Two Sides of the Same Coin: Uncovering Differential Roles of Cognitive and Affective Empathy in Adolescent Multidimensional Well-Being Network

Two Sides of the Same Coin: Uncovering Differential Roles of Cognitive and Affective Empathy in Adolescent Multidimensional Well-Being Network | 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 Two Sides of the Same Coin: Uncovering Differential Roles of Cognitive and Affective Empathy in Adolescent Multidimensional Well-Being Network Yanhe Deng, Xuan Han, Haichun Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9194600/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While empathy is traditionally championed as a cornerstone of positive psychological functioning, its structural role within multidimensional well-being systems remains surprisingly paradoxical. This research applied network analysis within the Well-Being Profile (WB-Pro) framework to elucidate the distinct dynamics of cognitive and affective empathy among adolescents. Study 1 ( N = 2,201, M age = 12.07, SD = 3.01) revealed a cross-sectional paradox: while general empathy strongly correlated with prosocial behavior, it was negatively associated with self-acceptance, optimism, and positive emotions. To resolve this, Study 2 ( N = 587, M age = 14.74, SD = 2.73) utilized a six-month longitudinal cross-lagged panel network analysis to disentangle the two subtypes, revealing a stark functional divergence. Cognitive empathy emerged as a consistently adaptive capacity, predicting broad improvements within the well-being network, most notably in prosocial behavior and resilience. Conversely, affective empathy functioned as a systemic vulnerability. Despite being positively predicted by early prosocial behavior, it failed to generate subsequent adaptive outcomes; instead, it undermined interpersonal well-being (e.g., positive relationships), eroded intrapersonal well-being (e.g., self-acceptance), and impaired adaptive functioning (e.g., resilience) over time. These findings challenge the global endorsement of empathy in positive psychology, indicating that the path to flourishing requires not merely feeling with others, but the capacity to understand them while maintaining self-integrity, offering a precise, structurally targeted approach to cultivate well-being. Adolescents Affective empathy Cognitive empathy Multidimensional well-being Network analysis Positive Psychology Figures Figure 1 Figure 2 Introduction In the 21st century, interest in well-being has grown exponentially in both scientific research and professional psychological practice, reflecting public values and individuals’ subjective experiences of life. Crucially, well-being is not a unitary construct but a complex system composed of multiple psychological components (Huppert & Ruggeri, 2018)—a perspective that underscores the need for comprehensive conceptualizations. Building on the theoretical approach that defines well-being as equivalent to positive mental health, Marsh et al. (2020) developed the Well-Being Profile (WB-Pro), which defines 15 dimensions of well-being as the positive opposites of common mental disorders. This theoretical perspective enables the structure of well-being to be contrasted with the internationally agreed framework of ill-being (e.g., Diagnostic and Statistical Manual of Mental Disorders, DSM; American Psychiatric Association , 1994, 2013). One benefit of this positive mental health approach is to provide a systematic framework of well-being to resolve the lack of consensus regarding the constituents of well-being. Notably, empathy was introduced to the WB-Pro as a novel and key component of well-being because it is essential to positive social functioning among adults. However, empirical evidence indicates that empathy does not consistently correlate with reduced ill-being; particularly in adolescence, cognitive and affective empathy differentially predict positive mental health (Benítez-Sillero et al., 2024; Cui et al., 2022; Johander et al., 2022). These findings highlight the urgency of investigating how two subtypes of empathy—when conceptualized as the dimension of well-being—exert distinct interactions with other components within the adolescent well-being network. While the WB-Pro demonstrated good psychometric properties in adults (Marsh et al., 2020; Scalas et al., 2023), evidence also indicates conceptual and measurement challenges—particularly for the empathy dimension (Ni et al., 2025). Little is known about how this dimension functions in adolescence, where cognitive and affective empathy may divergently shape the well-being system. This study aims to address this gap by applying network analysis to investigate multidimensional well-being in adolescents, with a specific focus on cognitive and affective empathy, to elucidate their distinct roles within the well-being network during this developmental stage. Empathy in the Well-Being Profile Empathy occupies an important yet ambiguously operationalized position within the 15-dimensional framework of the WB-Pro. As a multidimensional measure of positive mental health, the WB-Pro categorizes empathy under the domain of “Prosocial emotions and behaviors”, a choice rooted in the recognition that such capacities are fundamental to human functioning and vitality (Eisenberg et al., 2007). This inclusion underscores empathy’s acknowledged role in fostering positive psychological states and prosocial behavior. Psychological theory (Berluti et al., 2023; Engert et al., 2014; Pow et al., 2016) distinguishes two core components of empathy: cognitive empathy (the ability to comprehend others’ perspectives and emotions) and affective empathy (the capacity to experience or share others’ feelings). Cognitive empathy enables perspective-taking, which enhances positive social functioning, interpersonal satisfaction, and prosocial behavior (Levy et al., 2018; Mwilambwe-Tshilobo et al., 2023). Affective empathy, by contrast, involves emotional resonance, deepening emotional understanding and motivating prosocial responses (Gaspar & Esteves, 2022; Levy et al., 2018). However, the WB-Pro does not explicitly differentiate between cognitive and affective empathy in its operationalization. Instead, the WB-Pro defines empathy as the ability to understand and share others’ emotions (Davis, 1983; Marsh et al., 2020)—a definition that emphasizes facilitating the anticipation of others’ motivations, forming emotional connections, and promoting prosocial behaviors (Cornish et al., 2020; Li et al., 2024). Notably, its current items primarily tap into emotional susceptibility and emotional contagion, such as “ I easily get caught up in other people’s feelings ”, and even capture negative emotional responses, for example, “ Other people’s misfortunes usually disturb me a great deal ”. Validation studies with Italian adults further suggest that three of the four empathy items retain much specific variance—even after controlling for global well-being—indicating poor alignment with the general well-being factor in the WB-Pro (Scalas et al., 2023). Consistently, a cross-sectional network study with Chinese adolescents using a shortened 15-item WB-Pro found that single-item empathy, while strongly correlated with prosocial behavior, exhibited a negative association with emotional stability and autonomy and had the lowest centrality (Expected Influence = –2.621) among all dimensions (Ni et al., 2025). These results suggest that when conceptualized primarily as emotional contagion, empathy may have limited—or even detrimental—contributions to the adolescent well-being system. Taken together, these limitations highlight the necessity of disentangling cognitive and affective empathy, as well as their valence, for more precise well-being assessment. Although several studies have adopted a network approach to examine the multidimensional structure of well-being, few have specifically investigated the associative patterns between empathy and other well-being components within adolescent networks. Existing network-based studies have primarily focused on overall network structures and have largely relied on cross-sectional designs (e.g., Landvreugd et al., 2024), which limits their ability to capture the dynamic role of empathy within the well-being system. In addition, limitations in the measurement of empathy further constrain prior network-based research. For example, Ni et al. (2025) employed the 15-item version of WB-Pro, in which empathy is assessed as a single, undifferentiated component. Differential Roles of Cognitive and Affective Empathy in Adolescents’ Well-Being Empathy, a critical mechanism for navigating social responsibilities and integrating into social groups (Berluti et al., 2023; Engert et al., 2014), is crucial for fostering adaptation, strengthening interpersonal relationships, and reducing antisocial behaviors (Marzilli et al., 2021; Xing et al., 2023). Converging evidence demonstrates that during adolescence, cognitive and affective empathy follow different developmental trajectories, thereby constituting well-being through unique pathways. Cognitive empathy, on the one hand, is strongly associated with adolescents’ well-being and plays a central role in promoting perspective-taking and social competence. During this developmental phase, teenagers transition from childhood egocentrism to more nuanced, context-sensitive understandings of others’ motivations—a shift driven by expanded exposure to diverse perspectives, maturation of executive functions, and increasing social role-taking in schools and communities (Levy et al., 2018; Mwilambwe-Tshilobo et al., 2023). As cognitive abilities (e.g., executive functioning) mature, adolescents become better equipped to engage in perspective-taking, which allows adolescents to understand others’ needs and express empathy (Pow et al., 2016). Research has shown that this ability to adopt others’ perspectives is significantly associated with well-being, as adolescents demonstrating strong cognitive empathy receive greater peers and adult approval, reinforcing its adaptive value. Conversely, deficits in cognitive empathy correlate with increased aggression (Deng et al., 2025). Affective empathy, on the other hand, develops from primitive emotional contagion to more intense, shared, and personally involving responses. While this progression enhances adolescents’ ability to recognize others’ feelings and motivates prosocial or comforting behaviors, it also heightens vulnerability to personal distress, negative affect, or burnout, highlighting its potential “double-edged” impact on mental health. Adolescents, whose executive functions and emotional regulation skills are still maturing, may be overly sensitive or overwhelmed by others’ emotions, due to underdeveloped capacity to set emotional boundaries. This vulnerability is partly attributable to the ongoing maturation of the amygdala, which underlies adolescents’ relatively limited capacity for emotional regulation (Skyberg et al., 2023). As a result, they may struggle to balance their own emotions with those of others (Gaspar & Esteves, 2022; Levy et al., 2018). Unlike adults, who can strategically regulate empathy across contexts, children and adolescents often lack the cognitive resources to modulate empathic responses to meet dynamic interpersonal and cultural demands. Empirical evidence further reveals that heightened affective empathy in adolescence correlates with increased depression and anxiety, as adolescents may struggle to cope with emotional overload (Jauniaux et al., 2020; Vecchio & De Pascalis, 2023). In summary, adolescence represents a critical developmental stage in which cognitive and affective empathy follow distinct trajectories. While cognitive empathy advances rapidly and supports perspective-taking, affective empathy often remains less regulated, with limited boundary-setting capacities. These developmental differences likely explain the distinct associations between empathy subtypes