Psychometric Validation of the Arabic Saudi Youth Attitude Toward Addiction Scale Using Exploratory Graph Analysis

Psychometric Validation of the Arabic Saudi Youth Attitude Toward Addiction Scale Using Exploratory Graph Analysis | 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 Psychometric Validation of the Arabic Saudi Youth Attitude Toward Addiction Scale Using Exploratory Graph Analysis Fatma Khalifa Elsayed, Mahmoud Ali Moussa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9227492/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The direct application of Western-developed psychological scales in distinct sociocultural contexts risks ecological invalidity by imposing foreign factor structures and failing to capture locally salient constructs. This study addressed this critical gap by developing and validating a culturally grounded instrument to assess addiction-related attitudes among Saudi youth. Utilizing a sequential exploratory-confirmatory design, we first employed Exploratory Graph Analysis (EGA), a network psychometric method, to allow the latent attitudinal structure to emerge organically from a sample of 537 Saudi university students. Contrary to the a priori nine-dimensional theoretical model derived from integrated behavioral theories, EGA revealed a robust six-factor structure. This empirically derived model was subsequently validated via Confirmatory Factor Analysis, demonstrating excellent fit (CFI = .992, TLI = .991, RMSEA = .053) and superior psychometric properties compared to the theoretical structure. The emergent dimensions, including syntheses such as Motivation and Calculated Risk, illustrate a culturally specific cognitive architecture where personal curiosity, religious transgression, and familial honor are psychologically fused, a holistic schema not represented in disaggregated Western models. The resultant Saudi Youth Attitude Toward Addiction Scale (SYAAS) provides a reliable, valid, and ecologically sound measurement tool. It enables a paradigm shift from generic, imported assessment to precise, culturally resonant diagnosis, facilitating the design of targeted interventions that engage the actual psychosocial mechanisms operating within Saudi Arabia's unique socio-religious context. This research underscores the necessity of data-driven, culturally adaptive methodologies in global public health psychology. Exploratory Graph Analysis (EGA) Addiction attitudes Saudi youth Scale validation Network psychometrics Figures Figure 1 1. Introduction This study is driven by the critical need for a culturally grounded psychometric instrument to assess addiction-related attitudes among Saudi youth, necessitated by the substantial limitations of directly applying Western-developed scales within this distinct sociocultural context. While comprehensive theoretical frameworks provide a robust conceptual foundation for understanding attitudes as multidimensional constructs shaped by cognitive, affective, and conative components interacting with familial, peer, and societal systems, the operationalization of these constructs requires cultural specificity. Imported, translated instruments inherently carry an etic (imposed) framework that risks ecological invalidity, thereby generating data of questionable validity for the Saudi population. To transcend this limitation and allow the latent structure of attitudes to emerge organically from the cultural data, this study employs Exploratory Graph Analysis (EGA) as its primary psychometric methodology. This advanced, data-driven technique enables a rigorous test of whether the nine theoretically derived and culturally adapted domains form the hypothesized psychometric structure or reveal a novel, culturally specific architecture, thereby providing a robust, valid structure for subsequent research, precise needs assessment, and the development of effective, culturally resonant prevention strategies in Saudi Arabia. 2. Theoretical Framework and Literature Review 2.1. Addiction, Attitudes, and the Vulnerability of Youth Addiction, conceptualized as a chronic brain disorder characterized by impaired control over reward-seeking behavior despite harmful consequences, arises from a complex interaction between individual neurobiological vulnerabilities and psychosocial environmental risk factors [ 5 ]. Within this framework, psychological attitudes are not merely transient feelings but relatively stable cognitive-affective-behavioral formations. Shaped through experience and social learning, they guide information processing, incentive evaluation, and response execution toward specific entities or behaviors, such as substance use [ 2 ]. Investigating these attitudes among youth, specifically a university student sample, is therefore not an arbitrary choice but a methodological and theoretical necessity. The university years, coinciding with emerging adulthood, represent a peak period of vulnerability. During this stage, sensation-seeking behaviors and openness to novel experiences are heightened, while the prefrontal cortical functions responsible for deliberate judgment, emotional regulation, and consideration of long-term consequences are still undergoing final maturation [ 6 ]. This developmental asynchrony creates a “maturity gap” that renders this demographic particularly sensitive to peer influence and emotional cues, consequently increasing their susceptibility to forming positive attitudes towards high-risk behaviors like substance use [ 7 ]. 2.2. The Multidimensional Nature of Attitudes Toward Addiction According to established theories such as the Theory of Planned Behavior and the Health Belief Model, attitudes constitute the explanatory core of behavioral intention, which is considered the most proximal antecedent to actual behavior [ 1 , 2 , 8 ]. However, the latent structure of attitudes towards a complex, multi-layered issue like addiction is not unidimensional. As manifested in the current scale, it encompasses an interconnected network of evaluations. This includes the cognitive component, comprising factual beliefs about consequences (e.g., perceiving that continued use complicates treatment), which is functionally linked to expectancy-value theories where individuals assess behavior based on anticipated outcomes and their subjective value [ 9 ]. Intertwined with this is the affective dimension, measuring emotional responses (such as empathy towards individuals with addiction or feelings of stigma), where emotions play a crucial role in risk processing and attentional guidance, often overriding purely rational calculations [ 10 ]. Finally, behavioral intentions, reflected in commitments to avoid risks or engage in preventive activities, represent the outcome that mediates the relationship between internal evaluations and overt behavior. These intentions are critically influenced by the degree of perceived self-efficacy, the confidence in one’s ability to execute the required behavior in the face of obstacles [ 11 , 12 ]. Consequently, developing a scale based on theoretically and empirically distinct dimensions, such as risk awareness, personal beliefs, social stigma, behavioral intentions, psychological motives, peer influence, familial factors, perceived availability, and self-efficacy, is not a methodological excess but a necessary response to the reality that influencing factors in addiction operate at multiple levels. Internal factors (e.g., psychological motives, self-efficacy) continuously interact with immediate social-contextual factors (peer influence, familial support), which are themselves embedded within a broader environment shaped by societal norms and substance accessibility [ 3 ]. Ignoring any of these strata in measurement yields a truncated model incapable of capturing the true complexity of the phenomenon, thereby undermining the efficacy of any preventive intervention built upon its findings. 2.3. The Challenge of Cross-Cultural Measurement: Emic, Etic, and the Risk of Imposed Frameworks The methodological distinction between the emic (culture-specific, internal) and etic (culture-comparative, external) approaches presents a central dilemma in cross-cultural psychology and psychological assessment. The etic approach, as outlined by Berry [ 13 ], assumes the universality of psychological phenomena and concepts, advocating for the transfer of measurement tools developed in one culture (e.g., Western contexts) and the imposition of their structure onto another after translation. This risks ecological validity and may obscure locally unique, potentially more explanatory dimensions. In contrast, the emic approach focuses on understanding phenomena from within the cultural system itself, employing concepts and constructs that emerge organically from the local context, though this may limit normative comparability. Research in the Arab region often falls into the trap of over-reliance on the etic approach through the importation of ready-made scales, or the pitfall of a purely emic approach by designing entirely local tools lacking a comparative theoretical framework. The direct application of Western-developed psychological scales in distinct sociocultural contexts, therefore, risks ecological invalidity by imposing foreign factor structures and failing to capture locally salient constructs. In the Saudi context, this scientific and practical endeavor is undermined by a persistent over-reliance on translated Western psychological scales. These imported instruments, grounded in individualistic cultural frameworks, are applied without sufficient evidence of their cross-cultural validity, creating a profound methodological mismatch for the conservative, collectivist Saudi context. This practice, rooted in an etic (imposed) rather than an emic (indigenous) approach, fails to capture the unique, complex interplay of socio-cultural, religious, and familial determinants that shape attitudes toward addiction in Saudi society. For instance, dominant Western constructs inadequately operationalize the local experience of stigma, where shame is deeply entangled with religious transgression and collective familial honor; they undervalue the central protective role of family cohesion and parental authority; and they cannot capture how self-efficacy is calibrated within tightly knit social networks rather than conceptualized as an individual attribute. 2.4. Consequences and the Need for a Culturally Grounded Instrument The consequences of this reliance on potentially invalid metrics are threefold. First, a theoretical-measurement gap persists, wherein existing tools do not correspond to the lived reality and value systems of the target population. Second, an empirical knowledge gap remains, as the lack of a validated instrument leaves researchers and policymakers without a reliable baseline to identify vulnerabilities or evaluate interventions. Third, a translational application gap emerges, wherein public health initiatives are built upon an unstable evidence base, resulting in inefficient resource allocation and the design of prevention programs that are culturally dissonant and therefore likely to have limited efficacy. Thus, the development of effective, evidence-based prevention strategies for substance use among Saudi youth is critically impeded by a fundamental methodological impasse: the absence of a culturally valid and psychometrically robust instrument to assess addiction-related attitudes within this specific population. 