The Attachment theory was first described by John Bowlby (1969) and subsequently examined through observational and laboratory studies (Ainsworth et al., 2015). It emphasizes the relationship between early caregiving and the later capacity to establish affective bonds, insofar as the child is provided with the availability to create either a secure or insecure base (Bowlby, 1982; Kobak et al., 2016).

Attachment models have allowed the identification of two broad groups: secure and insecure (Lippold et al., 2022). The first occurs when the mother adequately interprets and responds to the child’s signals, establishing communication attuned to the child’s needs (Pineda, 2021). Accordingly, individuals with secure attachment develop a mindset that values trust in others as much as self-confidence. These individuals can recognize their needs, express emotions without resorting to aggression, establish bonds, and remain cognitively flexible (Hernández-Cadeño et al., 2019).

Insecure attachment, in turn, involves inconsistent or rejecting interactions on the part of the mother (Ainsworth, 1982). In adulthood, this pattern is associated with the use of maladaptive emotion regulation strategies (Bekaroglu & Bozo, 2017). More specifically, one line of research in this field has identified four types of attachment: secure, preoccupied, fearful, and dismissing (Bartholomew & Horowitz, 1991; Griffin & Bartholomew, 1994).

Building on the studies of Bowlby (1969, 1973, 1988), Bartholomew and Horowitz (1991), and Griffin and Bartholomew (1994), proposed that individuals with secure attachment exhibit a positive model of both self and other. The dismissing pattern is characterized by a positive model of self and a negative model of other. By contrast, the preoccupied pattern is characterized by a negative model of self and a positive model of other. Finally, individuals with both negative self and other representations tend to display a fearful attachment style. In this sense, insecure attachment patterns have been linked to various psychological conditions, among which emotional dysregulation stands out (Milozzi & Marmo, 2022).

Emotional dysregulation (ED) is defined as the maladaptive strategies used to cope with intense emotions (Gross, 2013). It has been shown to predict psychopathological symptoms, such as depression (Cole et al., 2019; Thompson, 2019). Research has reported a correlation between ED and depressive symptoms (Tartaro et al., 2024), suggesting that ED may represent a relevant variable in the manifestation of depressive symptomatology (Vanderlind et al., 2020; Yoon & Rottenberg, 2020).

In this regard, it is essential to recognize that depressive symptoms in university students are closely related to affective processes, as emotions are central to these processes and emerge as intense, temporary responses to contextual stimuli (Gómez & Calleja, 2016; Mestre & Guil, 2012; Perera-Medina et al., 2019). ED may therefore operate as a negative mediator, increasing vulnerability and fostering the development of depression (Dryman & Heimberg, 2018; Jackson-Koku & Grime, 2019; Leehr et al., 2015; Malik et al., 2015).

Depression is an affective disorder that has accompanied human experience throughout history and represents a leading cause of social disability (American Psychiatric Association [APA], 2014); World Health Organization [WHO], 2020) From a pathological perspective, it is characterized by mood alterations and emotional disturbance, with well-defined signs and symptoms (França et al., 2022). It is conceptualized as Major Depressive Disorder (MDD) in the DSM-5 TR (APA, 2022). However, depressive symptoms are highly prevalent among university students, even in the absence of a formal diagnosis, along with other psychopathological manifestations (Oswalt et al., 2020).

MDD is characterized by persistent feelings of sadness, emptiness, or irritability, accompanied by alterations that significantly impair functional capacity. According to the WHO (2023), approximately 280 million people worldwide suffer from depression. In Brazil, data from the Brazilian Institute of Geography and Statistics (IBGE, 2019) indicate that 10.2 % of individuals aged 18 years or older had been diagnosed with depression by a health professional.

In the university context, the mental health of students has received increasing attention in recent years, not only from health specialists but also from society at large (Serra et al., 2015), given that academic trajectories involve considerable pressure related to study and practical demands (Flesch et al., 2020). More specifically, the prevalence of depression and/or depressive symptoms has been reported among students in medicine (22.73 %), nursing (71.02 %), and dentistry (60.64 %) programs (Lima et al., 2019). Thus, depressive symptoms are included within the broader set of Minor Mental Disorders (MMD), given their high prevalence and impact on individuals across different regions of the world (Perini et al., 2019; Steel et al., 2014).

MMD are characterized by non-psychotic symptoms, generally related to subclinical conditions of stress, anxiety, and depression, which compromise normal functioning-conditions that are less severe but more frequent than major psychiatric disorders (Silva & Santos, 2019; Silva et al., 2020; Toneli et al., 2024). Experiencing MMD does not necessarily require a formal psychiatric diagnosis; however, it is associated with psychological distress and can negatively influence quality of life (Fiorotti et al., 2010).

