Introduction
The assessment of personality pathology in college students is important because it provides a framework of individual description and individuality (Sharp & Wall, 2018). It can even predict academic motivation and the likelihood of future academic success or failure (Lin, 2012). So, understanding and developing the knowledge about factors that affect academic motivation can help to improve educational performance (Hazrati-Viari, Rad, & Torabi, 2012). However, personality dysfunction is commonly misdiagnosed or completely missed in this population (Au-Yeung et al., 2019). Indeed, clinically significant emotional dysregulation and externalizing behaviors are sometimes overlooked during this period because they are mistaken for transient, age-related abnormal behaviors (Bleiberg, Rossouw, & Fonagy, 2012; Irwin, Burg, & Uhler Cart, 2002; Korhonen, Luoma, Salmelin, Siirtola, & Puura, 2018).
Personality disorders (PD) are conceived by the Diagnostic and Statistical Manual of Mental Disorders (DSM) with regard to 10 discrete diagnoses based on the approval of a determined subgroup of diagnostic criteria (American Psychiatric Association [APA]; 2013). Not only the high rate of comorbidity among diagnoses has caused criticism of this model of personality pathology, but also the heterogeneity of symptomology within a single diagnosis, the prevalence of the personality disorder not otherwise specified (PD-NOS) diagnosis, and the arbitrary boundaries between normal and abnormal functioning (Krueger, 2013; Krueger & Eaton, 2010; Widiger & Trull, 2007). To tackle these criticisms, a substitute, dimensional model of personality pathology is located in Section III (Emerging Measures and Models) of the DSM-5 to promote research while sustaining permanence with current clinical practice (APA, 2013).
The Section III alternative model conceives personality pathology in terms of both general personality dysfunction or severity (Criterion A) and dimensional pathological traits or style (Criterion B; (Skodol, 2012). The Personality Inventory for the DSM-5 (PID-5; Krueger, Derringer, Markon, Watson, & Skodol, 2012) is a measure of Criterion B, assessing 25 pathological personality facets covering the five domains of Negative Affect, Detachment, Antagonism, Disinhibition, and Psychoticism (Dowgwillo, Ménard, Krueger, & Pincus, 2016).
Despite the constant progression and the high level of research occurring during the last decades, PD have been largely debated (Bronchain, Raynal, & Chabrol, 2019). Classically, PD have been described among others as categorical entities consisting of affective (callousness, lack of remorse/guilt), interpersonal (manipulative tendencies, grandiose sense of self-worth), erratic lifestyle (impulsivity, irresponsibility), and even antisocial (disinhibition, criminal versatility) factors (Hare & Neumann, 2005). Recent approaches conceptualize PD as multidimensional constructs referring to various profiles and associated with inconsistent manifestations at clinical and subclinical levels (Lilienfeld, Watts, Francis Smith, Berg, & Latzman, 2015). The identification of PD is therefore of considerable importance, as it could impact the evaluation, prevention, and treatment of individuals displaying these characteristics.
For example, a study validated the Personality Inventory PID-5 (Krueger, 2013) for DSM-5 (APA, 2013), in a Colombian clinical population (Ferrer, Londoño, Calvete, & Krueger, 2019a). Participants were 341 patients between 18 and 60 years of age, 60% of women. Men scored significantly higher than women on grandiosity (t = 3.95, p < 0.001, d= 0.43), irresponsibility (t = 2.53, p = 0.012, d = 0.27), manipulativeness (t = 3.27, p < 0.001, d = 0.35), risk taking (t = 2.50, p = 0.013, d = 0.28), antagonism (t=2.80, p= 0.005, d = 0.32), disinhibition (t = 2.30, p = 0.022, d = 0.23).
In two facets the scores for women were significantly higher than for men: emotional lability (t = -2.34, p = 0.020, d= 0.27), intimacy avoidance (t = -3.36, p < 0.001, d= -0.36). Overall, the traits in which men had a result significantly higher than women were associated with anti-social features (irresponsibility, manipulativeness, risk-taking, antagonism and disinhibition). Moreover, women scored higher than men in facets and domains that are associated with borderline traits (emotional lability) and avoidant (intimacy avoidance).
