The Unique Role of Neuroticism in Social Avoidance and Distress Symptoms: A Cross-Lagged Network Analysis Model

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Methodology: A cohort of 397 first-year university students participated, with data collected across three follow-ups using the Neuroticism Personality Questionnaire and the Social Avoidance and Distress Scale. Cross-lagged panel and network analysis were used to examine the relationships between these variables. Findings: Neuroticism significantly predicted social avoidance and distress, with phase-specific effects. In the early period (T1-T2), "anxiety" and "self-consciousness" were key predictors, while in the later period (T2-T3), "vulnerability" emerged as a significant factor. Conclusion: The study highlights the temporal dynamics and specific dimensions of neuroticism in social behaviors, suggesting their importance for psychological assessment and interventions. Neuroticism Social Avoidance and Distress Cross-Lagged Analysis Cross-Lagged Network Analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Interpersonal communication is essential for mental health and personality development, playing a central role in psychological well-being (Wang Huan et al., 2014; Zhao Xin et al., 2014). College freshmen, in particular, face significant social challenges as they transition to university life, often encountering psychological difficulties such as social avoidance, distress, and anxiety (Zhang Yali et al., 2019; Zhang Qingqing et al., 2022). These challenges are closely associated with social avoidance and distress, a concept introduced by Watson and Friend ( 1969 ) to describe withdrawal from social interactions and the negative emotional states that follow. Neuroticism, a personality trait closely tied to the autonomic nervous system, predisposes individuals to intense emotional reactions to relatively minor external stimuli, especially in those with high levels of neuroticism (Riese et al., 2014 ). Such individuals often struggle with interpersonal challenges, and a direct link has been established between high neuroticism and increased tendencies toward social avoidance and distress (Wiesenfeller et al., 2020 ). Characterized by emotional instability and poor emotional regulation, individuals with high neuroticism tend to exhibit negative self-perceptions, which manifest as heightened anxiety, avoidance behaviors, and distress in social situations (Sheppes et al., 2015 ; Miers et al., 2014 ). Eysenck’s personality theory regards neuroticism as a core trait characterized by rapid emotional arousal and delayed inhibition, leading to emotional instability and susceptibility to feelings such as irritability, anger, sadness, anxiety, and hostility (Amiel & Sargent, 2004 ). The Behavioral Inhibition System (BIS) theory further suggests that individuals with high neuroticism exhibit heightened sensitivity to potential threats, triggering anxiety and fear in response to novel social situations, which in turn leads to avoidant behaviors as a coping mechanism (Carver & White, 1994 ; Cooper et al., 2007 ; Heponiemi et al., 2004 ; Sommer et al., 2016 ). Research has consistently shown that individuals with high neuroticism are more vulnerable to various psychological conditions, including anxiety, depression, and loneliness (Lund et al., 2017 ). These findings support the first hypothesis of this study: H1 – Neuroticism is a predictive factor for social avoidance and distress. The Five-Factor Model (FFM) defines neuroticism as the "tendency to experience negative emotions such as fear, sadness, embarrassment, anger, guilt, and disgust" (McCrae & Costa, 2010 , p. 19). This heightened emotional sensitivity is linked to various forms of psychopathology, including social anxiety, which correlates with broader neuroticism traits (Naragon-Gainey & Watson, 2011 ). The FFM further posits that neuroticism consists of six distinct facets, each reflecting specific aspects of emotional reactivity: anxiety (tendency to experience fear, worry, and tension), angry hostility (tendency to experience anger), depression (tendency to experience depressive moods), self-consciousness (tendency to feel embarrassment and shame), impulsiveness (difficulty controlling urges), and vulnerability (tendency to feel overwhelmed by stress) (McCrae & Costa, 2010 , pp. 21–22). Recent studies have found that social anxiety, a key component of social avoidance and distress, is particularly linked to heightened self-consciousness and vulnerability. Individuals prone to social anxiety tend to score higher on these facets compared to healthy controls, suggesting that they are more likely to experience intense feelings of shame and are particularly vulnerable to stress (Bienvenu et al., 2004 ; Gamez et al., 2007 ; Kotov, 2006 ). In line with these findings, social anxiety appears to be differentially associated with specific facets of neuroticism, particularly those emphasizing emotional instability and stress sensitivity, such as self-consciousness and vulnerability (Bienvenu et al., 2004 ). Based on these associations, this study proposes Hypothesis 2: The anxiety, vulnerability, and self-consciousness dimensions of neuroticism uniquely predict social avoidance and distress. Despite the predominance of cross-sectional studies examining social avoidance and distress, longitudinal research on these phenomena remains limited. Therefore, this study adopts a longitudinal approach to examine the temporal dynamics of social avoidance and distress, leading to Hypothesis 3: The influence of neuroticism on social avoidance and distress is characterized by temporal specificity. Traditional models have often overlooked the complex interactions and causal dynamics among symptoms. In response, Borsboom (2008) proposed a network theory of psychopathology, which conceptualizes symptoms as interconnected elements within psychological disorders. This theory emphasizes that the onset and persistence of psychological conditions are driven by intricate interrelations and feedback loops among symptoms (Borsboom & Cramer, 2013 ; Jones et al., 2017 ). Network analysis, a methodological innovation derived from this theory, represents symptoms as nodes and their interconnections as edges. The intensity of these relationships is conveyed through the thickness of the edges, shifting the focus from traditional disease models to the exploration of symptom relationships within a network framework (Liang et al., 2022 ). This study explores the impact of neuroticism on social avoidance and distress using a cross-lagged network analysis model and longitudinal data from freshmen. The goal is to uncover the causal dynamics between neuroticism and social avoidance/distress. Using R programming, the study carefully analyzes the interplay of these variables over time, with a particular focus on identifying the symptoms that have significant predictive power and influence within the network. 