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Introduction Enneagram typologies may impact psychological well-being and stressful situations in college students. However, the literature is still limited in the study of dynamic personality models such as the Enneagram in Spanish-speaking university students, and a better understanding is needed. Objective To analyze network associations and centrality measures of Enneagram personality typologies in Peruvian university students. Methods A total of 859 Peruvian university students responded to two instruments assessing: The Pangrazzi’s Enneagram personality types and healthy personality to psychosocial stress. All instruments showed good psychometric values (validity and consistency). A regularized cross-sectional network structure was estimated with Gaussian graphical model and the graphical LASSO. Results Enneagram types 4, 5, and 6 presented the highest and positive associations in the network structure. Type 6 emerged as the node with the highest predictability. The healthy personality and type 7 acted as bridges between the communities, with types 6, 7, and 8 being the most central nodes. Conclusion The findings suggest that Enneagram type 7 with healthy personality to psychosocial stress plays an important role in the development of the causal activation of the network model. The network shows causal associations between psychosocial stress and types 6, 7, 8, and 9.
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Frontiers in Psychology 01
Enneagram typologies and
healthy personality to
psychosocial stress: A network
Cristian Ramos-Vera
1,2, Antonio Serpa Barrientos
Jonatan Baños-Chaparro
2, José Vallejos Saldarriaga
1 and
Jacksaint Saintila
1 Research Area, Health Science, Universidad Cesar Vallejo, Lima, Peru, 2 Peruvian Society of
Psychometry, Lima, Peru, 3 Facultad de Psicología, Universidad Nacional Mayor de San Marcos,
Lima, Peru, 4 Escuela de Medicina Humana, Universidad Señor de Sipán, Chiclayo, Peru
Introduction: Enneagram typologies may impact psychological well-being
and stressful situations in college students. However, the literature is still
limited in the study of dynamic personality models such as the Enneagram in
Spanish-speaking university students, and a better understanding is needed.
Objective: To analyze network associations and centrality measures of
Enneagram personality typologies in Peruvian university students.
Methods: A total of 859 Peruvian university students responded to two
instruments assessing: The Pangrazzi’s Enneagram personality types and
healthy personality to psychosocial stress. All instruments showed good
psychometric values (validity and consistency). A regularized cross-sectional
network structure was estimated with Gaussian graphical model and the
graphical LASSO.
Results: Enneagram types 4, 5, and 6 presented the highest and positive
associations in the network structure. Type 6 emerged as the node with the
highest predictability. The healthy personality and type 7 acted as bridges
between the communities, with types 6, 7, and 8 being the most central nodes.
Conclusion: The findings suggest that Enneagram type 7 with healthy
personality to psychosocial stress plays an important role in the development
of the causal activation of the network model. The network shows causal
associations between psychosocial stress and types 6, 7, 8, and 9.
personality, Enneagram, psychosocial stress, network analysis, personality styles,
university students
TYPE Original Research
PUBLISHED 24 November 2022
DOI 10.3389/fpsyg.2022.1051271
Manuel Fernández-Alcántara,
University of Alicante,
Vilda Purutçuoğlu,
Middle East Technical University, Turkey
Yuehan Yang,
Central University of Finance and
Economics, China
Jacksaint Saintila
This article was submitted to
Health Psychology,
a section of the journal
Frontiers in Psychology
RECEIVED 22 September 2022
ACCEPTED 08 November 2022
PUBLISHED 24 November 2022
Ramos-Vera C, Barrientos AS,
Baños-Chaparro J, Saldarriaga JV and
Saintila J (2022) Enneagram typologies and
healthy personality to psychosocial stress:
A network approach.
Front. Psychol. 13:1051271.
doi: 10.3389/fpsyg.2022.1051271
© 2022 Ramos-Vera, Barrientos, Baños-
Chaparro, Saldarriaga and Saintila. This is
an open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 02
Networks allow to represent complex behaviors and
phenomena based on a set of interacting variables. Network
models are represented by nodes (variables) and edges connecting
the nodes representing mutual associations in the network system
(Hevey, 2018; Ramos-Vera, 2021). It is possible to assess the
complexity of psychological phenomena using these network
models that consider patterns of individual dierences in normal
or psychopathological personality traits (Costantini etal., 2019).
Personality models can beidentied as an ecosystem in which
some characteristics, behaviors, domains, or facets interact with
each other, while other connections are reduced given their degree
of importance in the network, this allows structuring a unique
causal system composed of associations between such personality
measures (Costantini etal., 2019).
Personality research has included several models in general
populations where well-known personality systems of major
research, such as the Big Five or the Hexaco stand out (ielmann
etal., 2021). However, it is important to evaluate other models that
have gained less interest to foster important new insights in the
assessment of dynamic personality processes (Kuper etal., 2021).
e generation of new ndings is less likely in the face of the
exclusive use of the Big Five model (Mõttus etal., 2020).
One of the least researched personality systems is the
Enneagram model that provides insight into psychological
structure based on 9 character orientations (types) that include
traits, dispositions, and behaviors that are unique to the individual
(Alexander and Schnipke, 2020). In this system, each personality
type has characteristics based on an underlying motivation
grounded in ego responses to a core fear and desire. Individuals
may develop a predominant type with other positive and negative
characteristics of their Enneagram typological style. However,
traits of other typologies are also present in the face of stress and
security events that, depend on lifestyle, tend to psychological
development, or conversely produce potentially pathological
psychological distress (Sutton etal., 2013). is model can help
people to understand the mechanisms involved in their own and
others’ personality, as well as to know better the dominant styles
in social and coping situations involving emotional, cognitive,
motivational, volitional, and value aspects.