A factor associated with MMD is the transition into higher education, as students’ efforts to achieve strong academic performance may increase their susceptibility to stress and depressive symptoms, potentially leading to course withdrawal (Melo et al., 2020; Silva et al., 2019). In this regard, Auerbach et al. (2018) study conducted among university students from eight countries found that 35 % screened positive for at least one minor mental disorder.

Although there is evidence suggesting a negative association between anxious attachments and emotional dysregulation with psychopathological symptomatology, no investigations have yet been conducted aiming at a confirmatory factor analysis that integrates the concepts of attachment, emotional dysregulation, self-evaluation, and psychopathological symptoms. This is due to the fact that, in the literature, these constructs have been based in isolation. Furthermore, most previous investigations in this area have focused predominantly on intrapersonal approaches to emotional regulation, disregarding social interactions in the mechanisms of emotional dysregulation (Messina et al., 2023) and self-evaluation, as well as their symptomatic consequences.

Accordingly, the present study aimed to conduct a confirmatory factor analysis of the path analysis model examining the relationships among attachment, emotional dysregulation, minor mental disorders, and depressive symptomatology. The following hypotheses were proposed: H1. Anxious attachment (preoccupied and fearful) will be positive-associated with emotional dysregulation (ED), and negative-associated with a positive auto-perception; H2. ED will have heavier weight than auto-perception regarding psychopathological symptoms; H3. The dismissing attachment will show a low capacity to explain the model.

Methods

A total of 540 university students of both genders, aged 18 years or older (M = 21.6 years, SD = 5.2), enrolled in public or private higher education institutions, participated in the study, as shown in Table 1.

Instruments

Identification Questionnaire (QI): The questionnaire contained both closed- and open-ended questions regarding age, biological sex, marital status, and type of university (public or private).

Brazilian Attachment Scale – Adult Version (EBRAPEG-A; Tartaro et al., 2024): The scale assesses adult attachment and comprises 24 items rated on a 4-point Likert-type scale, ranging from “strongly disagree” to “strongly agree.” It has a multifactorial structure with four attachment styles: secure, fearful, dismissing, and preoccupied. Reliability indices ranged from 0.88 to 0.92 across subscales.

Depression Indicators Assessment Battery [HUMOR-A] (Baptista & Borges, 2025): This battery was designed to evaluate various indicators related to depression (e.g., self-assessment). It comprises 49 items and assesses seven factors: loneliness/social isolation, helplessness, hopelessness, self-esteem, self-concept/self-image, and self-efficacy. Responses are given on a 3-point Likert-type scale (0 = “no/never,” 1 = “sometimes,” 2 = “yes/always”). Reliability indices ranged from 0.84 to 0.93 across factors.

Emotional Dysregulation Scale – Adult Version (EDE-A; Baptista et al., 2023): The EDE-A contains 15 items across four dimensions: adaptive coping strategies (+), externalization of aggression (-), pessimism (-), and freezing/paralysis (-). Responses are rated on a 5-point Likert-type scale, ranging from “never/not at all” to “always.” Reliability indices ranged from 0.79 to 0.95 across dimensions.

Minor Mental Disorders Scale (MENTAL; Baptista & Franco, 2025): this scale comprises 36 items rated on a 4-point Likert-type scale, ranging from “never/none” to “always/every day.” Reliability indices ranged from 0.80 to 0.90 across subscales.

Procedure

Data were collected individually online via Google Forms, following participants’ signing of the Informed Consent Form. The session lasted approximately 30 minutes. After completing the identification questionnaire, the scales were administered in the following order: EDE-A, HUMOR-A, EBRAPEG-A, and MENTAL. The study protocol was approved by the institutional ethics committee (CAAE: 78143824.5.0000.5481).

Data analysis

Data were compiled in an Excel spreadsheet and analyzed using Jamovi (The Jamovi Project, 2024) and RStudio [version 2024.12.1] (R Core Team, 2023). Analyses were conducted in multiple stages. First, the measurement model was tested via Confirmatory Factor Analysis (CFA) within a Structural Equation Modeling (SEM) framework. This measurement model specified latent variables based on observed indicators, without including structural paths between latent variables (Kline, 2016). The model supports a linear association from attachment, as an early form of socioemotional and cognitive learning, to the development of minor mental disorder symptoms (Figure 1).

Second, descriptive analyses were performed (Table 2). As normality assumptions were not met, non-parametric analyses were conducted using the R packages dplyr (Wickham et al., 2023), psych (Revelle, 2025), and moments (Komsta & Novomestky, 2022).

Third, reliability analyses were conducted for the instruments used, employing Cronbach’s alpha and omega coefficients. Fourth, the proposed model was tested using confirmatory factor analysis (CFA) with diagonally weighted least squares (DWLS) estimation, recommended for violations of multivariate normality (Fitriyati & Wijaya, 2022; Izquierdo et al., 2014; Li, 2016). Model fit was evaluated using incremental fit indices (NFI, CFI), absolute fit indices (GFI, AGFI), and error approximation indices (RMSEA, SRMR) (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1998; Kline, 2016). Estimates were computed with the lavaan package (Rosseel, 2012), and graphical representations were created using semPlot (Epskamp et al., 2026).