Another work examined the association between maladaptive personality dysfunction and traits using a Singaporean college students sample (Lim, Gwee, & Hong, 2019). It was found that hostility double-loaded on Antagonism and Detachment, which came as a surprise because it typically loaded on Antagonism and Negative Affectivity. The authors decided to subsume Hostility under Antagonism based on theoretical considerations. Second, Eccentricity loaded most strongly on Disinhibition rather than Psychoticism, though it had a secondary loading on the latter. Despite these deviations, the current PID-5 factor structure was consistent with the Krueger colleagues’ structure; congruence coefficients were as follows: Negative Affectivity (.93), Detachment (.96), Antagonism (.96), Disinhibition (.90), and Psychoticism (.93).
Likewise, a recent study examined the relationship between the five broad maladaptive traits included in the DSM-5 (Negative Affect, Detachment, Antagonism, Disinhibition and Psychoticism) and a wide range of criteria of college students functioning: behavioral (bullying, cyberbullying, victimization, cybervictimization, problematic Internet use, substance use) (Romero & Alonso, 2019). As for gender comparisons, women scored higher on Negative Affect (p < .001, d= 0.28) and lower on Detachment (p < .01, d=-0.20), Antagonism (p < .001, d=-0.40) and Disinhibition (p < .01, d= -0.19) than men. Considering age, it was positively (although only slightly) correlated with the maladaptive traits: Detachment (r= 0.12, p < 0.001), Antagonism (r=0.09, p <0.01), Disinhibition (r= 0.08, p < 0.05), Psychoticism (r= 0.10, p <0.01).
Regarding the behavioral criteria, all of them were significantly correlated with at least some of the maladaptive traits. Antagonism: bullying-aggression (r= 0.46, p<.00045), bullying-victimization (r=0.23, p<.00045), cyberbullying aggression (r=0.35, p<.00045), cyberbullying-victimization (r=0.29, p<.00045), problematic internet use (r=0.36, p<.00045), monthly consumption of alcohol (r=0.22, p<.00045), monthly consumption of cannabis (r=0.18, p<.00045); and Disinhibition: bullying-aggression (r= 0.31, p<.00045), bullying-victimization (r= 0.29, p<.00045), cyberbullying aggression (r=0.23, p<.00045), cyberbullying-victimization (r=0.25, p<.00045), problematic internet use (r=0.37, p<.00045), monthly consumption of alcohol (r=0.14, p<.00045), monthly consumption of cannabis (r=0.17, p<.00045) appear as relevant correlates for all of the behavioral criteria. p<.00045, on the basis of Bonferroni’s correction.
These results support the utility of unsuitable traits for helping to understand behavioral dimensions in undergraduate students, comprising behaviors such as bullying, which is of substantial social, clinical and educational concern. This work also demonstrated how bullying and cyberbullying are differentially forecast by personality dimensions.
The PID-5 (Krueger et al., 2012) measures have gained outstanding renown, and their correlates and consequences are being broadly investigated in adults (Al-Dajani, Gralnick, & Bagby, 2016). Application of the notions of personality pathology to college students has customarily been debatable, due to the stigmatizing effect which has been assigned to personality disturbances. However, an increasing number of researchers are currently interested in the dysfunctional traits of personality in young people (De Clercq et al., 2014; Romero & Alonso, 2019). As a matter of fact, promising evidence for the usefulness of the PID-5 (Krueger et al., 2012) measures in young people has been reported (Somma et al., 2016). Generally, researchers of personality pathology are encouraged to study the nomological net of maladaptive traits (McCabe, Vrabel, & Zeigler-Hill, 2017), but this need is even more required for adolescent populations, where the lack of studies on the PID model is noteworthy.
The aim of this study is to describe pathological personality traits identified in the Diagnostic and Statistical Manual of Mental Disorders - 5th ed. (DSM-5; APA, 2013) Section III alternative model of personality disorder in a group of college students.
