Psychometric Properties of the Spanish Version of the University Student Stressors Questionnaire (USSQ): Structure, Reliability, Validity, and Measurement Invariance

Abstract

The scarcity of psychometrically robust instruments limits the assessment of academic stressors among Spanish university students. This study aimed to adapt the University Student Stressors Questionnaire (USSQ) to the Spanish context and evaluate its psychometric properties in a sample of undergraduate and postgraduate students. An instrumental, cross-sectional census survey design was used to evaluate the psychometric properties of the instrument. A total of 4925 students from a Spanish public university participated. Exploratory and confirmatory factor analyses, measurement invariance analyses (by sex, gender, and study level), and convergent validity analyses with measures of depression, anxiety, and stress (DASS-21) and psychological well-being (Ryff’s PWB) were conducted. The final 27-item, seven-factor model demonstrated optimal fit and adequate reliability (McDonald’s omega: 0.73–0.89). Partial measurement invariance was achieved for sex and gender, and full invariance for study level. Convergent validity was supported through positive correlations with psychological distress and negative correlations with well-being. The University Student Stressors Questionnaire (USSQ) is a valid and reliable instrument for assessing academic stressors in the Spanish university population. “Fear of failure” emerges as the most prominent factor associated with psychological distress.

1. Introduction

Academic stress is a salient issue during emerging adulthood, a developmental stage marked by increasing demands and a transition toward autonomy (Matud et al., 2020). Consequently, university students exhibit elevated mental health problems compared to the general population (American Psychological Association, 2020; Ibrahim et al., 2013), as they navigate rigorous academic requirements and critical decision-making (Ruiz-Camacho & Gozalo, 2025). The prevalence and intensity of this stress are becoming increasingly prominent (Beiter et al., 2015).

Academic stress, defined as the perception that university demands exceed available coping resources (Cohen et al., 2007), aligns with the Transactional Model of Stress and Coping (Lazarus & Folkman, 1984). Stress levels vary significantly by sex, with females reporting higher intensity (Asensio-Martínez et al., 2023; Ruiz-Camacho & Gozalo, 2025); by academic year, particularly affecting postgraduate students facing work–life and financial challenges (Stojadinović et al., 2025); and by field of study, peaking in health sciences (Ruiz-Robledillo et al., 2022).

Chronically elevated academic stress is linked to poorer academic performance (Zając et al., 2024) and increased risk of university dropout (López-Aguilar et al., 2026) particularly when met with ineffective coping strategies (Kamtsios & Karagiannopoulou, 2015). Psychologically, academic stress is associated with overall stress (Casuso-Holgado et al., 2019), anxiety and depression (Koppenborg et al., 2024; Rastogi et al., 2025), in this order of strength (Castañeda Quirama et al., 2025; Luo et al., 2025; Ramesh et al., 2024; Vinet et al., 2022). Physically, it correlates with headaches, insomnia, fatigue, and unhealthy lifestyle habits (Monserrat-Hernández et al., 2023).

It is relevant to distinguish academic stressors from the psychological responses associated with them. Academic stressors refer to demands or circumstances within the university environment that students perceive as stressful, whereas psychological distress reflects the emotional responses that may be associated with these demands. Identifying specific sources of academic stress, rather than focusing exclusively on their psychological consequences, is therefore necessary to understand the challenges faced by university students. In the Spanish-speaking context, the Cuestionario de Estrés Académico en la Universidad (CEAU; García-Ros et al., 2012) uses the terms ‘academic stress’ and ‘potentially stressful situations’ interchangeably from the outset, blurring the conceptual boundary between triggers and consequences. The SISCO inventory (Barraza, 2007) assesses not only stressors but also symptoms and coping strategies. The Escala de Estresores Académicos (ECEA; Cabanach et al., 2016) focused specifically on academic stressors but includes 54 items, making it burdensome for students and less practical for studies using multiple questionnaires. These limitations highlight the need for a new, brief, reliable and valid academic stress measure in Spanish that focuses specifically on the sources of academic stress, not on its effects.

The Undergraduate Stressors Questionnaire (USQ; Kamtsios & Karagiannopoulou, 2015) was developed to assess different sources of stress among university students. The authors reviewed several existing instruments and found that they assessed a wide range of stressors, including interpersonal, environmental, personal, academic, and financial stressors (Crandall et al., 1992; Gadzella, 1994; Kohn & Frazer, 1986). However, few studies had focused specifically on identifying stressors related to educational settings and academic achievement. The USQ was developed to address this gap (Kamtsios & Karagiannopoulou, 2015).

The original USQ covers different sources of academic stress, including lack of leisure time, academic performance, fear of failure, academic overload, financial difficulties, competition between students, and relationships within the university. Its seven-factor structure explained 61.6% of the total variance, and the questionnaire showed good overall internal consistency (α = 0.90). Given that academic stressors may be perceived differently across cultural and educational contexts (Cho & Hayter, 2020; Kamtsios & Karagiannopoulou, 2015), it is important to examine whether the USQ maintains adequate psychometric properties among Spanish university students.

