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RESEARCH ARTICLE
Validity evidence for assessing social-
emotional psychological strengths in
Colombian adolescents using the SEHS-S
Diana Riaño-Herna
´ndez
1
, Iwin LeenenID
2
*, Angelli Ramı
´rez-Conde
3
, Paula A. Atehortua-
Rivera
3
, Jose
´A. PiquerasID
4
1Facultad de Psicologı
´a, Universidad del Valle, Valle del Cauca, Cali, Colombia, 2Facultad de Psicologı
´a,
Universidad Nacional Auto
´noma de Me
´xico, Ciudad de Me
´xico, Me
´xico, 3Facultad de Ciencias Humanas,
Sociales y de la Educacio
´n, Universidad Cato
´lica de Pereira, Risaralda, Pereira, Colombia, 4Departamento
de Psicologı
´a de la Salud, Universidad Miguel Herna
´ndez, Elche, Alicante, España
*iwin.leenen@unam.mx
Abstract
Background
Covitality is a multidimensional hierarchical construct of core psychological strengths that
synergistically promote resilience and well-being and that has been shown to be effective in
preventing mental health problems in individuals of different age groups. The Covitality
Model consists of 12 first-order latent factors, 4 second-order factors, and one general
higher-order Covitality factor.
Purpose
In this study, we aim at obtaining validity evidence for the assessment of Covitality in Colom-
bian adolescents by means of the Social Emotional Health Survey-Secondary (SEHS-S).
Method
A sample of 1461 adolescents responded the SEHS-S and four other instruments that mea-
sure well-being and distress. The internal structure of the SEHS-S was examined through
confirmatory factor analyses as well as its relations with other variables.
Results
The hierarchical factor structure of the SEHS-S was supported (with goodness-of-fit statis-
tics: χ
2
= 1727.6, df = 578, p <.001; RMSEA = .037; SRMSR = .044; AGFI = .962; CFI =
.940; and NNFI = .935) and configural and metric invariance across gender and age was
confirmed; however, the assumption of scalar invariance across males and females and
across age groups was violated for some items. Furthermore, we found moderate to high
correlations (r= .56 –.68) of Covitality with related constructs.
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OPEN ACCESS
Citation: Riaño-Herna
´ndez D, Leenen I, Ramı
´rez-
Conde A, Atehortua-Rivera PA, Piqueras JA (2025)
Validity evidence for assessing social-emotional
psychological strengths in Colombian adolescents
using the SEHS-S. PLoS ONE 20(2): e0314488.
https://doi.org/10.1371/journal.pone.0314488
Editor: Majed Sulaiman Alamri, University of Hafr
Al-Batin, SAUDI ARABIA
Received: June 21, 2024
Accepted: November 11, 2024
Published: February 10, 2025
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0314488
Copyright: ©2025 Riaño-Herna
´ndez et al. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data from which
all results presented in this study are derived are
publicly available in different file formats from a
Conclusion
As a conclusion, the SEHS-S can be considered a valid tool to assess psychological
strengths, well-being, and resilience (i.e., Covitality) in Colombian adolescents, though fur-
ther research is needed to explore the differences in item functioning across gender and
age.
Introduction
The 2015 National Mental Health Survey in Colombia highlighted that around 12% of the ado-
lescents showed signs of emotional or mental health symptoms [1] and 2.8% presented high-
risk alcohol abuse [2]. Moreover, suicide rates among children and adolescents increased sub-
stantially, with minors representing 10.5% of the overall national figure [3]. Yet, Colombia’s
public health system lacks the resources to provide adequate psychological support, preventa-
tive initiatives have limited reach, and private psychological services are unaffordable for most
of the population [4].
Developing strategies that identify and address emotional and behavioral issues early, with
a focus on proactive prevention, is crucial [5,6]. Many authors have studied mental health in
children and adolescents and, as such, provided evidence supporting the Bidimensional Model
of Mental Health [7–10]. This model, rather than viewing psychological well-being as the
absence of mental dysfunction, considers well-being and psychopathology as separate (but
connected) dimensions that work together to establish and maintain mental health [11–14].
This insight led to a fundamental shift, not only in the understanding but also in the treatment
of mental health problems [15–18]. Interventions should contribute to socio-emotional well-
being by fortifying the individual’s strengths and protective factors directly, besides minimiz-
ing psychosocial risk factors and symptoms of psychological damage [19–21]. As an example,
a study by Martinotti and colleagues offers a comprehensive analysis of alcohol consumption
patterns among young people and shows that gaining a deeper understanding of these behav-
ioral patterns and psychological strengths of adolescents is beneficial for developing effective
interventions, such as those aimed at preventing binge drinking [22].
