for the neuroticism–harassment relation ( Milam et al., 2009 ; Nielsen et al., 2017 ).

Next, following the negative perception mechanism, individuals with high gelotophobia scores might have a lowered threshold for interpreting behaviors as harassing/bullying, especially behaviors related to humor. Moreover, the perceived bullying could constitute a “false alarm,” or in reality, there is a lack of objective proof for it ( Platt et al., 2009 ; Ruch, 2009 ; Hofmann et al., 2017 ). In other words, gelotophobes might misinterpret the good-humored teasing from their colleagues, superiors, or subordinates, as being ridiculed/bullied ( Platt, 2008 ).

Finally, workplace bullying might in fact be an antecedent of gelotophobia, as proposed by the reverse causality mechanism. This especially could be true in workplaces with extreme humor culture ( Plester, 2016 ; Plester et al., 2022 ) and/or negative humor climate ( Cann et al., 2014 ), and is in accordance with the proposed causes of gelotophobia. According to Ruch et al. (2014) , in adulthood, intense traumatic experiences of being laughed at or ridiculed is one of the causes of gelotophobia. Therefore, individuals can become gelotophobic as a consequence of working in an extreme ‘fun culture.’

The incremental validity of gelotophobia

To the best of our knowledge, our study is the first to provide evidence for the incremental validity of gelotophobia beyond the personality domains. Although negative emotionality and gelotophobia overlap to a certain degree, the results from the regression analysis indicated that gelotophobia predicted exposure to workplace bullying (operationalized with the total score on the NAQ-R) over and above negative emotionality. More interestingly, gelotophobia was the only significant predictor of humor-related bullying, which is in line with theoretical considerations (e.g., Hofmann et al., 2017 ). In sum, we provide initial evidence that the construct of gelotophobia should be considered, over and above the personality dimensions, in order to understand exposure to workplace bullying.

Limitations and future directions

Besides the general limitations common to cross-sectional design studies, and utilizing convenience sampling, there are also some further limitations to our study. First, we have not focused on investigating whether there are different homogenous groups, which may vary based on the nature and extent of their exposure to bullying ( Notelaers et al., 2006 ; Einarsen et al., 2009 ). Therefore, future studies might employ latent class cluster analysis [e.g., Magidson and Vermunt, 2004 ; see also Reknes et al. (2021) ] and also investigate how the identified groups differ regarding the personality dimensions and gelotophobia. Second, recent methodological considerations propose that incremental validity should be tested using structural equation modeling (SEM) ( Wang and Eastwick, 2020 ; Feng and Hancock, 2022 ). For example, Wang and Eastwick (2020) warn that standard multiple regression inflates Type 1 error. Therefore, our results need to be replicated using SEM. It should be stressed that such research would require large sample sizes; possibly in the thousands, in order to reach a desirable level of power ( Wang and Eastwick, 2020 ). Large sample sizes are also required if one is utilizing the latent class cluster approach [see Nylund-Gibson and Choi (2018) ].

Furthermore, we did not explore the contribution of the facets of personality dimensions, and future investigations could focus on the relation between specific facets of personality and their relation to exposure to workplace bullying. Moreover, we used the short version of the BFI-2, while future research should employ the full version ( Soto and John, 2017a ); especially when the interest is on the facets of personality. Research is needed that will focus particularly on the humor-related bullying and its relation to gelotophobia. Platt (2021) expressed the need to include samples which include greater levels of the higher ranges on the distribution of gelotophobia on the continuum of fear – from no fear to extreme fear of being laughed at. This would be an important consideration of further studies, given the reported prevalence of gelotophobia in Hřebíčková’s et al. (2009) study that indicated the low percentage of gelotophobes in the Czech Republic [e.g., slight (5.24 %) marked (1.05 %)] and no reported extreme gelotophobes being identified [for comparable countries see Platt and Forabosco (2012) ]. As Platt (2021) reports, it is only at the marked and extreme levels that strong pathological effects of gelotophobia are observed. These investigations should also consider the humor culture/climate in the specific organizations, which could include qualitative focus groups statements that would identify any mis-interpretation of pro-social humorous interaction as bullying.