Method
This is a quantitative, non-experimental, cross-sectional study. Participants were 81 college students (60 female, 21 male) who attend the psychology program from a Colombian University. The participants ranged in age between 17 and 39 years (mean 21.6; SD = 4.31)
Instruments
Personality Inventory for the DSM-5. An alternative model of PD appears in DSM-5 Section III (Skodol, 2012). This model defines PD in terms of both self and interpersonal dysfunction (Criterion A) and constellations of pathological traits (Criterion B). The hierarchical trait model includes 25 pathological personality facets, which load onto the five domains of Negative Affect, Detachment, Antagonism, Disinhibition, and Psychoticism. The PID-5 (Krueger et al., 2012) is a 220-item self-report measure of these 25 pathological facets and five broad trait domains. Participants rate each item as “very false or often false,” “sometimes or somewhat false,” “sometimes or somewhat true,” or “very true or often true.” (Krueger et al., 2012).
Procedure
The compliance of ethical standards was considered throughout the study procedure, with data collection being approved by the ethics Committee from a Colombian University. The data collection was conducted under conditions of anonymity and confidentiality, after written consent of the participants had been obtained. The participants did not receive any reward for participating in the study. The PID-5 (Krueger et al., 2012) was administered in class under the supervision of the researchers during an hour and a half.
Data Analysis
Univariate descriptive analysis was performed to get mean values and standard deviations of the measures used and the associations with gender (comparisons made with t-tests and Mann-Whitney test according to data distribution). Data normal distribution was examined through Kolmogorov - Smirnov test. Moreover, effect sizes for the differences were estimated through eta-squared (( 2 ). Procedures and interpretation were performed as suggested by others (Fritz, Morris, & Richler, 2012). The analyses were conducted on IBM SPSS Statistics 25.
Results
This section introduces both a statistical analysis and a comparative analysis of every personality facet and domain. Several personality traits are shown as dysfunctional and the final comparison is controlled by gender in order to present individual performance (males vs females). Table 1 shows statistical analyses for personality dysfunctional facets in college students. It was found that risk taking, intimacy avoidance, emotional lability, impulsivity, unusual beliefs and experiences, rigid perfectionism, restricted affectivity and eccentricity are the personality facets that have reached a percentage of dysfunctionality equal to or greater than 10%. Dysfunctional traits were identified through the validated Colombian version of the Personality Inventory for the DSM-5 (Ferrer, Londoño, Calvete, & Krueger, 2019b).
Findings suggest that anxiousness, distractibility, emotional lability, hostility, impulsivity, restricted affectivity, rigid perfectionism, risk taking had the highest mean scores. On the other hand, the personality domain related to negative affect had the highest mean score and antagonism the lowest mean score.