In this context emerged the meta-construct of covitality — the theoretical counterpart of
comorbidity —, which is defined as “the synergistic effect of positive mental health resulting
from the interplay among multiple positive-psychological building blocks (. . .) [or] more tech-
nically as the latent, second-order positive mental health construct accounting for the presence
of several co-occurring, first-order positive mental health indicators” [23]. That is, the covital-
ity model does not just rely on individual psychological abilities but rather on developing and
combining a large number of strengths and actives, with special emphasis on their interplay
and conjunction. These psychological skills are then further consolidated into cognitive
schemes that organize and give meaning to life experiences and that gain importance as they
foster positive development and turn into protective factors that help to overcome emotional
distress [21,23,24]. Covitality must be understood as the joint effect and interaction of these
cognitive schemes, which lead directly to subjective well-being [25,26].
Prevention strategies rooted in socio-emotional strengths emphasize the importance of fos-
tering positive mental health through early, school-based interventions [27–29]. Educational
institutions provide a natural environment for promoting psychological well-being and pro-
grams that cultivate socio-emotional skills are crucial for both prevention and promotion of
mental health. Examples include Positive Psychology Interventions, which aim to build skills
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project registered at the Open Science Framework
with doi: 10.17605/OSF.IO/ND7HU.
Funding: This research received funding from the
Universidad Cato
´lica de Pereira (Project 2019/018)
and the Colombian Ministerio de Ciencia y
Tecnologı
´a (DRH; Project 2019/850). The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
such as self-regulation, coping, and interpersonal communication [30]. These can be imple-
mented as part of broader school culture programs or targeted individual support, fostering
resilience and emotional well-being among students. Additionally, transitional support pro-
grams, such as pre- and post-transfer interventions during school transitions, help students
adjust to new educational environments by reinforcing a positive mindset and emotional pre-
paredness. Programs like the “Student Strengths Safari” [31] or Growth Psychoeducation
Interventions (GPI) [32] focus on developing covitality by enhancing executive functioning
and social-emotional skills. These efforts underline the importance of early intervention and a
supportive school community in fostering socio-emotional strengths.
Furlong and colleagues developed a self-report measure called the Social Emotional Health
Survey (SEHS-S) to assess covitality in adolescents. This measure is based on a 1 !4!12 !
36 model of 36 items organized in 12 subscales that load onto four domains, culminating into
one higher-order covitality latent construct (see the Instruments section below for further
details). The SEHS-S has gained worldwide interest and validity evidence has been obtained
for its use in various cultures and contexts. The original studies by Furlong and colleagues
[33–36] with Californian middle and high school students validated the SEHS-S internal struc-
ture using confirmatory factor analyses (CFA), which subsequently was confirmed by studies
in Japan, Korea, China, Lithuania, Turkey, Iran, and Spain [5,6,18,37–41]. Further studies
added evidence for measurement invariance across gender, age, and ethnic groups [42,43]
and others showed positive associations of the higher-order covitality construct with subjective
well-being, resilience, prosocial behavior, quality of life, and school adjustment, and mental
health and negative associations with psychopathology [5,6,18,37,44,45]. It is worth men-
tioning that in 2020, Furlong et al. proposed an updated version of the SEHS-S, with a stan-
dardized four-point response scale for all 36 items and minimal changes to enhance
readability.
In this paper, we provide validity evidence for using the SEHS-S with Colombian adoles-
cents by studying its internal structure (factorial structure, measurement invariance and reli-
ability) and relations with external variables. Currently in Colombia mental health is still
approached from the traditional model, with a focus on negative indicators such as anxiety,
and instruments based on individual’s strengths and protective factors are rarely used or even
unavailable. In that sense, the SEHS-S can be used to shed a light on the assets and socio-emo-
tional abilities of Colombian adolescents and how they contribute to maintaining their mental
health; the needs of these students for an adequate socio-emotional development may be iden-
tified more precisely and more efficient intervention strategies may be proposed to increase
their well-being and prevent or reduce psychosocial and behavioral problems. As such, this
study contributes to the body of validity evidence for the use of the SEHS-S in different coun-
tries and cultures.
Materials and methods
Participants
A total of 1,473 students belonging to four public and rural secondary schools from the
Colombian department of Risaralda volunteered to participate in this study. These schools
were selected by convenience, for their proximity, accessibility, and for being the largest
schools in the department. To be eligible for participation, students had to demonstrate basic
reading and writing skills and be free from any diagnosed mental or cognitive disabilities.
These inclusion and exclusion criteria were verified by both the students’ teachers and the psy-
chologists at the participating schools.
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Of the 1,473 students who participated, 12 were excluded due to largely incomplete
responses on the instruments (i.e., over 50% of missing responses overall). Note that, following
recommendations and common practice regarding sample size in factor analysis [46,47], the
remaining 1,461 participants are sufficient for the planned data analysis.
The students were about evenly distributed between sixth and eleventh grade, all were
between 10 and 19 years old (mean: 14.3 years; with 95% between 11 and 17 years), and 44%
were women. The participants largely resided in small municipalities with low socioeconomic
status and their primary livelihoods revolving around activities such as farming, fishing, and
agriculture.
