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Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship between Stress, Adolescent Academic Performance, and Depression

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Title: Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship between Stress, Adolescent Academic Performance, and Depression
Language: English
Authors: Xiaoyan Bi, Yankun Ma, Xianghong Sun, Jianhui Wu, Xiaoyu Wang, Liang Zhang (ORCID 0000-0001-8888-9550)
Source: Journal of Adolescence. 2025 97(5):1297-1313.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Anxiety, Academic Achievement, Depression (Psychology), Foreign Countries, Adolescents, Cognitive Processes, Emotional Adjustment
Geographic Terms: China
DOI: 10.1002/jad.12497
ISSN: 0140-1971
1095-9254
Abstract: Introduction: The Challenge and Hindrance Stress Framework is an influential theoretical model for measuring individuals' perceptions of stress. However, its structure has not been validated among Chinese adolescents, and the effects of different forms of stress on their short-term and long-term outcomes remain unclear. Methods: Study 1 validated the Student Version Challenge and Hindrance Stress Scale with a sample of 3,376 adolescents in China (Time 1, September 2023, M[subscript age] = 14.57, SD = 1.46). Studies 2a and 2b extended Study 1 by analyzing cross-sectional and longitudinal data from 1,083 participants in China (Time 2, March 2024, M[subscript age] = 14.32, SD = 1.01) to examine the effects of various forms stress on academic performance and depression, with cognitive emotion regulation strategies used as mediators. Results: The results showed that adaptive strategies mediated the positive effects of challenge stress on academic performance and depression, whereas maladaptive strategies mediated the negative impacts of challenge or hindrance stress on depression. Conclusion: These findings emphasize the importance of distinguishing stress forms, offering insights for educators, researchers, and policymakers to enhance adolescent well-being and performance.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476101
Database: ERIC
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  Value: <anid>AN0186343121;jaa01jul.25;2025Jul04.06:05;v2.2.500</anid> <title id="AN0186343121-1">Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship Between Stress, Adolescent Academic Performance, and Depression </title> <p>Introduction: The Challenge and Hindrance Stress Framework is an influential theoretical model for measuring individuals' perceptions of stress. However, its structure has not been validated among Chinese adolescents, and the effects of different forms of stress on their short‐term and long‐term outcomes remain unclear. Methods: Study 1 validated the Student Version Challenge and Hindrance Stress Scale with a sample of 3,376 adolescents in China (Time 1, September 2023, Mage = 14.57, SD = 1.46). Studies 2a and 2b extended Study 1 by analyzing cross‐sectional and longitudinal data from 1,083 participants in China (Time 2, March 2024, Mage = 14.32, SD = 1.01) to examine the effects of various forms stress on academic performance and depression, with cognitive emotion regulation strategies used as mediators. Results: The results showed that adaptive strategies mediated the positive effects of challenge stress on academic performance and depression, whereas maladaptive strategies mediated the negative impacts of challenge or hindrance stress on depression. Conclusion: These findings emphasize the importance of distinguishing stress forms, offering insights for educators, researchers, and policymakers to enhance adolescent well‐being and performance.</p> <p>Keywords: academic performance; adolescents; challenge and hindrance stress; cognitive emotion regulation strategies; depression</p> <hd id="AN0186343121-2">Introduction</hd> <p>Although stress is commonly considered negative, as shown in early studies (Cohen et al. [<reflink idref="bib20" id="ref1">20</reflink>]; McEwen [<reflink idref="bib47" id="ref2">47</reflink>]), recent research has also shown the beneficial effects of stress on individual well‐being (Cavanaugh et al. [<reflink idref="bib12" id="ref3">12</reflink>]; Travis et al. [<reflink idref="bib64" id="ref4">64</reflink>]; Zhu et al. [<reflink idref="bib75" id="ref5">75</reflink>]). These findings suggest that the role of stress in individual outcomes is complex (LePine [<reflink idref="bib43" id="ref6">43</reflink>]; Pindek et al. [<reflink idref="bib53" id="ref7">53</reflink>]; Podsakoff et al. [<reflink idref="bib54" id="ref8">54</reflink>]). Understanding the role of stress in adolescents is particularly critical, given that they experience substantial physical, social, and psychological changes that are linked to stress (Association [<reflink idref="bib2" id="ref9">2</reflink>]; WHO–UNICEF, E [<reflink idref="bib68" id="ref10">68</reflink>]). Therefore, in this study, we aimed to elucidate the complicated mechanisms of stress on academic performance and mental health in Chinese adolescents by (a) characterizing the underlying structure of perceived stress based on the Challenge and Hindrance Stress Framework (CHSF) and (b) examining how the challenge or hindrance stress (HS) is potentially mediated by cognitive emotion regulation strategies.</p> <hd id="AN0186343121-3">CHSF</hd> <p>A prominent theory to explain the role of stress is CHSF, initially introduced by Cavanaugh et al. ([<reflink idref="bib12" id="ref11">12</reflink>]). According to the CHSF, stress can be conceptualized into two categories: CS and HS. Specifically, CS refers to those presenting an opportunity for growth and personal accomplishment, with a positive effect. Examples of CS include time pressure and responsibility. In contrast, HS refers to those hindering personal growth or achievement, triggering negative emotions, and leading to passive or emotional responses. Examples of HS include role conflict, demand ambiguity, and unfair treatment, all of which are likely to be considered uncontrollable and therefore hindering.</p> <p>The CHSF has gained popularity over the past several decades in stress research (LePine [<reflink idref="bib43" id="ref12">43</reflink>]; O'Brien and Beehr [<reflink idref="bib50" id="ref13">50</reflink>]; Podsakoff et al. [<reflink idref="bib54" id="ref14">54</reflink>]), especially in the field of occupational stress (Mazzola and Disselhorst [<reflink idref="bib45" id="ref15">45</reflink>]). However, few studies merging research extends CHSF to educational contexts, revealing: CS enhances learning motivation and achievement, whereas HS correlates with emotional exhaustion and resource depletion. These studies in the field of education provide support for the CHSF. However, existing research has focused predominantly on college students, whereas studies on adolescents are lacking (LePine et al. [<reflink idref="bib41" id="ref16">41</reflink>]; Zhu et al. [<reflink idref="bib75" id="ref17">75</reflink>]; Travis et al.[<reflink idref="bib64" id="ref18">64</reflink>]). Furthermore, previous studies predominantly adapted and used existing questionnaires of the challenge and HS scale, which were not comprehensively examined for their reliability and validity in the Chinese context (Byron et al. [<reflink idref="bib11" id="ref19">11</reflink>]; Cavanaugh et al. [<reflink idref="bib12" id="ref20">12</reflink>]). Therefore, the first goal of this study was to uncover the underlying structure of the challenge and HS scale with Chinese adolescent students and assess its reliability and validity.</p> <hd id="AN0186343121-4">The CS–HS and Adolescent Academic Performance and Depression</hd> <p>Researchers have explored the relationship between CS, HS, and various outcomes. Emerging evidence suggests that CS tends to enhance academic performance, whereas HS is associated with adverse academic consequences. For instance, LePine et al. ([<reflink idref="bib41" id="ref21">41</reflink>]) used the CHSF to assess how stress predicts academic performance among American undergraduates. Their findings indicated that CS was positively associated with learning motivation and academic performance, while HS exhibited a negative association. However, both forms of stress were linked to increased exhaustion. Similarly, Zhu et al. ([<reflink idref="bib75" id="ref22">75</reflink>]) conducted a study among Chinese undergraduates, reporting that CS positively predicted academic achievement, whereas HS had a detrimental effect. Notably, their study relied on self‐reported academic performance, which may introduce bias. Expanding on this, Travis et al. ([<reflink idref="bib64" id="ref23">64</reflink>]) examined multiple academic outcomes and further confirmed the opposing effects of CS and HS on positive and negative academic achievements, respectively.</p> <p>Beyond academic performance, stress plays a critical role in adolescent mental health. In China, adolescent depression rates have reached 14.8%, significantly surpassing the 10.6% prevalence observed in adults. The CHSF posits that CS and HS exert distinct influences on psychological well‐being, with CS generally linked to positive psychological states and HS contributing to negative emotional outcomes in adults (Dimitrova [<reflink idref="bib23" id="ref24">23</reflink>]; French et al. [<reflink idref="bib28" id="ref25">28</reflink>]; Schilbach et al. [<reflink idref="bib59" id="ref26">59</reflink>]). However, limited research has investigated these differential effects on adolescent depression. This gap highlights the need to explore how CS and HS uniquely shape depressive symptoms in adolescents, providing a more nuanced understanding of stress‐related mental health risks during this developmental stage.</p> <p>Although many studies suggest that CS has beneficial effects, some meta‐analyses indicate that its impact on various outcomes is inconsistent. For example, Clarke ([<reflink idref="bib19" id="ref27">19</reflink>]) conducted a meta‐analysis across three studies and found that the average mediation effect for CS on safety outcomes was small and negative (<emph>ρ</emph> = −0.12, 95% CI [−0.18, −0.06]). Similarly, Mazzola and Disselhorst ([<reflink idref="bib45" id="ref28">45</reflink>]) indicated that the average mediation effect of CS on performance was not significant (<emph>ρ</emph> = 0.01, 95% CI [−0.14, 0.08]) across five studies. Why does the impact of CS on outcomes exhibit inconsistency? Is this variability attributable to the intrinsic characteristics of CS or to differences in the types of outcomes assessed?</p> <p>To address these questions, we will investigate the effects of CS and HS on two distinct outcomes (academic performance and depression) among adolescent students. By examining both subjective (depression) and objective assessment (academic performance), this study aims to provide a more comprehensive understanding of how stress affects adolescent development. Furthermore, most studies testing the CHSF employed cross‐sectional designs, making it impossible to determine whether the measured stress is the cause or consequence of the observed relationships (Podsakoff et al. [<reflink idref="bib54" id="ref29">54</reflink>]). Therefore, in this study, we use a combination of cross‐sectional and longitudinal designs to investigate the mechanisms of CS and HS.