2. Method 2.1. Participants The cohort for this study comprised 397 first-year university students (44.3% male, age = 19.32 ± 1.02), selected from a university in Henan, China. Participants voluntarily provided written informed consent, and three follow-up assessments were conducted in September 2022, December 2022, and March 2023. The initial survey (T1) had 520 responses, the second (T2) had 480, and the final (T3) had 420. After excluding inconsistent responses, 397 valid participants remained, yielding a response rate of 76%. Logistic regression analysis of missing data revealed no significant differences between participants who completed all phases and those who did not ( p > 0.05), suggesting a random pattern of missing data.2.2. Instruments 2.2.1 Neuroticism Personality Scale In this study, neuroticism was assessed using the Neuroticism subscale from the streamlined version of the NEO Five-Factor Inventory (NEO-FFI) as revised by Nie Yangang et al. (2008). The NEO-FFI is a shortened form of the NEO Personality Inventory (NEO-PI), consisting of the 12 items with the highest factor loadings from each of the five dimensions of the NEO-PI(Costa & McCrae, 1992 ). Responses were rated on a 5-point Likert scale, ranging from 1 ("strongly disagree") to 5 ("strongly agree"), with items such as "I often feel nervous or easily agitated." The cumulative score reflects the respondent's level of neuroticism. The internal consistency of this scale, measured by Cronbach’s alpha, demonstrated excellent reliability across the three assessments in this study, with coefficients of 0.95, 0.96, and 0.95, respectively. 2.2.2 Social Avoidance and Distress Scale The study incorporated the Social Avoidance and Distress Scale (SAD) as refined by Peng Chunzi et al. (2003), originally developed by Watson and Friend. This instrument consists of 28 items bifurcated into two principal dimensions: social avoidance, with items such as "I often want to leave social gatherings," and social distress, exemplified by "I usually feel anxious around people unless I am very familiar with them." The scale's reliability, indicated by Cronbach's alpha coefficients for the three evaluations within this study, was robust, evidenced by scores of 0.95, 0.93, and 0.95, respectively. 2.3 Procedures The analysis of the data collected in this study was executed utilizing SPSS version 21.0 for preliminary descriptive statistics and correlation analyses. To explore the temporal dynamics between variables, cross-lagged analysis was conducted using AMOS version 23.0. Furthermore, a more sophisticated cross-lagged panel network (CLPN) analysis was performed with the R programming language, scrutinizing data collected at three distinct time intervals (T1, T2, T3). In the initial phase, data preprocessing was imperative to ensure coherent alignment and comparability across the various time points. This step included merging datasets and refining column names to facilitate accurate linkage and analysis of the data. The datasets corresponding to each time point (Data1, Data2, Data3) were meticulously cleaned and structured for subsequent analysis. The variables analyzed encompassed aspects of the neuroticism personality (denoted as N1-12) and social avoidance and distress (indicated as SAD1-28). 2.4 Data Analysis For the construction of the cross-lagged network model, the glmnet and cv.glmnet functions within R were employed, leveraging LASSO regression and cross-validation techniques. This modeling process necessitated the establishment of a random seed to ensure the reproducibility of results. A bespoke function, CLPN.fun, was crafted to generate adjacency matrices, elucidating the magnitude of associations between the variables. This function facilitated the estimation of both autoregressive effects—wherein a symptom's presence at one time point predicts its recurrence at a subsequent time point—and cross-lagged effects—where a symptom at one juncture predicts a different symptom at a later stage. Utilizing bootstrapping methods, the analysis extended to assess the network model's non-parametric stability and case stability. This involved repeated resampling and examination of the network's edge strengths' resilience, centrality indices, and expected influence. Such comprehensive analyses were instrumental in unmasking the steadfastness of the variable interrelations within the network and elucidating the temporal evolution of these relationships. 3. Results 3.1 Descriptive Statistics and Correlation Analysis The mean values, standard deviations, and correlation coefficients for neuroticism, social avoidance, and distress at the three time points are presented in Table 1 . Table 1 Mean, Standard Deviation, and Correlation Coefficients of Variables (n = 397) 1 T1 Neuroticism M ± SD 1 2 3 4 5 6 29.33 ± 13.44 1 2 T2 Neuroticism 28.31 ± 13.84 0.21** 1 3 T3 Neuroticism 30.39 ± 13.39 0.07 0.31** 1 4 T1 Social Avoidance and Distress 16.37 ± 8.81 0.14** 0.11* 0.07 1 5 T2 Social Avoidance and Distress 17.55 ± 8.11 0.13** 0.21** 0.09 0.22** 1 6 T3 Social Avoidance and Distress 17.66 ± 9.17 0.17** 0.25** 0.07 0.14** 0.26** 1 Note : T1, T2, and T3 represent the three measurement times, *** p < 0.001, ** p < 0.01, * p < 0.05. From Table 1 , it can be observed that neuroticism shows varying degrees of positive correlation with social avoidance and distress at time points T1 and T2. 3.2 Cross-Lagged Analysis Cross-lagged analysis was conducted using AMOS 23.0 software. The model fit indices demonstrated good fit: χ²/df = 3.10, NFI = 0.97, TLI = 0.97, CFI = 0.98, RMSEA = 0.05. These indices reflect the accuracy and utility of the model's predictions ((see Fig. 1 ). Table 2 Prediction Effects of Neuroticism on Social Avoidance and Distress Path β SD C.R p T1 N→T2 SAD 0.427 0.027 9.569 < 0.001 T2 N→T2 SAD 0.200 0.032 4.122 < 0.001 Note : T1 N = Neuroticism at the first time point; T2 SAD = Social Avoidance and Distress at the second time point; T2 N = Neuroticism at the second time point; T3 SAD = Social Avoidance and Distress at the third time point. From Table 2 , it is evident that neuroticism significantly predicts social avoidance and distress at Time 2 (T2) from Time 1 (T1). Similarly, neuroticism at Time 2 (T2) significantly predicts social avoidance and distress at Time 3 (T3). These results support the research hypothesis, indicating that neuroticism significantly and positively predicts levels of social avoidance and distress at different time points. 