Differential effects of personality traits and resilience on Ghanaian students' academic engagement: A quantile regression approach

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Using a cross-sectional survey data from 288 Ghanaian higher education students from the Jasikan College of Education students. The study employed quantile regression (QR) for the data analysis beyond the ordinary least square (OLS) to uncover the nuanced differential effects. The OLS results showed that agreeableness and resilience have a uniform positive significant association with students’ academic engagement, whereas openness was negatively associated. The QR analysis reveals heterogeneity in these relationships, emphasising that while openness negatively affects academic engagement among disengaged students (q25–q30), agreeableness provides the greatest benefits to learners with low academic engagement (q15). Surprisingly, resilience’s strongest positive impact occurs at higher academic engagement levels (q70–q80). This study contributes in two key ways. Firstly, the QR results are more effective than those of OLS at detecting distributional effects, thereby advancing methodological approaches in educational psychology research. Secondly, the findings have practical implications, highlighting the need for interventions tailored to students' levels of academic engagement, as psychological resources vary accordingly. Academic engagement Personality traits Resilience Quantile regression Self-Determination Theory Ghana Higher education students Figures Figure 1 Introduction Academic engagement remains a cornerstone of student success, playing a pivotal role in retention, performance, and long-term career achievement (Obeng et al., 2025 ; Yang & Xiang, 2025 ). Defined as a multidimensional construct encompassing behavioral, emotional, and cognitive investment in learning (Fredricks et al., 2004 , 2016 ), academic engagement is critical for fostering inclusive, equitable education and advancing Sustainable Development Goal 4 (SDG 4) (Cottafavas et al., 2019). Despite its significance, many students, particularly in Sub-Saharan Africa (SSA) continue to struggle with disengagement, high dropout rates, and inadequate institutional support (Akyeampong et al., 2013; Mohammed & Kuyini, 2021). In Ghana, for instance, only 58% of college students complete their degrees, with disengagement being a key contributing factor (Ministry of Education, 2022; Obeng et al., 2025 ). This alarming trend underscores the urgent need to identify the psychological and behavioral drivers of academic engagement to inform targeted interventions. While prior research has extensively the school climate as predictors of engagement and academic success (Zynuddin et al., 2023 ), emerging evidence highlights the crucial role of non-cognitive factors particularly personality traits and resilience in shaping students' engagement levels (Amoadu et al., 2025 ; Sultanova, 2025 ). The Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism that provide a robust framework for understanding how dispositional characteristics influence learning behaviors (Chen et al., 2022 ; Turner & Hodis, 2025 ). Conscientiousness, characterized by self-discipline and goal-directed behavior, is strongly linked to higher academic engagement (Kipuru et al., 2024 ; Rocha et al., 2023 ), whereas neuroticism (often referred to as emotional instability) may hinder persistence under stress (Daniel et al., 2024 ). Similarly, resilience operationalised as the capacity to adapt to adversity and maintain psychological well-being has been shown to buffer against students’ academic stressors, thereby sustaining motivation and academic engagement (Romano et al., 2021 ). Despite these insights, critical gaps persist in the literature. First, while the impact of psychological constructs on academic engagement has been widely studied in Western contexts, their differential effects on academic engagement among higher education students in the colleges of education remain underexplored in resource-constrained settings like Ghana, where cultural and socioeconomic factors may alter their impact. Second, most studies have relied on regression models such as ordinary least squares (OLS) or structural equation modeling (SEM), which assume uniform effects across all psychological constructs like personality, thereby obscuring its potential variations influence on high- versus low levels of an outcome (Polemis, 2021 ). In line with Polemis ( 2021 ) notion, we argued that that unlike these traditional linear models, quantile regression reveals how personality and resilience operate differently among high- versus low levels of academic engagement among students, offering nuanced insights for intervention strategies. However, prior research has not sufficiently leveraged advanced analytical techniques, using the quantile regression to uncover heterogeneous effects across the engagement spectrum. Addressing these gaps is essential for developing tailored interventions that cater to students at different engagement levels. This study advances the literature in three key ways. First, it draws on Self-Determination Theory (SDT) (Deci & Ryan, 2000 , 2017 ; Ryan et al., 2022 ) to elucidate how personality traits and resilience fulfill students' intrinsic psychological needs for autonomy, competence, and relatedness, thereby fostering sustained engagement (Chiu, 2022 ; Obeng et al., 2025 ). For instance, conscientious students exhibit greater self-regulation (competence), while resilient students maintain motivation despite setbacks (autonomy) (Liu & Huang, 2021 ). Second, it employs quantile regression analysis, a robust methodological approach that captures differential effects across the engagement distribution (Hughes et al., 2023 ; Liu & Huang, 2021 ; Ryan et al., 2019 ). Third, this study focuses on Ghanaian higher education students, addressing a critical gap in the literature by examining how these non-cognitive factors function in a resource-constrained setting. The findings hold significant implications for educators and policymakers. By identifying which personality traits and resilience factors most strongly predict engagement at different quantiles, institutions can design targeted interventions. Integrating SDT with quantile regression, this study provides a novel, contextually grounded understanding of how personality and resilience differentially shape academic engagement, offering actionable insights for enhancing student success in Ghana and similar settings. The remainder of this paper is organized as follows: Section 2 reviews the literature on personality, resilience, and academic engagement. Section 3 presents the theoretical framework (SDT) and hypotheses. Section 4 details the methodology, including quantile regression analysis. Section 5 reports the findings, while Section 6 discusses implications, limitations, and future research directions. Literature Review To understand the ongoing arguments on the effect of student personality traits and resilience, there is the need to review literature from both theoretical and empirical angles to form the foundation of the study. Self-Determination as Theoretical Lens This study is anchored in Self-Determination Theory (SDT) to provide a theoretical underpinning of academic engagement behaviours (Chiu, 2022; Obeng et al., 2025). SDT is well-established theoretical framework by which posits that human behavior is driven by three innate psychological needs: autonomy, competence, and relatedness (Deci & Ryan, 2000, 2017; Ryan et al., 2022). Studies have shown that SDT provides a dynamic framework that aligns with personality traits and resilience function as key psychological resources that can either facilitate or hinder task engagement (He et al., 2025; Hughes et al., 2023; Liu & Huang, 2021; Ryan et al., 2019; Skinner & Pitzer, 2012). As per SDT, autonomy reflects students' sense of volition in their learning and personality traits such as openness (associated with curiosity) and low neuroticism (linked to emotional stability) are perceived to enhance this autonomous motivation, fostering deeper engagement (Weinstein et al., 2012). Competence pertains to perceived efficacy in academic tasks. Resilient students, who view challenges as surmountable, are more likely to sustain effort and engagement (Liu & Huang, 2021). Moreover, the relatedness involves feeling connected to peers and educators. Extraversion and Agreeableness may strengthen social bonds, indirectly boosting engagement (Mahama et al., 2022). SDT further distinguishes between intrinsic motivation (engaging for inherent satisfaction) and extrinsic motivation (driven by external rewards). Therefore, personality traits may enhance intrinsic motivation, while resilience buffers against disengagement in extrinsically motivated students. Empirical Literature In pursuit of SDG 4's goals of inclusive quality education and lifelong learning, academic engagement has become crucial in the educational context (Yang et al., 2025). High academic engagement levels are correlated with improved academic achievement, reduced dropouts, and better psychological well-being (Laranjeira & Teixeira, 2025; Wong et al., 2024). Scholars have demonstrated through research that enhancing student engagement, is a central to fostering students’ academic performance and achieving educational quality (Azila-Gbettor & Abiemo, 2021; Obeng et al., 2025; Owusu-Agyeman & Amoakohene, 2021). In the context of understanding predictors of academic engagement, the personality traits, based on the Five-Factor Model (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) help explain students’ engagement outcomes across educational settings (Mahama et al., 2022; Ramirez-Arellano, 2024). Studies in educational psychology have highlighted the influence of personality traits on academic engagement and performance (Chen et al., 2025; Tan et al., 2024). Conscientiousness strongly predicts academic engagement through goal-directed behavior and self-discipline (Polemis, 2021; Rocha et al., 2023), while openness positively correlates with deep learning strategies and intellectual curiosity, enhancing engagement in cognitive tasks (Poort et al., 2023). Conversely, neuroticism is correlated with lower engagement due to anxiety and academic stress (Yusoff et al., 2021). Beyond personality, resilience explaining the capacity to bounce back from adversity, has emerged as a protective psychological resource that sustains students' academic engagement pursuits during times of adversity (Masten, et al., 2022). Several empirical studies have emphasised the positive effect of academic resilience that enables students to maintain engagement despite challenges, such as poor performance, financial constraints, and stress (Biggs et al., 2024; Doll & Song, 2023; Liu et al., 2022; Romano et al., 2021; Theron, 2023; Yu et al., 2022; Zhang, 2021). Resilient students engage more in learning, use coping strategies more effectively, and show greater perseverance. Resilience aligns with SDT, which states that fulfilling basic psychological needs, including autonomy, competence, and relatedness, fosters motivation and engagement (Deci and Ryan 2020). Both personality and resilience enable psychological needs, as students with high conscientiousness and resilience are likely to perceive themselves as competent and capable, promoting intrinsic motivation and academic engagement. In Ghana, studies have shown that resilience positively enhance students’ academic engagement (Amoadu et al., 2025; Mahama et al., 2023). Despite there is increasing scholarly attention on personality traits, resilience and academic engagement, empirical evidence remains elusive. While existing studies found that both personality traits and resilience predict student engagement, they failed to relied on analytical that may unmask the differential effects of these variables across engagement levels (Polemis, 2021). Following Polemis (2021), this current employed quantile regression to examine the effect of personality traits across low, medium, and high levels of students’ academic engagement. Quantile regression estimates the differential effects of these critical psychological constructs across the engagement distribution and disparities. We argued that quantile regression surpasses OLS by revealing how personality and resilience affect different student segments, which may impact lower-engaged students more strongly, whereas resilience may better predict engagement among students facing greater challenges. Given the persistent educational challenges in Ghanaian higher education institutions, especially within Colleges of Education, identifying which psychological traits predict higher levels of engagement at different levels can inform more targeted interventions. For example, students with low engagement may benefit more from resilience-building programs, while those at higher engagement levels may require different motivational strategies. Based on this, the following hypotheses are proposed: H1: Personality traits have significant differential of on students’ academic engagement. H2: Resilience has significant differential effects on students’ academic engagement. Methods Research design and participants The study employed a predictive correlational design to understand the differential effects students' personality traits and resilience on academic engagement. This design is deemed fit because of the use of quantitative variables in the prediction of relationships. A stratified random sampling technique was used to select 288 college students from the Jasikan College of Education of Ghana. First, the students were put into separate strata based on gender, level of study and programme of study and, within each stratum. Finally, a random sample of 288 SHS students was selected from the strata. The descriptive statistics for the study group are presented in Table 1 Measures Data were collected using measures adapted from the Higher Education Student Engagement Scale (HESES) (Zhoc, Webster, King, Li, & Chung (2019), the Big Five Inventory (BFI) (Soto & John, 2017) for measuring personality traits, and the Brief Resilience Scale (BRS) for measuring resilience. Except for the demographic profiles of the respondents, all the items on the questionnaire were rated using a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Academic engagement Students' academic engagement was measured using items modified from the the Higher Education Student Engagement Scale (HESES) (Zhoc, Webster, King, Li, & Chung (2019). The scale has 18 items with dimensions that focus on academic engagement. Some of the items are as follows: ‘I spend a lot of time to study on my own’ ‘I rarely skip classes’. The scale has been validated using different cohorts, confirming the three-facture structure with sound psychometric properties (). In this current study, the three-factor structure of the USEI has been confirmed with a high reliability coefficient (α = 0.843). Personality traits Personality traits were operationalised using the Big Five Inventory (BFI) scale (John & Srivastava, 1999; Soto & John, 2017). Participants rated 20 items across the five domains including openness (e.g., “has an active imagination”), conscientiousness (e.g., “does a thorough job”), extraversion (e.g., “is outgoing, sociable”), agreeableness (e.g., “tends to find fault”), and neuroticism e.g., “gets nervous easily”) on a 5-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). Composite scores for each trait were calculated by averaging the relevant items, with higher scores indicating greater manifestation of the trait (McCrae & Costa, 2023). The validity and cross-cultural applicability of the BFI have been confirmed in recent meta-analyses, particularly in African educational contexts (Ofori et al., 2023; Rammstedt et al., 2023). Openness and conscientiousness have been shown to have robust associations with academic persistence in Ghanaian tertiary institutions (Adu et al., 2022). Resilience Resilience, operationalised as the ability to bounce back from adversity (Smith et al., 2008), has been posited as a critical antecedent to gritty persistence and passion for long-term goals (Duckworth et al., 2016). In Ghanaian higher education, where systemic challenges intersect (Aboagye et al., 2023), resilience may serve as a psychological scaffold to nurture students' grit in terms of efforts and interests. This used the Brief Resilience Scale (BRS) for measuring resilience. The BRS comprised of six items (three positive and three negative), and each respondent were assessed based on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree), with higher scores reflecting greater resilience. Research highlights the role of resilience in moderating stress responses, enabling students to sustain their efforts in the midst of academic setbacks (Masten, 2018). Ethical Procedure Approval for data collection was obtained from the college administration. Participants were asked to clarify any misunderstandings in the questionnaire. Researchers informed participants of their freedom to continue or withdraw without consequence. Participants were assured that their responses would be used strictly for research purposes and that only the researchers would have access to the data. The questionnaires were administered after written informed consent was obtained from each participant. Teachers assisted the researchers in guiding participants to respond appropriately. Data were collected over 2 weeks in January 2025. Each questionnaire took 15–20 minutes to complete. Empirical Strategy This section describes our empirical strategies. We used quantile regression (QR) analysis to examine how personality traits and resilience affect students’ academic engagement. QR estimates the functions of conditional distributions, with each quantile characterizing a distribution point. The QR provides a complete description of conditional distributions by considering the covariate impacts on the entire dependent variable distribution, not just the conditional means. QR parameters estimate changes in specific quantiles from unit changes in independent variables, enabling quantile comparisons (Halkos and Polemis, 2019). OLS regression estimates the influence of independent variables on the conditional distribution mean, while QR analyzes the full conditional distribution with parameters varying across it. Quantile regression is nonparametric, requires no functional form, and minimizes residuals rather than squares. QR provides a better distribution analysis than mean regression, particularly for extreme responses (Taddy & Kottas, 2010). It effectively handles heteroscedasticity, non-normal disturbances (Buchinsky, 1998), and non-identically distributed data (Distante et al., 2018), resulting in varying slopes at different quantiles (Machado & Mata, 2000). Quantile estimators are obtained by solving the following optimization problem: For the \(\:\theta\:\) th quantile (0 < θ < 1), \(\:{y}_{i}\) is psychological outcome (academic engagement), and \(\:{x}_{i}\) is a vector of explanatory variables including the openness, conscientiousness, extraversion, agreeableness, and neuroticism and resilience, and other covariates. Based on this, we estimate the following linear model: Results Profile of socio-demographics The descriptive results as displayed in Table 2 presents the profile of survey participants which reveals a balanced gender distribution (52.08% male; 47.92% female), with respondents averaging 22.19 years (SD = 2.14), reflecting a typical undergraduate cohort. Notably, 40.63% were first-year students, potentially indicating greater attrition risks among novices (Credé et al., 2017), while 54.86% pursued junior high school education programs, suggesting domain-specific demands may influence grit development. Table 1 Descriptive statistics of the demographic profile. Variables Mean Std. Deviation Frequency Percentage Age 22.19 2.14 - - Gender Male - - 150 52.08 Female - - 138 47.92 Total - - 288 100 Level of Study Level 100 - - 117 40.63 Level 200 - - 85 29.51 Level 300 - - 86 29.86 Total - - 288 100 Programme of Study Early Childhood Education - - 44 15.28 Primary Education - - 86 29.86 Junior High School Education - - 158 54.86 Total - - 288 100 Descriptive statistics of the latent variables The summary statistics indicate a generally high level of psychological capital among students, with mean scores for conscientiousness ( M = 4.39), openness ( M = 4.07), agreeableness ( M = 4.08), and resilience ( M = 4.21) all exceeding the scale midpoint. Academic engagement was also notably high ( M = 3.80), while neuroticism scores were relatively lower ( M = 2.97), suggesting that most students exhibit strong emotional stability. These findings provide a robust empirical foundation for employing quantile regression to examine how these constructs differentially influence academic engagement across varying levels. Collectively, the results underscore the multidimensional and context-dependent role of personality traits and resilience in shaping academic behaviour further validating the need for a quantile regression approach to guide policy formulation, pedagogical strategies, and psychosocial interventions. Table 2 Summary statistics for the latent variables Variable Obs Mean Median SD Min Max Cronbach Alpha Academic engagement 288 3.802 3.750 0.855 1 5 0.72 Openness 288 4.069 4.200 0.645 1 5 0.74 Conscientiousness 288 4.388 4.600 0.631 1 5 0.74 Extraversion 288 3.433 3.400 0.769 1 5 0.76 Agreeableness 288 4.081 4.200 0.750 1 5 0.76 Neuroticism 288 2.972 2.800 1.00 1 5 0.78 Resilience 288 4.211 4.333 0.6307 1 5 0.79 Distribution of personality traits and resilience The histograms reveal distinct distributions for students' personality traits and resilience, highlighting the heterogeneity of psychological capital among learners. Notably, traits such as openness, conscientiousness, agreeableness, and resilience exhibit strong right-skewness, indicating that a substantial proportion of students report elevated levels in these dimensions. In contrast, neuroticism follows a more uniform distribution, reflecting varying degrees of emotional stability across the sample. These distributional variations justify the use of quantile regression, which enables a more nuanced analysis than traditional mean-focused approaches like ordinary least squares (OLS) or structural equation modeling (SEM). While these conventional models assume effect homogeneity across the academic engagement spectrum, quantile regression captures how the influence of personality and resilience varies at different engagement levels such as low, median, and high. For example, resilience may have a stronger predictive effect on highly engaged students than on their less engaged peers, a dynamic that OLS or SEM would overlook. Similarly, traits like conscientiousness might play a more critical role at the upper quantiles of engagement, aligning with the heightened self-regulation and motivation required for advanced academic performance. Thus, quantile regression is not only methodologically advantageous in this context but also enriches theoretical understanding by revealing how personality and resilience differentially interact with academic engagement across its full spectrum. Bivariate correlations of key constructs The correlation analysis reveals significant relationships between personality traits, resilience, and academic engagement. Among the Big Five traits, both agreeableness (r = 0.321, p < 0.001) and conscientiousness (r = 0.250, p < 0.001) demonstrated strong positive associations with engagement, indicating that students who are more cooperative and disciplined tend to exhibit greater academic investment. Resilience showed an equally robust correlation (r = 0.334, p < 0.001), emphasizing its critical role in maintaining academic focus during challenges. More moderate yet significant relationships emerged for openness (r = 0.135, p < 0.01) and extraversion (r = 0.181, p < 0.001), suggesting that curiosity and sociability provide modest support for engagement. In contrast, neuroticism displayed a negligible and non-significant association (r = 0.057), implying that emotional instability may not substantially affect academic involvement in this population. Notably, the positive intercorrelations among resilience, conscientiousness, and agreeableness highlight the synergistic effects of these psychological resources on student engagement. When examined through a quantile regression framework, these findings suggest that the strength of these relationships may vary across different levels of academic engagement. This underscores the need for differentiated intervention strategies that account for students' distinct personality profiles and resilience capacities. Regression results This section presents the regression results of the study, which examines the differential effects of personality traits and resilience on students’ academic engagement using both Ordinary Least Squares (OLS) and quantile regression (QR) approaches. The analysis begins with an interpretation of the OLS results as a baseline model, followed by a detailed interpretation of the QR estimates, which provide a more nuanced understanding of how these psychological constructs influence engagement at different points of the engagement distribution. OLS regression estimates The OLS regression results (Table 3 , Column 1) offer preliminary insights into the relationship between personality traits, resilience, and academic engagement. With an adjusted R ² of 0.467, the model accounts for a substantial proportion of the variance in academic engagement. Notably, Openness demonstrated a significant negative effect (β = −0.143, p < 0.05), a finding that contrasts with prior research linking Openness to intellectual curiosity (Poort et al., 2023 ). Conversely, Agreeableness emerged as the strongest positive predictor (β = 0.271, p < 0.01), aligning with Self-Determination Theory’s (SDT) emphasis on relatedness, as agreeable students may cultivate stronger social bonds that foster engagement (Mahama et al., 2022 ). In contrast, Conscientiousness, Extraversion, and Neuroticism were statistically insignificant a surprising result, given existing literature