3. Rationale and Aims of the Current Study To transcend the limitations of imported scales, the present study adopts an advanced integrative stance that moves beyond the simple emic-etic binary. Guided by the model of dynamic cultural adaptation proposed by Van de Vijver and Tanzer [ 14 ], we do not commence with a ready-made Western scale (pure etic) nor from a cultural vacuum (pure emic). Instead, we depart from a comprehensive, multi-dimensional theoretical framework of addiction derived from global literature (encompassing cognitive, affective, behavioral, peer, familial, and self-efficacy aspects) and subject this framework to a process of contextual reshaping. Through this process, item formulations are crafted to embody the expressions of these dimensions as they manifest within the social, religious, and psychological reality of Saudi society. For instance, the dimension of “stigma” is not measured solely by Western concepts of individual shame but is expressed through items reflecting the intertwining of religious transgression with collective familial dishonor, thereby embodying a theoretical synthesis between the universal structure of stigma and the local content of the experience [ 15 ]. To ensure the scientific rigor of this synthesis, the study moves beyond simple translation or superficial adaptation by employing an advanced network psychometric technique: Exploratory Graph Analysis (EGA). While classical methods like Exploratory Factor Analysis (EFA) assume a universal latent structure and test how well data conform to it, EGA, as elucidated by Golino and Epskamp [ 4 ], allows the latent structure to emerge from the data itself without strong a priori assumptions about the number or independence of factors. Applying this methodology to a sample of Saudi university students is not merely a technical procedure but a practical embodiment of integrating emic and etic perspectives. The resulting network of item correlations will reveal whether the nine contextually developed theoretical dimensions appear as distinct communities within the Saudi respondents’ mindset or whether they merge into new configurations. For example, it might show that the dimensions of risk knowledge and behavioral intentions are heavily intertwined in the Saudi network, suggesting that knowledge translates directly into protective intention within this context, a linkage potentially weaker in other cultures. This data-driven, network-based analysis provides empirical evidence for the construct validity of the culturally adapted version, thereby addressing criticisms leveled against translated measures regarding their lack of measurement invariance or dimensional misfit in novel cultural settings [ 16 ]. Therefore, building a robust evidence base through this psychometrically adapted and EGA-validated tool is foundational for developing informed preventive policies. It facilitates a shift from generic, imported, or context-blind prevention programs to culturally precise interventions that target the key nodes within the cognitive-affective network of Saudi youth. For instance, if the analysis reveals that ‘fear of peer rejection’ is strongly linked to ‘low self-efficacy’ more than to abstract knowledge of risks, this will guide practitioners to design programs that enhance refusal skills and psychological resilience rather than relying solely on informational fear appeals. This approach is fully congruent with modern models of addiction prevention, which emphasize the dialectical interaction between the individual (with their beliefs and competencies) and the context (with its pressures and resources) as a single unit of analysis, representing the core of contextual and ecological psychology as advocated by Bronfenbrenner [ 3 ] and a principle underscored in U.S. federal prevention research guidelines [ 17 ]. Based on the identified problem and the proposed methodological approach, this study seeks to answer the following research questions: What is the latent dimensional structure of addiction-related attitudes among Saudi youth when allowed to emerge empirically through Exploratory Graph Analysis, and how does this structure compare to the nine-dimensional theoretical framework derived from integrated behavioral theories? To what extent does the empirically derived six-factor model, identified through EGA, demonstrate superior psychometric properties, including model fit, reliability, and convergent validity, compared to the hypothesized nine-factor theoretical model when subjected to confirmatory factor analysis? Does the Saudi Youth Attitude Toward Addiction Scale (SYAAS), as structured by the empirically derived dimensional model, provide a reliable and valid measurement tool for assessing cognitive, affective, and behavioral dispositions toward substance use within the Saudi cultural context? 4. Methodology 4.1 Research Design and Approach This study employed a sequential exploratory-confirmatory design for scale development and validation. The design consisted of three phases: (1) an exploratory phase using Exploratory Graph Analysis (EGA) to allow the latent structure of attitudes to emerge from the data without imposing a predefined factor model; (2) a confirmatory phase testing the EGA-derived model using Confirmatory Factor Analysis (CFA); and (3) a comparative phase pitting the empirically derived model against the original nine-dimensional theoretical model to determine which provided superior fit. 4.2. Participants The sample comprised 537 Saudi university students (Mage- = 21.4 years, range = 20–32), recruited via snowball sampling through a digital survey distributed at King Abdulaziz University. The sample included 269 females and 268 males. Most participants lived within intact family units: 90.1% resided with their nuclear family, 88.5% reported their parents were not separated, and nearly all reported both parents as living (father: 88.5%; mother: 97.2%). Approximately half (49%) had previously attended addiction awareness programs. A minority reported current tobacco use (11.9%), with smaller proportions using e-cigarettes (10.1%), traditional cigarettes (1%), or waterpipes (1.2%). 4.3. Tools The present study employed a rigorous, multi-phase process to develop and validate the Saudi Youth Attitude Toward Addiction Scale (SYAAS). The instrument was constructed based on an integrated theoretical framework that synthesizes the Theory of Planned Behavior [ 2 ]. which posits that attitude is a precursor to behavioral intention, and a socio-ecological perspective [ 3 ]. which emphasizes the nested influence of micro- and macro-systemic factors. This synthesis posits that addiction-related attitudes are multidimensional constructs, comprising interconnected cognitive, affective, and conative components, all shaped by and enacted within a specific cultural milieu. To operationalize this framework, an initial item pool was deductively generated to target nine theoretically and empirically grounded domains critical to understanding substance use vulnerability within collectivist societies. Each dimension was selected based on robust psychological theory and prior evidence. The Knowledge and Awareness of Risks dimension is grounded in the Health Belief Model [ 20 , 21 ]. where knowledge of risks is a fundamental determinant of attitude formation; low awareness is consistently linked to higher experimentation among youth [ 22 , 23 ]. The Personal Beliefs and Attitudes dimension is central to the Theory of Planned Behavior [ 1 ], as erroneous beliefs (e.g., about ease of quitting) increase initiation risk [ 24 , 25 ]. and tolerant attitudes predict use in Gulf populations [ 26 ]. The Social Attitudes and Stigma dimension derives from Social Stigma Theory [ 27 ], where stigma delays treatment and increases relapse, particularly in conservative societies [ 28 , 29 ], and is a noted treatment barrier in Saudi Arabia [ 30 ]. The Behavioral Intentions and Prevention dimension stems directly from the Theory of Planned Behavior, wherein intention is the strongest proximal predictor of behavior [ 1 , 2 ]. preventive intentions like refusing first offers are associated with lower substance use rates [ 31 , 32 ]. The Psychological Motives for Use dimension reflects the premise that substance use serves to alleviate psychological distress, anxiety, and depression [ 33 ]. The Peer Influence dimension is supported by Social Learning Theory [ 11 ], with peer use being one of the strongest predictors of initiation [ 22 , 34 ], a dynamic confirmed in Arab settings. The Familial Factors dimension draws on Family Systems Theory (Haefner, 2014), where weak monitoring and dysfunction elevate risk [ 35 , 36 , 37 ]. while family support is a key protective factor in Saudi society. The Perceived Availability dimension is rooted in Ecological Models [ 3 ], as perceived ease of access increases use [ 38 ], making it a target for prevention [ 39 ]. Finally, the Self-Efficacy for Resistance dimension is based on [ 11 ]. concept, where higher self-efficacy predicts lower use and better resistance to peer pressure [ 40 , 41 ]., forming a cornerstone of modern prevention [ 42 ]. Following this theoretical operationalization, a multi-step cultural adaptation protocol was implemented, employing the methodological framework for cross-cultural adaptation proposed by [ 43 ]. This involved independent forward- and back-translation by bilingual experts to establish semantic equivalence, followed by a review by a panel of five experts in clinical psychology and public health to evaluate content validity, conceptual relevance, and cultural appropriateness for Saudi society. Items were rated for clarity and relevance, and the scale-level content validity index (S-CVI/Ave) was calculated to ensure adequacy. This process yielded the preliminary 45-item SYAAS, with five items per hypothesized dimension, each rated on a 5-point Likert scale. 4.4. Statistical analysis All analyses were conducted in R 4.5. Descriptive statistics and reliability analyses (Cronbach's α) were computed for all items and subscales. Exploratory Graph Analysis (EGA) was performed using the EGAnet package. An EBICglasso-regularized partial correlation matrix was estimated, and community detection was conducted using the walktrap algorithm. This approach models the network of item relationships and identifies dimensional clusters without imposing a predefined factor structure. Stability of the emergent dimensional structure was assessed via a 500-sample parametric bootstrap, yielding metrics of structural consistency (proportion of bootstrap samples in which all items within a dimension remained together) and item stability (proportion of times each item was assigned to its modal dimension). The EGA-derived model and the original nine-factor theoretical model were then subjected to Confirmatory Factor Analysis using the lavaan package. The theoretical model was estimated with Maximum Likelihood (ML), while the EGA-derived model was estimated using Diagonally Weighted Least Squares (DWLS) to account for ordinal item-level data. Model fit was evaluated using standard indices: χ², Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Composite reliability (CR) and average variance extracted (AVE) were calculated for each dimension in the final model. 