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Article

Descriptive Study on State and Trait Anxiety Levels in University Students and Their Potential Influencing Factors

by
Itziar Quevedo-Bayona
1,
María-Camino Escolar-Llamazares
1,*,
María-Ángeles Martínez-Martín
1 and
Francisco Luis Adell Carrasco
2
1
Health Sciences Faculty, Burgos University, 09001 Burgos, Spain
2
Health Sciences Faculty, Universidad Isabel I, 09003 Burgos, Spain
*
Author to whom correspondence should be addressed.
Societies 2025, 15(10), 287; https://doi.org/10.3390/soc15100287
Submission received: 28 July 2025 / Revised: 1 September 2025 / Accepted: 11 October 2025 / Published: 14 October 2025
(This article belongs to the Special Issue Mental Health, Anxiety, and Emerging Technologies: Assessment, Social Implications, and Interventions)
Review Reports Versions Notes

Abstract

While anxiety can be adaptive at moderate levels, it may also develop into a clinical disorder when it becomes excessively intense. In the university academic environment, such disorders not only affect the students’ academic performance but also their overall well-being. This study aims to analyze anxiety levels among students at the University of Burgos and identify factors that may influence the manifestation of anxiety. The State-Trait Anxiety Inventory (STAI) was administrated digitally to 302 students (74 men, 228 women) from the University of Burgos via Microsoft Forms, following a cross-sectional quantitative research design. The study revealed high levels of both state and trait anxiety among the student sample. While trait anxiety levels were similar between genders, women tended to report higher state anxiety. Students in technical fields and those fully dedicated to their studies exhibited higher trait anxiety. Additionally, age, academic discipline, and living away from one’s hometown significantly predicted higher state anxiety. Age and exclusive academic dedication also predicted higher trait anxiety. These findings emphasize the importance of addressing anxiety differences across student subgroups and providing targeted support during this critical developmental period.

1. Introduction

Anxiety is a complex emotion that arises as an individual’s response to situations perceived as threatening or dangerous [1]. From an evolutionary perspective, it has played a fundamental role in human survival, acting as an alert and preparation mechanism in the face of potential threats. This adaptive response activates an automatic physiological reaction known as the “fight-or-flight response,” as described by Cannon [2]. This biological mechanism is triggered through the stimulation of the autonomic nervous system, unleashing a series of physiological changes intended to optimize the individual’s ability to confront identified dangers. On a cognitive level, this entails a state of mental alertness that momentarily enhances concentration and the ability to make quick decisions under pressure [3]. However, although this response is clearly beneficial in the face of real danger, it can become problematic when triggered in situations where no real threat exists, ceasing to serve as a useful survival tool and instead becoming a barrier that compromises the person’s well-being and quality of life [4,5].
Anxiety disorders are among the most common mental health problems worldwide, with an approximate global prevalence of 4% [1]. This figure represents millions of people experiencing symptoms that interfere with their daily lives. These disorders are characterized by constant and disproportionate worry, intrusive thoughts that are difficult to control, and high emotional tension, which often impacts their interpersonal relationships [6].
Anxiety disorders can manifest in individuals of all ages, genders, and sociocultural contexts, which often makes detection difficult. However, studies indicate that women are more prone to experiencing anxiety. For example, in Spain, research shows that 16.5% of women suffer from anxiety disorders, compared to 8.7% of men [7]. In addition to gender differences, other factors may increase the risk of developing these disorders. For instance, individuals exposed to chronic stress—such as those working in highly demanding environments or living in poorly structured family and social contexts, have a greater likelihood of experiencing anxiety [8,9]. Similarly, people facing unfavorable socioeconomic conditions, such as poverty or lack of access to basic resources, are also at higher risk [10]. Another particularly affected group is the youth population, especially adolescents and young adults, who are exposed to many of the aforementioned stressors, along with academic, professional, and social pressures, as well as the search for personal identity [11].

2. Theoretical Framework

2.1. Vulnerability of University Students

In this context, university students appear as a particularly vulnerable group due to the interplay of various factors that co-occur during this stage of life. In addition to academic pressure, students must learn to adapt to new environments and responsibilities that affect both their social and personal lives. Often, young people leave behind their family homes and familiar environments to face new responsibilities that arise both academically and personally, frequently resulting in feelings of loneliness and increased psychological pressure [9].
Additionally, the transition to university life coincides with the redefinition of personal identity that characterizes this developmental stage. Students begin to make more autonomous decisions regarding their personal goals and values, situations that often generate internal conflict and existential doubt. On top of social and familial expectations, there is a growing need to fit into new social groups, which becomes particularly important at this stage. While some students succeed in forming strong and healthy relationships, others struggle to find new friendships or to manage social conflicts within these new groups. Continuous interaction with peers, roommates, or other members of the university community can create a social environment in which fear of judgment or rejection arises, further increasing anxiety. Balancing all these aspects can become a complex task for students who lack the necessary tools [12,13]. The tools required in this context include emotional regulation skills, effective communication strategies, time management techniques, and the ability to seek social or institutional support when needed. Students who face these challenges without such resources are more likely to experience heightened stress, as they may find it harder to articulate their needs, establish boundaries, or maintain a healthy balance between academic responsibilities and personal well-being. This lack of tools not only can amplify feelings of isolation and anxiety but can also limit their capacity to adapt successfully to the demands of university life [14,15].