that highlights Conscientiousness as a key driver of goal-directed behavior (Polemis, 2021 ). Resilience proved to be a significant positive predictor of academic engagement (β = 0.316, p < 0.01), supporting SDT’s proposition that resilience fulfills competence needs, thereby empowering students to persevere despite challenges (Liu & Huang, 2021 ). Among the control variables, female students exhibited higher engagement levels (β = 0.275, p < 0.05), consistent with studies documenting gender-based differences in academic behavior (Chen et al., 2025 ). Additionally, Level 300 students were more engaged than their peers (β = 0.341, p < 0.01), possibly reflecting greater academic maturity. Conversely, both primary and junior high school education backgrounds negatively impacted engagement, suggesting that socioeconomic factors may undermine student motivation (Yang et al., 2025 ). While OLS regression provides a useful baseline, its assumption of uniform effects across all engagement levels is a key limitation. To address this constraint, quantile regression offers a more nuanced analysis by uncovering differential effects. Quantile regression estimates The QR analysis (Table 3 , Columns 2–10) reveals heterogeneous effects across engagement quantiles, supporting H1 (differential personality effects) and H2 (stronger resilience effects at lower engagement levels). For openness, significant negative effects emerge at q25 (β = −0.305, p < 0.05) and q30 (β = −0.335, p < 0.01), suggesting that highly open yet disengaged students may struggle with focus. This aligns with prior findings that excessive openness can lead to distractibility (Yusoff et al., 2021 ). Agreeableness, in contrast, exhibits consistently positive effects across quantiles, peaking at lower engagement levels ( q15 : β = 0.557, p < 0.01). This supports Self-Determination Theory’s (SDT) relatedness hypothesis, as agreeable students likely benefit from social support—a critical factor for engagement, particularly among struggling learners (Ryan et al., 2019 ). otably, conscientiousness and extraversion were statistically insignificant in most quantiles, diverging from OLS expectations. This implies their influence may be context-dependent or mediated by other factors (Polemis, 2021 ). Conversely, neuroticism demonstrates negative effects at q60 (β = −0.020, p < 0.05), indicating that emotional instability primarily hampers engagement among moderately engaged students, a pattern consistent with studies linking neuroticism to academic anxiety (Tan et al., 2024 ). Resilience emerged as a key predictor, exerting its strongest effects at higher engagement quantiles ( q60–q80 ), with significant coefficients at q70 (β = 0.466, p < 0.01) and q80 (β = 0.569, p < 0.01). Contrary to H2, resilience mattered more for highly engaged students, a finding that may reflect their ability to sustain motivation in demanding academic environments (Biggs et al., 2024 ). Regarding demographic characteristics, female students demonstrated higher engagement at mid-quantiles ( q40–q60 ), consistent with prior research on gender differences in persistence (Laranjeira & Teixeira, 2025 ). Similarly, Level 300 students exhibited greater engagement at mid-quantiles ( q25–q50 ), suggesting that advanced students develop more effective coping strategies over time. Overall, this study enhances our understanding of how personality and resilience differentially influence engagement. While OLS regression provided a baseline, quantile regression (QR) uncovered nuanced, distributional effects, underscoring the importance of tailored interventions based on engagement levels. Future research should investigate potential cultural and institutional moderators of these relationships to further refine academic support strategies. Table 3 Bivariate correlations among the sample variables Variables 1 2 3 4 5 6 7 8 9 10 11 1.Academic engagement - 2. openness 0.135 ** - 3. conscientious 0.250 *** 0.427 *** - 4. extraversion 0.181 *** 0.126 ** 0.508 *** - 5. agreeableness 0.321 *** 0.341 *** 0.380 *** 0.447 *** - 6. neuroticism 0.057 0.086 0.044 0.272 *** 0.276 *** - 7. Resilience 0.334 *** 0.439 *** 0.557 *** 0.275 *** 0.427 *** -0.005 - 8. Age -0.050 -0.089 -0.023 -0.004 -0.130 *** -0.115 * -0.097 - 9. sex 0.204 *** 0.014 -0.043 -0.074 0.122 ** -0.018 0.011 -0.250 *** - 10. level 0.040 -0.022 -0.220 *** -0.214 *** -0.266 *** -0.041 -0.086 0.150 ** -0.026 - 11. programme -0.178 -0.005 -0.040 -0.016 -0.063 0.023 0.004 0.028 -0.251 *** 0.165 ** - Table 4 Quantile regression of the effects of students’ personality traits and resilience on academic engagement Engagement (Dep. Var.) OLS Quantile Regression (QR) Predictor Variables q15 q20 q25 q30 q40 q50 q60 q70 q80 Openness -0.143 ** -0.185 -0.305 ** -0.335 *** -0.234 ** -0.150 -0.179 -0.214 * -0.151 -0.024 (0.066) (0.172) (0.123) (0.105) (0.097) (0.092) (0.112) (0.116) (0.104) (0.098) Conscientiousness 0.104 0.374 0.244 0.303 0.160 0.115 0.083 -0.005 0.146 0.138 (0.107) (0.241) (0.196) (0.193) (0.197) (0.173) (0.173) (0.178) (0.168) (0.165) Extraversion 0.053 -0.083 0.043 0.025 0.119 0.130 0.0761 0.029 0.014 0.129 (0.072) (0.161) (0.128) (0.122) (0.115) (0.103) (0.100) (0.097) (0.084) (0.095) Agreeableness 0.271 *** 0.557 *** 0.469 *** 0.489 *** 0.452 *** 0.371 *** 0.355 *** 0.374 *** 0.283 * 0.024 (0.082) (0.163) (0.146) (0.123) (0.112) (0.107) (0.117) (0.112) (0.153) (0.173) Neuroticism 0.025 0.187 0.113 0.116 * 0.058 0.003 -0.020 ** -0.078 -0.029 -0.057 (0.049) (0.117) (0.079) (0.065) (0.060) (0.057) (0.073) (0.093) (0.082) (0.086) Resilience 0.316 *** 0.009 0.180 0.195 0.266 ** 0.329 *** 0.393 *** 0.466 *** 0.569 *** 0.390 ** (0.092) (0.118) (0.158) (0.146) (0.129) (0.105) (0.102) (0.136) (0.154) (0.176) Age 0.029 0.025 -0.012 0.026 0.035 0.041 0.047 0.019 -0.025 0.012 (0.028) (0.041) (0.042) (0.043) (0.038) (0.031) (0.031) (0.034) (0.036) (0.043) Female 0.275 ** 0.308 0.195 0.199 0.310 ** 0.320 ** 0.406 *** 0.234 0.036 0.057 (0.102) (0.220) (0.180) (0.156) (0.134) (0.113) (0.126) (0.146) (0.145) (0.081) Level 200 students 0.027 -0.180 0.000 0.000 0.000 0.000 0.172 0.056 0.053 0.000 (0.116) (0.215) (0.151) (0.124) (0.117) (0.108) (0.134) (0.097) (0.161) (0.124) Level 300 students 0.341 *** 0.189 0.273 * 0.325 ** 0.310 ** 0.370 ** 0.383 ** 0.244 0.089 -0.071 (0.114) (0.230) (0.164) (0.156) (0.149) (0.133) (0.144) (0.172) (0.181) (0.134) Primary education -0.490 *** --0.634 * -0.408 -0.826 ** -0.733 ** -0.699 *** -0.418 ** -0.457 ** -0.320 -0.120 (0.163) (0.340) (0.330) (0.300) (0.259) (0.203) (0.120) (0.212) (0.196) (0.203) Junior high education -0.471 *** -0.364 -0.323 -0.615 ** -0.567 *** -0.369 *** -0.418 ** -0.457 ** -0.284 * -0.164 (0.120) (0.270) (0.239) (0.216) (0.181) (0.151) (0.154) (0.170) (0.156) (0.123) Constant 0.739 -0.658 0.714 0.011 -2.261 -0.176 -0.150 1.307 1.429 1.764 * (0.709) (1.475) (1.265) (1.214) (1.094) (0.853) (0.927) (1.061) (1.113) (1.037) Observation 288 288 288 288 288 288 288 288 288 288 Adj R 2 0.467 - - - - - - - - - Pseudo R 2 0.173 0.185 0.183 0.186 0.206 0.212 0.200 0.185 0.174 Note: Bootstrap at 500 replications. Standard errors in parentheses *** p < 0.01 (1%), ** p < 0.05 (5%), and * p < 0.10 (10%). Discussion The present study investigated the differential effects of personality traits and resilience on students' academic engagement using both Ordinary Least Squares (OLS) and quantile regression (QR) approaches. Grounded in Self-Determination Theory (SDT), this research explored how psychological capital resources, particularly personality traits and resilience influence engagement across varying levels of the engagement distribution. Below, we discuss the key findings, their theoretical implications, and their alignment (or divergence) with prior literature. The OLS regression provided a baseline understanding, indicating that agreeableness and resilience were the strongest positive predictors of academic engagement, whereas openness exhibited a significant negative effect. Notably, conscientiousness, extraversion, and neuroticism were statistically insignificant in the OLS model, a finding that contradicts some prior research (Polemis, 2021 ; Rocha et al., 2023 ). However, the QR analysis revealed more nuanced effects across engagement quantiles. These results supported H1 (differential personality effects) and partially supported H2, as the impact of resilience varied significantly across different engagement levels. The analysis revealed nuanced patterns across personality traits and engagement levels. Openness exhibited significant negative effects at lower engagement quantiles ( q25–q30 ), suggesting that highly open yet disengaged students may struggle with focus. This aligns with Yusoff et al. ( 2021 ), who found that excessive openness can lead to distractibility, but contrasts with Poort et al. ( 2023 )'s association of openness with intellectual curiosity. This divergence underscores the context-dependent nature of openness while potentially beneficial for curiosity, it may hinder engagement when not effectively channeled. Agreeableness demonstrated consistently positive effects across quantiles, with the strongest influence at lower engagement levels ( q15 ). This supports SDT's relatedness hypothesis, as agreeable students likely benefit from stronger social connections that foster engagement (Mahama et al., 2022 ; Ryan et al., 2019 ). In contrast, conscientiousness and extraversion were statistically insignificant in most quantiles, diverging from studies linking conscientiousness to goal-directed behavior (Polemis, 2021 ). This suggests their effects may be context-dependent or mediated by institutional factors. Neuroticism showed negative effects at mid-level engagement ( q60 ), corroborating Tan et al. ( 2024 )'s findings linking neuroticism to academic anxiety. This implies that emotional instability primarily hampers moderately engaged students, who may be particularly susceptible to stress-related disengagement. Contrary to H2, resilience had its strongest effects at higher engagement quantiles ( q70–q80 ). This suggests resilient students excel at sustaining motivation in demanding academic environments (Biggs et al., 2024 ), challenging Liu & Huang's (2021) view that resilience primarily buffers disengaged students. Instead, our findings indicate resilience functions as an enhancement factor for already-engaged learners, amplifying their persistence. Our findings align with the SDT framework of autonomy, competence, and relatedness driving student engagement (Deci & Ryan, 2017 ). Our results validated the relatedness through agreeableness, showing that social bonds matter most at lower engagement levels (Ryan et al., 2019 ). For competence, resilience proved critical for highly engaged students, supporting SDT's link between efficacy and sustained effort (SDT) (Liu & Huang, 2021 ). Openness, linked to intellectual curiosity, demonstrated the importance of autonomy but showed negative effects among disengaged students, indicating that autonomy requires structure (Weinstein et al., 2012 ). While prior research found conscientiousness to be crucial (Polemis, 2021 ), our QR results showed minimal effects, possibly due to cultural differences. The stronger impact of resilience among engaged students contrasts with that of studies focusing on struggling learners (Liu et al., 2022 ). This study enhances the understanding of personality and resilience in academic engagement, with QR revealing nuanced effects that support context-specific interventions within SDT's framework of SDT. Theoretical Implications This study makes significant theoretical and methodological contributions to understanding how personality traits and academic resilience differentially influence students' academic engagement, advancing the application of SDT in educational psychology. By employing quantile regression (QR) alongside traditional Ordinary Least Squares (OLS) regression, the study reveals nuanced patterns that challenge and refine existing theoretical assumptions, offering a more granular perspective on the interplay between psychological capital resources and engagement. The findings reinforce and expand SDT’s core tenets by demonstrating that autonomy, competence, and relatedness operate differently across engagement levels. While OLS regression provided a generalized view—highlighting agreeableness and resilience as key predictors the QR analysis uncovered heterogeneous effects that align with SDT’s dynamic framework (Deci & Ryan, 2017 ; Ryan et al., 2022 ). The findings on openness align with SDT, which posits that autonomy-supportive environments enhance intrinsic motivation (Weinstein et al., 2012 ). However, the QR results reveal that openness negatively impacts engagement at lower quantiles (q25–q30), suggesting that excessive intellectual curiosity without structured guidance may lead to distractibility (Yusoff et al., 2021 ). This challenges the assumption that openness universally fosters engagement, emphasizing instead that autonomy must be balanced with academic structure to prevent disengagement. Contrary to prior research (Liu & Huang, 2021 ), this study found that resilience had its strongest