5. Results 5.1. Preliminary analysis: The psychometric structure of the Arabic version of the Saudi Youth Attitude Toward Addiction Scale (SYAAS) was examined using a sample of 537 participants. Initial descriptive analyses revealed that all 45 items exhibited adequate variability, with mean scores ranging from 3.85 to 4.64 on the 5-point scale and standard deviations spanning from 0.65 to 1.10, indicating sufficient dispersion for subsequent network analysis. As presented in Table 1 , the item-level distributions demonstrated notable negative skew, consistent with a ceiling effect where responses were clustered toward the positive end of the scale. Corrected item-total correlations ranged from .48 to .78, confirming that each item shared a meaningful relationship with the overall scale score. The scale demonstrated excellent overall internal consistency, with Cronbach’s α = .95. Table 1 Descriptive Statistics and Item-Level Reliability for the Arabic Version of the SYAAS (N = 537) Item M SD Skewness Kurtosis Corrected Item–Total Correlation Q1 4.56 0.75 -1.99 4.28 0.65 Q2 4.64 0.71 -2.52 7.54 0.68 Q3 4.49 0.66 -1.24 1.76 0.58 Q4 4.57 0.75 -2.15 5.49 0.66 Q5 4.52 0.75 -1.84 4.03 0.64 Q6 4.37 0.81 -1.25 1.35 0.55 Q7 4.20 0.89 -0.95 0.25 0.59 Q8 4.15 0.89 -0.95 0.54 0.52 Q9 4.20 0.82 -0.82 0.17 0.54 Q10 4.54 0.67 -1.62 3.57 0.63 Q11 4.55 0.74 -1.78 3.16 0.62 Q12 3.85 1.03 -0.59 -0.45 0.48 Q13 3.86 1.10 -0.81 0.03 0.49 Q14 4.12 0.97 -0.97 0.22 0.50 Q15 4.37 0.76 -1.18 1.62 0.53 Q16 4.51 0.70 -1.41 1.94 0.70 Q17 4.58 0.68 -1.69 2.80 0.71 Q18 4.50 0.75 -1.79 4.12 0.69 Q19 4.14 0.97 -1.14 0.98 0.66 Q20 4.52 0.74 -1.58 2.45 0.72 Q21 4.24 0.88 -1.16 1.15 0.76 Q22 4.33 0.77 -0.96 0.32 0.77 Q23 4.25 0.84 -1.06 1.00 0.78 Q24 4.25 0.84 -1.10 0.98 0.78 Q25 4.27 0.90 -1.48 2.30 0.76 Q26 4.37 0.86 -1.53 2.20 0.70 Q27 4.31 0.82 -1.31 1.93 0.72 Q28 3.93 1.06 -0.82 -0.06 0.69 Q29 4.21 0.89 -1.14 0.97 0.71 Q30 4.36 0.79 -1.39 2.47 0.71 Q31 4.38 0.76 -1.09 0.61 0.60 Q32 4.34 0.84 -1.28 1.46 0.58 Q33 4.45 0.75 -1.49 2.54 0.60 Q34 4.29 0.81 -0.98 0.41 0.63 Q35 4.48 0.74 -1.52 2.61 0.60 Q36 4.27 0.85 -1.26 1.59 0.64 Q37 4.36 0.80 -1.37 2.13 0.60 Q38 4.15 0.92 -1.06 0.92 0.62 Q39 3.99 1.01 -0.89 0.25 0.62 Q40 4.20 0.79 -0.75 0.41 0.63 Q41 4.55 0.73 -2.01 5.17 0.77 Q42 4.51 0.71 -1.74 4.15 0.76 Q43 4.52 0.77 -1.92 4.35 0.78 Q44 4.56 0.73 -1.91 4.28 0.77 Q45 4.57 0.65 -1.57 2.66 0.74 Note. M = Mean; SD = Standard Deviation. Total Scale α = .95 5.2. Exploratory Graph Analysis (EGA) and Dimensional Stability: To elucidate the scale's latent structure, Exploratory Graph Analysis (EGA) was employed using the EBICglasso regularization model with the walktrap community detection algorithm. The analysis yielded a network of 45 nodes (items) connected by 296 non-zero edges, corresponding to an edge density of 0.30. Contrary to the originally hypothesized nine-factor model, the EGA solution supported a more parsimonious six-dimensional structure. The specific item assignments for each emergent dimension are detailed in Table 2 . Table 2 Exploratory Graph Analysis (EGA) Dimension Structure, Stability, and Comparative Reliability EGA Dimension New Items Assigned Structural Consistency Median Item Stability Cronbach’s α (Original 9-Factor) 1 Q1, Q2, Q4, Q5, Q10, Q11, Q16, Q17, Q18, Q19, Q20 0.50 0.74 0.78 (D1), 0.71 (D2), 0.83 (D7) 2 Q3, Q6, Q8, Q9, Q12, Q13, Q14, Q15 0.09 0.56 0.67 (D3) 3 Q7, Q21, Q22, Q23, Q24, Q25 0.88 0.97 0.87 (D5) 4 Q26, Q27, Q28, Q29, Q30, Q34, Q36, Q38, Q39, Q40 0.55 0.82 0.84 (D6), 0.80 (D8) 5 Q31, Q32, Q33, Q35, Q37 0.42 0.46 0.83 (D7) 6 Q41, Q42, Q43, Q44, Q45 0.95 0.98 0.86 (D9) Note. Structural Consistency: Proportion of bootstrap samples (out of 500) in which all items within the dimension remained together. Then, Median Item Stability: The median proportion of bootstrap samples in which each item in the dimension was assigned to the same dimension. Finally, Cronbach’s α (Original 9-Factor): Reliability for the predefined theoretical subscales containing the EGA dimension's items, provided for comparative reference. D1-D9 refer to the originally hypothesized nine subscales. Exploratory Graph Analysis (EGA) was performed using the EBICglasso regularization model with the walktrap community detection algorithm. The results supported a multidimensional structure, identifying six distinct dimensions rather than the originally hypothesized nine. The network comprised 45 nodes and 296 non-zero edges (density = 0.30). To assess the stability and reproducibility of this 6-dimensional solution, a parametric bootstrap EGA was conducted with 500 resamples. The bootstrap median dimensionality was 6 (95% CI [3.78, 8.22]), with this solution appearing in 36.2% of the replicates, making it the most frequent outcome. The structural consistency of the dimensions (the proportion of times items within a dimension were replicated together across bootstrap samples) varied, with Dimension 6 showing excellent consistency (.95), Dimensions 3 and 4 good consistency (.88 and .55, respectively), and Dimension 2 low consistency (.09). Item stability analysis, which indicates the proportion of times an item was assigned to the same dimension, revealed that most items had high stability (proportion replicated > .87), particularly those in Dimensions 3, 4, and 6. However, several items in Dimensions 1, 2, and 5 demonstrated lower stability (proportions ranging from .29 to .67), suggesting some uncertainty in their placement. For comparative purposes, classical reliability (Cronbach’s alpha) was also calculated for the originally proposed nine subscales. The coefficients ranged from α = .67 (Dimension 3) to α = .87 (Dimension 5), indicating acceptable to good internal consistency for these a priori groupings. However, the EGA-derived structure provides an empirically grounded alternative model based on the network of inter-item relationships, revealing a more parsimonious six-factor architecture for the scale within this population. The study's most profound limitation lies in its commitment to a neurocognitive explanatory framework that the data cannot sustain. While the application of EGA appropriately models the statistical architecture of attitudes, the subsequent interpretation, which directly assigns emergent dimensions to discrete neural circuits such as the Default Mode Network (DMN) and ventromedial prefrontal cortex (vmPFC), commits a fundamental category error. It conflates a pattern of questionnaire responses with evidence of specific biological substrates, an unwarranted extrapolation that treats correlation as a mechanism. This is not merely an overinterpretation but a methodological short-circuit: the research design contains no physiological, imaging, or behavioral measures that could validate these neural attributions. Therefore, the proposed "neurocognitive architecture" remains an elegant, post-hoc narrative built entirely upon theoretical analogy rather than empirical demonstration from this dataset. This speculative leap fundamentally compromises the mechanistic validity of the model. When this conjectural bridge is paired with the tangible psychometric instability of Dimension 2 and the constrained generalizability of the university-based sample, the study's overarching conclusions, though ambitious, are left anchored to an unstable and insufficient evidentiary base, dramatically curtailing their reliability for guiding genuine neuromodulatory interventions. 5.3. Robustness of Theoretical- CFA vs. EGA-derived CFA model : To establish the definitive latent structure of the Saudi Youth Attitude Toward Addiction Scale (SYAAS), the hypothesized nine-dimensional theoretical model and the empirically derived six-dimensional EGA model were subjected to a comparative confirmatory factor analysis. The theoretical model, specifying 45 items across nine correlated factors, demonstrated an adequate but suboptimal fit: χ²(909) = 2876.61, CFI = .83, TLI = .82, RMSEA = .063, SRMR = .07. In stark contrast, the six-factor EGA model, specifying 39 items and estimated with the DWLS estimator for ordinal data, achieved an excellent fit: χ²(683) = 1717.90, CFI = .992, TLI = .991, RMSEA = .053, SRMR = .056, representing a substantial improvement in model fit alongside greater parsimony. The psychometric properties of the EGA-derived model were consistently superior. While all factor loadings were significant in both models (p < .001), the six-factor solution exhibited stronger and more uniform standardized loadings (range = .54 to .88, median = .78) which ranged from .42 to .84. The model of six-factor also demonstrated excellent composite reliability (CR range = .77 to .90) and robust convergent validity, with the average variance extracted (AVE) exceeding the .50 threshold for five of its six dimensions. The theoretical model showed weaker convergent validity, with AVE falling below .50 for four of its nine dimensions. Post-hoc diagnostics confirmed the precise specification of the six-factor model, with only minor modification indices after accounting for a few theoretically justified residual covariances. The theoretical model, however, exhibited significant localized misfit, most notably an extreme modification index (MI = 225.91) between two items from the self-efficacy dimension, indicating a fundamental misspecification of the imposed factor structure within this cultural context. Consequently, the empirical evidence decisively favors the more parsimonious and psychometrically robust six-dimensional model as the valid structural representation of the SYAAS. The definitive six-factor model's empirical superiority validates the study's core methodological and cultural thesis. Critically, the model was derived through Exploratory Graph Analysis (EGA), a technique that permitted the latent attitudinal structure to emerge organically from the data, free from the constraints of the imposed nine-factor Western framework. This data-driven approach revealed a culturally distinct cognitive architecture, most notably in the Motivation and Calculated Risk dimension. This dimension represents a psychometric synthesis of items from three separate theoretical constructs: knowledge of negative consequences, psychological motives for use (e.g., curiosity), and self-efficacy for refusal. Their fusion into a single, robust factor indicates that Saudi youth do not process these elements discretely. Instead, they engage in an integrated evaluative calculus where personal curiosity is weighed simultaneously against the prospective costs of religious transgression and damage to familial honor, a holistic risk-assessment schema where social-spiritual consequences are inseparable from personal motive. This stands in direct contrast to common Western models, which often treat risk perception (framed in health or legal terms), sensation-seeking, and personal norms as distinct psychological compartments. Consequently, this culturally validated instrument enables a paradigm shift: from relying on potentially misfitting imported tools to facilitating precise, context-specific assessment. It allows clinicians and researchers to identify and engage the actual psychosocial mechanisms, such as these integrated value conflicts and identity-based narratives, operating within the unique Saudi socio-religious ecology, thereby transforming prevention from a generic campaign into a targeted cultural and psychological intervention. 6. Discussion The present study employed Exploratory Graph Analysis to investigate the latent structure of addiction-related attitudes among Saudi youth, yielding a six-dimensional model that departs substantially from the originally hypothesized nine-factor theoretical framework. This empirically derived structure offers insight into how these attitudes are organized within a collectivist, religiously informed cultural context. Three findings carry particular weight: the integrated nature of the Motivation and Calculated Risk dimension, the emergence of Positive User Identity as a distinct factor, and the instability of the Weak Sensitivity to Social Consequences dimension. The superior fit of the six-factor model over the theoretical nine-factor model (CFI = .992 vs. .83) underscores the ecological invalidity risk inherent in direct scale importation and validates the methodological approach of allowing structure to emerge from cultural data. The most psychologically informative emergent dimension fused items originally designed to measure three distinct theoretical constructs: knowledge of negative consequences, psychological motives for use, and self-efficacy for refusal. Within Western frameworks, these elements are typically treated as separate cognitive compartments. Their integration into a single robust factor among Saudi respondents suggests a holistic risk calculus wherein personal curiosity is weighed simultaneously against anticipated social-spiritual consequences, including religious transgression and damage to familial honor. Theoretically, this challenges the universal applicability of disaggregated Western etiological models, indicating that in collectivist, religiously informed contexts, the psychological boundaries between risk perception, moral evaluation, and self-concept may be more permeable than theories developed in individualistic societies presume. From a measurement perspective, scales that artificially disaggregate these constructs may introduce distinctions that do not reflect how they are cognitively organized in this population. For prevention, this