Thus, the present study employed a confirmatory SEM composed of two rival measurement models, whose comparison allowed for an evaluation of the robustness of the proposed associations and provided a basis for the presentation of results. Based on the data, criteria for good model fit were defined according to various indices. For NFI (Normed Fit Index), CFI (Comparative Fit Index), GFI (Goodness of Fit Index), and AGFI (Adjusted Goodness of Fit Index), fit is considered acceptable if values exceed 0.90 and good if values exceed 0.95. For RMSEA (Root Mean Square Error of Approximation) and SRMR (Standardized Root Mean Square Residual), fit is considered acceptable if values are below 0.08 and good if below 0.05. Additionally, for RMSEA, the upper bound of the 90 % confidence interval should be below 0.08 to be considered acceptable.

Finally, a rival model was developed. A Spearman correlation analysis was first conducted to examine the associations among components. After performing a theoretical and empirical clustering of the factors, a model including these factors was constructed and refined to assess its fit.

Results

A reliability analysis was conducted for each instrument, using both Cronbach’s alpha and omega coefficients. Results indicate good reliability for all instruments, except for the Externalization of Aggression factor in the EDE-A (Table 3).

Next, a confirmatory analysis of the proposed model was conducted. The first model tested was based on a theoretical interpretation of attachment theory, in which secure attachment occurs when the caregiver accurately interprets and responds to the child’s signals (Pineda, 2021). In contrast, insecure attachment involves inconsistent or rejecting interactions by the caregiver (Ainsworth, 1982), which in adulthood leads to the use of maladaptive emotion regulation strategies (Bekaroglu & Bozo, 2017). These strategies are associated with depression and/or MMD symptoms (Aslan et al., 2024; Tartaro et al., 2024). Model fit is presented in Table 4 and in the model summary diagram in Figure 2. The results indicate a good model fit to the data, with appropriate factor loadings.

Considering the fit of the previous model, a model with improved fit was designed. Spearman correlations were used to examine the associations between variables (Figure 3). Based on these results, a new model was constructed (Figure 4) and subsequently tested (Table 4).

Although the second model showed good fit, variables with lower correlations with other factors, as well as the EDEA aggressiveness externalization variable—which displayed lower reliability—were removed. Accordingly, a third model was constructed (Figure 5). Testing the fit of this model revealed results similar to the previous ones, while providing a more theoretically coherent structure and regularized parameter estimates (Table 4).

Discussion

This study aimed to examine, using confirmatory factor analysis (CFA) within a structural equation modeling (SEM) framework, the associations among attachment styles, emotional dysregulation (ED), depression, and additional indicators of minor mental disorders (MMD), including self-evaluation. To this end, a series of competing models were specified and compared.

Model 1 demonstrated acceptable-to-good fit indices. However, despite its satisfactory statistical performance, it was considered theoretically underspecified, as it did not incorporate self-perception–related variables, which are conceptually relevant to the proposed framework. Model 2 was subsequently specified to address this limitation; nevertheless, it lacked theoretically and statistically robust structural paths linking mediating constructs and symptom-related latent variables, resulting in a less coherent structural configuration.

In Model 3, dismissing attachment and aggressiveness were removed from the structural model. This decision was based on both statistical and substantive criteria: these variables showed weak loadings and limited contribution to overall model fit, and their inclusion reduced model parsimony. Moreover, given that the sample consisted of non-clinical university students, these traits exhibited restricted variance, which may have compromised parameter stability and inflated estimation error. The final model therefore prioritized theoretical coherence, empirical adequacy, and parsimony.

The results aligned with previous literature, showing correlations between insecure attachment styles, ED, and depressive indicators (Tartaro et al., 2024). As highlighted by Vanderlind et al. (2020), ED may play a crucial role in the development of depression, as well as other psychopathological symptoms (Paulus et al., 2021).

It is essential to understand that exposure to insufficiently supportive caregiving in childhood can lead to insecure/anxious attachment styles. These styles, in turn, show a significant association with psychopathological symptoms in adulthood (Cassidy et al., 2013; Fonagy et al., 2011; Hayre et al., 2019; Mikulincer & Shaver, 2017; Schindler, 2019; Williams et al., 2019). In this context, analyses indicated that insecure attachment could account for ED (Mediator 1, composed of aggressiveness, pessimism, and freezing). This association may arise from the lack of consistent protection and support from primary caregivers, a factor that can contribute to the development of insecure attachment styles (Simons et al., 2014; Smith et al., 2016). Similarly, insecure attachment was also able to explain the self-evaluation processes of participants, including self-esteem, self-concept, and self-efficacy, as reported by Vacaru et al. (2018).