effects at higher engagement levels (q70–q80), indicating that it functions more as an enhancement factor than merely a buffer against disengagement. This refines SDT’s competence dimension by showing that resilience sustains engagement primarily among already motivated students, reinforcing their persistence in demanding academic environments (Biggs et al., 2024 ). Additionally, this study validates the positive effects of agreeableness across quantiles, particularly at lower engagement levels (q15), supporting SDT’s relatedness hypothesis (Mahama et al., 2022 ). This suggests that social connectedness is most critical for disengaged students, as peer and instructor support may help reintegrate them into academic life. Moreover, the study resolves inconsistencies in prior research by demonstrating that personality traits exhibit context-dependent effects. For instance, conscientiousness and extraversion often linked to engagement (Polemis, 2021 ) were statistically insignificant in most quantiles, suggesting that their influence may be mediated by institutional or cultural factors. This finding calls for further cross-cultural SDT research to explore boundary conditions. Neuroticism’s negative effects at mid-level engagement (q60) align with findings on academic anxiety (Tan et al., 2024 ), indicating that emotional instability most disrupts moderately engaged students, who may lack the coping mechanisms of either highly engaged or disengaged peers. Collectively, these findings necessitate a more conditional application of SDT, where personality and resilience effects are interpreted relative to engagement baselines rather than as universal predictors. Practical Implications This study offers critical insights into the differential effects of personality traits and resilience on academic engagement, presenting actionable strategies for policymakers, educational administrators, NGOs, and students in Ghana and similar contexts. By employing quantile regression (QR), the research advances beyond conventional OLS estimates, uncovering nuanced patterns that inform targeted interventions. The differential impact of personality traits across engagement levels necessitates tailored pedagogical approaches. Given that agreeableness significantly enhances engagement, particularly among disengaged students, policymakers should prioritize social-emotional learning (SEL) programs that foster stronger peer and teacher-student relationships. Meanwhile, resilience most benefits highly engaged students; thus, institutions should integrate advanced resilience training into honors programs and high-achieving cohorts to sustain motivation. Conversely, the negative effect of openness among disengaged students suggests that curricula should balance exploratory learning with structured tasks to mitigate distractibility. Educational administrators should also recognize that conscientiousness and extraversion, often assumed to be universally beneficial had minimal effects in this context. This suggests that institutional culture may mediate their impact, necessitating localized assessments before implementing broad interventions. Furthermore, given that neuroticism undermines mid-level engagement, mental health support systems such as counseling services and stress-management workshops should target moderately engaged students who are at risk of anxiety-induced disengagement. NGOs working in education can leverage these findings to design context-specific interventions. For instance, resilience-building programs should differentiate between struggling and high-performing students, providing foundational coping skills to the former and advanced persistence strategies to the latter. Additionally, since agreeableness enhances engagement through social connectedness, NGOs should facilitate peer-mentoring networks and community-building activities in colleges of education. Moreover, students can apply these insights through deliberate self-reflection: those scoring high in openness should implement structured study plans to counter distractibility, while disengaged students may benefit significantly from relationship-building activities to boost motivation. Highly engaged students, conversely, should proactively develop resilience through targeted goal-setting and stress-regulation techniques to sustain performance under academic pressure. This study decisively challenges universal intervention models, empirically demonstrating that psychological resources exert differential effects across engagement levels. By adopting quantile-sensitive approaches, institutions can optimize resource allocation directing social-support programs toward disengaged students while channeling resilience-building initiatives to high achievers. These findings strongly advocate for personality-informed pedagogy, wherein educators customize engagement strategies according to students' distinct psychological profiles, thereby promoting sustainable academic success in Ghana and comparable educational contexts. Ultimately, the study establishes an evidence-based framework for enhancing academic engagement through precision interventions. This model not only aligns with Self-Determination Theory's fundamental principles but also addresses the heterogeneous needs of diverse student populations with unprecedented specificity. Conclusion remarks and future research This study makes significant methodological contributions to educational psychology by employing quantile regression (QR) to reveal differential effects of personality traits and resilience across academic engagement levels. Unlike traditional OLS regression that assumes uniform effects, QR provides nuanced insights into how psychological capital resources operate across the engagement spectrum. Our findings refine SDT by demonstrating that core needs: autonomy (manifested through openness), competence (via resilience), and relatedness (through agreeableness) function differentially depending on baseline engagement levels, thereby challenging universal assumptions in existing literature. Methodologically, this research advances the field by demonstrating QR's superior capacity to detect heterogeneous effects, particularly revealing: (1) resilience's heightened impact among highly engaged students, and (2) openness's counterintuitive negative association among disengaged learners. These insights carry important practical implications, advocating for precisely tailored interventions, including targeted support programs for disengaged students and advanced resilience training for high achievers. Despite this, there are some limitations that warrant consideration. First, reliance on self-reported measures of engagement and resilience may introduce response bias. Second, the cross-sectional design precludes causal inferences, necessitating future longitudinal studies to examine temporal dynamics. Third, the Ghanaian college of education context may limit generalizability, as cultural and institutional factors could mediate personality traits, resilience and engagement relationships. Finally, while QR captures distributional heterogeneity, it cannot account for potential unobserved confounders (e.g., teaching quality, institutional policies). Future research should address these limitations while further exploring QR's applicability in educational settings. Such efforts will optimize precision-based interventions for diverse student populations and advance our understanding of psychological factors in academic achievement. Declarations CRediT authorship contribution statement Hansen Akoto-Baako : Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. Ethical Approval The study was conducted in accordance with ethical standards and all applicable institutional guidelines and regulations. Informed consent was obtained from all participants. This study received approval from the “Institutional Review Board (IRB)” of the “University of Cape Coast”. All procedures followed in the study complied with the principles outlined in the “Declaration of Helsinki”. Clinical Trial Number: Not Applicable Consent of Participants Informed consent was obtained from all participants before data collection. Data Availability The data are, however, available from the authors upon reasonable request. Conflict of interest The authors declare that they have no conflicts of interest. Funding The authors did not receive any fund for this study. References Amoadu, M., Hagan Jr, J. E., Obeng, P., Agormedah, E. K., Srem-Sai, M., & Schack, T. (2025). 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Zickafoose, A., Ilesanmi, O., Diaz-Manrique, M., Adeyemi, A. E., Walumbe, B., Strong, R., ... & Dooley, K. (2024). Barriers and challenges affecting quality education (Sustainable Development Goal# 4) in sub-Saharan Africa by 2030. Sustainability , 16 (7), 2657. Zynuddin, S. N., Kenayathulla, H. B., & Sumintono, B. (2023). The relationship between school climate and students' non-cognitive skills: a systematic literature review. Heliyon , 9 (4). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6787024","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473330967,"identity":"51e86604-43b8-4985-b442-a12eeeaf3d51","order_by":0,"name":"Akoto-Baako Hansen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACHgY2BsYGGwYGZhK1pJGu5TAJ7uLvOXzswc8d5xO3s7M//MBQY8cg334AvxaJs23phr1nbifubOYxlmA4lszA2JNAwJrzPGYSvG23Ezcc5mGQYGA7APQTAS3yQC2Sf9vOAbWwP/7B8O8AAxv/A/xaDM72mEnzth0AamEwk2BsO8DAI0HAFsMzx9KNZduSjYEOM7NI7EvmkZAgYIvcmeRjD9+22cluOH/88Y0P3+zk5PsJ2IIKEkDxNApGwSgYBaOAcgAADHhBRoOI5LcAAAAASUVORK5CYII=","orcid":"","institution":"University of South Africa","correspondingAuthor":true,"prefix":"","firstName":"Akoto-Baako","middleName":"","lastName":"Hansen","suffix":""}],"badges":[],"createdAt":"2025-05-30 18:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85071303,"identity":"ead91c42-b97d-4f80-8828-1d82a7bcd3a8","added_by":"auto","created_at":"2025-06-20 15:39:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31407,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of personality traits and resilience\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6787024/v1/56cf2363aa85a3b718952650.png"},{"id":100787662,"identity":"bc961ccd-0b11-48b3-bd1a-4fcf82f22dbb","added_by":"auto","created_at":"2026-01-21 12:02:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787024/v1/a57744e1-0f06-440f-bbba-a6b564dc7069.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential effects of personality traits and resilience on Ghanaian students' academic engagement: A quantile regression approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcademic engagement remains a cornerstone of student success, playing a pivotal role in retention, performance, and long-term career achievement (Obeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yang \u0026amp; Xiang, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Defined as a multidimensional construct encompassing behavioral, emotional, and cognitive investment in learning (Fredricks et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), academic engagement is critical for fostering inclusive, equitable education and advancing Sustainable Development Goal 4 (SDG 4) (Cottafavas et al., 2019). Despite its significance, many students, particularly in Sub-Saharan Africa (SSA) continue to struggle with disengagement, high dropout rates, and inadequate institutional support (Akyeampong et al., 2013; Mohammed \u0026amp; Kuyini, 2021). In Ghana, for instance, only 58% of college students complete their degrees, with disengagement being a key contributing factor (Ministry of Education, 2022; Obeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This alarming trend underscores the urgent need to identify the psychological and behavioral drivers of academic engagement to inform targeted interventions.