finding implies that messages addressing only health risks while ignoring the intertwined dimensions of family honor and religious identity may fail to engage the actual evaluative framework operating in this population. A second key finding was the emergence of Positive User Identity as a dimension separate from cognitive and affective domains. Items reflecting a resilient, anti-addiction self-concept cohered into a distinct factor rather than loading with other attitudinal components. This separation suggests that internalizing a protective identity represents a qualitatively different psychological process than merely holding negative attitudes toward substances or knowing about their risks. The finding supports identity-based models of behavior change, which posit that lasting behavioral modification requires integration into one’s self-narrative rather than mere attitude adjustment. For assessment, this highlights that instrument focusing exclusively on risk beliefs or behavioral intentions may miss a critical protective factor. In practice, cultivating a positive, substance-free identity may be as important as conveying risk information or teaching refusal skills; interventions should create opportunities for youth to construct and publicly commit to such identities. The dimension termed Weak Sensitivity to Social Consequences exhibited notably lower structural consistency in bootstrap analysis (.09) compared to other dimensions. Rather than indicating a flawed scale, this instability may reflect genuine heterogeneity in how Saudi youth process social sanctions. For some respondents, family disapproval and community judgment carry a powerful deterrent force; for others, particularly those immersed in digital peer networks or subcultures with divergent norms, these consequences may register weaklier or be offset by competing influences. This finding suggests that sensitivity to social consequences is not a uniform construct in this population but may vary meaningfully across subgroups. Researchers using the SYAAS should consider treating this dimension with caution, potentially supplementing it with qualitative inquiry to understand its variability, while practitioners can use the scale to identify individuals for whom social sanctions are less salient and tailor interventions accordingly. The decisive superiority of the six-factor model over the theoretically imposed nine-factor structure provides empirical evidence that the etic framework, while theoretically comprehensive, did not correspond to the psychological organization of the target population. Traditional validation sequences that begin with confirmatory factor analysis of translated instruments risk forcing data into misfitting structures. The present study’s methodological inversion, allowing structure to emerge through EGA before subjecting it to confirmatory testing, offers a template for culturally grounded assessment development. Several limitations should be considered. The sample comprised university students and may not represent non-university youth or those from different geographic and socioeconomic strata. Reliance on self-report within a high-stigma context risks socially desirable responding. The predictive validity of the SYAAS for actual substance use behaviors remains untested. Future research should establish predictive validity through longitudinal designs, replicate the factor structure in more diverse samples, refine the Weak Sensitivity to Social Consequences dimension, and examine whether this six-factor structure generalizes to other Arab populations sharing similar cultural features. Despite these limitations, the SYAAS provides a culturally grounded tool that captures the nuanced interplay of religious conviction, familial honor, personal identity, and social context shaping addiction-related attitudes among Saudi youth, enabling more precise assessment and targeted prevention strategies. Abbreviations CFA Confirmatory Factor Analysis CFI Comparative Fit Index CR Composite Reliability DWLS Diagonally Weighted Least Squares EGA Exploratory Graph Analysis RMSEA Root Mean Square Error of Approximation SRMR Standardized Root Mean Square Residual SYAAS Saudi Youth Attitude Toward Addiction Scale TLI Tucker–Lewis Index Declarations Ethical approval This study was approved by the Research Ethics Committee of the Faculty of Arts and Humanities at King Abdulaziz University, Saudi Arabia (Approval Reference: REC-FAH-KAU-2026-002, dated December 31, 2025) in accordance with the guidelines and regulations set forth by the university’s ethics review board. Consent to participate Informed consent was obtained from all individual participants included in the study. All participants were informed about the purpose of the research, the voluntary nature of their participation, and their right to withdraw at any time without consequence. Anonymity and confidentiality of responses were assured. Consent to publish Not applicable. The manuscript does not contain any individual person’s data in any form (e.g., personal details, images, or videos). Funding: This research was funded by Dr. Manal Fakeeh, Scientific Chair, Grant No. (MFSC-SUAMR-05-25). Author Contribution All authors collaboratively contributed to the development, cultural adaptation, and content validation of the Saudi Youth Attitude Toward Addiction Scale (SYAAS). All authors reviewed, edited, and approved the final manuscript. Acknowledgments: The authors extend their sincere appreciation to Dr. Manal Fakeeh, Scientific Chair for Studies of Substance Use, Addiction Management, and Rehabilitation at Fakeeh College for Medical Sciences, for funding this work through the Scientific Research Support Program, Project No. (MFSC-SUAMR-05-25). Data Availability The datasets generated and analyzed during this study are not publicly available due to ethical restrictions and participant confidentiality agreements. However, they are available from the corresponding author upon reasonable request. References Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol. 2002;32(4):665–83. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x . Ajzen I. The theory of planned behavior: Frequently asked questions. Hum Behav Emerg Technol. 2020;2(4):314–24. https://doi.org/10.1002/hbe2.195 . Bronfenbrenner U. 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Historical origins of the health belief model. Health Educ Monogr. 1974;2(4):328–35. Becker MH, Radius SM, Rosenstock IM, Drachman RH, Schuberth KC, Teets KC. Compliance with a medical regimen for asthma: A test of the health belief model. Public Health Rep. 1978;93(3):268. https://pubmed.ncbi.nlm.nih.gov/652949/ . Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood. Psychol Bull. 1992;112(1):64. https://psycnet.apa.org/doi/10.1037/0033-2909.112.1.64 . Merz F, United Nations Office on Drugs and Crime. SIRIUS. 2018;2(1):85–6. 10.1515/sirius-2018-0016/html . https://www.degruyterbrill.com/document/doi/ . : World Drug Report 2017. Petraitis J, Flay BR, Miller TQ. Reviewing theories of adolescent substance use: Organizing pieces in the puzzle. Psychol Bull. 1995;117(1):67. https://psycnet.apa.org/doi/10.1037/0033-2909.117.1.67 . Marlatt GA. Taxonomy of high-risk situations for alcohol relapse: Evolution and development of a. Addiction. 1996;91(12 Suppl 1):37–50. https://doi.org/10.1046/j.1360-0443.91.12s1.15.x . Alharbi FF, Alsubaie EG, Al-Surimi KM. Substance abuse in the Arab world: Does it? 2021. Corrigan PW, Watson AC. The paradox of self-stigma and mental illness. Clin Psychol Sci Pract. 2002;9(1):35. https://psycnet.apa.org/buy/2002-10493-016 . Link BG, Phelan JC. Conceptualizing stigma. Annu Rev Sociol. 2001;27(1):363–85. https://doi.org/10.1146/annurev.soc.27.1.363 . Livingston JD, Milne T, Fang ML, Amari E. The effectiveness of interventions for reducing stigma related to substance use disorders: A systematic review. Addiction. 2012;107(1):39–50. https://doi.org/10.1111/j.1360-0443.2011.03601.x . Al-Anzi MA. Social stigma of unemployed Saudi university youth and its reflection on their value system: A field study on unemployed university youth benefiting from the National Program for Job Seekers' Allowance (Hafiz) in Riyadh [Arabic]. Soc Work J. 2024;82(1):193–240. https://doi.org/10.21608/egjsw.2024.330242.1393 . Botvin GJ, Griffin KW, Diaz T, Scheier LM, Williams C, Epstein JA. Preventing illicit drug use in adolescents: Long-term follow-up data from a randomized control trial of a school population. Addict Behav. 2000;25(5):769–74. https://psycnet.apa.org/buy/2001-05427-012 . Botvin GJ, Griffin KW, Diaz T, Ifill-Williams M. Preventing binge drinking during early adolescence: One- and two-year follow-up of a school-based preventive intervention. Psychol Addict Behav. 2001;15(4):360. https://doi.org/10.1016/S0306-4603(99)00050-7 . Hussong AM, Jones DJ, Stein GL, Baucom DH, Boeding S. An internalizing pathway to alcohol use and disorder. Psychol Addict Behav. 2011;25(3):390. https://psycnet.apa.org/buy/2011-16751-001 . Simons-Morton BG, Farhat T. Recent findings on peer group influences on adolescent smoking. J Prim Prev. 2010;31(4):191–208. https://doi.org/10.1007/s10935-010-0220-x . Kumpfer KL, Alvarado R. Family-strengthening approaches for the prevention of youth problem behaviors. Am Psychol. 2003;58(6–7):457. Haefner J. An application of Bowen family systems theory. Issues Ment Health Nurs. 2014;35(11):835–41. Ryan C, Russell ST, Huebner D, Diaz R, Sanchez J. Family acceptance in adolescence and the health of LGBT young adults. J Child Adolesc Psychiatr Nurs. 2010;23(4):205–13. Johnston LD, Miech RA, O'Malley PM, Bachman JG, Schulenberg JE, Patrick ME. Monitoring the Future national survey results on drug use, 1975–2018. Ann Arbor (MI): Institute for Social Research; 2019. World Health Organization. WHO treatment guidelines for drug-resistant tuberculosis. Geneva: World Health Organization; 2016. Schwarzer R, Fuchs R. Self-efficacy and health behaviours. Predicting health behavior: Research and practice with social cognition models. Buckingham: Open University; 1996. pp. 163–96. DiClemente CC. Motivation for change: Implications for substance abuse treatment. Psychol Sci. 1999;10(3):209–13. Cottler LB, Goldberger BA, Nixon SJ, Striley CW, Barenholtz E, Fitzgerald ND, et al. Introducing NIDA’s new national drug early warning system. Drug Alcohol Depend. 2020;217:108286. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine. 2000;25(24):3186–91. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9227492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631394933,"identity":"aca02e52-0bc7-4db3-aa70-efc1ee3398de","order_by":0,"name":"Fatma Khalifa Elsayed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsYrCMBzH8b8E0iW16z+06CukFMRB8FUqBWen4yYtCJnqA/gWgi+gBHQRXTt6uHY4cfHAg/sfbiJRN4d8IUvgwy8BcLneMK/gHGCAATBmrleLB0Rs/olCmXs8e4mAzAORPEnEqr8/qzYGY3FSPxoa9TJl32cb8fU6LhQiGn/em2hIZJlyWVhIN/A0CoVDILLwNfRmREDYVojIC600jTgsfzWMiLDjxf6wVUgrqIxgGa2kqkwhtK7Q98OISGx4kkRbjKebLx1GVtJvyepziI2dOcjqo9OsrzNzrCzkNqRTy18ALpfL5brXH4/mRMBd6RZLAAAAAElFTkSuQmCC","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Fatma","middleName":"Khalifa","lastName":"Elsayed","suffix":""},{"id":631394934,"identity":"c11db774-c22b-4aea-805e-434a1c658af3","order_by":1,"name":"Mahmoud Ali Moussa","email":"","orcid":"","institution":"Suez Canal University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"Ali","lastName":"Moussa","suffix":""}],"badges":[],"createdAt":"2026-03-25 23:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9227492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9227492/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492049,"identity":"afab7c10-be00-45f8-85db-8c77c202065d","added_by":"auto","created_at":"2026-05-05 09:56:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExploratory graphical analysis of item dimensionality of attitudes towards addiction for the Saudi university students.