and adolescent well-being, underscoring their unique roles in navigating social relationships and societal expectations (Gaspar & Esteves, 2022; Padilla-Walker et al., 2018). The Present Study Despite growing interest in the empathy-well-being relationship among adolescents, the distinct roles of cognitive and affective empathy—considered as components of well-being—remain largely unexplored, particularly in light of the evolving empathic capacities and unique challenges of this stage. To bridge this gap, the present research employs a network approach across two studies to investigate empathy’s systemic role within the multidimensional well-being system. Study 1 adopts a cross-sectional design to map the position of empathy—measured as a single, affective-dominant construct within the WB-Pro framework—relative to other well-being components. Study 2 extends this inquiry via a six-month longitudinal design, explicitly differentiating between cognitive and affective empathy. By capturing temporal dynamics, Study 2 aims to elucidate how these distinct subtypes differentially shape the stability and fluctuations of adolescent well-being over time. Study 1: Cross-Sectional Network of Empathy in the Well-Being Profile This study focused on the positioning of the empathy dimension within the adolescent well-being network. Drawing on the “double-edged sword” perspective from prior literature (e.g., Lai et al., 2021; Wang et al., 2025), we hypothesized that this dimension—which predominantly captures emotional contagion—would exhibit a dual nature: positively associating with interpersonal functioning, while potentially showing negative associations with self-directed affects, reflecting the inherent cost of emotional resonance. Methods Participants and Procedure A total of 2,582 adolescents were initially recruited from Beijing, China. After excluding cases that failed data-quality checks (e.g., unrealistic age reports or failure on an attention-check item), the final analytic sample comprised 2,201 participants (males = 1041, 47.3%), with a mean age of 12.07 years ( SD = 3.01; range = 8–19 years). The collection of self-report questionnaires on the spot, via an online survey platform Wenjuanxing, ensured the immediate retrieval of all data. Measures Empathy and well-being dimensions. The 48-item Well-Being Profile (WB-Pro; Herbert et al., 2020), comprises 15 dimensions, including empathy, with each dimension measured by 3–4 items. It was rated on a 9-point Likert scale (1 = strongly disagree, 9 = strongly agree), with higher scores reflecting higher levels of each dimension (Empathy: Cronbach’s α = 0.597; Well-being dimensions: Cronbach’s α = 0.735–0.904, Table 1 ). Table 1 Descriptive statistics of key variables in Study 1. Variables M SD Skewness Kurtosis Cronbach’s α AU 6.955 1.611 -0.668 0.167 0.808 CT 7.398 1.429 -0.916 0.809 0.832 CO 7.126 1.500 -0.706 0.431 0.848 ES 6.713 1.729 -0.596 -0.100 0.803 AE 6.553 1.366 -0.470 0.714 0.597 EN 7.231 1.490 -0.760 0.274 0.805 ME 7.119 1.652 -0.815 0.302 0.871 OP 7.175 1.723 -0.919 0.394 0.898 PE 7.302 1.660 -1.105 1.078 0.904 PR 7.357 1.376 -0.976 1.385 0.735 PB 7.215 1.512 -0.852 0.740 0.856 RE 6.809 1.752 -0.670 -0.023 0.855 SA 7.049 1.546 -0.714 0.191 0.823 SE 7.159 1.541 -0.742 0.213 0.846 VI 7.248 1.629 -0.948 0.548 0.876 Note. AU = Autonomy; CT = Clear Thinking; CO = Competence; ES = Emotional Stability; AE = Affective Empathy; EN = Engagement; ME = Meaning; OP = Optimism; PE = Positive Emotions; PR = Positive Relationships; PB = Prosocial Behavior; RE = Resilience; SA = Self-Acceptance; SE = Self-Esteem; VI = Vitality. Data Analyses Network analysis. All statistical analyses were conducted in R (version 4.4.1). The estimateNetwork function from the bootnet package (Epskamp et al., 2018 ) was applied to estimate networks of well-being and empathy. A Gaussian Graphical Model (GGM) was employed, with network structure regularization via the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) algorithm to minimize spurious associations and reduce pseudo-correlations (Costantini et al., 2015 ). Network visualization was performed using the qgraph package in R (Epskamp et al., 2012 ). Centrality indices were calculated for each node to identify those most central to network structure maintenance, as highly connected nodes play key roles in network integrity. For networks containing negative edges, centrality was calculated by expected influence (EI), and visualized using the centralityPlot function in the qgraph package (Epskamp et al., 2012 ; Robinaugh et al., 2016 ). Accuracy and Stability Estimation. We assessed the estimated networks by examining both the accuracy of edges and the stability of centrality indices. 95% bootstrap confidence intervals (CIs) for the edge weights were computed using the bootnet package. The centrality stability coefficient (CS-coefficient) was used to quantify the stability of the centrality indices. Following Epskamp et al. ( 2018 ), CS-coefficients above 0.25 indicate acceptable stability, and values exceeding 0.50 reflect good stability. Results Descriptive Statistics As shown in Table 1 , all items fell within acceptable ranges for normality, with skewness (-1.105 to -0.470) and kurtosis (-0.100 to 1.385) well below the conventional thresholds of 3 and 10, respectively. [Insert Table 1 ] Network Analyses of Empathy and Other Well-Being Components The cross-sectional network (Fig. 1 ) revealed differential associations of the empathy dimensions. Edge coefficients (Table 2 ) revealed that empathy exhibited significant negative partial correlations with self-acceptance ( β = -0.009), optimism ( β = -0.006), and positive emotions ( β = -0.014). Conversely, it was positively correlated with engagement ( β = 0.016), autonomy ( β = 0.004), competence ( β = 0.016), and positive relationships ( β = 0.082). Notably, the empathy-prosocial behavior association ( β = 0.355) was the strongest edge, significantly exceeding all other associations ( Figure S1 a ). Optimism emerged as the most central node (EI z-score = 1.422; Table S1 ), underscoring its core role in the network. Table 2 Edge coefficients of key variables in Study 1 and Study 2 networks. AU CT CO ES EN ME OP PE PR PB RE SA SE VI Study1 AE 0.004 – 0.016 – 0.016 – -0.006 -0.014 0.082 0.355 – -0.009 – – Study2 AE – -0.095 – – – – – – -0.055 – -0.085 -0.119 -0.066 – 0.028 – – 0.005 – – – – – 0.139 – – – – CE 0.043 0.071 0.072 0.014 0.077 – 0.033 0.016 0.050 0.101 0.098 – 0.018 0.051 0.007 – – 0.036 – – – – – 0.058 – 0.007 – – Note. Only non-zero edges retained by the LASSO are displayed in the table. In study 2, black represents the edges parameters from empathy to other well-being variables, and red represents edge parameters in the opposite direction. AE = Affective Empathy; CE = Cognitive Empathy; AU = Autonomy; CT = Clear Thinking; CO = Competence; ES = Emotional Stability; EN = Engagement; ME = Meaning; OP = Optimism; PE = Positive Emotions; PR = Positive Relationships; PB = Prosocial Behavior; RE = Resilience; SA = Self-Acceptance; SE = Self-Esteem; VI = Vitality. [Insert Fig. 1 ] [Insert Table 2 ] To further dissect the specific sources of these associations observed at the dimension level, we conducted an item-level empathy network analysis (detailed in Supplementary Material, Figure S2 and Table S2 and S3 ). These granular results clarified that items capturing emotional contagion and susceptibility (e.g., Item 4: “ I easily get caught up in other people’s feelings ”, and Item 3: “ I feel others’ emotions ”) drove the broad negative associations. Accuracy and stability estimation of networks Edge-weight bootstrap procedure ( Figures S1 b and S3b ) confirmed acceptable accuracy for all edges. The bootstrapped case-dropping stability analyses ( Figures S1 c and S3c ) confirmed high stability of EI centrality for the network (CS coefficient = 0.75). Discussion Study 1 results suggest that the WB-Pro empathy dimension functions primarily as affective empathy, characterized by emotional susceptibility. The network structure reveals a distinctive trade-off: while this dimension is robustly linked to interpersonal connectivity (e.g., prosocial behavior), it shows negative associations with subjective well-being (e.g., positive emotions). This pattern implies that while emotional resonance co-occurs with high social capacity, it may simultaneously deplete internal emotional resources. These cross-sectional findings highlight the limitations of a single-dimension approach and underscore the need to introduce cognitive empathy and longitudinal design to disentangle these complex dynamics. Study 2: Longitudinal Network of Dual-Dimensional Empathy in Well-Being Profile Study 2 employed a six-month longitudinal network analysis to examine the specific contributions of empathy subtypes to adolescent well-being. This investigation operationalized the dual-dimensional nature of empathy by retaining the empathy dimension from the WB-Pro as affective empathy, and incorporating cognitive empathy via the perspective-taking subscale of the Interpersonal Reactivity Index. By assessing the temporal associations between these subtypes and other well-being components, we aimed to clarify their differential roles. Based on prior theoretical distinctions (Thompson et al., 2022 ), we hypothesized that cognitive empathy would show positive links across the network. In contrast, affective empathy was expected to exhibit a “double-edged” dynamic: positively predicting interpersonal outcomes while negatively predicting self-related well-being. Methods Participants and Procedure Data were collected from 730 adolescents in Qingdao, China at Time 1(T1; February 2025). Six months later (T2; August 2025), 606 adolescents were retained (82.9% retention rate). After excluding participants with invalid responses (e.g., abnormal answer patterns, unrealistic age reports, or invalid student IDs), the final longitudinal sample consisted of 587 adolescents (303 males, 51.6%). At T2, the mean age was 14.74 years ( SD = 2.73; range = 12–18). Self-report questionnaires were administered on-site to ensure complete data retrieval. Measures Affective empathy and well-being dimensions. Consistent with Study 1, the WB-Pro was used to assess affective empathy and well-being dimensions (Affective Empathy: Cronbach’s α = 0.649, 0.732 at T1–T2; Well-being dimensions: Cronbach’s α = 0.826–0.927, 0.855–0.940 at T1–T2, Table 3 ). Table 3 Descriptive statistics of key variables at T1 and T2 in Study 2. Variables M SD Skew Kurtosis Cronbach’s α Variables M SD Skew Kurtosis Cronbach’s α T1_AU 6.939 1.550 -0.624 0.227 0.866 T2_AU 7.116 1.487 -0.696 0.579 0.867 T1_CT 7.044 1.506 -0.702 0.482 0.888 T2_CT 7.196 1.479 -0.791 0.777 0.895 T1_CO 6.924 1.564 -0.575 0.074 0.897 T2_CO 7.102 1.539 -0.800 0.867 0.924 T1_ES 6.779 1.610 -0.483 -0.143 0.826 T2_ES 6.955 1.600 -0.626 0.129 0.869 T1_CE 6.428 1.100 -0.219 1.177 0.745 T2_CE 6.431 1.081 -0.042 0.442 0.768 T1_EN 7.065 1.518 -0.707 0.419 0.893 T2_EM 7.113 1.517 -0.746 0.607 0.899 T1_ME 6.838 1.728 -0.765 0.495 0.911 T2_ME 7.053 1.656 -0.844 0.533 0.933 T1_OP 6.988 1.678 -0.753 0.173 0.926 T2_OP 7.131 1.623 -0.886 0.685 0.935 T1_PE 7.111 1.556 -0.832 0.492 0.919 T2_PE 7.170 1.561 -0.944 1.117 0.936 T1_PR 7.177 1.423 -0.716 0.604 0.850 T2_PR 7.237 1.401 -0.710 0.635 0.855 T1_PB 7.115 1.443 -0.837 1.320 0.903 T2_PB 7.214 1.469 -0.927 1.362 0.900 T1_RE 6.722 1.756 -0.593 -0.134 0.902 T2_RE 6.931 1.698 -0.703 0.137 0.924 T1_SA 6.983 1.530 -0.643 0.104 0.882 T2_SA 7.068 1.540 -0.691 0.444 0.904 T1_SE 6.993 1.555 -0.742 0.645 0.908 T2_SE 7.127 1.554 -0.922 1.079 0.930 T1_VI 6.915 1.706 -0.814 0.438 0.927 T2_VI 6.995 1.649 -0.880 0.794 0.940 T1_AE 6.635 1.275 -0.460 0.829 0.649 T2_AE 6.785 1.359 -0.493 1.197 0.732 Note. SA = Self-Acceptance; VI = Vitality; EN = Engagement; OP = Optimism; ES = Emotional Stability; RE = Resilience; ME = Meaning; AU = Autonomy; SE = Self-Esteem; PB = Prosocial Behavior; CO = Competence; CT = Clear Thinking; PE = Positive Emotions; PR = Positive Relationships; AE = Affective empathy; CE = Cognitive Empathy. Cognitive empathy. The perspective-taking subscale of Interpersonal Reactivity Index (IRI; Davis, 1980), contains 7 items assessing the tendency to adopt others’ viewpoints (e.g., “ I believe that there are two sides to every question and try to look at them both ”). It was rated on a 9-point Likert scale (1 = strongly disagree, 9 = strongly agree), with higher score reflecting higher level of perspective-taking (Cronbach’s α = 0.745, 0.768 at T1–T2). Data Analyses Confirmatory Factor Analysis. Confirmatory Factor Analysis (CFA) with Maximum Likelihood (ML) estimation was conducted to validate the dual-dimensional structure of empathy. We compared a single-factor model (Model 1) against a two-factor model (Model 2). Model fit was evaluated using χ 2 / df , CFI, TLI, RMSEA, and SRMR. Acceptable model fit was designed as CFI and TLI>0.90, SRMR<0.08, and RMSEA<0.10 (Byrne, 2013 ). Model comparisons were performed using the chi-square difference test (Kline, 2023 ). Network estimation and visualization. To examine the dynamic associations between empathy and well-being, we employed the glmnet package in R to estimate cross-lagged panel networks (CLPN) model (Friedman et al., 2010 ). Two types of regression models were constructed: autoregressive models, where each variable at T1 predicts its own value at T2 while controlling for all other T1 variables, and cross-lagged models, examining how each T1 variable predicts other T2 variables after adjusting for all T1 variables (Funkhouser et al., 2021 ). Model parameters were tuned via 10-fold cross-validation tuning parameter selection method, and LASSO were applied to regularize the regression coefficients in order to reduce false-positive edges (Wysocki et al., 2022 ). We used the qgraph package in R to visualized these results as networks (Epskamp et al., 2018 ). In these networks, nodes represent dimensions and edges represent predictive relationships across time (e.g., an arrow from variable A to variable B indicates that variable A at time T significantly predicts variable B at time T + 1). We further calculated the centrality index of the networks: out-prediction and in-prediction (Wysocki et al., 2022 ). In-prediction reflects the extent to which a node is predicted by other nodes, whereas out-prediction captures the extent to which a node predicts other nodes. Dimensions with high out-prediction values occupy central positions in the network and have strong impacts on the network’s maintenance and dynamic changes. Results Descriptive Statistical Analysis As shown in Table 3 , skewness (-0.944 to -0.042) and kurtosis (-0.143 to 1.362) values all fell within acceptable limits for normality (skewness < 3 and kurtosis < 10). [Insert Table 3 ] Confirmatory Factor Analysis Regarding the factor structure of empathy, minor adjustments based on Modification Indices (MIs) were made to the two-factor model (Model 2). This revised model demonstrated acceptable fit across all indices ( Table S4 ). A chi-square difference test confirmed that the two-factor model provided a significantly better fit than the single-factor model at both time points (T1: Δ χ 2 ∕Δ df = 138.9, p < .001; T2: Δ χ 2 ∕Δ df = 381.03, p < .001; see Table S5 and S6 ). These results support the structural distinction between cognitive and affective empathy. Cross-Lagged Panel Network Analysis of Cognitive Empathy and Well-Being The network (Fig. 2 a) revealed that cognitive empathy positively predicted most dimensions of well-being (except self-acceptance and meaning), and was in turn positively predicted by prosocial behavior, emotional stability, autonomy, and self-acceptance (Table 2 ). Notably, the strongest lagged associations for cognitive empathy were with prosocial behavior, followed by resilience. In addition, cognitive empathy showed a significant positive autoregressive effect, indicating temporal stability. Within the broader network, the strongest cross-lagged edge was from vitality to engagement ( β = 0.214). Centrality indices (Fig. 2 c and 2 d, Table S7 ) identified optimism as having the highest out-prediction, marking it as a key predictor, while engagement showed the highest in-prediction, indicating it is a primary outcome. Cross-Lagged Panel Network Analysis of Affective Empathy and Well-Being The affective empathy network (Fig. 2 b) showed a distinct pattern. As detailed in Table 2 , affective empathy negatively predicted self-acceptance, resilience, positive relationships, self-esteem, and clear thinking. The strongest negative path was from affective empathy to self-acceptance. Conversely, affective empathy was positively predicted by prosocial behavior, emotional stability, and autonomy. Consistent with the cognitive empathy network, vitality to engagement remained the strongest cross-lagged edge ( β = 0.215). Centrality indices (Fig. 2 e and 2 f, Table S8 ) confirmed optimism and engagement as the highest out-prediction and in-prediction nodes, respectively. Discussion The confirmation of the two-factor model supports the structural distinction between affective and cognitive empathy, establishing a necessary prerequisite for examining their differential functions. Longitudinal networks further revealed their divergent roles. Specifically, cognitive empathy exerted broad positive predictive effects across interpersonal and intrapersonal well-being, serving as a stable resilience-promoting capacity. Contrary to the the “double-edged” expectation, affective empathy failed to predict future prosocial behavior. Instead, it functioned strictly as a vulnerability factor, anticipating broad declines across the interpersonal (positive relationships), intrapersonal (self-acceptance, self-esteem), and functional domains (clear thinking, resilience). These findings underscore the critical necessity of distinguishing empathy subtypes to capture their opposing long-term dynamics on adolescent well-being. General Discussion By utilizing network analysis, the present study integrates cross-sectional and longitudinal data to provide a nuanced understanding of how empathy functions within the adolescent well-being system. Crucially, while Study 1 examined empathy primarily through the affective-dominant WB-Pro lens, Study 2 introduced the measurement of cognitive empathy, allowing for a decisive comparison of their distinct predictive roles. Our findings demonstrate that incorporating cognitive empathy is essential for a valid representation of the WB-Pro framework, as the two-factor structure significantly outperformed the unitary model. This distinction proved critical: cognitive empathy consistently links to adaptive outcomes, whereas affective empathy exhibits a paradoxical vulnerability effect. Affective Empathy as a Vulnerable Factor Contradicting the “double-edged sword” hypothesis, Study 2 results indicate that affective empathy functions strictly as a vulnerability over time. The discrepancy between Study 1 and Study 2 clarifies this dynamic. In Study 1, affective empathy was positively associated with interpersonal assets (e.g., prosocial behavior, positive relationships) and motivational functioning (e.g., competence, autonomy). Without longitudinal directionality, these correlations masqueraded as adaptive effects. However, Study 2 debunked this illusion. Over six months, affective empathy failed to positively predict any adaptive outcomes. Instead, it functioned solely as a depleting factor, simultaneously undermining interpersonal well-being (e.g., positive relationships), eroding intrapersonal well-being (e.g., self-acceptance, self-esteem), and impairing adaptive functioning (e.g., clear thinking, resilience). Crucially, although affective empathy was rooted in high-functioning antecedents (e.g., positively predicted by prosocial behavior), this emotional resonance does not feed back into the system to promote future growth. Instead, it becomes a “sink” that consumes psychological resources without generating future benefits (Chávez et al., 2025 ; Laninga-Wijnen et al., 2019 ). A striking divergence emerged regarding positive relationships, which were positively associated with affective empathy in Study 1 but negatively predicted by it in Study 2. This reversal underscores the distinction between immediate intimacy and chronic depletion. Cross-sectionally, the immediate experience of emotional sharing fosters a sense of connection, appearing as high positive relationships. However, longitudinally, unmitigated emotional contagion incurs an intrapersonal debt. The deterioration of intrapersonal strength (e.g., self-acceptance) and adaptive functioning (e.g., resilience) driven by affective empathy likely creates a “spillover effect”, where the adolescent’s internal depletion eventually undermines their capacity to sustain healthy social interactions, leading to the deterioration of positive relationships over time (Deng et al., 2026 ). Cognitive Empathy as a Protective Factor In stark contrast to the vulnerability profile of affective empathy, cognitive empathy emerged as a comprehensive protective factor within the adolescent well-being network. Longitudinal results demonstrated that cognitive empathy positively predicted improvements across interpersonal well-being (e.g., prosocial behavior, positive relationships), intrapersonal well-being (e.g., self-esteem, optimism), motivational resources (e.g., engagement, competence), and most notably, adaptive functioning (e.g., resilience, clear thinking). Of particular significance is the robust prediction for resilience—a component negatively predicted by affective empathy. This specific link suggests that the capacity to adopt others’ perspectives functions as a critical “top-down” regulatory mechanism that facilitates the stress regulation inherent to resilience (Wang et al., 2024 ). Unlike affective empathy, which relies on automatic emotional contagion that blurs self-other boundaries, cognitive empathy involves effortful cognitive processing (Wellman et al., 2001 ). This allows adolescents to understand others’ distress while maintaining psychological distance, thereby buffering against emotional over-arousal and self-depletion. Consequently, while affective empathy becomes a cost of caring, cognitive empathy serves as a sustainable resource, fostering adaptive functioning that supports both the self (Benítez-Sillero et al., 2024 ) and the social bond (Georgiou et al., 2019 ). This functional divergence underscores that without differentiating these subtypes, the critical trade-off between interpersonal utility and intrapersonal costs would remain obscured by global empathy measures. Shared Developmental Roots and Divergent Pathway While our findings highlight the functional divergence between affective and cognitive empathy, the longitudinal network also unveiled their shared developmental antecedents. Both subtypes were significantly and positively predicted by prosocial behavior, autonomy, and emotional stability. This suggests that empathy, regardless of its form, is fundamentally rooted in a high-functioning psychological profile. Adolescents who are socially engaged, self-governing, and emotionally regulated are predisposed to developing both the capacity to resonate with others’ emotions and the capacity to understand their perspectives. Prosociality likely provides the necessary social exposure to cultivate these sensitivities, while autonomy and emotional stability serve as the secure psychological foundation that allows adolescents to turn their attention outward to others. Crucially, however, cognitive empathy was uniquely predicted by self-acceptance. This distinction is theoretically revealing. Unlike affective empathy, which can be triggered as an automatic emotional response to prosociality, cognitive empathy is an effortful, top-down process requiring the suppression of egocentric bias (Deng et al., 2025 ). Self-acceptance