Mapping Digital Adolescence: Assessing online activities and responses to cyberbullying with the Online Behavior Questionnaire

Mapping Digital Adolescence: Assessing online activities and responses to cyberbullying with the Online Behavior Questionnaire | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Digital Adolescence: Assessing online activities and responses to cyberbullying with the Online Behavior Questionnaire Sharon Cayzer-Haller, Hila Segal, Ariel Knafo-Noam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8256781/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The Online Behavior Questionnaire (OBQ) was developed as a brief, multidimensional instrument to assess adolescents’ digital behaviors that are directly relevant to bullying prevention: general online activities, exposure to harmful content, parental online involvement, and bystanders’ feelings and behaviors in response to ecologically valid cyberbullying scenarios. Two adolescent samples (total N = 617) were used to establish the OBQ’s structure and psychometric properties: a panel sample of 13-17-year-olds (Study 1; N = 308) and a twin-based sample of 17-19-year-olds (Study 2; N = 309). Confirmatory factor analyses in both samples, estimated in R (lavaan) with cluster-robust standard errors for the nested twin data and residual correlations for parallel items, supported a replicated six-factor model with adequate global fit indices and predominantly moderate-to-strong item loadings. Internal consistency was acceptable for most factors (α = .67–.89), with weaker reliability for offensive bystander emotions when indexed by McDonald’s ω. Parental online behaviors showed positive associations with sympathetic emotions and defending intentions, and small negative or negligible associations with general online activity and exposure to harmful content. The OBQ appears suitable for research, school screening, and program evaluation that seek to identify at-risk digital profiles, activate supportive bystanders, and inform policies and interventions aimed at creating safer online climates for adolescents. cyberbullying bystanders online behavior adolescents parental mediation questionnaire validation Figures Figure 1 Introduction The rapid development of technology has significantly affected various aspects of humanity, raising questions about the ways that online communication and activities may impact the social and emotional development of children and adolescents (Hasebrink et al., 2009 ; Smahel et al., 2020 ). The integration of online activities into daily lives has become a defining feature of the 21st century and many adolescents are now reporting “almost constant” online connectivity (Rideout, 2015 ). Understanding how adolescents engage online, and how they respond to the social dynamics that unfold there, is therefore crucial for advancing both research and practice regarding adolescents’ online activities. The current two studies introduce and validate the Online Behavior Questionnaire (OBQ), designed to capture the multifaceted nature of adolescents’ online experiences and engagements, as well as their feelings and behaviors as online bystanders. To situate the development of this measure, we first review prior research on the quantity and quality of adolescents’ online engagements, on exposure to online harmful content and responses to cyberbullying, and on online parental involvement in adolescents’ online activities. We then describe results from two different samples using the OBQ, with the goal of providing researchers and practitioners with a reliable tool for assessment, prevention, and interventions. Quantity and Quality of Adolescents’ Online Engagements Millennial childhood and adolescence are characterized by the early and intensive use of online activities and platforms that play a central role in how adolescents learn, play, and engage socially (Keeley & Little, 2017 ). The COVID-19 pandemic further accelerated this digital immersion, significantly altering both the quantity and quality of adolescents’ online activities (Organization for Economic Co-operation and Development, 2020 ). During periods of lockdown and remote learning, adolescents reported increased engagement in online educational content, digital reading, and virtual social communication, highlighting the adaptive role of technology in maintaining continuity in learning and relationships (Nagata et al., 2022 ). The Pew Research Center survey of American teenagers aged 13 to 17, reported that most American adolescents have access to digital devices, and 95% of them use personal smartphones. Studies show that 97% of American adolescents are daily internet users; 46% of them reported using the internet almost constantly (Anderson et al., 2023 ). Following this massive use of technology, the quantity and quality of adolescents’ online activities, and their impact on development have become a central concern in cyberpsychology. For example, Martin et al. (2020) reviewed longitudinal studies that examined the associations between adolescents’ daily screen time, sleep, and mental health. Their review highlights associations between excessive screen time and sleep deprivation, anxiety, low self-esteem and depression. A different perspective of these online engagements suggests that online social interactions may serve as an extension of offline relationships. From this perspective, adolescents may use online communication to help them cope with negative feelings (Mýlek et al., 2023 ). This increasing integration of online engagements, and their impact on the lives of children and adolescents have prompted the development of various instruments aimed at measuring their daily screen time and the different activities in which they are engaged. These tools vary in methodology and psychometric robustness, reflecting the complexity of digital engagement in children and adolescents. A broad synthesis of measurement tools for assessing children’s digital media use was conducted by Browne et al. ( 2021 ) in a comprehensive scoping review encompassing 162 instruments. This review highlighted the considerable diversity in methodological approaches, ranging from simple screen time logs to complex multi-dimensional assessments. This review underscores a critical shift in the field, from measuring mere exposure to understanding the broader implications of digital media in children’s lives. One of the central findings was the inadequacy of screen time as a standalone metric, given its limited capacity to capture the nuanced realities of children’s digital engagements. The authors emphasized the need for more comprehensive, multi-method instruments that assess not only the quantity of media use but also the quality, context, and psychosocial correlates. Specifically, they called for tools that examine content types, usage environments, emotional and behavioral outcomes, and family dynamics. Online Exposure to Harmful Content and Cyberbullying Although the quantity and quality of adolescents’ online engagements are widely studied, they provide only a partial view of the multifaceted nature of adolescents’ digital behaviors. Online exposure to harmful content can shape adolescents’ emotions, beliefs, and behaviors by normalizing observed behaviors, increasing anxiety or fear, and triggering harmful cycles of online engagements. The EU Kids and the Global Kids Online (GKO) programs show that greater use and specific online activities are linked to greater exposure to online risks (Hasebrink, et al., 2009 ), and the exposure to online harmful content was linked to being a perpetrator of online hate (Wachs et al., 2022 ). Recent U.S. survey data indicate that nearly half of U.S. adolescents (ages 13–17) report experiencing at least one form of cyberbullying, such as name-calling and rumor spreading (Anderson et al., 2022), and in the United States, data from CDC’s 2019 Youth Risk Behavior Survey indicate that nearly 16% of high school students has been electronically bullied in the last 12 months (CDC – Centre for Disease Control and Prevention, 2020). Perpetration is not rare either: across large school-based samples, adolescents reported having cyberbullied others, when offending behaviors were associated with perceptions of peers behaving similarly (Hinduja & Patchin, 2013 ). Most incidents unfold in front of peers: bystanders are present during the vast majority of bullying episodes, offline or online, though active defense remains comparatively infrequent (Jenkins et al., 2019; Lynn Hawkins et al., 2001 ; Olweus, 2013 ). Classic participant role work identifies several bystander roles: bystander-assistants and reinforcers who join or encourage the aggressor (laughing, sharing, “liking”), bystander-defenders who support or protect the victim (comforting, reporting, counter-messaging), and bystander-outsiders who stay passive or avoid the situation (Salmivalli, 2010 ). Studies have been searching for the impact of bystanders’ feelings on their behavioral choices, showing that higher empathy (both cognitive and emotional empathy) is associated with supportive bystander behaviors (Barlińska et al., 2013 ; Macháčková & Pfetsch, 2016). Conversely, offensive bystander responses align with reinforcing or assistant roles, while moral disengagement (“It’s just a joke,” “Everyone does it”) predicts pro-bully and passive responses (Thornberg & Jungert, 2013 ). In this domain of online aggression, the Cyber Victim and Bullying Scale-SVBS (Çetin et al., 2011 ) is a validated instrument that measures both victimization and perpetration across multiple forms of online harassment. It includes frequency-based items and has demonstrated strong psychometric properties across multiple studies. Complementing this, a systematic review by Berne et al. ( 2013 ) identified 44 instruments related to cyberbullying and online aggressions. The review found that many tools lacked consistent definitions and robust validation, underscoring the need for standardized, theory driven measures. Taken together, online environments underscore the need for tools that capture not only daily screen time and activities but also adolescents’ levels of exposure to harmful content and the emotional and behavioral reactions to cyberbullying in ecologically valid online contexts. Because adolescents can experience a range of conflicting emotions and behaviors when observing online harmful content, it is essential to distinguish between sympathetic and offensive feelings, active versus reactive responses, and the degree to which they disengage from such situations. Parental Involvement and Online Boundaries An important context for understanding adolescent online behavior concerns their relationship with their parents (Liu et al., 2023 ) and personal characteristics of parents were related their concerns about bullying and to the way they monitor their children’s online activities (Cohen, 2023). Other studies show that parental behaviors are associated with adolescents’ online engagements and emotional reactions as well as with their vulnerability to online behavioral risks (Banić & Orehovački, 2024 ). Parental behaviors that are characterized by strict rules and boundaries may lead adolescents toward secretive or rebellious online behavior as they seek for autonomy, while close and sharing parental behaviors may lead to the exact opposite (Stattin & Kerr, 2000 ). Parents who stay involved and communicate openly about online engagements may provide emotional support that can help adolescents process fear and exposure to harmful content. Such support may even serve as a buffer for the negative impacts of online harmful exposure. Kvardova et al. ( 2021 ) examined the role of risk and protective factors among adolescents who were exposed to online harmful content, indicating that emotional problems and sensation seeking serve as significant risk factors, while positive family environments and social support networks serve as protective factors for adolescents who are exposed to online harmful content. A notable instrument for assessing children’s digital behavior is the SCREENS-Q, developed by Klakk et al. ( 2020 ). This parent-reported questionnaire is designed to evaluate screen-based media use among children aged 6 to 10 years. The SCREENS-Q provides details about the media environment, types of content consumed, and the presence of family routines for screen use. Even though the SCREENS-Q serves as a valuable resource for early childhood digital behavior, it lacks an adolescent-specific, multidimensional, scenario-anchored assessment that can capture time and quality of use as well as levels of exposure to harmful content and bystander responses. The Motivation for Developing the OBQ Considering the full spectrum of children’s and adolescents’ online engagements, behaviors, and emotional reactions, multiple instruments were developed to evaluate digital behaviors. One of the most comprehensive and widely utilized instruments for studying children’s digital experiences is the Global Kids Online Questionnaire, developed collaboratively by the London School of Economics and UNICEF (Byrne et al., 2016 ). This tool includes both child and parent modules, enabling a multi-perspective assessment of children’s online engagement. It evaluates a broad range of dimensions, including access to digital technologies, digital skills, online risks and opportunities, and parental mediation strategies. The questionnaire investigates potential risks associated with online activities, but it lacks the ability of integrating bystander responses alongside time and quality of use that can create a more holistic perspective of adolescent online engagements, feelings and behaviors. Despite the many existing research tools that were designed to assess adolescents’ digital engagements, no single, psychometrically evaluated instrument offers a comprehensive profile that simultaneously captures: quantitative and qualitative modes of use, exposure to harmful content, online parental behaviors as well as emotional and behavioral reactions to cyberbullying. Existing measures tend to focus on one or two of these domains (e.g., screen time logs, cyberbullying victimization scales, or parental mediation inventories), which limits both explanatory power and practical utility for schools, clinicians, and researchers. The OBQ was therefore developed to provide an integrated, theory-aligned assessment that brings these elements together within a single, brief, developmentally appropriate instrument. Adolescents do not use technology in a disconnected environment, and their online behaviors are associated with their environment and surroundings as well as their parents’ attitudes toward screens. A novel aspect of the OBQ is that it includes scenario-based items and measurements of emotional response to these scenarios. These patterns map onto the OBQ’s distinction between sympathetic and offensive feelings, behavioral reactivity, and disengagement when witnessing cyberbullying. Measuring both emotional reactions and concrete behavioral intentions is therefore essential for capturing adolescents’ bystander profiles in ecologically valid online scenarios, and for evaluating interventions that aim to move adolescents from passive or reinforcing bystander roles toward supportive defending roles. The OBQ was designed to enable researchers with a holistic observation of adolescents’ digital behaviors, by unifying six domains within one instrument: (a) quantity of online engagements (daily screen time); (b) quality of online engagements; (c) exposure level to harmful content; (d) emotional reactions to cyberbullying; (e) online bystander behaviors (sympathetic, indifferent, offensive); (f) online parental behavior. We aimed to validate these suggested six scales of the OBQ across two adolescent samples by establishing its structure, reliability, and higher-order organization. We examined internal consistency for each factor received and evaluated the latent inter-factor correlations to differentiate between two higher-order domains using hierarchical confirmatory factor models. Method The Online Behavior Questionnaire (OBQ) was designed to examine quantitative and qualitative aspects of online behavior as well as emotional and behavioral reactions to cyberbullying. It includes 43 closed items, measuring activities, perceptions, and reactions to online behavior, including a special section designed to measure bystanders’ emotions and behavior (See Appendix 1 for the full OBQ). The questionnaire includes six scales that address different aspects of adolescents’ online behavior and environments. Online Behaviors and Environments Quantity of online engagements (daily screen time) : Participants reported how many hours a day (weekends excluded) they spend online, ranging from 1 to over 10 hours. Quality of online engagements : Participants reported daily hours (weekends excluded) spent in different online engagements (writing and talking to friends, scrolling through content, watching tv and video-clips, etc). Each activity was reported separately on a scale ranging from 1 hour to over 10 hours. Exposure level to harmful content : Participants reported to what extent they have been exposed to online harmful content choosing a response that ranged from ‘not at all’ (1) to ‘all the time’ (5). Emotional reactions to cyberbullying : Adolescents viewed three screenshots depicting cyberbullying and read about a fourth hypothetical scenario, which were ecologically valid, selected to represent real, distressing situations. These screenshots were based on real-life interactions among adolescents that the first author of this article encountered in her clinical practice. In the first scenario, bullies laughed at a childhood picture of the victim, saying she was ugly. In the second scenario, bullies mocked the victim after failing an exam, saying he was stupid. In the third scenario, bullies laughed at a girl who posted a picture of herself in a bathing suit. After each of screenshot, participants rated the extent to which the correspondence made them feel (feelings ranged between being ‘amused’ to feeling ‘angry’). They used a response scale ranging from 'not at all' (1) to 'very much' (5) for each of the emotions described. Online bystanders’ behavior : After each of the three screenshots, participants answered how they would respond, from a response considered most non-social (laugh’, coded as 1) to a response considered most prosocial (try to help the victim’, coded as 5). In the hypothetical scenario, participants reported which of these responses is the best thing to do. Online Parental behavior : Participants reported their perception of their parents’ involvement and boundaries in different aspects of their online activities, answering six questions concerning the ways that their parents supervise, restrict and support their online activities. Samples and Procedures Two samples were tested with adolescent participants. For each sample, parents were contacted to invite their children to participate, and adolescents completed self-reported questionnaires about their online behaviors. All procedures were approved by the University Social Sciences Research Ethics Committee. Descriptive statistics for both samples are presented in Table 1 . First Sample (S1) Adolescent participants were recruited through a commercial research panel company. Parents were contacted through the panel and instructed to ask one eligible child to participate; if more than one child met the age criterion, parents were asked to invite the child whose name came first alphabetically. Participants received standard compensation provided by the panel company. The sample included 308 adolescents aged 13 to 17 years (M = 15.26, SD = 1.63; 57.1% female), selected to represent Native-speaking (predominantly Jewish) families across various income and religiosity levels within secular, traditional, and religious communities. Reported family affiliations were 47% secular, 30% traditional, and 23% religious (population distribution: 45.3%, 33.1%, 21.6%; Central Bureau of Statistics, 2022 ). Family income distribution was 37% lower, 20% average, and 43% higher income (population distribution: 52%, 22%, 26%; Central Bureau of Statistics, 2021). Second Sample (S2) Participants were drawn from the Longitudinal Study of Twins (LIST; Vertsberger et al., 2019 ), a study of social development that invited all Native-speaking families of twins born in 2004–2005 ito participate. Initial recruitment began when twins were 3 years old, with later follow-ups at ages 6, 7, 9, 13, 15, and 17. At the adolescent stages (ages 13, 15, and 17), both parents and twins completed questionnaires that included self-reported data on online behaviors. The Second Sample (S2) comprised 309 adolescents (M = 17.12, SD = 0.48; 51.6% male). Table 1 Samples Demographic Characteristics Variable Sample 1 (n = 308; 2021) Sample 2 (n = 309; 2023) Age range, years 13–17 17–19 Age, M ( SD ), years 15.26 (1.63) 17.12 (0.48) Gender 57.1% female 51.6% male Religiosity 47% secular, 30% traditional, 23% religious 44% secular, 32% traditional, 24% religious Income 37% low, 20% average, 43% high 35% low, 20% average, 45% high Daily screen time, hours per day, M ( SD ) 5.26 (2.73) 4.99 (2.49) Age of first smartphone, years, M ( SD ) 10.03 (2.28) 10.48 (2.28) Note. M = mean; SD = standard deviation. Daily screen time refers to adolescents’ self-reported average number of hours spent online per day. Results To evaluate and cross-validate the questionnaire’s factor structure, we ran a confirmatory factor analysis (CFA) on two independent samples. In Sample 2, because participants were nested within families, we computed cluster-robust (family-level) standard errors and test statistics using the family identifier. The CFA was performed in R, version 4.3.1, using the “lavaan” package. All latent factors were freely correlated. To account for methodological overlap due to identical wording across scenarios, we specified residual correlations between parallel items (e.g., Items 4.1, 15.1, and 19.1; Items 4.2, 15.2, and 19.2; Items 4.3, 15.3, and 19.3; Items 4.4, 15.4, and 19.4; Items 5, 16, and 20; Items 6, 17 and 21; Items 2 and 7.1; Items 9 and 11). Additional residual covariances were included between closely related indicators within the same scenario (e.g., Items 4–6, Items 15–17, Items 19–21). Both samples presented adequate fit indices (See Table 2 for the fit indices). Overall, the factor structure is replicated well across samples, with most items showing moderate-to-strong loadings (see Table 3 for the item’s loading on each factor). Table 2 CFA Fit Indices for S1 and S2 Sample χ²(418) CFI TLI RMSEA SRMR Sample 1 820.59 .93 .91 .05 .07 Sample 2 637.79 .95 .94 .04 .06 Note. χ² = chi-square; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual. Table 3 CFA Items’ Standardized Loadings for Sample 1 and Sample 2 Factor Item Sample 1 Sample 2 F1: General online activities 2. Daily screen time (weekends excluded) .55 .34 7.1 General screen time .66 .46 7.2 Messaging friends .83 .76 7.3 Social media apps .83 .71 7.4 Gaming hours .39 .60 7.5 Watching videos/series .61 .49 7.6 Learning/reading .41 .24 F2: Exposure to harmful content 6. Exposure to similar content (scenario 1) .74 .70 8. Exposure to unpleasant correspondence .84 .83 14. Exposure to harmful content .55 .56 17. Exposure to similar content (scenario 2) .79 .64 21. Exposure to similar content (scenario 3) .68 .60 F3: Sympathetic emotions 4.3 It makes me feel sad (scenario 1) .62 .62 4.4 It makes me feel angry (scenario 1) .63 .64 15.3 It makes me feel sad (scenario 2) .78 .75 15.4 It makes me feel angry (scenario 2) .86 .75 19.3 It makes me feel sad (scenario 3) .65 .76 19.4 It makes me feel angry (scenario 3) .66 .74 F4: Offensive emotions 4.1 It makes me laugh (reverse, scenario 1) .51 .50 4.2 It doesn’t really matter to me (reverse, scenario 1) .31 .40 15.1 It makes me laugh (reverse, scenario 2) .50 .48 15.2 It doesn’t really matter to me (reverse, scenario 2) .46 .61 19.1 It makes me laugh (reverse, scenario 3) .11 .46 19.2 It doesn’t really matter to me (reverse, scenario 3) .44 .50 F5: Online bystanders’ behavior 5. How would you respond (scenario 1) .72 .78 16. How would you respond (scenario 2) .71 .72 18. What do you think is the right thing to do? .53 .55 20. How would you respond (scenario 3) .69 .59 F6: Onlien parental behavior 9. Tell parents everything that happens online .58 .43 10. Clear internet rules at home .77 .70 11. Tell parents about unpleasant online content .57 .35 12. Parents make sure to be involved online .73 .74 13. Parents monitor online activities .72 .64 Note. Values are standardized factor loadings (β). Items marked “reverse” were reverse-scored prior to analysis. Factor 1: General online activities (7 items). This factor represents the breadth and intensity of adolescents’ online engagements (quality and quantity), and it includes daily screen time and the nature of online activities (such as communicating with friends, scrolling through social media activities, gaming, watching videos, and online learning). Loadings were more heterogeneous (Sample 1: .39-.83; Sample 2: .24-.76). Core engagement items (messaging, social media) loaded strongly in both samples (≥ .71), whereas “learning/reading” was weak, particularly in Sample 2 (β = .24), and “gaming hours” varied (Sample 1: β = .39; Sample 2: β = .60). Factor 2: Exposure level to harmful content (5 items). This factor reflects the frequency of encountering negative or disturbing online harmful content, either personally or when directed at others. This factor showed consistently solid loadings (Sample 1: .55-.84; Sample 2: .56-.83). Factor 3: Sympathetic emotional responses (6 items). This factor reflected adolescents’ sympathetic emotional reactions when exposed to online harmful interactions. It includes strong negative emotions such as sadness and anger while witnessing cyberbullying. Loadings were uniformly high (Sample 1: .62-.86; Sample 2: .62-.76), indicating a well-defined factor. Factor 4: Offensive emotional reaction (6 items). This factor represents adolescents’ minimizing, dismissive, or disengaged emotional reactions to online cyberbullying scenarios. This factor showed the weakest and least stable loadings, especially in Sample 1 (Sample 1: .11-.51; Sample 2: .40-.61). Item 19.1 (“It makes me laugh,” scenario 3) was particularly low in Sample 1 (β = .11) but showed higher loadings in Sample 2 (β = .46). Factor 5: Online bystanders’ behavior (4 items). This factor captures adolescents’ behavioral responses when exposed to cyberbullying scenarios. Loadings were moderate to high (Sample 1: .53-.72; Sample 2: .55-.78). Factor 6: Online parental behavior (5 items). This factor captured parental involvement in adolescents’ online activities, and it includes sharing online experiences with parents, parental boundaries and supervision, and adolescents’ tendencies to report unpleasant online experiences to their parents. Most items loaded in the moderate-to-strong range (Sample 1: .57-.77; Sample 2: .35-.74). In Sample 2, two parent-disclosure items presented weaker loadings (β = .35-.43), consistent with either heightened between-family variability or sensitivity to item wording. Internal Consistency. We examined internal consistency for each latent factor using Cronbach’s α in both samples. Additionally, we computed McDonald’s ω for both samples to provide a reliability estimate that aligns with the congeneric CFA model (i.e., allows unequal loadings and error variances) and is therefore less biased than α. Estimating ω in each sample enables direct cross-sample comparability of internal consistency for every factor. In Sample 2, standard errors were cluster-robust to account for twin nesting. Sample 1 used conventional estimation because clustering was not present (Table 4 ). Across Factors 1–3 and Factors 5–6, internal consistency was generally adequate (≥ .70), with slightly lower ω in Sample 2 for Factors 1 and 6 (ω = .69 and .65, respectively). Factor 4 showed the weakest internal consistency, particularly by ω (Sample 1: α = .67; ω = .41, Sample 2: α = .75, ω = .54). This lower reliability for Factor 4 likely reflects the broad range of behaviors it captures, spanning from overtly aggressive reactions to mere indifference, which reduces inter-item homogeneity and, consequently, internal consistency. We nevertheless retained Factor 4 due to its clear theoretical relevance (capturing offensive bystander affect distinct from sympathetic reactions), and its replication across samples with acceptable α. Table 4 Internal Consistency and Corrected Item – Total Correlation Sample Index F1: General online activities F2: Exposure level to harmful content F3: Sympathetic emotional responses F4: Offensive emotional reactions F5: Online bystanders’ behavior F6: Online parental behavior 1 Α .81 .85 .88 .67 .70 .82 Ω .81 .82 .77 .41 .83 .77 Corrected item-total correlations .40-.69 .50-.73 .65-.75 .37-.54 .48-.53 .58-.64 2 Α .73 .82 .89 .75 .73 .73 Ω .69 .75 .77 .54 .79 .65 Corrected item–total correlations .22-.51 .49-.69 .68-.75 .40-.55 .47-.60 .44-.56 Note. α = Cronbach’s alpha; Ω = McDonald’s omega. Values in the corrected item–total correlations rows represent the range of corrected item–total correlations across items within each