\u003c/p\u003e \u003cp\u003eWhile prior research has extensively the school climate as predictors of engagement and academic success (Zynuddin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), emerging evidence highlights the crucial role of non-cognitive factors particularly personality traits and resilience in shaping students' engagement levels (Amoadu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sultanova, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism that provide a robust framework for understanding how dispositional characteristics influence learning behaviors (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Turner \u0026amp; Hodis, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conscientiousness, characterized by self-discipline and goal-directed behavior, is strongly linked to higher academic engagement (Kipuru et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rocha et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas neuroticism (often referred to as emotional instability) may hinder persistence under stress (Daniel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, resilience operationalised as the capacity to adapt to adversity and maintain psychological well-being has been shown to buffer against students\u0026rsquo; academic stressors, thereby sustaining motivation and academic engagement (Romano et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these insights, critical gaps persist in the literature. First, while the impact of psychological constructs on academic engagement has been widely studied in Western contexts, their differential effects on academic engagement among higher education students in the colleges of education remain underexplored in resource-constrained settings like Ghana, where cultural and socioeconomic factors may alter their impact. Second, most studies have relied on regression models such as ordinary least squares (OLS) or structural equation modeling (SEM), which assume uniform effects across all psychological constructs like personality, thereby obscuring its potential variations influence on high- versus low levels of an outcome (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In line with Polemis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) notion, we argued that that unlike these traditional linear models, quantile regression reveals how personality and resilience operate differently among high- versus low levels of academic engagement among students, offering nuanced insights for intervention strategies. However, prior research has not sufficiently leveraged advanced analytical techniques, using the quantile regression to uncover heterogeneous effects across the engagement spectrum.\u003c/p\u003e \u003cp\u003eAddressing these gaps is essential for developing tailored interventions that cater to students at different engagement levels. This study advances the literature in three key ways. First, it draws on Self-Determination Theory (SDT) (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to elucidate how personality traits and resilience fulfill students' intrinsic psychological needs for autonomy, competence, and relatedness, thereby fostering sustained engagement (Chiu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Obeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, conscientious students exhibit greater self-regulation (competence), while resilient students maintain motivation despite setbacks (autonomy) (Liu \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Second, it employs quantile regression analysis, a robust methodological approach that captures differential effects across the engagement distribution (Hughes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Third, this study focuses on Ghanaian higher education students, addressing a critical gap in the literature by examining how these non-cognitive factors function in a resource-constrained setting.\u003c/p\u003e \u003cp\u003eThe findings hold significant implications for educators and policymakers. By identifying which personality traits and resilience factors most strongly predict engagement at different quantiles, institutions can design targeted interventions. Integrating SDT with quantile regression, this study provides a novel, contextually grounded understanding of how personality and resilience differentially shape academic engagement, offering actionable insights for enhancing student success in Ghana and similar settings. The remainder of this paper is organized as follows: Section 2 reviews the literature on personality, resilience, and academic engagement. Section 3 presents the theoretical framework (SDT) and hypotheses. Section 4 details the methodology, including quantile regression analysis. Section 5 reports the findings, while Section 6 discusses implications, limitations, and future research directions.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eTo understand the ongoing arguments on the effect of student personality traits and resilience, there is the need to review literature from both theoretical and empirical angles to form the foundation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Determination as Theoretical Lens\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is anchored in Self-Determination Theory (SDT) to provide a theoretical underpinning of academic engagement behaviours (Chiu, 2022; Obeng et al., 2025). SDT is well-established theoretical framework by which posits that human behavior is driven by three innate psychological needs: autonomy, competence, and relatedness (Deci \u0026amp; Ryan, 2000, 2017; Ryan et al., 2022). Studies have shown that SDT provides a dynamic framework that aligns with personality traits and resilience function as key psychological resources that can either facilitate or hinder task engagement (He et al., 2025; Hughes et al., 2023; Liu \u0026amp; Huang, 2021; Ryan et al., 2019; Skinner \u0026amp; Pitzer, 2012). As per SDT, autonomy reflects students' sense of volition in their learning and personality traits such as openness (associated with curiosity) and low neuroticism (linked to emotional stability) are perceived to enhance this autonomous motivation, fostering deeper engagement (Weinstein et al., 2012). Competence pertains to perceived efficacy in academic tasks. Resilient students, who view challenges as surmountable, are more likely to sustain effort and engagement (Liu \u0026amp; Huang, 2021). Moreover, the relatedness involves feeling connected to peers and educators. Extraversion and Agreeableness may strengthen social bonds, indirectly boosting engagement (Mahama et al., 2022). \u0026nbsp;SDT further distinguishes between intrinsic motivation (engaging for inherent satisfaction) and extrinsic motivation (driven by external rewards). Therefore, personality traits may enhance intrinsic motivation, while resilience buffers against disengagement in extrinsically motivated students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn pursuit of SDG 4's goals of inclusive quality education and lifelong learning, academic engagement has become crucial in the educational context (Yang et al., 2025). High academic engagement levels are correlated with improved academic achievement, reduced dropouts, and better psychological well-being (Laranjeira \u0026amp; Teixeira, 2025; Wong et al., 2024). Scholars have demonstrated through research that enhancing student engagement, is a central to fostering students’ academic performance and achieving educational quality (Azila-Gbettor \u0026amp; Abiemo, 2021; Obeng et al., 2025; Owusu-Agyeman \u0026amp; Amoakohene, 2021). In the context of understanding predictors of academic engagement, the personality traits, based on the Five-Factor Model (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) help explain students’ engagement outcomes across educational settings (Mahama et al., 2022; Ramirez-Arellano, 2024). Studies in educational psychology have highlighted the influence of personality traits on academic engagement and performance (Chen et al., 2025; Tan et al., 2024). Conscientiousness strongly predicts academic engagement through goal-directed behavior and self-discipline (Polemis, 2021; Rocha et al., 2023), while openness positively correlates with deep learning strategies and intellectual curiosity, enhancing engagement in cognitive tasks (Poort et al., 2023). Conversely, neuroticism is correlated with lower engagement due to anxiety and academic stress (Yusoff et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond personality, resilience explaining the capacity to bounce back from adversity, has emerged as a protective psychological resource that sustains students' academic engagement pursuits during times of adversity (Masten, et al., 2022). Several empirical studies have emphasised the positive effect of academic resilience that enables students to maintain engagement despite challenges, such as poor performance, financial constraints, and stress (Biggs et al., 2024; Doll \u0026amp; Song, 2023; Liu et al., 2022; Romano et al., 2021; Theron, 2023; Yu et al., 2022; Zhang, 2021). Resilient students engage more in learning, use coping strategies more effectively, and show greater perseverance. Resilience aligns with SDT, which states that fulfilling basic psychological needs, including autonomy, competence, and relatedness, fosters motivation and engagement (Deci and Ryan 2020). Both personality and resilience enable psychological needs, as students with high conscientiousness and resilience are likely to perceive themselves as competent and capable, promoting intrinsic motivation and academic engagement. In Ghana, studies have shown that resilience positively enhance students’ academic engagement (Amoadu et al., 2025; Mahama et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite there is increasing scholarly attention on personality traits, resilience and academic engagement, empirical evidence remains elusive. While existing studies found that both personality traits and resilience predict student engagement, they failed to relied on analytical that may unmask the differential effects of these variables across engagement levels (Polemis, 2021). Following Polemis (2021), this current employed quantile regression to examine the effect of personality traits across low, medium, and high levels of students’ academic engagement. Quantile regression estimates the differential effects of these critical psychological constructs across the engagement distribution and disparities. We argued that quantile regression surpasses OLS by revealing how personality and resilience affect different student segments, which may impact lower-engaged students more strongly, whereas resilience may better predict engagement among students facing greater challenges. Given the persistent educational challenges in Ghanaian higher education institutions, especially within Colleges of Education, identifying which psychological traits predict higher levels of engagement at different levels can inform more targeted interventions. For example, students with low engagement may benefit more from resilience-building programs, while those at higher engagement levels may require different motivational strategies. Based on this, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Personality traits have significant differential of on students’ academic engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Resilience has significant differential effects on students’ academic engagement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eResearch design and participants\u003c/h2\u003e\n \u003cp\u003eThe study employed a predictive correlational design to understand the differential effects students\u0026apos; personality traits and resilience on academic engagement. This design is deemed fit because of the use of quantitative variables in the prediction of relationships. A stratified random sampling technique was used to select 288 college students from the Jasikan College of Education of Ghana. First, the students were put into separate strata based on gender, level of study and programme of study and, within each stratum. Finally, a random sample of 288 SHS students was selected from the strata. The descriptive statistics for the study group are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eData were collected using measures adapted from the Higher Education Student Engagement Scale (HESES) (Zhoc, Webster, King, Li, \u0026amp; Chung (2019), the Big Five Inventory (BFI) (Soto \u0026amp; John, 2017) for measuring personality traits, and the Brief Resilience Scale (BRS) for measuring resilience. Except for the demographic profiles of the respondents, all the items on the questionnaire were rated using a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAcademic engagement\u003c/h2\u003e\n \u003cp\u003eStudents\u0026apos; academic engagement was measured using items modified from the the Higher Education Student Engagement Scale (HESES) (Zhoc, Webster, King, Li, \u0026amp; Chung (2019). The scale has 18 items with dimensions that focus on academic engagement. Some of the items are as follows: \u0026lsquo;I spend a lot of time to study on my own\u0026rsquo; \u0026lsquo;I rarely skip classes\u0026rsquo;. The scale has been validated using different cohorts, confirming the three-facture structure with sound psychometric properties (). In this current study, the three-factor structure of the USEI has been confirmed with a high reliability coefficient (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.843).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePersonality traits\u003c/h3\u003e\n\u003cp\u003ePersonality traits were operationalised using the Big Five Inventory (BFI) scale (John \u0026amp; Srivastava, 1999; Soto \u0026amp; John, 2017). Participants rated 20 items across the five domains including openness (e.g., \u0026ldquo;has an active imagination\u0026rdquo;), conscientiousness (e.g., \u0026ldquo;does a thorough job\u0026rdquo;), extraversion (e.g., \u0026ldquo;is outgoing, sociable\u0026rdquo;), agreeableness (e.g., \u0026ldquo;tends to find fault\u0026rdquo;), and neuroticism e.g., \u0026ldquo;gets nervous easily\u0026rdquo;) on a 5-point Likert-type scale (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree). Composite scores for each trait were calculated by averaging the relevant items, with higher scores indicating greater manifestation of the trait (McCrae \u0026amp; Costa, 2023). The validity and cross-cultural applicability of the BFI have been confirmed in recent meta-analyses, particularly in African educational contexts (Ofori et al., 2023; Rammstedt et al., 2023). Openness and conscientiousness have been shown to have robust associations with academic persistence in Ghanaian tertiary institutions (Adu et al., 2022).