2.2. Prevalence of Anxiety in University

Several recent studies have highlighted that anxiety is one of the most prevalent psychological disorders among university students, identifying that approximately 50% of university students present moderate to severe anxiety symptoms, and around 22% report suicidal ideation [14]. This phenomenon may be partly explained by the growing responsibilities they face at university, such as meeting academic deadlines and completing exams and projects, while also adapting to a new social environment. Another relevant factor during this period is, as mentioned earlier, the transition to a new environment far from their family home. Many students move to new cities in search of academic opportunities, experiencing physical separation from their families and close friends, which forces them to develop new strategies to cope with loneliness and make decisions independently. This adaptation process is particularly challenging for those without strong social support in their new residence or without prior skills for managing stress [15].

2.3. Preventive Strategies and Protective Factors

Despite their high global prevalence, especially among young people, anxiety disorders often go undiagnosed and untreated, which can lead to significant deterioration in the quality of life of those affected, including in areas such as interpersonal relationships, academic or professional performance, and the maintenance of physical and emotional well-being [6]. For this reason, promoting early detection, raising mental health awareness, and providing access to preventive strategies is crucial. Such strategies should address associated risk factors, including the development of adaptive stress management skills and the strengthening of support networks. Research indicates that these two tools can be particularly effective in reducing absenteeism and enhancing academic performance. Adaptative stress management techniques, such as cognitive reappraisal, mindfulness, and relaxation strategies, have been shown to lower levels of anxiety and improve concentration, which can contribute to sustained class attendance and better academic outcomes [14]. At the same time, strengthening social and institutional support networks plays a crucial protective role: students who perceive higher levels of support from peers, mentors, or university services report lower stress and greater engagement with their studies [15]. As Ullah et al. emphasize, the sense of belonging fostered by supportive environments enhances resilience and motivations, thereby preventing disengagement and academic decline [16].
Overall, anxiety plays a significant role in students’ academic lives, as high levels of anxiety have been associated with poorer academic performance, reduced concentration, increased absenteeism, and in some cases, higher dropout rates. Studies indicate that academic anxiety can interfere with learning processes and memory, leading to decreased efficiency in studying and test-taking, while also undermining students’ motivation and self-confidence [17,18]. Early detection of anxiety in this population is therefore crucial, as timely interventions can mitigate its impact, promote adaptive coping strategies, and prevent long-term negative consequences on both academic and personal development [19].

2.4. Assessment of Anxiety: State vs. Trait Anxiety

In this context, the State-Trait Anxiety Inventory (STAI), developed by Spielberger et al. [20], is one of the most widely used tools for assessing anxiety. Spielberger proposed two dimensions within the concept of anxiety: state anxiety and trait anxiety. Understanding the distinction between these two is essential to comprehending how anxiety is experienced and expressed in various situations.
Trait anxiety is understood as a relatively stable personality trait that predisposes individuals to interpret situations as threatening or stressful. This subtype of anxiety does not necessarily depend on immediate external events or circumstances but is part of the individual’s defense mechanisms, influencing how they perceive, process, and respond to different situations over time. People with high levels of trait anxiety tend to experience constant worry, even in the absence of real or imminent threats, compared to individuals with lower levels. This often manifests as intrusive thoughts, negative anticipation of future events, and heightened vigilance in various situations. These thought and behavioral patterns can affect the person in many areas of daily life, such as interpersonal relationships, academic or professional performance, and overall emotional well-being [21,22].
On the other hand, state anxiety is defined as a temporary emotional reaction that arises in response to a specific situation perceived as threatening or stressful. Unlike trait anxiety, which is more stable and continuous over time, state anxiety is fluctuating and limited to a specific context. This type of anxiety is common in evaluative or performance situations, such as exams, job interviews, or public speaking. An individual may not generally be anxious but may experience state anxiety in response to a particular stimulus. Although temporary, this type of anxiety can also severely impact a person’s well-being. The main difference between the two dimensions lies in their temporality: trait anxiety is more permanent, whereas state anxiety disappears once the stressful situation is resolved [21,22].
The State-Trait Anxiety Inventory (STAI) [23] measures both dimensions of anxiety, offering a more precise assessment of the anxiety experience, which is why it has been widely used in research and clinical settings.
Therefore, the present study aims to identify both state and trait anxiety levels in university students, analyzing the interaction of various demographic factors, such as age, gender, academic discipline, employment status, and place of residence, in relation to the manifestation of anxiety. The ultimate goal is to implement adequate prevention and intervention programs for this population.

2.5. Objectives

The general objective of this study is to analyze the levels of state and trait anxiety among the university population at the University of Burgos during the 2024–2025 academic year.
The specific objectives are as follows:
  • To explore the relationship between various sociodemographic and academic variables (age, gender, type of studies, study mode, field of knowledge, employment status, place of residence, and housing situation) and the levels of state and trait anxiety in students.
  • To analyze whether students’ employment status is a predictive factor for experiencing higher levels of state and trait anxiety compared to the reference population.
  • To explore whether academic factors such as year of study, field of knowledge, or study mode are predictors of higher levels of state and trait anxiety.
  • To examine whether personal factors such as age or gender predict higher levels of state and trait anxiety.

