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ORIGINAL RESEARCH
published: 14 May 2018
doi: 10.3389/fpsyg.2018.00692
Edited by:
Maicon Rodrigues Albuquerque,
Universidade Federal de Minas
Gerais, Brazil
Reviewed by:
Cesar Merino-Soto,
Universidad de San Martín de Porres,
Peru
Edson Filho,
University of Central Lancashire,
United Kingdom
*Correspondence:
Jesus Montero-Marin
jmonteromarin@hotmail.com
Specialty section:
This article was submitted to
Quantitative Psychology
and Measurement,
a section of the journal
Frontiers in Psychology
Received: 01 January 2018
Accepted: 20 April 2018
Published: 14 May 2018
Citation:
García-Campayo J, del Hoyo YL,
Barceló-Soler A, Navarro-Gil M,
Borao L, Giarin V, Tovar-Garcia RR
and Montero-Marin J (2018)
Exploring the Wisdom Structure:
Validation of the Spanish New Short
Three-Dimensional Wisdom Scale
(3D-WS) and Its Explanatory Power
on Psychological Health-Related
Variables. Front. Psychol. 9:692.
doi: 10.3389/fpsyg.2018.00692
Exploring the Wisdom Structure:
Validation of the Spanish New Short
Three-Dimensional Wisdom Scale
(3D-WS) and Its Explanatory Power
on Psychological Health-Related
Variables
Javier García-Campayo1,2 , Yolanda L. del Hoyo3, Alberto Barceló-Soler1,4,
Mayte Navarro-Gil1, Luis Borao1, Veronica Giarin5, R. Raziel Tovar-Garcia6and
Jesus Montero-Marin1*
1Red de Investigación en Actividades Preventivas y Promoción de la Salud, Zaragoza, Spain, 2Hospital Universitario Miguel
Servet, Zaragoza, Spain, 3Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain, 4Instituto de
Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain, 5Unidad de Investigación de Atención Primaria, Zaragoza,
Spain, 6Centro de Investigación y Desarrollo en Ciencias de la Salud, Universidad Autónoma de Nuevo León,
San Nicolás de los Garza, Mexico
Introduction: Personal wisdom has demonstrated important implications for the health
of individuals. The aim of the present study was to validate a Spanish version of the
Three-Dimensional Wisdom Scale (3D-WS), exploring the structure of a possible general
factor, and assessing its explanatory power on psychological health-related variables.
Methods: A cross-sectional study design was used, with a total sample of 624
Spanish participants recruited on the Internet and randomly split into two halves. The
following instruments were applied: 3D-WS, Purpose in Life (PIL), Multidimensional
State Boredom Scale (MSBS), Positive and Negative Affect Scale (PANAS), and
Difficulties in Emotion Regulation Scale (DERS). Factorial structures were analyzed
through exploratory and confirmatory factor analysis (EFA and CFA), and the general
factor was characterized by using bifactor models. The explanatory power of the 3D-
WS was established by multiple regression.
Results: The original long and short versions of the 3D-WS were not replicated in
the first subsample using EFA, and there was a high rate of cross-loadings. Thus, a
new short 3D-WS was proposed by ordering the original items according to factorial
weights. This three-correlated-factor (reflective, cognitive, and affective) proposal was
tested by means of CFA in the second subsample, with adequate psychometrics
and invariance, and a good fit (χ2/df = 1.98; CFI = 0.946; RMSEA = 0.056; 90%
CI = 0.040−0.072). A bifactor structure, in which the reflective trait of wisdom was
integrated into a general factor (G-Reflective) improved the model fit (χ2/df = 1.85;
CFI = 0.959; RMSEA = 0.052; 90% CI = 0.035−0.070). The explained common variance
of G-Reflective was 0.53; therefore, the new short 3D-WS should not be considered
essentially unidimensional. The new short 3D-WS showed positive relationships with
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García-Campayo et al. Validation of the Spanish 3D-WS
the PIL and PANAS-positive, and negative associations with the MSBS, PANAS-
negative and DERS, contributing to explain all the referred variables. These results were
consistent across subsamples.
Conclusion: The new short 3D-WS appears to be a reliable instrument for measuring
wisdom in the Spanish general population. The reflective facet might influence the
cognitive and affective wisdom components through the G-Reflective general factor.
There seems to be a high explanatory power of the 3D-WS on psychological health-
related variables. This study will facilitate the development of future research and
psychological knowledge regarding wisdom.
Keywords: wisdom, 3D-WS, validation, EFA, CFA, psychometrics, well-being, bifactor
INTRODUCTION
Personal wisdom has increasingly become a research subject in
psychology in previous years, despite the intrinsic difficulty of
establishing a broadly accepted definition on what appears to be
a very slippery subject. This may be a result of the inherently
cultural nature attributed to wisdom (Takahashi and Overton,
2005). In general, it has recently been indicated that wisdom
may have significant implications for individuals and health care
systems via improvements in physical and mental health (Ardelt,
2000, 2003;Jeste et al., 2013;Webster et al., 2014), and quality
of life (Ardelt, 1997, 2000;Jeste and Oswald, 2014), as well as
psychological health-related outcomes, such as resilience (Jeste
et al., 2013), happiness (Etezadi and Pushkar, 2013;Zacher et al.,
2013), self-efficacy (Glück et al., 2013), life satisfaction (Ferrari
et al., 2011;Le, 2011) and forgiveness (Taylor et al., 2011). In
addition, wisdom may be beneficial for other individuals and
society at large by promoting the well-being of other individuals,
and improving the quality of social relationships (Ardelt, 1997,
2000;Jeste and Oswald, 2014). However, wisdom is a complex
psychological construct −with various not necessarily equivalent
operationalizations that are focused on different definitions to
some extent (Glück et al., 2013)−that is extremely difficult to
study.