Bullying behavior is detrimental for the bully victim. This is further impacted on in a workplace which can have serious financial as well as emotional consequences. This study has supported the link between workplace bullying and gelotophobia from the framework of personality and providing initial incremental validity for this relationship.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the studies involving humans because the study adhered to ethical principles outlined by APA. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

FS: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. KP: Data curation, Formal analysis, Methodology, Writing – original draft. JG: Conceptualization, Data curation, Writing – original draft. TP: Writing – original draft, Writing – review & editing. MS: Data curation, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The funding for the present publication was provided by the Czech Ministry of Education, Youth and Sports for specific research (IGA_FF_2023_025).

Conflict of interest

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.

The handling editor WR declared a past co-authorship with the author TP.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1400940/full#supplementary-material

Balducci, C., Conway, P. M., and van Heugten, K. (2021). “The contribution of organizational factors to workplace bullying, emotional abuse and harassment” in Pathways of job-related negative behaviour. Handbooks of workplace bullying, emotional abuse and harassmen t . eds. P. D’Cruz, E. Noronha, E. Baillien, B. Catley, K. Harlos, and A. Høgh, et al., vol. 2 (Springer), 3–28.

Google Scholar

Bledsoe, T. S., and Baskin, J. J. (2014). Recognizing student fear: the elephant in the classroom. Coll. Teach. 62, 32–41. doi: 10.1080/87567555.2013.831022

Crossref Full Text | Google Scholar

Bowling, N. A., Beehr, T. A., Bennett, M. M., and Watson, C. P. (2010). Target personality and workplace victimization: a prospective analysis. Work Stress 24, 140–158. doi: 10.1080/02678373.2010.489635

Cakirpaloglu, P., Šmahaj, J., Cakirpaloglu, S. D., and Zielina, M. (2017). Šikana na pracovišti: Reliabilita a validita českého překladu revidované verze dotazníku negativních aktů—NAQ-R [bullying at work: reliability and validity of the Czech version of the negative acts questionnaire—revised]. Československá Psychol. Časopis Pro Psychol. Teorii Praxi 61, 546–558.

Cann, A., Watson, A. J., and Bridgewater, E. A. (2014). Assessing humor at work: the humor climate questionnaire. Humor Int. J. Humor Res. 27, 307–323. doi: 10.1515/humor-2014-0019

Champely, S., Ekstrom, C., and Dalgaard, P. (2020). Pwr: basic functions for power analysis. R package version 1.3-0 [computer software]. Available at: https://github.com/heliosdrm/pwr

Cortina, L. M., Magley, V. J., Williams, J. H., and Langhout, R. D. (2001). Incivility in the workplace: incidence and impact. J. Occup. Health Psychol. 6, 64–80. doi: 10.1037/1076-8998.6.1.64

PubMed Abstract | Crossref Full Text | Google Scholar

Djurkovic, N., McCormack, D., and Casimir, G. (2006). Neuroticism and the psychosomatic model of workplace bullying. J. Manag. Psychol. 21, 73–88. doi: 10.1108/02683940610643224

Dujo López, V., González Trijueque, D., Graña Gómez, J. L., and Andreu Rodríguez, J. M. (2020). A psychometric study of a Spanish version of the negative acts questionnaire-revised: confirmatory factor analysis. Front. Psychol. 11:1856. doi: 10.3389/fpsyg.2020.01856

Ďurka, R., and Ruch, W. (2015). The location of three dispositions towards ridicule in the five-factor personality model in the population of Slovak adults. Personal. Individ. Differ. 72, 177–181. doi: 10.1016/j.paid.2014.08.045

Einarsen, S., Hoel, H., and Notelaers, G. (2009). Measuring exposure to bullying and harassment at work: validity, factor structure and psychometric properties of the negative acts questionnaire-revised. Work Stress 23, 24–44. doi: 10.1080/02678370902815673

Einarsen, S., and Skogstad, A. (1996). Bullying at work: epidemiological findings in public and private organizations. Eur. J. Work Organ. Psy. 5, 185–201. doi: 10.1080/13594329608414854

Feng, Y., and Hancock, G. R. (2022). Model-based incremental validity. Psychol. Methods 27, 1039–1060. doi: 10.1037/met0000342

Führ, M. (2010). The applicability of the GELOPH< 15 > in children and adolescents: first evaluation in a large sample of Danish pupils. Psychol. Test Assess. Model. 52, 60–76.