2.6. Hypotheses

Based on the objectives described above, the following hypotheses are proposed:
H1. 
Students will exhibit high levels of both state and trait anxiety.
H2. 
Female students will present higher levels of state and trait anxiety than male students, with gender being a predictive factor in the manifestation of anxiety.
H3. 
Students enrolled in more technical fields such as engineering, architecture, chemistry, or health sciences will report higher levels of state anxiety compared to students in fields such as arts and humanities.
H4. 
Being fully dedicated to academic studies will be a predictive factor for experiencing trait anxiety, with those who study without combining it with a job reporting the highest levels of trait anxiety.
H5. 
Age is a predictive factor for experiencing higher levels of both state and trait anxiety, with older students presenting lower levels of anxiety.
H6. 
Students who do not live in their hometown will show higher levels of anxiety compared to those who do. This condition will be a predictive factor for increased state anxiety, with students living in shared apartments reporting the highest levels of state anxiety.

3. Materials and Methods

3.1. Design

A cross-sectional descriptive quantitative analysis was conducted for this study. This type of design allows for the collection and analysis of data at a specific point in time. It was chosen because the main objective of the study is to analyze the level of anxiety experienced by university students and to relate it to other relevant variables such as field of study, age, or employment status. This descriptive approach aims to identify patterns and trends within the sample in order to better understand how anxiety manifests in university contexts and how it could be appropriately addressed by institutions.
The study was conducted at the University of Burgos, located in the city of Burgos, Spain. Data collection was carried out remotely via an online questionnaire, which will be further explained below, between January and March 2025, targeting students in the second semester of their studies. The survey was distributed to the entire student population of the University of Burgos (n = 9020), with a total of 302 (3.35%) responses obtained.

3.2. Sample

The incidental sample consisted of 302 students, with a majority of female participants (75.5%), ranging in age from 17 to 63 years, with a mean age of 25.75 (SD = 9.81). Most students were enrolled in in-person study programs (75.8%), with the predominant academic field being “Social and Law Sciences” (52.3%). In addition, most participants were exclusively dedicated to their studies, without combining them with paid employment (61.9%). Most students lived outside their hometown (56.3%), and among those who had relocated, the vast majority did so due to their current studies (71.8%).

3.3. Assessment Instruments

For this study, a self-administered questionnaire was used, composed of three sections: first, the informed consent form; second, a battery of 13 questions concerning sociodemographic data (age, gender, current program of study, academic year, study mode, combination of study and work, type of job, place and type of residence); and finally, 40 items corresponding to the State-Trait Anxiety Inventory (STAI) [24].
This questionnaire is one of the most widely used instruments for measuring anxiety levels in psychological research. It has been cited in numerous studies and has more than 60 adaptations, including the Spanish version developed by C. D. Spielberger et al. [25]. The inventory assesses the two dimensions of anxiety proposed by its author: state anxiety and trait anxiety.
The questionnaire consists of 40 items answered on a 4-point Likert scale ranging from 0 = “not at all” to 3 = “very much.” The first 20 items correspond to the state anxiety subscale, while items 21 to 40 comprise the trait anxiety subscale. To reduce response bias and better assess the construct of anxiety, the STAI includes several reverse-scored items within each subscale.
The Spanish adaptation of the STAI demonstrates strong psychometric properties in both the general population and university students, with reliability coefficients above 0.8, indicating adequate internal consistency both globally (α = 0.958) and for each of the sub-scales: state anxiety (α = 0.950) and trait anxiety (α = 0.919) [22,26]. Furthermore, the questionnaire is norm-referenced for both the general and student populations, justifying its use in this study.

3.4. Procedure

First, a questionnaire was created using Microsoft Forms (Microsoft 365). The questionnaire was divided into three sections. The first contained a detailed description of the study, emphasizing that participation was voluntary and included the informed consent form. The second section collected relevant sociodemographic data through 13 questions. The third section included the 40 items of the State-Trait Anxiety Inventory (STAI).
Prior to data collection, approval was obtained from the Bioethics Committee of the Isabel I University, ensuring compliance with the ethical and legal principles governing this type of research. Once approved, permission was requested from the University of Burgos to conduct the study among its students. To facilitate this, contact was made with the director of the University Health Care Service (SUAS-UBU), who is also the Dean of the Faculty of Health Sciences. She informed the Vice-Rector for Student Affairs about the project and authorized the mass distribution of an invitation to participate in the study titled “Descriptive study on anxiety levels in university students and their potential influencing factors” via institutional email. The email briefly described the study and emphasized the voluntary nature of participation, along with a link to the Microsoft Forms platform.
After the initial email was sent in January 2025, students were given time to respond to the questionnaire. Several follow-up email reminders were sent during February, and in March 2025, data cleaning began. A total of 302 responses were collected, all automatically stored in an Excel file generated by Microsoft Forms.
To ensure anonymity, certain variables such as age and degree program were categorized (e.g., age ranges, academic fields). This process prevented direct identification of individual respondents. Once the sociodemographic data were categorized, the STAI responses were scored using the standardized correction template, yielding two scores per participant: one for state anxiety and one for trait anxiety.