</p> <hd id="AN0186343121-5">The Mediating Role of Cognitive Emotion Regulation Strategies</hd> <p>CS and HS influence individuals' psychological processes and outcomes through distinct mechanisms. Previous research has identified various mediating variables through which these stresses impact individual performance. Some of these variables exert negative effects, such as negative emotions, fatigue, and physical strains. While others contribute positively, including motivation, attitudes, and positive emotions (Cavanaugh et al. [<reflink idref="bib12" id="ref30">12</reflink>]; Chalabaev et al. [<reflink idref="bib14" id="ref31">14</reflink>]; Rodell and Judge [<reflink idref="bib57" id="ref32">57</reflink>]). Additionally, Crawford et al. ([<reflink idref="bib22" id="ref33">22</reflink>]) refined and extended the Job Demands‐Resources (JD‐R) model, emphasizing the critical role of appraisal patterns in differentiating the effects of challenge and hindrance stressors. Specifically, the relationship between demands and engagement depends on how individuals appraise those demands. Demands that are perceived as hindrances are negatively associated with engagement, while those perceived as challenges are positively associated with engagement. Based on these studies, we identify two primary mediating pathways: the emotional mediation pathway, where stress influences outcomes by shaping individuals' emotional responses; the motivational mediation pathway, through which stress affects engagement and effort by modulating motivation levels; and the demand appraisal pathway, where the type of demands individuals appraise ultimately influences outcomes.</p> <p>However, how individuals cognitively engage with CS and HS remains unclear. Therefore, we investigate cognitive engagement as a novel mechanism by examining the role and effectiveness of cognitive emotion regulation strategies (CERS) in moderating the impact of these stressors. Research indicates that individuals exhibit varying habitual uses of CERS (Gross and John [<reflink idref="bib32" id="ref34">32</reflink>]; John and Gross [<reflink idref="bib37" id="ref35">37</reflink>]), which may influence how they cope with different forms of stress. Generally, CERS can be divided into two categories: adaptive and maladaptive (Cherkil et al. [<reflink idref="bib16" id="ref36">16</reflink>]; Zhou et al. [<reflink idref="bib73" id="ref37">73</reflink>]). When adaptive emotion regulation (AER) is used to cope with stress or to regulate the emotional problems caused by stressful events, it is believed to help alleviate stress and its emotional impact. Conversely, when a negative approach is used to cope with stress, it is believed to increase the stress and its negative outcomes. A recent meta‐analysis (Zhang et al. [<reflink idref="bib72" id="ref38">72</reflink>]) examined the regulatory strategies that employees adopt to cope with different types of stressors in the workplace. They proposed that CS tend to evoke promotion‐focused coping, whereas HS tend to evoke prevention‐focused coping. Based on this, we hypothesize that individuals experiencing CS tend to adopt AER (e.g., plan to future, putting into perspective), while those experiencing HS tend to adopt maladaptive emotion regulation (MER, e.g., venting, denial).</p> <p>Emotion regulation has been shown to predict academic performance (Graziano et al. [<reflink idref="bib31" id="ref39">31</reflink>]), with different emotion regulation strategies having varying effects on academic outcomes (Andrés et al. [<reflink idref="bib1" id="ref40">1</reflink>]; Nadeem et al. [<reflink idref="bib49" id="ref41">49</reflink>]). AER, such as cognitive reappraisal, are generally associated with better academic performance (Nadeem et al. [<reflink idref="bib49" id="ref42">49</reflink>]). Cognitive reappraisal involves altering one's perception of potentially stressful situations to change their emotional impact. This approach enables individuals to approach academic challenges with a more constructive mindset, thereby enhancing their ability to effectively solve problems and sustain effort, ultimately improving academic performance (Gross and John [<reflink idref="bib32" id="ref43">32</reflink>]). In contrast, MER, such as expressive suppression, are typically associated with poorer academic performance (Nadeem et al. [<reflink idref="bib49" id="ref44">49</reflink>]). Longitudinal evidence further establishes the unidirectional predictive role of emotion regulation in depressive symptomatology, with clear temporal precedence. Multi‐wave longitudinal data utilizing cross‐lagged panel models (CLPMs) demonstrate that emotion regulation capacity significantly predicts subsequent depression levels, whereas the reverse pathway (depression→emotion regulation) remains statistically nonsignificant (Bardeen and Fergus [<reflink idref="bib3" id="ref45">3</reflink>]; Brenning et al. [<reflink idref="bib7" id="ref46">7</reflink>]). AER are posited to provide substantial protection against psychopathological symptoms by enabling individual to effectively manage emotionally challenging situations, a common occurrence during adolescence (Cavicchioli et al. [<reflink idref="bib13" id="ref47">13</reflink>]; Kraft et al. [<reflink idref="bib40" id="ref48">40</reflink>]; Schäfer et al. [<reflink idref="bib58" id="ref49">58</reflink>]). Conversely, the frequence use of MER such as rumination and avoidance is associated with the persistence and intensification of negative emotional states (Brzozowski and Philip Crossey [<reflink idref="bib10" id="ref50">10</reflink>]; Gross and John [<reflink idref="bib32" id="ref51">32</reflink>]). Based on this, we posit that AER exert a positive predictive effect on academic performance, whereas MER demonstrate a positive predictive association with depression.</p> <p>According to the conservation of resources (COR) theory (Hobfoll et al. [<reflink idref="bib34" id="ref52">34</reflink>]), individuals experiencing CS are motivated to invest and mobilize resources (such as emotional regulation strategies) to meet demands, which can lead to more constructive coping mechanisms like cognitive reappraisal and perspective‐taking. This adaptive regulation can promote better academic performance and mental health outcomes (Andrés et al. [<reflink idref="bib1" id="ref53">1</reflink>]; Cavicchioli et al. [<reflink idref="bib13" id="ref54">13</reflink>]; Nadeem et al. [<reflink idref="bib49" id="ref55">49</reflink>]). Contrarily, when individuals encounter HS, their negative appraisal and anticipated adverse consequences of stress may lead to a perceived depletion and threat to their resources. As a result, they are more likely to engage in maladaptive emotional regulation strategies (MER), such as emotional suppression or venting. These MER often contribute to declines in academic performance and exacerbate mental health issues, as they fail to effectively regulate emotions and may lead to cognitive overload, impairing essential cognitive processes such as concentration and memory retention (Brzozowski and Philip Crossey [<reflink idref="bib10" id="ref56">10</reflink>]; Gross and John [<reflink idref="bib32" id="ref57">32</reflink>]). Therefore, HS typically undermine academic performance and mental health by fostering MER, such as avoidance or denial.</p> <hd id="AN0186343121-6">The Present Study</hd> <p>To address the questions mentioned above, three studies were conducted. In Study 1, we validated a student version of the CS and HS Scale and comprehensively examined its factor structure, measurement invariance (MI), reliability, and validity in a rich data set. Based on the results of Study 1, we further explored the mechanisms through which CS and HS impact both academic performance and depression. In Study 2a, we used cross‐sectional and longitudinal data to identify the mechanisms through which CS and HS affect academic performance (Figure 1). We hypothesize that CS may enhance academic performance by promoting the use of AER, whereas HS may hinder academic performance by use of MER. In Study 2b, which utilized similar data methodologies, we explored the pathways through which CS and HS influence depression (Figure 2). Specifically, we hypothesize that CS may alleviate depression by fostering the use of AER, whereas HS may increase depression levels through the use of MER. This not only validates the framework of CS and HS but also enhances our understanding of how different forms of stress impact various outcomes.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/JAA/01jul25/jad12497-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="jad12497-fig-0001.jpg" title="1 Research Model 1 of Study 2a: Model 1 includes a cross‐sectional analysis using Time 2 data and a longitudinal analysis incorporating data from both Time 1 and Time 2. In both analyses, academic performance at Time 1 was included as a control variable." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/JAA/01jul25/jad12497-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="jad12497-fig-0002.jpg" title="2 Research Model 2 of Study 2b: Model 2 includes a cross‐sectional analysis using Time 2 data and a longitudinal analysis incorporating data from both Time 1 and Time 2. In both analyses, depression at Time 1 was included as a control variable." /> </p> <p></p> <hd id="AN0186343121-9">Study 1: Validation and Psychometric Evaluation of the Student CS and HS Scale (S‐CHSS) in Ch...</hd> <p></p> <hd id="AN0186343121-10">Method</hd> <p></p> <hd id="AN0186343121-11">Participants</hd> <p>In September 2023 (Time 1), data were collected from 4,297 students across four regions (Guangzhou, Changsha, Shihezi, and Shenyang) using paper questionnaires. To enhance data quality, we removed participants who did not accurately answer a series of attention checks (e.g., please select "agree"; <emph>N</emph> = 802 removed). The number of attention checks varied by data collection method and ranged from 4 to 5. The participants needed to answer all the checks accurately for inclusion. Additionally, we removed any participants who did not complete at least 80% of the measures used (<emph>N</emph> = 119 removed). Altogether a max total of Sample A (<emph>N</emph> = 3376, Supporting Information S1: Table S1) participants with clean data were analyzed. In three studies, all the students voluntarily participated in the research, and none of them experienced significant clinical psychological symptoms. All participants and their parents provided written informed consent before data collection.</p> <hd id="AN0186343121-12">Procedure</hd> <p>We translated the questionnaire into Chinese via an English version (Cavanaugh et al. [<reflink idref="bib12" id="ref58">12</reflink>]; LePine et al. [<reflink idref="bib41" id="ref59">41</reflink>]) and made multiple rounds of modifications during the process. The process of translation involves translating from English to Chinese and Chinese back to English (Erkut [<reflink idref="bib25" id="ref60">25</reflink>]). Firstly, the first author of this paper translated the S‐CHSS into Chinese and modified ambiguous terms to obtain the first draft of the Chinese version. Secondly, three psychologists with study abroad experience translated the Chinese version of the draft back into English. Next, the seven experts who participated in the translation and back‐translation formed an expert group. We revised the draft of the Chinese version. Finally, after comprehensively considering evaluations and suggestions, the first author modified and obtained the final Chinese version (Supporting Information S1: Table S2).</p> <hd id="AN0186343121-13">Measures</hd> <p> <bold>The Student Version Challenge and Hindrance Stress Scale (S‐CHSS, Time 1).</bold> The S‐CHSS was developed to measure stress in the past semester (Cavanaugh et al. [<reflink idref="bib12" id="ref61">12</reflink>]; LePine et al. [<reflink idref="bib41" id="ref62">41</reflink>]). The S‐CHSS version, comprising 10 items, was used in this study. The hindrance stress factor consists of five items, while the CS factor consists of five items. Each item is rated on a five‐point scale (1 = never stress to 5 = great deal of stress). Sample items from the CS scale include "The difficulty of the work required in your classes" and "The number of projects/assignments in your classes." Sample items from the hindrance stress scale include "The inability to clearly understand what is expected of you in your classes" and "The amount of time spent on busy work for your classes."</p> <p> <bold>The Perceived Stress Scale (PSS, Time 1).