Factors associated with depressive disorders in college students using quantile regression analysis

Abstract

Background

Depressive disorders among college students are a significant public health issue. Most existing studies on factors related to depressive disorders use traditional linear regression models, which have limited ability to reveal deeper insights. This study aims to explore the complexities of depressive disorders further by applying quantile regression.

Methods

The study was conducted at six universities in China from November 26 to December 6, 2022, using a cross-sectional questionnaire survey with a cluster sampling design. The questionnaire includes the Patient Health Questionnaire-9, the Interpersonal Sensitivity subscale of the Symptom Checklist-90, the Positive Psychological Capital Questionnaire, and the Perceived Social Support Scale. Data analysis was performed using quantile regression with SPSS 26.0.

Results

A total of 3,156 college students participated, and 2,580 valid questionnaires were collected. The prevalence of depressive disorders was 22.4%. Quantile regression indicated that depressive disorders were linked to social support (β _25th = -0.044, β _50th = -0.111, β _75th = -0.244, p < 0.001), interpersonal sensitivity (β_25th = 0.073, β_50th = 0.127, β_75th = 0.232, p < 0.001), psychological capital (β _25th= -0.077, β _50th = -0.154, β _75th = -0.252, p < 0.001), and regular contact with family (β _25th = -0.057, p < 0.05). Social support and psychological capital showed negative associations with depressive disorders, while interpersonal sensitivity had a positive association. The strength of these correlations varied across quartiles, with social support, psychological capital, and interpersonal sensitivity being more strongly associated with depressive disorders at higher quartile points.

Conclusion

This study identifies the factors influencing college students with varying levels of depressive disorders. Students with severe depression tend to exhibit higher levels of interpersonal sensitivity, psychological capital, and social support. Percentile analysis helps to explore mental health issues more thoroughly, providing valuable details for targeted intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-025-07334-w.

Introduction

Depression is a common mental disorder affecting 350 million people globally [1]. It is characterized by sadness, a loss of interest or pleasure, feelings of guilt or low self-esteem, disturbances in sleep or appetite, fatigue, and difficulty concentrating [2]. Depression ranks as the fourth most prevalent disease worldwide and is rapidly increasing [3]. Nearly 800,000 suicides each year are linked to depression, and it is projected to become the most burdensome disease globally by 2030 [2]. Furthermore, the intense psychological stress caused by the novel coronavirus has worsened depression worldwide [4].

Depression among college students is a critical public health issue due to its high prevalence, serious consequences, and urgent need for targeted interventions. From a public health perspective, young people’s mental health is a major global concern [5]. College years are a peak time for the onset of depression [6], which is affected by many factors. Students experience dual transitional stressors as they move from adolescence to adulthood and into college [7]. This period of multiple transitions often coincides with the start of depression [8]. A meta-analysis shows that about 33.6% of college students experience depression [9], which is much higher than the 20% found in the general population [10]. Additionally, depression is a strong predictor of poorer academic performance [11] and higher college dropout rates [12]. It is also associated with increased anxiety [13], physical health issues [14], higher smoking rates [15], greater alcohol use, and an increased risk of suicide [16]. Given its high prevalence and severe impacts, it is vital to identify the factors that contribute to depression among college students and to develop effective interventions.

The symptoms of depression among college students display distinct social and demographic characteristics involving many factors [17]. Several aspects of this impact have been identified in previous studies [8]. For example, female students have a higher prevalence of depression compared to male students [18], and younger students have a significantly greater likelihood of experiencing depression [19]. Furthermore, being an only child and coming from a rural area are also factors affecting depression in college students [20]. Certain behaviors can also influence depression. Healthy eating promotes emotional regulation and reduces distress associated with stressful environments and depression [21]. Participants who engage in moderate to high levels of physical activity tend to have lower depression levels than those with low activity levels [22].