Previous research suggests that undergraduate and postgraduate students may differ in both the intensity and the sources of perceived academic stress. For instance, MacKinnon et al. (2025) found that undergraduate students generally reported higher levels of perceived stress than graduate students, particularly in relation to academic demands, the learning environment, and campus culture. Similarly, Wyatt and Oswalt (2013) identified significant differences in stress and mental health outcomes between educational levels. Postgraduate education involves specific challenges, including financial concerns, research-related demands, and difficulties balancing academic responsibilities with personal life (Cho & Hayter, 2020; Zhang et al., 2024). In the Spanish context, Estupiñá et al. (2024) also highlighted the relevance of academic and organizational challenges for doctoral students’ mental health. Together, these findings suggest that educational level may be associated not only with differences in overall perceived stress but also with the relative importance of specific academic stressors. Therefore, examining differences between undergraduate and postgraduate students across the seven dimensions of the USQ may contribute to a more comprehensive understanding of academic stress in university populations.

Given the need for culturally attuned, psychometrically robust tools (Dagli et al., 2024), this study aims to: (a) adapt the USQ for the broader Spanish university population, encompassing undergraduate and postgraduate students, and (b) evaluate its psychometric properties (construct validity, convergent validity, reliability, and measurement invariance). Validating the instrument across educational levels will empirically justify renaming it the University Student Stressors Questionnaire (USSQ).

As far as convergent validity is concerned, the following hypotheses were established:

H1. 

The seven-factor structure of the original USQ will demonstrate an adequate fit to the data in a sample of Spanish university students.

H2. 

The USQ will demonstrate adequate internal consistency, both for the overall scale and its seven dimensions.

H3. 

The USQ will demonstrate measurement invariance across undergraduate and postgraduate students, supporting the comparability of academic stressor scores between these educational levels.

H4. 

The university stressors scores and the psychological distress scores will show a positive association.

H5. 

The university stressors scores will show the strongest correlations with the overall psychological distress scores, followed by, in this order, the correlations with stress scores, with anxiety scores, and with depression scores.

H6. 

The university stressors scores will show a negative correlation with the psychological wellbeing scores.

2. Materials and Methods

2.1. Procedure

Data were collected between 5 September and 28 October 2025, as part of a broader mental health census. Given that a censal study was intended, research personal visited classrooms of all studies and all academic years at the university. Undergraduate and postgraduate students accessed the 15–20 min assessment battery (up to 12 questionnaires) via classroom-projected QR codes, while doctoral and international mobility students received email links. Hosted on Office365^®, the survey was completed by 4928 participants. Prior to completing the survey, all participants provided digital informed consent ensuring anonymity and confidentiality. The study was conducted in accordance with the Declaration of Helsinki and approved by the Drug Research Ethics Committee of Navarra (CEIm; Protocol code: PI_2024/94, Protocol v.2_3sep2024, on 25 September 2024) as part of the research project “EVAPSI: Study and Assessment of University Psychological Health”.

With author permission, the USQ (Kamtsios & Karagiannopoulou, 2015) was adapted following the International Test Commission (2018) and Hernández et al. (2020) guidelines. Because the scale was developed in Greek but published in English, parallel forward translations into Spanish were conducted from English (two independent sources) and Greek (one native translator and one AI system; OpenAI, 2025). An expert panel (two psychology specialists and the English translators) synthesized a unified Spanish version using the Greek source to resolve discrepancies. Back-translations into Greek (one independent bilingual translator) and English (two blind bilingual translators) confirmed semantic equivalence with the original meaning and international literature. Finally, a pilot test (n = 20 students) verified item clarity and comprehensibility.

2.2. Participants

The questionnaire was presented to 5083 students from the Public University of Navarre (UPNA), of whom 4928 responded (effective response rate = 96.9%), which represents ~44% of the institution’s population. Excluding three invalid demographic entries yielded a final sample of N = 4925. The mean age was 20.5 years; most participants were female, undergraduates and enrolled in the faculties of Human, Social, and Education Sciences. See Table 1 for more details.

Table 1. Sample characteristics (N = 4925).

Variable	n (%)
Sex
Man	1964 (39.9%)
Woman	2951 (59.9%)
Other	10 (0.2%)
Gender
Man	1955 (39.7%)
Woman	2946 (59.8%)
Other	24 (0.5%)
Age
M (SD)	20.5 (3.51)
Median [Min, Max]	20.0 [16.0, 65.0]
Study level
Postgraduate	551 (11.2%)
Undergraduate	4374 (88.8%)
Faculty
Doctorate School	29 (0.6%)
Agronomic Engineering and Biosciences	649 (13.2%)
Industrial, Computer and Telecommunications Engineering	1174 (23.8%)
Health Sciences	785 (15.9%)
Economics and Business Sciences	696 (14.1%)
Human, Social and Education Sciences	1244 (25.3%)
Legal Sciences	348 (7.1%)

Note. N = total sample size; n = sub-sample size; M = mean; SD = standard deviation.

2.3. Instruments

Undergraduates Stressors Questionnaire (USQ). Developed by Kamtsios and Karagiannopoulou (2015), the USQ assesses academic stressors in university students. It comprises 29 items across seven factors: lack of leisure time (e.g., “I do not have time to rest”), academic performance (e.g., “I consider myself to be an unsuccessful student”), fear of failure (e.g., “I am afraid of failing exams”), academic overload (e.g., “The workload in terms of university papers is very demanding”), finances (e.g., “I have financial problems”), competition between students (e.g., “There is competition among university students”), and relationships with university departments (e.g., “The university bureaucratic procedures take too much time to complete”). Items are rated on a four-point Likert-type scale ranging from 1 (“Not at all stressful”) to 4 (“Very stressful”), with higher scores indicating greater perceived stress. Original internal consistency was high (overall α = 0.90; subscales α = 0.61–0.86).