Gamian-Wilk, M., Bjorkelo, B., Mikkelsen, E. G., D'Cruz, P., and Madeja-Bien, K. (2022). Workplace bullying: individual hostility, poor work environment or both? Exploring competing explanatory models in a single longitudinal study. Int. Arch. Occup. Environ. Health 95, 1955–1969. doi: 10.1007/s00420-022-01896-y

Goldberg, L. R. (1993). The structure of phenotypic personality traits. Am. Psychol. 48, 26–34. doi: 10.1037/0003-066X.48.1.26

Hair, J., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) . 3rd Edn. Thousand Oaks: SAGE Publications.

Hofmann, J., Ruch, W., Proyer, R. T., Platt, T., and Gander, F. (2017). Assessing dispositions toward ridicule and laughter in the workplace: adapting and validating the PhoPhiKat-9 questionnaire. Front. Psychol. 8:714. doi: 10.3389/fpsyg.2017.00714

Hřebíčková, M., Ficková, E., Klementová, M., Ruch, W., and Proyer, R. T. (2009). Strach ze zesměšněni: Česká a slovenská verze dotazníku GELOPH (15) pro zjištování gelotofobie [the fear of being laughed at: Czech and Slovak version of a questionnaire GELOPH (15) for gelotophobia]. Československá Psychol. Časopis Pro Psychol. Teorii Praxi 53, 468–479. doi: 10.5167/uzh-23696

Hřebíčková, M., Jelínek, M., Květon, P., Benkovič, A., Botek, M., Sudzina, F., et al. (2020). Pětifaktorový dotazník BFI-2: Hierarchický model s 15 subškálami [Big Five Inventory 2 (BFI-2): Hierarchial model]. Československá Psychol. Časopis Pro Psychol. Teorii Praxi 64, 437–460.

John, O. P., Naumann, L. P., and Soto, C. J. (2008). “Paradigm shift to the integrative big-five trait taxonomy: history, measurement, and conceptual issues” in Handbook of personality: theory and research . eds. O. P. John, R. W. Robins, and L. A. Pervin. 3rd ed (Guilford), 114–158.

Leymann, H. (1996). The content and development of mobbing at work. Eur. J. Work Organ. Psy. 5, 165–184. doi: 10.1080/13594329608414853

Magidson, J., and Vermunt, J. K. (2004). “Latent class models” in The Sage handbook for quantitative methodology . ed. D. Kaplan (Thousand Oaks: Sage Publications), 175–198.

McCrae, R. R., and Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. J. Pers. Soc. Psychol. 52, 81–90. doi: 10.1037/0022-3514.52.1.81

McCrae, R. R., and Costa, P. T. (2008). “The five-factor theory of personality” in Handbook of personality: Theory and research . eds. O. P. John, R. W. Robins, and L. A. Pervin. 3rd ed (Guilford), 159–181.

Meade, A. W., and Craig, S. B. (2012). Identifying careless responses in survey data. Psychol. Methods 17, 437–455. doi: 10.1037/a0028085

Mikkelsen, E. G., and Einarsen, S. (2001). Bullying in Danish work-life: prevalence and health correlates. Eur. J. Work Organ. Psy. 10, 393–413. doi: 10.1080/13594320143000816

Milam, A. C., Spitzmueller, C., and Penney, L. M. (2009). Investigating individual differences among targets of workplace incivility. J. Occup. Health Psychol. 14, 58–69. doi: 10.1037/a0012683