3.3 Network Estimation The cross-lagged network analysis delineates the intricate pathways through which neuroticism influences social avoidance and distress, transitioning from Time 1 (T1) to Time 2 (T2), as depicted on the left side of Fig. 2 . In this visual representation, green arrows symbolize positive predictors, while red arrows denote negative influences, with the thickness of each line mirroring the prediction's potency. The diagram categorizes the nodes into three colors: yellow for the 12 facets of neuroticism, green for social avoidance, and blue for social distress, facilitating an intuitive understanding of the network's structure.During the T1→T2 interval, the analysis identifies neuroticism items 2 and 9 as pivotal in forecasting network dynamics, encapsulating the dimensions of anxiety (defined as the tendency to experience fear and worry about potential threats; Newby, Pitura, Penney, et al., 2017) and self-consciousness (defined as the tendency to experience embarrassment and shame, often leading to a desire to withdraw from social situations; Newby, Pitura, Penney, et al., 2017), respectively.These symptoms notably contribute to the prediction of social avoidance and distress, with items 17 and 20—highlighting discomfort with strangers ("Even if a room is full of strangers, I might still go in") and unease in group settings ("When I am with a group of people, I usually feel uneasy")—emerging as frequently predicted elements. Shifting focus to the progression from Time 2 (T2) to Time 3 (T3), as depicted on the right side of Fig. 2 , the analysis highlights item 10 ("I often feel inferior to others") as a key predictor, emphasizing vulnerability as a critical dimension at this stage. The network further reveals items 10 and 13 as the most significant predictors of social avoidance and distress; (Newby et al., 2017 ). Specifically, these items capture anxiety in mixed-gender social contexts ("At informal gatherings, if members of the opposite sex are present, I usually feel anxious and tense") and a tendency towards social withdrawal ("I often think about leaving social situations"). This analysis underscores the evolving role of specific neuroticism symptoms in shaping social behaviors and emotional responses across different time points, providing valuable insights into the temporal dynamics of social avoidance and distress. 3.4 Centrality Estimation The centrality of items within the cross-lagged networks is depicted in Fig. 3 . Through the analysis of cross-lagged networks across two time stages, it was found that symptoms with high out-degree Expected Influence (out-EI) exhibit temporal specificity. In the T1→T2 stage, items 2 and 9 of neuroticism have the highest out-EI; while items 17 and 20 within social avoidance and distress have high in-degree Expected Influence (in-EI). In the T2→T3 stage, item 10 of neuroticism has the highest out-EI; and items 10 and 13 within social avoidance and distress have high in-EI. 3.5 Stability Analysis To evaluate the stability of centrality indices within the model, we calculated the Centrality Stability Coefficient (CS-coefficient). The CS-coefficient measures the maximum proportion of the sample that can be removed from the network while still maintaining a correlation of at least 0.7. A higher CS-coefficient value indicates that centrality indices can maintain high stability even within smaller subsamples. For the T1→T2 network, the centrality stability coefficients for the sum of in-EI and out-EI are 0.13 and 0.75, respectively, indicating that the correlation of in-EI remains above 0.7 even after removing up to 13% of the samples, and out-EI maintains high stability even with a significant reduction in the sample size; for the T2→T3 network, the centrality stability coefficients for in-EI and out-EI are both 0.36. Tests of centrality differences showed that, in the networks from T1→T2 and T2→T3, those symptoms with the strongest out-EI/in-EI are statistically stronger than most other symptoms in the network (see web appendix Figure S4), further indicating that the results of the centrality analysis are stable and generalizable. Results of the edge weight bootstrapping procedure (see web appendix Figure S2) show that both cross-lagged network estimates are moderately accurate: there is considerable overlap between the 95% confidence intervals (CI) of edge weights, while some of the strongest edges do not overlap with their confidence intervals. Subset bootstrapping results (see web appendix Figure S3) suggest that estimates of edges, out-EI, and in-EI in both networks are stable and generalizable. 4. Discussion 4.1 The Positive Predictive Effect of Neuroticism on Social Avoidance and Distress Using AMOS 23.0 software for cross-lagged analysis, this study examined the predictive role of neuroticism on social avoidance and distress among college freshmen. The results clearly indicate that neuroticism significantly and positively predicts both social avoidance and distress across different time points, consistent with findings from previous research (Yin Xunbao et al., 2010). Notably, this study distinguishes itself from earlier cross-sectional studies by employing a longitudinal approach, which reinforces the predictive power of neuroticism over time and further substantiates its impact on social avoidance and distress. 4.2 Evolution of Neuroticism: Transition from "Anxiety" and "Self-Consciousness" to "Vulnerability" as a Central Influence By utilizing R for cross-lagged network analysis, this study traced how the influence of neuroticism on social avoidance and distress evolved over time. In the initial period (T1-T2), "Anxiety" and "Self-Consciousness" were identified as the strongest predictors of social avoidance and distress. However, as the study progressed to the T2-T3 phase, "Vulnerability" emerged as the dominant predictor. This shift suggests that while neuroticism initially triggers a range of social avoidance and distress symptoms, over time, "Vulnerability" becomes the central factor driving the persistence and escalation of these symptoms. 4.3 Temporal Specificity of Neuroticism's Impact This study also explored the temporal specificity of neuroticism’s influence on social avoidance and distress. During the T1-T2 phase, the "Anxiety" and "Self-Consciousness" dimensions of neuroticism were strong predictors of freshmen's discomfort in social interactions, such as "discomfort with strangers" and "uneasiness in social situations." However, by the T2-T3 phase, "Vulnerability" emerged as the central predictor, with items like "At informal gatherings, if members of the opposite sex are present, I usually feel anxious and tense" and "I often think about leaving social situations" being prominently predicted by this dimension. This shift underscores the evolving role of neuroticism in shaping social avoidance and distress over time. These findings highlight the importance of addressing the dimensions of "Anxiety," "Self-Consciousness," and "Vulnerability" in interventions designed to reduce social avoidance and distress among freshmen. 