In the research of Roh etal. (Roh etal., 2019), the dierent
personality styles of the Enneagram (typological triads) are
reported as the three basic centers of the human psyche: feeling
(types 2, 3, and 4), thinking (types 5, 6 and 7), and instinct (types
1, 8, and 9). Harmonic groups are characterized by the way the
person faces and react to disappointment, frustration, or when he/
she does not obtain what he/she desires, conforming by positive
(types 2, 7, and 9), competitive (types 1, 3, and 5) and reactive
(types 6, 4, and 8) styles. ey have also been classied according
to interpersonal tendencies oriented to the pursuit of needs and
desires as referred by psychologist Karen Horney (Hornevian
groups), according to the most prevalent typology of the
individual, which are the combative style (types 3, 7, and 8),
reserved (types 4, 5, and 9), and obedient (types 1, 2, and 6; Roh
etal., 2019).
A higher prevalence of negative traits of the dominant
personality in stressful situations strengthens the usual patterns of
maladaptive coping with emotional distress, which are
conceptually similar to the maladaptive schemas referred to in
Cognitive-Behavioral erapy models (Beck, 2013). erefore, it
is likely that other negative traits of other typologies may
be manifested that reinforce personality patterns of greater
psychological vulnerability, while people with a higher prevalence
of positive traits in their typological centers tend to have better
psychological development and functioning. In this context, the
most important feature that distinguishes the Enneagram from
other personality models is the representation of a dynamic
system (Enneagram, 2019; Matise, 2019).
Hook etal. (2021) state that the theorization of Enneagram
typologies is more closely linked to modern psychodynamic
approaches, characterized by the identication of inexible
patterns conducive to emotional experiences aimed at learning
new alternatives for social interaction with others. People have
been considered to have a cyclical maladaptive pattern (CMP) or
a primary pattern of problematic relating in more recent
psychotherapeutic models such as Time-Limited Dynamic
Psychotherapy. PCM describes patterns of feelings toward the self,
expectations and perceptions of others, and ways of relating that
are dynamically interconnected and perpetuate dysfunctional
relationships (Levenson, 2012). is functioning can also
beexplained by an underlying motivation rooted in ego responses
to a core fear and desire as referenced by the Enneagram model
(Hook etal., 2021).
A brief review of the scientic literature on the Enneagram
found that most of the studies demonstrating positive eects on
college students were conducted in Asia. e little empirical
evidence on the integrity and legitimacy of the Enneagram
measure in Latin American practice is due to the lack of research
in Spanish-speaking participants; however, more research is
reported in the UnitedStates in diverse areas beyond the university
setting (Hook etal., 2021). During the last decade, there has been
an increase in studies evidencing the benets of the Enneagram in
the family and work area in various cultural-religious contexts
such as Brazil, Spain, Iran, Kenya, UnitedKingdom, SouthAfrica,
and ailand (Sutton etal., 2013; Burger and van Coller-Peter,
2019; Ndirangu etal., 2019; Navabifar etal., 2020; Romero etal.,
2020; Engelseth etal., 2021; Henrique etal., 2021).
rough the theoretical foundation of the Enneagram
proposed to the psychological eld by the Chilean psychiatrist
Claudio Naranjo in 1990 (Naranjo, 1990), several studies have
shown its importance in the integration of psychotherapeutic
processes, as it strengthens the therapeutic alliance, helps to
manage physical and emotional pain, and motivates people to take
control in their recovery process (La, 2014; Matise, 2019; Kam and
Vriend, 2021). According to the Enneagram model, a better
understanding of the mechanisms involved in personality allows
people to promote a higher degree of self-compassion and
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 03
self-acceptance from a greater awareness of the psychological
states of imbalance and balance of the most predominant
typology; this can motivate individuals to free themselves from
their maladaptive schemas, dysfunctional cycles, and limiting
defensive styles (Hook etal., 2021).
Some personality types have been reported, which use certain
coping strategies in certain contexts, such as the Grossarth-
Maticek and Eysenck (Grossarth-Maticek and Eysenck, 1990)
model, which refers to the existence of six types of reactions to
psychosocial stress that are associated with the presence of
symptoms and health-related behaviors such as nutrition, physical
exercise, self-medication, or frequency of visits to the doctor
(Hernández etal., 2007). Specically, of interest for the present
study is type 4 (healthy personality), similar to type B personality
(Shaw and Dimsdale, 2007). Individuals with these personality
patterns report a lower degree of emotional reaction of stress and
anger to social situations of criticism and competition (Jarašiūnaitė
and Perminas, 2014) and a better quality of interpersonal
relationships (Kyeum Lee and Jong, 2021). Individuals who
identify with other Grossarth-Maticek and Eysenck personalities
or types A or D present greater psychological vulnerability
(Chilicka etal., 2020; Sakitri, 2020; Fatrous, 2021) and addiction
to tobacco use (Mortazavi etal., 2020).
Grossarth-Maticek and Eysenck’s (Grossarth-Maticek and
Eysenck, 1990) healthy personality is based on emotional
autonomy associated with a higher degree of self-regulation and
psychological exibility (Kirk and Martin, 1998). Individuals
identied with a higher predominance of this personality
realistically cope with approach and avoidance behaviors with
respect to a given stressful event. In addition, they present
assertive emotional and behavioral reactions that are socially
desirable, such as tolerance, extreme patience, understanding,
kindness, and stoic acceptance of problems (Hisam etal., 2014).
is personality has been reported to benegatively associated with
stress, negative aectivity, aggression, and somatic
symptomatology in Spanish adults (Reyes del Paso and
Martínez, 2004).