may function as a critical “cognitive liberator” in this process. Adolescents who accept themselves are likely less consumed by ego-defensive processing or personal insecurities, thereby freeing up the cognitive resources required to objectively adopt others’ viewpoints. This finding suggests that while general psychosocial adjustment fuels the drive to empathize, a positive relationship with the self is specifically required to cultivate the skill of perspective-taking. Core Components within Adolescent Well-Being Network Beyond the empathy-specific dynamics, our analysis identified the central drivers of the broader well-being system. In the cross-sectional network, optimism emerged as the most central node, underscoring the pivotal role of positive future orientation in adolescent psychological functioning. This aligns with prior studies highlighting optimism as a core protective resource for navigating developmental transitions and uncertainty (Fredrickson, 2001 ; Ni et al., 2025 ). Notably, affective empathy showed an inverse association with optimism, implying that heightened sensitivity to others’ distress may dampen adolescents’ capacity to maintain positive expectations about their own future. Shifting to a dynamic perspective, the longitudinal network revealed that optimism, autonomy, and vitality emerged as the most influential nodes. These constructs represent self-sustaining resources that anchor systemic well-being over time. Importantly, cognitive empathy positively predicted subsequent levels of all three of the core nodes. It further reinforces the role of cognitive empathy as a system-wide energizer that not only promotes specific skills but also fortifies the central pillars of adolescent well-being. Practical Implications Our findings offer several actionable insights for fostering adolescent well-being. First, regarding assessment, the distinct profiles of the two subtypes suggest that the empathy subscale of the WB-Pro primarily captures the emotional susceptibility. Researchers applying this instrument in Chinese adolescent samples should interpret findings with caution, recognizing that observed patterns likely reflect emotional susceptibility rather than a broad, resilience-promoting capacity. Second, for intervention, clinical and educational programs should aim to assist adolescents in regulating emotional resonance while strengthening self-other differentiation. Specifically, training in perspective-taking and resilience-building could amplify the adaptive benefits of cognitive empathy while buffering against the intrapersonal costs of affective empathy. Third, from a systemic perspective, given that individual-level empathy changes may not suffice for holistic well-being, we advocate for multi-component approaches. Identifying and reinforcing high-centrality nodes—specifically optimism, autonomy, and vitality—provides effective entry points for system-wide promotion (Li et al., 2026 ). Interventions targeting these core resources are likely to yield cascading benefits across the entire well-being network. Limitations and Future Avenues Despite the strengths of employing longitudinal network analysis to disentangle the dual-dimensional nature of empathy, several limitations warrant consideration. First, the strictly negative associations between affective empathy and self-related outcomes necessitate further investigation into underlying mechanisms. Future research should explore potential moderators, such as emotion regulation strategies or peer conformity, that might mitigate these costs of caring. Second, although cognitive empathy appeared uniformly adaptive in this study, prior research suggests it may relate to poorer mental health in specific contexts (e.g., “Machiavellian” perspective-taking; Tan et al., 2023 ). Future work should employ contextualized assessments to delineate the boundary conditions under which cognitive empathy might exert adverse effects. Finally, reliance on self-reports introduces potential bias. Incorporating multi-informant perspectives (e.g., parents, teachers) and cross-cultural comparisons would enhance the generalizability and robustness of these findings. Conclusion This research elucidates the distinct roles of empathy within the multidimensional adolescent well-being system. Specifically, our findings reveal a stark functional divergence within the empathic dimension itself: while cognitive empathy serves as a resilience-promoting component that reinforces the broader system, affective empathy—when unregulated—acts as a “leak” within the network, and despite its social roots, this affective component functions strictly as a vulnerability that erodes overall well-being. Consequently, these results suggest that the path to flourishing in adolescence lies not merely in feeling with others, but in the capacity to understand them while maintaining the integrity of the self. Ultimately, this distinction is vital for moving beyond global endorsements of empathy toward more precise, structurally targeted approaches to youth development. Declarations This study was reviewed and approved by the ethical committee of Capital Normal University (Date: November 20, 2024; Reference No: CNU-202412001). Participant Consent Statement: Informed consent was obtained from all adolescent participants and their legal guardians (parents) prior to their inclusion in the study. Author Contribution Yanhe Deng: Conceptualization, Funding acquisition, Project administration, Supervision, Data collection, Writing – original draft, Writing – review & editing. Xuan Han: Methodology, Writing – original draft.Haichun Zhou: Writing – original draft. References American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). 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Advances.in/psychology , 2 , Article e739621. https://doi.org/10.56296/aip00037 Xing, L., Deng, S. W., & Ho, G. W. (2023). From empathy to resilience: The mediating role of emotional intelligence. Psychological Reports , 128 (6), 4533–4548. https://doi.org/10.1177/00332941231220299 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9194600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611774517,"identity":"0eff6cf9-4a77-42c2-b4d0-235c9d90b121","order_by":0,"name":"Yanhe Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDCCAxAqgYG9AcxgbCBeC88BkrVIJBCphe947+HXPDV38vglHz97zMNgI7vhAPOzB/i0SJ45l2bNc+xZseTsNHNjHoY04w0H2MwN8GkxuJFjZszDdjhxw+0cNmkeBiDjAA+bBF4t998Atfw7nLj/5hmQlv9EaLnBY/yYtw1ouAQPSMsBwlokz+SYMc7tO5w440yameQcg2TjmYfZzPBq4Tt+xvjDm2+HE/vbDz+TeFNhJ9t3vPkZXi1AwCbFg3AnEDMTUA9S8vEHYUWjYBSMglEwkgEAmMdMuohq0gkAAAAASUVORK5CYII=","orcid":"","institution":"Capital Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yanhe","middleName":"","lastName":"Deng","suffix":""},{"id":611774519,"identity":"97522a34-291c-4f07-83e0-0efdc3b4dcd2","order_by":1,"name":"Xuan Han","email":"","orcid":"","institution":"Capital Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Han","suffix":""},{"id":611774520,"identity":"13c11b65-a042-4133-a941-e3eb1f9e4094","order_by":2,"name":"Haichun Zhou","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Haichun","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-03-23 02:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9194600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9194600/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105502128,"identity":"d3d2e6b6-c57c-4b7c-ac2a-98acb1712664","added_by":"auto","created_at":"2026-03-26 17:51:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":406021,"visible":true,"origin":"","legend":"\u003cp\u003eCross-sectional network analysis of affective empathy and well-being dimensions\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003e(a) Network of affective empathy and other Well-Being nodes. Green lines indicate positive relationships and red lines indicate negative relationships between nodes. Thicker lines between nodes represent stronger relationships. (b) The expected influence centrality indices, shown as standardized z scores.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9194600/v1/e644438060eb95e9679a33e8.png"},{"id":105502133,"identity":"517f94f5-d391-444e-a49e-cf73b4893508","added_by":"auto","created_at":"2026-03-26 17:51:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":540507,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged panel network analysis in Study 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote.\u003c/strong\u003e\u003c/em\u003e (a) Network of cognitive empathy and other Well-Being nodes. (b)Network of affective empathy and other Well-Being nodes. An arrow from variable A to variable B represents the significant prediction of variable A at T and variable B at T+1. Blue lines indicate positive relationships and red line indicate negative relationships between nodes. Thicker lines between nodes represent stronger relationships. (c) and (d) Centrality indices for network of cognitive empathy and other Well-Being nodes. (e) and (f) Centrality indices for network of affective empathy and other Well-Being nodes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9194600/v1/b5c070dc5edfea2b455bd99c.png"},{"id":105567113,"identity":"91876a79-034a-4068-a4fb-4a2dcec62310","added_by":"auto","created_at":"2026-03-27 12:58:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2170983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9194600/v1/1b7c0ec5-f9c5-46ff-83a0-119cb0fb266a.pdf"},{"id":105502129,"identity":"0ca8e3e2-67f0-43fc-9b92-5062742c44f1","added_by":"auto","created_at":"2026-03-26 17:51:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":546966,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9194600/v1/646c4261caa79a7003fcdd32.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two Sides of the Same Coin: Uncovering Differential Roles of Cognitive and Affective Empathy in Adolescent Multidimensional Well-Being Network","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the 21st century, interest in well-being has grown exponentially in both scientific research and professional psychological practice, reflecting public values and individuals\u0026rsquo; subjective experiences of life. Crucially, well-being is not a unitary construct but a complex system composed of multiple psychological components (Huppert \u0026amp; Ruggeri, 2018)\u0026mdash;a perspective that underscores the need for comprehensive conceptualizations. Building on the theoretical approach that defines well-being as equivalent to positive mental health, Marsh et al. (2020) developed the Well-Being Profile (WB-Pro), which defines 15 dimensions of well-being as the positive opposites of common mental disorders. This theoretical perspective enables the structure of well-being to be contrasted with the internationally agreed framework of ill-being (e.g., Diagnostic and Statistical Manual of Mental Disorders, DSM; \u003cem\u003eAmerican Psychiatric Association\u003c/em\u003e, 1994, 2013). One benefit of this positive mental health approach is to provide a systematic framework of well-being to resolve the lack of consensus regarding the constituents of well-being.\u003c/p\u003e\n\u003cp\u003eNotably, empathy was introduced to the WB-Pro as a novel and key component of well-being because it is essential to positive social functioning among adults. However, empirical evidence indicates that empathy does not consistently correlate with reduced ill-being; particularly in adolescence, cognitive and affective empathy differentially predict positive mental health (Ben\u0026iacute;tez-Sillero et al., 2024; Cui et al., 2022; Johander et al., 2022). These findings highlight the urgency of investigating how two subtypes of empathy\u0026mdash;when conceptualized as the dimension of well-being\u0026mdash;exert distinct interactions with other components within the adolescent well-being network. While the WB-Pro demonstrated good psychometric properties in adults (Marsh et al., 2020; Scalas et al., 2023), evidence also indicates conceptual and measurement challenges\u0026mdash;particularly for the empathy dimension (Ni et al., 2025). Little is known about how this dimension functions in adolescence, where cognitive and affective empathy may divergently shape the well-being system. This study aims to address this gap by applying network analysis to investigate multidimensional well-being in adolescents, with a specific focus on cognitive and affective empathy, to elucidate their distinct roles within the well-being network during this developmental stage.