There is a consensus from different theoretical orientations
that wisdom is a multifaceted or multidimensional psychological
concept, and some of its component facets or dimensions may
feed and reinforce each other (Webster, 2003). Although not free
of debate regarding what are the essential components of wisdom,
both necessary and sufficient, and what constitutes predictors
and consequences, one basic definition of wisdom that spans an
especially broad range of facets of wisdom consider it formed by
cognitive (general), reflective (self-related) and affective (other-
related) components (Ardelt, 2011). This definition is able to
generate a parsimonious concept compatible with modern and
ancient descriptions of the topic (Clayton and Birren, 1980;
Ardelt, 2003). The cognitive component includes the ability to
understand and comprehend the deeper meaning of life events,
including the ambiguity of human nature, the limits of knowledge
and the uncertainty of life (Ardelt, 2000, 2003). The reflective
dimension, which seems to be essential to facilitate referred
understanding and cognitions, consists of the ability to acquire
different perspectives, overcome self-centeredness, subjectivity
and projections, attain insights into the true nature of things
and motivations, and avoid blaming other individuals for one’s
own circumstances (Ardelt, 2003). The affective factor is based
on the presence of positive emotions and a sympathetic and
compassionate behavior toward other individuals, as well as
the absence of indifferent or negative feelings and behaviors
toward other individuals. It may also depend on the reflective
dimension because a deep understanding of life and individuals
from a positive point of view is only possible if one can perceive
reality as it is with no major biases (Ardelt, 2003). This model
considers wisdom as an integration of the three above-mentioned
facets, which are conceptualized as developmental personality
qualities that may be measured by the long and short versions of
the “Three-Dimensional Wisdom Scale” (3D-WS) (Ardelt, 2003;
Thomas et al., 2017). There have been shown to be inter-factorial
correlations between the 3D-WS factors in the original study
with regard to ‘reflective–cognitive’ of 0.41−0.50, ‘reflective–
affective’ 0.46−0.50, and ‘affective–cognitive’ 0.30−0.33. The
factor loadings from a possible general second order factor
were of 0.83−0.84 for the reflective, 0.59−0.61 for the affective,
and 0.50−0.52 for the cognitive. According to the original
author, all of this suggests the reflective facet might be fostering
both cognitive and affective characteristics of wisdom (Ardelt,
2003). The 3D-WS was originally developed in English. To
date, no version exists in Spanish that enables investigations
of the implications of this wisdom model in Spanish-speaking
countries.
In this context, the main aim of the present study was to
validate a new Spanish-language version of the 3D-WS. As
secondary objectives, we aimed to explore the structure of
a possible general factor and the influence of the reflective
characteristics on all the aspects of wisdom; as well as to
estimate the explanatory power of the 3D-WS on psychological
health-related variables to evaluate the extent to which wisdom
may contribute to well-being. To date, no Spanish-language
studies have evaluated potential relationships between wisdom
and psychological outcomes, such as purpose in life, boredom,
positive and negative affectivity, and emotional regulation.
Purpose in life has been of interest in existential psychotherapy
(Crumbaugh and Maholick, 1969), and it has shown negative
associations with depression and positive relationships with
psychological well-being (Bonebright et al., 2000). Boredom
has been associated with several psychiatric disorders, such as
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anxiety, depression, somatisation, overeating and binge eating,
pathological gambling, and substance abuse (Alda et al., 2015).
In general, positive and negative affectivity explain an important
portion of psychological well-being (Menk Otto et al., 2010).
Emotion regulation is a central component of mental health, and
its imbalances may underlie several mental disorders (Mennin
and Farach, 2007). Therefore, we started with the exploratory
assumption that a new Spanish version of the 3D-WS could be
validated with adequate psychometric properties. We also stated
that the reflective facet might be contributing to the general
factor to a greater extent than the other components. Finally,
we hypothesized that wisdom may significantly explain all of
the above-mentioned psychological outcomes, and it may be
positively related to purpose in life and positive affectivity, and
negatively to boredom, negative affectivity and the absence of
emotional regulation.
MATERIALS AND METHODS
Design
An analytical cross-sectional design was developed within
a validation study, using back-translations of the original
questionnaire and an online survey.
Participants, Data Collection and Ethics
The online survey was developed on a commercial system1,
and it was disseminated through several websites from the
authors’ scientific research webpage. Individuals were invited to
participate in research on “general aspects related to wisdom.”
The link to the survey was accessible from September 2016 to
June 2017. Overall, 1,808 participants accessed the link, and 1,737
individuals voluntarily agreed to participate. Participants who did
not complete all items of the 3D-WS validation questionnaire
(n= 937) were excluded. Those participants with nationalities
or provenances other than Spain (n= 176) were also excluded,
given the intention that everyone should use a similar standard
variety of Spanish and be able to interpret the questionnaire
statements in the same way. Therefore, 624 Spanish individuals
were recruited. The majority of the participants were female
(75.6%), with a mean age of 44.70 (SD = 12.61; Range = 18−75),
and mainly with a partner in a stable relationship (63.1%), a
university education (79.5%) and in employment (72.9%). The
total sample was randomly split into two halves (312 participants
each) in order to develop exploratory and confirmatory analyses
using different subsamples. A sample size of n= 312 subjects,
with a null hypothesis that RMSEA would be equal to or less than
0.050 if the true value was 0.080 (close fit) and an alpha equal to
0.05 level, produces power coefficients ranging from 0.72 (lower-
powered analysis: exploratory factor analysis of the short 3D-WS
using a bifactor model, with 33 degrees of freedom) to 0.99
(higher-powered analysis: exploratory factor analysis of the long
3D-WS, with 627 degrees of freedom) (MacCallum et al., 1996).
The protocol used in this study was approved by the Ethical
Committee of the regional health authority of Aragon (CEICA,
1www.surveymonkey.com
PI16/0117), and all participants submitted a written informed
consent form online attesting to their willingness to participate.
Validation Procedure
We initially obtained permission from the original author
(Ardelt, 2003) to translate into Spanish and validate the 3D-WS.
Two researchers who were aware of the questionnaire’s objectives
subsequently performed the initial translation from English to
Spanish. Each researcher translated the questionnaire separately.
Two bilingual linguistic experts, who had no specific knowledge
regarding the instrument, produced back-translations. A native
English-speaking teacher subsequently determined whether the
two English versions were equivalent, and differences between the
translations were solved through mutual agreement. An accepted
guideline for cross-cultural adaptations was followed (Guillemin
et al., 1993). The final Spanish version of the 3D-WS is shown
in Additional File 1, and its corresponding English version in
Additional File 2.
Measures
Socio-Demographic
The general socio-demographic information obtained from the
participants included age, sex, nationality (Spain, South America,
Central America, others), marital status (with partner, single,
divorced, or widower), level of education (primary, secondary,
or university) and employment situation (student, employed, on
sick leave, unemployed).