</bold> The PSS developed by Cohen ([<reflink idref="bib21" id="ref63">21</reflink>]) is a self‐report measure used to assess the level of stress experienced. The PSS‐10 version was used in this study. Each item is rated on a five‐point scale (0 = never to 4 = always). The higher the overall score, the greater the perceived stress level.</p> <p> <bold>The Big Five Measurement (Time 1)</bold>. The Mini‐IPIP contains 20 items and the responses are rated on a 5‐point Likert‐type scale ranging from 1 (strongly disagree) to 5 (strongly agree) (Dimitrova [<reflink idref="bib23" id="ref64">23</reflink>]). Each item is a phrase describing a behavior (e.g., "Am the life of the party," "Sympathize with others feelings") and subjects were asked to respond to this question: How much do you agree with each statement about you as you generally are now, not as you wish to be in the future? The scale measures the five dimensions of the Big Five Model (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) with four items per dimension.</p> <p> <bold>The Self‐Rating Anxiety Scale (SAS, Time 1).</bold> The SAS is a self‐report scale whose 20 items cover a variety of anxiety symptoms (Zung [<reflink idref="bib76" id="ref65">76</reflink>]), both psychological (e.g., "I feel afraid for no reason at all") and somatic (e.g., "My arms and legs shake and tremble"). Responses are given on a 4‐point scale ranging from 1 (none, or a little of the time) to 4 (most, or all of the time). The participants are instructed to base their answers on their experiences over the previous week.</p> <p> <bold>The Center for Epidemiological Studies Depression Scale (CES‐D, Time 1).</bold> The CES‐D scale was developed to screen for depression symptoms by measuring the frequency of events and ideas over the past 2 weeks (Radloff [<reflink idref="bib56" id="ref66">56</reflink>]). The CES‐D scale is a 20‐item instrument with each item rated on a four‐point scale ranging from 0 ("rarely or none of the time") to 3 ("most or all of the time"). The total score ranges from 0 to 60, and a higher score indicates a greater risk of depression. The CES‐D has been validated in a variety of Chinese samples (Yang et al. [<reflink idref="bib71" id="ref67">71</reflink>]). The scale measures the four dimensions of the CES‐D (Depressed affect, Positive affect, Somatic and retarded activity, Interpersonal).</p> <p> <bold>Academic performance (AP, Time 1).</bold> Academic performance was measured by student exam average standardized scores for the core subjects provided by their school administration offices. In middle and high schools in China, the three core subjects for students include Chinese, mathematics and English. We focused on core academic courses because these courses are generally the most crucial to students' success and because they are the most stressful (Paunesku et al. [<reflink idref="bib52" id="ref68">52</reflink>]; Yang and Zhao [<reflink idref="bib70" id="ref69">70</reflink>]). The figure depicts the kernel density estimate (KDE) of standardized scores, from which the distribution characteristics of academic scores can be intuitively observed (Supporting Information S1: Figure S1).</p> <hd id="AN0186343121-14">Statistical Analysis</hd> <p>Statistical analysis was performed using SPSS 22.0, Python 3.0, and Mplus 8.3 (Muthen and Muthen [<reflink idref="bib48" id="ref70">48</reflink>]). Sample A was randomly split for exploratory factor analysis (EFA; Subsample 1 A, <emph>N</emph> = 1136) and confirmatory factor analysis (CFA; Subsample 2 A, <emph>N</emph> = 2240). Principal axis factoring (PAF) with direct oblimin rotation was used, with loadings ≥ 0.30 presented (De Winter and Dodou [<reflink idref="bib69" id="ref71">69</reflink>]). Parallel analysis, which is considered the best way to determine the number of factors (Fabrigar et al. [<reflink idref="bib26" id="ref72">26</reflink>]), as well as the scree plot and eigenvalue criteria, was used to determine the number of factors. We evaluated the fit of each model by examining multiple fit indices, including the comparative fit index (CFI), Tucker‐Lewis index (TLI), root mean square error of approximation (RMSEA) with 90% confidence intervals (CIs), and standardized root mean squared residual (SRMR). Generally, model fit is considered acceptable when the CFI and TLI are ≥ 0.90 and good when the CFI and TLI are ≥ 0.95. In addition, RMSEA and SRMR values ≤ 0.08 are indicative of acceptable fit, and 0.05 is indicative of good fit (Kline [<reflink idref="bib39" id="ref73">39</reflink>]).</p> <p>The measurement invariance (MI) was tested across genders. This was accomplished by conducting multigroup CFA (MCFA). The first level tested was configural invariance, in which no parameters were constrained. The second level was metric invariance. The third level was scalar invariance. The last level was error variance invariance (Van de Schoot et al. [<reflink idref="bib61" id="ref74">61</reflink>]). When the differences in the model chi‐square between the constrained and unconstrained factors are not significant, the MI can be considered to have been achieved. However, the chi‐square test is sensitive to sample size; therefore, the change in CFI (∆CFI) was also investigated to compare the nested models. A ∆CFI smaller than 0.01 indicates a definite existence of MI; if it is greater than 0.01 but less than 0.02, this indicates a likely presence of MI; if it is greater than 0.02, this shows an absence of invariance (Cheung and Rensvold [<reflink idref="bib17" id="ref75">17</reflink>]). Additionally, a ∆RMSEA smaller than 0.01 indicates that there is MI (Chen [<reflink idref="bib15" id="ref76">15</reflink>]).</p> <p>Concurrent, convergent, and incremental validity analyses were conducted on Sample A (<emph>N</emph> = 3376). Concurrent validity was assessed by examining positive correlations (ranging from > 0.20 to < 0.70) between the S‐CHSS and other measures of perceived stress (Borgogna et al. [<reflink idref="bib6" id="ref77">6</reflink>]). Convergent validity was evaluated by analyzing correlations (> |0.20| to < |0.70 |) between the S‐CHSS and personality traits and psychological health measures. Incremental validity was assessed using hierarchical regressions (Cavanaugh et al. [<reflink idref="bib12" id="ref78">12</reflink>]; Schlotz et al. [<reflink idref="bib60" id="ref79">60</reflink>]). The internal consistency of the S‐CHSS at each time point was also examined. Cronbach's alphas of < 0.60 are insufficient, those between 0.60 and 0.69 are marginal, those between 0.70 and 0.79 are acceptable, those between 0.80 and 0.89 are good, and those > 0.90 are excellent (Barker et al. [<reflink idref="bib4" id="ref80">4</reflink>]). Furthermore, calculating the means interitem correlation (MIC) can also be an indicator independent of scale numbers and can be considered acceptable when ranging from 0.15 to 0.50 (Clark and Watson [<reflink idref="bib18" id="ref81">18</reflink>]).</p> <hd id="AN0186343121-15">Results</hd> <p></p> <hd id="AN0186343121-16">EFA</hd> <p>We performed an EFA on Subsample 1 A (<emph>N</emph> = 1136) and conducted the KMO sample fitness test and the Bartlett sphericity test. The results indicated that the data were suitable for performing EFA (KMO = 0.91, <emph>χ</emph><sups>2</sups> = 6126.264, <emph>df</emph> = 45, <emph>p</emph> < 0.001). Next, factors were extracted via PAF, and an oblique rotation method was used. Based on the criterion, the eigenvalue should be greater than 1. The results suggested a two‐factor model (the eigenvalues were 5.19 and 1.34), with a 65.31% cumulative variance explanation rate and a factor loading between 0.57 and 0.90 for each item (Table 1). Furthermore, we conducted parallel analysis to further determine the number of factors by comparing the random eigenvalues to the observed data. The results indicated that the third factor's true data eigenvalue (0.71) was smaller than the random sample eigenvalue mean (1.00, Supporting Information S1: Figure S2). Therefore, it is reasonable to keep two factors.</p> <p>1 Table Descriptive statistics and factor loadings of the S‐CHSS. (Subsample 1A, N = 1136).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th /><th align="center">EFA (Loding)</th></tr><tr valign="bottom"><th /><th><italic>M</italic></th><th>SD</th><th>HS</th><th>CS</th></tr></thead><tbody valign="top"><tr><td>2. The degree to which favoritism rather than performance affects final grades in your classes.</td><td>2.10</td><td>1.02</td><td>0.80</td><td /></tr><tr><td>3. The inability to clearly understand what is expected of you in your classes.</td><td>2.78</td><td>1.00</td><td>0.74</td><td /></tr><tr><td>4. The amount of hassles you need to go through to get projects/assignments done.</td><td>2.77</td><td>1.00</td><td>0.68</td><td /></tr><tr><td>1. The amount of time spent on "busy work" for your classes.</td><td>2.47</td><td>0.94</td><td>0.64</td><td /></tr><tr><td>5. The degree to which your learning progression seems stalled.</td><td>3.18</td><td>1.12</td><td>0.57</td><td /></tr><tr><td>6. The number of projects/assignments in your classes.</td><td>3.15</td><td>0.96</td><td /><td>0.90</td></tr><tr><td>7. The amount of time spent working on projects/assignments for your classes.</td><td>3.14</td><td>0.98</td><td /><td>0.89</td></tr><tr><td>9. The volume of coursework that must be completed in your classes.</td><td>3.15</td><td>1.01</td><td /><td>0.87</td></tr><tr><td>8. The difficulty of the work required in your classes.</td><td>3.20</td><td>0.96</td><td /><td>0.80</td></tr><tr><td>10. The time pressures experienced for completing work required in your classes.</td><td>3.15</td><td>1.00</td><td /><td>0.78</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> Loadings < 0.4 omitted.</p> <p>2 Abbreviations: CS = challenge stress (items 6–10), EFA = exploratory factor analysis, HS = hindrance stress (items 1–5), M = mean, SD = standard deviation.</p> <hd id="AN0186343121-17">CFA</hd> <p>First, we constructed a single‐factor model and loaded all items onto one latent variable. Next, we built a two‐factor model, with HS and CS. The results showed that the two‐factor model fit was acceptable and significantly better than the one‐factor model (Table 2). We then proceeded to confirmatory two‐factor fit using CFA in Sample A (<emph>N</emph> = 3376). The results again indicated excellent fit: <emph>χ</emph><sups><emph>2</emph></sups> = 616.304, <emph>df</emph> = 34, <emph>p</emph> < 0.001, CFI = 0.962, TLI = 0.950, RMSEA = 0.071 (90% CI [0.066, 0.076]), and SRMR = 0.029. For two‐factor, all standardized loadings were large (0.41–0.84) and statistically significant (<emph>p</emph> < 0.001, Supporting Information S1: Figures S3 and S4).</p> <p>2 Table Model fit indices of confirmatory factor analysis for the S‐CHSS.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Model</th><th><italic>N</italic></th><th><italic>χ</italic><sup><italic>2</italic></sup></th><th><italic>df</italic></th><th>CFI</th><th align="center">TLI</th><th align="center">RMSEA</th><th>90% CI RMSEA</th><th>SRMR</th></tr></thead><tbody valign="top"><tr><td>One‐factor</td><td>2240</td><td>1427.812<ext-link href="***" /></td><td>35</td><td>0.853</td><td align="center">0.811</td><td>0.133</td><td>[0.127, 0.139]</td><td>0.073</td></tr><tr><td>Two‐factor</td><td>2240</td><td>398.935<ext-link href="***" /></td><td>34</td><td>0.962</td><td align="center">0.949</td><td>0.069</td><td>[0.063, 0.075]</td><td>0.031</td></tr><tr><td>Two‐factor</td><td>3376</td><td>616.304<ext-link href="***" /></td><td>34</td><td>0.962</td><td align="center">0.950</td><td>0.071</td><td>[0.066, 0.076]</td><td>0.029</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 Abbreviations: 90% CI RMSEA = 90% confidence interval for RMSEA, <emph>χ2</emph> = chi‐square, CFI = comparative fit index, <emph>df </emph>= degrees of freedom, TLI = Tucker Lewis index, RMSEA = root mean square error of approximation, SRMR = standardized root mean square residual.