\u003c/p\u003e\n\u003cp\u003eEmpathy in the Well-Being Profile\u003c/p\u003e\n\u003cp\u003eEmpathy occupies an important yet ambiguously operationalized position within the 15-dimensional framework of the WB-Pro. As a multidimensional measure of positive mental health, the WB-Pro categorizes empathy under the domain of \u0026ldquo;Prosocial emotions and behaviors\u0026rdquo;, a choice rooted in the recognition that such capacities are fundamental to human functioning and vitality (Eisenberg et al., 2007). This inclusion underscores empathy\u0026rsquo;s acknowledged role in fostering positive psychological states and prosocial behavior.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsychological theory (Berluti et al., 2023; Engert et al., 2014; Pow et al., 2016) distinguishes two core components of empathy: cognitive empathy (the ability to comprehend others\u0026rsquo; perspectives and emotions) and affective empathy (the capacity to experience or share others\u0026rsquo; feelings). Cognitive empathy enables perspective-taking, which enhances positive social functioning, interpersonal satisfaction, and prosocial behavior (Levy et al., 2018; Mwilambwe-Tshilobo et al., 2023). Affective empathy, by contrast, involves emotional resonance, deepening emotional understanding and motivating prosocial responses (Gaspar \u0026amp; Esteves, 2022; Levy et al., 2018). However, the WB-Pro does not explicitly differentiate between cognitive and affective empathy in its operationalization. Instead, the WB-Pro defines empathy as the ability to understand and share others\u0026rsquo; emotions (Davis, 1983; Marsh et al., 2020)\u0026mdash;a definition that emphasizes facilitating the anticipation of others\u0026rsquo; motivations, forming emotional connections, and promoting prosocial behaviors (Cornish et al., 2020; Li et al., 2024). Notably, its current items primarily tap into emotional susceptibility and emotional contagion, such as \u0026ldquo;\u003cem\u003eI easily get caught up in other people\u0026rsquo;s feelings\u003c/em\u003e\u0026rdquo;, and even capture negative emotional responses, for example, \u0026ldquo;\u003cem\u003eOther people\u0026rsquo;s misfortunes usually disturb me a great deal\u003c/em\u003e\u0026rdquo;. Validation studies with Italian adults further suggest that three of the four empathy items retain much specific variance\u0026mdash;even after controlling for global well-being\u0026mdash;indicating poor alignment with the general well-being factor in the WB-Pro (Scalas et al., 2023). Consistently, a cross-sectional network study with Chinese adolescents\u0026nbsp;using a shortened 15-item WB-Pro found that single-item empathy, while strongly correlated with prosocial behavior, exhibited a negative association with emotional stability and autonomy and had the lowest centrality (Expected Influence = \u0026ndash;2.621) among all dimensions (Ni et al., 2025). These results suggest that when conceptualized primarily as emotional contagion, empathy may have limited\u0026mdash;or even detrimental\u0026mdash;contributions to\u0026nbsp;the adolescent well-being system.\u0026nbsp;Taken together, these limitations highlight the necessity of disentangling cognitive and affective empathy, as well as their valence, for more precise well-being assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough several studies have adopted a network approach to examine the multidimensional structure of well-being, few have specifically investigated the associative patterns between empathy and other well-being components within adolescent networks. Existing network-based studies have primarily focused on overall network structures and have largely relied on cross-sectional designs (e.g., Landvreugd et al., 2024), which limits their ability to capture the dynamic role of empathy within the well-being system. In addition, limitations in the measurement of empathy further constrain prior network-based research. For example, Ni et al. (2025) employed the 15-item version of WB-Pro, in which empathy is assessed as a single, undifferentiated component.\u003c/p\u003e\n\u003cp\u003eDifferential Roles of Cognitive and Affective Empathy in Adolescents\u0026rsquo; Well-Being\u003c/p\u003e\n\u003cp\u003eEmpathy, a critical mechanism for navigating social responsibilities and integrating into social groups (Berluti et al., 2023; Engert et al., 2014), is crucial for fostering adaptation, strengthening interpersonal relationships, and reducing antisocial behaviors (Marzilli et al., 2021; Xing et al., 2023). Converging evidence demonstrates that during adolescence, cognitive and affective empathy follow different developmental trajectories, thereby constituting well-being through unique pathways.\u003c/p\u003e\n\u003cp\u003eCognitive empathy, on the one hand, is strongly associated with adolescents\u0026rsquo; well-being and plays a central role in promoting perspective-taking and social competence. During this developmental phase, teenagers transition from childhood egocentrism to more nuanced, context-sensitive understandings of others\u0026rsquo; motivations\u0026mdash;a shift driven by expanded exposure to diverse perspectives, maturation of executive functions, and increasing social role-taking in schools and communities (Levy et al., 2018; Mwilambwe-Tshilobo et al., 2023). As cognitive abilities (e.g., executive functioning) mature, adolescents become better equipped to engage in perspective-taking, which allows adolescents to understand others\u0026rsquo; needs and express empathy (Pow et al., 2016). Research has shown that this ability to adopt others\u0026rsquo; perspectives is significantly associated with well-being, as adolescents demonstrating strong cognitive empathy receive greater peers and adult approval, reinforcing its adaptive value. Conversely, deficits in cognitive empathy correlate with increased aggression (Deng et al., 2025).\u003c/p\u003e\n\u003cp\u003eAffective empathy, on the other hand, develops from primitive emotional contagion to more intense, shared, and personally involving responses. While this progression enhances adolescents\u0026rsquo; ability to recognize others\u0026rsquo; feelings and motivates prosocial or comforting behaviors, it also heightens vulnerability to personal distress, negative affect, or burnout, highlighting its potential \u0026ldquo;double-edged\u0026rdquo; impact on mental health. Adolescents, whose executive functions and emotional regulation skills are still maturing, may be overly sensitive or overwhelmed by others\u0026rsquo; emotions, due to underdeveloped capacity to set emotional boundaries. This vulnerability is partly attributable to the ongoing maturation of the amygdala, which underlies adolescents\u0026rsquo; relatively limited capacity for emotional regulation (Skyberg et al., 2023). As a result, they may struggle to balance their own emotions with those of others (Gaspar \u0026amp; Esteves, 2022; Levy et al., 2018). Unlike adults, who can strategically regulate empathy across contexts, children and adolescents often lack the cognitive resources to modulate empathic responses to meet dynamic interpersonal and cultural demands. Empirical evidence further reveals that heightened affective empathy in adolescence correlates with increased depression and anxiety, as adolescents may struggle to cope with emotional overload (Jauniaux et al., 2020; Vecchio \u0026amp; De Pascalis, 2023).\u003c/p\u003e\n\u003cp\u003eIn summary, adolescence represents a critical developmental stage in which cognitive and affective empathy follow distinct trajectories. While cognitive empathy advances rapidly and supports perspective-taking, affective empathy often remains less regulated, with limited boundary-setting capacities. These developmental differences likely explain the distinct associations between empathy subtypes and adolescent well-being, underscoring their unique roles in navigating social relationships and societal expectations (Gaspar \u0026amp; Esteves, 2022; Padilla-Walker et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe Present Study\u003c/p\u003e\n\u003cp\u003eDespite growing interest in the empathy-well-being relationship among adolescents, the distinct roles of cognitive and affective empathy\u0026mdash;considered as components of well-being\u0026mdash;remain largely unexplored, particularly in light of the evolving empathic capacities and unique challenges of this stage. To bridge this gap, the present research employs a network approach across two studies to investigate empathy\u0026rsquo;s systemic role within the multidimensional well-being system. Study 1 adopts a cross-sectional design to map the position of empathy\u0026mdash;measured as a single, affective-dominant construct within the WB-Pro framework\u0026mdash;relative to other well-being components. Study 2 extends\u0026nbsp;this inquiry via a six-month longitudinal design, explicitly differentiating between cognitive and affective empathy. By capturing temporal dynamics, Study 2 aims to elucidate how these distinct subtypes differentially shape the stability and fluctuations of adolescent well-being over time.\u003c/p\u003e"},{"header":"Study 1: Cross-Sectional Network of Empathy in the Well-Being Profile","content":"\u003cp\u003eThis study focused on the positioning of the empathy dimension within the adolescent well-being network. Drawing on the \u0026ldquo;double-edged sword\u0026rdquo; perspective from prior literature (e.g., Lai et al., 2021; Wang et al., 2025), we hypothesized that this dimension\u0026mdash;which predominantly captures emotional contagion\u0026mdash;would exhibit a dual nature: positively associating with interpersonal functioning, while potentially showing negative associations with self-directed affects, reflecting the inherent cost of emotional resonance.\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eA total of 2,582 adolescents were initially recruited from Beijing, China. After excluding cases that failed data-quality checks (e.g., unrealistic age reports or failure on an attention-check item), the final analytic sample comprised 2,201 participants (males\u0026thinsp;=\u0026thinsp;1041, 47.3%), with a mean age of 12.07 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.01; range\u0026thinsp;=\u0026thinsp;8\u0026ndash;19 years). The collection of self-report questionnaires on the spot, via an online survey platform Wenjuanxing, ensured the immediate retrieval of all data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eEmpathy and well-being dimensions.\u003c/b\u003e The 48-item Well-Being Profile (WB-Pro; Herbert et al., 2020), comprises 15 dimensions, including empathy, with each dimension measured by 3\u0026ndash;4 items. It was rated on a 9-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 9\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores reflecting higher levels of each dimension (Empathy: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.597; Well-being dimensions: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.735\u0026ndash;0.904, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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 of key variables in Study 1.\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSkewness\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth 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colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote.\u003c/b\u003e AU\u0026thinsp;=\u0026thinsp;Autonomy; CT\u0026thinsp;=\u0026thinsp;Clear Thinking; CO\u0026thinsp;=\u0026thinsp;Competence; ES\u0026thinsp;=\u0026thinsp;Emotional Stability; AE\u0026thinsp;=\u0026thinsp;Affective Empathy; EN\u0026thinsp;=\u0026thinsp;Engagement; ME\u0026thinsp;=\u0026thinsp;Meaning; OP\u0026thinsp;=\u0026thinsp;Optimism; PE\u0026thinsp;=\u0026thinsp;Positive Emotions; PR\u0026thinsp;=\u0026thinsp;Positive Relationships; PB\u0026thinsp;=\u0026thinsp;Prosocial Behavior; RE\u0026thinsp;=\u0026thinsp;Resilience; SA\u0026thinsp;=\u0026thinsp;Self-Acceptance; SE\u0026thinsp;=\u0026thinsp;Self-Esteem; VI\u0026thinsp;=\u0026thinsp;Vitality.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003e \u003cb\u003eNetwork analysis.\u003c/b\u003e All statistical analyses were conducted in R (version 4.4.1). The estimateNetwork function from the \u003cem\u003ebootnet\u003c/em\u003e package (Epskamp et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was applied to estimate networks of well-being and empathy. A Gaussian Graphical Model (GGM) was employed, with network structure regularization via the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) algorithm to minimize spurious associations and reduce pseudo-correlations (Costantini et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Network visualization was performed using the qgraph package in R (Epskamp et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Centrality indices were calculated for each node to identify those most central to network structure maintenance, as highly connected nodes play key roles in network integrity. For networks containing negative edges, centrality was calculated by expected influence (EI), and visualized using the centralityPlot function in the qgraph package (Epskamp et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Robinaugh et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccuracy and Stability Estimation.\u003c/b\u003e We assessed the estimated networks by examining both the accuracy of edges and the stability of centrality indices. 95% bootstrap confidence intervals (CIs) for the edge weights were computed using the bootnet package. The centrality stability coefficient (CS-coefficient) was used to quantify the stability of the centrality indices. Following Epskamp et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), CS-coefficients above 0.25 indicate acceptable stability, and values exceeding 0.50 reflect good stability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults \u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all items fell within acceptable ranges for normality, with skewness (-1.105 to -0.470) and kurtosis (-0.100 to 1.385) well below the conventional thresholds of 3 and 10, respectively.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analyses of Empathy and Other Well-Being Components\u003c/h2\u003e \u003cp\u003eThe cross-sectional network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed differential associations of the empathy dimensions. Edge coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that empathy exhibited significant negative partial correlations with self-acceptance (\u003cem\u003eβ\u003c/em\u003e = -0.009), optimism (\u003cem\u003eβ\u003c/em\u003e = -0.006), and positive emotions (\u003cem\u003eβ\u003c/em\u003e = -0.014). Conversely, it was positively correlated with engagement (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), autonomy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), competence (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), and positive relationships (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082). Notably, the empathy-prosocial behavior association (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.355) was the strongest edge, significantly exceeding all other associations (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea\u003c/b\u003e). Optimism emerged as the most central node (EI z-score\u0026thinsp;=\u0026thinsp;1.422; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), underscoring its core role in the network.\u003c/p\u003e \u003cp\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\u003eEdge coefficients of key variables in Study 1 and Study 2 networks.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eStudy1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eStudy2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cb\u003eNote.\u003c/b\u003e Only non-zero edges retained by the LASSO are displayed in the table. In study 2, black represents the edges parameters from empathy to other well-being variables, and red represents edge parameters in the opposite direction.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eAE\u0026thinsp;=\u0026thinsp;Affective Empathy; CE\u0026thinsp;=\u0026thinsp;Cognitive Empathy; AU\u0026thinsp;=\u0026thinsp;Autonomy; CT\u0026thinsp;=\u0026thinsp;Clear Thinking; CO\u0026thinsp;=\u0026thinsp;Competence; ES\u0026thinsp;=\u0026thinsp;Emotional Stability; EN\u0026thinsp;=\u0026thinsp;Engagement; ME\u0026thinsp;=\u0026thinsp;Meaning; OP\u0026thinsp;=\u0026thinsp;Optimism; PE\u0026thinsp;=\u0026thinsp;Positive Emotions; PR\u0026thinsp;=\u0026thinsp;Positive Relationships; PB\u0026thinsp;=\u0026thinsp;Prosocial Behavior; RE\u0026thinsp;=\u0026thinsp;Resilience; SA\u0026thinsp;=\u0026thinsp;Self-Acceptance; SE\u0026thinsp;=\u0026thinsp;Self-Esteem; VI\u0026thinsp;=\u0026thinsp;Vitality.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo further dissect the specific sources of these associations observed at the dimension level, we conducted an item-level empathy network analysis (detailed in Supplementary Material, \u003cb\u003eFigure S2 and Table S2 and S3\u003c/b\u003e). These granular results clarified that items capturing emotional contagion and susceptibility (e.g., Item 4: \u0026ldquo;\u003cem\u003eI easily get caught up in other people\u0026rsquo;s feelings\u003c/em\u003e\u0026rdquo;, and Item 3: \u0026ldquo;\u003cem\u003eI feel others\u0026rsquo; emotions\u003c/em\u003e\u0026rdquo;) drove the broad negative associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAccuracy and stability estimation of networks\u003c/h2\u003e \u003cp\u003eEdge-weight bootstrap procedure (\u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb and S3b\u003c/b\u003e) confirmed acceptable accuracy for all edges. The bootstrapped case-dropping stability analyses (\u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec and S3c\u003c/b\u003e) confirmed high stability of EI centrality for the network (CS coefficient\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion \u003c/h3\u003e\n\u003cp\u003eStudy 1 results suggest that the WB-Pro empathy dimension functions primarily as affective empathy, characterized by emotional susceptibility. The network structure reveals a distinctive trade-off: while this dimension is robustly linked to interpersonal connectivity (e.g., prosocial behavior), it shows negative associations with subjective well-being (e.g., positive emotions). This pattern implies that while emotional resonance co-occurs with high social capacity, it may simultaneously deplete internal emotional resources. These cross-sectional findings highlight the limitations of a single-dimension approach and underscore the need to introduce cognitive empathy and longitudinal design to disentangle these complex dynamics.\u003c/p\u003e"},{"header":"Study 2: Longitudinal Network of Dual-Dimensional Empathy in Well-Being Profile","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003eStudy 2 employed a six-month longitudinal network analysis to examine the specific contributions of empathy subtypes to adolescent well-being. This investigation operationalized the dual-dimensional nature of empathy by retaining the empathy dimension from the WB-Pro as affective empathy, and incorporating cognitive empathy via the perspective-taking subscale of the Interpersonal Reactivity Index. By assessing the temporal associations between these subtypes and other well-being components, we aimed to clarify their differential roles. Based on prior theoretical distinctions (Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we hypothesized that cognitive empathy would show positive links across the network. In contrast, affective empathy was expected to exhibit a \u0026ldquo;double-edged\u0026rdquo; dynamic: positively predicting interpersonal outcomes while negatively predicting self-related well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eData were collected from 730 adolescents in Qingdao, China at Time 1(T1; February 2025). Six months later (T2; August 2025), 606 adolescents were retained (82.9% retention rate). After excluding participants with invalid responses (e.g., abnormal answer patterns, unrealistic age reports, or invalid student IDs), the final longitudinal sample consisted of 587 adolescents (303 males, 51.6%). At T2, the mean age was 14.74 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.73; range\u0026thinsp;=\u0026thinsp;12\u0026ndash;18). Self-report questionnaires were administered on-site to ensure complete data retrieval.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAffective empathy and well-being dimensions.\u003c/b\u003e Consistent with Study 1, the WB-Pro was used to assess affective empathy and well-being dimensions (Affective Empathy: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.649, 0.732 at T1\u0026ndash;T2; Well-being dimensions: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.826\u0026ndash;0.927, 0.855\u0026ndash;0.940 at T1\u0026ndash;T2, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of key variables at T1 and T2 in Study 2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSkew\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eKurtosis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSkew\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eKurtosis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_CE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_CE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_EM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_ME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_ME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_OP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_OP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_RE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_RE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1_AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT2_AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cb\u003eNote.\u003c/b\u003e SA\u0026thinsp;=\u0026thinsp;Self-Acceptance; VI\u0026thinsp;=\u0026thinsp;Vitality; EN\u0026thinsp;=\u0026thinsp;Engagement; OP\u0026thinsp;=\u0026thinsp;Optimism; ES\u0026thinsp;=\u0026thinsp;Emotional Stability; RE\u0026thinsp;=\u0026thinsp;Resilience; ME\u0026thinsp;=\u0026thinsp;Meaning; AU\u0026thinsp;=\u0026thinsp;Autonomy; SE\u0026thinsp;=\u0026thinsp;Self-Esteem; PB\u0026thinsp;=\u0026thinsp;Prosocial Behavior; CO\u0026thinsp;=\u0026thinsp;Competence; CT\u0026thinsp;=\u0026thinsp;Clear Thinking; PE\u0026thinsp;=\u0026thinsp;Positive Emotions; PR\u0026thinsp;=\u0026thinsp;Positive Relationships; AE\u0026thinsp;=\u0026thinsp;Affective empathy; CE\u0026thinsp;=\u0026thinsp;Cognitive Empathy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCognitive empathy.\u003c/b\u003e The perspective-taking subscale of Interpersonal Reactivity Index (IRI; Davis, 1980), contains 7 items assessing the tendency to adopt others\u0026rsquo; viewpoints (e.g., \u0026ldquo;\u003cem\u003eI believe that there are two sides to every question and try to look at them both\u003c/em\u003e\u0026rdquo;). It was rated on a 9-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 9\u0026thinsp;=\u0026thinsp;strongly agree), with higher score reflecting higher level of perspective-taking (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.745, 0.768 at T1\u0026ndash;T2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003e \u003cb\u003eConfirmatory Factor Analysis.