Instruments
Social emotional health survey–Secondary. The SEHS-S is a self-report questionnaire
for adolescents of between 12 and 18 years old. The 36 items are organized in 12 subscales
(with each subscale consisting of three items and measuring one positive psychological compo-
nent), which in turn are grouped in four domains of positive mental health: belief-in-self (with
the subscales self-efficacy, self-awareness, and persistence), belief-in-others (school support,
family coherence, peer support), emotional competence (emotional regulation, empathy,
behavioral self-control), and engaged living (gratitude, zest, optimism). These four domains
contribute to the overall construct of covitality. Items are responded using a four-point Likert-
type format (1 = “not at all true of me”, 2 = “a little true of me”, 3 = “more or less true of me”,
and 4 = “very much true of me”). We used Piqueras’s [18] translation into Spanish of the
SEHS-S, with the adjustments by Furlong [33].
Kidscreen–10 Index. The Kidscreen–10 Index [48] assesses the subjective quality of life
— related to health and well-being during the week preceding the application — of children
and adolescents between 8 and 17 years. It consists of 10 items, with items 1 and 2 evaluating
the child/adolescent’s level of physical activity, condition and energy, items 3 and 4 their cur-
rent mood and unpleasant emotions, items 5 and 6 their freedom of choice about entertain-
ment and social activities, items 7 and 8 their relationship with parents (or caregivers) and
peers, and items 9 and 10 their perception of the own cognitive abilities and academic achieve-
ment. Responses are given using a five-point Likert-type format (1 = “never”, 2 = “almost
never”, 3 = “sometimes”, 4 = “almost always”, 5 = “always”). According to a recent review, this
instrument has been used up to six times previously in the Colombian population with ade-
quate psychometric properties [49].
The MHI-5 mental health inventory. The MHI-5 [50] consists of five (two positive and
three negative) items asking the respondent about how they felt during the last month. The
instrument has been translated to multiple languages and can rely on validity studies in many
countries, including Brazil [51], Mexico [52], Spain [53], and Peru [54]. Responses are given
using a four-level Likert-type format (with 0 = “never”, 1 = “sometimes”, 2 = “many times”, 3
= “always”).
The Trait Emotional Intelligence Questionnaire–Adolescent Short Form (TEIQue–
ASF). The Spanish version of the TEIQue-ASF [55], which was retrieved from http://www.
psychometriclab.com, is a simplified version of the abbreviated form of the TEIQue global
trait emotional intelligence measure for adults and comprises 30 short statements (two for
each of the 15 facets of emotional intelligence) with a seven-point Likert-response format (1 =
“never” to 7 = “always”). Ferrando et al. [56] obtained adequate psychometric properties for
the instrument in Spanish adolescents.
Pediatric Symptom Checklist-Youth self-report (PSC-17-Y). The short form of the
PSC-17-Y [57] assesses psychosocial problems, overall and in three main psychopathological
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domains: internalizing (anxiety and depression), externalizing (disruptive behavior) and atten-
tion deficit hyperactivity disorder (ADHD). It consists of 17 items, scored on a three-point
Likert scale (0 = “never”, 1 = “sometimes”, 2 = “always”). Piqueras et al. [58] provided validity
evidence for the instrument in Spanish adolescents.
Procedure
Cultural relevance, clarity, comprehensibility, and potential ambiguities in the SEHS-S ques-
tionnaire by Piqueras [18] were evaluated by three expert judges and through cognitive inter-
views with five adolescents from the target population; based on their suggestions, slight
adjustments to 22 of the 36 items were made. After obtaining informed consent (see the “Ethi-
cal approval and informed consent” section below), the instruments and a sociodemographic
questionnaire were administered in groups. The data collection took place on selected dates
between March 1 and October 17, 2019. Different collaborators visited all four schools on each
of the selected dates and administered the instruments to all students from a given grade who
were present at school that day and had provided their consent.
Data analysis
We calculated descriptive statistics (means, standard deviations, intercorrelations, Cronbach’s
alpha, and McDonald’s omega) for the scale scores (i.e., sums across all items) of the five
instruments. Additionally, for the SEHS-S, we present descriptive statistics (including percen-
tiles) separately for gender (male versus female) and age group (grades 7–8 versus grades 9–10
versus grades 11–12). Given the relatively large number of individuals with at least one missing
value, we employed multiple imputation (with 25 imputed data sets) for the missing values
based on the fully conditional specification (FCS) method and multivariate regression on the
full set of response variables, assuming data are missing at random (MAR) [59]. We used the
PROC MI and PROC MIANALYZE procedures of SAS V9.4 [60].