factor. Corrected item–total correlations were generally adequate to high (Table 4 ), except for Item 7.6 (Learning/reading online activities) in Sample 2 (CITC = .22). Dropping Item 7.6 would yield only a trivial gain for Factor 1 (a .01 increase in α), and only in Sample 2; α was unchanged in Sample 1 when dropping Item 7.6. In Sample 1, small α increases could have been achieved by removing Item 14 from Factor 2 and Item 19.1 from Factor 4 (.01 and .008 increase in α, respectively). Because these changes were negligible and all factors met conventional internal-consistency thresholds, we retained the original item set. Inter-Factors Correlations. Composite scores were created for each of the six factors, based on the mean of the items comprising each factor, for both samples. Notably, Factor 1 was computed without Item 2, because Item 2 was measured on an ordinal scale, whereas Items 7.1–7.6 were measured on an interval scale. In addition, Item 2 and Item 7.1 captured a similar construct. Similar patterns emerged in the two samples. The correlation patterns in the two samples were overall very similar, with a very high, r = .95 matrix correlation between the correlation matrices of the two samples, indicating high consistency in the overall pattern of intercorrelations among the six factors. Examination of the correlations revealed two groups of factors. Factor 3 (“sympathetic bystanders’ emotions”), Factor 4 (“Offensive Bystanders’ emotions-reversed”), and Factor 5 (“Online bystander behavior”) showed high positive associations across the two samples (r = .41 − .68, p < .001). Factor 6 (“online parental behavior”) was also positively related to these bystander factors, though more weakly (r = .17 − .29, p < .01). Factor 1 (“General online activities”) and Factor 2 (“Exposure level to harmful content”) presented positive, though modest, correlations (r = .14 − .17, p < .05). The associations between factors across the two groups were either nonsignificant or negative (r = − .22 − .03). These associations indicated two largely distinct domains: (a) bystander reactions to cyberbullying, including parental involvement and boundaries and (b) exposure level to harmful content and online activities. Within each domain, factors were positively interrelated, but the domains themselves were only weakly and negatively connected. Interestingly, Factor 6 (“Online parental behavior”) showed low positive associations with the bystanders’ emotional reactions to cyberbullying factors (Factors 3–5; r = .17-.26, p < .01, Table 5 ), and low to nonsignificant negative correlations with the exposure level to harmful content and general online activities factors (Factors 1–2; r = − .14- − .04). In other words, when adolescents experience higher parental involvement and boundaries in their online lives, they also tend to show more sympathetic emotional and behavioral reactions to cyberbullying. By contrast, online parental behaviors appear largely unrelated, or even slightly negatively related, to adolescents’ daily screen time or to their level of exposure to harmful content. To examine whether a more parsimonious, hierarchical representation of the six factors would provide a better account of the data, a second-order CFA model was specified. In this model, two higher-order latent constructs were defined: “exposure level to harmful content and general online activities” (loading on Factors 1 and 2) and “online bystanders’ responses to cyberbullying” (loading on Factors 3 to 6). The hierarchical model showed a good overall fit in both samples (Table 6 ). Comparing the six-factor model with the hierarchical (second-order) model indicated a significant loss of fit for the hierarchical specification in Sample 1 (Δχ²(8) = 21.47, p = .006). However, the practical impact was small, with ΔCFI = .004, ΔTLI = .002, ΔRMSEA = .000, and ΔSRMR = .003. Moreover, in Sample 2 the difference between the two models was not statistically significant (Δχ²(8) = 13.64, p = .092). Thus, the hierarchical model appears to provide a similarly adequate fit while offering a conceptually more parsimonious structure that aligns with the two broader domains identified in the correlation analyses: (a) coping and emotional regulation, and (b) online exposure and activity. See Fig. 1 for the hierarchical model’s loadings in the two samples. Table 5 Inter-Factor Correlations (S1: Top, S2: Bottom) Factor 1. General online 2. Exposure level to harmful content 3. Sympathetic emotions 4. Offensive emotions 5. Bystanders’ behavior 6. Online parental behavior Sample 1 (N = 308) 1. General online activities — .17** −.11 −.19** −.17** −.14* 2. Exposure to online harmful content — .03 .11 .02 −.04 3. Sympathetic emotional responses — .50*** .57*** .29*** 4. Offensive emotional reactions — .41*** .17** 5. Online bystanders’ behavior — .18** 6. Online parental behavior — M (SD) 3.21 (1.83) 2.32 (0.82) 3.41 (1.10) 4.11 (0.66) 4.17 (0.77) 2.95 (1.09) Range 0.00-9.67 1.00–5.00 1.00–5.00 2.33-5.00 1.00–5.00 1.00–5.00 Sample 2 (N = 309) 1. General online activities — .14* − .11 − .22*** − .16** − .08 2. Exposure level to harmful content — − .19*** − .22*** − .22*** − .14* 3. Sympathetic emotional responses — .68*** .68*** .26*** 4. Offensive emotional reactions — .58*** .22*** 5. Online bystanders’ behavior — .26*** 6. Online parental behavior — M (SD) 2.57 (1.10) 2.26 (0.79) 3.29 (1.15) 4.09 (0.81) 4.09 (0.75) 2.50 (0.93) Range 1.00-6.33 1.00–5.00 1.00–5.00 1.67-5.00 1.00–5.00 1.00–5.00 Note. Values are Pearson correlations. M = mean; SD = standard deviation. * p < .05, ** p < .01, *** p < .001. Table 6 Hierarchical CFA Fit Indices for Sample 1 and Sample 2 Sample χ²(426) CFI TLI RMSEA SRMR Sample 1 785.84 .92 .90 .05 .07 Sample 2 654.56 .95 .94 .04 .06 Note. χ² = chi-square; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual. Scale Descriptives. In addition to examining inter-factor correlations, adolescents’ online experiences and responses were also evaluated by comparing the average levels of each factor across both samples. Descriptive statistics for the composite scores are presented in Table 1 , separately for the PCA and CFA samples. Factor 2–4 presented a highly consistent pattern across the two samples, showing nearly identical mean levels and variability. This stability further supports the robustness of these constructs across groups. Slight differences emerged for Factors 1 and 6, which may be attributed to age-related differences in online activities and parental behaviors norms between the two samples. Discussion The OBQ offers a holistic portrait of adolescents’ digital lives and online behaviors. Rather than focusing on time or other specific digital aspects, it integrates adolescents’ daily screen time with the quality features of their online engagements (active vs. passive uses), the contexts surrounding that engagement (perceived parental involvement and boundaries), risky behaviors (exposure to online harmful content), and their in-the-moment behavioral and emotional responses to realistic cyberbullying scenarios. This multi-layered perspective captures mutually reinforcing processes: online engagements patterns that shape exposure to harmful content, family involvement that buffers or amplifies online risky behaviors, and emotions that channel their bystanders’ behaviors, yielding a deeper, more meaningful understanding of how children and adolescents act, feel, interact, and are influenced in digital environments. Across two distinct samples, we replicated a clear six-factor structure covering sympathetic and offensive bystander emotional reactions, bystander behavioral tendencies, general online activities, level of exposure to harmful content, and online parental behaviors. Global fit indices were good in both samples, and factor loadings were largely moderate-to-strong, supporting construct validity. Internal consistency estimates were acceptable for five of the six factors; the comparatively lower consistency of the Offensive Bystanders’ Emotions factor accords with prior difficulties observed for reverse-keyed affect items and likely reflects heterogeneity in disengaged or minimizing reactions to cyberbullying rather than mere measurement noise. Together, these results indicate that the OBQ captures a theoretically meaningful and practically actionable variation in adolescents’ online engagements. From a measurement standpoint, the OBQ’s design advances the field in three ways. First, it integrates previously separated strands of assessment: screen time and activities, exposure to harmful content, parental mediation, and bystander functioning. These differential aspects of online engagements are commonly studied in isolation, thereby supporting integrated models of risk and resilience. Second, it captures bystander emotions and behaviors in ecologically valid scenarios, which likely elevates predictive relevance for real-world responding beyond generic attitudinal items. Third, it provides replicable structure as well as adequate reliability across demographically distinct adolescent samples, an essential prerequisite for longitudinal and cross-context applications (e.g., program evaluation, policy-relevant surveillance). Parental Behaviors and Adolescents’ Online Engagements Consistent, though modest, positive association between parental online behaviors and sympathetic bystander reactions, coupled with negligible or slightly negative links to the activities/exposure domain, aligns with family-process perspectives that distinguish between quality of online mediation and quantity of use. In other words, parents that cultivate clear rules and are emotionally open to dialogue may nurture adolescents who experience stronger sympathetic emotions when witnessing cyberbullying and who are more behaviorally inclined to help, even if overall screen exposure does not change significantly. This pattern also underscores a potential buffering role of positive parental mediation for emotion regulation in the face of online harm. It is important to recognize that associations between parenting and adolescent online behaviors are likely bidirectional. Parents may shape their children’s norms as well as their online responses as bystanders, but adolescents’ own dispositions, distress, or online activities can also evoke and reshape their parents’ involvement and boundaries over time. In other words, what looks like a parental “effect” at one time point may partly reflect a child-driven influence on parenting practices. Future work should therefore use longitudinal designs to test directional pathways and reciprocal dynamics between parents and adolescents’ online behaviors, and to examine when supportive mediation buffers risk versus when adolescents’ behaviors aim for tighter control. Thus, socialization is a two-way process in which children actively influence their caregiving environment (Davidov et al., 2015 ). Two-Domain Architecture of Digital Experience A central substantive finding concerns the measurement’s higher-order organization. Inter-factor correlations converged on two coherent domains: (1) bystander coping and emotional regulation (sympathetic emotions, offensive emotions, behavioral reactivity) that also clusters with parental behaviors, and (2) online exposure and activities (exposure to harmful content and general online engagements). The replication of this pattern across samples, together with adequate fit of a second-order CFA, suggests that adolescents’ online reactions to harmful content form a domain that is relatively distinct from their overall level or style of online activity. Practically, this implies that interventions seeking to improve bystander functioning (e.g., enhancing empathic concern, increasing defending behaviors, reducing moral disengagement) can be targeted without necessarily attempting to reduce overall online activities. Results highlight opportunities for refinement: The weaker and less stable loadings for some items within the Offensive Emotions factor suggest testing alternative wording (e.g., direct endorsement of indifference/disengagement without humor cues), adding additional indicators that capture moral disengagement and trivialization, and considering method factors for reverse-coded items in future CFAs. In the online activity domain, the “learning/reading online” indicator showed lower item-total correlations, possibly reflecting broad variability in what adolescents consider “learning” (formal vs. informal, etc.). Future studies may differentiate school-mandated versus elective learning and short-form versus long-form content to improve construct precision. Finally, the hierarchical representation invites theoretically informed pathways: Parental involvement may shape bystander emotions and behaviors through empathy-related processes and norms of responsibility, whereas general activity levels and exposure to harmful content may be governed more by opportunity structures and platform ecologies. This separation suggests two complementary levers for policy and practice: (a) skills and climates (family-school programming to cultivate more empathic bystanders), and (b) environments and safeguards (content moderation literacy, safety-by-design, and exposure management). Strengths of the OBQ. The OBQ is ecological valid, and items are embedded in realistic screenshot scenarios reflecting appearance-based ridicule, academic shaming, and body-related mockery, common triggers of adolescent online harassment as well as a general “what is right to do” vignette. The questionnaire integrates the quantity and quality of adolescents’ online activities and online harmful exposure, parental behaviors and bystander feelings and behaviors without becoming unwieldy. The OBQ has proven to have a replicated structure, with the six-factor solution and inter-factor patterns replicated across an independent twin sample, with appropriate cluster-robust estimation. A central contribution of the OBQ is to bridge between what adolescents do online (daily screen time, quality of online activities and exposure to harmful content) with their online bystanders’ feelings and behaviors when they witness cyberbullying or shaming. Many established questionnaires emphasize daily screen time or problematic and addictive online tendencies. Other target bystander roles or intentions without simultaneously measuring general engagement, exposure, and parental processes. The OBQ integrates these online behavioral domains within a single, brief tool and enables the measurement of bystander items in ecologically rich screenshots derived from real adolescent online interactions. Practical implications. With 43 closed items that include three real-based cyberbullying