\u003c/b\u003e Confirmatory Factor Analysis (CFA) with Maximum Likelihood (ML) estimation was conducted to validate the dual-dimensional structure of empathy. We compared a single-factor model (Model 1) against a two-factor model (Model 2). Model fit was evaluated using \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e, CFI, TLI, RMSEA, and SRMR. Acceptable model fit was designed as CFI and TLI\u0026gt;0.90, SRMR\u0026lt;0.08, and RMSEA\u0026lt;0.10 (Byrne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Model comparisons were performed using the chi-square difference test (Kline, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNetwork estimation and visualization.\u003c/b\u003e To examine the dynamic associations between empathy and well-being, we employed the \u003cem\u003eglmnet\u003c/em\u003e package in R to estimate cross-lagged panel networks (CLPN) model (Friedman et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Two types of regression models were constructed: autoregressive models, where each variable at T1 predicts its own value at T2 while controlling for all other T1 variables, and cross-lagged models, examining how each T1 variable predicts other T2 variables after adjusting for all T1 variables (Funkhouser et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Model parameters were tuned via 10-fold cross-validation tuning parameter selection method, and LASSO were applied to regularize the regression coefficients in order to reduce false-positive edges (Wysocki et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used the \u003cem\u003eqgraph\u003c/em\u003e package in R to visualized these results as networks (Epskamp et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In these networks, nodes represent dimensions and edges represent predictive relationships across time (e.g., an arrow from variable A to variable B indicates that variable A at time T significantly predicts variable B at time T\u0026thinsp;+\u0026thinsp;1). We further calculated the centrality index of the networks: out-prediction and in-prediction (Wysocki et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In-prediction reflects the extent to which a node is predicted by other nodes, whereas out-prediction captures the extent to which a node predicts other nodes. Dimensions with high out-prediction values occupy central positions in the network and have strong impacts on the network\u0026rsquo;s maintenance and dynamic changes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistical Analysis\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, skewness (-0.944 to -0.042) and kurtosis (-0.143 to 1.362) values all fell within acceptable limits for normality (skewness\u0026thinsp;\u0026lt;\u0026thinsp;3 and kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;10).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eRegarding the factor structure of empathy, minor adjustments based on Modification Indices (MIs) were made to the two-factor model (Model 2). This revised model demonstrated acceptable fit across all indices (\u003cb\u003eTable S4\u003c/b\u003e). A chi-square difference test confirmed that the two-factor model provided a significantly better fit than the single-factor model at both time points (T1: Δ\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e∕Δ\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;138.9, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; T2: Δ\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e∕Δ\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;381.03, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; see \u003cb\u003eTable S5 and S6\u003c/b\u003e). These results support the structural distinction between cognitive and affective empathy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCross-Lagged Panel Network Analysis of Cognitive Empathy and Well-Being\u003c/h2\u003e \u003cp\u003eThe network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) revealed that cognitive empathy positively predicted most dimensions of well-being (except self-acceptance and meaning), and was in turn positively predicted by prosocial behavior, emotional stability, autonomy, and self-acceptance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the strongest lagged associations for cognitive empathy were with prosocial behavior, followed by resilience. In addition, cognitive empathy showed a significant positive autoregressive effect, indicating temporal stability. Within the broader network, the strongest cross-lagged edge was from vitality to engagement (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.214). Centrality indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cb\u003eTable S7\u003c/b\u003e) identified optimism as having the highest out-prediction, marking it as a key predictor, while engagement showed the highest in-prediction, indicating it is a primary outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCross-Lagged Panel Network Analysis of Affective Empathy and Well-Being\u003c/h2\u003e \u003cp\u003eThe affective empathy network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) showed a distinct pattern. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, affective empathy negatively predicted self-acceptance, resilience, positive relationships, self-esteem, and clear thinking. The strongest negative path was from affective empathy to self-acceptance. Conversely, affective empathy was positively predicted by prosocial behavior, emotional stability, and autonomy. Consistent with the cognitive empathy network, vitality to engagement remained the strongest cross-lagged edge (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.215). Centrality indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, \u003cb\u003eTable S8\u003c/b\u003e) confirmed optimism and engagement as the highest out-prediction and in-prediction nodes, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThe confirmation of the two-factor model supports the structural distinction between affective and cognitive empathy, establishing a necessary prerequisite for examining their differential functions. Longitudinal networks further revealed their divergent roles. Specifically, cognitive empathy exerted broad positive predictive effects across interpersonal and intrapersonal well-being, serving as a stable resilience-promoting capacity. Contrary to the the \u0026ldquo;double-edged\u0026rdquo; expectation, affective empathy failed to predict future prosocial behavior. Instead, it functioned strictly as a vulnerability factor, anticipating broad declines across the interpersonal (positive relationships), intrapersonal (self-acceptance, self-esteem), and functional domains (clear thinking, resilience). These findings underscore the critical necessity of distinguishing empathy subtypes to capture their opposing long-term dynamics on adolescent well-being.\u003c/p\u003e "},{"header":"General Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003cp\u003eBy utilizing network analysis, the present study integrates cross-sectional and longitudinal data to provide a nuanced understanding of how empathy functions within the adolescent well-being system. Crucially, while Study 1 examined empathy primarily through the affective-dominant WB-Pro lens, Study 2 introduced the measurement of cognitive empathy, allowing for a decisive comparison of their distinct predictive roles. Our findings demonstrate that incorporating cognitive empathy is essential for a valid representation of the WB-Pro framework, as the two-factor structure significantly outperformed the unitary model. This distinction proved critical: cognitive empathy consistently links to adaptive outcomes, whereas affective empathy exhibits a paradoxical vulnerability effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eAffective Empathy as a Vulnerable Factor\u003c/h2\u003e \u003cp\u003eContradicting the \u0026ldquo;double-edged sword\u0026rdquo; hypothesis, Study 2 results indicate that affective empathy functions strictly as a vulnerability over time. The discrepancy between Study 1 and Study 2 clarifies this dynamic. In Study 1, affective empathy was positively associated with interpersonal assets (e.g., prosocial behavior, positive relationships) and motivational functioning (e.g., competence, autonomy). Without longitudinal directionality, these correlations masqueraded as adaptive effects. However, Study 2 debunked this illusion. Over six months, affective empathy failed to positively predict any adaptive outcomes. Instead, it functioned solely as a depleting factor, simultaneously undermining interpersonal well-being (e.g., positive relationships), eroding intrapersonal well-being (e.g., self-acceptance, self-esteem), and impairing adaptive functioning (e.g., clear thinking, resilience). Crucially, although affective empathy was rooted in high-functioning antecedents (e.g., positively predicted by prosocial behavior), this emotional resonance does not feed back into the system to promote future growth. Instead, it becomes a \u0026ldquo;sink\u0026rdquo; that consumes psychological resources without generating future benefits (Ch\u0026aacute;vez et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Laninga-Wijnen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA striking divergence emerged regarding positive relationships, which were positively associated with affective empathy in Study 1 but negatively predicted by it in Study 2. This reversal underscores the distinction between immediate intimacy and chronic depletion. Cross-sectionally, the immediate experience of emotional sharing fosters a sense of connection, appearing as high positive relationships. However, longitudinally, unmitigated emotional contagion incurs an intrapersonal debt. The deterioration of intrapersonal strength (e.g., self-acceptance) and adaptive functioning (e.g., resilience) driven by affective empathy likely creates a \u0026ldquo;spillover effect\u0026rdquo;, where the adolescent\u0026rsquo;s internal depletion eventually undermines their capacity to sustain healthy social interactions, leading to the deterioration of positive relationships over time (Deng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Empathy as a Protective Factor\u003c/h2\u003e \u003cp\u003eIn stark contrast to the vulnerability profile of affective empathy, cognitive empathy emerged as a comprehensive protective factor within the adolescent well-being network. Longitudinal results demonstrated that cognitive empathy positively predicted improvements across interpersonal well-being (e.g., prosocial behavior, positive relationships), intrapersonal well-being (e.g., self-esteem, optimism), motivational resources (e.g., engagement, competence), and most notably, adaptive functioning (e.g., resilience, clear thinking).