Subsequently, we examined the internal structure of the SEHS-S adopting an approach
similar to the one used by previous validation studies (see the Introduction). We ran CFA
on the 36 response variables to fit the three-level higher-order factorial structure of the
SEHS-S. To evaluate goodness of fit, we report the chi-square statistic (with associated
degrees of freedom), the root mean square error of approximation (RMSEA), the standard-
ized root mean square residual (SRMSR), the adjusted goodness-of-fit index (AGFI), Ben-
tler’s comparative fit index (CFI), and the Bentler-Bonett nonnormed fit index (NNFI).
With the chi-square test being highly sensitive to sample size, we consider values below 0.05
for RMSEA and SRMSR and values above 0.95 for AGFI, CFI, and NNFI excellent fit, while
values between 0.05 and 0.08 for RMSEA and SRMSR and between 0.90 and 0.95 for CFI
and NNFI are still acceptable [61].
The invariance assumptions for the obtained factor model were tested across gender and
age groups (as defined above), considering three levels of invariance: configural invariance
(which is confirmed if the above model fits well in each subpopulation), metric invariance
(which holds if, additional to configural invariance, the factor loadings are equal across sub-
populations), and scalar invariance (which means that, additional to metric invariance, vari-
able intercepts are equal across subpopulations). Following Cheung and Rensvold [62], metric
and scalar invariance were tested by comparing a restricted model (equal loadings and/or
intercepts) with the more general model (unrestricted loadings and/or intercepts) through the
CFI of both models, with a difference (ΔCFI) of .01 or less indicating that the null hypothesis
of invariance can be maintained. All factor models were fitted by the SAS V9.4 PROC CALIS
procedure [60] specifying full-information maximum likelihood estimation and Levenberg-
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Marquardt optimization. Note that this software is known for its robustness and ability to han-
dle large datasets with missing values.
Ethical approval and informed consent
This study was reviewed and formally approved by the Ethics Committee of the Universidad
Cato
´lica de Pereira, Colombia, on November 19, 2018. The research procedures were con-
ducted in strict accordance with the guidelines and principles outlined in the Declaration of
Helsinki. Participant schools sent detailed information about the project to parents, asking
permission for their adolescent children to participate in the study. Both parents and adoles-
cents provided written informed consent before completing the questionnaires. Furthermore,
all data were analyzed anonymously and the study was designed to ensure the safety and well-
being of all subjects involved.
Inclusivity in global research
Additional information regarding the ethical, cultural, and scientific considerations specific to
inclusivity in global research is included in the Supporting Information (S1 Checklist).
Results
Descriptive statistics
Table 1 shows means, standard deviations, and reliability statistics through Cronbach’s alpha
and McDonald’s omega (with their respective standard errors) for the sum scores on each of
the five instruments applied to the 1,461 participants. Given the higher-order factor structure
for the SEHS-S, we also calculated McDonald’s hierarchical omega [63]: ⍵
H
= .923 (with a
standard error of .025). Furthermore, the table reports the intercorrelations (with 95%-confi-
dence intervals) among scales and the percentage of nonresponse in each scale. Whereas the
overall nonresponse rate is low (considering all scales, 1.0% of the item responses were miss-
ing), the percentage of individuals who did not respond to at least one item is relatively high,
especially in the longer instruments (viz., 22.7% for the TEIQue-ASF, the highest among the
instruments used). As explained in the Data Analysis section, missing responses were imputed
under the missing-at-random assumption. Given this assumption and the relatively low non-
response rate at the item level, their impact on the results and conclusions is expected to be
minimal. For the SEHS-S, Table 2 presents percentile scores, separated by gender and age
groups.
Factorial structure and invariance
Standardized loadings for Furlong et al.’s [33] three-order factor model, estimated by a CFA
on the sample SEHS-S data, are shown in Fig 1. The model had a good to excellent fit to the
data, with χ
2
= 1727.6 (df = 578, p <.001), RMSEA = .037, SRMSR = .044, AGFI = .962, CFI =
.940, and NNFI = .935.
Next, we tested the model for invariance. First, we checked invariance across gender. Con-
figural invariance was confirmed by separately fitting the model to males and females and
obtaining adequate fit indices in both subsamples (males: χ
2
= 1217.8, RMSEA = .037, SRMSR
= .047, AGFI = .953, CFI = .933, NNFI = .927; females: χ
2
= 1134.3, RMSEA = .038, SRMSR =
.046, AGFI = .949, CFI = .942, NNFI = .937). As to metric invariance, a model with the factor
loadings being restricted to be equal for males and females did not fit substantially worse than
a more general model that allowed for different factor loadings in both groups (ΔCFI = .001;
see Table 3 for details); consequently, we maintained the null hypothesis of metric invariance.
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Finally, with respect to scalar invariance, we did find a better fit for the model that allowed for
different intercepts across genders as compared to a model that restricts these intercepts to be
equal (ΔCFI = .018). To identify the items with significantly different intercepts for males and
Table 1. Means, standard deviations, percentage of nonresponse, intercorrelations, Cronbach’s alpha and McDonald’s omega for the five scales applied to the sam-
ple of 1461 adolescents.