Depression, Anxiety and Stress Scale (DASS-21). The DASS-21 (Lovibond & Lovibond, 1995a, 1995b; Spanish adaptation by Bados et al., 2005) assesses symptoms of depression, anxiety, and stress experienced during the past week. It consists of 21 items divided into three subscales of seven items each: depression (e.g., “I felt that life was meaningless”), anxiety (e.g., “I felt scared without any good reason”), and stress (e.g., “I found it difficult to relax”). Items are rated on a four-point Likert-type scale ranging from 0 (“Did not apply to me at all”) to 3 (“Applied to me very much or most of the time”), with higher scores indicating greater symptom severity. The Spanish version demonstrates adequate internal consistency both in the original adaptation (α = 0.70–0.84) (Bados et al., 2005) and in our sample (α = 0.91–0.97 and ω = 0.92–0.97). As to construct validity, Bados et al. (2005) confirmed the three-factor structure, which was also supported in our sample.

Ryff Scales of Psychological Well-Being (PWB). The PWB (Ryff, 1989; Spanish adaptation by Díaz et al., 2006) assesses psychological well-being through 29 items across six subscales: self-acceptance (e.g., “When I look at the story of my life, I am pleased with how things have turned out”), positive relations with others (e.g., “I often feel lonely because I have few close friends with whom to share my concerns”), autonomy (e.g., “I am not afraid to voice my opinions, even when they go against the general consensus”), environmental mastery (e.g., “In general, I feel I am in charge of the situation in which I live”), purpose in life (e.g., “I have a sense of direction and purpose in life”), and personal growth (e.g., “For me, life has been a continuous process of learning, changing, and growth”). Items are rated on a 6-point Likert scale, ranging from 1 (“Strongly disagree”) to 6 (“Strongly agree”). The Spanish version showed adequate internal consistency both in the original adaptation (α = 0.70–0.84) (Díaz et al., 2006) and in our sample (α = 0.69–0.89 and ω = 0.69–0.89).

2.4. Data Analysis

The original 29-item, seven-factor USQ model (Kamtsios & Karagiannopoulou, 2015) was initially evaluated via Confirmatory Factor Analysis (CFA) for ordered categorical data, using the weighted least squares mean and variance adjusted (WLSMV) estimator. Goodness-of-fit was assessed using standard cutoffs: χ²/df ratio (χ²/df ≤ 2; Schermelleh-Engel et al., 2003) comparative fit index (CFI > 0.95), Tucker–Lewis index (TLI > 0.95), root-mean square error of approximation with its 90% confidence interval (RMSEA, excellent fit ≤ 0.05 and moderate fit ≤ 0.10; Hu & Bentler, 1999), standardized root-mean-square residual (SRMR < 0.08; Schreiber et al., 2006).

Additionally, measurement invariance was assessed to compare the performance of the test between the original Greek subsample (n = 473) and a size-matched subsample of Spanish participants (n = 473) recruited from the corresponding faculty. After confirming the expected lack of invariance, we proceeded to empirically re-examine its dimensionality and develop an alternative factor structure for the Spanish version of the scale (Greiff & Iliescu, 2017; International Test Commission, 2018).

For the development of the Spanish factor structure, the dataset was randomly halved. Subsample 1 (n = 2462) was used for data-driven structure identification: an Exploratory Graph Analysis (EGA; Christensen & Golino, 2021) was conducted using the graphical LASSO with the walktrap community detection algorithm to estimate the optimal dimensionality of the USQ. In parallel, an exploratory factor analysis (EFA) was performed using principal axis factoring with oblimin rotation. Subsample 2 (n = 2463) was reserved for cross-validation, where a CFA was estimated for the EGA/EFA-derived structure and its fit was compared with that of the original seven-factor model.

Reliability was estimated using CFA-based ordinal alpha and categorical McDonald’s omega (Flora, 2020). Convergent validity was assessed via Spearman correlations, which were computed between the factor scores (${\widehat{F}}_1, {\widehat{F}}_2, \dots , {\widehat{F}}_7$) estimated via Empirical Bayes Modal, derived from the final CFA model and whose factor metric was set by the marker-variable method) and the dimensions and total score from DASS-21 and PWB. These Spearman correlations were considered strong when higher than 0.70 and significant if their p-values were smaller than 0.01 (Evans, 1996).

Measurement invariance was evaluated across biological sex, gender identity, and study level. A conventional sequential invariance-testing framework was applied, whereby increasingly restrictive equality constraints were imposed across groups (Millsap & Yun-Tein, 2004). Configural invariance was examined first, followed by tests of threshold invariance and loading invariance, consistent with recommendations for ordered-categorical indicators (Liu et al., 2017; Wu & Estabrook, 2016). When full invariance was not supported, partial measurement invariance was explored by identifying non-invariant parameters and selectively relaxing equality constraints to obtain a substantively interpretable and statistically adequate model (Tse et al., 2024). For any comparison between fits, the previously mentioned fit indices were used, with the addition of the likelihood ratio test (LRT’s p-value > 0.05; Arango-Lasprilla et al., 2025). Analyses were conducted in R 4.1.3 (R Core Team, 2024), with the packages lavaan (version 0.7.2; Rosseel, 2012), semTools (version 0.5.9; Jorgensen et al., 2022) and EGAnet (version 2.4.1; Golino & Epskamp, 2017).