\u003c/p\u003e \u003cp\u003eOf particular significance is the robust prediction for resilience\u0026mdash;a component negatively predicted by affective empathy. This specific link suggests that the capacity to adopt others\u0026rsquo; perspectives functions as a critical \u0026ldquo;top-down\u0026rdquo; regulatory mechanism that facilitates the stress regulation inherent to resilience (Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike affective empathy, which relies on automatic emotional contagion that blurs self-other boundaries, cognitive empathy involves effortful cognitive processing (Wellman et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This allows adolescents to understand others\u0026rsquo; distress while maintaining psychological distance, thereby buffering against emotional over-arousal and self-depletion. Consequently, while affective empathy becomes a cost of caring, cognitive empathy serves as a sustainable resource, fostering adaptive functioning that supports both the self (Ben\u0026iacute;tez-Sillero et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the social bond (Georgiou et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This functional divergence underscores that without differentiating these subtypes, the critical trade-off between interpersonal utility and intrapersonal costs would remain obscured by global empathy measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eShared Developmental Roots and Divergent Pathway\u003c/h2\u003e \u003cp\u003eWhile our findings highlight the functional divergence between affective and cognitive empathy, the longitudinal network also unveiled their shared developmental antecedents. Both subtypes were significantly and positively predicted by prosocial behavior, autonomy, and emotional stability. This suggests that empathy, regardless of its form, is fundamentally rooted in a high-functioning psychological profile. Adolescents who are socially engaged, self-governing, and emotionally regulated are predisposed to developing both the capacity to resonate with others\u0026rsquo; emotions and the capacity to understand their perspectives. Prosociality likely provides the necessary social exposure to cultivate these sensitivities, while autonomy and emotional stability serve as the secure psychological foundation that allows adolescents to turn their attention outward to others.\u003c/p\u003e \u003cp\u003eCrucially, however, cognitive empathy was uniquely predicted by self-acceptance. This distinction is theoretically revealing. Unlike affective empathy, which can be triggered as an automatic emotional response to prosociality, cognitive empathy is an effortful, top-down process requiring the suppression of egocentric bias (Deng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Self-acceptance may function as a critical \u0026ldquo;cognitive liberator\u0026rdquo; in this process. Adolescents who accept themselves are likely less consumed by ego-defensive processing or personal insecurities, thereby freeing up the cognitive resources required to objectively adopt others\u0026rsquo; viewpoints. This finding suggests that while general psychosocial adjustment fuels the drive to empathize, a positive relationship with the self is specifically required to cultivate the skill of perspective-taking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCore Components within Adolescent Well-Being Network\u003c/h2\u003e \u003cp\u003eBeyond the empathy-specific dynamics, our analysis identified the central drivers of the broader well-being system. In the cross-sectional network, optimism emerged as the most central node, underscoring the pivotal role of positive future orientation in adolescent psychological functioning. This aligns with prior studies highlighting optimism as a core protective resource for navigating developmental transitions and uncertainty (Fredrickson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ni et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, affective empathy showed an inverse association with optimism, implying that heightened sensitivity to others\u0026rsquo; distress may dampen adolescents\u0026rsquo; capacity to maintain positive expectations about their own future.\u003c/p\u003e \u003cp\u003eShifting to a dynamic perspective, the longitudinal network revealed that optimism, autonomy, and vitality emerged as the most influential nodes. These constructs represent self-sustaining resources that anchor systemic well-being over time. Importantly, cognitive empathy positively predicted subsequent levels of all three of the core nodes. It further reinforces the role of cognitive empathy as a system-wide energizer that not only promotes specific skills but also fortifies the central pillars of adolescent well-being.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePractical Implications\u003c/h3\u003e\n\u003cp\u003eOur findings offer several actionable insights for fostering adolescent well-being. First, regarding assessment, the distinct profiles of the two subtypes suggest that the empathy subscale of the WB-Pro primarily captures the emotional susceptibility. Researchers applying this instrument in Chinese adolescent samples should interpret findings with caution, recognizing that observed patterns likely reflect emotional susceptibility rather than a broad, resilience-promoting capacity. Second, for intervention, clinical and educational programs should aim to assist adolescents in regulating emotional resonance while strengthening self-other differentiation. Specifically, training in perspective-taking and resilience-building could amplify the adaptive benefits of cognitive empathy while buffering against the intrapersonal costs of affective empathy. Third, from a systemic perspective, given that individual-level empathy changes may not suffice for holistic well-being, we advocate for multi-component approaches. Identifying and reinforcing high-centrality nodes\u0026mdash;specifically optimism, autonomy, and vitality\u0026mdash;provides effective entry points for system-wide promotion (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Interventions targeting these core resources are likely to yield cascading benefits across the entire well-being network.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Avenues\u003c/h2\u003e \u003cp\u003eDespite the strengths of employing longitudinal network analysis to disentangle the dual-dimensional nature of empathy, several limitations warrant consideration. First, the strictly negative associations between affective empathy and self-related outcomes necessitate further investigation into underlying mechanisms. Future research should explore potential moderators, such as emotion regulation strategies or peer conformity, that might mitigate these costs of caring. Second, although cognitive empathy appeared uniformly adaptive in this study, prior research suggests it may relate to poorer mental health in specific contexts (e.g., \u0026ldquo;Machiavellian\u0026rdquo; perspective-taking; Tan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future work should employ contextualized assessments to delineate the boundary conditions under which cognitive empathy might exert adverse effects. Finally, reliance on self-reports introduces potential bias. Incorporating multi-informant perspectives (e.g., parents, teachers) and cross-cultural comparisons would enhance the generalizability and robustness of these findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research elucidates the distinct roles of empathy within the multidimensional adolescent well-being system. Specifically, our findings reveal a stark functional divergence within the empathic dimension itself: while cognitive empathy serves as a resilience-promoting component that reinforces the broader system, affective empathy\u0026mdash;when unregulated\u0026mdash;acts as a \u0026ldquo;leak\u0026rdquo; within the network, and despite its social roots, this affective component functions strictly as a vulnerability that erodes overall well-being. Consequently, these results suggest that the path to flourishing in adolescence lies not merely in feeling with others, but in the capacity to understand them while maintaining the integrity of the self. Ultimately, this distinction is vital for moving beyond global endorsements of empathy toward more precise, structurally targeted approaches to youth development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was reviewed and approved by the ethical committee of Capital Normal University (Date: November 20, 2024; Reference No: CNU-202412001). Participant Consent Statement: Informed consent was obtained from all adolescent participants and their legal guardians (parents) prior to their inclusion in the study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYanhe Deng: Conceptualization, Funding acquisition, Project administration, Supervision, Data collection, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Xuan Han: Methodology, Writing \u0026ndash; original draft.Haichun Zhou: Writing \u0026ndash; original draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. (1994). \u003cem\u003eDiagnostic and statistical manual of mental disorders\u003c/em\u003e (4th ed.). Author.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. (2013). \u003cem\u003eDiagnostic and statistical manual of mental disorders\u003c/em\u003e (5th ed.). Author.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez-Sillero, J. D., Falla, D., C\u0026oacute;rdoba-Alcaide, F., \u0026amp; Zych, I. (2024). 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From empathy to resilience: The mediating role of emotional intelligence. \u003cem\u003ePsychological Reports\u003c/em\u003e, \u003cem\u003e128\u003c/em\u003e(6), 4533\u0026ndash;4548. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00332941231220299\u003c/span\u003e\u003cspan address=\"10.1177/00332941231220299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adolescents, Affective empathy, Cognitive empathy, Multidimensional well-being, Network analysis, Positive Psychology","lastPublishedDoi":"10.21203/rs.3.rs-9194600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9194600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile empathy is traditionally championed as a cornerstone of positive psychological functioning, its structural role within multidimensional well-being systems remains surprisingly paradoxical. This research applied network analysis within the Well-Being Profile (WB-Pro) framework to elucidate the distinct dynamics of cognitive and affective empathy among adolescents. Study 1 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,201, \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 12.07, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.01) revealed a cross-sectional paradox: while general empathy strongly correlated with prosocial behavior, it was negatively associated with self-acceptance, optimism, and positive emotions. To resolve this, Study 2 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;587, \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 14.74, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.73) utilized a six-month longitudinal cross-lagged panel network analysis to disentangle the two subtypes, revealing a stark functional divergence. Cognitive empathy emerged as a consistently adaptive capacity, predicting broad improvements within the well-being network, most notably in prosocial behavior and resilience. Conversely, affective empathy functioned as a systemic vulnerability. Despite being positively predicted by early prosocial behavior, it failed to generate subsequent adaptive outcomes; instead, it undermined interpersonal well-being (e.g., positive relationships), eroded intrapersonal well-being (e.g., self-acceptance), and impaired adaptive functioning (e.g., resilience) over time. These findings challenge the global endorsement of empathy in positive psychology, indicating that the path to flourishing requires not merely feeling with others, but the capacity to understand them while maintaining self-integrity, offering a precise, structurally targeted approach to cultivate well-being.\u003c/p\u003e","manuscriptTitle":"Two Sides of the Same Coin: Uncovering Differential Roles of Cognitive and Affective Empathy in Adolescent Multidimensional Well-Being Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 17:51:04","doi":"10.21203/rs.3.rs-9194600/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69f73ab5-5ac2-4afe-a9f1-2cca24531b43","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"32171828599161125786343152802415898535","date":"2026-05-18T17:29:06+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"77342615556346315427021629647393700922","date":"2026-05-12T03:40:21+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T23:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 17:51:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9194600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9194600","identity":"rs-9194600","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}