SEHS-S (36 items) Kidscreen-10 (10 items) MHI-5 (5 items) TEIQue-ASF (30 items) PSC-17-Y (17 items)
Mean 116.03 ±0.41 38.63 ±0.15 9.91 ±0.07 139.78 ±0.62 11.64 ±0.14
Standard deviation 15.59 ±0.29 5.74 ±0.11 2.85 ±0.05 23.70 ±0.44 5.51 ±0.10
Nonresponse
Overall
a
0.6% 0.7% 0.5% 1.7% 0.9%
Cases
b
14.1% 4.8% 1.4% 22.7% 7.3%
Intercorrelations
SEHS-S .679 [.651,.706] .556 [.520,.591] .662 [.632,.690] −.563 [−.598,−.527]
Kidscreen-10 .714 [.688,.739] .618 [.585,.649] −.552 [−.587,−.515]
MHI-5 .602 [.568,.633] −.551 [−.586,−.514]
TEIQue-ASF −.609 [−.640,−.576]
Cronbach’s alpha .911 ±.003 .782 ±.008 .786 ±.009 .834 ±.006 .800 ±.008
McDonald’s omega .908 ±.005 .787 ±.009 .789 ±.009 .824 ±.008 .803 ±.008
Notes. For the mean, standard deviation, Cronbach’s alpha and McDonald’s omega, standard errors are reported, while the uncertainty about the intercorrelations is
accounted for by 95%-confidence intervals (based on a Fisher-Ztransformation of the correlations). For the standard errors for McDonald’s omega, bootstrapping was
used [64]. All statistics are based on 25 multiply-imputed data sets.
a
Overall nonresponse is the percentage of cells with missing values in the data matrix
b
Cases nonresponse is the percentage of individuals who did not respond one or more items of the scale.
https://doi.org/10.1371/journal.pone.0314488.t001
Table 2. Percentile scores for the SEHS-S, overall and by gender and age groups.
Grades 6–7 Grades 8–9 Grades 10–11
Percentiles Overall Females Males Females Males Females Males
(n= 1461) (n= 282) (n= 332) (n= 152) (n= 206) (n= 214) (n= 275)
1 72 62 76 69 74 73 76
5 88 87 92 84 90 86 89
10 95 92 100 93 94 92 97
15 100 100 103 96 99 96 101
20 104 105 106 101 104 100 103
25 107 109 109 103 105 105 107
30 110 112 110 106 108 109 110
40 114 117 115 111 113 111 115
50 117 121 118 114 117 115 117
60 121 126 123 118 121 120 120
70 126 130 127 122 125 124 123
75 127 132 129 125 126 125 125
80 130 134 131 127 128 127 127
85 132 136 134 131 131 130 129
90 135 138 136 132 133 132 132
95 138 140 139 137 136 135 136
99 143 143 144 144 141 139 140
Note. All percentiles are based on 25 multiply-imputed data sets.
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females, we tested the intercept parameters one-by-one in a stepwise procedure to obtain a sta-
ble division of the items in two groups: one with intercepts that were significantly different for
males and females and the other where this is not the case. Table 4 shows the results of this
procedure. Refitting the model with these partially constrained intercepts yielded an almost
equal fit as compared to the model with unconstrained intercepts (ΔCFI = .001, between
Model G2 and Model G4 in Table 3).
Fig 1. Standardized factor loadings from a confirmatory factor analysis on the SEHS-S (Furlong et al.’s [33] three-
order factor model).
https://doi.org/10.1371/journal.pone.0314488.g001
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The SEHS-S items are scored on a scale from 1 to 4 points; therefore, the intercepts indicate
the expected score for an individual with average latent factor scores. In Table 3, we read for
instance that on item 17 (“I have a friend of my age who talks with me about my problems”), a
female is expected to have a score that is almost 0.5 points higher than a male who has identical
scores on the Covitality constructs and subconstructs; in other words, a high score on that
item is not as indicative of high covitality in females as it is in males. Likewise, in item 5 (“I
understand my moods and feelings”) a male is expected to have a score of almost 0.3 points
higher than a female with the same level of self-efficacy, belief-in-self, and covitality; that is, a
high score on this item is more indicative of high covitality in females that in males. Interest-
ingly, when considering all items in the scale and summing all items into the overall test score,
it turns out that the expected difference between males and females with the same scores on
the latent factors largely cancels out and is almost negligible as females are expected to score
only 0.74 points (on a scale that ranges from 36 to 144) higher as compared to males of the
same covitality level. This means that, for a fair comparison of males and females (e.g., in
Table 2) test scores for females should be lowered (or scores for males increased) with 0.74
points.