5. Conclusion This study offers significant methodological contributions by utilizing a longitudinal design and cross-lagged network analysis, providing a novel perspective on the dynamic interplay between neuroticism and social avoidance and distress. The findings reveal that neuroticism plays a crucial role in predicting both social avoidance and distress over time, with distinct dimensions of neuroticism, such as "anxiety," "self-consciousness," and "vulnerability," demonstrating phase-specific effects. These insights offer a deeper understanding of the temporal dynamics of social behavior and provide a basis for future research on personality traits and mental health. However, the study acknowledges several limitations. One key limitation is the reliance on questionnaire items for symptom identification, rather than predefined scale dimensions, which introduces a degree of subjectivity in measuring social avoidance and distress. Additionally, the sample is limited to first-year university students, which may affect the generalizability of the findings to other populations, such as older adults or individuals from different cultural backgrounds. Despite these limitations, this study lays a foundational theoretical framework for further exploration into the complex role of neuroticism in social avoidance and distress. The findings suggest important implications for psychological assessments and interventions, especially for those struggling with social anxiety or distress. By emphasizing the importance of the dimensions of "anxiety," "self-consciousness," and "vulnerability," the study provides guidance for targeted interventions aimed at reducing social avoidance and distress. Future research could further investigate the role of other personality traits, such as extraversion and agreeableness, in shaping social behaviors and mental health outcomes. Additionally, exploring the mechanisms through which neuroticism influences social avoidance and distress—such as cognitive distortions, emotional regulation, or coping strategies—would help deepen our understanding of the pathways linking personality and mental health. In conclusion, this study represents a significant step forward in our understanding of the complex dynamics between neuroticism and social avoidance/distress. By employing a longitudinal design and advanced analytical methods, it offers new insights into how personality traits influence mental health over time and lays the groundwork for future research aimed at improving psychological interventions and mental health outcomes. Declarations Conflict of interest: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Ethics approval: The ethical approval for conducting this study was obtained from the [Key Laboratory of Psychology and Behavior of Henan Province], and all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Funding: No funding was received to assist with the preparation of this manuscript. Author Contribution Lanxi Liu was responsible for research design and correspondence. Qiannan Ma contributed to research design, data analysis, and manuscript writing. Yulian Ding was responsible for data validation, investigation, and manuscript revision. Jinjin Fu contributed to data collection and initial manuscript preparation. Maolin Qin was responsible for resources coordination and data investigation. All authors have read and approved the final manuscript. Data Availability Data and statistical code can be made available upon request to the corresponding author References Amiel T, Sargent SL. Individual differences in Internet usage motives. Comput Hum Behav. 2004;20(6):711–26. https://doi.org/10.1016/j.chb.2004.09.002 . Bienvenu OJ, Samuels JF, Costa PT, Reti IM, Eaton WW, Nestadt G. 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16:16:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54680,"visible":true,"origin":"","legend":"\u003cp\u003eCross-Lagged Analysis of Neuroticism and Social Avoidance and Distress\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9014912/v1/c8357c09f0ca6f82081d8036.png"},{"id":104587683,"identity":"9885870f-d6fb-4e43-9194-0eb9f698dd2c","added_by":"auto","created_at":"2026-03-13 16:16:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278461,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged networks of neuroticism with social avoidance and distress from T1 to T2 and T2 to T3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9014912/v1/9a275ae6b7ca47399f8cd089.png"},{"id":104587685,"identity":"434fff43-a1c3-4a8f-a0d8-9ae85fb35977","added_by":"auto","created_at":"2026-03-13 16:16:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10455,"visible":true,"origin":"","legend":"\u003cp\u003eThe out-degree Expected Influence (out-EI) and in-degree Expected Influence (in-EI) of symptoms in the cross-lagged networks of neuroticism with social avoidance and distress from T1 to T2 and T2 to T3.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9014912/v1/55bb6905f66f79ca6e11cd79.png"},{"id":104782396,"identity":"8ddc0b98-40bb-4997-9cee-61fbe95b4417","added_by":"auto","created_at":"2026-03-17 07:57:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":963375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9014912/v1/9f8eba50-96e2-42ff-abff-38c08248eb81.pdf"},{"id":104587684,"identity":"a734552d-e3ee-4905-8530-6c5f311ad860","added_by":"auto","created_at":"2026-03-13 16:16:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":699188,"visible":true,"origin":"","legend":"","description":"","filename":"AttachFiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9014912/v1/e494768eced4d95cfd8af68d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Unique Role of Neuroticism in Social Avoidance and Distress Symptoms: A Cross-Lagged Network Analysis Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInterpersonal communication is essential for mental health and personality development, playing a central role in psychological well-being (Wang Huan et al., 2014; Zhao Xin et al., 2014). College freshmen, in particular, face significant social challenges as they transition to university life, often encountering psychological difficulties such as social avoidance, distress, and anxiety (Zhang Yali et al., 2019; Zhang Qingqing et al., 2022). These challenges are closely associated with social avoidance and distress, a concept introduced by Watson and Friend (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) to describe withdrawal from social interactions and the negative emotional states that follow.