3.5. Data Analysis

The data obtained were recorded in a Microsoft Excel spreadsheet, ensuring at all times the anonymization of responses and compliance with confidentiality requirements. Subsequently, statistical analysis was performed using IBM SPSS Statistics, version 29. Before testing the hypotheses, the reliability of the STAI scale was assessed, confirming acceptable internal consistency for both state and trait anxiety subscales.
Given that the sample consisted of 302 participants, the normality of variable distributions was examined using the Kolmogorov–Smirnov test, in order to verify the assumptions required for parametric testing. Additionally, the homogeneity of variances was tested using Levene’s test to ensure comparability of group variances in subsequent analyses. All statistical tests were performed using a 95% confidence interval (CI).
For all analyses, STAI percentiles were employed. These percentiles were calculated based on the standardized (norm-referenced) scores provided by the STAI manual, which converts raw scores into scaled scores according to established norms for age and sex. This procedure allows each participant’s raw score on the state and trait anxiety subscales to be transformed into a percentile rank, indicating the proportion of the normative population scoring below that value. Using percentiles ensures that the results are interpretable in terms of relative standing within a reference population and allows comparisons across different groups of participants.
Next, descriptive statistics were calculated for sociodemographic and academic variables, as well as for state anxiety and trait anxiety levels, using measures of central tendency (mean) and dispersion (standard deviation).
To test Hypothesis H1, which stated that students will present high levels of both state and trait anxiety, an analysis of the means obtained from the standardized scores of the state and trait anxiety subscales was conducted. The percentile mean was chosen for the analysis.
Regarding Hypothesis H2, which posited that women would present higher levels of state and trait anxiety than men, with sex being a predictive factor for the manifestation of anxiety, an independent samples t-test was applied, comparing the means of men and women in the state and trait anxiety subscales measured with the STAI. To verify whether sex was a predictive factor for higher state anxiety levels, a linear regression analysis was conducted.
To test Hypothesis H3, which stated that students in more technical fields such as engineering, architecture, chemistry, or health sciences present significantly higher levels of state anxiety than students in fields such as arts and humanities or social and legal sciences, with the field of study being a predictive factor for higher state anxiety, a comparison of the mean scores obtained in the state anxiety subscales between the two groups of interest was conducted using an independent samples t-test. To determine whether the field of study was a predictive factor for higher state anxiety, a linear regression analysis was performed.
Hypothesis H4, which stated that dedicating oneself fully to studying will be a predictive factor for trait anxiety, with those studying without combining it with work reporting the highest levels of trait anxiety, was tested first with an independent samples t-test to examine differences in trait anxiety scores between students fully dedicated to studying and those combining studies with work. To confirm that full dedication to studying was a predictive factor for higher trait anxiety, a linear regression analysis was performed.
To test Hypothesis H5, which stated that age is a predictive factor for levels of both state and trait anxiety, with older students presenting lower levels of state and trait anxiety, an analysis of variance (ANOVA) was first conducted to compare mean anxiety levels according to age ranges. Subsequently, a linear regression analysis was performed to verify whether age was a predictive factor for higher anxiety levels.
Hypothesis H6 posited that students who do not live in their hometown will present higher levels of anxiety than those who live in their hometown, with living situation being a predictive factor for higher state anxiety, and those living in shared apartments showing the highest state anxiety levels. To test this hypothesis, an independent samples t-test was first conducted to determine whether there were significant differences in state and trait anxiety between students living in their hometown and those living in another city. Subsequently, a linear regression analysis was performed to determine whether living situation was a predictive factor.