Nielsen, M. B., Glasø, L., and Einarsen, S. (2017). Exposure to workplace harassment and the five factor model of personality: a meta-analysis. Personal. Individ. Differ. 104, 195–206. doi: 10.1016/j.paid.2016.08.015

Nielsen, M. B., and Knardahl, S. (2015). Is workplace bullying related to the personality traits of victims? A two-year prospective study. Work Stress 29, 128–149. doi: 10.1080/02678373.2015.1032383

Notelaers, G., Einarsen, S., De Witte, H., and Vermunt, J. K. (2006). Measuring exposure to bullying at work: the validity and advantages of the latent class cluster approach. Work Stress 20, 289–302. doi: 10.1080/02678370601071594

Nylund-Gibson, K., and Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Trans. Issues Psychol. Sci. 4, 440–461. doi: 10.1037/tps0000176

Platt, T. (2008). Emotional responses to ridicule and teasing: should gelotophobes react differently? Humor Int. J. Humor Res. 21, 105–128. doi: 10.1515/HUMOR.2008.005

Platt, T. (2021). Extreme gelotophobia: affective and physical responses to ridicule and teasing. Curr. Psychol. J. Div. Perspect. Div. Psychol. Issues 40, 6076–6084. doi: 10.1007/s12144-019-00510-8

Platt, T., and Forabosco, G. (2012). “Gelotophobia: the fear of being laughed at” in Humor and health promotion . ed. P. Gremigni (Hauppauge, NY: Nova Biomedical Books), 229–252.

Platt, T., Proyer, R. T., Hofmann, J., and Ventis, W. L. (2016). Gelotophobia in practice and the implications of ignoring it. Eur. J. Humour Res. 4, 46–56. doi: 10.7592/EJHR2016.4.2.platt

Platt, T., Proyer, R. T., and Ruch, W. (2009). Gelotophobia and bullying: the assessment of the fear of being laughed at and its application among bullying victims. Psychol. Sci. Q. 51, 135–147. doi: 10.5167/uzh-19396

Plester, B. (2016). The complexity of workplace humour: laughter, jokers and the dark side of humour . Cham: Springer International Publishing.

Plester, B., Bentley, T., and Brewer, E. (2022). “It only hurts when I laugh”: tolerating bullying humour in order to belong at work. Eur. J. Humour Res. 10, 116–134. doi: 10.7592/EJHR.2022.10.2.645

Podsiadly, A., and Gamian-Wilk, M. (2017). Personality traits as predictors or outcomes of being exposed to bullying in the workplace. Personal. Individ. Differ. 115, 43–49. doi: 10.1016/j.paid.2016.08.001

Proyer, R. T., Meier, L. E., Platt, T., and Ruch, W. (2013). Dealing with laughter and ridicule in adolescence: relations with bullying and emotional responses. Soc. Psychol. Educ. Int. J. 16, 399–420. doi: 10.1007/s11218-013-9221-y

Proyer, R. T., Neukom, M., Platt, T., and Ruch, W. (2012). Assessing gelotophobia, gelotophilia, and katagelasticism in children: an initial study on how six to nine-year-olds deal with laughter and ridicule and how this relates to bullying and victimization. Child Indic. Res. 5, 297–316. doi: 10.1007/s12187-011-9127-1

Proyer, R. T., and Ruch, W. (2010). Enjoying and fearing laughter: personality characteristics of gelotophobes, gelotophiles, and katagelasticists. Psychol. Test Assess. Model. 52, 148–160.

Reknes, I., Notelaers, G., Iliescu, D., and Einarsen, S. V. (2021). The influence of target personality in the development of workplace bullying. J. Occup. Health Psychol. 26, 291–303. doi: 10.1037/ocp0000272

Ruch, W. (2009). Fearing humor? Gelotophobia: the fear of being laughed at: introduction and overview [editorial]. Humor: Int. J. Humor Res. 22, 1–25. doi: 10.1515/HUMR.2009.001

Ruch, W., Harzer, C., and Proyer, R. T. (2013). Beyond being timid, witty, and cynical: big five personality characteristics of gelotophobes, gelotophiles, and katagelasticists. Int. Stud. Humour 2, 24–42. doi: 10.5167/uzh-81159