</item> <item>4 *** <emph>p</emph> < 0.001.</item> </ulist> <hd id="AN0186343121-18">MI</hd> <p>We proceeded by conducting our gender invariance analyses (Sample A; Table 3). The results of the baseline models of the S‐CHSS (i.e., configural model) were acceptable (i.e., CFI = 0.966, TLI = 0.956, RMSEA (90% CI) = 0.056 [0.052, 0.061]). The metric invariance model was then tested using ∆CFI and ∆RMSEA (both less than 0.01). These results indicated that there were no significant differences in item loading across genders. Similarly, the results of scalar invariance testing also revealed that the intercept was invariant across genders (∆CFI = −0.008, ∆RMSEA = −0.002). Finally, the strict invariance model was also supported in terms of ∆CFI (−0.002) and ∆RMSEA (0). Therefore, these results suggest that the items of the S‐CHSS operate nearly identically between females and males, supporting the direct comparison of scores across those two primary genders (Chen [<reflink idref="bib15" id="ref82">15</reflink>]; Cheung and Rensvold [<reflink idref="bib17" id="ref83">17</reflink>]; Wang and Fang [<reflink idref="bib66" id="ref84">66</reflink>]).</p> <p>3 Table Results of measurement invariance.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Model</th><th><italic>χ</italic><sup><italic>2</italic></sup></th><th><italic>df</italic></th><th>CFI</th><th>TLI</th><th>RMSEA (90% CI)</th><th>SRMR</th><th>Comparison</th><th><italic>∆χ2</italic></th><th><italic>∆df</italic></th><th>∆CFI</th><th align="center">∆RMSEA</th></tr></thead><tbody valign="top"><tr><td align="left">Gender invariance (N = 3376)</td></tr><tr><td>M1:Configural invariance</td><td>464.803<ext-link href="*" /></td><td>68</td><td>0.966</td><td>0.956</td><td>0.056 [0.052, 0.061]</td><td>0.030</td><td /><td /><td /><td /><td /></tr><tr><td>M2:Metric invariance</td><td>484.198<ext-link href="*" /></td><td>76</td><td>0.966</td><td>0.959</td><td>0.059 [0.055, 0.064]</td><td>0.031</td><td>M2–M1</td><td>19.395</td><td>8</td><td>0</td><td>0.003</td></tr><tr><td>M3:Scalar invariance</td><td>581.959<ext-link href="*" /></td><td>84</td><td>0.958</td><td>0.955</td><td>0.057 [0.053, 0.061]</td><td>0.035</td><td>M3–M2</td><td>97.761</td><td>8</td><td>−0.008</td><td>−0.002</td></tr><tr><td>M4:Strict invariance</td><td>622.280<ext-link href="*" /></td><td>94</td><td>0.956</td><td>0.958</td><td>0.057 [0.053, 0.061]</td><td>0.036</td><td>M4–M3</td><td>40.321</td><td>10</td><td>−0.002</td><td>0</td></tr><tr><td align="left">Longitudinal invariance (N = 1083)</td></tr><tr><td>M5:Configural invariance</td><td>425.612<ext-link href="*" /></td><td>154</td><td>0.967</td><td>0.959</td><td>0.040 [0.036, 0.045]</td><td>0.029</td><td /><td /><td /><td /><td /></tr><tr><td>M6:Metric invariance</td><td>432.330<ext-link href="*" /></td><td>160</td><td>0.967</td><td>0.960</td><td>0.040 [0.035, 0.046]</td><td>0.031</td><td>M6–M5</td><td>6.718</td><td>6</td><td>0</td><td align="center">0</td></tr><tr><td>M7:Scalar invariance</td><td>445.526<ext-link href="*" /></td><td>168</td><td>0.966</td><td>0.962</td><td>0.039 [0.035, 0.043]</td><td>0.031</td><td>M7–M6</td><td>13.196</td><td>8</td><td>−0.001</td><td align="center">−0.001</td></tr><tr><td>M8:Strict invariance</td><td>511.291<ext-link href="*" /></td><td>178</td><td>0.959</td><td>0.957</td><td>0.042 [0.037, 0.046]</td><td>0.037</td><td>M8–M7</td><td>65.765</td><td>10</td><td>−0.007</td><td align="center">0.003</td></tr></tbody></table> </ephtml> </p> <ulist> <item>5 Abbreviations: 90% CI RMSEA = 90% confidence interval for RMSEA, χ2 = chi‐square, ∆χ2 = change in χ2 relative to the preceding model, ∆CFI = change in comparative fit index relative to the preceding model, ∆df = change in degrees of freedom, ∆RMSEA = change in root mean square error of approximation relative to the preceding model, ∆TLI = change in Tucker–Lewis index relative to the preceding model, CFI = comparative fit index, df = degrees of freedom, RMSEA = root mean square error of approximation, SRMR = standardized root mean square residual, TLI = Tucker–Lewis index.</item> <item>6 * <emph>p</emph> < 0.001.</item> </ulist> <hd id="AN0186343121-19">Concurrent, Convergent, and Incremental Validity</hd> <p>We used Pearson's correlation analysis to evaluate the concurrent validity. The results revealed that both HS (<emph>r</emph> = 0.419) and CS (<emph>r</emph> = 0.369) were significantly positively correlated with the PSS (Table 4). We then proceeded with analyses of convergent validity using the same method. The HS was significantly correlated with personality measures, with <emph>|r</emph> | > 0.2 (consumiousness: <emph>r</emph> = −0.262; neuroticism: <emph>r</emph> = −0.415). Similarly, CS was significantly correlated with <emph>|r</emph> | > 0.2 (consumiousness: <emph>r</emph> = −0.230; neuroticism: <emph>r</emph> = 0.348). Concurrently, HS was significantly positively correlated with indicators of anxiety (<emph>r</emph> = 0.458) and depression (<emph>r</emph> = 0.348); CS was significantly positively correlated with indicators of anxiety (<emph>r</emph> = 0.393) and depression (<emph>r</emph> = 0.265). For academic performance, the results indicated a significant correlation with HS (<emph>r</emph> = −0.080).</p> <p>4 Table Correlations of the S‐CHSS with other scales.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Variables</th><th /><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th></tr></thead><tbody valign="top"><tr><td>Stress</td><td>1. HS</td><td>1</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td /><td>2. CS</td><td>0.560<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td /><td>3. PSS</td><td>0.419<ext-link href="**" /></td><td>0.369<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Personality</td><td>4. IPIP‐E</td><td>−0.086<ext-link href="**" /></td><td>−0.106<ext-link href="**" /></td><td>−0.191<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td /><td>5. IPIP‐A</td><td>−0.076<ext-link href="**" /></td><td>−0.127<ext-link href="**" /></td><td>−0.149<ext-link href="**" /></td><td>0.301<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /></tr><tr><td /><td>6. IPIP‐C</td><td>−0.262<ext-link href="**" /></td><td>−0.230<ext-link href="**" /></td><td>−0.449<ext-link href="**" /></td><td>0.080<ext-link href="**" /></td><td>0.254<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /></tr><tr><td /><td>7. IPIP‐N</td><td>0.415<ext-link href="**" /></td><td>0.348<ext-link href="**" /></td><td>0.603<ext-link href="**" /></td><td>−0.163<ext-link href="**" /></td><td>−0.119<ext-link href="**" /></td><td>−0.407<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /></tr><tr><td /><td>8. IPIP‐O</td><td>0.010</td><td>0.000</td><td>−0.053<ext-link href="**" /></td><td>0.197<ext-link href="**" /></td><td>0.193<ext-link href="**" /></td><td>0.035<ext-link href="*" /></td><td>0.02</td><td>1</td><td /><td /><td /></tr><tr><td>Health</td><td>9. SAS</td><td>0.458<ext-link href="**" /></td><td>0.393<ext-link href="**" /></td><td>0.520<ext-link href="**" /></td><td>−0.125<ext-link href="**" /></td><td>−0.181<ext-link href="**" /></td><td>−0.388<ext-link href="**" /></td><td>0.601<ext-link href="**" /></td><td>−0.02</td><td>1</td><td /><td /></tr><tr><td /><td>10. CES‐D</td><td>0.348<ext-link href="**" /></td><td>0.265<ext-link href="**" /></td><td>0.678<ext-link href="**" /></td><td>−0.162<ext-link href="**" /></td><td>−0.099<ext-link href="**" /></td><td>−0.384<ext-link href="**" /></td><td>0.564<ext-link href="**" /></td><td>0.00</td><td>0.515<ext-link href="**" /></td><td>1</td><td /></tr><tr><td>Performance</td><td>11. AP</td><td>−0.080<ext-link href="**" /></td><td>0.010</td><td>−0.127<ext-link href="**" /></td><td>0.002</td><td>0.079<ext-link href="**" /></td><td>0.122<ext-link href="**" /></td><td>−0.076<ext-link href="*" /></td><td>0.041</td><td>−0.089<ext-link href="**" /></td><td>−0.126<ext-link href="**" /></td><td>1</td></tr><tr><td /><td>M</td><td>2.60</td><td>2.99</td><td>1.90</td><td>2.46</td><td>3.14</td><td>2.71</td><td>2.46</td><td>2.87</td><td>33.07</td><td>30.75</td><td /></tr><tr><td /><td>SD</td><td>0.76</td><td>0.86</td><td>0.65</td><td>0.80</td><td>0.65</td><td>0.68</td><td>0.86</td><td>0.52</td><td>9.08</td><td>12.80</td><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>7 Abbreviations: AP = academic performance, CES‐D = center for Epidemiological studies depression scale, CS = challenge stress, HS = hindrance stress, PIPI‐A = agreeableness, PIPI‐C = consumption, PIPI‐E = extroversion, PIPI‐N = neuroticism, PIPI‐O = openness, PSS = perceived stress scale, SAS = the self‐rating anxiety scale.</item> <item>8 * <emph>p</emph> < 0.05</item> <item>9 ** <emph>p</emph> < 0.01.</item> </ulist> <p>We followed procedures from previous studies (Cavanaugh et al. [<reflink idref="bib12" id="ref85">12</reflink>]; Kashdan et al. [<reflink idref="bib38" id="ref86">38</reflink>]) and conducted hierarchical regression analysis to test incremental validity. We examined whether the S‐CHSS was better at accounting for incremental variance in anxiety, depression, and academic performance than the PSS. The S‐CHSS subscales explained a significant amount of additional variance (0.8%–15.3%, Table 5) in all the models. The results suggested that after controlling for the effects of conscientiousness, neuroticism, and the PSS on the variable, the impacts of the S‐CHSS subscales (<emph>∆R</emph><sups><emph>2</emph></sups>) on depression and anxiety remained significant. Similarly, controlling for conscientiousness and the PSS, the impacts of the S‐CHSS subscales (<emph>∆R</emph><sups><emph>2</emph></sups>) on academic performance remained significant. The influence of neuroticism on academic performance was not significant; hence, it was not included as a control.</p> <p>5 Table Results of regression analysis.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>Variables</th><th><italic>Β</italic></th><th><italic>SE</italic></th><th><italic>β</italic></th><th><italic>F</italic></th><th><italic>R</italic><sup><italic>2</italic></sup></th><th><italic>∆R</italic><sup><italic>2</italic></sup></th></tr></thead><tbody valign="top"><tr><td align="left">Anxiety (N = 3376)</td></tr><tr><td>Model 1</td><td>PIPI‐C</td><td>−2.292</td><td>0.198</td><td>−0.171</td><td /><td>0.340</td><td /></tr><tr><td /><td>PIPI‐N</td><td>5.619</td><td>0.156</td><td>0.531</td><td /><td /><td /></tr><tr><td>Model 2</td><td>PIPI‐C</td><td>−1.608</td><td>0.202</td><td>−0.12</td><td /><td>0.386</td><td>0.046<ext-link href="*" /></td></tr><tr><td /><td>PIPI‐N</td><td>4.507</td><td>0.178</td><td>0.426</td><td /><td /><td /></tr><tr><td /><td>PSS</td><td>2.932</td><td>0.242</td><td>0.209</td><td /><td /><td /></tr><tr><td>Model 3</td><td>PIPI‐C</td><td>−1.436</td><td>0.195</td><td>−0.107</td><td>553.398<ext-link href="*" /></td><td>0.451</td><td>0.065<ext-link href="*" /></td></tr><tr><td /><td>PIPI‐N</td><td>3.898</td><td>0.177</td><td>0.369</td><td /><td /><td /></tr><tr><td /><td>PSS</td><td>2.055</td><td>0.241</td><td>0.146</td><td /><td /><td /></tr><tr><td /><td>HS</td><td>1.933</td><td>0.193</td><td>0.162</td><td /><td /><td /></tr><tr><td /><td>CS</td><td>1.002</td><td>0.166</td><td>0.095</td><td /><td /><td /></tr><tr><td align="left">Depression (N = 3376)</td></tr><tr><td>Model 1</td><td>IPIP‐C</td><td>−3.48</td><td>0.287</td><td>−0.184</td><td /><td>0.347</td><td /></tr><tr><td /><td>IPIP‐N</td><td>7.291</td><td>0.227</td><td>0.489</td><td /><td /><td /></tr><tr><td>Model 2</td><td>IPIP‐C</td><td>−1.121</td><td>0.262</td><td>−0.059</td><td /><td>0.500</td><td>0.153<ext-link href="*" /></td></tr><tr><td /><td>IPIP‐N</td><td>3.451</td><td>0.232</td><td>0.231</td><td /><td /><td /></tr><tr><td /><td>PSS</td><td>10.125</td><td>0.314</td><td>0.512</td><td /><td /><td /></tr><tr><td>Model 3</td><td>IPIP‐C</td><td>−1.114</td><td>0.262</td><td>−0.059</td><td>679.840<ext-link href="*" /></td><td>0.562</td><td>0.062<ext-link href="*" /></td></tr><tr><td /><td>IPIP‐N</td><td>3.396</td><td>0.237</td><td>0.228</td><td /><td /><td /></tr><tr><td /><td>PSS</td><td>10.089</td><td>0.323</td><td>0.510</td><td /><td /><td /></tr><tr><td /><td>HS</td><td>0.809</td><td>0.260</td><td>0.248</td><td /><td /><td /></tr><tr><td /><td>CS</td><td>−0.645</td><td>0.223</td><td>−0.243</td><td /><td /><td /></tr><tr><td align="left">Academic performance (N = 1083)</td></tr><tr><td>Model 1</td><td>IPIP‐C</td><td>1.296</td><td>0.321</td><td>0.122</td><td /><td>0.015</td><td /></tr><tr><td>Model 2</td><td>IPIP‐C</td><td>0.918</td><td>0.345</td><td>0.086</td><td /><td>0.023</td><td>0.008<ext-link href="**" /></td></tr><tr><td /><td>PSS</td><td>−0.095</td><td>0.033</td><td>−0.095</td><td /><td /><td /></tr><tr><td>Model 3</td><td>IPIP‐C</td><td>0.893</td><td>0.351</td><td>0.084</td><td>8.553<ext-link href="*" /></td><td>0.031</td><td>0.008<ext-link href="**" /></td></tr><tr><td /><td>PSS</td><td>−0.104</td><td>0.034</td><td>−0.103</td><td /><td /><td /></tr><tr><td /><td>HS</td><td>−0.737</td><td>0.359</td><td>−0.079</td><td /><td /><td /></tr><tr><td /><td>CS</td><td>0.913</td><td>0.308</td><td>0.111</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>10 <emph>Note:</emph> Due to academic performance data being obtained from only one school in Guangzhou, the sample size is 1083.