Personality Facets | Dysfunctional Trait | Descriptive statistics | |||
---|---|---|---|---|---|
Yes | No | ||||
n(%) | n(%) | Mean | SD | Range | |
Anhedonia | 1 (1.2) | 80 (98.8) | 0.59 | 0.50 | 2.13 |
Anxiousness | 3 (3.7) | 78 (96.3) | 1.23 | 0.67 | 2.56 |
Attention Seeking | 81 (100) | 0.72 | 0.39 | 1.88 | |
Callousness | 3 (3.7) | 78 (96.3) | 0.27 | 0.34 | 1.64 |
Deceitfulness | 3 (3.7) | 78 (96.3) | 0.48 | 0.34 | 1.60 |
Depressivity | 3 (3.7) | 78 (96.3) | 0.48 | 0.51 | 2.43 |
Distractibility | 4 (4.9) | 77 (95.1) | 0.91 | 0.67 | 2.78 |
Eccentricity | 8 (10) | 73 (90) | 0.69 | 0.63 | 2.38 |
Emotional Lability | 10 (12.3) | 71 (87.7) | 1.30 | 0.70 | 2.71 |
Grandiosity | 2 (2.5) | 79 (97.5) | 0.67 | 0.41 | 2.50 |
Hostility | 3 (3.7) | 78 (96.3) | 0.94 | 0.53 | 2.60 |
Impulsivity | 10 (12.3) | 71 (87.7) | 0.92 | 0.76 | 3.00 |
Intimacy Avoidance | 12 (14.8) | 69 (85.2 | 0.54 | 0.54 | 2.17 |
Irresponsability | 3 (3.7) | 78 (96.3) | 0.47 | 0.40 | 1.71 |
Manipulativeness | 4 (4.9) | 77 (95.1) | 0.43 | 0.54 | 2.80 |
Perceptual dysregulation | 7 (8.6) | 74 (91.4) | 0.51 | 0.48 | 2.25 |
Perseveration | 3 (3.7) | 78 (96.3) | 0.87 | 0.52 | 2.22 |
Restricted Affectivity | 8 (10) | 73 (90) | 1.03 | 0.56 | 2.57 |
Rigid Perfectionism | 9 (11.1) | 72 (88.9) | 1.16 | 0.68 | 2.90 |
Risk Taking | 13 (16) | 68 (84) | 1.28 | 0.55 | 2.36 |
Separation insecurity | 3 (3.7) | 78 (96.3) | 0.63 | 0.62 | 2.29 |
Submissiveness | 3 (3.7) | 78 (96.3) | 0.59 | 0.53 | 2.00 |
Suspiciousness | 4 (4.9) | 77 (95.1) | 0.88 | 0.54 | 2.29 |
Unusual beliefs and experiences | 10 (12.3) | 71 (87.7) | 0.61 | 0.57 | 2.75 |
Avoidance | 5 (6.2) | 76 (93.8) | 0.78 | 0.58 | 3.40 |
Personality Domain | |||||
Negative Affect | 1.05 | 0.57 | 2.52 | ||
Detachment | 0.64 | 0.44 | 2.06 | ||
Antagonism | 0.53 | 0.36 | 2.30 | ||
Disinhibition | 0.77 | 0.51 | 2.14 | ||
Psychoticism | 0.61 | 0.50 | 2.41 |
Source: The authors
Table 2 shows a comparative analysis of every personality facet and domain when controlling gender. Due to the fact that nonparametric tests have more statistical power, both mean scores, standard deviation, average range and median scores were reported. Women scored significantly higher than men on hostility (z=-2.577; p=0.01; n 2 = 0.082). A medium size effect was found (p> 0.039) through the ( 2 index (Fritz et al., 2012). The remaining variables did not prove statistically significant differences (p < 0.05). (see table 2).
Personality Facet | Women (n=60) | Men (n=21) | z | t | p | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
M | SD | Ar | Me | M | DS | Ar | Me | ||||
Anhedonia | 0.58 | 0.48 | 40.71 | 0.50 | 0.63 | 0.57 | 41.83 | 0.50 | -0.190 | 0.85 | |
Anxiousness | 1.27 | 0.67 | 42.11 | 1.22 | 1.13 | 0.68 | 37.83 | 1.22 | 0.848 | 0.40 | |
Attention Seeking | 0.70 | 0.38 | 40.10 | 0.63 | 0.77 | 0.42 | 43.57 | 0.75 | -0.586 | 0.56 | |
Callousness | 0.26 | 0.33 | 40.88 | 0.14 | 0.28 | 0.36 | 41.33 | 0.21 | -0.077 | 0.94 | |
Deceitfulness | 0.47 | 0.31 | 40.73 | 0.40 | 0.51 | 0.41 | 41.79 | 0.40 | -0.180 | 0.86 | |