3. Results

The CFA of the original 29-item, seven-factor model yielded mixed evidence of fit. Although the RMSEA indicated moderate fit (0.071; 90%CI, 0.069–0.072) and incremental and residual-based indices were within acceptable ranges (CFI = 0.974; TLI = 0.970; SRMR = 0.050), the χ² test was statistically significant (χ² = 9082.07; df = 356; p < 0.001) and the χ²/df ratio was elevated (25.54).

Analysis of measurement invariance between equivalent-sized Greek and Spanish samples, supported configural invariance, but did not achieve threshold level (p < 0.001). Item-level analyses revealed non-invariant thresholds for 14 of the 29 items—Items 1, 2, 5, 6, 8, 9, 15, 18, 19, 20, 22, 26, 27, and 28—indicating that participants in the Greek and Spanish samples did not use the response categories equivalently for a substantial proportion of the scale. Given the magnitude of the χ² inflation, the moderate RMSEA and the lack of equivalent functioning, alternative model specifications were subsequently examined.

To identify an improved-fitting specification, an EGA was conducted in subsample 1 to determine the most appropriate number of dimensions. The EGA consistently supported a 7-community solution (see Figure 1), with community stability indices in the acceptable range (≥0.70–0.76), indicating sufficient structural stability (Christensen & Golino, 2021). In parallel, an EFA specifying 7 factors showed that these factors accounted for 61% of the total variance. Additionally, the multiple R2 of scores with factors range from 0.83 to 0.95, showing that all seven factors are important due to the variance they explain. The new model moves item 9 from factor 2 (academic performance) to factor 3 (fear of failure), and has the benefit of simplifying the test by removing two items (21 and 25), which were included in the analysis but turned out to not be relevant in the model itself (did not have loadings > 0.4 on any of the factors).

Figure 1. Exploratory Graph Analysis results: showing factor hubs. Numbers within nodes correspond to the USQ item numbers (1–29), while node colors represent the seven identified communities (factors), numbered 1–7 in the legend.

Based on the EFA-derived solution, a CFA was fitted in subsample 2 to evaluate the revised measurement model. Overall fit was optimum, with fit indicated by the RMSEA (0.064; 90%CI, 0.061–0.067) and favorable incremental and residual-based indices (CFI = 0.981; TLI = 0.978; SRMR = 0.044). The χ² test remained statistically significant (χ² = 3360.81; df = 303; p < 0.001), although the χ²/df ratio was substantially lower than for the original specification (11.09 vs. 25.54). Standardized loadings (λ_j) varied from 0.61 to 0.96, all p-values were <0.001, and omega (ω_u) values showed adequate reliability for all factors, ranging from 0.73 (factor 2) to 0.89 (factor 6). See Table 2, Table 3 and Table S1 for further details.

Table 2. Fit indices of the original and revised models.

Model	χ2	df	p	χ2/df	RMSEA	[90% CI]	CFI	TLI	SRMR
Original	9082.07	356	<0.001	25.51	0.071	[0.069, 0.072]	0.974	0.970	0.050
Revised	3360.81	303	<0.001	11.09	0.064	[0.061, 0.067]	0.981	0.978	0.044

Note: Both models were estimated in subsample 2 using WLSMV; χ² values are robust (scaled) statistics. χ² = chi-square; df = degrees of freedom; χ²/df = ratio of chi-square to degrees of freedom; RMSEA = root mean square error of approximation, with its 90% confidence interval; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root mean square residual.

Table 3. Construct validity, and reliability estimation.