</item> <item>11 Abbreviations: <emph>β</emph> = standardized coefficients, ∆<emph>R</emph><sups>2</sups> = final step accounted for variance, <emph>B</emph> = unstandardized coefficients, CS = challenge stress, HS =hindrance stress, IPIP‐C = conscientiousness, IPIP‐N = neuroticism, PSS = perceived stress scale, <emph>R</emph><sups>2</sups> = total model accounted for variance.</item> <item>12 * <emph>p</emph> < 0.001</item> <item>13 ** <emph>p</emph> < 0.01.</item> </ulist> <hd id="AN0186343121-20">Internal Consistency</hd> <p>The Cronbach's alpha values for the hindrance and CS factor scores in the study were 0.75 and 0.89 (Time 1, Sample A), respectively. Furthermore, the mean interitem correlations (MICs) for the HS and CS were 0.25 and 0.47 (Time 1, Sample A), respectively. Furthermore, the reliability results for the scales used in the study were satisfactory (Supporting Information S1: Table S3).</p> <hd id="AN0186343121-21">Study 2a: The Effect of CS and HS on Adolescents' Academic Performance</hd> <p></p> <hd id="AN0186343121-22">Methods</hd> <p></p> <hd id="AN0186343121-23">Participants and Procedure</hd> <p>Six months after the initial data collection (Time 2, March 2024), a follow‐up survey was conducted with participants from the original cohort of 3376 students in the Guangzhou area. A total of 1136 participants completed the follow‐up assessment on paper. Participant data from those subjects who did not complete the second survey were excluded (<emph>N</emph> = 53 removed). Finally, 1083 participants were included in the final analysis (Sample B). The attrition rate for the two data collection periods was 4.67%. The demographics of these participants are available in Supporting Information S1: Table S1.</p> <hd id="AN0186343121-24">Measures</hd> <p>For CS‐HS and academic performance, the same scales were used as in Study 1. Additionally, we calculated each student's end‐of‐semester academic performance in core courses (i.e., Math, English, and Chinese) in the spring (Time 2). The figure depicts the KDE of standardized scores (Supporting Information S1: Figure S1). The reliability results are presented in Supporting Information S1: Table S3. Additionally, the following measures were used in study 2.</p> <p> <bold>The cognitive emotion regulation questionnaire (CERQ, Time 1 and Time 2).</bold> Emotional regulation strategies were measured using the 36‐item cognitive emotion regulation questionnaire (Garnefski et al. [<reflink idref="bib30" id="ref87">30</reflink>]). The respondents were instructed to state on a five‐point Likert scale (ranging from almost never to almost always) how often they tend to react in certain ways to stressful life events. The CERQ comprises nine subscales, each measured by four items: self‐blame, other‐blame, rumination, catastrophizing, putting into perspective, positive refocusing, positive reappraisal, acceptance, and planning. The CERQ was used in the current study and has demonstrated good psychometric properties in previous studies among Chinese students (Zhu et al. [<reflink idref="bib74" id="ref88">74</reflink>]).</p> <hd id="AN0186343121-25">Statistical Analysis</hd> <p>After conducting preliminary calculations of descriptive statistics and variable intercorrelations, we proceeded with MCFA to assess the longitudinal invariance of CS and HS across two time points. For detailed measurement information, please refer to the MI section in the statistical analysis section of Study 1. The next step we used path analysis to test the predicted relationships among latent variables. Finally, based on a mediation model, we conducted sensitivity analysis using the causal mediation framework proposed by Imai et al. ([<reflink idref="bib36" id="ref89">36</reflink>]). The results indicated that the mediation effect demonstrates high robustness when |<emph>ρ</emph> | > 0.5 (i.e., the correlation between unobserved confounders and the outcome‐mediator relationship exceeds this threshold) and when <emph>R</emph>²_MR²_Y > 0.25 (i.e., the joint explanatory power of unobserved confounders on both the mediator and outcome variables surpasses this criterion).</p> <hd id="AN0186343121-26">Results</hd> <p></p> <hd id="AN0186343121-27">Longitudinal Invariance</hd> <p>The means, standard deviations, and correlations are presented in Table 6. In terms of longitudinal invariance (Table 3), the configural model had good model fit (CFI = 0.967, TLI = 0.959, RMSEA (90% CI) = 0.040 [0.036, 0.045], SRMR = 0.029). The fit indices of the metric invariance model were also acceptable, and the negligible differences between the metric and configural models supported metric invariance. Next, the model fits indicated that scalar invariance was acceptable (∆CFI = −0.001, ∆RMSEA = 0.001). Finally, strict invariance was also supported in terms of ∆CFI (−0.007) and ∆RMSEA (0.003).</p> <p>6 Table Descriptive Statistics for Study 2a and Study 2b (N = 1083).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th></tr></thead><tbody valign="top"><tr><td>1.T1CS</td><td>1</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>2.T1HS</td><td>0.582<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>3.T2CS</td><td>0.394<ext-link href="**" /></td><td>0.280<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>4.T2HS</td><td>0.315<ext-link href="**" /></td><td>0.382<ext-link href="**" /></td><td>0.608<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /><td /></tr><tr><td>5.T1CES‐D</td><td>0.297<ext-link href="**" /></td><td>0.345<ext-link href="**" /></td><td>0.284<ext-link href="**" /></td><td>0.355<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /><td /></tr><tr><td>6.T2CES‐D</td><td>0.215<ext-link href="**" /></td><td>0.299<ext-link href="**" /></td><td>0.379<ext-link href="**" /></td><td>0.485<ext-link href="**" /></td><td>0.621<ext-link href="**" /></td><td>1</td><td /><td /><td /><td /></tr><tr><td>7.T2MER</td><td>0.229<ext-link href="**" /></td><td>0.287<ext-link href="**" /></td><td>0.377<ext-link href="**" /></td><td>0.465<ext-link href="**" /></td><td>0.412<ext-link href="**" /></td><td>0.596<ext-link href="**" /></td><td>1</td><td /><td /><td /></tr><tr><td>8.T2AER</td><td>0.096<ext-link href="**" /></td><td>0.084<ext-link href="**" /></td><td>0.232<ext-link href="**" /></td><td>0.151<ext-link href="**" /></td><td>0.058</td><td>0.033</td><td>0.440<ext-link href="**" /></td><td>1</td><td /><td /></tr><tr><td>9.T1AP</td><td>0.038</td><td>−0.014</td><td>0.035</td><td>−0.017</td><td>−0.041</td><td>−0.045</td><td>0.012</td><td>0.098<ext-link href="**" /></td><td>1</td><td /></tr><tr><td>10.T2AP</td><td>0.01</td><td>−0.080<ext-link href="**" /></td><td>0.03</td><td>−0.080<ext-link href="**" /></td><td>−0.126<ext-link href="**" /></td><td>−0.105<ext-link href="**" /></td><td>−0.016</td><td>0.128<ext-link href="**" /></td><td>0.617<ext-link href="**" /></td><td>1</td></tr><tr><td>M</td><td>3.16</td><td>2.65</td><td>3.05</td><td>2.57</td><td>17.71</td><td>20.20</td><td>2.59</td><td>3.17</td><td>50.00</td><td>49.99</td></tr><tr><td>SD</td><td>0.84</td><td>0.74</td><td>0.84</td><td>0.73</td><td>10.97</td><td>10.43</td><td>0.62</td><td>0.55</td><td>7.03</td><td>6.93</td></tr></tbody></table> </ephtml> </p> <ulist> <item>14 <emph>Note:</emph> AP scores are standardized with a mean of 50.</item> <item>15 Abbreviations: AER = adaptive emotion regulation, AP = academic performance, CES‐D = center for epidemiological studies depression scale, CS = challenge stress, HS = hindrance stress, MER = maladaptive emotion regulation, T1 = Time 1, T2 = Time 2.</item> <item>16 * <emph>p</emph> < 0.01.</item> </ulist> <hd id="AN0186343121-28">Mediation Model Results</hd> <p>We initially tested our proposed research model 1 (Figure 1) using cross‐sectional data. We controlled for gender, age, and grader by adding paths from these variables to the mediators and outcomes. Additionally, we specified a direct path from academic performance at Time 1. The results indicated that the model fit the data well: <emph>χ</emph><sups><emph>2</emph></sups> = 55.358, <emph>df</emph> = 10, <emph>p</emph> < 0.001, CFI = 0.958, TLI = 0.913, RMSEA (90% CI) = 0.065 [0.049, 0.082], SRMR = 0.044. As shown in Table 7, each form of stress at Time 2 had different effects on academic performance at Time 2. Specifically, CS at Time2 is positively related to academic performance at Time2 (<emph>β</emph> = 0.078, <emph>p</emph> < 0.01), whereas HS at Time2 is negatively related to academic performance at Time2 (<emph>β</emph> = −0.109, <emph>p</emph> < 0.001). Furthermore, CS at Time2 was positively related to the AER (<emph>β</emph> = 0.215, <emph>p</emph> < 0.001) and MER at Time2 (<emph>β</emph> = 0.153, <emph>p</emph> < 0.001). The HS at Time 2 was positively related to the MER at Time 2 (<emph>β</emph> = 0.371, <emph>p</emph> < 0.001). Additionally, AER at Time 2 was positively related to academic performance at Time 2 (<emph>β</emph> = 0.086, <emph>p</emph> < 0.01). We also examined the significance of the mediation effects using 10,000 bootstrapping samples and bias‐corrected 95% CIs, as shown in Table 8. The results confirmed that AER partially mediated the relationship between CS and academic performance (<emph>β</emph><subs>indirect effect</subs> = 0.018, 95% CI = [0.070, 0.263]). The sensitivity analysis of the mediation model revealed that the average causal mediation effects (ACME) became statistically nonsignificant (95% CI included zero) when the correlation coefficient (<emph>ρ</emph>) between confounders and the outcome‐mediator relationship reached 0.3. The finding suggests that the mediation model demonstrates moderate robustness.</p> <p>7 Table Effects of CS and HS on academic performance (Study 2a).