4. Results

Firstly, a descriptive analysis was conducted to study the nature of the sample (see Table 1).
A test of homogeneity of variances was carried out for each of the 40 items on the questionnaire, as well as for the overall scores obtained. Levene’s test was used for this purpose, and it was observed that the assumption of homogeneity of variances was met, as the significance values for this statistic were greater than 0.05. Subsequently, the normality of the sample was analyzed. Given that the sample size was greater than 50 participants, the Kolmogorov–Smirnov test was used, and it was observed that the assumption of normality was not met. Despite this and considering the sample size (n = 302), parametric tests were used [27,28].
Next, the reliability of the scale used was assessed using Cronbach’s alpha. The reported reliability was satisfactory, as the scale yielded a value above 0.7, indicating adequate internal consistency both globally (α = 0.958) and for each of the subscales: state anxiety (α = 0.950) and trait anxiety (α = 0.919).
To test Hypothesis 1, which stated that students will show high levels of both state and trait anxiety scores on the state and trait anxiety subscales were analyzed using central tendency measures based on percentiles. It was observed that the mean values for both subscales were high, with the state and trait anxiety subscales yielding percentile scores of M = 70.97 (SD = 28.4) and M = 73.70 (SD = 25.08), respectively, thus confirming the hypothesis.
Hypothesis 2 proposed that female students will present higher levels of state and trait anxiety than male students, with gender being a predictive factor in the manifestation of anxiety. This hypothesis was tested by comparing the means of both groups on the STAI subscales. Although a trend was observed for women (M = 72.57, SD = 28.23) to have higher state anxiety levels than men (M = 66.07, SD = 28.56), an independent samples Student’s t-test showed that the differences were not statistically significant, t (300) = −1.716, p = 0.087, 95% CI: −13.951 to 0.955. On the trait anxiety subscale, however, women (M = 73.03, SD = 24.58) reported lower scores than men (M = 75.76, SD = 26.62), and a Student’s t-test again showed the difference was not significant, t (300) = −0.813, p = 0.417, 95% CI: −14.041 to 1.045.
A linear regression analysis that proved non-significant, F (1, 300) = 2.944, p = 0.087, indicated that gender is not a predisposing factor (B = 6.498, β = 0.099, p = 0.087) for higher levels of state anxiety. Another linear regression was conducted to determine whether gender predicted trait anxiety, which again showed non-significant results, F (1, 300) = 0.661, B = −2.730, β = −0.047, p = 0.417 (see Table 2).
To test Hypothesis 3, which stated that students in more technical fields such as engineering, architecture, chemistry, or health sciences present significantly higher levels of state anxiety compared to students in fields such as arts, humanities, social sciences, or law, with field of study being a predictive factor of higher state anxiety, a comparison was made between the mean scores on the state anxiety subscale across two groups: technical fields (engineering and architecture, sciences, and health sciences) and non-technical fields (social sciences and law, and arts and humanities). Results showed differences in state anxiety between fields, with students in engineering and architecture (M = 77.64, SD = 23.65), sciences (M = 76.47, SD = 31.56), and health sciences (M = 76.45, SD = 29.88) presenting higher anxiety levels compared to social sciences and law (M = 68.52, SD = 27.85) and arts and humanities (M = 61.28, SD = 28.43). An independent samples Student’s t-test indicated that these differences were statistically significant, t (300) = −2.824, p = 0.005. Additionally, a linear regression analysis yielded significant results, F (1, 300) = 7.974, p = 0.005, with field of study being a significant predictor (B = 9.396, β = 0.161, p = 0.005, 95% CI: 2.848 to 15.943) of state anxiety levels. Students in more technical fields such as sciences, health sciences, engineering, and architecture showed higher state anxiety. This variable explained 2.6% of the variance in state anxiety (see Table 3).
Hypothesis 4 stated that being solely dedicated to studying would be a predictor of trait anxiety, with students who do not combine studying with work reporting higher levels of trait anxiety. To test this, differences in trait anxiety scores were analyzed between those fully dedicated to studying and those who studied while working. Results showed that, in line with the hypothesis, those who only studied (M = 76.80, SD = 23.97) reported higher levels of trait anxiety than those who combined study with work (M = 68.65, SD = 26.11). An independent samples Student’s t-test was significant, t (300) = −2.771, p = 0.006, 95% CI: −13.929 to −2.360. Furthermore, a linear regression analysis showed that whether or not students combine study with work is a predictive factor for trait anxiety, F (1, 300) = 7.677, p = 0.006, with being solely a student a significant predictor (B = 8.145, β = 0.158, p = 0.006, 95% CI: 2.360 to 13.929) of higher trait anxiety. Therefore, students who are fully dedicated to their studies show higher trait anxiety levels than those who also work. This variable explained 2.5% of the variance in trait anxiety (see Table 4).
To test Hypothesis 5, which stated that age is a predictive factor for anxiety, with older students presenting lower levels of both state and trait anxiety, the mean anxiety scores of students were compared across age groups. Significant differences were found between age groups for both state anxiety, F (4,297) = 3.213, p = 0.013, and trait anxiety, F (4,297) = 3.435, p = 0.009. Multiple comparisons using the Bonferroni method showed that students over 45 years of age presented significantly lower levels of state anxiety compared to those aged 25–30, and also significantly lower levels of trait anxiety compared to those aged 21–24 (see Table 5).
To determine whether age is a predictive factor for the tendency to experience state and trait anxiety, two linear regression analyses were performed, one for each dependent variable. The analysis of the influence of age on state anxiety was significant, F (1, 300) = 5.147, p ≤ 0.024, indicating that age is a significant predictor (B = −0.376, β = −0.130, p = 0.024) of state anxiety, showing that the older the participant, the less prone they are to experiencing anxiety. The variable “age” explained 1.3% of the variance in state anxiety. On the other hand, when regression analyses were conducted to assess trait anxiety, the results were also significant, F (1, 300) = 9.223, p ≤ 0.003. This indicated that age, as in the previous case, was also a predictive factor (B = −0.442, β = −0.173, p = 0.003) of trait anxiety, explaining 3% of the variance in this dependent variable (see Table 6).
Hypothesis 6 stated that students who do not live in their hometown will present higher levels of anxiety than those who do live in their hometown, and those living in shared apartments showing the highest levels of state anxiety. To test this, differences in state anxiety levels were examined between students living in their hometown and those living in a different city. An independent samples Student’s t-test yielded significant results for the dependent variable state anxiety, t (300) = 2.486, p = 0.013, 95% CI: 1.694 to 14.551, indicating that students living outside their hometown (M = 74.52, SD = 27.13) reported significantly higher levels of state anxiety than those living in their hometown (M = 66.40, SD = 29.44). To determine whether not living in their hometown is a predictive factor for the tendency to experience state anxiety, a linear regression analyses was performed. The analysis was significant, F (1, 300) = 6.182, p = 0.013, indicating that not living in their hometown is a significant predictor (B = −8.122, β = −0.142, p = 0.013) of state anxiety, showing that the students who live in their hometown are less prone to experiencing anxiety. The variable “not living in their hometown” explained 1.42% of the variance in state anxiety (see Table 7).
To test the second part of the hypothesis, mean scores on the state anxiety subscale were compared according to students’ living arrangements: living alone in an apartment, in a shared apartment, with family, or in a university residence. It was observed that the highest levels of state anxiety were reported by students living in shared apartments (M = 77.67, SD = 24.38), followed by those living in university residences (M = 74.00, SD = 25.56). These differences were statistically significant, F (3,298) = 3.951, p = 0.009. Multiple comparisons revealed that students living in shared apartments showed significantly higher levels of state anxiety than those living with family (see Table 8).