\u003c/p\u003e \u003cp\u003eNeuroticism, a personality trait closely tied to the autonomic nervous system, predisposes individuals to intense emotional reactions to relatively minor external stimuli, especially in those with high levels of neuroticism (Riese et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such individuals often struggle with interpersonal challenges, and a direct link has been established between high neuroticism and increased tendencies toward social avoidance and distress (Wiesenfeller et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Characterized by emotional instability and poor emotional regulation, individuals with high neuroticism tend to exhibit negative self-perceptions, which manifest as heightened anxiety, avoidance behaviors, and distress in social situations (Sheppes et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Miers et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEysenck\u0026rsquo;s personality theory regards neuroticism as a core trait characterized by rapid emotional arousal and delayed inhibition, leading to emotional instability and susceptibility to feelings such as irritability, anger, sadness, anxiety, and hostility (Amiel \u0026amp; Sargent, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The Behavioral Inhibition System (BIS) theory further suggests that individuals with high neuroticism exhibit heightened sensitivity to potential threats, triggering anxiety and fear in response to novel social situations, which in turn leads to avoidant behaviors as a coping mechanism (Carver \u0026amp; White, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Cooper et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Heponiemi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sommer et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Research has consistently shown that individuals with high neuroticism are more vulnerable to various psychological conditions, including anxiety, depression, and loneliness (Lund et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings support the first hypothesis of this study: H1 \u0026ndash; Neuroticism is a predictive factor for social avoidance and distress.\u003c/p\u003e \u003cp\u003eThe Five-Factor Model (FFM) defines neuroticism as the \"tendency to experience negative emotions such as fear, sadness, embarrassment, anger, guilt, and disgust\" (McCrae \u0026amp; Costa, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, p. 19). This heightened emotional sensitivity is linked to various forms of psychopathology, including social anxiety, which correlates with broader neuroticism traits (Naragon-Gainey \u0026amp; Watson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The FFM further posits that neuroticism consists of six distinct facets, each reflecting specific aspects of emotional reactivity: anxiety (tendency to experience fear, worry, and tension), angry hostility (tendency to experience anger), depression (tendency to experience depressive moods), self-consciousness (tendency to feel embarrassment and shame), impulsiveness (difficulty controlling urges), and vulnerability (tendency to feel overwhelmed by stress) (McCrae \u0026amp; Costa, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, pp. 21\u0026ndash;22). Recent studies have found that social anxiety, a key component of social avoidance and distress, is particularly linked to heightened self-consciousness and vulnerability. Individuals prone to social anxiety tend to score higher on these facets compared to healthy controls, suggesting that they are more likely to experience intense feelings of shame and are particularly vulnerable to stress (Bienvenu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gamez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kotov, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with these findings, social anxiety appears to be differentially associated with specific facets of neuroticism, particularly those emphasizing emotional instability and stress sensitivity, such as self-consciousness and vulnerability (Bienvenu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Based on these associations, this study proposes Hypothesis 2: The anxiety, vulnerability, and self-consciousness dimensions of neuroticism uniquely predict social avoidance and distress.\u003c/p\u003e \u003cp\u003eDespite the predominance of cross-sectional studies examining social avoidance and distress, longitudinal research on these phenomena remains limited. Therefore, this study adopts a longitudinal approach to examine the temporal dynamics of social avoidance and distress, leading to Hypothesis 3: The influence of neuroticism on social avoidance and distress is characterized by temporal specificity.\u003c/p\u003e \u003cp\u003eTraditional models have often overlooked the complex interactions and causal dynamics among symptoms. In response, Borsboom (2008) proposed a network theory of psychopathology, which conceptualizes symptoms as interconnected elements within psychological disorders. This theory emphasizes that the onset and persistence of psychological conditions are driven by intricate interrelations and feedback loops among symptoms (Borsboom \u0026amp; Cramer, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Network analysis, a methodological innovation derived from this theory, represents symptoms as nodes and their interconnections as edges. The intensity of these relationships is conveyed through the thickness of the edges, shifting the focus from traditional disease models to the exploration of symptom relationships within a network framework (Liang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study explores the impact of neuroticism on social avoidance and distress using a cross-lagged network analysis model and longitudinal data from freshmen. The goal is to uncover the causal dynamics between neuroticism and social avoidance/distress. Using R programming, the study carefully analyzes the interplay of these variables over time, with a particular focus on identifying the symptoms that have significant predictive power and influence within the network.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eThe cohort for this study comprised 397 first-year university students (44.3% male, age\u0026thinsp;=\u0026thinsp;19.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02), selected from a university in Henan, China. Participants voluntarily provided written informed consent, and three follow-up assessments were conducted in September 2022, December 2022, and March 2023. The initial survey (T1) had 520 responses, the second (T2) had 480, and the final (T3) had 420. After excluding inconsistent responses, 397 valid participants remained, yielding a response rate of 76%. Logistic regression analysis of missing data revealed no significant differences between participants who completed all phases and those who did not ( \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting a random pattern of missing data.2.2. Instruments\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Neuroticism Personality Scale\u003c/h2\u003e \u003cp\u003eIn this study, neuroticism was assessed using the Neuroticism subscale from the streamlined version of the NEO Five-Factor Inventory (NEO-FFI) as revised by Nie Yangang et al. (2008). The NEO-FFI is a shortened form of the NEO Personality Inventory (NEO-PI), consisting of the 12 items with the highest factor loadings from each of the five dimensions of the NEO-PI(Costa \u0026amp; McCrae, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Responses were rated on a 5-point Likert scale, ranging from 1 (\"strongly disagree\") to 5 (\"strongly agree\"), with items such as \"I often feel nervous or easily agitated.