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Model 1</th><th>T2AER</th><th>T2MER</th><th align="center">T2AP</th></tr><tr valign="bottom"><th><italic>β</italic></th><th><italic>SE</italic></th><th>95%CI [lower, upper]</th><th><italic>β</italic></th><th><italic>SE</italic></th><th>95%CI [lower, upper]</th><th><italic>β</italic></th><th><italic>SE</italic></th><th>95%CI [lower, upper]</th></tr></thead><tbody valign="top"><tr><td>Intercept</td><td>3.825<ext-link href="***" /></td><td>0.839</td><td>[2.451, 5.218]</td><td>1.900<ext-link href="**" /></td><td>0.794</td><td>[0.627, 3.229]</td><td>2.263<ext-link href="**" /></td><td>0.731</td><td>[1.051, 3.471]</td></tr><tr><td>Control variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Gender</td><td>0.078</td><td>0.060</td><td>[−0.023, 0.176]</td><td>0.032</td><td>0.058</td><td>[−0.064, 0.127]</td><td>0.029</td><td>0.053</td><td>[−0.057, 0.117]</td></tr><tr><td>Age</td><td>0.017</td><td>0.030</td><td>[−0.031, 0.067]</td><td>−0.025</td><td>0.027</td><td>[−0.068, 0.02]</td><td>0.045<ext-link href="**" /></td><td>0.024</td><td>[0.005, 0.085]</td></tr><tr><td>Grade</td><td>−0.023</td><td>0.059</td><td>[−0.118, 0.075]</td><td>−0.047</td><td>0.058</td><td>[−0.142, 0.048]</td><td>−0.067</td><td>0.053</td><td>[−0.156, 0.02]</td></tr><tr><td>T1AP</td><td /><td /><td /><td /><td /><td /><td>0.615<ext-link href="***" /></td><td>0.019</td><td>[0.582, 0.645]</td></tr><tr><td>Independent variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T2CS</td><td>0.215<ext-link href="***" /></td><td>0.040</td><td>[0.148, 0.279]</td><td>0.153<ext-link href="***" /></td><td>0.035</td><td>[0.095, 0.211]</td><td>0.078<ext-link href="**" /></td><td>0.033</td><td>[0.023, 0.131]</td></tr><tr><td>T2HS</td><td>0.020</td><td>0.041</td><td>[‐0.046, 0.088]</td><td>0.371<ext-link href="***" /></td><td>0.035</td><td>[0.311, 0.427]</td><td>−0.109<ext-link href="***" /></td><td>0.033</td><td>[−0.162, −0.055]</td></tr><tr><td>Mediators</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T2AER</td><td /><td /><td /><td /><td /><td /><td>0.086<ext-link href="**" /></td><td>0.022</td><td>[0.038, 0.134]</td></tr><tr><td>T2MER</td><td /><td /><td /><td /><td /><td /><td>−0.036</td><td>0.030</td><td>[−0.084, 0.013]</td></tr><tr><td> R<sup>2</sup></td><td>0.056</td><td>0.231</td><td align="center">0.390</td></tr><tr><td>Intercept</td><td>4.323<ext-link href="***" /></td><td>0.879</td><td>[3.072, 5.943]</td><td>2.744<ext-link href="**" /></td><td>0.850</td><td>[1.685, 4.473]</td><td>0.612<ext-link href="**" /></td><td>0.022</td><td>[1.014, 3.444]</td></tr><tr><td>Control variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Gender</td><td>0.073</td><td>0.062</td><td>[−0.03, 0.230]</td><td>0.020</td><td>0.063</td><td>[−0.085, 0.174]</td><td>0.033</td><td>0.053</td><td>[−0.053, 0.171]</td></tr><tr><td>Age</td><td>0.014</td><td>0.031</td><td>[−0.037, 0.095]</td><td>−0.028</td><td>0.030</td><td>[−0.076, 0.05]</td><td>0.043</td><td>0.024</td><td>[0.003, 0.107]</td></tr><tr><td>Grade</td><td>0.002</td><td>0.062</td><td>[−0.097, 0.164]</td><td>−0.031</td><td>0.063</td><td>[−0.136, 0.126]</td><td>−0.066</td><td>0.054</td><td>[−0.155, 0.071]</td></tr><tr><td>T1AP</td><td /><td /><td /><td /><td /><td /><td>0.615<ext-link href="***" /></td><td>0.019</td><td>[0.582, 0.662]</td></tr><tr><td>Independent variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T1CS</td><td>0.062</td><td>0.039</td><td>[−0.002, 0.165]</td><td>0.095<ext-link href="**" /></td><td>0.037</td><td>[0.034, 0.188]</td><td>0.052</td><td>0.030</td><td>[0.002, 0.129]</td></tr><tr><td>T1HS</td><td>0.045</td><td>0.040</td><td>[−0.02, 0.146]</td><td>0.229<ext-link href="***" /></td><td>0.038</td><td>[0.167, 0.325]</td><td>−0.089<ext-link href="**" /></td><td>0.030</td><td>[−0.139, −0.011]</td></tr><tr><td>Mediators</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T2AER</td><td /><td /><td /><td /><td /><td /><td>0.095<ext-link href="***" /></td><td>0.029</td><td>[0.048, 0.171]</td></tr><tr><td>T2MER</td><td /><td /><td /><td /><td /><td /><td>−0.048</td><td>0.027</td><td>[−0.092, 0.021]</td></tr><tr><td>R<sup>2</sup></td><td>0.015</td><td>0.088</td><td align="center">0.388</td></tr></tbody></table> </ephtml> </p> <ulist> <item>17 Abbreviations: <emph>β = </emph>standardized coefficients, AER = adaptive emotion regulation, AP = academic performance; CS = challenge stress, HS = hindrance stress, MER = maladaptive emotion regulation, <emph>R</emph><sups><emph>2</emph></sups> = total model accounted variance, T1 = Time 1, T2 = Time 2.</item> <item>18 ** <emph>p</emph> < 0.01</item> <item>19 *** <emph>p</emph> < 0.001.</item> <item>8 Table Standardized direct and indirect effects from Study 2a and 2b.</item> </ulist> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Path of Study 2a</th><th><italic>β</italic></th><th>SE</th><th><italic>p</italic></th><th>95% CI [Lower, Upper]</th></tr></thead><tbody valign="top"><tr><td>Model 1: Cross‐sectional analysis</td><td /><td /><td /><td /></tr><tr><td>Direct effect: T2HS → T2AP</td><td>−0.109</td><td>0.033</td><td>0.001</td><td>[−1.521, −0.511]</td></tr><tr><td>Indirect effect 1: T2HS → T2AER → T2AP</td><td>0.002</td><td>0.004</td><td>0.644</td><td>[−0.031, 0.089]</td></tr><tr><td>Indirect effect 2: T2HS → T2MER → T2AP</td><td>−0.013</td><td>0.011</td><td>0.225</td><td>[−0.297, 0.045]</td></tr><tr><td>Total effect: T2HS → T2AP</td><td>−0.121</td><td>0.031</td><td>0.001</td><td>[−1.612, −0.652]</td></tr><tr><td>Direct effect: T2CS → T2AP</td><td>0.078</td><td>0.033</td><td>0.017</td><td>[0.187, 1.066]</td></tr><tr><td>Indirect effect 1: T2CS → T2AER → T2AP</td><td>0.018</td><td>0.007</td><td>0.009</td><td>[0.070, 0.263]</td></tr><tr><td>Indirect effect 2: T2CS → T2MER → T2AP</td><td>−0.006</td><td>0.005</td><td>0.257</td><td>[−0.121, 0.011]</td></tr><tr><td>Total effect: T2CS → T2AP</td><td>0.091</td><td>0.032</td><td>0.005</td><td>[0.296, 1.174]</td></tr><tr><td>Model 1: Longitudinal analysis</td><td /><td /><td /><td /></tr><tr><td>Direct effect: T1HS → T2AP</td><td>−0.089</td><td>0.030</td><td>0.003</td><td>[−0.139, −0.039]</td></tr><tr><td>Indirect effect 1: T1HS → T2AER → T2AP</td><td>0.004</td><td>0.004</td><td>0.309</td><td>[−0.001, 0.013]</td></tr><tr><td>Indirect effect 2: T1HS → T2MER → T2AP</td><td>−0.011</td><td>0.006</td><td>0.089</td><td>[−0.022, −0.001]</td></tr><tr><td>Total effect: T1HS → T2AP</td><td>−0.095</td><td>0.030</td><td>0.002</td><td>[−0.145, −0.046]</td></tr><tr><td>Direct effect: T1CS → T2AP</td><td>0.052</td><td>0.030</td><td>0.087</td><td>[0.002, 0.102]</td></tr><tr><td>Indirect effect 1: T1CS → T2AER → T2AP</td><td>0.006</td><td>0.004</td><td>0.165</td><td>[0.000, 0.015]</td></tr><tr><td>Indirect effect 2: T1CS → T2MER → T2AP</td><td>−0.005</td><td>0.003</td><td>0.163</td><td>[−0.012, −0.001]</td></tr><tr><td>Total effect: T1CS → T2AP</td><td>0.053</td><td>0.030</td><td>0.081</td><td>[0.003, 0.104]</td></tr><tr><td>Path of Study 2b</td><td /><td /><td /><td /></tr><tr><td>Model 2: Cross‐sectional analysis</td><td /><td /><td /><td /></tr><tr><td>Direct effect: T2HS → T2CES</td><td>0.127</td><td>0.030</td><td>0.000</td><td>[0.08, 0.179]</td></tr><tr><td>Indirect effect 1: T2HS → T2AER → T2CES</td><td>−0.005</td><td>0.009</td><td>0.617</td><td>[−0.02, 0.01]</td></tr><tr><td>Indirect effect 2: T2HS → T2MER → T2CES</td><td>0.168</td><td>0.019</td><td>0.000</td><td>[0.137, 0.2]</td></tr><tr><td>Total effect: T2HS → T2CES</td><td>0.291</td><td>0.032</td><td>0.000</td><td>[0.24, 0.343]</td></tr><tr><td>Direct effect: T2CS → T2CES</td><td>0.076</td><td>0.028</td><td>0.006</td><td>[0.031, 0.122]</td></tr><tr><td>Indirect effect 1: T2CS → T2AER → T2CES</td><td>−0.048</td><td>0.011</td><td>0.000</td><td>[−0.068, −0.032]</td></tr><tr><td>Indirect effect 2: T2CS → T2MER → T2CES</td><td>0.069</td><td>0.017</td><td>0.000</td><td>[0.043, 0.098]</td></tr><tr><td>Total effect: T2CS → T2CES</td><td>0.097</td><td>0.030</td><td>0.001</td><td>[0.048, 0.146]</td></tr></tbody></table> </ephtml> </p> <ulist> <item>20 <emph>Note:</emph> Bold values indicate statistically significant.</item> <item>21 Abbreviations: AER = adaptive emotion regulation, AP = academic performance, CES = depression, CS = challenge stress, HS = hindrance stress, MER = maladaptive emotion regulation, T1 = Time 1, T2 = Time 2.</item> </ulist> <p>Next, we tested research model 1 (Figure 1) using longitudinal data. Our control variables remain consistent with those used in the cross‐sectional data analysis. The results showed that the model fit the data well: <emph>χ</emph><sups><emph>2</emph></sups> = 65.123, <emph>df</emph> = 10, <emph>p</emph> < 0.001, CFI = 0.938, TLI = 0.869 RMSEA (90% CI) = 0.071 [0.055, 0.088], SRMR = 0.050. As shown in Table 7, each form of stress at Time 1 had different effects on academic performance at Time 2. Specifically, CS at Time 1 was not significantly related to academic performance at Time 2 (<emph>β</emph> = 0.052, <emph>p</emph> = 0.628), whereas HS at Time 1 was negatively related to academic performance at Time 2 (<emph>β</emph> = −0.089, <emph>p</emph> < 0.01). Furthermore, CS and HS at Time 1 were positively related to MER at Time 2 (<emph>β</emph><subs>CS</subs> = 0.095, <emph>p</emph> < 0.01; <emph>β</emph><subs>HS</subs> = 0.229, <emph>p</emph> < 0.001). Additionally, AER at Time2 was positively related to academic performance at Time2 (<emph>β</emph> = 0.095, <emph>p</emph> < 0.01). More importantly, the results confirmed that none of the mediation effects were significant (see Table 8).</p> <hd id="AN0186343121-29">Study 2b: The Effect of CS and HS on Adolescents' Depression</hd> <p></p> <hd id="AN0186343121-30">Method</hd> <p>The participants and procedures in Study 2b were consistent with those in Study 2a. For CS‐HS and cognitive emotion regulation, the same scales were used as in Study 2a. Additionally, we measured depression (Time 2). The reliability results of the questionnaire administered during the second round of data collection are presented in Supporting Information S1: Table S3. We used path analysis, both cross‐sectional and longitudinal, to test the predicted relationships among latent variables. Finally, based on a mediation model, we conducted sensitivity analysis using the causal mediation framework proposed.</p> <hd id="AN0186343121-31">Results</hd> <p>We initially tested our proposed research model 2 (Figure 2) via cross‐sectional data. We controlled for gender, age, and grader by adding paths from these variables to the mediators and outcomes. Additionally, we specified a direct path from depression at Time 1. The results indicated that the model fit the data well: <emph>χ</emph><sups><emph>2</emph></sups> = 319.096, <emph>df</emph> = 3, <emph>p</emph> < 0.001, CFI = 0.804, RMSEA (90% CI) = 0.031 [0.028, 0.034], SRMR = 0.080. As shown in Table 9, CS and HS at Time 2 are both positively related to depression at Time 2 (<emph>β</emph><subs>CS</subs> = 0.076, <emph>p</emph> < 0.01; <emph>β</emph><subs><emph>HS</emph></subs> = 0.127, <emph>p</emph> < 0.001). Furthermore, AER at Time 2 was negatively related to depression at Time 2 (<emph>β</emph> = −0.226, <emph>p</emph> < 0.001), whereas MER at Time 2 was positively related to depression at Time 2 (<emph>β</emph> = 0.452, <emph>p</emph> < 0.001). We also examined the significance of the mediation effects by using 10,000 bootstrapping samples and bias‐corrected 95% CIs, as shown in Table 8. The results confirmed that MER partially mediated the relationship between CS at Time 2 and depression at Time 2 (<emph>β</emph><subs>indirect effect</subs> = 0.069, 95% CI = [0.043, 0.098]) and partially mediated the relationship between HS at Time 2 and depression at Time 2 (<emph>β</emph><subs>indirect effect</subs> = 0.168, 95% CI = [0.137, 0.2]). Additionally, AER partially mediated the relationship between CS at Time 2 and depression at Time 2 <emph>(β</emph><subs>indirect effect</subs> = −0.048, 95% CI = [−0.068, −0.032]). Sensitivity analyses were conducted separately for the three mediators. The results showed that the ACME became statistically nonsignificant (95% confidence interval [CI] included zero) when the correlation coefficients (<emph>ρ</emph>) between unobserved confounders and the outcome‐mediator relationships reached 0.5, 0.4, and 0.5 for the three mediators, respectively. Additionally, the joint explanatory power of unobserved confounders on both the mediators and outcome variables (<emph>R</emph>²_MR²_Y) was 0.25, 0.24, and 0.16, respectively. These findings indicate that the observed mediation effects are less susceptible to attenuation by typical unobserved confounders and exhibit a moderate degree of robustness.</p> <p>9 Table Effects of CS and HS on depression (Study 2b).