Depressivity | 0.47 | 0.53 | 39.99 | 0.29 | 0.50 | 0.45 | 43.88 | 0.43 | -0.654 | 0.51 | |
Distractibility | 0.89 | 0.69 | 39.78 | 0.67 | 0.98 | 0.63 | 44.50 | 1.00 | -0.794 | 0.43 | |
Eccentricity | 0.66 | 0.61 | 40.03 | 0.54 | 0.79 | 0.69 | 43.76 | 0.69 | -0.626 | 0.53 | |
Emotional lability | 1.38 | 0.70 | 43.56 | 1.43 | 1.05 | 0.67 | 33.69 | 1.00 | 1.865 | 0.07 | |
Grandiosity | 0.62 | 0.37 | 38.17 | 0.67 | 0.83 | 0.49 | 49.10 | 0.83 | -1.861 | 0.06 | |
Hostility | 1.03 | 0.51 | 44.98 | 0.90 | 0.69 | 0.51 | 29.64 | 0.50 | -2.577 | 0.01 | |
Impulsivity | 1.01 | 0.82 | 43.37 | 1.00 | 0.64 | 0.47 | 34.24 | 0.50 | -1.536 | 0.12 | |
Intimacy avoidance | 0.53 | 0.56 | 39.84 | 0.33 | 0.57 | 0.49 | 44.31 | 0.33 | -0.758 | 0.45 | |
Irresponsability | 0.47 | 0.39 | 41.42 | 0.43 | 0.47 | 0.45 | 39.81 | 0.29 | -0.272 | 0.79 | |
Manipulativeness | 0.38 | 0.48 | 39.08 | 0.20 | 0.57 | 0.68 | 46.50 | 0.40 | -1.293 | 0.20 | |
Perceptual dysregulation | 0.53 | 0.53 | 40.23 | 0.37 | 0.47 | 0.29 | 43.19 | 0.42 | -0.497 | 0.62 | |
Perseveration | 0.86 | 0.53 | 40.45 | 0.89 | 0.89 | 0.53 | 42.57 | 0.78 | -0.222 | 0.82 | |
Restricted Affectivity | 1.06 | 0.54 | 42.27 | 0.93 | 0.96 | 0.62 | 37.38 | 1.00 | -0.822 | 0.41 | |
Rigid Perfectionism | 1.19 | 0.67 | 42.06 | 1.05 | 1.09 | 0.71 | 37.98 | 1.00 | -0.685 | 0.49 | |
Risk taking | 1.30 | 0.54 | 41.91 | 1.36 | 1.24 | 0.61 | 38.40 | 1.21 | -0.588 | 0.56 | |
Separation insecurity | 0.65 | 0.60 | 42.23 | 0.50 | 0.56 | 0.68 | 37.50 | 0.14 | -0.799 | 0.42 | |
Submissiveness | 0.55 | 0.52 | 39.00 | 0.50 | 0.70 | 0.53 | 46.71 | 0.75 | -1.313 | 0.19 | |
Suspiciousness | 0.90 | 0.57 | 41.48 | 0.71 | 0.82 | 0.43 | 39.64 | 0.71 | -0.308 | 0.76 | |
Unusual beliefs and experiences | 0.62 | 0.63 | 39.82 | 0.38 | 0.60 | 0.39 | 44.38 | 0.63 | -0.769 | 0.44 | |
Avoidance | 0.77 | 0,62 | 39.78 | 0.50 | 0.80 | 0.48 | 44.48 | 0.90 | -0.791 | 0.43 | |
Personality Domain | |||||||||||
Negative Affect | 1.10 | 0,57 | 43.12 | 1.08 | 0.91 | 0.57 | 34.95 | 0.85 | 1.316 | 0.19 | |
Detachment | 0.63 | 0,46 | 39.58 | 0.44 | 0.66 | 0.37 | 45.07 | 0.61 | -0.922 | 0.36 | |
Antagonism | 0.49 | 0,31 | 39.23 | 0.42 | 0.64 | 0.47 | 46.05 | 0.58 | -1.143 | 0.25 | |
Disinhibition | 0.79 | 0,55 | 41.53 | 0.71 | 0.70 | 0.40 | 39.50 | 0.71 | -0.339 | 0.73 | |
Psychoticism | 0.60 | 0,54 | 39.42 | 0.45 | 0.62 | 0.38 | 45.52 | 0.58 | -1.024 | 0.31 |
Note: M=Mean; SD=Standard Deviation; Ar= Average range; Me= Median; z= Mann-Whitney U test; t= Student´s t-test; p= Statistical significance value. Source: The authors
Despite finding a single statistically significant difference when controlling gender, it is also reported that women scored higher on anxiousness, emotional lability, hostility, impulsivity, perceptual dysregulation, restricted affectivity, rigid perfectionism, risk taking, separation insecurity, suspiciousness, negative affect and disinhibition. On the other hand, men scored higher on anhedonia, attention seeking, callousness, deceitfulness, depressivity, distractibility, eccentricity, grandiosity, intimacy avoidance, manipulativeness, submissiveness, detachment, antagonism and psychoticism.