\u003c/p\u003e\n\u003ch3\u003eResilience\u003c/h3\u003e\n\u003cp\u003eResilience, operationalised as the ability to bounce back from adversity (Smith et al., 2008), has been posited as a critical antecedent to gritty persistence and passion for long-term goals (Duckworth et al., 2016). In Ghanaian higher education, where systemic challenges intersect (Aboagye et al., 2023), resilience may serve as a psychological scaffold to nurture students\u0026apos; grit in terms of efforts and interests. This used the Brief Resilience Scale (BRS) for measuring resilience. The BRS comprised of six items (three positive and three negative), and each respondent were assessed based on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores reflecting greater resilience. Research highlights the role of resilience in moderating stress responses, enabling students to sustain their efforts in the midst of academic setbacks (Masten, 2018).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical Procedure\u003c/h2\u003e\n \u003cp\u003eApproval for data collection was obtained from the college administration. Participants were asked to clarify any misunderstandings in the questionnaire. Researchers informed participants of their freedom to continue or withdraw without consequence. Participants were assured that their responses would be used strictly for research purposes and that only the researchers would have access to the data. The questionnaires were administered after written informed consent was obtained from each participant. Teachers assisted the researchers in guiding participants to respond appropriately. Data were collected over 2 weeks in January 2025. Each questionnaire took 15\u0026ndash;20 minutes to complete.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEmpirical Strategy\u003c/h2\u003e\n \u003cp\u003eThis section describes our empirical strategies. We used quantile regression (QR) analysis to examine how personality traits and resilience affect students\u0026rsquo; academic engagement. QR estimates the functions of conditional distributions, with each quantile characterizing a distribution point. The QR provides a complete description of conditional distributions by considering the covariate impacts on the entire dependent variable distribution, not just the conditional means. QR parameters estimate changes in specific quantiles from unit changes in independent variables, enabling quantile comparisons (Halkos and Polemis, 2019). OLS regression estimates the influence of independent variables on the conditional distribution mean, while QR analyzes the full conditional distribution with parameters varying across it. Quantile regression is nonparametric, requires no functional form, and minimizes residuals rather than squares. QR provides a better distribution analysis than mean regression, particularly for extreme responses (Taddy \u0026amp; Kottas, 2010). It effectively handles heteroscedasticity, non-normal disturbances (Buchinsky, 1998), and non-identically distributed data (Distante et al., 2018), resulting in varying slopes at different quantiles (Machado \u0026amp; Mata, 2000). Quantile estimators are obtained by solving the following optimization problem:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"835\" height=\"100\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFor the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e quantile (0\u0026thinsp;\u0026lt;\u0026thinsp;\u0026theta;\u0026thinsp;\u0026lt;\u0026thinsp;1), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003eis psychological outcome (academic engagement), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of explanatory variables including the openness, conscientiousness, extraversion, agreeableness, and neuroticism and resilience, and other covariates. Based on this, we estimate the following linear model:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"829\" height=\"97\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProfile of socio-demographics\u003c/h2\u003e \u003cp\u003eThe descriptive results as displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the profile of survey participants which reveals a balanced gender distribution (52.08% male; 47.92% female), with respondents averaging 22.19 years (SD\u0026thinsp;=\u0026thinsp;2.14), reflecting a typical undergraduate cohort. Notably, 40.63% were first-year students, potentially indicating greater attrition risks among novices (Cred\u0026eacute; et al., 2017), while 54.86% pursued junior high school education programs, suggesting domain-specific demands may influence grit development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the demographic profile.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgramme of Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Childhood Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics of the latent variables\u003c/h2\u003e \u003cp\u003eThe summary statistics indicate a generally high level of psychological capital among students, with mean scores for conscientiousness (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.39), openness (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.07), agreeableness (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.08), and resilience (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.21) all exceeding the scale midpoint. Academic engagement was also notably high (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.80), while neuroticism scores were relatively lower (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.97), suggesting that most students exhibit strong emotional stability. These findings provide a robust empirical foundation for employing quantile regression to examine how these constructs differentially influence academic engagement across varying levels. Collectively, the results underscore the multidimensional and context-dependent role of personality traits and resilience in shaping academic behaviour further validating the need for a quantile regression approach to guide policy formulation, pedagogical strategies, and psychosocial interventions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for the latent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCronbach Alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of personality traits and resilience\u003c/h2\u003e \u003cp\u003eThe histograms reveal distinct distributions for students' personality traits and resilience, highlighting the heterogeneity of psychological capital among learners. Notably, traits such as openness, conscientiousness, agreeableness, and resilience exhibit strong right-skewness, indicating that a substantial proportion of students report elevated levels in these dimensions. In contrast, neuroticism follows a more uniform distribution, reflecting varying degrees of emotional stability across the sample. These distributional variations justify the use of quantile regression, which enables a more nuanced analysis than traditional mean-focused approaches like ordinary least squares (OLS) or structural equation modeling (SEM). While these conventional models assume effect homogeneity across the academic engagement spectrum, quantile regression captures how the influence of personality and resilience varies at different engagement levels such as low, median, and high. For example, resilience may have a stronger predictive effect on highly engaged students than on their less engaged peers, a dynamic that OLS or SEM would overlook. Similarly, traits like conscientiousness might play a more critical role at the upper quantiles of engagement, aligning with the heightened self-regulation and motivation required for advanced academic performance. Thus, quantile regression is not only methodologically advantageous in this context but also enriches theoretical understanding by revealing how personality and resilience differentially interact with academic engagement across its full spectrum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBivariate correlations of key constructs\u003c/h2\u003e \u003cp\u003eThe correlation analysis reveals significant relationships between personality traits, resilience, and academic engagement. Among the Big Five traits, both agreeableness (r\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and conscientiousness (r\u0026thinsp;=\u0026thinsp;0.250, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated strong positive associations with engagement, indicating that students who are more cooperative and disciplined tend to exhibit greater academic investment. Resilience showed an equally robust correlation (r\u0026thinsp;=\u0026thinsp;0.334, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing its critical role in maintaining academic focus during challenges. More moderate yet significant relationships emerged for openness (r\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and extraversion (r\u0026thinsp;=\u0026thinsp;0.181, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that curiosity and sociability provide modest support for engagement. In contrast, neuroticism displayed a negligible and non-significant association (r\u0026thinsp;=\u0026thinsp;0.057), implying that emotional instability may not substantially affect academic involvement in this population. Notably, the positive intercorrelations among resilience, conscientiousness, and agreeableness highlight the synergistic effects of these psychological resources on student engagement. When examined through a quantile regression framework, these findings suggest that the strength of these relationships may vary across different levels of academic engagement. This underscores the need for differentiated intervention strategies that account for students' distinct personality profiles and resilience capacities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRegression results\u003c/h2\u003e \u003cp\u003eThis section presents the regression results of the study, which examines the differential effects of personality traits and resilience on students\u0026rsquo; academic engagement using both Ordinary Least Squares (OLS) and quantile regression (QR) approaches. The analysis begins with an interpretation of the OLS results as a baseline model, followed by a detailed interpretation of the QR estimates, which provide a more nuanced understanding of how these psychological constructs influence engagement at different points of the engagement distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOLS regression estimates\u003c/h2\u003e \u003cp\u003eThe OLS regression results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Column 1) offer preliminary insights into the relationship between personality traits, resilience, and academic engagement. With an adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; of 0.467, the model accounts for a substantial proportion of the variance in academic engagement. Notably, Openness demonstrated a significant negative effect (β = \u0026minus;0.143, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a finding that contrasts with prior research linking Openness to intellectual curiosity (Poort et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, Agreeableness emerged as the strongest positive predictor (β\u0026thinsp;=\u0026thinsp;0.271, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), aligning with Self-Determination Theory\u0026rsquo;s (SDT) emphasis on relatedness, as agreeable students may cultivate stronger social bonds that foster engagement (Mahama et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, Conscientiousness, Extraversion, and Neuroticism were statistically insignificant a surprising result, given existing literature that highlights Conscientiousness as a key driver of goal-directed behavior (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResilience proved to be a significant positive predictor of academic engagement (β\u0026thinsp;=\u0026thinsp;0.316, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting SDT\u0026rsquo;s proposition that resilience fulfills competence needs, thereby empowering students to persevere despite challenges (Liu \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among the control variables, female students exhibited higher engagement levels (β\u0026thinsp;=\u0026thinsp;0.275, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with studies documenting gender-based differences in academic behavior (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, Level 300 students were more engaged than their peers (β\u0026thinsp;=\u0026thinsp;0.341, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), possibly reflecting greater academic maturity. Conversely, both primary and junior high school education backgrounds negatively impacted engagement, suggesting that socioeconomic factors may undermine student motivation (Yang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While OLS regression provides a useful baseline, its assumption of uniform effects across all engagement levels is a key limitation. To address this constraint, quantile regression offers a more nuanced analysis by uncovering differential effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eQuantile regression estimates\u003c/h2\u003e \u003cp\u003eThe QR analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Columns 2\u0026ndash;10) reveals heterogeneous effects across engagement quantiles, supporting H1 (differential personality effects) and H2 (stronger resilience effects at lower engagement levels). For openness, significant negative effects emerge at \u003cem\u003eq25\u003c/em\u003e (β = \u0026minus;0.305, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eq30\u003c/em\u003e (β = \u0026minus;0.335, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that highly open yet disengaged students may struggle with focus. This aligns with prior findings that excessive openness can lead to distractibility (Yusoff et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Agreeableness, in contrast, exhibits consistently positive effects across quantiles, peaking at lower engagement levels (\u003cem\u003eq15\u003c/em\u003e: β\u0026thinsp;=\u0026thinsp;0.557, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This supports Self-Determination Theory\u0026rsquo;s (SDT) relatedness hypothesis, as agreeable students likely benefit from social support\u0026mdash;a critical factor for engagement, particularly among struggling learners (Ryan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). otably, conscientiousness and extraversion were statistically insignificant in most quantiles, diverging from OLS expectations. This implies their influence may be context-dependent or mediated by other factors (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, neuroticism demonstrates negative effects at \u003cem\u003eq60\u003c/em\u003e (β = \u0026minus;0.020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that emotional instability primarily hampers engagement among moderately engaged students, a pattern consistent with studies linking neuroticism to academic anxiety (Tan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResilience emerged as a key predictor, exerting its strongest effects at higher engagement quantiles (\u003cem\u003eq60\u0026ndash;q80\u003c/em\u003e), with significant coefficients at \u003cem\u003eq70\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.466, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eq80\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.569, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Contrary to H2, resilience mattered more for highly engaged students, a finding that may reflect their ability to sustain motivation in demanding academic environments (Biggs et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Regarding demographic characteristics, female students demonstrated higher engagement at mid-quantiles (\u003cem\u003eq40\u0026ndash;q60\u003c/em\u003e), consistent with prior research on gender differences in persistence (Laranjeira \u0026amp; Teixeira, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, Level 300 students exhibited greater engagement at mid-quantiles (\u003cem\u003eq25\u0026ndash;q50\u003c/em\u003e), suggesting that advanced students develop more effective coping strategies over time. Overall, this study enhances our understanding of how personality and resilience differentially influence engagement. While OLS regression provided a baseline, quantile regression (QR) uncovered nuanced, distributional effects, underscoring the importance of tailored interventions based on engagement levels. Future research should investigate potential cultural and institutional moderators of these relationships to further refine academic support strategies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate correlations among the sample variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Academic engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. conscientious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. extraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. agreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.380\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. neuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.130\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.115\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.220\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.214\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.266\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.150\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. programme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.251\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.165\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantile regression of the effects of students\u0026rsquo; personality traits and resilience on academic engagement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement (Dep. Var.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eQuantile Regression (QR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eq15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eq20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eq30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eq40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eq50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eq60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eq70\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eq80\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.143\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.305\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.335\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.234\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.214\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.095)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.271\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.557\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.452\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.355\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.374\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.283\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.020\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.086)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.266\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.329\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.393\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.466\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.569\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.390\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.310\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.406\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 200 students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.124)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 300 students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.341\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.310\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.370\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.383\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.490\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--0.634\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.826\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.733\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.699\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.418\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.457\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.471\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.615\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.567\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.418\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.457\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.284\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.764\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(1.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: Bootstrap at 500 replications. Standard errors in parentheses \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (1%), \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (5%), and \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 (10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigated the differential effects of personality traits and resilience on students' academic engagement using both Ordinary Least Squares (OLS) and quantile regression (QR) approaches. Grounded in Self-Determination Theory (SDT), this research explored how psychological capital resources, particularly personality traits and resilience influence engagement across varying levels of the engagement distribution. Below, we discuss the key findings, their theoretical implications, and their alignment (or divergence) with prior literature. The OLS regression provided a baseline understanding, indicating that agreeableness and resilience were the strongest positive predictors of academic engagement, whereas openness exhibited a significant negative effect. Notably, conscientiousness, extraversion, and neuroticism were statistically insignificant in the OLS model, a finding that contradicts some prior research (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rocha et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the QR analysis revealed more nuanced effects across engagement quantiles. These results supported H1 (differential personality effects) and partially supported H2, as the impact of resilience varied significantly across different engagement levels.\u003c/p\u003e \u003cp\u003eThe analysis revealed nuanced patterns across personality traits and engagement levels. Openness exhibited significant negative effects at lower engagement quantiles (\u003cem\u003eq25–q30\u003c/em\u003e), suggesting that highly open yet disengaged students may struggle with focus. This aligns with Yusoff et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that excessive openness can lead to distractibility, but contrasts with Poort et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)'s association of openness with intellectual curiosity. This divergence underscores the context-dependent nature of openness while potentially beneficial for curiosity, it may hinder engagement when not effectively channeled. Agreeableness demonstrated consistently positive effects across quantiles, with the strongest influence at lower engagement levels (\u003cem\u003eq15\u003c/em\u003e). This supports SDT's relatedness hypothesis, as agreeable students likely benefit from stronger social connections that foster engagement (Mahama et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, conscientiousness and extraversion were statistically insignificant in most quantiles, diverging from studies linking conscientiousness to goal-directed behavior (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests their effects may be context-dependent or mediated by institutional factors. Neuroticism showed negative effects at mid-level engagement (\u003cem\u003eq60\u003c/em\u003e), corroborating Tan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)'s findings linking neuroticism to academic anxiety. This implies that emotional instability primarily hampers moderately engaged students, who may be particularly susceptible to stress-related disengagement.\u003c/p\u003e \u003cp\u003eContrary to H2, resilience had its strongest effects at higher engagement quantiles (\u003cem\u003eq70–q80\u003c/em\u003e). This suggests resilient students excel at sustaining motivation in demanding academic environments (Biggs et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), challenging Liu \u0026amp; Huang's (2021) view that resilience primarily buffers disengaged students. Instead, our findings indicate resilience functions as an enhancement factor for already-engaged learners, amplifying their persistence. Our findings align with the SDT framework of autonomy, competence, and relatedness driving student engagement (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results validated the relatedness through agreeableness, showing that social bonds matter most at lower engagement levels (Ryan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For competence, resilience proved critical for highly engaged students, supporting SDT's link between efficacy and sustained effort (SDT) (Liu \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Openness, linked to intellectual curiosity, demonstrated the importance of autonomy but showed negative effects among disengaged students, indicating that autonomy requires structure (Weinstein et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While prior research found conscientiousness to be crucial (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our QR results showed minimal effects, possibly due to cultural differences. The stronger impact of resilience among engaged students contrasts with that of studies focusing on struggling learners (Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study enhances the understanding of personality and resilience in academic engagement, with QR revealing nuanced effects that support context-specific interventions within SDT's framework of SDT.