scenarios, the OBQ is brief enough for school screening and clinical intake while retaining multidimensional coverage. It can be used to evaluate digital profile activities and bystander tendencies, as well as identifying potential bystander-helpers – those experiencing strong sympathetic feelings but lacking action strategies. The OBQ can help map potential roles of bystanders and help build direct psycho-educational programs to encourage potential helpers as well as directing potential offensive-bystanders. The OBQ can be used to evaluate children and adolescents’ digital citizenship and bystander activation programs by tracking domain-specific change. As mentioned above, one of the strengths of the OBQ is the way it separates offensive and sympathetic bystanders’ feelings and behaviors. This advantage can lead to tailor-made educational programs, after initially evaluating the different roles of online bystanders within a group of people. For example, emotion-to-action bridging programs for adolescents who feel distress but hesitate to intervene, or norm-setting and moral reframing programs for more offensive bystanders’ tendencies. Above all, it may be a directing tool for designing online efficacy skills (what to write, who to tell, safe reporting, etc.) for those willing but unsure how to act. Parental directing programs can focus on communication and monitoring practices that relate to stronger emotional atonement and helping intentions, as well as getting deeper acquaintances and communicating with adolescents. Study Limitations and Future Directions Several limitations should inform interpretation and future refinement. First, the data was cross-sectional with self-reports, limiting causal inference and raising the possibility of shared-method variance. Second, some reverse-keyed items of bystanders’ feelings exhibited comparatively lower internal consistency, suggesting potential gains from rewording or adding positively keyed indicators of disengagement/minimization to mitigate response-style artifacts. Third, despite the real-life-based scenarios and their ecological validity, their cultural and platform specificity may limit generalizability: norms around humor, shaming, and gendered content can vary across communities and platforms. Fourth, while activity items span several modalities, more granular differentiation of active vs. passive uses (and prosocial vs. competitive gaming; creative vs. consumptive video engagement) could sharpen predictive precision. Fifth, although the twin sample supports replication and modelling with clustered data, broader cross-cultural and age band replications (early vs. late adolescence) are yet to be tested. Future directions should enable longitudinal and experimental validation, testing whether parental processes and level of exposure to harmful content can predict changes in bystanders’ feelings and behaviors, and whether OBQ scales are sensitive to intervention (such as empathy training, bystander efficacy workshops, etc.). Future measurement invariance can also expand across gender, age, religiosity, socioeconomic status, and cultural groups. Further research should also try and isolate passive and active engagements and their potential influence on bystanders’ feelings and behaviors. Conclusion The Online Behavior Questionnaire offers a psychometrically sound, ecologically valid, and practical assessment of adolescents’ online behaviors and feelings, which uniquely integrates between six domains: (a) quantity and quality of online engagements, (b) level of exposure to online harmful content, (c) sympathetic bystanders’ feelings, (d) offensive bystanders’ feelings, (e) online bystanders’ behaviors and (f) online parental behaviors. The replicated six-factor structure, acceptable reliability, and theoretically coherent inter-factor architecture provide a strong foundation for research that moves beyond daily screen time toward understanding how adolescents feel and act when witnessing cyberbullying, and how family processes and parental involvement patterns shape these responses. As schools and communities seek to foster prosocial bystander engagement and safer online climates, the OBQ can serve both as a diagnostic lens (who needs what kind of support?) and as an outcome metric for evaluating targeted interventions. With continued refinement and cross-cultural extension, the OBQ is well positioned to become a core instrument for studying and improving adolescents’ well-being in the digital century. Declarations Ethics approval This research was reviewed and approved by the Hebrew University Social Sciences Research Ethics Committee. All procedures complied with institutional guidelines and the Declaration of Helsinki. Consent to participate Participation was voluntary and respondents could discontinue at any time without penalty. Parents or legal guardians provided informed consent, and adolescents provided assent prior to data collection through the research panel’s online system. Consent for publication Participants and their guardians were informed that findings would be reported in aggregate form only. No identifying information is disclosed in this manuscript, and consent to publish anonymized findings was obtained as part of the consent procedure. Funding Declaration: Not applicable. The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Author Contribution S.C.H.: Conceptualization, methodology, investigation, writing of original draft, visualization, project administration.H.S.: Investigation, methodology, formal analysis, data curation, prepared all figures and tables, writing (review and editing).A.K.N.: Conceptualization, methodology, supervision, writing (review and editing). Acknowledgement We would like to express our deepest gratitude to all adolescents and parents who generously agreed to participate in this study and facilitated the data collection. We are also grateful to the research assistants who supported questionnaire administration, data entry, and initial coding. Their help was invaluable to the completion of this project.Last, we would like to thank the department of Psychology in the Hebrew University of Jerusalem, for providing the necessary resources and facilities that made this research possible. 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06:11:57","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170270,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8256781/v1/76d50d119c3e09a65deabfd0.html"},{"id":100851734,"identity":"d2e4bf9e-33be-413f-a591-d68be17543e5","added_by":"auto","created_at":"2026-01-22 06:11:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHierarchical Model Loadings in S1 (Top) and S2 (Bottom)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8256781/v1/f58df86edcd61ef17a2bd991.png"},{"id":100952568,"identity":"7f828325-118d-41a5-8a09-0af9c5456c10","added_by":"auto","created_at":"2026-01-23 07:17:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697176,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8256781/v1/6d6971cc-f8cb-48ac-93a2-5582a927b9a6.pdf"},{"id":100851755,"identity":"bd70a77f-680b-4f19-9cfa-48c53da69e04","added_by":"auto","created_at":"2026-01-22 06:11:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":427349,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8256781/v1/0f5318655e4dc28f9c35db3e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Digital Adolescence: Assessing online activities and responses to cyberbullying with the Online Behavior Questionnaire","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid development of technology has significantly affected various aspects of humanity, raising questions about the ways that online communication and activities may impact the social and emotional development of children and adolescents (Hasebrink et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Smahel et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The integration of online activities into daily lives has become a defining feature of the 21st century and many adolescents are now reporting \u0026ldquo;almost constant\u0026rdquo; online connectivity (Rideout, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Understanding how adolescents engage online, and how they respond to the social dynamics that unfold there, is therefore crucial for advancing both research and practice regarding adolescents\u0026rsquo; online activities. The current two studies introduce and validate the Online Behavior Questionnaire (OBQ), designed to capture the multifaceted nature of adolescents\u0026rsquo; online experiences and engagements, as well as their feelings and behaviors as online bystanders.\u003c/p\u003e \u003cp\u003eTo situate the development of this measure, we first review prior research on the quantity and quality of adolescents\u0026rsquo; online engagements, on exposure to online harmful content and responses to cyberbullying, and on online parental involvement in adolescents\u0026rsquo; online activities. We then describe results from two different samples using the OBQ, with the goal of providing researchers and practitioners with a reliable tool for assessment, prevention, and interventions.\u003c/p\u003e\n\u003ch3\u003eQuantity and Quality of Adolescents’ Online Engagements\u003c/h3\u003e\n\u003cp\u003eMillennial childhood and adolescence are characterized by the early and intensive use of online activities and platforms that play a central role in how adolescents learn, play, and engage socially (Keeley \u0026amp; Little, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The COVID-19 pandemic further accelerated this digital immersion, significantly altering both the quantity and quality of adolescents\u0026rsquo; online activities (Organization for Economic Co-operation and Development, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). During periods of lockdown and remote learning, adolescents reported increased engagement in online educational content, digital reading, and virtual social communication, highlighting the adaptive role of technology in maintaining continuity in learning and relationships (Nagata et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Pew Research Center survey of American teenagers aged 13 to 17, reported that most American adolescents have access to digital devices, and 95% of them use personal smartphones. Studies show that 97% of American adolescents are daily internet users; 46% of them reported using the internet almost constantly (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Following this massive use of technology, the quantity and quality of adolescents\u0026rsquo; online activities, and their impact on development have become a central concern in cyberpsychology. For example, Martin et al. (2020) reviewed longitudinal studies that examined the associations between adolescents\u0026rsquo; daily screen time, sleep, and mental health. Their review highlights associations between excessive screen time and sleep deprivation, anxiety, low self-esteem and depression.\u003c/p\u003e \u003cp\u003eA different perspective of these online engagements suggests that online social interactions may serve as an extension of offline relationships. From this perspective, adolescents may use online communication to help them cope with negative feelings (M\u0026yacute;lek et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis increasing integration of online engagements, and their impact on the lives of children and adolescents have prompted the development of various instruments aimed at measuring their daily screen time and the different activities in which they are engaged. These tools vary in methodology and psychometric robustness, reflecting the complexity of digital engagement in children and adolescents.\u003c/p\u003e \u003cp\u003eA broad synthesis of measurement tools for assessing children\u0026rsquo;s digital media use was conducted by Browne et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in a comprehensive scoping review encompassing 162 instruments. This review highlighted the considerable diversity in methodological approaches, ranging from simple screen time logs to complex multi-dimensional assessments. This review underscores a critical shift in the field, from measuring mere exposure to understanding the broader implications of digital media in children\u0026rsquo;s lives. One of the central findings was the inadequacy of screen time as a standalone metric, given its limited capacity to capture the nuanced realities of children\u0026rsquo;s digital engagements. The authors emphasized the need for more comprehensive, multi-method instruments that assess not only the quantity of media use but also the quality, context, and psychosocial correlates. Specifically, they called for tools that examine content types, usage environments, emotional and behavioral outcomes, and family dynamics.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOnline Exposure to Harmful Content and Cyberbullying\u003c/h2\u003e \u003cp\u003eAlthough the quantity and quality of adolescents\u0026rsquo; online engagements are widely studied, they provide only a partial view of the multifaceted nature of adolescents\u0026rsquo; digital behaviors. Online exposure to harmful content can shape adolescents\u0026rsquo; emotions, beliefs, and behaviors by normalizing observed behaviors, increasing anxiety or fear, and triggering harmful cycles of online engagements. The EU Kids and the Global Kids Online (GKO) programs show that greater use and specific online activities are linked to greater exposure to online risks (Hasebrink, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the exposure to online harmful content was linked to being a perpetrator of online hate (Wachs et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent U.S. survey data indicate that nearly half of U.S. adolescents (ages 13\u0026ndash;17) report experiencing at least one form of cyberbullying, such as name-calling and rumor spreading (Anderson et al., 2022), and in the United States, data from CDC\u0026rsquo;s 2019 Youth Risk Behavior Survey indicate that nearly 16% of high school students has been electronically bullied in the last 12 months (CDC \u0026ndash; Centre for Disease Control and Prevention, 2020).