Discussion
The present study empirically describes pathological personality traits identified in the Diagnostic and Statistical Manual of Mental Disorders - 5th ed. (DSM-5; APA, 2013) Section III alternative model of personality disorder in college students. In this regard, many psychologists reject this because most of them have been trained within the traditional framework that personality pathology is not properly measurable before the age of 18 (De Clercq et al., 2014). Still, the field of developmental personality psychology has expanded in the last two decades (Caspi, Roberts, & Shiner, 2005) and substantially underscored that the roots of personality can be traced back to childhood (Caspi, 2000; Chapman & Goldberg, 2011). Besides, recent works have indicated that personality disorder symptoms have their developmental antecedents in childhood or adolescence (Cicchetti & Crick, 2009; Cohen, 2008; Tackett, Balsis, Oltmanns, & Krueger, 2009), can be measured accurately in younger age groups, and represent a dimensional trait structure akin to that suggested for adulthood (De Clercq, De Fruyt, Van Leeuwen, & Mervielde, 2006).
From this developmentally oriented perspective on personality pathology, and given that 27% of the sample was 18 or younger, the present study corroborates existing evidence on the validity of a dimensional trait perspective in youth and has explored in different ways how the proposed PID-5 (Krueger et al., 2012) trait measure for adults may also serve as a viable tool for describing adolescent manifestations of personality pathology (De Clercq et al., 2014). Confirming and extending De Clercq and colleagues' (De Clercq et al., 2014) data on non-clinically referred college students, our data suggested that the PID-5 (Krueger et al., 2012) may represent a reliable and useful instrument to assess dysfunctional personality traits, at least in its Spanish translation. The rationale behind this study lies in understanding that there are behavioral tendencies reflected in personality traits that can affect certain habits that influence academic achievement such as perseverance, conscientiousness and talkativeness. Furthermore, whereas cognitive ability reflects what an individual can do personality traits reflect what an individual will do (Rammstedt, Lechner, & Danner, 2018). Finally, personality as well as cognitive proficiency would predict subsequent performance better in older students, especially motivation-related personality variables. For instance, research has shown that conscientiousness influences on academic motivation (Fuertes, Blanco, García, Rebaque, & Pascual, 2020), and academic performance (Morales, Camps, & Dueñas, 2020). It has even been counted as a valid and unique predictor of college performance (Wang et al., 2019).
The results can be summarized in three different key points that all contribute to answering the empirical question of the pathological personality traits identified in college students. First, the personality domain that reached the most dysfunctionality was disinhibition (risk taking, impulsivity, rigid perfectionism). It has been reported that high levels of disinhibition forecast higher alcohol consumption in older and younger individuals, respectively (Creswell, Bachrach, Wright, Pinto, & Ansell, 2016; McCabe, Vrabel, & Zeigler-Hill, 2016). Likewise, disinhibition turned out to be positively associated with self-defeating humor and aggressive humor (Zeigler-Hill, McCabe, & Vrabel, 2016), which implies that individuals with greater levels of disinhibition may be less involved on the possibility of hurting others in the process of improving either the self or connections through humor. On the other hand, when making moral decisions, these individuals are less concerned about the rights and well-being of others.
An interpretation is that disinhibited subjects have a greater likelihood to engage in behaviors that are both impulsive and harmful to themselves and others (APA, 2013; Zeigler-Hill et al., 2016). For instance, one phenomenon that has been shown to be characteristic of online communication among college students is the online disinhibition effect, defined as a lowering of behavioral inhibitions in the online environment (Shih, 2014). Many of the student behaviors displayed online (including violence, incitement, flaming, and verbal attacks, on the one hand, and self-disclosure, kindness, and the dispensing of help and advice, on the other) may be attributed to the online disinhibition effect (Stuart & Scott, 2021).
Disinhibition has also significant consequences for the consideration of workplace behavior. As large levels of disinhibition predict large levels of work achievement (Barrick, Neubert, Mount, & Stewart, 1998; Brown, Lent, Telander, & Tramayne, 2011) and low levels of staff rotation (Salgado, 2002), abnormal and dysfunctional work behaviors (Bowling & Eschleman, 2010). High levels of disinhibition anticipate poor and defiant behaviors in the workplace. This is especially important in law enforcement and medical professions where poor work performance and dysfunctional behavior threatens life (McCabe et al., 2017).