5. Discussion

The present study aimed to analyze the levels of state and trait anxiety among university students at the University of Burgos during the 2024–2025 academic year, while also exploring how sociodemographic and academic variables influence anxiety manifestation. The findings offer relevant insights into the significant presence of anxiety in the university population, as well as several contributing factors to its intensification.
The main hypothesis (H1) was confirmed: both state and trait anxiety levels, measured using percentile scores, were notably higher than normative benchmarks. This outcome reinforces growing concerns regarding the mental health status of university students, highlighting anxiety as one of the most prevalent and persistent problems within this demographic [29]. Literature has consistently emphasized that the university stage is a psychologically vulnerable life phase due to students facing a complex combination of academic, social, and personal demands that may surpass their coping abilities [30].
Additionally, academic pressures to excel and the increasingly competitive university environment can produce significant levels of state anxiety [31]. The high levels of trait anxiety observed suggest a stable predisposition toward anxiety in various everyday situations, pointing not only to situational responses but also to entrenched cognitive and emotional patterns [32]. Moreover, the current sociocultural context, marked by job insecurity, fear of unemployment, market demands, and pervasive hyperconnectivity, contributes to constant social and professional comparison. These factors may intensify students’ psychological discomfort, indicating that anxiety is not purely academic, but also reflects broader internal and external pressures [33].
Contrary to expectations, the second hypothesis (H2) was not supported. Although female students tended to report higher levels of state anxiety than their male peers, the difference was not statistically significant. In fact, trait anxiety scores were slightly higher among males. This finding diverges from a significant body of research identifying women as more susceptible to anxiety symptoms [34,35]. This greater vulnerability has been attributed to biological and psychosocial factors such as ruminative coping styles, emotional internalization, and socialization processes that promote worry and environmental caretaking [36].
However, the present results, in line with some emerging studies, suggest a need to reconsider this traditional viewpoint [37]. One plausible explanation is the sample composition: 75.5% of participants identified as female, potentially reducing score variability and complicating comparison with the male group. Also, the smaller male subsample may be more susceptible to outliers and individual variability.
It is also important to consider generational shifts in emotional expression and stress coping. Traditional gender stereotypes, where women externalize emotions and men suppress them, may be eroding in more egalitarian, mental health-conscious environments. Recent research has shown that young men are increasingly open to acknowledging and verbalizing emotional distress, which may be closing the gender gap observed in earlier emotional studies [38,39]. In contemporary university settings that promote equality and emotional openness, both women and men increasingly have the opportunity to express their distress. Recent studies in Spanish universities show that while women have traditionally reported higher anxiety, these differences do not always translate into disparities in well-being [40]. Moreover, emotional skills such as clarity and repair, together with social support, have been identified as protective factors across genders [41]. Within a university context, which fosters a culture of equality and mental health awareness, these dynamics may enable male students to verbalize and report anxiety more openly than in the past [41,42]. This perspective suggests that the results of the present study, where gender differences in anxiety were less pronounced than expected, may be partly explained by the evolving role of universities as spaced where emotional expression is normalized and supported for all students [40].
The third hypothesis (H3) was confirmed. Significant differences in state anxiety were found according to academic discipline. Students in Engineering, Sciences, and Health Sciences reported higher levels than those in Social Sciences or Arts and Humanities. This finding aligns with prior research indicating that STEM (Science, Technology, Engineering, and Mathematics) and biomedical programs generate higher academic stress and anxiety [43]. Several stressors have been identified in these fields, including dense course loads, rigid curricula, and sustained performance demands [44]. Teaching methodologies in these disciplines often emphasize precision, closed problem-solving, and strict objective standards, creating competitive environments with little tolerance for error. These dynamics can increase academic anxiety, particularly among perfectionist students or those without effective coping strategies [45].