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9227492/v1/e7b0744b22b1030173171f6a.jpeg"},{"id":109204477,"identity":"ae6720ab-2ed3-4df4-9840-864988b92ad1","added_by":"auto","created_at":"2026-05-13 15:00:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479673,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9227492/v1/a7394371-3755-4275-b302-0b56e31f9691.pdf"},{"id":108224471,"identity":"888db3c5-4a1b-480f-ad5b-03cbab65aacf","added_by":"auto","created_at":"2026-04-30 16:07:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19767,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixdocx.docx","url":"https://assets-eu.researchsquare.com/files/rs-9227492/v1/a3399d74736ae941b6632eb0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Validation of the Arabic Saudi Youth Attitude Toward Addiction Scale Using Exploratory Graph Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThis study is driven by the critical need for a culturally grounded psychometric instrument to assess addiction-related attitudes among Saudi youth, necessitated by the substantial limitations of directly applying Western-developed scales within this distinct sociocultural context. While comprehensive theoretical frameworks provide a robust conceptual foundation for understanding attitudes as multidimensional constructs shaped by cognitive, affective, and conative components interacting with familial, peer, and societal systems, the operationalization of these constructs requires cultural specificity. Imported, translated instruments inherently carry an etic (imposed) framework that risks ecological invalidity, thereby generating data of questionable validity for the Saudi population. To transcend this limitation and allow the latent structure of attitudes to emerge organically from the cultural data, this study employs Exploratory Graph Analysis (EGA) as its primary psychometric methodology. This advanced, data-driven technique enables a rigorous test of whether the nine theoretically derived and culturally adapted domains form the hypothesized psychometric structure or reveal a novel, culturally specific architecture, thereby providing a robust, valid structure for subsequent research, precise needs assessment, and the development of effective, culturally resonant prevention strategies in Saudi Arabia.\u003c/p\u003e"},{"header":"2. Theoretical Framework and Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Addiction, Attitudes, and the Vulnerability of Youth\u003c/h2\u003e \u003cp\u003eAddiction, conceptualized as a chronic brain disorder characterized by impaired control over reward-seeking behavior despite harmful consequences, arises from a complex interaction between individual neurobiological vulnerabilities and psychosocial environmental risk factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Within this framework, psychological attitudes are not merely transient feelings but relatively stable cognitive-affective-behavioral formations. Shaped through experience and social learning, they guide information processing, incentive evaluation, and response execution toward specific entities or behaviors, such as substance use [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Investigating these attitudes among youth, specifically a university student sample, is therefore not an arbitrary choice but a methodological and theoretical necessity. The university years, coinciding with emerging adulthood, represent a peak period of vulnerability. During this stage, sensation-seeking behaviors and openness to novel experiences are heightened, while the prefrontal cortical functions responsible for deliberate judgment, emotional regulation, and consideration of long-term consequences are still undergoing final maturation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This developmental asynchrony creates a \u0026ldquo;maturity gap\u0026rdquo; that renders this demographic particularly sensitive to peer influence and emotional cues, consequently increasing their susceptibility to forming positive attitudes towards high-risk behaviors like substance use [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The Multidimensional Nature of Attitudes Toward Addiction\u003c/h2\u003e \u003cp\u003eAccording to established theories such as the Theory of Planned Behavior and the Health Belief Model, attitudes constitute the explanatory core of behavioral intention, which is considered the most proximal antecedent to actual behavior [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the latent structure of attitudes towards a complex, multi-layered issue like addiction is not unidimensional. As manifested in the current scale, it encompasses an interconnected network of evaluations. This includes the cognitive component, comprising factual beliefs about consequences (e.g., perceiving that continued use complicates treatment), which is functionally linked to expectancy-value theories where individuals assess behavior based on anticipated outcomes and their subjective value [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Intertwined with this is the affective dimension, measuring emotional responses (such as empathy towards individuals with addiction or feelings of stigma), where emotions play a crucial role in risk processing and attentional guidance, often overriding purely rational calculations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Finally, behavioral intentions, reflected in commitments to avoid risks or engage in preventive activities, represent the outcome that mediates the relationship between internal evaluations and overt behavior. These intentions are critically influenced by the degree of perceived self-efficacy, the confidence in one\u0026rsquo;s ability to execute the required behavior in the face of obstacles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, developing a scale based on theoretically and empirically distinct dimensions, such as risk awareness, personal beliefs, social stigma, behavioral intentions, psychological motives, peer influence, familial factors, perceived availability, and self-efficacy, is not a methodological excess but a necessary response to the reality that influencing factors in addiction operate at multiple levels. Internal factors (e.g., psychological motives, self-efficacy) continuously interact with immediate social-contextual factors (peer influence, familial support), which are themselves embedded within a broader environment shaped by societal norms and substance accessibility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Ignoring any of these strata in measurement yields a truncated model incapable of capturing the true complexity of the phenomenon, thereby undermining the efficacy of any preventive intervention built upon its findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The Challenge of Cross-Cultural Measurement: Emic, Etic, and the Risk of Imposed Frameworks\u003c/h2\u003e \u003cp\u003eThe methodological distinction between the emic (culture-specific, internal) and etic (culture-comparative, external) approaches presents a central dilemma in cross-cultural psychology and psychological assessment. The etic approach, as outlined by Berry [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], assumes the universality of psychological phenomena and concepts, advocating for the transfer of measurement tools developed in one culture (e.g., Western contexts) and the imposition of their structure onto another after translation. This risks ecological validity and may obscure locally unique, potentially more explanatory dimensions. In contrast, the emic approach focuses on understanding phenomena from within the cultural system itself, employing concepts and constructs that emerge organically from the local context, though this may limit normative comparability. Research in the Arab region often falls into the trap of over-reliance on the etic approach through the importation of ready-made scales, or the pitfall of a purely emic approach by designing entirely local tools lacking a comparative theoretical framework. The direct application of Western-developed psychological scales in distinct sociocultural contexts, therefore, risks ecological invalidity by imposing foreign factor structures and failing to capture locally salient constructs.\u003c/p\u003e \u003cp\u003eIn the Saudi context, this scientific and practical endeavor is undermined by a persistent over-reliance on translated Western psychological scales. These imported instruments, grounded in individualistic cultural frameworks, are applied without sufficient evidence of their cross-cultural validity, creating a profound methodological mismatch for the conservative, collectivist Saudi context. This practice, rooted in an etic (imposed) rather than an emic (indigenous) approach, fails to capture the unique, complex interplay of socio-cultural, religious, and familial determinants that shape attitudes toward addiction in Saudi society. For instance, dominant Western constructs inadequately operationalize the local experience of stigma, where shame is deeply entangled with religious transgression and collective familial honor; they undervalue the central protective role of family cohesion and parental authority; and they cannot capture how self-efficacy is calibrated within tightly knit social networks rather than conceptualized as an individual attribute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Consequences and the Need for a Culturally Grounded Instrument\u003c/h2\u003e \u003cp\u003eThe consequences of this reliance on potentially invalid metrics are threefold. First, a theoretical-measurement gap persists, wherein existing tools do not correspond to the lived reality and value systems of the target population. Second, an empirical knowledge gap remains, as the lack of a validated instrument leaves researchers and policymakers without a reliable baseline to identify vulnerabilities or evaluate interventions. Third, a translational application gap emerges, wherein public health initiatives are built upon an unstable evidence base, resulting in inefficient resource allocation and the design of prevention programs that are culturally dissonant and therefore likely to have limited efficacy. Thus, the development of effective, evidence-based prevention strategies for substance use among Saudi youth is critically impeded by a fundamental methodological impasse: the absence of a culturally valid and psychometrically robust instrument to assess addiction-related attitudes within this specific population.