Second, negative affect had the highest mean score. This implies that individuals who are subject to undergo negative emotions, that is, subjects with high levels of negative affectivity (Krueger et al., 2012) and those with an inclination to be cold and avoidant, namely individuals with high levels of detachment (Wright et al., 2012) hardly will engage in harmless humor styles aimed to improve the self or enhance relationships. This also suggests that individuals with high levels of negative affectivity and detachment may be less likely to use kind humor styles (McCabe et al., 2017). At the same time, negative affectivity has been found to be positively associated with engaging in behaviors that point out the beneficial aspects of preserving one's current romantic relationship and cost-inflicting mate withholding behaviors, namely behaviors that produce costs to the partner if he/she determines to leave the relationship or behave disloyally. Such results show that individuals who undergo excessive negative emotions or aggression may be more likely to use several approaches for the sake of retaining their romantic partners (Noser et al., 2015).
Furthermore, negative affectivity has both positively predicted pragmatic reasons for being friends with a former romantic partner, and sentimental motives for remaining friends with a former romantic partner (Mogilski & Welling, 2017).
Third, gender differences in facets and domains showed that women scored significantly higher than men on hostility. Research has found that shame-prone individuals experience issues sustaining their interpersonal relationships because the internalized hostility that accompanies shame is easily reoriented toward others (hostile interpersonal behaviors) (Tangney, Wagner, Fletcher, & Gramzow, 1992).
Research about gender differences in hostility expression is essential both because previous research has proposed that gender stereotypes exist and because former studies have failed to review the existence of a potential gender bias when examining the observation and rating of hostility (Davidson, Hall, & MacGregor, 1996). Of the different kinds of hostility expression, verbal and nonverbal/behavioral hostility are both easily amenable to observation and are seen as more characteristic of women and men, respectively.
For example, one study found that participants perceived females as exhibiting greater nonverbal/behavioral hostility compared to males and males as exhibiting greater verbal hostility compared to female actors (MacGregor & Davidson, 2000). Hence, it has been suggested that men and women may actually differ in the amount or type of emotional expressions they display (Brody, 1985). Consistent with this speculation, it has also been suggested that women and men display their anger/ hostility through different means (Stoney & Engebretson, 1994).
Finally, disinhibition, negative affect and hostility were found as key pathological personality traits to be studied in college students. These mental health problems can have a profound impact on college students´ functioning. At the individual level, they can affect all aspects of physical, emotional, cognitive and interpersonal functioning. They also have a negative impact on the academic performance -students with higher levels of psychological distress have higher test anxiety and lower self-efficacy (Tosevski, Milovancevic, & Gajic, 2010). Besides, students with mental health problems influence many other people on campuses, including roommates, classmates, faculty members and staff. On the other hand, they have been shown to have a high degree of consciousness and therefore a higher tendency toward social phobia (Yamamoto, Tomotake, & Ohmori, 2008). When public self-consciousness is too high, the difficulty in interpersonal relationships may emerge and self-esteem may decrease, leading to various mental health problems.
Limitations and Future directions
Of course, our findings should be considered in the light of several limitations. The sample size was definitively too small to legitimate the use of sophisticated multivariate analyses in order to evaluate the predictive role of PID-5 scale. Moreover, most participants included in the present sample were female, and this inherently limits the generalizability of our findings to samples composed of male adolescents.
There are obvious weaknesses related to the categorical model of PD used by the DSM (McCabe et al., 2016, 2017). Nevertheless, despite the growing evidence endorsing the alternative model of personality pathology that was proposed in DSM-5 (APA, 2013), there is still substantial defiance to this dimensional model). More studies into the pathological personality traits established by the PID-5 is required if the shift to a dimensional model is likely to occur in future editions of the DSM.
On the other hand, this study has solely focused on college students and future research should contrast both clinical and nonclinical samples. For instance, in this study all comparisons (5 of 5 domains) the participants scored lower than expected. However, comparisons with another sample from the general population would have allowed to make further and deeper analysis.