Three-Dimensional Wisdom Scale (3D-WS)
The long 3D-WS (Ardelt, 2003) includes 39 items: 14 items
for the cognitive dimension (e.g., “I am hesitant about making
important decisions after thinking about them”), 12 items for
the reflective dimension (e.g., “When I look back on what has
happened to me, I can’t help feeling resentful”), and 13 items
for the affective dimension (e.g., “I don’t like to get involved
in listening to another person’s troubles”). The short 3D-WS
(Thomas et al., 2017) includes only 12 items of the total pool,
with four items for each of the three dimensions previously
described. The items are self-rated using five options, and they
are scaled from 1 (strongly agree or definitely true of myself)
to 5 (strongly disagree or not true of myself); 5 items from the
reflective dimension and 3 items from the affective dimension are
reverse-scored (they are marked with an “r” in Table 2). The scale
structure also supports a total second-order factor of wisdom, in
which higher scores indicate greater wisdom levels, with adequate
psychometric properties in its first proposal and both the long
and short versions (Ardelt, 2003;Thomas et al., 2017).
Purpose in Life (PIL)
The PIL (Crumbaugh and Maholick, 1969) is one of the
tools most commonly employed to measure the meaning of
life. It was used in Part A of the questionnaire, which has
20 items Likert-type distributed among the components of
general perception of the meaning of life (e.g., “My personal
existence is: utterly meaningless, without purpose/purposeful and
meaningful”) and satisfaction with life (e.g., “Life to me seems:
completely routine/always exciting”), with a general total score of
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purpose in life, in which higher scores indicate a higher purpose
in life level. As indicated, each item has specific response anchors
with respect to categories 1–7, whereas category 4 entails a neutral
attitude toward the statements. The Spanish version of the PIL
has shown good psychometric properties (Martínez et al., 2012),
with a total alpha value in the present study for the total scale of
α= 0.94 and a 95% confidence interval (95% CI) = 0.93−0.95,
using Fisher’s method (Fisher, 1950) because of its efficiency
(Dominguez-Lara and Merino-Soto, 2015) [general perception
of the meaning of life α= 0.90 (95% CI = 0.88−0.91), and
satisfaction with life α= 0.79 (95% CI = 0.76−0.82)].
Multidimensional State Boredom Scale (MSBS)
The MSBS (Fallman et al., 2013) is a self-reported 29-item
questionnaire that measures state boredom using the dimensions
of disengagement (e.g., “I am wasting time that would be better
spent on something else”), high arousal (e.g., “Everything seems
to be irritating me right now”), low arousal (e.g., “It seems like
there’s no one around for me to talk to”), inattention (e.g., “I am
easily distracted”), and time perception (e.g., “Time is passing by
slower than usual”). It also permits a total score, in which higher
scores indicate higher boredom levels. Each item is rated on a
scale from 1 (strongly disagree) to 7 (strongly agree) in relation to
the respondent’s present experience. The scale has recently been
validated in Spanish with appropriate psychometric parameters
(Alda et al., 2015), with an alpha value in the present study for
the total scale of α= 0.97 (95% CI = 0.97−0.98) [disengagement
α= 0.94 (95% CI = 0.93−0.95); high arousal α= 0.87 (95%
CI = 0.85−0.89), low arousal α= 0.92 (95% CI = 0.90−0.93),
inattention α= 0.91 (95% CI = 0.89−0.92), time perception
α= 0.92 (95% CI = 0.91−0.93)].
Positive and Negative Affect Scale (PANAS)
The PANAS is a brief measure of positive (e.g., “Enthusiastic”),
and negative (e.g., “Distressed”) affectivity (Watson et al., 1988).
It consists of a list of 20 adjectives, 10 per subscale, rated
on a 5-point Likert-type scale from 1 (very slightly or not at
all) to 5 (extremely). Present moment temporary instructions
were used in this study. Higher scores indicate greater levels
of positive/negative affectivity. This questionnaire has been
validated in Spanish with good psychometrics (Sandín et al.,
1999), with α= 0.92 (95% CI = 0.91−0.93) and α= 0.91 (95%
CI = 0.89−0.92) for the positive and negative scales, respectively,
in the present study.
Difficulties in Emotion Regulation Scale (DERS)
The DERS is a questionnaire that assesses aspects of the emotion
regulation process in which individuals may have difficulties.
The Spanish version (Hervás and Jódar, 2008) consists of 28
items grouped into the subscales of lack of emotional awareness
(e.g., “I am attentive to my feelings” −item reversed), lack of
emotional clarity (e.g., “I have difficulty making sense out of
my feelings”), non-acceptance (e.g., “When I’m upset, I become
angry with myself for feeling that way”), goals (e.g., “When I’m
upset, I have difficulty concentrating”), and impulse (e.g., “When
I’m upset, I have difficulty controlling my behaviors”); it also
permits a global score. Participants are asked to indicate how
often the items apply to themselves, with responses that range
from 1 (almost never) to 5 (almost always). Higher scores indicate
greater difficulties in emotion regulation. This scale has shown
evidence of adequate psychometric properties (Hervás and Jódar,
2008), with an internal consistence in the present study for the
total scale of α= 0.96 (95% CI = 0.95−0.97) [lack of emotional
awareness α= 0.86 (95% CI = 0.84−0.87); lack of emotional
clarity α= 0.83 (95% CI = 0.80−0.85); non-acceptance α= 0.94
(95% CI = 0.93−0.95); goals α= 0.91 (95% CI = 0.89−0.92),
impulse α= 0.93 (95% CI = 0.92−0.94)].
Statistical Analysis
The socio-demographics were described using means (SDs)
and frequencies (percentages) according to their nature, and
possible differences between subsamples were tested using the
tfor independent groups and χ2(or Fisher when necessary)
tests. The items behavior was assessed using means (SDs),
skewness, kurtosis and item-rest (factor/total) correlations.
Mardia’s coefficients (Mardia, 1974) were calculated to evaluate
their multivariate distribution. We verified the KMO sampling
adequacy values, the Barlett’s test of sphericity on the redundancy
levels and the matrix determinants to discard multi-collinearity
problems (Muthén and Kaplan, 1992;Field, 2000).
Exploratory factor analysis (EFA) −using subsample 1−was
conducted to discover the underlying factorial structure of the
3D-WS items. Schwartz’s Bayesian Information Criterion (BIC)
was used as a dimensionality test to decide the number of factors
to be retained. The unweighted least squares (ULS) method, with
correcting for robust mean and variance-scaled, was employed
for factor extraction in view of its robustness (Jöreskog, 1977).