It is important to consider this favorable measure for mental
health and physical well-being, veried by the evidence of a higher
prevalence of this positive personality to psychosocial stress in
young adults in Spain, UnitedStates, Norway, and Peru (Sandin
etal., 1992; Smedslund, 1995; Martínez-Correa and Reyes Del
Paso, 2007; Condori, 2013; Taller, 2018; Núñez, 2020) in contrast
to other personality factors with a greater tendency to symptoms
of emotional distress and psychosomatic risk (Sandin etal., 1992;
Hernández etal., 2007).
e joint evaluation of the network relationships of
Enneagram typologies with healthy personality to psychosocial
stress reactions allows exploring new ndings on the role of
variables in the relationship and activation of connections in
the network in a systemic way (Epskamp and Fried, 2018;
Ramos-Vera, 2021). is allows to know the associative
patterns that identify those styles, states, and personality
proles are more inuential according to the Enneagram
theorization represented in a multivariate network, and to
know which ones are more associated with healthy personality
(Matise, 2019). erefore, the present study aims to evaluate
network associations and report centrality measures of such
personality typologies in male and female university students
in Peru.
Materials and methods
e study sample consisted of 859 university students from a
private university in the Peruvian city of Ica, who were selected by
non-probabilistic convenience sampling. ose university
students over 18 years of age, who were registered at the university
regular cycle and accepted the informed consent form, were
considered. Students who did not meet the inclusion criteria did
not participate in the research. In that sense, the nal sample was
composed of 598 females (69.6%) and 261 males (30.4%), aged 18
to 37 years (Mean: 23.49; Standard deviation: 2.9). e highest
percentage of students were from the professional careers of
psychology (28.2%) and environmental engineering (26.3%),
followed by obstetrics (24.9%) and nursing (20.5%).
All measures showed good internal consistency in dierent
Peruvian university groups with adequate psychometric evidence
(Vicuña etal., 2001; Condori, 2013; Núñez, 2020).
Personality according to the Enneagram
Pangrazzi’s (Pangrazzi, 1997) Enneagram questionnaire was
used, composed of 9 enneatypes with dichotomous responses
(0 = No and 1 = Yes). e enneatypes included 20 items each,
which were type 1: Perfectionist (items 1 to 20, for example, “I
have an instinctive tendency to evaluate situations”), type 2:
Helper (items 21 to 40, for example, “Many people depend on my
help and my generosity”), type 3: Accomplishing (items 41 to 60,
e.g., “I have a very high energy level”), type 4: Romantic (items 61
to 80, e.g., “I appreciate the beauty of life more than most people”),
type 5: Observant (items 81 to 100, e.g., “I generally hide my
feelings”), type 6: Loyal (items 100 to 120, e.g., “Fundamentally
Iama fairly balanced person”), type 7: Adventurous (items 121 to
140, e.g., “I amthe type of person who likes to try a little bit of
everything in life”), type 8: Challenger (items 141 to 160, e.g., “I
feel able to take a stand and ght for what Ibelieve in”), and type
9: Pacist (items 161 to 180, e.g., “by nature Iamcalm, quiet and
conciliatory”). e Kuder–Richardson 20 coecients between the
enneatypes were between 0.84 and 0.86, which show adequate
values of internal consistency.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 04
Healthy personality to psychosocial
e type 4 personality measure (healthy personality) of the
Short Interpersonal Reactions Inventory (SIRI) by Grossarth-
Maticek and Eysenck was considered (Grossarth-Maticek and
Eysenck, 1990). It contains 10 items (items 4, 11, 18, 25, 32, 39, 46,
53, 60, and 67) with dichotomous response (0 = No and 1 = Yes).
is personality measure reported an internal consistency of 0.78
according to the Kuder–Richardson coecient 20. e Spanish
version of Martínez-Correa and Reyes was used, which has
adequate psychometric properties (Martínez-Correa and Reyes
Del Paso, 2007).
Permission was requested from the director of the university
center with the respective information on the purpose of the
research and academic purposes. Heagreed to carry out the
project and provided information to the administrative sta of
each faculty to facilitate coordination with the tutor, teachers, and
students. e collection of information was carried out during the
last 3 months of 2019, in the academic period of cycle II during
the tutoring courses in charge of one of the researchers with the
support of the tutor in charge.
During the application, each student was explained the
objective of the current research and the objectives of the study,
and the condentiality of the participants, who responded
voluntarily and anonymously to the survey for an average time of
approximately 30 min. Likewise, all procedures used in this study
guarantee the condentiality of the responses and are in
accordance with the ethical requirements of the research ethics
committee given article 27 of the professional code of Ethics of the
Peruvian College of Psychologists and the Helsinki Declaration
of 1964.
Statistical analysis
e Gaussian graphical model (GGM) performed presents a
regularized partial correlation network to model the interaction
between dierent variables or psychological phenomena. In this
graph, each variable is represented as circles, called “nodes” (or
“vertices”). ese are connected by lines, called “edges.” In this
network variant, the conditional dependency relationships
between the variables are characterized: if two variables are
connected in the resulting network, they are dependent aer
adjusting for all other variables. e graphical LASSO (selection
operator and absolute minimum shrinkage) was used to estimate
the GGM (Epskamp and Fried, 2018) and avoid spurious edges,
representing a sparse network describing the data with
parsimony. e Fruchterman-Reingold algorithm was used for
network visualization, which allows determining the position of
a node based on the sum of connections it has with other nodes
using the qgraph package (Epskamp etal., 2012). In addition,
this model is characterized by coming from a normal
distribution, understanding that a pair of random variables
comply with the joint normal distribution, on the basis of which
it is assumed that their marginal and conditional distribution is
also normal.
e precision of the edge weights at 95% condence intervals
was estimated by Bootstrapping 1,000 samples around each edge
in the network. e 1,000-sample Bootstrap method was
considered to strengthen the stability of the network results;
moreover, the strength stability was estimated by calculating the
correlation stability coecient (CS), where the value should not
beless than 0.25 and preferably greater than.50 (Fruchterman and
Reingold, 1991; Mcnally, 2021). We report the measures of
frequency centrality and magnitude of connections that each node
has from the number of connections (strength centrality), which
have been reported in previous studies (Ramos-Vera etal., 2022).