Additionally, individual psychological factors have been linked to depression among college students [23–25], such as interpersonal sensitivity and psychological capital [26, 27]. Interpersonal sensitivity involves feelings of personal inadequacy and inferiority, particularly when comparing oneself to others, accompanied by self-deprecation, uneasiness, and discomfort during social interactions. It also includes intense self-awareness and negative expectations about interpersonal communication [28]. This sensitivity is associated with depression [29]. People with high interpersonal sensitivity tend to experience increased self-consciousness and worry about criticism and rejection from others [28]. According to self-perception theory, high self-consciousness in adolescents may raise the risk of depression [30]. Psychological capital (PsyCap) refers to a positive psychological state that individuals display during growth and development, comprising four components: self-efficacy, optimism, hope, and resilience [31]. Previous research shows that PsyCap has a substantial direct and indirect effect on depression among college freshmen [26] and graduate students [32]. Some studies also suggest that social support helps improve mental health and reduce depression [33], with a lack of support being a predictor of depression in college students [34]. Although these findings are valuable, most existing studies rely on regression models, such as logistic or linear regression, which primarily examine average effects. This approach often overlooks how factors influence individuals with different depression levels [35]. Evidence indicates that people at various depression levels may have distinct psychological structures [36], and the same factor might affect them differently [37]. We believe that linear regression based on mean modeling may not fully capture this substantial variability, and more advanced statistical methods are needed to gain a better understanding of the factors influencing depressive disorders.

To address the limitations of ordinary least squares (OLS) in regression analysis, Koenker and Bassett introduced Quantile Regression in 1978 [38]. Quantile regression models the conditional quantile of the dependent variable based on the independent variable, enabling analysis at different quantile levels [39]. This approach offers several benefits over traditional regression models. For example, traditional regression mainly focuses on the conditional mean of the dependent variable and does not provide insights into other parts of the data distribution [40]. Risk factors for depression at different depression levels may be masked in the overall mean analysis [41]. Several studies indicate that groups with more severe depression are more intensely affected by certain risk factors [41, 42]. Therefore, using quantile regression to analyze the high quartile depression group can accurately identify its specific predictors and help guide targeted interventions. Moreover, linear regression is more susceptible to outliers, as a few outliers can significantly influence the results [43]. In contrast, quantile regression is more robust because it minimizes absolute errors rather than squared errors, thus reducing the impact of outliers. Over recent decades, quantile regression has experienced rapid growth and diverse applications, including fields like economics [44], medical science [45], environmental science [46], power engineering [47], and others. Although it has many clear advantages and has been widely adopted across various disciplines, quantile regression remains underutilized in the analysis of depressive disorders. Motivated by these considerations, we employed quantile regression to identify the factors influencing depressive disorders among college students and to elucidate the roles of these factors in developing targeted intervention strategies.

Methods

Participants and procedure

The study involved college students from six universities across three provinces. Two universities are located in northern China, while four are in central China. To facilitate questionnaire collection, we contacted the mental health education and counseling centers at these universities, which then assigned student counselors from each college to randomly select students from each grade using a cluster sampling method. Each university randomly chose at least 500 students from 1 to 2 colleges. The designated person effectively coordinated and supervised data collection within the specified timeframe. From November 26 to December 6, 2022, all participants accessed and completed the questionnaire by scanning a provided QR code. A total of 3,156 participants completed the questionnaire.

To ensure data quality and reliability, the following criteria are used for inclusion and exclusion. Inclusion criteria included: (1) being an undergraduate college student; (2) being 18 years old or older; and (3) reading through the guidelines and voluntarily participating in the survey. Exclusion criteria included: (1) questionnaires with logical errors (e.g., age = 999); (2) incorrect responses to test questions (which have no real meaning and require selecting a prescribed option); (3) questionnaires taking too long to complete (response time exceeding three times the standard deviation of the total response time, i.e., more than 921 s) or with an average answer time per question of less than two seconds [48]. If respondents met any of these criteria, their entire set of answers was omitted from the dataset. In the end, 2,580 valid questionnaires were gathered, resulting in an overall validity rate of 81.75%. See Fig. 1.