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Model 2</th><th align="center">T2AER</th><th align="center">T2MER</th><th align="center">T2CES</th></tr><tr valign="bottom"><th><italic>β</italic></th><th><italic>SE</italic></th><th>95% CI [lower, upper]</th><th><italic>β</italic></th><th><italic>SE</italic></th><th>95% CI [lower, upper]</th><th><italic>β</italic></th><th><italic>SE</italic></th><th align="center">95% CI [lower, upper]</th></tr></thead><tbody valign="top"><tr><td>Intercept</td><td>3.82<ext-link href="***" /></td><td>0.837</td><td>[2.45, 5.214]</td><td>1.899<ext-link href="**" /></td><td>0.794</td><td>[0.729, 0.812]</td><td>−0.415</td><td>0.576</td><td align="center">[−1.356, 0.537]</td></tr><tr><td>Control variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>AGE</td><td>0.078</td><td>0.06</td><td>[−0.023, 0.176]</td><td>0.032</td><td>0.058</td><td>[−0.064, 0.127]</td><td>0.036</td><td>0.042</td><td align="center">[−0.033, 0.103]</td></tr><tr><td>Gender</td><td>0.017</td><td>0.03</td><td>[−0.031, 0.067]</td><td>−0.025</td><td>0.027</td><td>[−0.068, 0.02]</td><td>−0.029</td><td>0.020</td><td align="center">[−0.063, 0.003]</td></tr><tr><td>Grade</td><td>−0.023</td><td>0.059</td><td>[−0.118, 0.075]</td><td>−0.047</td><td>0.058</td><td>[−0.142, 0.048]</td><td>−0.031</td><td>0.040</td><td align="center">[−0.097, 0.036]</td></tr><tr><td>T1CES</td><td /><td /><td /><td /><td /><td /><td>0.379<ext-link href="***" /></td><td>0.026</td><td align="center">[0.336, 0.422]</td></tr><tr><td>Independent variables</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T2CS</td><td>0.214<ext-link href="***" /></td><td>0.040</td><td>[0.148, 0.279]</td><td>0.152<ext-link href="***" /></td><td>0.035</td><td>[0.095, 0.211]</td><td>0.076<ext-link href="**" /></td><td>0.028</td><td align="center">[0.031, 0.122]</td></tr><tr><td>T2HS</td><td>0.020</td><td>0.041</td><td>[−0.046, 0.087]</td><td>0.371<ext-link href="***" /></td><td>0.035</td><td>[0.311, 0.427]</td><td>0.127<ext-link href="***" /></td><td>0.030</td><td align="center">[0.08, 0.179]</td></tr><tr><td>Mediators</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>T2AER</td><td /><td /><td /><td /><td /><td /><td>‐0.226<ext-link href="***" /></td><td>0.025</td><td align="center">[−0.268, −0.184]</td></tr><tr><td>T2MER</td><td /><td /><td /><td /><td /><td /><td>0.452<ext-link href="***" /></td><td>0.028</td><td align="center">[0.405, 0.498]</td></tr><tr><td> R<sup>2</sup></td><td align="center">0.058</td><td align="center">0.232</td><td align="center">0.586</td></tr></tbody></table> </ephtml> </p> <ulist> <item>22 Abbreviations: <emph>β = </emph>standardized coefficients, AER = adaptive emotion regulation, CES = depression, CS = challenge stress; HS = hindrance stress, MER = maladaptive emotion regulation, <emph>R</emph><sups><emph>2</emph></sups> = total model accounted for variance, T1 = Time 1, T2 = Time 2.</item> <item>23 ** <emph>p</emph> < 0.01</item> <item>24 *** <emph>p</emph> < 0.001.</item> </ulist> <p>Next, we tested research model 2 (Figure 2) using longitudinal data. Our control variables remain consistent with those used in the cross‐sectional data analysis. The results showed that the model did not fit the data: <emph>χ</emph><sups><emph>2</emph></sups> = 574.220, <emph>df</emph> = 11, <emph>p</emph> < 0.001, CFI = 0.594, TLI = 0.225, RMSEA (90% CI) = 0.271 [0.202, 0.233], SRMR = 0.014. Therefore, the longitudinal model was not supported. We directly analyzed the predictive effects of change and HS at Time 1 on depression at Time 2. The results revealed that CS at Time 1 was not significantly related to depression at Time 2 (<emph>β</emph> = −0.023, <emph>p</emph> = 0.440), whereas HS at Time 1 was positively related to depression at Time 2 (<emph>β</emph> = 0.108, <emph>p</emph> < 0.001).</p> <hd id="AN0186343121-32">General Discussion</hd> <p></p> <hd id="AN0186343121-33">Two Forms of Stress: CS and HS</hd> <p>Our findings from Study 1 and Study 2a demonstrate that the S‐CHSS is an effective measurement tool for evaluating academic stress. Through EFA and parallel analysis, the S‐CHSS was shown to have a two‐factor structure: CS and HS. Furthermore, the two‐factor model demonstrated a significantly better fit than the one‐factor model in CFA, which is consistent with the original scale (Cavanaugh et al. [<reflink idref="bib12" id="ref90">12</reflink>]; LePine et al. [<reflink idref="bib41" id="ref91">41</reflink>]). This finding also further confirms the existence of two distinct forms of stress. In addition, we constructed multiple models to test the measurement equivalence of the S‐CHSS across genders and over time. The S‐CHSS items showed configural, metric, scalar, and strict invariance, offering benefits for researchers and educators. In terms of concurrent and convergent validity, our findings suggest that the S‐CHSS has good validity. The S‐CHSS subscales explained additional variance in anxiety, depression, and academic performance (0.8%–15.3%). CS was linked to lower levels of depression and improved academic performance, whereas HS was associated with greater depression and poorer academic performance. Both forms of stress negatively affect anxiety, indicating that anxiety levels can increase regardless of the type of stress. These results highlight the effectiveness of the S‐CHSS in capturing unique aspects of academic stress and supporting the CHSF. This two‐factor model provides a more balanced perspective and explains the inconsistent relationships between stress and performance reported in the literature.</p> <hd id="AN0186343121-34">Short‐Term Effects and Long‐Term Effects</hd> <p>Through analyses of both cross‐sectional and longitudinal data, we found that HS has a negative effect on academic performance and depression through both cross‐sectional and longitudinal data analysis. However, CS is significantly associated with academic performance and depression only in cross‐sectional data. These results suggest that CS and HS have different dynamic effects on outcomes.</p> <p>Regardless of the short‐term or long‐term perspective, HS consistently has a negative effect on academic performance and depression. In the long term, the cumulative effect of HS can leads to increased levels of emotional exhaustion, a form of resource depletion that is characterized by feelings of emotional detachment, including numbness, isolation, and a desire to withdraw (Liu et al. [<reflink idref="bib44" id="ref92">44</reflink>]; Sheppes and Meiran [<reflink idref="bib62" id="ref93">62</reflink>]). When these resources are continually drained without adequate replenishment, an individual's academic performance is likely to decline. In terms of mental health, prolonged exposure to HS results in persistent deterioration of psychological well‐being. Similar to the effects of chronic stress, previous evidence suggests that the detrimental impact of chronic stressors accumulates gradually over time (Duchaine et al. [<reflink idref="bib24" id="ref94">24</reflink>]).</p> <p>CS has a positive impact on academic performance only in cross‐sectional data, suggesting that its beneficial effects may be primarily evident in the short term. CS is generally thought to stimulate individuals' motivation and proactive behaviors (e.g., setting clear goals, investing more effort), which can quickly translate into improved academic performance in the short term. These findings are consistent with those of LePine et al. ([<reflink idref="bib41" id="ref95">41</reflink>]) and Travis et al. ([<reflink idref="bib64" id="ref96">64</reflink>]), as well as the meta‐analytic results linking CS to work performance (LePine et al. [<reflink idref="bib42" id="ref97">42</reflink>]; Pindek et al. [<reflink idref="bib53" id="ref98">53</reflink>]; Podsakoff et al. [<reflink idref="bib55" id="ref99">55</reflink>]). Although CS may have a positive effect on academic performance in the short term, it may also induce short‐term emotional distress (Brutus et al. [<reflink idref="bib9" id="ref100">9</reflink>]; McCauley et al. [<reflink idref="bib46" id="ref101">46</reflink>]). Our study revealed a positive relationship between CS and depression, which further illustrates the complex dual impact of CS on performance. According to longitudinal data analysis, the effects of CS on both academic performance and depression were not significant, indicating that its effects may not be sustained over the long term or may be offset by other long‐term factors (such as sustained fatigue and resource depletion), which is consistent with the COR theory (Hobfoll et al. [<reflink idref="bib34" id="ref102">34</reflink>]; Palmwood and McBride [<reflink idref="bib51" id="ref103">51</reflink>]). These findings explain the inconsistent and unstable results in previous research on CS (Clarke [<reflink idref="bib19" id="ref104">19</reflink>]; Mazzola and Disselhorst [<reflink idref="bib45" id="ref105">45</reflink>]).</p> <hd id="AN0186343121-35">Mechanisms of Influence: Different Stress and Different Outcomes</hd> <p>Moreover, Study 2a revealed that AER is a mediator of the relationship between CS and academic performance. While CS has a direct positive impact on academic performance, AER further amplifies this effect by facilitating effective emotional management. This allows students to better navigate challenges, ultimately enhancing their academic outcomes. AER, such as positive reappraisal, refocusing on planning, and putting into perspective, enables students to maintain a constructive mindset when confronted with CS, thereby bolstering their capacity to overcome obstacles. By employing these strategies, students can more effectively capitalize on the motivational benefits of CS rather than succumb to its potential negative emotional consequences. Thus, AER not only enhances academic performance but also provides a protective function (Garnefski et al. [<reflink idref="bib30" id="ref106">30</reflink>]), enabling students to manage and mitigate the adverse effects of stress more effectively.</p> <p>Study 2b further elucidates the mechanisms by which different forms of stress impact depression. Specifically, the findings indicate that the effect of CS on depression is mediated through two distinct emotional regulation pathways: AER and MER. First, AER serves as a protective factor in the relationship between CS and depression. This positive mediating effect is consistent with previous research (Folkman and Moskowitz [<reflink idref="bib27" id="ref107">27</reflink>]; Troy and Mauss [<reflink idref="bib65" id="ref108">65</reflink>]), demonstrating that AER can effectively mitigate the negative impact of stress, thereby promoting mental health. Conversely, MER amplifies the negative effects of CS, potentially intensifying the stress experience and leading to an increased risk of depression (Brown et al. [<reflink idref="bib8" id="ref109">8</reflink>]; Gadassi‐Polack et al. [<reflink idref="bib29" id="ref110">29</reflink>]). This dual‐pathway model provides an explanation for the previously inconsistent and unstable findings regarding the effects of CS on psychological outcomes (Bennett et al. [<reflink idref="bib5" id="ref111">5</reflink>]; Clarke [<reflink idref="bib19" id="ref112">19</reflink>]; Mazzola and Disselhorst [<reflink idref="bib45" id="ref113">45</reflink>]; Webster and Adams [<reflink idref="bib67" id="ref114">67</reflink>]). In contrast, HS reveals that MER significantly mediates the relationship between HS and depression. These findings further confirm the detrimental impact of HS on depression while also emphasizing the critical role that MER play in this process. When faced with HS, individuals tend to resort to MER, thereby exacerbating the adverse effects of HS on depression.