To identify the bridging variables, the strength-bridge index
was considered in the network model, in which Enneagram
typologies are connected to healthy personality. Considering Jones
etal. (Jones etal., 2021), those variables of interest with the highest
bridging centrality were selected based on the percentile
parameter >0.80. Such centrality measures have been reported in
personality-oriented network research (Goh etal., 2020; Jordan
etal., 2021).
Table1 shows the descriptive statistics of the participants’
responses to the measures used. In the predictability values, the
network mean was 37.4%, with type 6 (46.3%) having the highest
predictability, followed by type 4 (45.9%), type 5 (45.9%), and type
7 (41.2%). All network structures presented positive correlations,
being type 4 with type 5 (0.24) and type 6 (0.21) the highest
network associations. Covariances between HP with type 7 (0.16),
type 6 (0.11), and type 8 (0.10) were also evident (Table 1).
Likewise, in the network graph, the thickness of the connection is
evidenced by the magnitude of the correlation, and the shaded
proportions of the rings represent the degree of predictability
variance between the network nodes (Figure 1).
Figure2 shows the measures of strength centrality, where type
6 has a greater inuence; other measures of greater centrality were
types 7, 8, and 5.
Figure 3 refers to the bridge strength centrality measures
where those greater than 0.80 percentiles are considered, which
were healthy personality and type 7, these measures being the ones
that play an important role in the development of causal activation
of the Enneagram typologies with healthy personality.
e precision of the edge weights is shown in Figure 4. It is
evident that most of the estimated edges were greater than zero
and in general, did not overlap with other edges, reecting a
precise estimation.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 05
e stability of the strength centrality index is presented in
Figure 5. In that sense, it is observed that the strength estimate is
maintained even aer removing large proportions of the sample
and the CS coecient showed a value of.71, indicating the stability
of the strength of the nodes.
e Bootstrap dierence test for node strength is presented in
Figure 6. is result refers that the healthy personality measure of
psychosocial stress was signicantly dierent from all other
strength values, while types 1, 9, and 2 were signicantly dierent
from most Enneagram typologies in the network.
Figure 7 presents the Bootstrap dierence test for edge weights
based on the 95% Bootstrap interval, where the dierences of any two
edges can include a zero value (dark squares) or not (gray squares),
which allows determining whether the two edges are dierent from
each other. e diagonal displays the magnitude of the original edge,
where blue squares are positive values and red squares are negative
edges, and the color saturation indicates absolute values (the more
saturated the color, the stronger the edge). e edge weights between
nodes type 4 and type 5, type 1 and type 3, type 4 and type 6, type 3,
and type 8 are signicantly dierent from most edges in the network.
e network results refer causal associations of positive
reaction to psychosocial stress with types 6 (loyal), 7
TABLE1 Descriptive data, predictability, and network relationships.
Va r i a b l e MSD P T1 T2 T3 T4 T5 T6 T7 T8 T9 HP
T1 13.82 3.41 31.50% -
T2 12.49 3.59 37.60% 0.01 -
T3 13.27 3.39 37.80% 0.21 0.18 -
T4 12.54 3.66 41.90% 0.10 0.20 0.01 -
T5 12.49 3.45 43.10% 0.05 0.09 0.09 0.24 -
T6 13.63 3.19 46.30% 0.16 0.08 0.02 0.21 0.07 -
T7 13.61 3.23 41.30% 0.03 0.12 0.11 0 0.03 0.15 -
T8 13.39 3.23 40.20% 0.11 0.01 0.2 0.02 0.13 0.11 0.18 -
T9 13.08 3.48 35.20% 0 0.10 0 0.08 0.20 0.14 0.13 0.02 -
HP 6.53 2.03 20.20% 0 0 0.01 0 0 0.11 0.16 0.10 0.09 -
M: Media; SD: standard deviation; P: predictability; e T_i values correspond to the partial correlation coecients. T1: type 1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6;
T7: type 7; T8: type 8; T9: type 9; HP: healthy personality.
Network analysis of the nine personality types of the Enneagram and healthy personality to psychosocial stress. The greater the thickness of the
observed connections, the greater the magnitude of the statistical relationships. It should bespecified that the thickness of the line equals the
magnitude of the relationship.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 06
(adventurous), 8 (challenging), and 9 (peacemaker), which
evidences that university students with healthy behaviors to
interpersonal stress may beidentied with a more trustworthy,
responsible, decisive, outgoing, enthusiastic, innovative,
optimistic, and peaceful character. Such typologies are related to
higher healthy and assertive behavior (Wagner, 2012), better self-
control and positive emotion management (Araghi etal., 2015),
and reduced stress (Karabulut etal., 2021) and fear (Al-Obaidi,
2021). Other ndings indicate that the healthy personality of
lower stressor reactivity is associated with a higher Myers-Briggs
perceptual temperament, which is characterized by a high degree
of quietness, self-control, and adaptation to new situations
(Fretwell etal., 2013).
Enneatype 6 (loyal) is the most inuential measure (high
degree of strength centrality and predictability) on the other
Enneagram typologies and styles in the network, which is
characterized by a personality composed of prosocial and
emotional traits that contribute to resilience strategies, linked to
problem solving. In addition, according to the systemic theory of
the Enneagram, it is possible to identify the integration of type 6
with type 9 (peacemaker), which may bean indicator of a better
mental health status (Enneagram, 2019), given that both types
refer to a network causal relationship and are associated with
healthy personality to psychosocial stress. e assessed university
students are likely to present a calmer, more mature, and
emotionally stable character in the face of interpersonal situations.