Fig. 1.

Flowchart of the participants

The ethics committee of Xuzhou Medical University approved this study (approval number: XMUs-22/0406), and all participants provided informed consent. All procedures were conducted in accordance with relevant ethical guidelines and regulations.

Measurement tools

The questionnaire was divided into two sections. The first section included demographic variables, mainly focusing on individual and lifestyle factors. Individual factors covered age, gender, hometown, and whether the participant was an only child. Lifestyle factors involved regular breakfast consumption (defined as having breakfast nearly every day), engaging in moderate-intensity physical activity for five or more days a week, or walking for at least 30 min per day for a week (as defined by the International Physical Activity Questionnaire) [49], and maintaining regular contact with family members (four or more times a week). The second section of the survey involved four scales used as assessment tools (see below for details):

Depressive disorders

The Patient Health Questionnaire 9-item depression scale (PHQ-9) was used to measure depression severity. It is currently the most widely used scale for depressive disorders worldwide and has demonstrated good reliability and validity across different countries and research populations [50]. The scale contains nine items, each rated from 0 (“never”) to 3 (“almost every day”). A PHQ-9 total score of 8 or higher serves as a detection criterion for depressive disorders, as this cutoff score provided good sensitivity and specificity for diagnosis [51]. The OLS regression and quantile regression analyses in this study included college students with PHQ-9 scores of 8 or higher. The PHQ-9 has been shown to possess acceptable psychometric properties for screening depression among Chinese undergraduates [52]. In this study, the Cronbach’s α for PHQ-9 was 0.896.

Interpersonal sensitivity

The Interpersonal Sensitivity subscale of the Symptom Checklist 90 (SCL-90) was used to assess the severity of interpersonal sensitivity, which is the most common mental disorder and mental illness screening tool for outpatients [28]. The subscale includes 9 items, each rated on a scale of 0 to 4 as “never”, “very mild”, “moderate”, or “severe.” A higher total score indicates greater interpersonal sensitivity. The scale has demonstrated good reliability among Chinese university students [53]. In this study, the Cronbach’s α for SCL-90 was 0.908.

Psychological capital

Based on the core definition of psychological capital [31], Zhang et al. [54] developed a Positive Psychological Capital Questionnaire (PPQ-26) for Chinese college students. The scale consists of 26 items and employs a 7-point Likert scale. Total scores range from 26 to 182, with higher scores indicating greater PsyCap. In this study, the Cronbach’s α for PPQ-26 was 0.948.

Social support

The Perceived Social Support Scale (PSSS) was used to assess social support, which has been validated and shown to be reliable in Chinese populations [55]. The scale includes three dimensions—family support, friend support, and other support—and consists of 12 items rated on a 7-point Likert scale. Total scores range from 12 to 84, with higher scores indicating a greater perceived level of social support. In this study, the Cronbach’s α for the PSSS was 0.942.

Statistical analysis

The SPSS 26.0 software was used for statistical analysis. First, the socio-demographic characteristics of the sample are presented in terms of numbers and frequencies. Second, to compare with quantile regression, we used OLS regression to display the overall mean model. Third, we examined the regression relationship between the study variables and depressive disorders using quantile regression. Additionally, due to the nature of the dependent variables, we refer to the previous studies [37] and the P25, P50, and P75 conditional quartiles as proxies to describe the results of the quantile regression analysis. Quantile regression can analyze the relationship between independent variables and the dependent variable at different quantiles, providing a comprehensive view of their connection. When the depression level is considered in the P25 quantile, quantile regression can show the influence of factors on depression at a low level of depression. When the depression level is in the P50 quantile, it reveals the impact of factors on depression at a middle level. When depression levels are in the P75 quantile, it shows the influence of factors on depression at higher levels. p < 0.05 is considered statistically significant.