Based on the previous study, the main explanation for the relationship between low self-concept and insecure attachment is that exploratory behavior acts as a mediator. Children who develop insecure attachment with their caregivers tend to experience less freedom or safety to explore their surroundings. This limitation in exploration prevents them from mastering new skills and acquiring competencies, which are crucial for the development of a positive self-image. Instead, the lack of opportunities to engage with the world leads to a perception of inefficacy or incapacity, resulting directly in a low self-concept.

However, dismissive attachment was removed from the explanatory model due to its lack of association with other constructs (Tartaro et al., 2021) and absence of statistical significance. In addition, insecure attachment was found to negatively predict self-perception (mediator 2, composed of self-esteem, self-concept, and self-efficacy), consistent with evidence that negligent caregiving in childhood affects the expression of insecure attachment styles (Ensink et al., 2020). Lyvers et al. (2019) reported associations between insecure attachment and low emotional regulation skills, supporting the idea that the presence of insecure attachment styles can contribute significantly to the development of psychopathological symptoms (Tartaro et al., 2021).

Secure attachment, in contrast, negatively predicted emotional dysregulation, although this effect was modest in the current results, suggesting that attachment quality emerges as a key factor enabling individuals to face environmental challenges across the lifespan (Eikenæs et al., 2015). Secure attachment may therefore serve as a protective factor for maltreated children at high risk of emotional dysregulation (Gause et al., 2022). Secure attachment was also positively associated with self-perception, reflecting the influence of early learning experiences and perceptions from a secure base (e.g., caregiver) that promote positive views of self and others (Bartholomew & Horowitz, 1991; Griffin & Bartholomew, 1994). This attachment style is characterized by adaptive affect regulation, emotional closeness combined with interpersonal autonomy, and the ability to understand mental states and regulate emotions (Bowlby, 1988). Accordingly, individuals with secure attachment tend to report higher life satisfaction, more positive self-image, and higher self-esteem (Frederick et al., 2016).

The results indicate that emotional dysregulation provided the strongest explanation for psychopathological symptomatology (Mediator 3, comprising somatocognitive factors, social insecurity, hopelessness, dysphoria, hyperactivation, depression, and feelings of helplessness), with a very high loading (0.90), indicating that nearly all psychopathological symptoms are explained by emotional dysregulation. Furthermore, this factor acts as a key mediator of attachment styles, particularly insecure attachment. This association may be explained by the well-established link between insecure attachment and emotional dysregulation, which increases individuals’ tendency to ruminate on problems and attribute blame to themselves or others, processes that are closely related to the development of psychopathological symptoms (Ye et al., 2024).

Finally, regarding study limitations, despite the sample size, the participant pool was largely concentrated in the Southeast region, especially in the city of Campinas, and predominantly female. Future research should include university students from other regions to assess the prevalence of depressive symptoms and minor mental disorders across diverse institutions, both public and private, as well as among groups with diagnosed psychopathologies.

Conclusions and final considerations

This study aimed to examine an explanatory model involving attachment styles, emotional dysregulation, self-evaluation, and psychopathological symptoms in university students. The results indicated that supportive relationships established during childhood may be associated with the manifestation of psychopathological symptoms in university students, highlighting the importance of early caregiving experiences for mental health in adulthood.

CFA demonstrated that insecure attachment, particularly the preoccupied and fearful types, was directly associated with higher levels of emotional dysregulation and negative self-evaluation (low self-esteem and self-efficacy), which in turn were significant predictors of depressive symptoms and other minor mental disorders. In contrast, secure attachment was identified as a protective factor, showing a positive association with self-evaluation and a negative association with emotional dysregulation, albeit modest.

These findings underscore the role of emotional dysregulation as a crucial mediator linking attachment style to psychopathological manifestations. Accordingly, the results highlight the need for psychotherapeutic and preventive interventions in university settings that focus not only on overt symptoms but also on emotional regulation strategies and attachment patterns, aiming to foster a safer and more resilient emotional foundation for students.

Acknowledgments

This research was funded by FAPIC/RECTORY, under the research project “Do Attachment, Emotional Dysregulation, and Self-Evaluation Explain Psychopathological Symptoms in University Students?”. The authors also thank CNPq for supporting the research through the productivity scholarship awarded to the first author.

References

Notes

^* Research article

Author notes

a Correspondence autor. Email: makilim01@gmail.com

Additional information

How to cite: Nunes-Baptista, M., Carvalho de Almeida, M. S. de A., Francisco-Ritoni, C. de F., & García, M. J. (2026). ¿Do attachment, emotional dysregulation, and self-evaluation explain psychopathological symptoms in university students?. Universitas Psychologica, 25, 1-14. https://doi.org/10.11144/Javeriana.upsy25.aeds

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