\u003c/p\u003e \u003cp\u003ePerpetration is not rare either: across large school-based samples, adolescents reported having cyberbullied others, when offending behaviors were associated with perceptions of peers behaving similarly (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost incidents unfold in front of peers: bystanders are present during the vast majority of bullying episodes, offline or online, though active defense remains comparatively infrequent (Jenkins et al., 2019; Lynn Hawkins et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Olweus, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Classic participant role work identifies several bystander roles: bystander-assistants and reinforcers who join or encourage the aggressor (laughing, sharing, \u0026ldquo;liking\u0026rdquo;), bystander-defenders who support or protect the victim (comforting, reporting, counter-messaging), and bystander-outsiders who stay passive or avoid the situation (Salmivalli, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Studies have been searching for the impact of bystanders\u0026rsquo; feelings on their behavioral choices, showing that higher empathy (both cognitive and emotional empathy) is associated with supportive bystander behaviors (Barlińska et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mach\u0026aacute;čkov\u0026aacute; \u0026amp; Pfetsch, 2016). Conversely, offensive bystander responses align with reinforcing or assistant roles, while moral disengagement (\u0026ldquo;It\u0026rsquo;s just a joke,\u0026rdquo; \u0026ldquo;Everyone does it\u0026rdquo;) predicts pro-bully and passive responses (Thornberg \u0026amp; Jungert, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this domain of online aggression, the Cyber Victim and Bullying Scale-SVBS (\u0026Ccedil;etin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) is a validated instrument that measures both victimization and perpetration across multiple forms of online harassment. It includes frequency-based items and has demonstrated strong psychometric properties across multiple studies. Complementing this, a systematic review by Berne et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified 44 instruments related to cyberbullying and online aggressions. The review found that many tools lacked consistent definitions and robust validation, underscoring the need for standardized, theory driven measures. Taken together, online environments underscore the need for tools that capture not only daily screen time and activities but also adolescents\u0026rsquo; levels of exposure to harmful content and the emotional and behavioral reactions to cyberbullying in ecologically valid online contexts. Because adolescents can experience a range of conflicting emotions and behaviors when observing online harmful content, it is essential to distinguish between sympathetic and offensive feelings, active versus reactive responses, and the degree to which they disengage from such situations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParental Involvement and Online Boundaries\u003c/h3\u003e\n\u003cp\u003eAn important context for understanding adolescent online behavior concerns their relationship with their parents (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and personal characteristics of parents were related their concerns about bullying and to the way they monitor their children\u0026rsquo;s online activities (Cohen, 2023). Other studies show that parental behaviors are associated with adolescents\u0026rsquo; online engagements and emotional reactions as well as with their vulnerability to online behavioral risks (Banić \u0026amp; Orehovački, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Parental behaviors that are characterized by strict rules and boundaries may lead adolescents toward secretive or rebellious online behavior as they seek for autonomy, while close and sharing parental behaviors may lead to the exact opposite (Stattin \u0026amp; Kerr, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Parents who stay involved and communicate openly about online engagements may provide emotional support that can help adolescents process fear and exposure to harmful content. Such support may even serve as a buffer for the negative impacts of online harmful exposure. Kvardova et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the role of risk and protective factors among adolescents who were exposed to online harmful content, indicating that emotional problems and sensation seeking serve as significant risk factors, while positive family environments and social support networks serve as protective factors for adolescents who are exposed to online harmful content.\u003c/p\u003e \u003cp\u003eA notable instrument for assessing children\u0026rsquo;s digital behavior is the SCREENS-Q, developed by Klakk et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This parent-reported questionnaire is designed to evaluate screen-based media use among children aged 6 to 10 years. The SCREENS-Q provides details about the media environment, types of content consumed, and the presence of family routines for screen use. Even though the SCREENS-Q serves as a valuable resource for early childhood digital behavior, it lacks an adolescent-specific, multidimensional, scenario-anchored assessment that can capture time and quality of use as well as levels of exposure to harmful content and bystander responses.\u003c/p\u003e\n\u003ch3\u003eThe Motivation for Developing the OBQ\u003c/h3\u003e\n\u003cp\u003eConsidering the full spectrum of children\u0026rsquo;s and adolescents\u0026rsquo; online engagements, behaviors, and emotional reactions, multiple instruments were developed to evaluate digital behaviors. One of the most comprehensive and widely utilized instruments for studying children\u0026rsquo;s digital experiences is the Global Kids Online Questionnaire, developed collaboratively by the London School of Economics and UNICEF (Byrne et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This tool includes both child and parent modules, enabling a multi-perspective assessment of children\u0026rsquo;s online engagement. It evaluates a broad range of dimensions, including access to digital technologies, digital skills, online risks and opportunities, and parental mediation strategies. The questionnaire investigates potential risks associated with online activities, but it lacks the ability of integrating bystander responses alongside time and quality of use that can create a more holistic perspective of adolescent online engagements, feelings and behaviors.\u003c/p\u003e \u003cp\u003eDespite the many existing research tools that were designed to assess adolescents\u0026rsquo; digital engagements, no single, psychometrically evaluated instrument offers a comprehensive profile that simultaneously captures: quantitative and qualitative modes of use, exposure to harmful content, online parental behaviors as well as emotional and behavioral reactions to cyberbullying. Existing measures tend to focus on one or two of these domains (e.g., screen time logs, cyberbullying victimization scales, or parental mediation inventories), which limits both explanatory power and practical utility for schools, clinicians, and researchers. The OBQ was therefore developed to provide an integrated, theory-aligned assessment that brings these elements together within a single, brief, developmentally appropriate instrument.\u003c/p\u003e \u003cp\u003eAdolescents do not use technology in a disconnected environment, and their online behaviors are associated with their environment and surroundings as well as their parents\u0026rsquo; attitudes toward screens. A novel aspect of the OBQ is that it includes scenario-based items and measurements of emotional response to these scenarios. These patterns map onto the OBQ\u0026rsquo;s distinction between sympathetic and offensive feelings, behavioral reactivity, and disengagement when witnessing cyberbullying. Measuring both emotional reactions and concrete behavioral intentions is therefore essential for capturing adolescents\u0026rsquo; bystander profiles in ecologically valid online scenarios, and for evaluating interventions that aim to move adolescents from passive or reinforcing bystander roles toward supportive defending roles.\u003c/p\u003e \u003cp\u003eThe OBQ was designed to enable researchers with a holistic observation of adolescents\u0026rsquo; digital behaviors, by unifying six domains within one instrument: (a) quantity of online engagements (daily screen time); (b) quality of online engagements; (c) exposure level to harmful content; (d) emotional reactions to cyberbullying; (e) online bystander behaviors (sympathetic, indifferent, offensive); (f) online parental behavior. We aimed to validate these suggested six scales of the OBQ across two adolescent samples by establishing its structure, reliability, and higher-order organization. We examined internal consistency for each factor received and evaluated the latent inter-factor correlations to differentiate between two higher-order domains using hierarchical confirmatory factor models.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThe Online Behavior Questionnaire (OBQ) was designed to examine quantitative and qualitative aspects of online behavior as well as emotional and behavioral reactions to cyberbullying. It includes 43 closed items, measuring activities, perceptions, and reactions to online behavior, including a special section designed to measure bystanders\u0026rsquo; emotions and behavior (See Appendix 1 for the full OBQ). The questionnaire includes six scales that address different aspects of adolescents\u0026rsquo; online behavior and environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOnline Behaviors and Environments\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuantity of online engagements (daily screen time)\u003c/b\u003e: Participants reported how many hours a day (weekends excluded) they spend online, ranging from 1 to over 10 hours.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuality of online engagements\u003c/b\u003e: Participants reported daily hours (weekends excluded) spent in different online engagements (writing and talking to friends, scrolling through content, watching tv and video-clips, etc). Each activity was reported separately on a scale ranging from 1 hour to over 10 hours.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExposure level to harmful content\u003c/b\u003e: Participants reported to what extent they have been exposed to online harmful content choosing a response that ranged from \u0026lsquo;not at all\u0026rsquo; (1) to \u0026lsquo;all the time\u0026rsquo; (5).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEmotional reactions to cyberbullying\u003c/b\u003e: Adolescents viewed three screenshots depicting cyberbullying and read about a fourth hypothetical scenario, which were ecologically valid, selected to represent real, distressing situations. These screenshots were based on real-life interactions among adolescents that the first author of this article encountered in her clinical practice. In the first scenario, bullies laughed at a childhood picture of the victim, saying she was ugly. In the second scenario, bullies mocked the victim after failing an exam, saying he was stupid. In the third scenario, bullies laughed at a girl who posted a picture of herself in a bathing suit. After each of screenshot, participants rated the extent to which the correspondence made them feel (feelings ranged between being \u0026lsquo;amused\u0026rsquo; to feeling \u0026lsquo;angry\u0026rsquo;). They used a response scale ranging from 'not at all' (1) to 'very much' (5) for each of the emotions described.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOnline bystanders\u0026rsquo; behavior\u003c/b\u003e: After each of the three screenshots, participants answered how they would respond, from a response considered most non-social (laugh\u0026rsquo;, coded as 1) to a response considered most prosocial (try to help the victim\u0026rsquo;, coded as 5). In the hypothetical scenario, participants reported which of these responses is the best thing to do.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOnline Parental behavior\u003c/b\u003e: Participants reported their perception of their parents\u0026rsquo; involvement and boundaries in different aspects of their online activities, answering six questions concerning the ways that their parents supervise, restrict and support their online activities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eSamples and Procedures\u003c/h3\u003e\n\u003cp\u003eTwo samples were tested with adolescent participants. For each sample, parents were contacted to invite their children to participate, and adolescents completed self-reported questionnaires about their online behaviors. All procedures were approved by the University Social Sciences Research Ethics Committee. Descriptive statistics for both samples are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFirst Sample (S1)\u003c/h2\u003e \u003cp\u003eAdolescent participants were recruited through a commercial research panel company. Parents were contacted through the panel and instructed to ask one eligible child to participate; if more than one child met the age criterion, parents were asked to invite the child whose name came first alphabetically. Participants received standard compensation provided by the panel company. The sample included 308 adolescents aged 13 to 17 years (M\u0026thinsp;=\u0026thinsp;15.26,\u003c/p\u003e \u003cp\u003eSD\u0026thinsp;=\u0026thinsp;1.63; 57.1% female), selected to represent Native-speaking (predominantly Jewish) families across various income and religiosity levels within secular, traditional, and religious communities. Reported family affiliations were 47% secular, 30% traditional, and 23% religious (population distribution: 45.3%, 33.1%, 21.6%; Central Bureau of Statistics, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Family income distribution was 37% lower, 20% average, and 43% higher income (population distribution: 52%, 22%, 26%; Central Bureau of Statistics, 2021).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSecond Sample (S2)\u003c/h3\u003e\n\u003cp\u003eParticipants were drawn from the Longitudinal Study of Twins (LIST; Vertsberger et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a study of social development that invited all Native-speaking families of twins born in 2004\u0026ndash;2005 ito participate. Initial recruitment began when twins were 3 years old, with later follow-ups at ages 6, 7, 9, 13, 15, and 17. At the adolescent stages (ages 13, 15, and 17), both parents and twins completed questionnaires that included self-reported data on online behaviors. The Second Sample (S2) comprised 309 adolescents (M\u0026thinsp;=\u0026thinsp;17.12, SD\u0026thinsp;=\u0026thinsp;0.48; 51.6% male).