People who present a personality with greater characteristics of
both typological states may have greater control of their
psychological needs and eciently manage anxiety and anger in
conictive social interactions, as referred to in the Enneagram
theorizing. is is similar to the development of growth potential
and well-being following the satisfaction of basic psychological
needs according to self-determination theory (Vansteenkiste and
Ryan, 2013) including basic humanistic aspects related to
motivation and personality (DeRobertis and Bland, 2018).
Individuals with a core personality characterized by type 6 can
achieve greater satisfaction of their desire for security and trust
when they identify with peaceful personality behaviors (type 9)
such as kindness, psychological exibility, sociability, and
empathic listening to others that make it easier for them to cope
Network analysis strength centrality indexes of network analysis. Centrality refers to the measure with the highest number of connections along
with the sum of the relationships it presents. T1: type 1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6; T7: type 7; T8: type 8; T9: type 9;
S4AT: healthy personality to psychosocial stress.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 07
with distressing interpersonal situations (Hook etal., 2021). ese
characteristics are related to the Big Five personality facets of
extraversion and openness.
Given the greater inuence of type 6 on the associative activation
of other traits of the intellectual center style (types 5, 6, and 7), iit is
probable that cognitive abilities such as intellectual curiosity and
openness, critical and analytical thinking are more prelevant (Hook
etal., 2021; Kam, 2019). ey would promote legislative, judicial,
global, hierarchical, and liberal thinking styles that are considered
the most adaptive and refer to a complex mode of information
processing, as well as a preference for unstructured and holistic
situations. is allows individuals to eectively implement their
intelligence and creativity in various domains, favoring a high level
of self-condence and autonomy to make decisions and solve
problem situations independently (Zhang, 2010). Curiosity is one of
the most characteristic traits in people with greater mastery of the
intellectual center that strengthens social and intellectual interest,
and can even bea motivating force for academic learning (Lauriola
etal., 2015). Likewise, people with high levels of motivation and in
the face of new experiences report higher degrees of positive
emotions and acquisition of novel information that favor
psychological well-being and autonomy (Schutte and Malou, 2019).
Precedent research indicates that people identied with the
intellectual center report lower levels of aggression than other
people characterized with other typological centers (Wagner,
2012; Shameli etal., 2020). It is probable that personality patterns
with lower aggression characteristics share a link with Grossarth-
Maticek & Eysenck’s (Grossarth-Maticek and Eysenck, 1990)
favorable interpersonal reactions to healthy personality, as this
personality type is negatively related to aggression in adults (Reyes
del Paso and Martínez, 2004).
In the network, the type 6 measure is localized within the
associative patterns of the positive coping style (nexus of
relationships of types 2, 7, and 9); therefore, it is more likely that
university students with greater condence and positive attitudes
will beable to cope and react eectively to various social situations.
Likewise, people who identify with the positive style tend to have
more enjoyable experiences, are helpful, and are oriented to
identify positive qualities in others, even in adverse situations.
Causal connections are also shown between the traits of the other
coping typological triads: reactive (types 4, 6, and 8) and
competence (types 1, 3, and 5) that indicate a balance in the
interactive functioning of mental, emotional, and instinctive
capacities in coping with social situations linked to healthy
Network analysis bridge strength centrality index. Centrality refers to the measure with the highest number of connections together with the sum
of the relationships it presents. T1: type 1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6; T7: type 7; T8: type 8; T9: type 9; S4AT: healthy
personality to psychosocial stress.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 08
behaviors and reactions to psychosocial stress and physical well-
being (Hernández and Froján, 2005; Whiteld etal., 2020).
e healthy personality to psychosocial stress demonstrated
causal relationships with the three enneatypes of the combative
social style (types 3, 7, and 8) which is identied by problem solving,
active coping style, assertiveness, and greater identity with the self,
compared to the other personal styles of the Enneagram (Nettmann,
2013). e highest magnitude connections with healthy personality
were with types 7 and 8, which according to the systematic review
of Hook etal. (Hook etal., 2021) evidenced signicant positive
relationships between the Big Five personality facets of the
Enneagram typologies. Eight reviewed investigations presented
direct associations between extraversion and type 8, while 10
previous studies reported relationships between the domains of
extraversion and openness with type 7 (Hook etal., 2021). Likewise,
Hook etal. (Hook etal., 2021) evidenced that a greater preference
Accuracy of edge weight estimation and 95% CIs based on the Bootstrapping method. The precision of the edge weights is shown, where the red
line indicates the sample edge weight (ordered in increasing order) and the gray bars are the 95% CIs based on the Bootstrapping method. T1: type
1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6; T7: type 7; T8: type 8; T9: type 9; S4AT: healthy personality to psychosocial stress.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 09
Stability of the strength centrality index. The stability of the strength centrality index is shown, where the red line is the correlation between the
strength index estimate and the subsamples that would beused from the total sample.
Bootstrap dierence test for the strength of nodes. Bootstrap dierence test for node strength is evident, where gray boxes indicate non-
significant dierences and black boxes indicate significant dierences. T1: type 1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6; T7: type
7; T8: type 8; T9: type 9; S4AT: healthy personality to psychosocial stress.