Ruch, W., Hofmann, J., Platt, T., and Proyer, R. (2014). The state-of-the art in gelotophobia research: a review and some theoretical extensions. Humor Int. J. Humor Res. 27, 23–45. doi: 10.1515/humor-2013-0046

Ruch, W., and Proyer, R. T. (2008a). The fear of being laughed at: individual and group differences in gelotophobia. Humor Int. J. Humor Res. 21, 47–67. doi: 10.1515/HUMOR.2008.002

Ruch, W., and Proyer, R. T. (2008b). Who is gelotophobic? Assessment criteria for the fear of being laughed at. Swiss J. Psychol. 67, 19–27. doi: 10.1024/1421-0185.67.1.19

Ruch, W., and Proyer, R. T. (2009). Who fears being laughed at? The location of gelotophobia in the Eysenckian PEN-model of personality. Personal. Individ. Differ. 46, 627–630. doi: 10.1016/j.paid.2009.01.004

Ruch, W., Proyer, R. T., and Popa, D. E. (2008). The fear of being laughed at (gelotophobia) and personality. Anuarul Institutului de Istorie “G. Baritiu” din Cluj-Napoca (Ed.), Series Humanistica (VI, 53–68). Cluj-Napoca: Romania.

Ruch, W., and Stahlmann, A. G. (2020). Toward a dynamic model of Gelotophobia: social support, workplace bullying and stress are connected with diverging trajectories of life and job satisfaction among Gelotophobes. Curr. Psychol. 42, 16368–16380. doi: 10.1007/s12144-020-01046-y

Soto, C. J., and John, O. P. (2017a). The next big five inventory (BFI-2): developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. J. Pers. Soc. Psychol. 113, 117–143. doi: 10.1037/pspp0000096

Soto, C. J., and John, O. P. (2017b). Short and extra-short forms of the big five inventory–2: the BFI-2-S and BFI-2-XS. J. Res. Pers. 68, 69–81. doi: 10.1016/j.jrp.2017.02.004

Titze, M. (1997). “Das Komische als schamauslösende Bedingung [the funny as an elicitor of shame]” in Scham – ein menschliches Gefühl [Shame – A human emotion] . eds. R. Kühn, M. Raub, and M. Titze (Opladen, Germany: Westdeutscher Verlag), 169–178.

Titze, M. (2009). Gelotophobia: the fear of being laughed at. Humor Int. J. Humor Res. 22, 27–48. doi: 10.1515/HUMR.2009.002

Tsuno, K., Kawakami, N., Inoue, A., and Abe, K. (2010). Measuring workplace bullying: reliability and validity of the Japanese version of the negative acts questionnaire. J. Occup. Health 52, 216–226. doi: 10.1539/joh.l10036

Wang, Y. A., and Eastwick, P. W. (2020). Solutions to the problems of incremental validity testing in relationship science. Pers. Relat. 27, 156–175. doi: 10.1111/pere.12309

Yentes, R., and Wilhelm, F. (2018). Careless (version 1.1.3) [computer software]. Available at: https://cran.r-project.org/web/packages/careless

Zapf, D., and Einarsen, S. (2001). Bullying in the workplace: recent trends in research and practice− an introduction. Eur. J. Work Organ. Psy. 10, 369–373. doi: 10.1080/13594320143000807

Keywords: workplace bullying, NAQ-R, big five, gelotophobia, incremental validity

Citation: Sulejmanov F, Petr K, Gambová J, Platt T and Seitl M (2024) Exposure to workplace bullying: the incremental effect of gelotophobia beyond the big five. Front. Psychol . 15:1400940. doi: 10.3389/fpsyg.2024.1400940

Received: 14 March 2024; Accepted: 11 April 2024; Published: 25 April 2024.

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Copyright © 2024 Sulejmanov, Petr, Gambová, Platt and Seitl. 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) and the copyright owner(s) 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.

*Correspondence: Filip Sulejmanov, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.