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSamples Demographic Characteristics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample 1 (n\u0026thinsp;=\u0026thinsp;308; 2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample 2 (n\u0026thinsp;=\u0026thinsp;309; 2023)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, \u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.26 (1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.12 (0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.1% female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6% male\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligiosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47% secular, 30% traditional, 23% religious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44% secular, 32% traditional, 24% religious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37% low, 20% average, 43% high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35% low, 20% average, 45% high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily screen time, hours per day, \u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.26 (2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99 (2.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of first smartphone, years, \u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.03 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.48 (2.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote. M\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard deviation. Daily screen time refers to adolescents\u0026rsquo; self-reported average number of hours spent online per day.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo evaluate and cross-validate the questionnaire\u0026rsquo;s factor structure, we ran a confirmatory factor analysis (CFA) on two independent samples. In Sample 2, because participants were nested within families, we computed cluster-robust (family-level) standard errors and test statistics using the family identifier. The CFA was performed in R, version 4.3.1, using the \u0026ldquo;lavaan\u0026rdquo; package. All latent factors were freely correlated. To account for methodological overlap due to identical wording across scenarios, we specified residual correlations between parallel items (e.g., Items 4.1, 15.1, and 19.1; Items 4.2, 15.2, and 19.2; Items 4.3, 15.3, and 19.3; Items 4.4, 15.4, and 19.4; Items 5, 16, and 20; Items 6, 17 and 21; Items 2 and 7.1; Items 9 and 11). Additional residual covariances were included between closely related indicators within the same scenario (e.g., Items 4\u0026ndash;6, Items 15\u0026ndash;17, Items 19\u0026ndash;21). Both samples presented adequate fit indices (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the fit indices). Overall, the factor structure is replicated well across samples, with most items showing moderate-to-strong loadings (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the item\u0026rsquo;s loading on each factor).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCFA Fit Indices for S1 and S2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;(418)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e820.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e637.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e χ\u0026sup2; = chi-square; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; TLI\u0026thinsp;=\u0026thinsp;Tucker\u0026ndash;Lewis index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; SRMR\u0026thinsp;=\u0026thinsp;standardized root mean square residual.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCFA Items\u0026rsquo; Standardized Loadings for Sample 1 and Sample 2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1: General online activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. Daily screen time (weekends excluded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 General screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2 Messaging friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 Social media apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4 Gaming hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 Watching videos/series\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 Learning/reading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2: Exposure to harmful content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6. Exposure to similar content (scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8. Exposure to unpleasant correspondence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14. Exposure to harmful content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17. Exposure to similar content (scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21. Exposure to similar content (scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3: Sympathetic emotions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 It makes me feel sad (scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 It makes me feel angry (scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3 It makes me feel sad (scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.4 It makes me feel angry (scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.3 It makes me feel sad (scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.4 It makes me feel angry (scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4: Offensive emotions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 It makes me laugh (reverse, scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 It doesn\u0026rsquo;t really matter to me (reverse, scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1 It makes me laugh (reverse, scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.2 It doesn\u0026rsquo;t really matter to me (reverse, scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.1 It makes me laugh (reverse, scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2 It doesn\u0026rsquo;t really matter to me (reverse, scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5: Online bystanders\u0026rsquo; behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5. How would you respond (scenario 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16. How would you respond (scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18. What do you think is the right thing to do?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20. How would you respond (scenario 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6: Onlien parental behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9. Tell parents everything that happens online\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10. Clear internet rules at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11. Tell parents about unpleasant online content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12. Parents make sure to be involved online\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13. Parents monitor online activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values are standardized factor loadings (β). Items marked \u0026ldquo;reverse\u0026rdquo; were reverse-scored prior to analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 1: General online activities (7 items).\u003c/b\u003e This factor represents the breadth and intensity of adolescents\u0026rsquo; online engagements (quality and quantity), and it includes daily screen time and the nature of online activities (such as communicating with friends, scrolling through social media activities, gaming, watching videos, and online learning). Loadings were more heterogeneous (Sample 1: .39-.83; Sample 2: .24-.76). Core engagement items (messaging, social media) loaded strongly in both samples (\u0026ge;\u0026thinsp;.71), whereas \u0026ldquo;learning/reading\u0026rdquo; was weak, particularly in Sample 2 (β\u0026thinsp;=\u0026thinsp;.24), and \u0026ldquo;gaming hours\u0026rdquo; varied (Sample 1: β\u0026thinsp;=\u0026thinsp;.39; Sample 2: β\u0026thinsp;=\u0026thinsp;.60).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 2: Exposure level to harmful content (5 items).\u003c/b\u003e This factor reflects the frequency of encountering negative or disturbing online harmful content, either personally or when directed at others. This factor showed consistently solid loadings (Sample 1: .55-.84; Sample 2: .56-.83).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 3: Sympathetic emotional responses (6 items).\u003c/b\u003e This factor reflected adolescents\u0026rsquo; sympathetic emotional reactions when exposed to online harmful interactions. It includes strong negative emotions such as sadness and anger while witnessing cyberbullying. Loadings were uniformly high (Sample 1: .62-.86; Sample 2: .62-.76), indicating a well-defined factor.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 4: Offensive emotional reaction (6 items).\u003c/b\u003e This factor represents adolescents\u0026rsquo; minimizing, dismissive, or disengaged emotional reactions to online cyberbullying scenarios. This factor showed the weakest and least stable loadings, especially in Sample 1 (Sample 1: .11-.51; Sample 2: .40-.61). Item 19.1 (\u0026ldquo;It makes me laugh,\u0026rdquo; scenario 3) was particularly low in Sample 1 (β\u0026thinsp;=\u0026thinsp;.11) but showed higher loadings in Sample 2 (β\u0026thinsp;=\u0026thinsp;.46).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 5: Online bystanders\u0026rsquo; behavior (4 items).\u003c/b\u003e This factor captures adolescents\u0026rsquo; behavioral responses when exposed to cyberbullying scenarios. Loadings were moderate to high (Sample 1: .53-.72; Sample 2: .55-.78).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactor 6: Online parental behavior (5 items).\u003c/b\u003e This factor captured parental involvement in adolescents\u0026rsquo; online activities, and it includes sharing online experiences with parents, parental boundaries and supervision, and adolescents\u0026rsquo; tendencies to report unpleasant online experiences to their parents. Most items loaded in the moderate-to-strong range (Sample 1: .57-.77; Sample 2: .35-.74). In Sample 2, two parent-disclosure items presented weaker loadings (β\u0026thinsp;=\u0026thinsp;.35-.43), consistent with either heightened between-family variability or sensitivity to item wording.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInternal Consistency.\u003c/b\u003e We examined internal consistency for each latent factor using Cronbach\u0026rsquo;s α in both samples. Additionally, we computed McDonald\u0026rsquo;s ω for both samples to provide a reliability estimate that aligns with the congeneric CFA model (i.e., allows unequal loadings and error variances) and is therefore less biased than α. Estimating ω in each sample enables direct cross-sample comparability of internal consistency for every factor. In Sample 2, standard errors were cluster-robust to account for twin nesting. Sample 1 used conventional estimation because clustering was not present (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Across Factors 1\u0026ndash;3 and Factors 5\u0026ndash;6, internal consistency was generally adequate (\u0026ge;\u0026thinsp;.70), with slightly lower ω in Sample 2 for Factors 1 and 6 (ω\u0026thinsp;=\u0026thinsp;.69 and .65, respectively). Factor 4 showed the weakest internal consistency, particularly by ω (Sample 1: α\u0026thinsp;=\u0026thinsp;.67; ω\u0026thinsp;=\u0026thinsp;.41, Sample 2: α\u0026thinsp;=\u0026thinsp;.75, ω\u0026thinsp;=\u0026thinsp;.54). This lower reliability for Factor 4 likely reflects the broad range of behaviors it captures, spanning from overtly aggressive reactions to mere indifference, which reduces inter-item homogeneity and, consequently, internal consistency. We nevertheless retained Factor 4 due to its clear theoretical relevance (capturing offensive bystander affect distinct from sympathetic reactions), and its replication across samples with acceptable α.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInternal Consistency and Corrected Item\u003c/em\u003e\u0026ndash;\u003cem\u003eTotal Correlation\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF1:\u003c/p\u003e \u003cp\u003eGeneral online activities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF2:\u003c/p\u003e \u003cp\u003eExposure level to harmful content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF3: Sympathetic emotional responses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF4: Offensive emotional reactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF5:\u003c/p\u003e \u003cp\u003eOnline bystanders\u0026rsquo; behavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF6:\u003c/p\u003e \u003cp\u003eOnline parental behavior\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΑ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΩ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrected item-total correlations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.40-.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50-.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.65-.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.37-.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.48-.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.58-.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΑ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΩ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrected item\u0026ndash;total correlations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.22-.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.49-.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.68-.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.40-.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.47-.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.44-.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e α\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha; Ω\u0026thinsp;=\u0026thinsp;McDonald\u0026rsquo;s omega. Values in the corrected item\u0026ndash;total correlations rows represent the range of corrected item\u0026ndash;total correlations across items within each factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrected item\u0026ndash;total correlations were generally adequate to high (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), except for Item 7.6 (Learning/reading online activities) in Sample 2 (CITC\u0026thinsp;=\u0026thinsp;.22). Dropping Item 7.6 would yield only a trivial gain for Factor 1 (a .01 increase in α), and only in Sample 2; α was unchanged in Sample 1 when dropping Item 7.6. In Sample 1, small α increases could have been achieved by removing Item 14 from Factor 2 and Item 19.1 from Factor 4 (.01 and .008 increase in α, respectively). Because these changes were negligible and all factors met conventional internal-consistency thresholds, we retained the original item set.