Results

Common method bias test

The Harman single-factor method often employs exploratory factor analysis to detect common method bias [56]. It is generally accepted that the variation explained by a single factor should be less than 40% [57]. In our study, the variance explained by the first common factor across all sample data is 26.49%, indicating that common method bias is not a significant issue.

Sociodemographic characteristics

Out of 2,580 college students who submitted valid questionnaires, 579 (22.4%) individuals with a PHQ-9 score of 8 or higher were identified as having depressive disorders [51]. Among these students with depressive disorder, 90.7% were between the ages of 18 and 20 (average age = 19). Additionally, 399 (69.0%) participants were female, 250 (43.2%) were only children, and 377 were urban residents. Notably, 233 (40.2%) participants regularly had breakfast, only 75 (13.0%) engaged in regular sports, and 183 (31.9%) maintained regular contact with their families. Details are shown in Table 1.

Table 1.

General demographic characteristics statistics (N = 2580)

Variables	Depressive disorders (N = 579, scores ≥ 8)		No depressive disorders (N = 2001, scores < 8)
	n	%	n	%
Age
18	221	36.4	766	29.7
19	156	26.9	529	26.4
20	126	21.8	380	19.0
≥ 21	76	9.3	326	16.3
Gender
Male	180	31.1	601	30.0
Female	399	68.9	1400	70.0
Only-child
Yes	250	43.2	918	45.9
NO	329	56.8	1083	54.1
Hometown
Urban	377	65.1	1212	60.6
Rural	202	34.9	789	39.4
Regular breakfast
Yes (a)	233	40.2	1039	51.9
No	246	59.8	962	48.1
Regular sport
Yes (b)	75	13.0	383	19.1
No	504	87.0	1618	80.9
Regular contact with family
Yes (c)	183	31.6	911	45.5
No	396	68.4	1090	54.5

(a), having breakfast almost every day

(b), engaging in moderate-intensity physical activity for ≥ 5 days per week and/or walking for ≥ 30 minutes per day for a week

(c), ≥ 4 times a week

Linear regression

In the OLS regression analyses, college students from rural areas were significantly associated with higher depressive disorders (β = −0.160, p = 0.017). Still, it is worth noting that this factor is not significant in the quantile regression analysis. Those who had regular contact with family were significantly associated with lower depressive disorders (β = −0.134, p = 0.047). Additionally, interpersonal sensitivity was significantly positively associated with depressive disorders (β = 0.168, p < 0.001); higher levels of interpersonal sensitivity correspond to more severe depressive disorders. Social support (β = −0.242, p < 0.001) and PsyCap (β = −0.273, p < 0.001) were significantly negatively associated with depressive disorders, meaning more social support and better PsyCap led to less severe depressive disorders. See Table 2.

Table 2.

The OLS regression of college students with depressive disorders (N = 579)

Variables	OLS regression
	β	95%CI	p
Age(years)	0.002	(−0.046, 0.050)	0.939
Gender
Male	−0.121	(−0.258, 0.016)	0.084
Female	Ref.
Only-child
Yes	0.048	(−0.078, 0.174)	0.452
No	Ref.
Hometown
Urban	−0.160	(−0.291, −0.029)	0.017
Rural	Ref.
Regular breakfast
Yes	0.061	(−0.059, 0.182)	0.320
No	Ref.
Regular sport
Yes	0.0011	(−0.175, 0.197)	0.908
No	Ref.
Regular contact with family
Yes	−0.134	(−0.267, −0.002)	0.047
No	Ref.
Social support	−0.242	(−0.235, −0.114)	< 0.001
Interpersonal sensitivity	0.168	(0.088, 0.248)	< 0.001
Psychological capital	−0.273	(−0.368, −0.185)	< 0.001