ULS does not provide significance p-values for the parameters;
however: (a) it does not require distributional assumptions; (b)
it is robust and typically converges because of its high efficiency
in terms of computation; and (c) it tends to supply less biased
estimates of the true parameter values than classical methods
or far more complex procedures (Knol and Berger, 1991;Parry
and McArdle, 1991;Briggs and MacCallum, 2003;Lee et al.,
2012). Polychoric correlations, which are specially adapted to
the analysis of relationships between polytomous categorical
variables, were used to build the input matrices. The raw loading
matrices were rotated using the Promin procedure, which allows
factors to be oblique so that factor simplicity is maximized,
without the assumption that all the variables are pure measures of
a single dimension (Lorenzo-Seva, 1999;Ferrando and Lorenzo-
Seva, 2000). Uniqueness terms (δ) were calculated as a measure
of item unexplained variance. We evaluated factorial simplicity
by means of: (a) the index of factor simplicity (IFS); (b) the scale
fit index (SFI); (c) Bentler’s scale-free matrix measure; and d)
hyperplane counts. IFS and SFI values of 0.80 are meritorious;
Bentler’s measure ranges from 0 for very complex structures, to 1
for very simple ones; and hyperplane counts (loadings essentially
zero except for random error) were estimated through the
−0.15/+0.15 interval and using the Kaiser and Cerny procedure
(Fleming, 2003). Factor scores were calculated by means of Bayes
Expected a Posteriori (EAP) estimates because these scores have
the highest correlations with the common factors they measure
(Mulaik, 2010). Effectiveness and quality of factor score estimates
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were quantified by using the factor determinacy index (FDI)
and marginal reliability estimates. FDI is the correlation between
the factor score estimates and the levels on the latent factors
they estimate (Beauducel, 2011), and values of around 0.80
are adequate (Gorsuch, 1983). Marginal reliability was obtained
by FDI squared, and it is interpreted as the reliability of the
corresponding factor score estimates (Brown and Croudace,
2015). Construct replicability, the proportion of the factor
variance that can be accounted for by its indicators, was measured
by the H index −bounded between 0 and 1, with reasonable
values when ≥0.70 (Hancock and Mueller, 2000), or more
strictly ≥0.80 (Rodriguez et al., 2016). We explored closeness to
unidimensionality by the mean of item residual absolute loadings
(MIREAL) and the explained common variance (ECV). MIREAL
is a measure of departure from unidimensionality, with <0.30
indicating no substantial bias if a unidimensional solution is fitted
(Grice, 2001;Ferrando and Loranzo-Seva, 2017). ECV represents
the proportion of common variance attributable to the general
factor, and it should be in the range of 0.70−0.85 if a solution is to
be accepted as unidimensional (Rodriguez et al., 2016). We tested
an exploratory second order factor solution (Schmid and Leiman,
1957) and an exploratory bifactor model as two general factor (G)
approaches for wisdom that would reflect what is common to all
the items.
Confirmatory factor analysis (CFA) −using subsample 2−was
used to ensure a clear distinction between the factors emerged
from the EFA by loading each item onto its corresponding single
component, all of them correlated. The Maximum Likelihood
method (ML) was used, which employs Pearson correlations
based on the covariance matrix as input data. The ML
method is the most popular structural equation modeling (SEM)
estimation procedure, as it provides asymptotically unbiased
and consistent parameter estimates (Bollen, 1989), and permits
inferential estimations based on the χ2distribution, providing
significance p-values. It implies the assumption of multivariate
normality −particularly in terms of skewness (Coenders and
Saris, 1995), but it is relatively robust to its non-observance
(Hu and Bentler, 1999;Schermelleh-Engel et al., 2003). This
method also assumes the continuous measurement of both
latent and observed variables (DiStefano, 2002); however, the
covariance matrix enables robust analysis to be made of
ordinal data when the latent variables present more than one
indicator (Coenders et al., 1997). From an analytical perspective,
inter-factor correlations, standardized factor saturations (λ),
uniqueness terms and discrepancy values −as unstandardized
residual covariance estimates−were taken into account. From
a general perspective, the goodness-of-fit was assessed by
chi-square (χ2), chi-square/degrees of freedom (χ2/df), the
comparative fit index (CFI) and the root mean square error
of approximation (RMSEA). χ2is very sensitive to sample size
(Bollen and Long, 1993), so use was made of χ2/df, which
indicates a good fit when <5, and an excellent fit if <3 (Hu
and Bentler, 1999;Schermelleh-Engel et al., 2003). CFI examines
the discrepancy between the data and the hypothesized model
while adjusting for the sample size, and it indicates adequate
fit with a value of >0.90 and an excellent fit >0.95 (Burnham
and Anderson, 1998;Hu and Bentler, 1999). RMSEA is a
measurement of the error of approximation to the population,
and it indicates adequate fit when <0.08 and an excellent fit
<0.06 (Burnham and Anderson, 1998;Hu and Bentler, 1999).
An estimation was also made of the average variance extracted
(AVE), as the amount of variance that is captured by the construct
in relation to the variance due to measurement error. It shows
good values of construct convergent validity when ≥0.50, but
also has acceptable values if it is around 0.40 and composite
reliability (CR) is >0.60. In addition, when AVE values are greater
than the squared correlation between factors, it can be said
there are discriminant validity among them (Fornell and Larcker,
1981).
The structure of the possible general factor of the new
proposed 3D-WS was evaluated −using CFA and subsample 2−
by testing a second-order solution, as well as a bifactor model
that would reflect the influence of the reflective characteristics
on the other wisdom aspects through the G-Reflective general
factor (Figure 1). The omega CR for the total scale (ω) and for
each subscale (ωS) were calculated, which may be interpreted
as the square of the correlation between the scale (ω)−or
subscale (ωS)−score and the latent variable common to the
corresponding indicators. This reliability value has the advantage
of taking into account the strength of association between items
and constructs as well as item-specific measurement errors,
while providing a more realistic estimate of true reliability than
other classical methods (McDonald, 1999). We also estimated
the omega hierarchical (ωH), as the proportion of reliable
variance in total scores that can be attributed to the single
general factor, and the omega hierarchical subscale (ωHS), as
the proportion of reliable variance associated with each factor
once partitioning out variance associated with the single general
factor (Reise, 2012;Green and Yang, 2015). The percentage
of uncontaminated correlations (PUC) were also estimated,
as the number of correlations between items from different
factors divided by the total number of correlations, which
indicates the proportion of correlations reflecting the general
FIGURE 1 | Hypothetical G-reflective structure of wisdom by means of the
G-Reflective bifactor model without considering the final number of items.
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factor. When ECV and PUC are >0.70 common variance can
be regarded as essentially unidimensional (Rodriguez et al.,
2016).