The fourth hypothesis (H4) was also supported. Students who only studied showed significantly higher levels of trait anxiety than those who combined studies with employment. While traditionally the combination of work and study has been seen as a source of added stress, recent findings suggest the opposite. Work experience may offer psychological benefits such as time structuring, feelings of efficacy and autonomy, the development of transferable skills, and access to broader social networks [46]. Employment may also help students relativize academic performance by providing alternative sources of personal and professional validation. In this way, work not only provides income but also cultivates a more diversified sense of purpose and identity, reducing overreliance on academic success [47]. Students who do not work may experience heightened pressure to excel academically, given that it is their primary responsibility and perceived path to future success. The exclusive focus on academic achievement may intensify threat perception in the face of failure and increase tendencies toward rumination and hypervigilance [38].
The fifth hypothesis (H5) was confirmed: age was a significant predictor of both state and trait anxiety. Older students, especially those over 45, reported lower anxiety levels compared to younger students. This aligns with existing research showing that emotional reactivity to stress decreases with age [48]. Developmental studies show that aging is associated with greater use of adaptive coping strategies—such as cognitive reappraisal, acceptance, and solution-focused behavior, and less use of maladaptive strategies like avoidance or rumination [49]. Consequently, older students may be more capable of interpreting academic demands as less threatening, thereby reducing anxiety. Socioemotional selectivity theory suggests that aging leads people to prioritize emotionally positive goals and to regulate affect more effectively. This emotional self-regulation could result in lower susceptibility to both transient (state) and persistent (trait) anxiety [50]. Additionally, life experience, managing complex situations and personal or professional responsibilities, offers perspective. For adult learners, academic performance is important but less identity-defining than for younger students who are still shaping their personal and professional identities [51,52].
The sixth and last hypothesis (H6) was also confirmed. Students not living in their hometown reported significantly higher levels of state anxiety. This supports prior research indicating that geographic relocation, particularly during the transition to higher education, poses significant emotional and psychological challenges [53]. Distance from family, friends, and community support networks may lead to increased feelings of loneliness, vulnerability, and uprootedness, known contributors to anxiety [54]. Additionally, type of cohabitation emerged as relevant: students living in shared apartments had higher anxiety than those living with family. Shared housing often involves privacy limitations, unaligned routines, cultural or lifestyle clashes, and interpersonal conflicts—all of which can foster sustained emotional tension [55]. By contrast, students living with family generally benefit from stronger support networks and stable routines, which can reduce uncertainty and promote emotional resilience in the face of academic stress [56].
Some methodological limitations should be considered. First, the cross-sectional design precludes establishing causal relationships. Although associations between sociodemographic factors and anxiety levels were identified, it remains unclear which variables are causes or effects, or whether third variables are involved. Second, the sample was collected through voluntary self-selection, introducing possible bias, as individuals with heightened awareness or concern for their mental health may have been more likely to participate. This limits the generalizability of the findings. Furthermore, the gender imbalance (75.5% female) may have hindered the ability to detect significant gender differences or to extrapolate results to the broader student population. Thirdly, anxiety was measured through self-report instruments, which are susceptible to social desirability and self-assessment bias. Lastly, while the study included several sociodemographic variables, other potentially important factors, such as socioeconomic status, perceived social support, clinical background, and coping strategies, were not examined. Moreover, while the study included several sociodemographic variables, other potentially important factors, such as socioeconomic status, perceived social support, clinical background, and coping strategies, were not examined. Finally, no confounders such as age were adjusted for, which may have influenced the observed associations.
Future studies should aim to employ longitudinal designs that allow for the tracking of anxiety over time, the identification of peak periods of vulnerability, and the detection of both risk and protective factors. It is also essential to utilize more representative and randomized sampling methods to address potential self-selection biases and enhance the generalizability of the results. Additionally, future research would benefit from the inclusion of a broader range of psychosocial variables, such as coping mechanisms, perceived social support, and socioeconomic status, which could play a mediating or moderating role in the relationship between academic stressors and anxiety. Complementing quantitative self-report measures with qualitative methodologies, including in-depth interviews and even physiological assessments, would provide a more comprehensive and nuanced understanding of how anxiety manifests in academic contexts.