β, coefficient of parameter; CI, confidence interval

Quantile regressions

Table 3 shows the correlation effects for the P25, P50, and P75 conditional quantiles of depressive disorder distribution. Although OLS regression analyses indicated a significant impact of urban-rural differences on depressive disorders, this difference was not significant at the 25th, 50th, and 75th quantiles in the quantile regressions. Additionally, while the effect of regular contact with family was substantial in the OLS regression, the quantile regression analysis revealed that this effect was only significant at the 25th quantile (β _25th = −0.057). Social support, interpersonal sensitivity, and PsyCap were significantly linked to depressive disorders across all quartiles, with their association strengthening at higher quartiles. The regression coefficients for social support, interpersonal sensitivity, and PsyCap changed from − 0.044, 0.073, and − 0.077 at the 25th quantile to −0.244, 0.232, and − 0.252 at the 75th quantile. This indicates that social support, PsyCap, and interpersonal sensitivity have a greater influence on college students with severe depressive disorders. Additionally, the effect of social support increased most notably as depression scores rose. At the 25th quantile, the regression coefficient for social support was smaller than for interpersonal sensitivity, but at the 75th quantile, it was higher. The impact of social support varies nearly sixfold between the 25th and 75th quantiles. Plotting the slopes of the quantile regression lines provides a clearer visual of how these factors affect different levels of depressive disorders (Fig. 2).

Table 3.

Quantile regressions (dependent variable: depressive disorders, N = 579)

Variables	P25		P50		P75
	β	95%CI	β	95%CI	β	95%CI
Age(years)	0.001	(−0.018, 0.019)	−0.007	(−0.039, 0.025)	−0.002	(−0.069, 0.065)
Gender
Male	−0.028	(−0.024, 0.081)	−0.046	(−0.138, 0.045)	−0.132	(−0.322, 0.059)
Female	Ref.		Ref.		Ref.
Only-child
Yes	−0.024	(−0.024, 0.072)	0.034	(−0.050, 0.119)	0.028	(−0.146, 0.203)
No	Ref.		Ref.		Ref.
Hometown
Urban	−0.012	(−0.062, 0.038)	−0.068	(−0.156, 0.020)	−0.159	(−0.341, 0.023)
Rural	Ref.		Ref.		Ref.
Regular breakfast
Yes	0.032	(−0.014, 0.078)	0.074	(−0.007, 0.154)	0.069	(−0.098, 0.236)
No	Ref.		Ref.		Ref.
Regular sport
Yes	0.001	(−0.071, 0.071)	0.039	(−0.085, 0.163)	0.098	(−0.160, 0.355)
No	Ref.		Ref.		Ref.
Regular contact with family
Yes	−0.057	(−0.108, −0.007)	−0.057	(−0.145, 0.031)	−0.127	(−0.310, 0.057)
No	Ref.		Ref.		Ref.
Social support	−0.044	(−0.067, −0.021)	−0.111	(−0.151, −0.071)	−0.244	(−0.328, −0.161)
Interpersonal sensitivity	0.073	(0.043, 0.104)	0.127	(0.073, 0.180)	0.232	(0.120, 0.343)
Psychological capital	−0.077	(−0.112, −0.042)	−0.154	(−0.215, −0.093)	−0.252	(−0.379, −0.126)

β, coefficient of parameter. CI, confidence interval. Ref., reference category

Fig. 2.

Quantile regression plots of the effect of influencing factors on the quantile of depressive disorders distribution among college students Blue Block, Confidence intervals (95% CI) of the parameter estimates Black dotted line, Parameter estimates at the different regression quantiles Red solid line, Parameter estimates for the ordinary linear regression with the same predictors; Red dotted line, Confidence interval (95% CI) bounds for the ordinary linear regression with the same predictors

Discussion

The detection rate of depressive disorders among college students in this study was 22.4%, which is higher than in previous research [58]. On one hand, this could be due to the higher proportion of females in this study, who are often considered more vulnerable to depressive disorders [59]. On the other hand, it might be associated with the ongoing effects of the novel coronavirus and the worsening mental health of college students, increasing the likelihood of depressive disorders [60].