\" The cumulative score reflects the respondent's level of neuroticism. The internal consistency of this scale, measured by Cronbach\u0026rsquo;s alpha, demonstrated excellent reliability across the three assessments in this study, with coefficients of 0.95, 0.96, and 0.95, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Social Avoidance and Distress Scale\u003c/h2\u003e \u003cp\u003eThe study incorporated the Social Avoidance and Distress Scale (SAD) as refined by Peng Chunzi et al. (2003), originally developed by Watson and Friend. This instrument consists of 28 items bifurcated into two principal dimensions: social avoidance, with items such as \"I often want to leave social gatherings,\" and social distress, exemplified by \"I usually feel anxious around people unless I am very familiar with them.\" The scale's reliability, indicated by Cronbach's alpha coefficients for the three evaluations within this study, was robust, evidenced by scores of 0.95, 0.93, and 0.95, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Procedures\u003c/h2\u003e \u003cp\u003eThe analysis of the data collected in this study was executed utilizing SPSS version 21.0 for preliminary descriptive statistics and correlation analyses. To explore the temporal dynamics between variables, cross-lagged analysis was conducted using AMOS version 23.0. Furthermore, a more sophisticated cross-lagged panel network (CLPN) analysis was performed with the R programming language, scrutinizing data collected at three distinct time intervals (T1, T2, T3).\u003c/p\u003e \u003cp\u003eIn the initial phase, data preprocessing was imperative to ensure coherent alignment and comparability across the various time points. This step included merging datasets and refining column names to facilitate accurate linkage and analysis of the data. The datasets corresponding to each time point (Data1, Data2, Data3) were meticulously cleaned and structured for subsequent analysis. The variables analyzed encompassed aspects of the neuroticism personality (denoted as N1-12) and social avoidance and distress (indicated as SAD1-28).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eFor the construction of the cross-lagged network model, the glmnet and cv.glmnet functions within R were employed, leveraging LASSO regression and cross-validation techniques. This modeling process necessitated the establishment of a random seed to ensure the reproducibility of results. A bespoke function, CLPN.fun, was crafted to generate adjacency matrices, elucidating the magnitude of associations between the variables. This function facilitated the estimation of both autoregressive effects\u0026mdash;wherein a symptom's presence at one time point predicts its recurrence at a subsequent time point\u0026mdash;and cross-lagged effects\u0026mdash;where a symptom at one juncture predicts a different symptom at a later stage.\u003c/p\u003e \u003cp\u003eUtilizing bootstrapping methods, the analysis extended to assess the network model's non-parametric stability and case stability. This involved repeated resampling and examination of the network's edge strengths' resilience, centrality indices, and expected influence. Such comprehensive analyses were instrumental in unmasking the steadfastness of the variable interrelations within the network and elucidating the temporal evolution of these relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Statistics and Correlation Analysis\u003c/h2\u003e \u003cp\u003eThe mean values, standard deviations, and correlation coefficients for neuroticism, social avoidance, and distress at the three time points are presented in 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\u003eMean, Standard Deviation, and Correlation Coefficients of Variables (n\u0026thinsp;=\u0026thinsp;397)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1 T1 Neuroticism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.33\u0026thinsp;\u0026plusmn;\u0026thinsp;13.44\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 T2 Neuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 T3 Neuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e30.39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 T1 Social Avoidance and Distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 T2 Social Avoidance and Distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.55\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 T3 Social Avoidance and Distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: T1, T2, and T3 represent the three measurement times, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be observed that neuroticism shows varying degrees of positive correlation with social avoidance and distress at time points T1 and T2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cross-Lagged Analysis\u003c/h2\u003e \u003cp\u003eCross-lagged analysis was conducted using AMOS 23.0 software. The model fit indices demonstrated good fit: \u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.10, NFI\u0026thinsp;=\u0026thinsp;0.97, TLI\u0026thinsp;=\u0026thinsp;0.97, CFI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.05. These indices reflect the accuracy and utility of the model's predictions ((see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003ePrediction Effects of Neuroticism on Social Avoidance and Distress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\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\u003eC.R\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 N\u0026rarr;T2 SAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 N\u0026rarr;T2 SAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: T1 N\u0026thinsp;=\u0026thinsp;Neuroticism at the first time point; T2 SAD\u0026thinsp;=\u0026thinsp;Social Avoidance and Distress at the second time point; T2 N\u0026thinsp;=\u0026thinsp;Neuroticism at the second time point; T3 SAD\u0026thinsp;=\u0026thinsp;Social Avoidance and Distress at the third time point.