Factor	Items	Standardized Loadings (λj)	ω Value
F1	1. I don’t have time for my hobbies [No tengo tiempo para mis aficiones]	0.742	0.835
	2. My schedule is overloaded [Mi agenda diaria está sobrecargada]	0.784
	3. I don’t have time for myself [No tengo tiempo suficiente para divertirme]	0.664
	4. The demands of university affect my personal life [Las exigencias de la universidad afectan a mi vida personal]	0.786
	5. I don’t have time to rest [No tengo tiempo para descansar]	0.805
F2	6. I have been getting low grades for a long time [Llevo mucho tiempo sacando notas bajas]	0.820	0.732
	7. I consider myself not to be a successful student [Considero que no soy un/a estudiante exitoso/a]	0.770
	8. I have conflicts with my parents because of my grades at university [Tengo conflictos con mis padres debido a mis notas en la universidad]	0.664
F3	9 † I think that I will not be able to meet the academic requirements to graduate [Pienso que no voy a ser capaz de cumplir con las exigencias académicas para graduarme]	0.664	0.870
	10. I am afraid of failing my exams [Tengo miedo de suspender los exámenes]	0.823
	11. I am worried about the results of my exams [Me preocupan los resultados de mis exámenes]	0.811
	12. I feel anxious about my exam grades [Siento ansiedad ante las notas de mis exámenes]	0.858
	13. I am very concerned about the grades I will get this semester [Me preocupan mucho las notas que voy a sacar este semestre]	0.868
F4	14. The number of assignments we have to submit at university is vast [La cantidad de trabajos que tenemos que entregar en la universidad es muy elevada]	0.843	0.844
	15. The academic workload is very demanding [Los trabajos que tenemos que entregar suelen ser extensos]	0.873
	16. The deadlines for assignments are very strict [Las fechas de entrega de los trabajos son muy estrictas]	0.721
	17. The assignments some teachers give us are very hard to complete [Los trabajos que nos piden algunos docentes son difíciles de hacer]	0.793
F5	18. I have many financial obligations [Tengo muchas obligaciones económicas]	0.913	0.802
	19. I have to cope with my financial obligations [Tengo que gestionar eficazmente mi economía]	0.746
	20. I have financial problems [Tengo dificultades económicas]	0.807
F6	22. University students compete with each other [El estudiantado universitario compite entre sí]	0.847	0.892
	23. I think there is a highly competitive environment among the undergraduate student community [Percibo que en la universidad hay un ambiente académico competitivo]	0.955
	24. There is a high level of competition regarding exams and assignments [Hay una gran competencia por las notas de los exámenes]	0.917
F7	26. The bureaucratic procedures at university take a long time [Los trámites burocráticos en la universidad llevan mucho tiempo]	0.695	0.759
	27. The relationship between the university department and its students is deficient [La relación entre la administración de la universidad y los estudiantes es deficiente]	0.760
	28. Sometimes we need books or extra material that are not available [A veces necesitamos libros o material extra que no están disponibles]	0.617
	29. The university department does not provide adequate or sufficient guidelines on specific topics [La universidad no proporciona directrices claras sobre determinados temas]	0.762

Note: p-values for all standardized loadings were <0.001. Empty cells in the item columns denote an item not being part of that factor within that model. † item 9 was moved from F2 to F3 in the proposed model; F1 = lack of leisure time [falta de tiempo libre]; F2 = academic performance [rendimiento académico]; F3 = fear of failure [miedo al fracaso académico]; F4 = academic overload [sobrecarga académica]; F5 = finances [aspectos económicos]; F6 = competition between students [competencia entre estudiantes]; F7 = relationships with university faculty [relaciones con la universidad].

Given that the correlations between our factors were generally low, ranging from 0.17 to 0.59, no higher-order model was tested. To compare both specifications on a common set of items, the original and revised assignments of Item 9 were each tested against a parent model in which this item loaded on both F2 and F3. Relative to the parent model, constraining Item 9 to its original factor (F2) significantly worsened fit (scaled Δχ²(1) = 202.89, p < 0.001, RMSEAD = 0.37), as did constraining it to its revised factor (F3; scaled Δχ²(1) = 148.31, p < 0.001, RMSEAD = 0.31), although the deterioration was markedly smaller for the revised assignment. In the parent model, Item 9 loaded somewhat more strongly on F3 (λ = 0.42, 95% CI [0.38, 0.47]) than on F2 (λ = 0.36, 95% CI [0.31, 0.42]). Collectively, these results supported the adequacy of the revised model. Descriptions and results regarding CFA have closely followed the checklist from Nye (2023).

Spearman correlations between the USSQ CFA-derived latent factor score (${\widehat{F}}_1, {\widehat{F}}_2, \dots , {\widehat{F}}_7$) and the DASS-21 scores were uniformly positive and small-to-moderate. Coefficients ranged from ρ= 0.204–0.440 across the DASS-21 subscales and ρ = 0.239–0.460 for the DASS-21 Total. The strongest and most consistent associations were observed for F3 (D = 0.395, A = 0.428, S = 0.440; Total = 0.460), followed by F1 (notably S = 0.403; Total = 0.401). The remaining factors (F2, F4–F7) showed weaker but still positive correlations (ρ ≈ 0.20–0.31). With the PWB, these correlations are negative and small-to-moderate, ranging in strength from −0.31 (between our second factor and environmental mastery) to −0.03 (between the sixth factor of our scale and purpose in life) (see Table 4).

Table 4. Correlations with DASS-21 and Ryff PWB scores.

USSQ Factors *	DASS-21 †			DASS-21 Total	PWB +
	D	A	S		Sel	Pos	Aut	Env	Pur	Per
F1	0.342	0.347	0.403	0.401	−0.223	−0.199	−0.136	−0.250	−0.095	−0.143
F2	0.302	0.278	0.262	0.300	−0.286	−0.189	−0.160	−0.310	−0.217	−0.300
F3	0.395	0.428	0.440	0.460	−0.296	−0.188	−0.247	−0.300	−0.116	−0.213
F4	0.206	0.238	0.249	0.252	−0.133	−0.117	−0.122	−0.167	−0.060	−0.090
F5	0.280	0.281	0.299	0.314	−0.187	−0.202	−0.088	−0.213	−0.086	−0.105
F6	0.221	0.241	0.267	0.267	−0.112	−0.123	−0.115	−0.137	−0.031	−0.039
F7	0.204	0.217	0.236	0.239	−0.138	−0.116	−0.082	−0.174	−0.084	−0.119

Note: p-values for all correlations were <0.001. * F1 (lack of leisure time), F2 (academic performance), F3 (fear of failure), F4 (academic overload), F5 (finances), F6 (competition between students), F7 (relationships with university faculty). † D (depression), A (anxiety), S (stress). + Sel (self-acceptance), Pos (positive relations with others), Aut (autonomy), Env (environmental mastery), Pur (purpose in life), Per (personal growth).