The quantile regression analysis uncovered detailed characteristics. This study employed quantile regression to identify factors affecting depressive disorders in college students, aiming to offer more adaptable interventions. Unlike OLS regression, which analyzes the mean, quantile regression can provide additional insights. For instance, while OLS detected a significant impact of regular contact with family on depressive disorders, it was only at the average level. In contrast, quantile regression identified a significant effect at the 25th quantile, suggesting that students with milder depression levels benefit notably from regular family contact. We also performed quantile regression on students without depressive disorders, and these results confirmed that regular family contact significantly helps reduce depression. Family serves as a crucial source of emotional support [61], as students share their experiences and challenges with their families promptly, and family members offer positive feedback and advice to assist with problems.

Social support and PsyCap can greatly enhance an individual’s interpersonal relationships, helping to reduce depressive disorders among college students. They act as protective factors against depression in this study. Social support is a temporary resource that stabilizes and reduces the impact of stressful events by influencing how people evaluate and respond to them [62]. Under stress, social support helps by improving coping ability, which lowers the risk of depression [62]. Moreover, strong social support can boost self-esteem and self-efficacy, further preventing depression [63]. PsyCap includes self-efficacy, optimism, hope, and resilience, all vital for adapting to change and managing challenges [64]. People with high PsyCap tend to cope more positively and inhibit negative responses, thereby reducing depression indirectly [65]. Importantly, PsyCap and social support are interconnected; they influence each other and together strengthen defenses against depression. Past research has shown that encouragement from others can help build confidence [66]. Emotional support and practical help from social connections also open up more options during tough times, increasing motivation and fostering hope [67]. Conversely, PsyCap is a positive predictor of perceived social support [68]. High PsyCap individuals often perceive and utilize support from their environment [69]. Studies indicate that perceived support can boost PsyCap, leading to improved thoughts and feelings [70], and help individuals internalize external resources, thereby enhancing their overall well-being [71]. In this study, interpersonal sensitivity was strongly linked to depressive disorders. Healthy relationships are vital for college students’ academic and social lives [72]. When individuals struggle with relationships or lose support, they may react with tension and stress. According to self-perception theory, stress can cause individuals to activate their ego schemas with depressive traits, leading to cognitive biases. This process can cause interpersonal sensitivity issues. If it persists, it may lead to a negative view of one’s surroundings and raise the risk of depression [30].

New findings in this study reveal that the effects of social support, PsyCap, and interpersonal sensitivity on depressive disorders become stronger as the severity of depression increases. The impact of these factors at the 75th quantile was several times greater than at the 25th quantile. This aligns with previous research, which found that individuals at the higher end of the depressive disorder spectrum—meaning those with more severe depression—are more affected by these factors [73]. The main effect model, as mentioned in [74], suggests that certain forms of social support are only effective when stress levels are high. College students experiencing intense depressive disorders often face higher stress levels, making social support more impactful for them. The Conservation of Resources (COR) theory also supports this insight. The third principle of COR states that resource gain becomes more noticeable in the face of resource loss. In other words, when circumstances involve significant resource depletion, resource gains become more valuable—they increase in importance [75]. The more severely depressed someone is, the fewer psychological resources they typically have. In areas where resources are scarce, they need to be replenished and increased to help reduce depressive symptoms. Therefore, PsyCap and social support—representing internal and external resources—can be especially beneficial for individuals with severe depression. Additionally, COR theory states that individuals with more resources are less vulnerable to resource loss, while those with fewer resources are at greater risk [75]. When college students experience interpersonal sensitivity, it indicates that their interpersonal resources have been depleted, which can worsen outcomes for those with fewer psychological resources and more severe depression. In conclusion, individuals with severe depressive disorders are more likely to benefit from interventions involving social support, PsyCap, and interpersonal sensitivity. This highlights the importance of prioritizing interventions for more vulnerable groups to maximize the limited resources available.