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 10
Bootstrap dierence test for edge weights. T1: type 1; T2: type 2; T3: type 3; T4: type 4; T5: type 5; T6: type 6; T7: type 7; T8: type 8; T9: type 9;
S4AT: healthy personality to psychosocial stress.
for extraversion according to the Myers-Briggs Typological
Inventory was related to type 7in six of the seven studies reviewed,
and to type 8in ve of the seven investigations. It is possible that
those university students identied with these typologies present
characteristics such as boldness in social situations, attention to
internal experiences, and a greater tendency to experience positive
emotions, which reinforce a healthier personality to psychosocial
stress, since they are more associated with psychological well-being
(Hernández etal., 2007). e ndings suggest that people identied
with types 7 and 8 present higher levels of self-control and
assertiveness, as well as a lower tendency to negative emotions,
attachment, and dependence (Wagner, 2012; Araghi etal., 2015).
According to Enneagram theory, the integration of type 7 with
its wings (types 6 and 8) promotes the development of personal
traits of determination, tenacity, responsibility, motivation,
reection, temperance, greater spiritual interest, they are more
organized, do not worry about being judged, and are more likely
to become leaders (Enneagram, 2019). Research by Roh etal. (Roh
et al., 2019) indicated that college students with a higher
predominance of type 7 are noted for cognitive empathy, who have
a higher degree of consideration of another persons point of view
to appreciate the situation from their perspective (Hojat etal.,
2002). Optimism is the main character of type 7 (bridging
enneatype) that favors mindfulness and reduces levels of
depression, anxiety, and stress. It is also recognized as a protective
factor for mental health in the face of the current pandemic (Vos
etal., 2021) and is related to a lower risk of mortality according to
a recent meta-analysis (Craig etal., 2021). Optimistic individuals
are noted for a personality with a positive attitude, more carefree,
and exible to others, these typological characteristics are likely to
bethe underlying mediating traits inuencing the relationship of
the more network-centric (loyal) personality and the more
emotionally autonomous healthy personality.
e greater relationships in the network of types 2 and 4 linked
to the emotional center consolidated feeling-centered characteristics
such as emotional awareness and regulation that allow individuals to
improve communicative and interpersonal skills in an empathic
manner (Shin and Lee, 2020). ese individuals pay greater attention
to their emotions, have a higher degree of understanding and valuing
emotions, clearly identify their own and others’ negative emotions,
and then manage them assertively in social relationships. Such
characteristics of awareness and self-regulation of emotions are
signicantly associated with Grossarth-Maticek and Eysenck’s healthy
personality (Grossarth-Maticek and Eysenck, 1990). However, in the
Ramos-Vera et al. 10.3389/fpsyg.2022.1051271
Frontiers in Psychology 11
network, typologies 2 and 4 are only related to the healthy personality
through the loyal personality (type 6) characterized by trust
and harmony.
is study has some limitations that should be mentioned.
First, it should be noted that this is a cross-sectional study;
therefore, it cannot beinferred whether a given node causes or is
caused by another node to which it is connected due to the use of
undirected networks. Second, it is that the cross-sectional edges
represent both within- and between-subject eects that cannot
bedisentangled, i.e., it is not possible to interpret these results at
the individual level. Experimental and prospective designs are
needed to more rigorously test those assumptions underlying the
causal systems perspective for theoretical models of personality.
However, the application of this variant of network analysis is
important since it guarantees better technical information on the
interaction of the variables under study because it estimates the
associations aer multivariate control of all elements of the system.
Additionally, it is recommended that network analysis beapplied
in future research that considers various protective and risk factors for
mental health during the current pandemic related to personality by
type of profession and academic performance in university students
for a better interpretation of the results in specic groups and to
provide new evidence of personality typologies that are favorable to
university education and psychological well-being from the models
of complex network systems in diverse sociocultural contexts.
To conclude, the present research refers the greater importance
of the strength of type 6 (number and magnitude of connections) in
the network. Type 7 is related to the healthy personality with
bridging measures suggesting a direct and indirect associative
interconnection pathway of the Enneagram typologies to the
healthy personality with psychosocial stress where the characteristics
of optimism, curiosity, and psychological exibility. In this system,
higher associations were found between enneatypes 2 and 4, and
greater relationships were identied between the personality healthy
to psychosocial stress and types 7 and 8. e application of the
Bootstrapping method indicates that the relationships and centrality
indexes in the network are stable measures.
Data availability statement
The raw data supporting the conclusions of this article
will be made available by the authors, without
undue reservation.
Ethics statement
e studies involving human participants were reviewed and
approved by Professional code of Ethics of the Peruvian College
of Psychologists. e patients/participants provided their written
informed consent to participate in this study.
Author contributions
CR-V and AB designed the study. JB-C and JVS performed
the statistical analysis. CR-V and JS wrote the rst dra of the
manuscript. All authors contributed to the article and approved
the submitted version.
Open access funding provided by Universidad Señor de Sipán,
Chiclayo, Perú (DIRECTORY RESOLUTION N°015-2022/
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
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authors and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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... This provides a more complete understanding of the relationships between categorical variables and their influence on the phenomenon studied. 61,62 In addition, the accuracy and robustness of the association weights were evaluated by running 3000 bootstrap samples. 63 Finally, to assess the relationships between all study variables and sociodemographics, network analysis was employed as the methodology. ...