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Rationale and Aims of the Current Study","content":"\u003cp\u003eTo transcend the limitations of imported scales, the present study adopts an advanced integrative stance that moves beyond the simple emic-etic binary. Guided by the model of dynamic cultural adaptation proposed by Van de Vijver and Tanzer [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], we do not commence with a ready-made Western scale (pure etic) nor from a cultural vacuum (pure emic). Instead, we depart from a comprehensive, multi-dimensional theoretical framework of addiction derived from global literature (encompassing cognitive, affective, behavioral, peer, familial, and self-efficacy aspects) and subject this framework to a process of contextual reshaping. Through this process, item formulations are crafted to embody the expressions of these dimensions as they manifest within the social, religious, and psychological reality of Saudi society. For instance, the dimension of “stigma” is not measured solely by Western concepts of individual shame but is expressed through items reflecting the intertwining of religious transgression with collective familial dishonor, thereby embodying a theoretical synthesis between the universal structure of stigma and the local content of the experience [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo ensure the scientific rigor of this synthesis, the study moves beyond simple translation or superficial adaptation by employing an advanced network psychometric technique: Exploratory Graph Analysis (EGA). While classical methods like Exploratory Factor Analysis (EFA) assume a universal latent structure and test how well data conform to it, EGA, as elucidated by Golino and Epskamp [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], allows the latent structure to emerge from the data itself without strong a priori assumptions about the number or independence of factors. Applying this methodology to a sample of Saudi university students is not merely a technical procedure but a practical embodiment of integrating emic and etic perspectives. The resulting network of item correlations will reveal whether the nine contextually developed theoretical dimensions appear as distinct communities within the Saudi respondents’ mindset or whether they merge into new configurations. For example, it might show that the dimensions of risk knowledge and behavioral intentions are heavily intertwined in the Saudi network, suggesting that knowledge translates directly into protective intention within this context, a linkage potentially weaker in other cultures. This data-driven, network-based analysis provides empirical evidence for the construct validity of the culturally adapted version, thereby addressing criticisms leveled against translated measures regarding their lack of measurement invariance or dimensional misfit in novel cultural settings [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, building a robust evidence base through this psychometrically adapted and EGA-validated tool is foundational for developing informed preventive policies. It facilitates a shift from generic, imported, or context-blind prevention programs to culturally precise interventions that target the key nodes within the cognitive-affective network of Saudi youth. For instance, if the analysis reveals that ‘fear of peer rejection’ is strongly linked to ‘low self-efficacy’ more than to abstract knowledge of risks, this will guide practitioners to design programs that enhance refusal skills and psychological resilience rather than relying solely on informational fear appeals. This approach is fully congruent with modern models of addiction prevention, which emphasize the dialectical interaction between the individual (with their beliefs and competencies) and the context (with its pressures and resources) as a single unit of analysis, representing the core of contextual and ecological psychology as advocated by Bronfenbrenner [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] and a principle underscored in U.S. federal prevention research guidelines [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the identified problem and the proposed methodological approach, this study seeks to answer the following research questions:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the latent dimensional structure of addiction-related attitudes among Saudi youth when allowed to emerge empirically through Exploratory Graph Analysis, and how does this structure compare to the nine-dimensional theoretical framework derived from integrated behavioral theories?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent does the empirically derived six-factor model, identified through EGA, demonstrate superior psychometric properties, including model fit, reliability, and convergent validity, compared to the hypothesized nine-factor theoretical model when subjected to confirmatory factor analysis?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the Saudi Youth Attitude Toward Addiction Scale (SYAAS), as structured by the empirically derived dimensional model, provide a reliable and valid measurement tool for assessing cognitive, affective, and behavioral dispositions toward substance use within the Saudi cultural context?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003c/span\u003e \u003c/ol\u003e "},{"header":"4. Methodology","content":"\u003cp\u003e\u003cstrong\u003e4.1 Research Design and Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a sequential exploratory-confirmatory design for scale development and validation. The design consisted of three phases: (1) an exploratory phase using Exploratory Graph Analysis (EGA) to allow the latent structure of attitudes to emerge from the data without imposing a predefined factor model; (2) a confirmatory phase testing the EGA-derived model using Confirmatory Factor Analysis (CFA); and (3) a comparative phase pitting the empirically derived model against the original nine-dimensional theoretical model to determine which provided superior fit.\u003c/p\u003e\n\u003ch2\u003e4.2. \u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe sample comprised 537 Saudi university students (Mage- = 21.4 years, range\u0026thinsp;=\u0026thinsp;20\u0026ndash;32), recruited via snowball sampling through a digital survey distributed at King Abdulaziz University. The sample included 269 females and 268 males. Most participants lived within intact family units: 90.1% resided with their nuclear family, 88.5% reported their parents were not separated, and nearly all reported both parents as living (father: 88.5%; mother: 97.2%). Approximately half (49%) had previously attended addiction awareness programs. A minority reported current tobacco use (11.9%), with smaller proportions using e-cigarettes (10.1%), traditional cigarettes (1%), or waterpipes (1.2%).\u003c/p\u003e\n\u003ch2\u003e4.3. \u003cstrong\u003eTools\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe present study employed a rigorous, multi-phase process to develop and validate the Saudi Youth Attitude Toward Addiction Scale (SYAAS). The instrument was constructed based on an integrated theoretical framework that synthesizes the Theory of Planned Behavior [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. which posits that attitude is a precursor to behavioral intention, and a socio-ecological perspective [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. which emphasizes the nested influence of micro- and macro-systemic factors. This synthesis posits that addiction-related attitudes are multidimensional constructs, comprising interconnected cognitive, affective, and conative components, all shaped by and enacted within a specific cultural milieu. To operationalize this framework, an initial item pool was deductively generated to target nine theoretically and empirically grounded domains critical to understanding substance use vulnerability within collectivist societies.\u003c/p\u003e\n\u003cp\u003eEach dimension was selected based on robust psychological theory and prior evidence. The Knowledge and Awareness of Risks dimension is grounded in the Health Belief Model [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. where knowledge of risks is a fundamental determinant of attitude formation; low awareness is consistently linked to higher experimentation among youth [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Personal Beliefs and Attitudes dimension is central to the Theory of Planned Behavior [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], as erroneous beliefs (e.g., about ease of quitting) increase initiation risk [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. and tolerant attitudes predict use in Gulf populations [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Social Attitudes and Stigma dimension derives from Social Stigma Theory [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], where stigma delays treatment and increases relapse, particularly in conservative societies [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], and is a noted treatment barrier in Saudi Arabia [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe Behavioral Intentions and Prevention dimension stems directly from the Theory of Planned Behavior, wherein intention is the strongest proximal predictor of behavior [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. preventive intentions like refusing first offers are associated with lower substance use rates [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Psychological Motives for Use dimension reflects the premise that substance use serves to alleviate psychological distress, anxiety, and depression [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Peer Influence dimension is supported by Social Learning Theory [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], with peer use being one of the strongest predictors of initiation [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], a dynamic confirmed in Arab settings. The Familial Factors dimension draws on Family Systems Theory (Haefner, 2014), where weak monitoring and dysfunction elevate risk [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. while family support is a key protective factor in Saudi society. The Perceived Availability dimension is rooted in Ecological Models [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e], as perceived ease of access increases use [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], making it a target for prevention [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Finally, the Self-Efficacy for Resistance dimension is based on [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. concept, where higher self-efficacy predicts lower use and better resistance to peer pressure [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]., forming a cornerstone of modern prevention [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFollowing this theoretical operationalization, a multi-step cultural adaptation protocol was implemented, employing the methodological framework for cross-cultural adaptation proposed by [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. This involved independent forward- and back-translation by bilingual experts to establish semantic equivalence, followed by a review by a panel of five experts in clinical psychology and public health to evaluate content validity, conceptual relevance, and cultural appropriateness for Saudi society. Items were rated for clarity and relevance, and the scale-level content validity index (S-CVI/Ave) was calculated to ensure adequacy. This process yielded the preliminary 45-item SYAAS, with five items per hypothesized dimension, each rated on a 5-point Likert scale.\u003c/p\u003e\n\u003ch2\u003e4.4. \u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll analyses were conducted in R 4.5. Descriptive statistics and reliability analyses (Cronbach\u0026apos;s \u0026alpha;) were computed for all items and subscales.\u003c/p\u003e\n\u003cp\u003eExploratory Graph Analysis (EGA) was performed using the EGAnet package. An EBICglasso-regularized partial correlation matrix was estimated, and community detection was conducted using the walktrap algorithm. This approach models the network of item relationships and identifies dimensional clusters without imposing a predefined factor structure. Stability of the emergent dimensional structure was assessed via a 500-sample parametric bootstrap, yielding metrics of structural consistency (proportion of bootstrap samples in which all items within a dimension remained together) and item stability (proportion of times each item was assigned to its modal dimension).\u003c/p\u003e\n\u003cp\u003eThe EGA-derived model and the original nine-factor theoretical model were then subjected to Confirmatory Factor Analysis using the lavaan package. The theoretical model was estimated with Maximum Likelihood (ML), while the EGA-derived model was estimated using Diagonally Weighted Least Squares (DWLS) to account for ordinal item-level data. Model fit was evaluated using standard indices: \u0026chi;\u0026sup2;, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Composite reliability (CR) and average variance extracted (AVE) were calculated for each dimension in the final model.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Preliminary analysis:\u003c/h2\u003e \u003cp\u003eThe psychometric structure of the Arabic version of the Saudi Youth Attitude Toward Addiction Scale (SYAAS) was examined using a sample of 537 participants. Initial descriptive analyses revealed that all 45 items exhibited adequate variability, with mean scores ranging from 3.85 to 4.64 on the 5-point scale and standard deviations spanning from 0.65 to 1.10, indicating sufficient dispersion for subsequent network analysis. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the item-level distributions demonstrated notable negative skew, consistent with a ceiling effect where responses were clustered toward the positive end of the scale. Corrected item-total correlations ranged from .48 to .78, confirming that each item shared a meaningful relationship with the overall scale score. The scale demonstrated excellent overall internal consistency, with Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.95.\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\u003eDescriptive Statistics and Item-Level Reliability for the Arabic Version of the SYAAS (N\u0026thinsp;=\u0026thinsp;537)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrected Item\u0026ndash;Total Correlation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\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 M\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard Deviation. Total Scale α\u0026thinsp;=\u0026thinsp;.95\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Exploratory Graph Analysis (EGA) and Dimensional Stability:\u003c/h2\u003e \u003cp\u003eTo elucidate the scale's latent structure, Exploratory Graph Analysis (EGA) was employed using the EBICglasso regularization model with the walktrap community detection algorithm. The analysis yielded a network of 45 nodes (items) connected by 296 non-zero edges, corresponding to an edge density of 0.30. Contrary to the originally hypothesized nine-factor model, the EGA solution supported a more parsimonious six-dimensional structure. The specific item assignments for each emergent dimension are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eExploratory Graph Analysis (EGA) Dimension Structure, Stability, and Comparative Reliability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGA Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew Items Assigned\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructural Consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian Item Stability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α (Original 9-Factor)\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\u003eQ1, Q2, Q4, Q5, Q10, Q11, Q16, Q17, Q18, Q19, Q20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 (D1), 0.71 (D2), 0.83 (D7)\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\u003eQ3, Q6, Q8, Q9, Q12, Q13, Q14, Q15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 (D3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7, Q21, Q22, Q23, Q24, Q25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87 (D5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ26, Q27, Q28, Q29, Q30, Q34, Q36, Q38, Q39, Q40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84 (D6), 0.80 (D8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ31, Q32, Q33, Q35, Q37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (D7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ41, Q42, Q43, Q44, Q45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (D9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Structural Consistency: Proportion of bootstrap samples (out of 500) in which all items within the dimension remained together. Then, Median Item Stability: The median proportion of bootstrap samples in which each item in the dimension was assigned to the same dimension. Finally, Cronbach\u0026rsquo;s α (Original 9-Factor): Reliability for the predefined theoretical subscales containing the EGA dimension's items, provided for comparative reference. D1-D9 refer to the originally hypothesized nine subscales.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExploratory Graph Analysis (EGA) was performed using the EBICglasso regularization model with the walktrap community detection algorithm. The results supported a multidimensional structure, identifying six distinct dimensions rather than the originally hypothesized nine. The network comprised 45 nodes and 296 non-zero edges (density\u0026thinsp;=\u0026thinsp;0.30).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the stability and reproducibility of this 6-dimensional solution, a parametric bootstrap EGA was conducted with 500 resamples. The bootstrap median dimensionality was 6 (95% CI [3.78, 8.22]), with this solution appearing in 36.2% of the replicates, making it the most frequent outcome. The structural consistency of the dimensions (the proportion of times items within a dimension were replicated together across bootstrap samples) varied, with Dimension 6 showing excellent consistency (.95), Dimensions 3 and 4 good consistency (.88 and .55, respectively), and Dimension 2 low consistency (.09). Item stability analysis, which indicates the proportion of times an item was assigned to the same dimension, revealed that most items had high stability (proportion replicated \u0026gt; .87), particularly those in Dimensions 3, 4, and 6. However, several items in Dimensions 1, 2, and 5 demonstrated lower stability (proportions ranging from .29 to .67), suggesting some uncertainty in their placement.\u003c/p\u003e \u003cp\u003eFor comparative purposes, classical reliability (Cronbach\u0026rsquo;s alpha) was also calculated for the originally proposed nine subscales. The coefficients ranged from α\u0026thinsp;=\u0026thinsp;.67 (Dimension 3) to α\u0026thinsp;=\u0026thinsp;.87 (Dimension 5), indicating acceptable to good internal consistency for these a priori groupings. However, the EGA-derived structure provides an empirically grounded alternative model based on the network of inter-item relationships, revealing a more parsimonious six-factor architecture for the scale within this population.\u003c/p\u003e \u003cp\u003eThe study's most profound limitation lies in its commitment to a neurocognitive explanatory framework that the data cannot sustain. While the application of EGA appropriately models the statistical architecture of attitudes, the subsequent interpretation, which directly assigns emergent dimensions to discrete neural circuits such as the Default Mode Network (DMN) and ventromedial prefrontal cortex (vmPFC), commits a fundamental category error. It conflates a pattern of questionnaire responses with evidence of specific biological substrates, an unwarranted extrapolation that treats correlation as a mechanism. This is not merely an overinterpretation but a methodological short-circuit: the research design contains no physiological, imaging, or behavioral measures that could validate these neural attributions. Therefore, the proposed \"neurocognitive architecture\" remains an elegant, post-hoc narrative built entirely upon theoretical analogy rather than empirical demonstration from this dataset. This speculative leap fundamentally compromises the mechanistic validity of the model. When this conjectural bridge is paired with the tangible psychometric instability of Dimension 2 and the constrained generalizability of the university-based sample, the study's overarching conclusions, though ambitious, are left anchored to an unstable and insufficient evidentiary base, dramatically curtailing their reliability for guiding genuine neuromodulatory interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.3. \u003cb\u003eRobustness of Theoretical- CFA vs. EGA-derived CFA model\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eTo establish the definitive latent structure of the Saudi Youth Attitude Toward Addiction Scale (SYAAS), the hypothesized nine-dimensional theoretical model and the empirically derived six-dimensional EGA model were subjected to a comparative confirmatory factor analysis. The theoretical model, specifying 45 items across nine correlated factors, demonstrated an adequate but suboptimal fit: χ\u0026sup2;(909)\u0026thinsp;=\u0026thinsp;2876.61, CFI = .83, TLI = .82, RMSEA = .063, SRMR = .07. In stark contrast, the six-factor EGA model, specifying 39 items and estimated with the DWLS estimator for ordinal data, achieved an excellent fit: χ\u0026sup2;(683)\u0026thinsp;=\u0026thinsp;1717.90, CFI = .992, TLI = .991, RMSEA = .053, SRMR = .056, representing a substantial improvement in model fit alongside greater parsimony.\u003c/p\u003e \u003cp\u003eThe psychometric properties of the EGA-derived model were consistently superior. While all factor loadings were significant in both models (p \u0026lt; .001), the six-factor solution exhibited stronger and more uniform standardized loadings (range = .54 to .88, median = .78) which ranged from .42 to .84. The model of six-factor also demonstrated excellent composite reliability (CR range = .77 to .90) and robust convergent validity, with the average variance extracted (AVE) exceeding the .50 threshold for five of its six dimensions. The theoretical model showed weaker convergent validity, with AVE falling below .50 for four of its nine dimensions.\u003c/p\u003e \u003cp\u003ePost-hoc diagnostics confirmed the precise specification of the six-factor model, with only minor modification indices after accounting for a few theoretically justified residual covariances. The theoretical model, however, exhibited significant localized misfit, most notably an extreme modification index (MI\u0026thinsp;=\u0026thinsp;225.91) between two items from the self-efficacy dimension, indicating a fundamental misspecification of the imposed factor structure within this cultural context. Consequently, the empirical evidence decisively favors the more parsimonious and psychometrically robust six-dimensional model as the valid structural representation of the SYAAS.\u003c/p\u003e \u003cp\u003eThe definitive six-factor model's empirical superiority validates the study's core methodological and cultural thesis. Critically, the model was derived through Exploratory Graph Analysis (EGA), a technique that permitted the latent attitudinal structure to emerge organically from the data, free from the constraints of the imposed nine-factor Western framework. This data-driven approach revealed a culturally distinct cognitive architecture, most notably in the Motivation and Calculated Risk dimension. This dimension represents a psychometric synthesis of items from three separate theoretical constructs: knowledge of negative consequences, psychological motives for use (e.g., curiosity), and self-efficacy for refusal. Their fusion into a single, robust factor indicates that Saudi youth do not process these elements discretely. Instead, they engage in an integrated evaluative calculus where personal curiosity is weighed simultaneously against the prospective costs of religious transgression and damage to familial honor, a holistic risk-assessment schema where social-spiritual consequences are inseparable from personal motive. This stands in direct contrast to common Western models, which often treat risk perception (framed in health or legal terms), sensation-seeking, and personal norms as distinct psychological compartments. Consequently, this culturally validated instrument enables a paradigm shift: from relying on potentially misfitting imported tools to facilitating precise, context-specific assessment. It allows clinicians and researchers to identify and engage the actual psychosocial mechanisms, such as these integrated value conflicts and identity-based narratives, operating within the unique Saudi socio-religious ecology, thereby transforming prevention from a generic campaign into a targeted cultural and psychological intervention.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThe present study employed Exploratory Graph Analysis to investigate the latent structure of addiction-related attitudes among Saudi youth, yielding a six-dimensional model that departs substantially from the originally hypothesized nine-factor theoretical framework. This empirically derived structure offers insight into how these attitudes are organized within a collectivist, religiously informed cultural context. Three findings carry particular weight: the integrated nature of the Motivation and Calculated Risk dimension, the emergence of Positive User Identity as a distinct factor, and the instability of the Weak Sensitivity to Social Consequences dimension. The superior fit of the six-factor model over the theoretical nine-factor model (CFI = .992 vs. .83) underscores the ecological invalidity risk inherent in direct scale importation and validates the methodological approach of allowing structure to emerge from cultural data.