Overall, our study has significant implications for reducing depressive disorders among college students. The identified factors can guide effective interventions. It is recommended that college students’ mental health education and counseling centers assess and screen students’ mental health annually and implement different intervention strategies based on depression scores. Based on our findings, college students experiencing mild depression should stay connected with their families and actively seek help when facing emotional challenges. Additionally, school counselors should inform students’ families about their emotional well-being and work together through home-school cooperation to reduce depression and prevent it from worsening into major depression. Among college students with different levels of depressive disorders, social support, interpersonal sensitivity, and PsyCap are closely connected to depression. Therefore, these three key factors should be prioritized in students with depressive disorders. Concerning social support, college mental health departments should encourage students to develop strong relationships with classmates and family members to ensure they receive timely assistance when needed. For interpersonal sensitivity, colleges can offer online relationship education videos for students to watch. They should also promote participation in club activities to foster social interaction and enhance communication skills [27]. Concerning PsyCap, colleges can offer PsyCap lectures to increase students’ awareness of PsyCap and motivate them to further develop it. Students should also actively participate in practical activities to learn from their experiences and effectively enhance their PsyCap. For students experiencing high levels of depressive disorders, mental health departments need to implement targeted interventions such as Psychodynamic group therapy [76], cognitive behavioral therapy [77], and positive PsyCap interventions [78] to help reduce their depression. Additionally, schools can develop mental health profiles for students with severe depressive disorders and monitor their conditions regularly to respond with appropriate measures.

Limitations

Several limitations were present in our study. First, although quantile regression captures more potential information, cross-sectional surveys make it difficult to establish a causal relationship between depressive disorders and the risk factors considered among college students. Second, this study only included students from six universities across three provinces, which limits the generalizability of the findings and may mean they do not fully represent the situation of college students nationwide. Third, the study was conducted during a time when the novel coronavirus was widespread, which could have slightly affected the results. Fourth, the sampling method did not account for the gender ratio, resulting in an overrepresentation of girls. This could affect the strength of some predictors. It has been shown that social support seems to have a more protective effect against mental distress in females than in males [79]. Fifth, this study used an anonymous self-report method, which may have issues with social validation. Although the CMB test does not indicate a problem with common method bias, future research could replace it with an external depression measurement scale (multi-center collection, multi-time period collection could help avoid common method bias). Finally, some key factors influencing depressive disorders among college students were not included, such as family history of depressive disorders [80], smoking status [81], sleep [82], academic pressure [83], and economic pressure [84].

Conclusion

This study used quantile regression to explore factors influencing depressive disorders among college students, aiming to support personalized interventions. Findings identified irregular family contact, high interpersonal sensitivity, low social support, and low PsyCap as key factors, guiding targeted treatments. The research shows different relationships between these factors and depression severity—students with more severe depression are more affected by interpersonal sensitivity, PsyCap, and social support, highlighting the need to focus on this group. These results could expand quantile regression’s use in mental health research and offer insights into COR theory across depression levels.

Abbreviations

CMB

Common method bias

COR

Conservation of resources

OLS

Ordinary least squares

PHQ-9

Patient health questionnaire-9

PPQ-26

Positive psychological capital questionnaire

PSSS

Perceived social support scale

PsyCap

Psychological capital

SCL-90

Symptom checklist-90

Authors’ contributions

Haibo Xu: Conceptualization, Methodology, Writing - review and editing, Funding acquisition, Resources and Supervision, Resources. Xin Liu: Conceptualization, Resources, and Supervision. Chaoran Zhang: Methodology, Formal analysis, and investigation, and Writing - original draft preparation. Zhen Wang: Writing - original draft preparation. Chen Zhang: Writing -original draft preparation. Lixin Peng: Formal analysis and investigation, and Writing - original draft preparation.

Funding

This study was funded by the Special Subject Key Project “Jiangsu Higher Education” of Jiangsu Higher Education Association (Project number: 2022JSGJKT008).

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

The ethics committee of Xuzhou Medical University approved this study (approval number: XMUs-22/0406), and all participants provided informed consent. All procedures were conducted in accordance with relevant ethical guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.