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInter-Factors Correlations.\u003c/b\u003e Composite scores were created for each of the six factors, based on the mean of the items comprising each factor, for both samples. Notably, Factor 1 was computed without Item 2, because Item 2 was measured on an ordinal scale, whereas Items\u003c/p\u003e \u003cp\u003e7.1\u0026ndash;7.6 were measured on an interval scale. In addition, Item 2 and Item 7.1 captured a similar construct. Similar patterns emerged in the two samples. The correlation patterns in the two samples were overall very similar, with a very high, r\u0026thinsp;=\u0026thinsp;.95 matrix correlation between the correlation matrices of the two samples, indicating high consistency in the overall pattern of intercorrelations among the six factors. Examination of the correlations revealed two groups of factors. Factor 3 (\u0026ldquo;sympathetic bystanders\u0026rsquo; emotions\u0026rdquo;), Factor 4 (\u0026ldquo;Offensive Bystanders\u0026rsquo; emotions-reversed\u0026rdquo;), and Factor 5 (\u0026ldquo;Online bystander behavior\u0026rdquo;) showed high positive associations across the two samples (r\u0026thinsp;=\u0026thinsp;.41 \u0026minus;\u0026thinsp;.68, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Factor 6 (\u0026ldquo;online parental behavior\u0026rdquo;) was also positively related to these bystander factors, though more weakly (r\u0026thinsp;=\u0026thinsp;.17 \u0026minus;\u0026thinsp;.29, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Factor 1 (\u0026ldquo;General online activities\u0026rdquo;) and Factor 2 (\u0026ldquo;Exposure level to harmful content\u0026rdquo;) presented positive, though modest, correlations (r\u0026thinsp;=\u0026thinsp;.14 \u0026minus;\u0026thinsp;.17, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). The associations between factors across the two groups were either nonsignificant or negative (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.22 \u0026minus;\u0026thinsp;.03). These associations indicated two largely distinct domains: (a) bystander reactions to cyberbullying, including parental involvement and boundaries and (b) exposure level to harmful content and online activities. Within each domain, factors were positively interrelated, but the domains themselves were only weakly and negatively connected.\u003c/p\u003e \u003cp\u003eInterestingly, Factor 6 (\u0026ldquo;Online parental behavior\u0026rdquo;) showed low positive associations with the bystanders\u0026rsquo; emotional reactions to cyberbullying factors (Factors 3\u0026ndash;5; r\u0026thinsp;=\u0026thinsp;.17-.26, p\u0026thinsp;\u0026lt;\u0026thinsp;.01, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and low to nonsignificant negative correlations with the exposure level to harmful content and general online activities factors (Factors 1\u0026ndash;2; r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.14- \u0026minus;\u0026thinsp;.04). In other words, when adolescents experience higher parental involvement and boundaries in their online lives, they also tend to show more sympathetic emotional and behavioral reactions to cyberbullying. By contrast, online parental behaviors appear largely unrelated, or even slightly negatively related, to adolescents\u0026rsquo; daily screen time or to their level of exposure to harmful content.\u003c/p\u003e \u003cp\u003eTo examine whether a more parsimonious, hierarchical representation of the six factors would provide a better account of the data, a second-order CFA model was specified. In this model, two higher-order latent constructs were defined: \u0026ldquo;exposure level to harmful content and general online activities\u0026rdquo; (loading on Factors 1 and 2) and \u0026ldquo;online bystanders\u0026rsquo; responses to cyberbullying\u0026rdquo; (loading on Factors 3 to 6). The hierarchical model showed a good overall fit in both samples (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Comparing the six-factor model with the hierarchical (second-order) model indicated a significant loss of fit for the hierarchical specification in Sample 1 (Δχ\u0026sup2;(8)\u0026thinsp;=\u0026thinsp;21.47, p\u0026thinsp;=\u0026thinsp;.006). However, the practical impact was small, with ΔCFI\u0026thinsp;=\u0026thinsp;.004, ΔTLI\u0026thinsp;=\u0026thinsp;.002, ΔRMSEA\u0026thinsp;=\u0026thinsp;.000, and ΔSRMR\u0026thinsp;=\u0026thinsp;.003. Moreover, in Sample 2 the difference between the two models was not statistically significant (Δχ\u0026sup2;(8)\u0026thinsp;=\u0026thinsp;13.64, p\u0026thinsp;=\u0026thinsp;.092). Thus, the hierarchical model appears to provide a similarly adequate fit while offering a conceptually more parsimonious structure that aligns with the two broader domains identified in the correlation analyses: (a) coping and emotional regulation, and (b) online exposure and activity. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the hierarchical model\u0026rsquo;s loadings in the two samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInter-Factor Correlations (S1: Top, S2: Bottom)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003cp\u003eGeneral online\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003cp\u003eExposure level to harmful content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003cp\u003eSympathetic emotions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003cp\u003eOffensive emotions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003cp\u003eBystanders\u0026rsquo;\u003c/p\u003e \u003cp\u003ebehavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003cp\u003eOnline parental behavior\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1 (N\u0026thinsp;=\u0026thinsp;308)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. General online activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;.14*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Exposure to online harmful content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Sympathetic emotional responses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.57***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.29***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Offensive emotional reactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.17**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Online bystanders\u0026rsquo; behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.18**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Online parental behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.21 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.11 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.17 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.95 (1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00-9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample 2 (N\u0026thinsp;=\u0026thinsp;309)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. General online activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Exposure level to harmful content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Sympathetic emotional responses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.26***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Offensive emotional reactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.58***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.22***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Online bystanders\u0026rsquo; behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.26***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Online parental behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.09 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.50 (0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00-6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values are Pearson correlations. \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHierarchical CFA Fit Indices for Sample 1 and Sample 2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;(426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e785.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e654.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e χ\u0026sup2; = chi-square; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; SRMR\u0026thinsp;=\u0026thinsp;standardized root mean square residual.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eScale Descriptives.\u003c/b\u003e In addition to examining inter-factor correlations, adolescents\u0026rsquo; online experiences and responses were also evaluated by comparing the average levels of each factor across both samples. Descriptive statistics for the composite scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, separately for the PCA and CFA samples. Factor 2\u0026ndash;4 presented a highly consistent pattern across the two samples, showing nearly identical mean levels and variability. This stability further supports the robustness of these constructs across groups. Slight differences emerged for Factors 1 and 6, which may be attributed to age-related differences in online activities and parental behaviors norms between the two samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe OBQ offers a holistic portrait of adolescents\u0026rsquo; digital lives and online behaviors. Rather than focusing on time or other specific digital aspects, it integrates adolescents\u0026rsquo; daily screen time with the quality features of their online engagements (active vs. passive uses), the contexts surrounding that engagement (perceived parental involvement and boundaries), risky behaviors (exposure to online harmful content), and their in-the-moment behavioral and emotional responses to realistic cyberbullying scenarios. This multi-layered perspective captures mutually reinforcing processes: online engagements patterns that shape exposure to harmful content, family involvement that buffers or amplifies online risky behaviors, and emotions that channel their bystanders\u0026rsquo; behaviors, yielding a deeper, more meaningful understanding of how children and adolescents act, feel, interact, and are influenced in digital environments.\u003c/p\u003e \u003cp\u003eAcross two distinct samples, we replicated a clear six-factor structure covering sympathetic and offensive bystander emotional reactions, bystander behavioral tendencies, general online activities, level of exposure to harmful content, and online parental behaviors. Global fit indices were good in both samples, and factor loadings were largely moderate-to-strong, supporting construct validity. Internal consistency estimates were acceptable for five of the six factors; the comparatively lower consistency of the Offensive Bystanders\u0026rsquo; Emotions factor accords with prior difficulties observed for reverse-keyed affect items and likely reflects heterogeneity in disengaged or minimizing reactions to cyberbullying rather than mere measurement noise. Together, these results indicate that the OBQ captures a theoretically meaningful and practically actionable variation in adolescents\u0026rsquo; online engagements.\u003c/p\u003e \u003cp\u003eFrom a measurement standpoint, the OBQ\u0026rsquo;s design advances the field in three ways. First, it integrates previously separated strands of assessment: screen time and activities, exposure to harmful content, parental mediation, and bystander functioning. These differential aspects of online engagements are commonly studied in isolation, thereby supporting integrated models of risk and resilience. Second, it captures bystander emotions and behaviors in ecologically valid scenarios, which likely elevates predictive relevance for real-world responding beyond generic attitudinal items. Third, it provides replicable structure as well as adequate reliability across demographically distinct adolescent samples, an essential prerequisite for longitudinal and cross-context applications (e.g., program evaluation, policy-relevant surveillance).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParental Behaviors and Adolescents\u0026rsquo; Online Engagements\u003c/h2\u003e \u003cp\u003eConsistent, though modest, positive association between parental online behaviors and sympathetic bystander reactions, coupled with negligible or slightly negative links to the activities/exposure domain, aligns with family-process perspectives that distinguish between quality of online mediation and quantity of use. In other words, parents that cultivate clear rules and are emotionally open to dialogue may nurture adolescents who experience stronger sympathetic emotions when witnessing cyberbullying and who are more behaviorally inclined to help, even if overall screen exposure does not change significantly. This pattern also underscores a potential buffering role of positive parental mediation for emotion regulation in the face of online harm.\u003c/p\u003e \u003cp\u003eIt is important to recognize that associations between parenting and adolescent online behaviors are likely bidirectional. Parents may shape their children\u0026rsquo;s norms as well as their online responses as bystanders, but adolescents\u0026rsquo; own dispositions, distress, or online activities can also evoke and reshape their parents\u0026rsquo; involvement and boundaries over time. In other words, what looks like a parental \u0026ldquo;effect\u0026rdquo; at one time point may partly reflect a child-driven influence on parenting practices. Future work should therefore use longitudinal designs to test directional pathways and reciprocal dynamics between parents and adolescents\u0026rsquo; online behaviors, and to examine when supportive mediation buffers risk versus when adolescents\u0026rsquo; behaviors aim for tighter control. Thus, socialization is a two-way process in which children actively influence their caregiving environment (Davidov et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTwo-Domain Architecture of Digital Experience\u003c/h2\u003e \u003cp\u003eA central substantive finding concerns the measurement\u0026rsquo;s higher-order organization. Inter-factor correlations converged on two coherent domains: (1) bystander coping and emotional regulation (sympathetic emotions, offensive emotions, behavioral reactivity) that also clusters with parental behaviors, and (2) online exposure and activities (exposure to harmful content and general online engagements). The replication of this pattern across samples, together with adequate fit of a second-order CFA, suggests that adolescents\u0026rsquo; online reactions to harmful content form a domain that is relatively distinct from their overall level or style of online activity. Practically, this implies that interventions seeking to improve bystander functioning (e.g., enhancing empathic concern, increasing defending behaviors, reducing moral disengagement) can be targeted without necessarily attempting to reduce overall online activities.