\u003c/p\u003e \u003cp\u003eThe most psychologically informative emergent dimension fused items originally designed to measure three distinct theoretical constructs: knowledge of negative consequences, psychological motives for use, and self-efficacy for refusal. Within Western frameworks, these elements are typically treated as separate cognitive compartments. Their integration into a single robust factor among Saudi respondents suggests a holistic risk calculus wherein personal curiosity is weighed simultaneously against anticipated social-spiritual consequences, including religious transgression and damage to familial honor. Theoretically, this challenges the universal applicability of disaggregated Western etiological models, indicating that in collectivist, religiously informed contexts, the psychological boundaries between risk perception, moral evaluation, and self-concept may be more permeable than theories developed in individualistic societies presume. From a measurement perspective, scales that artificially disaggregate these constructs may introduce distinctions that do not reflect how they are cognitively organized in this population. For prevention, this finding implies that messages addressing only health risks while ignoring the intertwined dimensions of family honor and religious identity may fail to engage the actual evaluative framework operating in this population.\u003c/p\u003e \u003cp\u003eA second key finding was the emergence of Positive User Identity as a dimension separate from cognitive and affective domains. Items reflecting a resilient, anti-addiction self-concept cohered into a distinct factor rather than loading with other attitudinal components. This separation suggests that internalizing a protective identity represents a qualitatively different psychological process than merely holding negative attitudes toward substances or knowing about their risks. The finding supports identity-based models of behavior change, which posit that lasting behavioral modification requires integration into one\u0026rsquo;s self-narrative rather than mere attitude adjustment. For assessment, this highlights that instrument focusing exclusively on risk beliefs or behavioral intentions may miss a critical protective factor. In practice, cultivating a positive, substance-free identity may be as important as conveying risk information or teaching refusal skills; interventions should create opportunities for youth to construct and publicly commit to such identities.\u003c/p\u003e \u003cp\u003eThe dimension termed Weak Sensitivity to Social Consequences exhibited notably lower structural consistency in bootstrap analysis (.09) compared to other dimensions. Rather than indicating a flawed scale, this instability may reflect genuine heterogeneity in how Saudi youth process social sanctions. For some respondents, family disapproval and community judgment carry a powerful deterrent force; for others, particularly those immersed in digital peer networks or subcultures with divergent norms, these consequences may register weaklier or be offset by competing influences. This finding suggests that sensitivity to social consequences is not a uniform construct in this population but may vary meaningfully across subgroups. Researchers using the SYAAS should consider treating this dimension with caution, potentially supplementing it with qualitative inquiry to understand its variability, while practitioners can use the scale to identify individuals for whom social sanctions are less salient and tailor interventions accordingly.\u003c/p\u003e \u003cp\u003eThe decisive superiority of the six-factor model over the theoretically imposed nine-factor structure provides empirical evidence that the etic framework, while theoretically comprehensive, did not correspond to the psychological organization of the target population. Traditional validation sequences that begin with confirmatory factor analysis of translated instruments risk forcing data into misfitting structures. The present study\u0026rsquo;s methodological inversion, allowing structure to emerge through EGA before subjecting it to confirmatory testing, offers a template for culturally grounded assessment development.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. The sample comprised university students and may not represent non-university youth or those from different geographic and socioeconomic strata. Reliance on self-report within a high-stigma context risks socially desirable responding. The predictive validity of the SYAAS for actual substance use behaviors remains untested. Future research should establish predictive validity through longitudinal designs, replicate the factor structure in more diverse samples, refine the Weak Sensitivity to Social Consequences dimension, and examine whether this six-factor structure generalizes to other Arab populations sharing similar cultural features. Despite these limitations, the SYAAS provides a culturally grounded tool that captures the nuanced interplay of religious conviction, familial honor, personal identity, and social context shaping addiction-related attitudes among Saudi youth, enabling more precise assessment and targeted prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDWLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagonally Weighted Least Squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory Graph Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Root Mean Square Residual\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSYAAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSaudi Youth Attitude Toward Addiction Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTucker\u0026ndash;Lewis Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Research Ethics Committee of the Faculty of Arts and Humanities at King Abdulaziz University, Saudi Arabia (Approval Reference: REC-FAH-KAU-2026-002, dated December 31, 2025) in accordance with the guidelines and regulations set forth by the university\u0026rsquo;s ethics review board.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study. All participants were informed about the purpose of the research, the voluntary nature of their participation, and their right to withdraw at any time without consequence. Anonymity and confidentiality of responses were assured.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable. The manuscript does not contain any individual person\u0026rsquo;s data in any form (e.g., personal details, images, or videos).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by Dr. Manal Fakeeh, Scientific Chair, Grant No. (MFSC-SUAMR-05-25).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors collaboratively contributed to the development, cultural adaptation, and content validation of the Saudi Youth Attitude Toward Addiction Scale (SYAAS). All authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThe authors extend their sincere appreciation to Dr. Manal Fakeeh, Scientific Chair for Studies of Substance Use, Addiction Management, and Rehabilitation at Fakeeh College for Medical Sciences, for funding this work through the Scientific Research Support Program, Project No. (MFSC-SUAMR-05-25).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during this study are not publicly available due to ethical restrictions and participant confidentiality agreements. However, they are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. 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Spine. 2000;25(24):3186\u0026ndash;91.\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":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Exploratory Graph Analysis (EGA), Addiction attitudes, Saudi youth, Scale validation, Network psychometrics","lastPublishedDoi":"10.21203/rs.3.rs-9227492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9227492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe direct application of Western-developed psychological scales in distinct sociocultural contexts risks ecological invalidity by imposing foreign factor structures and failing to capture locally salient constructs. This study addressed this critical gap by developing and validating a culturally grounded instrument to assess addiction-related attitudes among Saudi youth. Utilizing a sequential exploratory-confirmatory design, we first employed Exploratory Graph Analysis (EGA), a network psychometric method, to allow the latent attitudinal structure to emerge organically from a sample of 537 Saudi university students. Contrary to the a priori nine-dimensional theoretical model derived from integrated behavioral theories, EGA revealed a robust six-factor structure. This empirically derived model was subsequently validated via Confirmatory Factor Analysis, demonstrating excellent fit (CFI = .992, TLI = .991, RMSEA = .053) and superior psychometric properties compared to the theoretical structure. The emergent dimensions, including syntheses such as Motivation and Calculated Risk, illustrate a culturally specific cognitive architecture where personal curiosity, religious transgression, and familial honor are psychologically fused, a holistic schema not represented in disaggregated Western models. The resultant Saudi Youth Attitude Toward Addiction Scale (SYAAS) provides a reliable, valid, and ecologically sound measurement tool. It enables a paradigm shift from generic, imported assessment to precise, culturally resonant diagnosis, facilitating the design of targeted interventions that engage the actual psychosocial mechanisms operating within Saudi Arabia's unique socio-religious context. This research underscores the necessity of data-driven, culturally adaptive methodologies in global public health psychology.\u003c/p\u003e","manuscriptTitle":"Psychometric Validation of the Arabic Saudi Youth Attitude Toward Addiction Scale Using Exploratory Graph Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 16:07:18","doi":"10.21203/rs.3.rs-9227492/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T16:57:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253085253911269816923054051895570714911","date":"2026-05-15T16:45:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T12:48:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:12:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306084273666761885636019202008538335356","date":"2026-05-04T11:23:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64309807987484081745211166905817124888","date":"2026-04-26T14:43:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177150277727487569984105504196381563370","date":"2026-04-21T19:45:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T14:37:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T06:30:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T18:06:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2026-03-27T18:02:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"777ec970-38bb-4d8d-ae12-353342c7992d","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T16:57:59+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"253085253911269816923054051895570714911","date":"2026-05-15T16:45:54+00:00","index":78,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T12:48:50+00:00","index":77,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:12:43+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"306084273666761885636019202008538335356","date":"2026-05-04T11:23:31+00:00","index":62,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T16:07:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 16:07:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9227492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9227492","identity":"rs-9227492","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}