6. Conclusions

The findings of this study confirm that both state and trait anxiety are highly prevalent among university students, with levels significantly surpassing normative ranges. This suggests that students are experiencing considerable external pressures that extend beyond academic demands and include personal and contextual stressors. Among the key predictive factors identified are age, where older students tend to report lower anxiety; academic dedication, with higher anxiety found in students who only study full-time; field of study, where those in STEM disciplines report higher anxiety levels; and living situation, as students living away from home, especially in shared housing, exhibit greater levels of anxiety.
These findings highlight the urgent need to develop targeted policies and programs focused on the prevention and promotion of mental health within university environments [57,58]. Institutions should take an active role in the early detection of emotional distress, provide accessible, continuous, and personalized psychological support services, and implement stress-management workshops and socio-emotional skills training. Ultimately, fostering an institutional culture that legitimizes mental health care, free from stigma, will contribute not only to improved academic performance but also to the personal development and long-term well-being of students.

Author Contributions

Conceptualization, I.Q.-B.; methodology, I.Q.-B., M.-C.E.-L. and F.L.A.C.; software, I.Q.-B. and M.-C.E.-L.; validation, I.Q.-B., M.-C.E.-L. and F.L.A.C.; formal analysis, I.Q.-B.; investigation, I.Q.-B.; resources, I.Q.-B. and M.-Á.M.-M.; data curation, I.Q.-B.; writing—original draft preparation, I.Q.-B.; writing—review and editing, I.Q.-B., M.-C.E.-L. and F.L.A.C.; visualization, I.Q.-B., M.-C.E.-L. and F.L.A.C.; supervision, I.Q.-B., M.-C.E.-L., M.-Á.M.-M. and F.L.A.C.; project administration, I.Q.-B., M.-C.E.-L., M.-Á.M.-M. and F.L.A.C.; funding acquisition, I.Q.-B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a Predoctoral Contract awarded to the first author (University of Burgos, 2024 call, Resolution of 8 January 2025) within the project “Voice assistants and artificial intelligence in Moodle: a path to a smart university” (SmartLearnUni, PID2020-117111RB-I00), funded by the State Research Agency, Ministry of Science, Innovation and Universities, Government of Spain.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Universidad Isabel I (16 December 2024) and approved by Ethics Committee of the University of Burgos (No.: REGAGE24s00086337595/2024/REGSED-8724, 14 November 2024), for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The AI tools ChatGPT-5 and Copilot (Microsoft 365) were used exclusively for the purpose of correcting a translation previously carried out by the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Sample Description: Frequencies and Percentages.
Table 1. Sample Description: Frequencies and Percentages.
N%
Gender
   Female 228 75.5
   Male 74 24.5
Age
   18–20 years old 12340.7
   21–24 years old 84 27.8
   25–30 years old 29 9.6
   31–44 years old 44 14.6
   +45 years old 22 7.3
Academic discipline
   Arts and Humanities 29 9.6
   Engineering and Architecture 33 10.9
   Social and Law Sciences 158 52.3
   Sciences 15 5
   Health Sciences 67 22.2
Combining studies with employment
   Yes 115 38.1
   No 187 61.9
Living in their hometown
   Yes 132 43.7
   No 170 56.3
Type of cohabitation
   Living with family 143 47.4
   Living in shared apartments 11 36.8
   Living alone in an apartment 34 11.3
   Living in university residences 14 4.6
Table 2. Linear Regression Analysis of Gender as a Predictor of State and Trait Anxiety.
Table 2. Linear Regression Analysis of Gender as a Predictor of State and Trait Anxiety.
BβR2R2 std.FSig.
State Anxiety6.4980.0990.0100.0062.9440.087
Trait Anxiety−2.730−0.0470.002−0.0010.6610.417
Table 3. Linear Regression Analysis of Field of Study as a Predictor of State Anxiety.
Table 3. Linear Regression Analysis of Field of Study as a Predictor of State Anxiety.
BβR2R2 std.FSig.
State Anxiety9.3960.1610.0260.0237.9740.005
Table 4. Linear Regression Analysis of Being Solely a Student as a Predictor of Trait Anxiety.
Table 4. Linear Regression Analysis of Being Solely a Student as a Predictor of Trait Anxiety.
BβR2R2 std.FSig.
Trait Anxiety8.1450.1580.0250.0227.6770.006
Table 5. Comparison of Scores on the State Anxiety and Trait Anxiety Subscales According to Student Age Range. Analysis of Variance (ANOVA).
Table 5. Comparison of Scores on the State Anxiety and Trait Anxiety Subscales According to Student Age Range. Analysis of Variance (ANOVA).
18–20 Years Old21–24 Years Old25–30 Years Old31–44 Years Old+45 Years OldANOVA
MSDMSDMSDMSDMSDFp
State Anxiety70.529.375.026.079.923.566.328.055.232.43.210.013
Trait Anxiety74.224.978.221.777.723.968.026.659.029.83.430.009
Table 6. Linear Regression Analysis of Age as a Predictor of State and Trait Anxiety.
Table 6. Linear Regression Analysis of Age as a Predictor of State and Trait Anxiety.
BβR2R2 std.FSig.
State Anxiety−0.376−0.1300.0170.0145.1470.024
Trait Anxiety−0.442−0.1730.0300.0279.2230.003
Table 7. Linear Regression Analysis of Not Living in their Hometowns as a Predictor of State Anxiety.
Table 7. Linear Regression Analysis of Not Living in their Hometowns as a Predictor of State Anxiety.
BβR2R2 std.FSig.
State Anxiety−8.122−0.1420.0200.0176.1820.013
Table 8. Comparison of Scores on the State Anxiety and Trait Anxiety Subscales According to Type of Living Arrangement. Analysis of Variance (ANOVA).
Table 8. Comparison of Scores on the State Anxiety and Trait Anxiety Subscales According to Type of Living Arrangement. Analysis of Variance (ANOVA).
Living with FamilyLiving in a Shared ApartmentLiving Alone in an ApartmentLiving in University ResidencesANOVA
MSDMSDMSDMSDFp
State Anxiety65.5 30.70 77.67 24.38 70.62 27.98 74.00 25.56 3.951 0.009
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MDPI and ACS Style

Quevedo-Bayona, I.; Escolar-Llamazares, M.-C.; Martínez-Martín, M.-Á.; Adell Carrasco, F.L. Descriptive Study on State and Trait Anxiety Levels in University Students and Their Potential Influencing Factors. Societies 2025, 15, 287. https://doi.org/10.3390/soc15100287

AMA Style

Quevedo-Bayona I, Escolar-Llamazares M-C, Martínez-Martín M-Á, Adell Carrasco FL. Descriptive Study on State and Trait Anxiety Levels in University Students and Their Potential Influencing Factors. Societies. 2025; 15(10):287. https://doi.org/10.3390/soc15100287

Chicago/Turabian Style

Quevedo-Bayona, Itziar, María-Camino Escolar-Llamazares, María-Ángeles Martínez-Martín, and Francisco Luis Adell Carrasco. 2025. "Descriptive Study on State and Trait Anxiety Levels in University Students and Their Potential Influencing Factors" Societies 15, no. 10: 287. https://doi.org/10.3390/soc15100287

APA Style

Quevedo-Bayona, I., Escolar-Llamazares, M.-C., Martínez-Martín, M.-Á., & Adell Carrasco, F. L. (2025). Descriptive Study on State and Trait Anxiety Levels in University Students and Their Potential Influencing Factors. Societies, 15(10), 287. https://doi.org/10.3390/soc15100287

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