We further checked invariance across age groups. Again, configural invariance (with ade-
quate fit in the three age groups; grades 6–7: χ
2
= 1054.6, RMSEA = .036, SRMSR = .046, AGFI
= .945, CFI = .938, NNFI = .932; grades 8–9: χ
2
= 1078.1, RMSEA = .049, SRMSR = .059, AGFI
= .912, CFI = .899, NNFI = .890; grades 10–11: χ
2
= 1,070.2, RMSEA = .042, SRMSR = .061,
AGFI = .943, CFI = .929, NNFI = .923) as well as metric invariance hold (with ΔCFI = .002
between a model where factor loadings are restricted to be equal across age groups versus a
model where this is not the case, see Table 3), whereas the assumption of scalar invariance is
violated: A model with unconstrained intercepts fits substantially better than a model with the
intercepts being constrained to be equal across age groups (ΔCFI = .014). Identifying the sub-
set of items for which a constrained intercept leads to a significantly worse fit (using a similar
stepwise procedure as the one described above), yields the results shown in the last columns of
Table 4. The model with partially restricted intercepts does not fit substantially worse than the
model with free intercepts (ΔCFI = .002, between Model A2 and Model A4 in Table 3). The
interpretation follows the same lines as in the case of gender non-invariance for the intercepts.
Table 3. Goodness-of-fit indices for models used to test invariance across gender and age groups.
χ
2
(df) RMSEA SRMSR AGFI CFI NNFI
Gender
Model G1 2352.2 (1158) .038 .047 .951 .937 .932
Model G2 2410.8 (1192) .037 .051 .951 .936 .933
Model G3 2786.8 (1227) .042 .056 .949 .918 .916
Model G4 2439.9 (1206) .038 .051 .951 .935 .933
Age groups
Model A1 3202.9 (1737) .042 .055 .936 .925 .918
Model A2 3309.7 (1807) .041 .062 .935 .923 .919
Model A3 3645.4 (1878) .044 .065 .935 .909 .909
Model A4 3398.7 (1851) .041 .063 .936 .921 .919
Notes. Model G1/A1: Model with unequal factor loadings and unequal intercepts across gender/age groups; Model G2/A2: Model with equal factor loadings and unequal
intercepts across gender/age groups; Model G3/A3: Model with equal factor loadings and equal intercepts across gender/age groups. Model G4/A4: Model with equal
factor loadings and partially unequal intercepts (see, Table 4) across gender/age groups. In all models, unique variances are allowed to differ across gender/age groups.
Gender refers to two groups (males and females); age groups to three groups (Grades 6–7, Grades 8–9, Grades 10–11).
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Item 8 (“I try to respond all questions that are asked us during the lessons”) shows the largest
difference between the groups: Adolescents of grades 6–7 are expected to score about .30
points higher than adolescents of grades 10–11 who have the same scores on the latent Covital-
ity construct and subconstructs. Conversely, for item 17, older adolescents are expected to
score about .26 points higher as compared to younger adolescents of the same covitality level:
Table 4. Estimated intercepts for the 36 items in the higher-order factor model for the SEHS-S, by gender and age groups.
Gender Age groups (grades)
Item Female Male 6 – 7 8–9 10–11
1 3.22 3.32 3.28 3.36 3.54
2 — 3.46 — 3.50 3.58 3.69
3 — 3.24 — 3.29 3.31 3.49
4 3.68 3.59 3.67 3.74 3.80
5 2.85 3.12 — 3.16 —
6 3.08 3.20 3.18 3.30 3.38
7 — 2.59 — — 2.72 —
8 2.81 2.90 3.10 2.99 2.81
9 2.70 2.80 2.98 2.79 2.79
10 — 3.35 — — 3.46 —
11 — 3.00 — — 3.13 —
12 — 3.23 — — 3.36 —
13 — 3.43 — — 3.55 —
14 3.30 3.35 — 3.45 —
15 — 3.33 — — 3.44 —
16 3.29 2.97 — 3.20 —
17 3.22 2.74 2.91 3.12 3.17
18 3.27 2.91 — 3.17 —
19 — 3.45 — 3.45 3.48 3.67
20 — 3.31 — 3.31 3.36 3.56
21 — 3.08 — 3.10 3.13 3.26
22 3.34 3.06 — 3.23 —
23 3.44 3.19 — 3.37 —
24 3.36 3.11 — 3.29 —
25 — 3.26 — — 3.36 —
26 3.00 2.89 — 3.03 —
27 2.73 2.93 — 2.95 —
28 3.25 3.41 — 3.48 —
29 — 3.42 — 3.49 3.61 3.64
30 — 3.49 — — 3.61 —
31 2.95 3.18 — 3.24 —
32 3.00 3.17 — 3.26 —
33 2.95 3.09 — 3.17 —
34 — 3.39 — — 3.52 —
35 3.38 3.22 — 3.42 —
36 3.65 3.58 3.68 3.73 3.77
Sum score 115.52 114.78 118.52 119.08 120.13
Note. If only one intercept is reported for both genders or for the three age groups, the intercepts were constrained to be equal after a previous hypothesis test showed
that these intercepts were not significantly different (i.e., p>.05). In this model, factor loadings were restricted to be equal across groups.