Before testing the USSQ measure invariance, model fit was tested in each category separately, showing good fit indices (see Table S2). Within the analyses, the results with the biological sex and gender identity variables were fairly similar. In both cases, the “Other” category was excluded, as its sample size was too small and would skew the results. Both configural levels were achieved, with adequate fit indices (see Table 5). The full threshold levels were not achieved, as they were significantly different (LRT’s p-value of 0.006 for biological sex and of 0.004 for gender identity), so two thresholds were freed in order to reach partial threshold invariance in both variables. The partial threshold and full loading levels were also not achieved (LRT’s p-value of 0.0002 for biological sex and of 4.39 × 10⁻⁶ for gender identity), so three loadings were freed in order to reach partial loading invariance in both cases.

Table 5. Measurement Invariance.

Variable	Invariance Level	χ2	df	p	χ2/df	RMSEA	[90% CI]	CFI	TLI	SRMR	p-Value
Biological sex	1. Configural	4976.648	606	<0.001	8.21	0.054	[0.053, 0.056]	0.967	0.961	0.045
	2. Threshold	5065.908	633	<0.001	8.00	0.053	[0.052, 0.055]	0.966	0.962	0.045	0.006
	with 1 freed	5058.541	632	<0.001	8.00	0.053	[0.052, 0.055]	0.966	0.962	0.045	0.036
	with 2 freed	5051.409	631	<0.001	8.01	0.053	[0.052, 0.055]	0.966	0.962	0.045	0.184
	3. Loading	4983.516	651	<0.001	7.66	0.052	[0.051, 0.053]	0.967	0.964	0.046	0.0002
	with 1 freed	4974.831	650	<0.001	7.65	0.052	[0.051, 0.053]	0.967	0.964	0.046	0.005
	with 2 freed	4972.066	649	<0.001	7.66	0.052	[0.051, 0.053]	0.967	0.964	0.046	0.081
	with 3 freed	4967.130	648	<0.001	7.67	0.052	[0.051, 0.053]	0.967	0.964	0.046	0.190
Gender identity	1. Configural	4996.927	606	<0.001	8.25	0.054	[0.053, 0.056]	0.966	0.961	0.046
	2. Threshold	5087.111	633	<0.001	8.04	0.054	[0.052, 0.055]	0.966	0.962	0.046	0.004
	with 1 freed	5079.568	632	<0.001	8.04	0.054	[0.052, 0.055]	0.966	0.962	0.046	0.028
	with 2 freed	5072.075	631	<0.001	8.04	0.054	[0.052, 0.055]	0.966	0.962	0.046	0.170
	3. Loading	5012.755	651	<0.001	7.70	0.052	[0.051, 0.054]	0.967	0.964	0.046	4.39 × 10−6
	with 1 freed	4998.293	650	<0.001	7.69	0.052	[0.051, 0.054]	0.967	0.964	0.046	0.0006
	with 2 freed	4990.454	649	<0.001	7.69	0.052	[0.051, 0.054]	0.967	0.964	0.046	0.009
	with 3 freed	4986.714	648	<0.001	7.70	0.052	[0.051, 0.054]	0.967	0.964	0.046	0.161
Study level	1. Configural	4343.305	606	<0.001	7.17	0.050	[0.049, 0.051]	0.970	0.965	0.043
	2. Threshold	4396.001	633	<0.001	6.94	0.049	[0.048, 0.051]	0.970	0.967	0.043	0.764
	3. Loading	4311.575	653	<0.001	6.60	0.048	[0.046, 0.049]	0.971	0.969	0.043	0.910

Note: χ² = chi-square value; df = degrees of freedom; p = p-value; χ²/df = chi-square and degrees of freedom ratio; CFI: comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation, with its 90% confidence interval; SRMS = standardized root-mean-square residual; p-value = likelihood ratio test’s p-value.

In the measurement invariance analysis across study levels (undergraduate versus graduate students), configural invariance was established, yielding adequate fit indices. Full metric (loadings) and full scalar (thresholds) invariance were also supported, as the constrained models did not significantly degrade model fit compared to the baseline models (LRT’s p = 0.910 for loading and p = 0.764 for threshold invariance) and maintained adequate fit values (see Table 5). Based on this comprehensive structural support, this adapted version will hereafter be designated as the University Student Stressors Questionnaire (USSQ).