\u003c/p\u003e \u003cp\u003eResults highlight opportunities for refinement: The weaker and less stable loadings for some items within the Offensive Emotions factor suggest testing alternative wording (e.g., direct endorsement of indifference/disengagement without humor cues), adding additional indicators that capture moral disengagement and trivialization, and considering method factors for reverse-coded items in future CFAs. In the online activity domain, the \u0026ldquo;learning/reading online\u0026rdquo; indicator showed lower item-total correlations, possibly reflecting broad variability in what adolescents consider \u0026ldquo;learning\u0026rdquo; (formal vs. informal, etc.). Future studies may differentiate school-mandated versus elective learning and short-form versus long-form content to improve construct precision. Finally, the hierarchical representation invites theoretically informed pathways: Parental involvement may shape bystander emotions and behaviors through empathy-related processes and norms of responsibility, whereas general activity levels and exposure to harmful content may be governed more by opportunity structures and platform ecologies. This separation suggests two complementary levers for policy and practice: (a) skills and climates (family-school programming to cultivate more empathic bystanders), and (b) environments and safeguards (content moderation literacy, safety-by-design, and exposure management).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths of the OBQ.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe OBQ is ecological valid, and items are embedded in realistic screenshot scenarios reflecting appearance-based ridicule, academic shaming, and body-related mockery, common triggers of adolescent online harassment as well as a general \u0026ldquo;what is right to do\u0026rdquo; vignette. The questionnaire integrates the quantity and quality of adolescents\u0026rsquo; online activities and online harmful exposure, parental behaviors and bystander feelings and behaviors without becoming unwieldy. The OBQ has proven to have a replicated structure, with the six-factor solution and inter-factor patterns replicated across an independent twin sample, with appropriate cluster-robust estimation. A central contribution of the OBQ is to bridge between what adolescents do online (daily screen time, quality of online activities and exposure to harmful content) with their online bystanders\u0026rsquo; feelings and behaviors when they witness cyberbullying or shaming. Many established questionnaires emphasize daily screen time or problematic and addictive online tendencies. Other target bystander roles or intentions without simultaneously measuring general engagement, exposure, and parental processes. The OBQ integrates these online behavioral domains within a single, brief tool and enables the measurement of bystander items in ecologically rich screenshots derived from real adolescent online interactions.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePractical implications.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWith 43 closed items that include three real-based cyberbullying scenarios, the OBQ is brief enough for school screening and clinical intake while retaining multidimensional coverage. It can be used to evaluate digital profile activities and bystander tendencies, as well as identifying potential bystander-helpers \u0026ndash; those experiencing strong sympathetic feelings but lacking action strategies. The OBQ can help map potential roles of bystanders and help build direct psycho-educational programs to encourage potential helpers as well as directing potential offensive-bystanders. The OBQ can be used to evaluate children and adolescents\u0026rsquo; digital citizenship and bystander activation programs by tracking domain-specific change.\u003c/p\u003e \u003cp\u003eAs mentioned above, one of the strengths of the OBQ is the way it separates offensive and sympathetic bystanders\u0026rsquo; feelings and behaviors. This advantage can lead to tailor-made educational programs, after initially evaluating the different roles of online bystanders within a group of people. For example, emotion-to-action bridging programs for adolescents who feel distress but hesitate to intervene, or norm-setting and moral reframing programs for more offensive bystanders\u0026rsquo; tendencies. Above all, it may be a directing tool for designing online efficacy skills (what to write, who to tell, safe reporting, etc.) for those willing but unsure how to act. Parental directing programs can focus on communication and monitoring practices that relate to stronger emotional atonement and helping intentions, as well as getting deeper acquaintances and communicating with adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations should inform interpretation and future refinement. First, the data was cross-sectional with self-reports, limiting causal inference and raising the possibility of shared-method variance. Second, some reverse-keyed items of bystanders\u0026rsquo; feelings exhibited comparatively lower internal consistency, suggesting potential gains from rewording or adding positively keyed indicators of disengagement/minimization to mitigate response-style artifacts. Third, despite the real-life-based scenarios and their ecological validity, their cultural and platform specificity may limit generalizability: norms around humor, shaming, and gendered content can vary across communities and platforms. Fourth, while activity items span several modalities, more granular differentiation of active vs. passive uses (and prosocial vs. competitive gaming; creative vs. consumptive video engagement) could sharpen predictive precision. Fifth, although the twin sample supports replication and modelling with clustered data, broader cross-cultural and age band replications (early vs. late adolescence) are yet to be tested.\u003c/p\u003e \u003cp\u003eFuture directions should enable longitudinal and experimental validation, testing whether parental processes and level of exposure to harmful content can predict changes in bystanders\u0026rsquo; feelings and behaviors, and whether OBQ scales are sensitive to intervention (such as empathy training, bystander efficacy workshops, etc.). Future measurement invariance can also expand across gender, age, religiosity, socioeconomic status, and cultural groups. Further research should also try and isolate passive and active engagements and their potential influence on bystanders\u0026rsquo; feelings and behaviors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Online Behavior Questionnaire offers a psychometrically sound, ecologically valid, and practical assessment of adolescents\u0026rsquo; online behaviors and feelings, which uniquely integrates between six domains: (a) quantity and quality of online engagements, (b) level of exposure to online harmful content, (c) sympathetic bystanders\u0026rsquo; feelings, (d) offensive bystanders\u0026rsquo; feelings, (e) online bystanders\u0026rsquo; behaviors and (f) online parental behaviors. The replicated six-factor structure, acceptable reliability, and theoretically coherent inter-factor architecture provide a strong foundation for research that moves beyond daily screen time toward understanding how adolescents feel and act when witnessing cyberbullying, and how family processes and parental involvement patterns shape these responses. As schools and communities seek to foster prosocial bystander engagement and safer online climates, the OBQ can serve both as a diagnostic lens (who needs what kind of support?) and as an outcome metric for evaluating targeted interventions. With continued refinement and cross-cultural extension, the OBQ is well positioned to become a core instrument for studying and improving adolescents\u0026rsquo; well-being in the digital century.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThis research was reviewed and approved by the Hebrew University Social Sciences Research Ethics Committee. All procedures complied with institutional guidelines and the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eParticipation was voluntary and respondents could discontinue at any time without penalty. Parents or legal guardians provided informed consent, and adolescents provided assent prior to data collection through the research panel\u0026rsquo;s online system.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eParticipants and their guardians were informed that findings would be reported in aggregate form only. No identifying information is disclosed in this manuscript, and consent to publish anonymized findings was obtained as part of the consent procedure.\u003c/p\u003e \u003ch2\u003eFunding Declaration:\u003c/h2\u003e \u003cp\u003eNot applicable. The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.C.H.: Conceptualization, methodology, investigation, writing of original draft, visualization, project administration.H.S.: Investigation, methodology, formal analysis, data curation, prepared all figures and tables, writing (review and editing).A.K.N.: Conceptualization, methodology, supervision, writing (review and editing).\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our deepest gratitude to all adolescents and parents who generously agreed to participate in this study and facilitated the data collection. We are also grateful to the research assistants who supported questionnaire administration, data entry, and initial coding. Their help was invaluable to the completion of this project.Last, we would like to thank the department of Psychology in the Hebrew University of Jerusalem, for providing the necessary resources and facilities that made this research possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson, M., Faverio, M., \u0026amp; Gottfried, J. 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F., Kansok-Dusche, J., Krause, N., \u0026amp; Ballaschk, C. (2022). Associations between witnessing and perpetrating online hate speech among adolescents: Testing moderation effects of moral disengagement and empathy. \u003cem\u003ePsychology of Violence\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(6), 371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/vio0000422\u003c/span\u003e\u003cspan address=\"10.1037/vio0000422\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bullying-prevention","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbp","sideBox":"Learn more about [International Journal of Bullying Prevention](https://rd.springer.com/journal/42380)","snPcode":"42380","submissionUrl":"https://submission.springernature.com/new-submission/42380/3","title":"International Journal of Bullying Prevention","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cyberbullying, bystanders, online behavior, adolescents, parental mediation, questionnaire, validation","lastPublishedDoi":"10.21203/rs.3.rs-8256781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8256781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Online Behavior Questionnaire (OBQ) was developed as a brief, multidimensional instrument to assess adolescents\u0026rsquo; digital behaviors that are directly relevant to bullying prevention: general online activities, exposure to harmful content, parental online involvement, and bystanders\u0026rsquo; feelings and behaviors in response to ecologically valid cyberbullying scenarios. Two adolescent samples (total N\u0026thinsp;=\u0026thinsp;617) were used to establish the OBQ\u0026rsquo;s structure and psychometric properties: a panel sample of 13-17-year-olds (Study 1; N\u0026thinsp;=\u0026thinsp;308) and a twin-based sample of 17-19-year-olds (Study 2; N\u0026thinsp;=\u0026thinsp;309). Confirmatory factor analyses in both samples, estimated in R (lavaan) with cluster-robust standard errors for the nested twin data and residual correlations for parallel items, supported a replicated six-factor model with adequate global fit indices and predominantly moderate-to-strong item loadings. Internal consistency was acceptable for most factors (α\u0026thinsp;=\u0026thinsp;.67\u0026ndash;.89), with weaker reliability for offensive bystander emotions when indexed by McDonald\u0026rsquo;s ω. Parental online behaviors showed positive associations with sympathetic emotions and defending intentions, and small negative or negligible associations with general online activity and exposure to harmful content. The OBQ appears suitable for research, school screening, and program evaluation that seek to identify at-risk digital profiles, activate supportive bystanders, and inform policies and interventions aimed at creating safer online climates for adolescents.\u003c/p\u003e","manuscriptTitle":"Mapping Digital Adolescence: Assessing online activities and responses to cyberbullying with the Online Behavior Questionnaire","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 06:09:16","doi":"10.21203/rs.3.rs-8256781/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T15:43:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T11:30:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126197770718124066729253698356189382718","date":"2026-04-15T11:23:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T16:35:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24244688814890453494408108191395584868","date":"2026-02-03T14:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169945783129097970477433331456047053996","date":"2026-01-20T18:37:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T16:50:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T05:34:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T05:33:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Bullying Prevention","date":"2025-12-02T06:38:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bullying-prevention","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbp","sideBox":"Learn more about [International Journal of Bullying Prevention](https://rd.springer.com/journal/42380)","snPcode":"42380","submissionUrl":"https://submission.springernature.com/new-submission/42380/3","title":"International Journal of Bullying Prevention","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"baae9313-cdc3-46a8-a7d0-e1e32fcd80d4","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T14:09:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 06:09:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8256781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8256781","identity":"rs-8256781","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}