</p> <p>The differences in the impacts of HS and CS on outcomes can be explained from three perspectives. (<reflink idref="bib1" id="ref115">1</reflink>) Nature of Stress. CS are typically viewed as an opportunity for growth and development (LePine et al. [<reflink idref="bib42" id="ref116">42</reflink>]; Pindek et al. [<reflink idref="bib53" id="ref117">53</reflink>]). When faced with CS, individuals tend to perceive it as a controllable and positive task rather than merely a burden. This perception encourages the use of AER. In contrast, HSors are often seen as obstacles to achieving personal goals. Such stressors are usually perceived as uncontrollable, making individuals more likely to adopt MER. (<reflink idref="bib2" id="ref118">2</reflink>) Goal orientation and motivation. When faced with CS, individuals usually set clear goals and employ effective emotional regulation in the process of striving to achieve these goals (LePine et al. [<reflink idref="bib41" id="ref119">41</reflink>]). On the other hand, HS can lead to a lack of clear goals and intrinsic motivation, thereby weakening an individual's ability to engage in AER. (<reflink idref="bib3" id="ref120">3</reflink>) Resource Perspective. According to COR theory, individuals are motivated to acquire, protect, and conserve their resources (Hobfoll [<reflink idref="bib33" id="ref121">33</reflink>]; Podsakoff et al. [<reflink idref="bib54" id="ref122">54</reflink>]). CS can be understood as a "risky investment" (Horan et al. [<reflink idref="bib35" id="ref123">35</reflink>]). These are circumstances where there is opportunity for resource gain (e.g., learning) but also the possibility for loss of invested resources (e.g., time). When CS is associated with anticipated gains, the greater the anticipated gain is, the more likely individuals are to adopt active coping. Conversely, when the anticipated gains are lower, individuals may be more inclined toward negative coping. This finding is consistent with the findings from the dual‐pathway model. HS may lead to a loss of key resources or an inability to utilize them effectively (Siu et al. [<reflink idref="bib63" id="ref124">63</reflink>]), causing individuals to experience resource depletion, such as increased anxiety, fatigue, and reduced motivation. As a result, individuals are more likely to conserve resources and invest less to avoid further resource loss.</p> <hd id="AN0186343121-36">Practical Implications and Limitations</hd> <p>Our studies have several practical implications for educational institutions, educators, and mental health practitioners. First, the S‐CHSS provides a reliable tool for assessing academic stress among students. By distinguishing between CS and HS, educators and counselors can develop targeted interventions. For example, interventions could focus on minimizing HS, which is linked to poorer academic performance and higher levels of depression. Second, educators can adjust their instructional design and manage the learning environment on the basis of the impacts of CS and HS. Creating challenging tasks or clarifying learning goals can help students cope with academic stress more effectively, turning stress into a motivating challenge. Additionally, using organizational strategies such as charts and outlines can reduce vague expectations and lessen HS. Finally, our research emphasizes the need for strategies that enhance positive emotional coping mechanisms, particularly AER. Interventions such as stress management and resilience training can teach students to view stress as a challenge, not a threat, and develop proactive coping strategies. These programs focus on skills such as positive reappraisal, problem solving, and planning, which improve students' ability to manage stress and enhance their academic performance.</p> <p>Despite these contributions, our study has some limitations. First, although we employed a longitudinal design, data were only collected at two time points (T1 for X and T2 for both M and Y). This design constrains our ability to establish a clear temporal sequence of mediation effects. Future research should consider incorporating a more detailed temporal structure (e.g., measuring X at T1, M at T2, and Y at T3) to strengthen causal inferences. Second, our study relies on self‐reported data to assess mental health outcomes, which may introduce biases such as social desirability effects, recall biases, or common method bias. Future studies should consider using multiple data sources, such as teacher evaluations, behavioral assessments, or physiological measures, to enhance the objectivity and validity of the findings. Third, the mediation effects observed in our study may be influenced by individual differences (e.g., personality traits) and contextual factors (e.g., social support). For instance, psychological resilience and cognitive flexibility might influence how students respond to CS and HS, affecting their choice of emotion regulation strategies and subsequent academic or mental health outcomes. Additionally, social support from peers, teachers, or family may buffer the negative effects of HS. Future research should incorporate moderation analyses to explore these boundary conditions more thoroughly.</p> <hd id="AN0186343121-37">Conclusion</hd> <p>Our research highlights the distinct impacts of CS and HS on Chinese adolescents via the validated S‐CHSS. Study 1 confirmed that these two types of stress are separate constructs with unique effects on depression and academic performance. Study 2a revealed that AER partially mediates the positive effect of CS on academic performance, emphasizing its role in enhancing academic success. Conversely, HS negatively impacted academic performance without mediating the effect of cognitive emotion regulation. Study 2b revealed that CS has a dual‐path effect on depression. For HS, only MER mediated the relationship with depression. In conclusion, understanding the specific impacts and mechanisms of CS and HS can help educators and mental health professionals develop targeted support strategies for students.</p> <hd id="AN0186343121-38">Author Contributions</hd> <p>Xiaoyan Bi contributed to the design, data interpretation, and manuscript drafting; Liang Zhang conceptualized the study, contributed to the study's design and coordination, conducted measurements, performed statistical analyses, and revised the manuscript, making significant contributions to each stage of the study; Yankun Ma and Xianghong Sun were involved in the conception of the study, participated in statistical analyses, and contributed to the manuscript drafting; Jianhui Wu and Xiaoyu Wang supported the data collection, interpretation, and manuscript review. All authors read and approved the final manuscript, and each has made a significant intellectual contribution to the work.</p> <hd id="AN0186343121-39">Acknowledgments</hd> <p>We are grateful to the adolescents participating in this study. We are grateful to the teachers who assisted in the data collection.</p> <hd id="AN0186343121-40">Ethics Statement</hd> <p>The studies reported in this article were approved by the Ethical Committee of Chinese Academy of Science, Beijing Province, China, ID H23093 ("Research on Stress Risk and Mechanisms in Adolescents").</p> <hd id="AN0186343121-41">Consent</hd> <p>For three studies, all students voluntarily participated in the research and none of them had significant clinical psychological symptoms. And all participants and participants' parents gave written informed consent before data collection.</p> <hd id="AN0186343121-42">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0186343121-43">Data Availability Statement</hd> <p>The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.</p> <p>GRAPH: Supplementary_Material20250227.</p> <ref id="AN0186343121-44"> <title> References </title> <blist> <bibl id="bib1" idref="ref40" type="bt">1</bibl> <bibtext> Andrés, M. L., F. Stelzer, L. Canet Juric, I. M. Introzzi, R. Rodríguez Carvajal, and J. I. Navarro Guzmán. 2017. "Emotion Regulation and Academic Performance: A Systematic Review of Empirical Relationships." Psicologia: Estudo e Pesquisa 22, no. 3: 299–311.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref9" type="bt">2</bibl> <bibtext> Association, A. P. 2021. 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  Label: Title
  Group: Ti
  Data: Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship between Stress, Adolescent Academic Performance, and Depression
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xiaoyan+Bi%22">Xiaoyan Bi</searchLink><br /><searchLink fieldCode="AR" term="%22Yankun+Ma%22">Yankun Ma</searchLink><br /><searchLink fieldCode="AR" term="%22Xianghong+Sun%22">Xianghong Sun</searchLink><br /><searchLink fieldCode="AR" term="%22Jianhui+Wu%22">Jianhui Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaoyu+Wang%22">Xiaoyu Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Liang+Zhang%22">Liang Zhang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-8888-9550">0000-0001-8888-9550</externalLink>)
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Adolescence%22"><i>Journal of Adolescence</i></searchLink>. 2025 97(5):1297-1313.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Depression+%28Psychology%29%22">Depression (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+Adjustment%22">Emotional Adjustment</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jad.12497
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0140-1971<br />1095-9254
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Introduction: The Challenge and Hindrance Stress Framework is an influential theoretical model for measuring individuals' perceptions of stress. However, its structure has not been validated among Chinese adolescents, and the effects of different forms of stress on their short-term and long-term outcomes remain unclear. Methods: Study 1 validated the Student Version Challenge and Hindrance Stress Scale with a sample of 3,376 adolescents in China (Time 1, September 2023, M[subscript age] = 14.57, SD = 1.46). Studies 2a and 2b extended Study 1 by analyzing cross-sectional and longitudinal data from 1,083 participants in China (Time 2, March 2024, M[subscript age] = 14.32, SD = 1.01) to examine the effects of various forms stress on academic performance and depression, with cognitive emotion regulation strategies used as mediators. Results: The results showed that adaptive strategies mediated the positive effects of challenge stress on academic performance and depression, whereas maladaptive strategies mediated the negative impacts of challenge or hindrance stress on depression. Conclusion: These findings emphasize the importance of distinguishing stress forms, offering insights for educators, researchers, and policymakers to enhance adolescent well-being and performance.
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  Data: As Provided
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  Label: Entry Date
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  Data: 2025
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  Label: Accession Number
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  Data: EJ1476101
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1476101
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        Value: 10.1002/jad.12497
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 1297
    Subjects:
      – SubjectFull: Anxiety
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Depression (Psychology)
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Emotional Adjustment
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship between Stress, Adolescent Academic Performance, and Depression
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            NameFull: Xiaoyan Bi
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            NameFull: Jianhui Wu
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            NameFull: Xiaoyu Wang
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            NameFull: Liang Zhang
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            – D: 01
              M: 07
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              Y: 2025
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              Value: 97
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