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is evident that neuroticism significantly predicts social avoidance and distress at Time 2 (T2) from Time 1 (T1). Similarly, neuroticism at Time 2 (T2) significantly predicts social avoidance and distress at Time 3 (T3). These results support the research hypothesis, indicating that neuroticism significantly and positively predicts levels of social avoidance and distress at different time points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Network Estimation\u003c/h2\u003e \u003cp\u003eThe cross-lagged network analysis delineates the intricate pathways through which neuroticism influences social avoidance and distress, transitioning from Time 1 (T1) to Time 2 (T2), as depicted on the left side of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In this visual representation, green arrows symbolize positive predictors, while red arrows denote negative influences, with the thickness of each line mirroring the prediction's potency. The diagram categorizes the nodes into three colors: yellow for the 12 facets of neuroticism, green for social avoidance, and blue for social distress, facilitating an intuitive understanding of the network's structure.During the T1\u0026rarr;T2 interval, the analysis identifies neuroticism items 2 and 9 as pivotal in forecasting network dynamics, encapsulating the dimensions of anxiety (defined as the tendency to experience fear and worry about potential threats; Newby, Pitura, Penney, et al., 2017) and self-consciousness (defined as the tendency to experience embarrassment and shame, often leading to a desire to withdraw from social situations; Newby, Pitura, Penney, et al., 2017), respectively.These symptoms notably contribute to the prediction of social avoidance and distress, with items 17 and 20\u0026mdash;highlighting discomfort with strangers (\"Even if a room is full of strangers, I might still go in\") and unease in group settings (\"When I am with a group of people, I usually feel uneasy\")\u0026mdash;emerging as frequently predicted elements.\u003c/p\u003e \u003cp\u003eShifting focus to the progression from Time 2 (T2) to Time 3 (T3), as depicted on the right side of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the analysis highlights item 10 (\"I often feel inferior to others\") as a key predictor, emphasizing vulnerability as a critical dimension at this stage. The network further reveals items 10 and 13 as the most significant predictors of social avoidance and distress; (Newby et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Specifically, these items capture anxiety in mixed-gender social contexts (\"At informal gatherings, if members of the opposite sex are present, I usually feel anxious and tense\") and a tendency towards social withdrawal (\"I often think about leaving social situations\"). This analysis underscores the evolving role of specific neuroticism symptoms in shaping social behaviors and emotional responses across different time points, providing valuable insights into the temporal dynamics of social avoidance and distress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Centrality Estimation\u003c/h2\u003e \u003cp\u003eThe centrality of items within the cross-lagged networks is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Through the analysis of cross-lagged networks across two time stages, it was found that symptoms with high out-degree Expected Influence (out-EI) exhibit temporal specificity. In the T1\u0026rarr;T2 stage, items 2 and 9 of neuroticism have the highest out-EI; while items 17 and 20 within social avoidance and distress have high in-degree Expected Influence (in-EI). In the T2\u0026rarr;T3 stage, item 10 of neuroticism has the highest out-EI; and items 10 and 13 within social avoidance and distress have high in-EI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Stability Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the stability of centrality indices within the model, we calculated the Centrality Stability Coefficient (CS-coefficient). The CS-coefficient measures the maximum proportion of the sample that can be removed from the network while still maintaining a correlation of at least 0.7. A higher CS-coefficient value indicates that centrality indices can maintain high stability even within smaller subsamples. For the T1\u0026rarr;T2 network, the centrality stability coefficients for the sum of in-EI and out-EI are 0.13 and 0.75, respectively, indicating that the correlation of in-EI remains above 0.7 even after removing up to 13% of the samples, and out-EI maintains high stability even with a significant reduction in the sample size; for the T2\u0026rarr;T3 network, the centrality stability coefficients for in-EI and out-EI are both 0.36. Tests of centrality differences showed that, in the networks from T1\u0026rarr;T2 and T2\u0026rarr;T3, those symptoms with the strongest out-EI/in-EI are statistically stronger than most other symptoms in the network (see web appendix Figure S4), further indicating that the results of the centrality analysis are stable and generalizable. Results of the edge weight bootstrapping procedure (see web appendix Figure S2) show that both cross-lagged network estimates are moderately accurate: there is considerable overlap between the 95% confidence intervals (CI) of edge weights, while some of the strongest edges do not overlap with their confidence intervals. Subset bootstrapping results (see web appendix Figure S3) suggest that estimates of edges, out-EI, and in-EI in both networks are stable and generalizable.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The Positive Predictive Effect of Neuroticism on Social Avoidance and Distress\u003c/h2\u003e \u003cp\u003eUsing AMOS 23.0 software for cross-lagged analysis, this study examined the predictive role of neuroticism on social avoidance and distress among college freshmen. The results clearly indicate that neuroticism significantly and positively predicts both social avoidance and distress across different time points, consistent with findings from previous research (Yin Xunbao et al., 2010). Notably, this study distinguishes itself from earlier cross-sectional studies by employing a longitudinal approach, which reinforces the predictive power of neuroticism over time and further substantiates its impact on social avoidance and distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Evolution of Neuroticism: Transition from \"Anxiety\" and \"Self-Consciousness\" to \"Vulnerability\" as a Central Influence\u003c/h2\u003e \u003cp\u003eBy utilizing R for cross-lagged network analysis, this study traced how the influence of neuroticism on social avoidance and distress evolved over time. In the initial period (T1-T2), \"Anxiety\" and \"Self-Consciousness\" were identified as the strongest predictors of social avoidance and distress. However, as the study progressed to the T2-T3 phase, \"Vulnerability\" emerged as the dominant predictor. This shift suggests that while neuroticism initially triggers a range of social avoidance and distress symptoms, over time, \"Vulnerability\" becomes the central factor driving the persistence and escalation of these symptoms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Temporal Specificity of Neuroticism's Impact\u003c/h2\u003e \u003cp\u003eThis study also explored the temporal specificity of neuroticism\u0026rsquo;s influence on social avoidance and distress. During the T1-T2 phase, the \"Anxiety\" and \"Self-Consciousness\" dimensions of neuroticism were strong predictors of freshmen's discomfort in social interactions, such as \"discomfort with strangers\" and \"uneasiness in social situations.