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Having a friend with whom you can talk about your problems is less indicative of high covital-
ity in older adolescents. Summing all the intercepts shows that the overall test score is only
slightly biased, with adolescents in grades 10–11 having an advantage of 1.61 points over ado-
lescents from grades 6–7 and an advantage of 1.05 points over adolescents of grades 8–9.
Gender and age differences
As explained in the previous section, differences in Covitality between males and females or
among adolescents of different age can be examined through the overall test score, after taking
into account the above mentioned differences due to lack of scalar invariance. A more direct
comparison (using the CFA results) is based on the estimated latent scores on the Covitality
super-factor. With respect to sex, we found that, on average, men have a slightly higher latent
score than women (of 0.044, on a scale with a standard deviation of 0.39 for women and 0.33
for men; p= .047). As to age groups, the average latent Covitality score for adolescents in
grades 6–7 is 0.130 units higher than for those in grades 8–9 and 0.149 higher as compared to
adolescents of grades 10–11 (p<.01 for both differences; the standard deviations for the three
groups are 0.38, 0.37, and 0.33, respectively).
Discussion
Covitality refers to the positive aspects of mental health, such as well-being, positive emotions,
and social support [20,22–24]. In this study, we provide a validation of the SEHS-S, the most
wide-spread measure of covitality, in a population of Colombian adolescents aged 10 to 19
years. The results provide validity evidence based on the SEHS-S internal structure (including
factor structure, measurement invariance, and reliability) as well as on its relations with exter-
nal variables.
Regarding the descriptive statistics, we found that the mean in our sample was slightly
above the mean reported in Furlong et al.’s [34] recent study. However, further research is
required to interpret this difference because, in the first place, cross-cultural measurement
invariance should be established (e.g., equivalence with respect to the instrument used in both
populations) and then, possibly, this difference may be validly attributed to differences in the
living conditions between Californian and Colombian adolescents. Furthermore, Furlong et al.
[33] consider SEHS-S scores between percentiles 16 and 84 as indicative of normal covitality,
whereas scores below the 15th percentile and above the 85th percentile are regarded as “weak”
and “strong” covitality, respectively. Translated to the population in this study, these percen-
tiles correspond to overall sum scores of 100 and 132, respectively, which may be relevant cut-
offs when the SEHS-S is used for educational or clinical purposes in Colombia.
The CFAs in this study support the a priori hierarchical factor structure of the super-con-
struct Covitality, where Covitality is as a third-order factor, composed of 4 second-order fac-
tors and 12 first-order factors measured by 36 items. As such, our results are in line with the
original Covitality model, which has been corroborated in a wide variety of other cultures and
contexts [18,33,34,41,43]. This suggests that configural invariance of the Covitality model
across cultures is plausible.
Furthermore, this study examined measurement invariance across different subpopula-
tions, specifically across boys and girls and across age groups (based on the participants’ school
grade). We found that, whereas the hierarchical factor structure fits well in both male and
female subpopulations as well as in three different age groups, and both configural and metric
invariance was confirmed, differences in the intercepts for different items pointed to a lack of
scalar invariance. This lack of scalar invariance means that two individuals from different gen-
ders and/or from different age groups, although they have equal scores on the latent covitality
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factors, have different expected scores for at least some of the items. Examples include item 17
(“I have a friend my age who talks to me about my problems”, which is more common in girls
and in older adolescents), item 5 (“I understand my moods and feelings”, more common in
boys) and item 8 (“I try to answer all the questions asked in class”, more common in younger
students). It is important to take this lack of invariance into account as it implies that the same
response is more indicative of covitality in some subpopulations than in others (e.g., item 17
points to a higher level of covitality if endorsed by a younger boy as compared to an older girl).
In this respect, our results are notably different from most previous work with the SEHS-S,
where scalar invariance as a function of gender and age received support [18,33,34,43]. Over-
all, our findings suggest that while the SEHS-S can be validly used to measure social and emo-
tional health in Colombian adolescents, there are (small) differences in response patterns
between boys and girls and adolescents of different age that are worthwhile to be further inves-
tigated. In general, it is important to test for measurement invariance in different subpopula-
tions when using self-report measures of social and emotional health and to be aware of
potential biases in the interpretation of results.
With respect to the reliability of the SEHS-S, the values for Cronbach’s alpha and McDo-
nald’s omega we obtained in this study were high (above .90), which means that the SEHS-S is
a reliable measure in this population of Colombian adolescents. Also within subgroups defined
by sex and age the values for Cronbach’s alpha were high (between .89 and .93). These results
are fully in line with previous studies, which report reliability indices in the range of .89–.96
[5,6,18,23,33–36,38].