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Background: Although the importance of the therapeutic alliance in the treatment process and health outcomes is recognized, so far, there has been no evaluation in the Peruvian context that considers possible individual differences that could influence this assessment. Purpose: This study assessed the psychometric properties of the WAI-S-P in a sample of individuals from Peru who are receiving psychological therapy. Furthermore, a network analysis was conducted to investigate the direct relationships between the therapeutic alliance and several relevant sociodemographic variables. Methods: The short version of the Working Alliance Inventory was used in a sample of 241 participants (Mage=32.58, SD=12.67) that had attended less than 6 sessions. Three models were considered, including a three-factor and a two-factor correlated model, as well as a bifactor model. In addition, a network of partial associations was created including the overall therapeutic alliance, sex, age, and number of psychotherapeutic sessions. Results: The bifactor model, with an overall therapeutic alliance factor and two specific factors (“contact” and “contract”), better fit the data. Invariance of the structure by sex and age showed equitable measurement. On the other hand, network analysis revealed a positive correlation between total session attendance and therapeutic alliance. Men reported higher therapeutic alliance, while women had higher total session attendance. Conclusion: The results of this study suggest that the therapeutic alliance is better represented by a bifactor model and demonstrates invariance across sex and age in Peruvian adults. Additionally, findings indicate that differences in life experiences and the sex of patients may need to be verified in future studies to better understand nuanced needs in forming therapeutic alliances at least in the early stages of session attendance.
Personality clusters such as the Dark and Light Triad are going through an exponential investigation. The Dark Triad traits characterize behaviors associated primarily with socially undesirable outcomes, while the Light Triad traits are composed of behaviors linked to prosocial responses. Most studies that sought to map and investigate both triads are performed in North American or European contexts. Thus, we analyzed the structure of two of the most used short composite measures (i.e., Dirty Dozen and Light Triad Scale) in a sample of 2335 adults from Poland, Brazil, Nigeria, Colombia, and Peru. We performed structural equation models to understand better the structure of both instruments in the five countries. Subsequently, we conducted a network analysis to observe the dynamics of both triads in countries with different cultures. Our results found that Machiavellianism is one of the more relevant traits in Latin American and European countries, while humanism is in Nigeria. Other findings reaffirm that light and dark are not opposite traits, but at the same time, they represent distinct constructs.
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Background: Depressive symptoms can affect people’s quality of life and social environment. In addition, in severe situations, they can lead to suicidal behaviors. Objective: This study aimed to analyze the differences in depressive symptoms in underweight and obese Peruvian adults. Methods: A cross-sectional study was carried out based on secondary data obtained from the Instituto Nacional de Estadística e Informática (INEI), Lima, Peru. A sample of 10 053 participants was considered, of which 55.96% were women. Two Gaussian plot models were estimated and the levels of depressive symptomatology were compared between the 2 groups (adults with underweight and obese). Results: A total of 1510 (15.02%) were underweight adults and 8543 (84.98%) were obese adults. There were differences in the reporting of depressive symptoms in the underweight group; the most central items were “Depressed mood” (PH2), “Tiredness/low energy” (PH4), and “Psychomotor difficulties” (PH8). Conclusion: This study provides new evidence on the dynamic relationship between depressive symptoms according to the body mass index categories (underweight and obese) assessed.
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The study aimed to determine the COVID-19 fear pattern, the common Enneagram pattern in Yemeni society, and the relationship between COVID-19 fear and Enneagram patterns. The study sample consisted of (360) individuals (youth - adults) who were randomly selected. The researcher used the Ingram scale according to (RISO) theory. The results showed that there is no fear of COVID-19 as a dominant pattern. The peacemaker type is the dominant personality type in Yemeni society, followed by the achieved type. Furthermore, there was a negative correlation between peacemaker and courage from COVID-19. The study concluded that The attitude of no (COVID-19) fear was the prevailing personality pattern among members of Yemeni society. The Enneagram pattern (the peacemaker) was the dominant pattern in the personality of Yemeni society members, followed by the achieved pattern. There was a negative, strong and relationship between fear of (COVID-19) and between the two patterns of Enneagram (unique) and (peacemaker) in the personality of the Yemeni community members. The attitude of no (COVID-19) fear was related negatively with the unhealthy aspect of the (singular) and (peacemaker) patterns of Enneagram (singular) and (peacemaker) in the personality of Yemeni community members.
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Objective: Whenever the subject of coronary artery disease (CAD) and myocardial infarctions is discussed, the focus is usually shifted towards biological factors such as smoking, diabetes, or obesity; consequently, the management aims at addressing these factors. This paper approaches the subject from a psychosocial perspective and highlights the importance of these risk factors and their inclusion in CAD screening. Background: CAD is one of the most common diseases worldwide and also one of the leading causes of death in multiple countries. Although we have a proper understanding of its pathogenesis and risk factors, we sometimes tend to overlook the psychological factors that affect the patient both pre- and post-diagnosis. The purpose of this paper is to present these underestimated factors and convey their importance. Methods: To accomplish this, an extensive review of the literature was done using PubMed and Google Scholar, and articles were chosen based on the specified keywords. The references of these articles were also screened to identify more related studies and clinical trials. Discussion: This paper is composed of multiple subsections that go over the epidemiology of the disease as well as its pathogenesis and known biological risk factors, before delving into the psychosocial aspects associated with CAD including the effects of depression, anxiety, social support, and sex differences on a patient’s prognosis. Conclusion: CAD is a disease for which the management is through multifactorial interventions. Although the pathogenesis is well understood, there is a clear gap when it comes to appreciating the patients’ mental health when living with this diagnosis. Additionally, it has been shown that there is an increase in morbidity and mortality in the patients struggling on a psychosocial level, thus these factors should be included in the screening process.
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The narcissistic admiration and rivalry concept (NARC) model of grandiose narcissism posits that striving for uniqueness, grandiose fantasies, and charmingness define narcissistic admiration, whereas striving for supremacy, devaluation, and aggressiveness define narcissistic rivalry. Given these complex interrelationships, we explored the structure of grandiose narcissism using the Narcissistic Admiration and Rivalry Questionnaire (NARQ) and Narcissistic Personality Inventory (NPI) via network analysis in four separate samples which allowed us to assess the extent to which these networks replicated across these samples (total N = 3,868). Overall, grandiose cognitions from the NARQ emerged as a highly central node in each network, providing compound evidence for its replicability and generalizability as an important feature of grandiose narcissism within the NARC model. Charmingness from the NARQ emerged as a central node throughout Samples 1, 2, and 3, with strong connections to features of narcissistic admiration and narcissistic rivalry (e.g., grandiose fantasies and aggressiveness), but was less central in Sample 4. To our knowledge, this is the first research to examine the replicability of the network structure of grandiose narcissism across various samples. These findings add to an increasingly important dialogue regarding replicability in psychological network science.