\" However, by the T2-T3 phase, \"Vulnerability\" emerged as the central predictor, with items like \"At informal gatherings, if members of the opposite sex are present, I usually feel anxious and tense\" and \"I often think about leaving social situations\" being prominently predicted by this dimension. This shift underscores the evolving role of neuroticism in shaping social avoidance and distress over time. These findings highlight the importance of addressing the dimensions of \"Anxiety,\" \"Self-Consciousness,\" and \"Vulnerability\" in interventions designed to reduce social avoidance and distress among freshmen.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study offers significant methodological contributions by utilizing a longitudinal design and cross-lagged network analysis, providing a novel perspective on the dynamic interplay between neuroticism and social avoidance and distress. The findings reveal that neuroticism plays a crucial role in predicting both social avoidance and distress over time, with distinct dimensions of neuroticism, such as \"anxiety,\" \"self-consciousness,\" and \"vulnerability,\" demonstrating phase-specific effects. These insights offer a deeper understanding of the temporal dynamics of social behavior and provide a basis for future research on personality traits and mental health.\u003c/p\u003e \u003cp\u003eHowever, the study acknowledges several limitations. One key limitation is the reliance on questionnaire items for symptom identification, rather than predefined scale dimensions, which introduces a degree of subjectivity in measuring social avoidance and distress. Additionally, the sample is limited to first-year university students, which may affect the generalizability of the findings to other populations, such as older adults or individuals from different cultural backgrounds.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study lays a foundational theoretical framework for further exploration into the complex role of neuroticism in social avoidance and distress. The findings suggest important implications for psychological assessments and interventions, especially for those struggling with social anxiety or distress. By emphasizing the importance of the dimensions of \"anxiety,\" \"self-consciousness,\" and \"vulnerability,\" the study provides guidance for targeted interventions aimed at reducing social avoidance and distress.\u003c/p\u003e \u003cp\u003eFuture research could further investigate the role of other personality traits, such as extraversion and agreeableness, in shaping social behaviors and mental health outcomes. Additionally, exploring the mechanisms through which neuroticism influences social avoidance and distress\u0026mdash;such as cognitive distortions, emotional regulation, or coping strategies\u0026mdash;would help deepen our understanding of the pathways linking personality and mental health.\u003c/p\u003e \u003cp\u003eIn conclusion, this study represents a significant step forward in our understanding of the complex dynamics between neuroticism and social avoidance/distress. By employing a longitudinal design and advanced analytical methods, it offers new insights into how personality traits influence mental health over time and lays the groundwork for future research aimed at improving psychological interventions and mental health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of interest:\u003c/h2\u003e \u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval:\u003c/strong\u003e \u003cp\u003e The ethical approval for conducting this study was obtained from the [Key Laboratory of Psychology and Behavior of Henan Province], and all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLanxi Liu was responsible for research design and correspondence. Qiannan Ma contributed to research design, data analysis, and manuscript writing. Yulian Ding was responsible for data validation, investigation, and manuscript revision. Jinjin Fu contributed to data collection and initial manuscript preparation. Maolin Qin was responsible for resources coordination and data investigation. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and statistical code can be made available upon request to the corresponding author\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmiel T, Sargent SL. Individual differences in Internet usage motives. 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Approach and avoidance behavior in female patients with borderline personality disorder. Front Behav Neurosci. 2020;14:588874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnbeh.2020.588874\u003c/span\u003e\u003cspan address=\"10.3389/fnbeh.2020.588874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neuroticism, Social Avoidance and Distress, Cross-Lagged Analysis, Cross-Lagged Network Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9014912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9014912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: T\u003c/strong\u003ehis study explores the role of neuroticism in social avoidance and distress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eA cohort of 397 first-year university students participated, with data collected across three follow-ups using the Neuroticism Personality Questionnaire and the Social Avoidance and Distress Scale. Cross-lagged panel and network analysis were used to examine the relationships between these variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eNeuroticism significantly predicted social avoidance and distress, with phase-specific effects. In the early period (T1-T2), \"anxiety\" and \"self-consciousness\" were key predictors, while in the later period (T2-T3), \"vulnerability\" emerged as a significant factor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study highlights the temporal dynamics and specific dimensions of neuroticism in social behaviors, suggesting their importance for psychological assessment and interventions.\u003c/p\u003e","manuscriptTitle":"The Unique Role of Neuroticism in Social Avoidance and Distress Symptoms: A Cross-Lagged Network Analysis Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 16:16:16","doi":"10.21203/rs.3.rs-9014912/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-09T23:58:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-07T10:40:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T01:12:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T01:12:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-03T02:14:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e7b7141-18cf-490a-8e1c-be1020a63005","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T16:16:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 16:16:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9014912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9014912","identity":"rs-9014912","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}