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Implications\u003c/h2\u003e \u003cp\u003eThis study makes significant theoretical and methodological contributions to understanding how personality traits and academic resilience differentially influence students' academic engagement, advancing the application of SDT in educational psychology. By employing quantile regression (QR) alongside traditional Ordinary Least Squares (OLS) regression, the study reveals nuanced patterns that challenge and refine existing theoretical assumptions, offering a more granular perspective on the interplay between psychological capital resources and engagement. The findings reinforce and expand SDT’s core tenets by demonstrating that autonomy, competence, and relatedness operate differently across engagement levels. While OLS regression provided a generalized view—highlighting agreeableness and resilience as key predictors the QR analysis uncovered heterogeneous effects that align with SDT’s dynamic framework (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings on openness align with SDT, which posits that autonomy-supportive environments enhance intrinsic motivation (Weinstein et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the QR results reveal that openness negatively impacts engagement at lower quantiles (q25–q30), suggesting that excessive intellectual curiosity without structured guidance may lead to distractibility (Yusoff et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This challenges the assumption that openness universally fosters engagement, emphasizing instead that autonomy must be balanced with academic structure to prevent disengagement. Contrary to prior research (Liu \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this study found that resilience had its strongest effects at higher engagement levels (q70–q80), indicating that it functions more as an enhancement factor than merely a buffer against disengagement. This refines SDT’s competence dimension by showing that resilience sustains engagement primarily among already motivated students, reinforcing their persistence in demanding academic environments (Biggs et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, this study validates the positive effects of agreeableness across quantiles, particularly at lower engagement levels (q15), supporting SDT’s relatedness hypothesis (Mahama et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that social connectedness is most critical for disengaged students, as peer and instructor support may help reintegrate them into academic life. Moreover, the study resolves inconsistencies in prior research by demonstrating that personality traits exhibit context-dependent effects. For instance, conscientiousness and extraversion often linked to engagement (Polemis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were statistically insignificant in most quantiles, suggesting that their influence may be mediated by institutional or cultural factors. This finding calls for further cross-cultural SDT research to explore boundary conditions. Neuroticism’s negative effects at mid-level engagement (q60) align with findings on academic anxiety (Tan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), indicating that emotional instability most disrupts moderately engaged students, who may lack the coping mechanisms of either highly engaged or disengaged peers. Collectively, these findings necessitate a more conditional application of SDT, where personality and resilience effects are interpreted relative to engagement baselines rather than as universal predictors.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eThis study offers critical insights into the differential effects of personality traits and resilience on academic engagement, presenting actionable strategies for policymakers, educational administrators, NGOs, and students in Ghana and similar contexts. By employing quantile regression (QR), the research advances beyond conventional OLS estimates, uncovering nuanced patterns that inform targeted interventions. The differential impact of personality traits across engagement levels necessitates tailored pedagogical approaches. Given that agreeableness significantly enhances engagement, particularly among disengaged students, policymakers should prioritize social-emotional learning (SEL) programs that foster stronger peer and teacher-student relationships. Meanwhile, resilience most benefits highly engaged students; thus, institutions should integrate advanced resilience training into honors programs and high-achieving cohorts to sustain motivation. Conversely, the negative effect of openness among disengaged students suggests that curricula should balance exploratory learning with structured tasks to mitigate distractibility.\u003c/p\u003e \u003cp\u003eEducational administrators should also recognize that conscientiousness and extraversion, often assumed to be universally beneficial had minimal effects in this context. This suggests that institutional culture may mediate their impact, necessitating localized assessments before implementing broad interventions. Furthermore, given that neuroticism undermines mid-level engagement, mental health support systems such as counseling services and stress-management workshops should target moderately engaged students who are at risk of anxiety-induced disengagement. NGOs working in education can leverage these findings to design context-specific interventions. For instance, resilience-building programs should differentiate between struggling and high-performing students, providing foundational coping skills to the former and advanced persistence strategies to the latter. Additionally, since agreeableness enhances engagement through social connectedness, NGOs should facilitate peer-mentoring networks and community-building activities in colleges of education.\u003c/p\u003e \u003cp\u003eMoreover, students can apply these insights through deliberate self-reflection: those scoring high in openness should implement structured study plans to counter distractibility, while disengaged students may benefit significantly from relationship-building activities to boost motivation. Highly engaged students, conversely, should proactively develop resilience through targeted goal-setting and stress-regulation techniques to sustain performance under academic pressure. This study decisively challenges universal intervention models, empirically demonstrating that psychological resources exert differential effects across engagement levels. By adopting quantile-sensitive approaches, institutions can optimize resource allocation directing social-support programs toward disengaged students while channeling resilience-building initiatives to high achievers. These findings strongly advocate for personality-informed pedagogy, wherein educators customize engagement strategies according to students' distinct psychological profiles, thereby promoting sustainable academic success in Ghana and comparable educational contexts. Ultimately, the study establishes an evidence-based framework for enhancing academic engagement through precision interventions. This model not only aligns with Self-Determination Theory's fundamental principles but also addresses the heterogeneous needs of diverse student populations with unprecedented specificity.\u003c/p\u003e "},{"header":"Conclusion remarks and future research","content":"\u003cp\u003eThis study makes significant methodological contributions to educational psychology by employing quantile regression (QR) to reveal differential effects of personality traits and resilience across academic engagement levels. Unlike traditional OLS regression that assumes uniform effects, QR provides nuanced insights into how psychological capital resources operate across the engagement spectrum. Our findings refine SDT by demonstrating that core needs: autonomy (manifested through openness), competence (via resilience), and relatedness (through agreeableness) function differentially depending on baseline engagement levels, thereby challenging universal assumptions in existing literature. Methodologically, this research advances the field by demonstrating QR's superior capacity to detect heterogeneous effects, particularly revealing: (1) resilience's heightened impact among highly engaged students, and (2) openness's counterintuitive negative association among disengaged learners. These insights carry important practical implications, advocating for precisely tailored interventions, including targeted support programs for disengaged students and advanced resilience training for high achievers. Despite this, there are some limitations that warrant consideration. First, reliance on self-reported measures of engagement and resilience may introduce response bias. Second, the cross-sectional design precludes causal inferences, necessitating future longitudinal studies to examine temporal dynamics. Third, the Ghanaian college of education context may limit generalizability, as cultural and institutional factors could mediate personality traits, resilience and engagement relationships. Finally, while QR captures distributional heterogeneity, it cannot account for potential unobserved confounders (e.g., teaching quality, institutional policies). Future research should address these limitations while further exploring QR's applicability in educational settings. Such efforts will optimize precision-based interventions for diverse student populations and advance our understanding of psychological factors in academic achievement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHansen Akoto-Baako\u003c/strong\u003e: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with ethical standards and all applicable institutional guidelines and regulations. Informed consent was obtained from all participants. This study received approval from the “Institutional Review Board (IRB)” of the “University of Cape Coast”. All procedures followed in the study complied with the principles outlined in the “Declaration of Helsinki”.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are, however, available from the authors upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any fund for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmoadu, M., Hagan Jr, J. 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The relationship between school climate and students\u0026apos; non-cognitive skills: a systematic literature review. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Academic engagement, Personality traits, Resilience, Quantile regression, Self-Determination Theory, Ghana, Higher education students","lastPublishedDoi":"10.21203/rs.3.rs-6787024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study grounded in the Self-Determination Theory (SDT), explores how personality traits and resilience differentially affect academic engagement. Using a cross-sectional survey data from 288 Ghanaian higher education students from the Jasikan College of Education students. The study employed quantile regression (QR) for the data analysis beyond the ordinary least square (OLS) to uncover the nuanced differential effects. The OLS results showed that agreeableness and resilience have a uniform positive significant association with students\u0026rsquo; academic engagement, whereas openness was negatively associated. The QR analysis reveals heterogeneity in these relationships, emphasising that while openness negatively affects academic engagement among disengaged students (q25\u0026ndash;q30), agreeableness provides the greatest benefits to learners with low academic engagement (q15). Surprisingly, resilience\u0026rsquo;s strongest positive impact occurs at higher academic engagement levels (q70\u0026ndash;q80). This study contributes in two key ways. Firstly, the QR results are more effective than those of OLS at detecting distributional effects, thereby advancing methodological approaches in educational psychology research. Secondly, the findings have practical implications, highlighting the need for interventions tailored to students' levels of academic engagement, as psychological resources vary accordingly.\u003c/p\u003e","manuscriptTitle":"Differential effects of personality traits and resilience on Ghanaian students' academic engagement: A quantile regression approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 15:39:17","doi":"10.21203/rs.3.rs-6787024/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85daabad-db3c-408b-ba7d-3af7a96193d0","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T11:46:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-20 15:39:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6787024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787024","identity":"rs-6787024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}