The configurational, metric, scalar and strict invariance of the
new 3D-WS model across subsamples, and age, sex and level of
studies −as relevant socio-demographic factors that might affect
wisdom (Ardelt, 2003) and that are recommended in validation
studies (Ayman and Korabik, 2010)−was sequentially evaluated
using the ML method (Van de Schoot et al., 2012). A nested model
adding covariances between latent factors was also incorporated
to the strict invariance model. These five nested models were
compared in order to allow some degree of invariance. Owing
to the sensitivity to sample size of changes in χ2(Hair et al.,
1999), we ensured that both decreases in CFI and increases in
RMSEA were ≤0.010 and ≤0.015, respectively (Chen, 2007).
Because the goodness-of-fit indices corrected for parsimony (e.g.,
RMSEA) can be improved with the addition of model constraints,
they were considered to be random. Possible differences in latent
factor means (1Mn) were tested across subsample, age, sex and
level of studies using structured means modelling (SMM), and
by setting the means of ‘subsample 1,’ ‘<55 years,’ ‘males,’ and
‘primary/secondary education’ to 0 (Sörbom, 1974). Effect sizes
(ESs) of differences in latent means were also assessed by using
Cohen’s d, dividing the referred differences by the within-groups
pooled variance estimate for scores on latent variables (Hancock,
2001).
The relationships between the EAP estimates of the long
3D-WS and the new proposed 3D-WS were assessed across
subsamples by applying Pearson’s rcoefficients, adjusting for
correlated errors (adj-r) (Levy, 1967). The raw correlations
between the new proposed 3D-WS and the psychological health-
related variables were estimated by applying rcoefficients, and
the explanatory power of the new proposed 3D-WS factors
in relation to the psychological health-related variables by
multiple linear regression models, which were examined using
analysis of variance. The total scores of the psychological
health-related variables were considered dependent variables,
whereas the new proposed 3D-WS factors by EPA estimates
were considered independent variables. Adjusted multiple
determination coefficients (R2) were calculated to evaluate the
explanatory power of the new proposed 3D-WS. The individual
contribution of the independent variables in each regression
model was estimated via calculation of the standardized
slope coefficients (Beta). The Wald test was used to evaluate
the significance of the contribution of each independent
variable (Etxeberrìa, 2007). The assumptions of regression were
tested using the K-Stest over the conditional distribution
of residuals, in order to check whether they were normally
distributed; the Durbin-Watson test, in order to rule out
possible autocorrelations in the error terms; as well as tolerance
(T) and variance inflation factor (VIF) values, in order
to discard collinearity problems (Martínez-González et al.,
2006).
All the tests were bilateral and were performed with a
significance level of α<0.05. Data analysis was conducted with
the SPSS-19, FACTOR-10, SIMLOAD, and AMOS-7 statistical
packages.
RESULTS
Socio-Demographics
The socio-demographics of the study participants, depending on
their randomly selected subsample, are shown in Table 1. No
significant differences were found between them in terms of age,
sex, marital status, education level, or employment status.
Exploratory Factor Analyses
Long 3D-WS
The descriptive statistics of all 3D-WS items (subsample 1) are
shown in Table 2. All the items showed item-rest correlations
in the same direction, with low values in some cases. Results of
the BIC dimensionality test advised a 3-factor solution (Table 3).
This solution explained 38.3% of the total variance, and only 20
out of 39 items (51.3%) loaded onto their theoretical belonging
factor (Table 4). Goodness of model-data fit for the 3-factor
proposal (Table 5), and general simplicity values were adequate
(IFS = 0.89; SFI = 0.82; Bentler = 0.98). However, individual
IFS values (Table 4), and the SFI value for the second factor
(SFI1= 0.89; SFI2= 0.75; SFI3= 0.81) suggested there was
space for improvement. The factor determinacy of the EAP
scores was good (FDI = 0.97 in all the factors). The marginal
reliability estimates were appropriate (F1= 0.93; F2= 0.95;
F3= 0.93). Construct replicability was good (H1= 0.91, H2= 0.87,
H3= 0.88). The inter-factor correlations were moderately high
(‘reflective−affective’ φ= 0.48, ‘reflective–cognitive’ φ= 0.54,
‘affective−cognitive’ φ= 0.46).
The loadings in a Schmid–Leiman general factor, which
maintained the same explained variance and fit that the three-
correlated factors model, are shown in Table 4. A general factor
by means of exploratory bifactor analysis improved the fit indices
(Table 5) and the percentage of explained variance (43.3%). FDI
values remained ≥0.91; marginal reliability was ≥0.84 in all the
factors; and construct replicability was good in the general factor
(HG= 0.90), and appropriate but fair in the rest (H1= 0.74,
H2= 0.81, H3= 0.77). The ECV and MIREAL values were
0.49 and 0.22, respectively. General simplicity was worsened
(IFS = 0.85; SFI = 0.69; Bentler = 0.96), as was the problem of item
loadings out of the corresponding theoretical factor (Table 5),
with only 15 out of 39 loading where they corresponded (38.5%),
and with inacceptable factorial SFI values (all of them ≤0.75).
Short 3D-WS
In view of this, we explored the possibility of improving the model
by discarding certain items. Firstly, the original short SD-WS
version (Table 6) was explored by means of EFA. Results of the
BIC dimensionality test advised a 1-factor solution (Table 3),
which was not in line with the theoretical background, it only
explained 35.4% of the variance and did not show adequate
fit (Table 5). Therefore, we explored a forced 3-factor solution
(explaining 53.9% of the variance), which presented a better fit
to the data in addition to good general simplicity (IFS = 0.93;
SFI = 0.91; Bentler = 0.98) and SFI values (SFI1= 0.83; SFI2= 0.91;
SFI3= 0.99). However, this solution had poor interpretability −
only 7 out of 12 items (58.3%) loaded onto their theoretical factor.
Factor determinacy values (FDI1= 0.86; FDI2= 0.87; FDI3= 0.96)
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TABLE 1 | Socio-demographics of the participants according to subsample.
Variables/subsamples Subsample 1 (n= 312) Subsample 2 (n= 312)
Mn SD Mn SD p
Age 44.51 11.50 44.88 10.53 0.678
Freq. % Freq. %
Sex Female 240 76.9 232 74.4 0.454
Male 72 23.1 80 25.6
Marital status With partner 205 65.7 189 60.6 0.411
Single 65 20.8 69 22.1
Separated 35 11.2 48 15.4
Widower 7 2.2 6 1.9
Education Primary 15 4.8 15 4.8 0.803
Secondary 46 14.8 52 16.7
University 251 80.4 245 78.5
Employment Student 30 9.6 22 7.1 0.630
Employed 226 72.4 229 73.4
Sick leave 19 6.1 18 5.8
Unemployed 37 11.9 43 13.7
Mn, mean; SD, standard deviation; Freq., frequencies; %, percentages.
and marginal reliability (F1= 0.74; F2= 0.76; F3= 0.92) were
appropriate, but construct replicability did not reach acceptable
values in the first and second components (H1= 0.65, H2= 0.67,
H3= 0.87). The inter-factor correlations were moderately high
(‘reflective−affective’ φ= 0.58, ‘reflective–cognitive’ φ= 0.42,
‘affective–cognitive’ φ= 0.56).