4. Discussion

This study’s objective was to adapt the USQ into Spanish, and evaluate its psychometric properties, including its convergent validity, in a Spanish university student population. An optimized factorial model demonstrated robust construct validity, reliability, and convergent validity with the DASS-21 and PWB scales. Factor analyses (EFA, CFA) led to the removal of items 21 and 25 due to negligible loadings (<0.40). Conceptually, it can be hypothesized that item 21 (“I work during the period of my studies”) diverges from Factor 5 (finances) because it describes an objective condition that does not necessarily imply a perceived threat. Similarly, item 25 (“I work with students that are non-cooperative”) lacks coherence with Factor 6 (competition among students), as a lack of cooperation reflects an individualistic climate rather than explicit academic competition (Deutsch, 1962; Johnson & Johnson, 1989). Furthermore, item 9 (“I think that I will not be able to meet the academic requirements to graduate”) was reassigned from Factor 2 (academic performance) to Factor 3 (fear of failure), where it demonstrated stronger correlations. From a psychological perspective, this realignment is consistent with the transactional theory of stress (Lazarus, 1991; Lazarus & Folkman, 1984). Item 9 captures a future-oriented threat appraisal, contrasting with the retrospective evaluations of the remaining Factor 2 items.

The removal of items 21 and 25 and the relocation of item 9 improve the psychometric properties reported in the original study (Kamtsios & Karagiannopoulou, 2015), supporting hypotheses 1 and 2. Furthermore, our results demonstrating that the questionnaire -initially designed solely for undergraduate students- exhibits equivalent measurement properties among postgraduate populations, justifying its renaming to the University Student Stressors Questionnaire (USSQ) and supporting hypothesis 3. Regarding convergent validity, the USSQ exhibited positive associations with indicators of psychological distress (DASS-21), aligning with hypothesis 4 and with extensive literature (Andargeery, 2024; Haruna et al., 2025; Karki et al., 2022; Li et al., 2022; Luo et al., 2025; Mirza et al., 2021; Ramesh et al., 2024; Vinet et al., 2022). All seven USSQ factors correlated significantly and positively with the DASS-21 scales. Fear of failure, lack of leisure time and academic performance showed the highest correlations, in this order. However, all associations were more moderate than typically reported. This divergence likely stems from the fact that other academic stress scales often conflate stressors with emotional symptoms, inherently overlapping with the construct of psychological distress. Conversely, by strictly focusing on academic stressors rather than the symptoms they generate, the moderate correlations found in our study reinforce the conceptual boundary between these constructs, thereby providing evidence of discriminant validity. Furthermore, the multidimensional structure of the scale may also account for the magnitude of the observed correlations. Rather than reflecting a single global dimension of academic stress, the instrument comprises seven distinct factors, each capturing a specific facet of the construct. Consequently, associations with external measures are expected to be more nuanced, variable, or selective, depending on the degree of conceptual correspondence of each specific dimension.

Hypothesis 5 was also confirmed. In alignment with the expected pattern, the strongest associations were observed with the DASS-21 total score—a finding consistent with research utilizing alternative stressor questionnaires (Luo et al., 2025; Ramesh et al., 2024); among the specific subscales, correlations followed a descending order of magnitude from stress to anxiety, with the weakest associations found with the depression subscale. The only exception to this pattern was Factor 2 (academic performance), which presented its strongest correlation with the DASS-21 depression subscale. This finding is theoretically congruent; while the anticipation of tasks or exams predominantly elicits anxiety and tension, sustained worry regarding academic performance -or the perception of failing to meet required standards- is more closely linked to feelings of failure, diminished self-efficacy, and hopelessness, which are core features of depressive symptomatology (Beck, 1967).

Finally, all USSQ factors were negatively associated with Ryff’s Psychological Well-Being scale, in line with hypothesis 6 and consistent with previous evidence documenting the detrimental impact of academic stress on university student well-being (Abdul Aziz et al., 2024; Delgado-Tenorio et al., 2021; Jabin, 2025; Ofori et al., 2018). The highest correlations were found between environmental mastery and self-acceptance on the one hand and academic performance and fear of failure, on the other. This could indicate that high academic demands erode students’ perceived capability to govern and adaptively manage their daily environment, as well as their positive self-evaluation. Alternatively, it could imply that students with a more negative self-evaluation and a decreased sense of environmental mastery are more vulnerable to academic stressors.

At the stressor level, academic performance and fear of failure showed the most extensive negative associations across well-being dimensions. A more negative perception of academic performance was associated with lower environmental mastery and personal growth, suggesting that chronic perceived underachievement undermines feelings of competence and personal development. Meanwhile, the stronger negative correlation of fear of failure with self-acceptance and autonomy might reflect how evaluation-related anxiety fosters dependency on external validation and undermines self-worth. The alternative interpretation would be that students with lower well-being are more vulnerable to academic stressors. In contrast, dimensions such as purpose in life remained largely unaffected by interpersonal and organizational stressors (e.g., student competition or bureaucratic procedures).

Factor 3 (fear of failure) accounted for the largest proportion of variance (0.12) and exhibited the strongest positive association with psychological distress (DASS-21) alongside the strongest negative association with well-being (PWB). Specifically, its constituent items encompass anxiety and worry regarding academic performance, conceptually positioning it closer to psychological distress and suggesting it may constitute the core of academic stress within our student sample. These results are consistent with contemporary evidence indicating that worry over evaluations, exams, and beliefs about one’s capacity to meet academic demands represent primary sources of stress for university students (Rastogi et al., 2025; Ruiz-Camacho & Gozalo, 2025).