Further validity evidence is provided by the moderate to strong associations of the SEHS-S
with other variables that measure theoretically related constructs. In particular, our results
show that covitality has positive and strong correlations with health-related quality of life and
trait emotional intelligence, moderate positive correlations with well-being, and moderate neg-
ative correlations with psychopathological symptoms. These correlations are consistent with
results from previous studies which report associations with similar measures of mental health
and well-being [5,6,18,33,37,41,44,45,65].
Finally, our study explored differences on the overall Covitality construct both between males
and females and among individuals of different age. Regarding gender, we found slightly higher
social and emotional skills in boys, a result that is consistent with previous studies; in effect,
whenever gender differences come across, it turns out that males are more likely to have high
scores on global Covitality [5,18,23,38]. Regarding age, our results reveal a trend of lower Covi-
tality scores in older individuals. Although we have not found any other studies that have com-
pared the level of global Covitality as a function of age or grade, this result is in line with the
general decrease —reported for both boys and girls, although more pronounced in girls — of
subjective well-being in adolescents with increasing age, starting at 11–12 years [66]. Some
authors attribute this decline in well-being to the onset of adolescence, which is characterized by
significant physical, cognitive, emotional, and social changes, as well as by an increase in risky
situations that lead to greater emotional, psychological, and social vulnerability [67]. These gen-
der and age differences are relevant for the development and implementation of interventions
aimed at improving social and emotional health, both in general and in this population of
Colombian adolescents. However, further research, especially with a longitudinal design, is nec-
essary to examine how covitality evolves over time, potentially differing between boys and girls.
Limitations
Our study faces the following limitations: In the first place, we used a convenience sample of
adolescents from four specific schools in a particular area in Colombia, characterized by its
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rather low social economic status. Moreover, data collection took place in 2019, before the
COVID-19 outbreak. Therefore, any generalizations of the obtained results, whether in space
(to other regions inside or outside Colombia) or time (e.g., post-COVID), are highly
speculative.
Furthermore, the self-report nature of the instruments poses a potential risk of bias, as ado-
lescents may struggle to accurately assess their own emotional states or may be reluctant to
share their feelings. Studies that correlate adolescents’ responses on the SEHS-S with qualita-
tive data from in-depth interviews may shed a light on the extent to which participants’ self-
report measures on this instrument overestimate their actual social-emotional strengths. Con-
versely, Corneille and Gawronski, in a recent paper, highlight several advantages of self-report
measures and argue for their superiority over implicit measures [68].
Finally, our analytic approach is fully quantitative and focuses exclusively on validity evi-
dence based on internal structure and relations with other variables. Future research may
include other sources of validity evidence, such as those based on response processes and con-
sequences, and employ qualitative techniques, such as cognitive interviews or focus groups
[69].
Conclusions
Overall, the findings of this study support the use of the SEHS-S as a tool for promoting posi-
tive mental health and assessing social and emotional skills in adolescents. The hierarchical
structure of the Covitality super-construct provides a comprehensive framework for under-
standing these skills. Although future research should examine gender and age differences, the
results from the CFAs, high reliability indices, and correlations with similar constructs provide
ample evidence for the SEHS-S as a valid tool for assessing social and emotional skills in
Colombian adolescents. This can help develop targeted interventions to promote Covitality in
this population.
Supporting information
S1 Checklist. Inclusivity in global research.
(DOCX)
Acknowledgments
We gratefully acknowledge the invaluable support received from the educational institutions
He
´ctor A
´ngel Arcila, Instituto Santuario INSA, Nuestra Señora de la Presentacio
´n, and Institu-
cio
´n Educativa Francisco Jose
´de Caldas, as well as from the Secretaria de Educacio
´n of the
department of Risaralda, in facilitating the data collection process.
Author Contributions
Conceptualization: Diana Riaño-Herna
´ndez, Jose
´A. Piqueras.
Data curation: Iwin Leenen.
Formal analysis: Iwin Leenen.
Funding acquisition: Diana Riaño-Herna
´ndez.
Investigation: Diana Riaño-Herna
´ndez, Angelli Ramı
´rez-Conde, Paula A. Atehortua-Rivera.
Methodology: Diana Riaño-Herna
´ndez, Iwin Leenen.
Project administration: Diana Riaño-Herna
´ndez.
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Resources: Diana Riaño-Herna
´ndez, Angelli Ramı
´rez-Conde, Paula A. Atehortua-Rivera.
Supervision: Diana Riaño-Herna
´ndez.
Validation: Iwin Leenen.
Writing – original draft: Diana Riaño-Herna
´ndez, Iwin Leenen, Angelli Ramı
´rez-Conde,
Paula A. Atehortua-Rivera, Jose
´A. Piqueras.
Writing – review & editing: Iwin Leenen, Jose
´A. Piqueras.
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