The loadings in a general factor by means of the Schmid–
Leiman solution and exploratory bifactor analysis can be seen in
Table 6. Exploratory bifactor analysis improved the fit (Table 5)
and explained variance (61.7%), although worsening general
(IFS = 0.65; SFI = 0.61; Bentler = 0.40), factorial (SFI1= 0.59;
SFI2= 0.17; SFI3= 0.91) and individual (Table 6) simplicity, and
making interpretation difficult due to the appearance of negative
weights in the third factor. FDI values (FDI1= 0.75; FDI2= 0.74;
FDI3= 0.88; FDIG= 0.87) and marginal reliability estimates
(F1= 0.56; F2= 0.54; F3= 0.78; FG= 0.76) were insufficient for the
first and second factors, and construct replicability values were
not acceptable (all of them H≤0.70). MIREAL presented a value
of 0.24, but ECV was 0.43.
New Short 3D-WS
Considering the observed limitations, we explored a new
operational definition of 3D-WS by sorting factorial weights
into the corresponding theoretical factor. The 12 selected items
can be seen in Table 6. The fit of the EFA (Table 5) for the
advised 3-dimensional model (Table 3), which explained 60.9%
of the variance, as well as the general (IFS = 0.97; SFI = 0.98;
Bentler = 0.99), factorial (SFI1= 0.98; SFI2= 0.98; SFI3= 0.98)
and individual (Table 7) simplicity values was adequate,
with all the items loading onto their corresponding factor.
Factor determinacy (FDI1= 0.95; FDI2= 0.93; FDI3= 0.91),
marginal reliability (F1= 0.90; F2= 0.87; F3= 0.83) and
construct replicability (H1= 0.86, H2= 0.83, H3= 0.74) were
appropriate. The inter-factor correlations were moderately high
(‘reflective−affective’ φ= 0.51, ‘reflective–cognitive’ φ= 0.46,
‘affective–cognitive’ φ= 0.55).
The loadings in a Schmid–Leiman general factor solution
and exploratory bifactor analysis are shown in Table 6. The
exploratory bifactor analysis improved the model fit (Table 5),
explaining 67.8% of the variance. Factorial simplicity remained
adequate at the general (IFS = 0.89; SFI = 0.89; Bentler = 0.98),
factorial (SFI1= 0.78; SFI2= 0.94; SFI3= 0.91) and individual
(Table 6) levels, with all the items loading onto the corresponding
factor. FDI was not adequate for the third factor (FDI1= 0.90;
FDI2= 0.81; FDI3= 0.67; FDIG= 0.95), and marginal reliability
was only sufficient for the first and general factors (F1= 0.80;
F2= 0.66; F3= 0.44; FG= 0.90). Construct replicability was
appropriate for the first and general factor, but not for the second
and third (H1= 0.78, H2= 0.59, H3= 0.62; HG= 0.94). ECV and
MIREAL values were 0.52 and 0.29, respectively.
Confirmatory Factor Analyses
New Short 3D-WS
The descriptive of the new short 3D-WS proposal (subsample 2),
are shown in Table 7. The BIC dimensionality test showed a
3-factor solution (Table 3), explaining 63.8% of the variance.
The CFA for a 3-correlated factors solution showed adequate
loadings, ranging from 0.51 to 0.87 (Table 8), and presented
adequate fit without introducing covariances between the
errors (Table 5). Uniqueness was similar to that obtained
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TABLE 2 | Descriptive statistics of the Spanish 3D-WS items∗.
Factor Item Mn SD Skew Kurt Item-rest (f) Item-rest (t)
Reflective 6 3.41 1.20 −0.36 −0.80 0.43 0.39
10 3.70 1.03 −0.53 −0.32 0.38 0.51
16r 4.16 0.79 −1.08 2.09 0.40 0.17
18r 3.85 0.83 −0.51 0.00 0.45 0.26
20r 4.10 0.78 −0.86 1.01 0.39 0.17
23 3.96 1.06 −0.99 0.45 0.55 0.56
26 3.62 1.06 −0.54 −0.35 0.62 0.49
29 3.56 1.10 −0.70 −0.10 0.60 0.33
32r 3.81 0.91 −0.76 0.43 0.37 0.20
35r 3.77 0.90 −0.66 0.25 0.41 0.16
37 3.95 1.03 −0.79 0.01 0.55 0.54
39 3.90 0.93 −0.84 0.61 0.51 0.31
Affective 2 3.12 1.18 −0.06 −0.93 0.28 0.28
4 3.85 1.18 −0.85 −0.12 0.21 0.31
8 3.21 1.22 −0.12 −1.09 0.42 0.38
12r 3.14 1.10 −0.17 −0.92 0.27 0.18
14 4.21 0.88 −1.26 1.74 0.43 0.32
17r 4.23 0.71 −0.53 −0.31 0.36 0.21
19 3.82 1.03 −0.74 0.04 0.42 0.31
21r 3.79 1.01 −0.78 0.33 0.40 0.28
24 4.13 0.90 −1.09 1.21 0.33 0.35
27 3.71 1.07 −0.66 −0.29 0.34 0.31
30 3.46 1.01 −0.47 −0.12 0.39 0.41
33 4.08 0.90 −0.82 0.08 0.37 0.29
36 3.38 1.00 −0.38 −0.43 0.42 0.43
Cognitive 1 4.12 0.99 −1.15 0.93 0.20 0.18
3 3.72 1.12 −0.79 −0.18 0.38 0.45
5 3.89 1.00 −0.74 0.07 0.47 0.37
7 4.42 0.81 −1.72 3.53 0.48 0.43
9 3.90 1.04 −0.84 0.17 0.50 0.43
11 3.32 1.04 −1.70 2.28 0.47 0.39
13 3.70 1.01 −0.64 −0.20 0.41 0.39
15 4.26 0.95 −1.45 1.82 0.54 0.42
22 3.54 1.14 −0.55 −0.39 0.34 0.42
25 3.33 1.01 −0.05 −0.57 0.17 0.27
28 3.29 1.03 −0.36 −0.46 0.39 0.46
31 3.50 1.04 −0.32 −0.57 0.22 0.17
34 3.38 1.12 −0.38 −0.67 0.33 0.47
38 3.66 1.12 −0.65 −0.42 0.17 0.11
∗Subsample 1. Factor, theoretical belonging factor; Item, item number; Mn, mean; SD, standard deviation; skew, skewness; kurt, kurtosis; item-rest, discrimination
coefficient for the factor (f) and total (t); r, reversed item.
from EFA using subsample 1, and residual covariances were
low and equally distributed among all the items −average
absolute value = 0.04. FDI values were adequate (FDI-
reflective = 0.78; FDI-cognitive = 0.87; FDI-affective = 0.80).