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Models of basic personality structure are among the most widely used frameworks in psychology and beyond, and they have considerably advanced the understanding of individual differences in a plethora of consequential outcomes. Over the past decades, two such models have become most widely used: the Five Factor Model (FFM) or Big Five, respectively, and the HEXACO Model of Personality. However, there is no large-scale empirical evidence on the general comparability of these models. Here, we provide the first comprehensive meta-analysis on (i) the correspondence of the FFM/Big Five and HEXACO dimensions, (ii) the scope of trait content the models cover, and (iii) the orthogonality (i.e., degree of independence) of dimensions within the models. Results based on 152 (published and unpublished) samples and 6,828 unique effects showed that the HEXACO dimensions incorporate notable conceptual differences compared to the Big Five, resulting in a broader coverage of the personality space and less redundancy between dimensions. Moreover, moderator analyses revealed substantial differences between operationalizations of the Big Five. Taken together, these findings have important theoretical and practical implications for the understanding of basic personality dimensions and their assessment.
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The COVID-19 pandemic has a substantial impact on mental health. Prior reports have shown that depression, anxiety, and stress have increased throughout the pandemic. Nonetheless, not everyone is affected by these negative consequences and some people may be relatively unaffected. In this online study in a predominantly Dutch and Belgian sample (N = 546), we investigated whether positive personality traits such as optimism, mindfulness, and resilience may protect against the negative mental health consequences (i.e., fear of the coronavirus, depression, stress, and anxiety) of the COVID-19 pandemic. We found that fear of COVID-19 was related to higher depression, stress, and anxiety. However, for participants scoring high on mindfulness, optimism, and resilience, this relationship was weakened. In addition to these findings, we present the results of network analyses to explore the network structure between these constructs. These results help to identify possible ways through which psychological well-being can be promoted during the COVID-19 pandemic.
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La presente carta tiene como finalidad presentar el modelo de red de relaciones estadísticas (dirigidas o no dirigidas), compuesta por relaciones de orden cero o asociaciones parciales3 que conectan los nodos (varia- bles) y estructuran el modelo dinámico. Este método es incluyente con diversas medidas clínicas vincula- das a la psiquiatría4,5: cognitivas y neuropsicológicas6, neuroanatómicas7, bioquímicas8,9, genómicas10,11, antropo- métricas y fisiológicas,
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The Enneagram and Narrative Therapy are two frameworks that have potential for unique integration to combine therapeutic effects for issues related to Adult Ego Development. The Enneagram offers a centuries-tested personality framework that has accumulated ancient wisdom from multiple spiritual traditions. Narrative Therapy offers postmodern insights into helping clients overcome their self-limiting narratives through creative interventions involving the medium of story. The nature of these two frameworks as well as their commonalities, complementary differences, and synergistic potential can help clients with Adult Ego Development issues. These issues will be explored along with an example of their integration.
The study aimed to evaluate the effect of nursing students’ personality types and perceived stress on their attitudes towards the nursing profession during the Covid-19 pandemic. This is a descriptive and cross-sectional study. The study consisted of the first-, second-, third- and fourth-year nursing students in the Nursing Faculty (Branch A) at a state university located in Eastern Anatolia of Turkey during the academic year 2019–2020. Sample selection was not made in the study, and 359 nursing students who agreed to participate in the study and completed the online questionnaire were reached. The data were collected between June and July 2020 via an online questionnaire form. The mean age of the nursing students was 20.88 ± 1.94. Of the students, 86.9% were female and 35.1% were second-year students. There was a positive significant relationship between the mean PSS and personality types – helper and romantic; however, a negative relationship was found between the mean PSS and personality types – adventurer and peacemaker (p < .05). In this study, a positive relationship was found between the mean total ASNP and ASNP subscale scores – properties of nursing profession and all personality types (p = .000). The findings of this study showed that some demographic variables influenced the stress perceived by nursing students during the Covid-19 pandemic, their personality types and their attitudes towards the nursing profession. All personality types positively affected the nursing students’ attitudes towards their profession. While the helper and romantic personality types had a positive effect on the students’ perceived stress.
Research suggests that optimism and pessimism should be studied as two distinct constructs rather than as unidimensional, at opposing ends of a continuum. Optimism and pessimism have different associations with health-related behaviours, coping style, and a number of health outcomes. This study aimed to systematically review the evidence on the association of optimism and pessimism and all-cause mortality. A systematic search of MEDLINE, EMBASE and PsycINFO was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From 6837 retrospective/prospective cohort studies identified, 25 were included, with a total of 217,256 participants. Eleven of the 18 studies assessing optimism reported that optimism was associated with lower risk of mortality, while seven of eight studies measuring pessimism and the two assessing unrealistic optimism reported that higher pessimism/unrealistic optimism was associated with an increased mortality risk. Results of a meta-analysis indicated that optimism measured categorically was associated with lower risk of mortality (pooled RR = 0.85, 95% CI: 0.79-0.91). We suggest optimism and pessimism be measured as separate constructs, as both of these have distinct effects on mortality risk. Future research is required to investigate whether psychological interventions to increase optimism or decrease pessimism can reduce risk of all-cause mortality.