Regarding measurement invariance, our findings demonstrated that the USSQ’s factor structure is fully equivalent across study levels. The achievement of full invariance between undergraduate and postgraduate students is a critical finding, as it confirms that both groups interpret the items and use the response scale identically. This robust equivalence empirically justifies the instrument’s renaming as University Student Stressors Questionnaire (USSQ) and validates its use for making direct, unbiased comparisons of academic stress across different stages of higher education. Furthermore, the analyses across biological sex and gender identity achieved partial threshold and loading invariance. Although a few parameters (two thresholds and three loadings) had to be freed, achieving partial scalar invariance implies that the core meaning of the construct remains fundamentally stable across the different sex and gender groups evaluated. In practical terms, this level of partial invariance is widely considered sufficient for conducting meaningful comparisons between groups, indicating that the USSQ is a reliable and unbiased tool for examining both sex and gender differences in academic stress. Importantly, these findings suggest that observed group differences in USSQ scores are more likely to reflect genuine variations in stress experiences rather than measurement artifacts. This strengthens the utility of the instrument for institutional monitoring, epidemiological research, and the evaluation of preventive and support interventions targeting university students.

Several limitations should be acknowledged. First, data collection occurred via in-person classroom visits, inherently excluding students who were absent on those days. This may introduce a degree of selection bias, as regular attendees might perceive university stressors differently than their frequently absent peers. Second, while the substantial sample size partially mitigates concerns regarding representativeness, the generalizability of our findings to students at other Spanish universities remains to be established. Third, although the present study centers on traditional academic stressors that have consistently dominated the literature, future research must also address emerging sources of strain. These include digital stress (Chaouali et al., 2025), fear of missing out (FoMO), social media fatigue (Zhan et al., 2025), and poor sleep quality, all of which are strongly linked to elevated anxiety and depression. Furthermore, successive crises in the aftermath of COVID-19 have increased loneliness (Pérez-Jorge et al., 2025) and identity-related challenges (Brata et al., 2025). Contemporary students are required to shape their identities within an environment characterized by fluidity and uncertainty, a context that heavily amplifies psychological strain. Finally, it is noteworthy that these emerging variables do not operate in isolation; rather, they interact dynamically with traditional academic demands to form a complex, multilayered network of stress.

Our study presents several notable strengths. Primary among these is the large, census-based sample, which provides comprehensive representation of students across all degree programs and academic years at the university. Furthermore, unlike the original validation (Kamtsios & Karagiannopoulou, 2015), which focused solely on undergraduates, the current study expands the instrument’s scope by including postgraduate students, for whom the scale yielded equally robust psychometric properties.

This study equips the scientific community with a robust psychometric tool to examine the perception of academic stressors among both undergraduate and postgraduate students. To streamline the scoring process and mitigate potential manual calculation errors, an open-access web application was developed using R Shiny (version 1.13.0), allowing researchers and practitioners to simply input item responses to generate results. This tool is freely available to all users at https://diegorivera.shinyapps.io/calculator_ussq/ (accessed on 4 November 2026). Ultimately, it is anticipated that the accessibility of the USSQ will foster international research, yielding data that elucidate the similarities and differences in perceived university stressors across diverse cultural contexts and higher education systems.

Our findings highlight several promising avenues for future research. First, subsequent studies should investigate variations in perceived university stressors across diverse academic disciplines. Particular focus is warranted on health sciences programs, which literature consistently identifies as highly stressful environments for students (Chen et al., 2020; Ruiz-Robledillo et al., 2022). Second, longitudinal designs tracking student cohorts from their first year (T1) to their final year (T2) would be highly valuable for analyzing developmental trajectories of perceived academic stressors and establishing causal relationships between these stressors and students’ distress and well-being. Furthermore, such designs would enable researchers to evaluate the predictive validity of the USSQ—specifically, whether stressor dimensions measured at T1 forecast mental health outcomes at T2—as well as to explore the complex mediators and moderators underlying these effects.

5. Conclusions

This study provides the first comprehensive adaptation and psychometric validation of the University Student Stressors Questionnaire (USSQ) in a large Spanish university population, offering significant theoretical and practical advancements.

From a theoretical perspective, by optimizing the scale into a robust seven-factor structure, the USSQ clearly isolates academic stressors from emotional symptoms. This empirical distinction reinforces the conceptual boundary within the transactional theory of stress, offering further evidence that the triggers of stress are distinct from the resulting psychological distress. Furthermore, establishing full measurement invariance across both undergraduate and postgraduate educational levels provides evidence that the underlying multidimensional architecture of academic stress remains stable throughout higher education, justifying the expansion of the construct beyond undergraduate populations.

From a practical and applied standpoint, our findings could directly inform institutional policies and clinical interventions. Given that fear of failure emerges as the most salient academic stressor and the strongest correlate of psychological distress, preventive efforts in universities should specifically target this domain through broad educational measures and individualized psychological support. Similarly, the high correlations with lack of leisure time suggest that interventions might be accompanied by time management training, while the negative impact of academic performance on personal growth implies that universities should consider offering underperforming students support at the level of personal resources, not just academic tutoring.

Ultimately, the USSQ equips university mental health services and academic policymakers with a precise screening instrument. Because it delineates seven specific stress factors, university health services can move beyond generic stress-management programs and instead design targeted psychological interventions. Supported by the availability of an open-access web application that automates scoring, it can facilitate early risk detection and the design of targeted preventive interventions to foster student resilience.