Construct replicability was appropriate −a bit fair in the affective
factor: H-reflective = 0.87, H-cognitive = 0.86, H-affective = 0.70.
The inter-factor correlations were moderate and significant for
‘reflective–cognitive’ (φ= 0.43; p<0.001) and ‘reflective–
affective’ (φ= 0.41; p<0.001), and low but significant between
‘cognitive−affective’ (φ= 0.21; p<0.05). The AVE and CR
values were adequate in the reflective (AVE = 0.60; ωS= 0.85)
and cognitive (AVE = 0.57; ωS= 0.84) factors, although they
were fair in the affective component (AVE = 0.36; ωS= 0.69)
−the effectiveness of alpha coefficients was corroborated in the
reflective (α= 0.82; 95% CI = 0.78−0.85), and cognitive (α= 0.78;
95% CI = 0.73−0.82) components, but it was not in the affective
(α= 0.60; 95% CI = 0.52−0.67), showing certain impact of the
difference in the size of factorial loadings. Nevertheless, all AVE
values were higher than the corresponding squared correlation
between factors, which suggested discriminant validity among
them. Moreover, CR values were >0.60 and with AVE values close
to or higher than 0.40, suggesting construct convergent validity.
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TABLE 3 | 3D-WS dimensionality tests.
n◦factors BIC
Subsample 1 39 items 3D-WS original
0 18,661.78
1 2,144.29
2 1,967.87
3 1,866.25∗
4 1,895.80
5 1,984.63
Subsample 1 12 items 3D-WS original
0 1,990.04
1 265.34∗
2 267.78
3 314.57
Subsample 1 12 items 3D-WS proposed
0 2,545.99
1 403.24
2 335.16
3 323.12∗
4 372.66
5 433.70
Subsample 2 12 items 3D-WS proposed
0 2,595.14
1 469.80
2 403.09
3 326.69∗
4 377.30
5 430.16
Results based on Schwartz’s Bayesian Information Criterion (BIC) dimensionality
test. ∗Advised number of factors.
General Factor Structure
The general factor structure of the new short 3D-WS was tested
by CFA and a second-order solution using subsample 2. It
presented the same fit as that obtained in the three-correlated-
factor model, with high and significant second-order loadings in
the reflective (γ−reflective = 0.90, R2= 0.81, p<0.001), and
moderate and significant in the cognitive (γ−cognitive = 0.48,
R2= 0.23, p<0.001) and affective (γ−affective = 0.44, R2= 0.19,
p<0.001). The G-Reflective bifactor structure improved the fit
(Table 5), showing a G-Reflective general factor with significant
loadings in all the items (Figure 2). Loadings of the G-Reflective
were greater in those items of the reflective theoretical factor
(ranging from 0.45 to 0.78), compared to the items from
the cognitive and affective components (ranging from 0.22 to
0.38). All the items ranged from 0.16 to 0.76 in the one-
factor solution taken as a reference, which showed poor fit
(Table 5). We also explored the fit of an affective bifactor
structure (in which the general factor would incorporate the
affective traits), and a cognitive bifactor structure (in which
the general factor would incorporate the cognitive traits), but
both the G-Affective and G-Cognitive general factor solutions
showed worse fit to the data than the G-Reflective (Table 5).
Moreover, a bifactor solution maintaining the reflective, affective
and cognitive factors at the same orthogonal level did not reach
identification. The CR for the total scale was ω= 0.81 −alpha
coefficient was α= 0.77; 95% CI = 0.72−0.81, and removing any
item did not improve this value−, with ωH= 0.60. Therefore,
almost 3/4 of the reliable variance in total scores came from
the G-reflective. The subscale score variance after controlling
for the effects of the G-Reflective was ωHS = 0.58 for the
cognitive (2/3 of the reliable variance in the cognitive factor
were out of the influence of the G-Reflective), and ωHS = 0.45
for the affective (2/3 of the reliable variance in the affective
factor were out of the influence of the G-Reflective). FD values
were rather fair (FD−G-Reflective = 0.78, FD−cognitive = 0.76,
FD−affective = 0.66), but the H index was only appropriate for
the G-Reflective (H−G-Reflective = 0.80, H−cognitive = 0.67,
H−affective = 0.51). The ECV was 0.53, reflecting that common
variance was equally spread across the G-Reflective and the other
two factors (i.e., affective, cognitive). The PUC was 0.67 (around
2/3 of correlations informed directly on the general factor). All
these results suggested that common variance should not be
regarded as essentially unidimensional.
Invariance Analyses
The three-correlated-factor model was used to explore the
invariance of the new short 3D-WS across subsamples
(n1= 312 vs. n2= 312), age (<55 years = 515 vs.
≥55 years = 109), sex (female = 472 vs. male = 152) and
studies (primary/secondary = 128 vs. university = 496). CFA
showed appropriate fit in the overall sample (Table 5). None of
the increasingly restrictive nested models of invariance exceeded
both cut-off recommendations (e.g., 1CFI and 1RMSEA) at
the same time with respect to subsample, sex, age and level of
studies (Table 5), providing a reasonable level of approximate
fit to the data. There were no differences when comparing
latent means according to subsample (reflective: 1Mn = −0.01,
p= 0.846, d= 0.01; affective: 1Mn = −0.01, p= 0.703, d= 0.03;
cognitive: 1Mn = −0.07, p= 0.207, d= 0.10). However, there
were differences according to age, with higher values in the
older group in the reflective (1Mn = 0.27, p= 0.003, d= 0.35)
and affective (1Mn = 0.13, p= 0.010, d= 0.38), but not in the
cognitive (1Mn = 0.02, p= 0.845, d= 0.03). There were also
differences according to sex, with lower values in females in the
reflective (1Mn = −0.18, p= 0.037, d= 0.22), and higher values
in the affective (1Mn = 0.10, p= 0.024, d= 0.30), but there were
no differences in the cognitive (1Mn = 0.06, p= 0.414, d= 0.09).
There were differences according to education level, with higher
values for having university studies in the cognitive component
(1Mn = 0.21, p= 0.008, d= 0.33), but there were no differences
in the reflective (1Mn = 0.18, p= 0.051, d= 0.22) and affective
(1Mn = 0.01, p= 0.874, d= 0.02) factors.
Convergence and Explanatory Power
The raw correlations between the same dimension of the original
long and new proposed short 3D-WS using EAP factor scores
were significant (all of them p<0.001), and high [subsample 1:
reflective r= 0.84 (adj-r= 0.68), cognitive: r= 0.87 (adj-r= 0.67),
affective: r= 0.87 (adj-r= 0.55); subsample 2: reflective r= 0.84
(adj-r= 0.69), cognitive r= 0.88 (adj-r= 0.72), affective r= 0.82
(adj-r= 0.52)].
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TABLE 4 | Exploratory factor analyses of the Spanish long 3D-WS∗.
Long three-correlated-factors Long exploratory bifactor
Factor item λ1λ2λ3S-L δIFS†λ1λ2λ3GδIFS†
Reflective 6 0.57 −0.17 0.09 0.39 0.68 0.85 0.26 −0.02 0.44 0.21 0.67 0.65
10 0.31 −0.06 0.42 0.50 0.63 0.55 0.35 −0.14 0.19 0.46 0.63 0.55
16r 0.03 0.69 −0.13 0.37 0.57 0.95 −0.10 0.63 0.01 0.37 0.50 0.96
18r −0.03 0.70 0.08 0.48 0.48 0.98 0.07 0.61 −0.06 0.47 0.38 0.97
20r −0.02 0.78 −0.14 0.38 0.48 0.96 −0.18 0.59 −0.03 0.46 0.48 0.87
23 0.64 −0.10 0.17 0.54 0.51 0.87 0.19 −0.16 0.46 0.48 0.49 0.66
26 0.81 0.06 −0.14 0.54 0.41 0.95 0.08 0.18 0.63 0.37 0.39 0.87
29 0.81 0.10 −0.15 0.57 0.37 0.93 −0.03 0.06 0.62 0.51 0.36 0.98
32r 0.04 0.66 −0.11 0.37 0.61 0.96 −0.19 0.42 0.01 0.47 0.61 0.76
35r 0.06 0.58 −0.07 0.36 0.67 0.96 −0.05 0.51 0.03 0.35 0.63 0.98
37 0.74 −0.12 0.09 0.55 0.45 0.94 0.22 −0.05 0.55 0.41 0.45 0.79
39 0.42 0.40 −0.13 0.47 0.60 0.43 −0.05 0.35 0.31 0.43 0.60 0.49
Affective 2 0.20 −0.09 0.39 0.28 0.87 0.44 0.32 −0.08 −0.06 0.16 0.84 0.79
4−0.04 0.02 0.48 0.33 0.78 0.99 0.33 −0.12 −0.08 0.36 0.78 0.76
8 0.30 −0.09 0.33 0.41 0.74 0.45 0.42 0.02 0.21 0.24 0.69 0.71
12r 0.17 0.25 0.19 0.28 0.76 0.24 −0.05 0.32 0.15 0.24 0.82 0.71
14 0.22 0.30 0.57 0.45 0.57 0.56 0.29 −0.01 −0.24 0.57 0.56 0.52
17r −0.08 0.48 0.16 0.36 0.71 0.83 0.02 0.26 −0.09 0.45 0.72 0.83
19 0.26 −0.03 0.23 0.34 0.83 0.50 0.24 −0.03 0.18 0.28 0.82 0.56
21r 0.04 0.38 0.22 0.44 0.70 0.64 0.20 0.31 0.00 0.40 0.66 0.61
24 0.19 0.33 −0.11 0.42 0.83 0.56 0.12 −0.01 0.09 0.48 0.74 0.57
27 0.03 0.28 0.23 0.37 0.79 0.53 −0.01 −0.04 −0.03 0.54 0.71 0.57
30 0.51 0.14 −0.08 0.41 0.70 0.86 −0.11 −0.01 0.38 0.46 0.64 0.90
33 −0.03 0.43 0.22 0.41 0.70 0.71 −0.06 0.04 −0.08 0.63 0.59 0.41
36 0.50 0.21 −0.01 0.50 0.62 0.78 0.08 0.21 0.37 0.41 0.61 0.61
Cognitive 1 −0.04 −0.09 0.39 0.19 0.89 0.91 0.34 −0.08 −0.06 0.13 0.88 0.89
3 0.38 −0.01 0.26 0.47 0.69 0.59 0.26 −0.05 0.26 0.41 0.69 0.47
5 0.05 −0.09 0.59 0.41 0.66 0.96 0.57 −0.06 −0.01 0.29 0.62 0.98
7 0.09 0.00 0.65 0.54 0.51 0.97 0.58 −0.03 0.01 0.44 0.48 0.99
9 0.17 −0.02 0.47 0.45 0.68 0.84 0.36 −0.13 0.08 0.44 0.68 0.78
11 0.03 0.24 0.48 0.52 0.59 0.72 0.28 −0.01 −0.04 0.59 0.58 0.96
13 −0.11 −0.15 0.77 0.38 0.56 0.92 0.64 −0.18 −0.15 0.30 0.55 0.82
15 −0.09 −0.03 0.77 0.47 0.49 0.98 0.61 −0.12 −0.14 0.43 0.48 0.88
22 0.42 0.01 0.14 0.42 0.73 0.85 0.12 −0.09 0.29 0.42 0.72 0.70
25 0.01 0.06 0.25 0.22 0.92 0.92 0.05 −0.20 −0.04 0.36 0.83 0.88
28 0.44 0.15 0.08 0.48 0.67 0.80 0.08 0.05 0.31 0.47 0.66 0.88
31 −0.09 0.30 0.11 0.21 0.89 0.73 −0.22 −0.14 −0.13 0.52 0.62 0.35
34 0.61 −0.06 0.03 0.44 0.65 0.98 0.08 −0.09 0.45 0.45 0.63 0.89
38 −0.07 0.31 0.03 0.17 0.91 0.92 −0.24 −0.05 −0.10 0.42 0.74 0.75
Loadings in the (−0.15/+0.15) hyperplane: 56 (47.9%)
Kaiser-Cerny hyperplane count: 64 (54.7%)
Kaiser-Cerny hyperplane values: 0.19; 0.18; 0.21
Ideal hyperplane count: 78 (66.7%)
Loadings in the (−0.15/+0.15) hyperplane: 64 (54.7%)
Kaiser-Cerny hyperplane count: 62 (53.0%)
Kaiser-Cerny hyperplane values: 0.18; 0.12; 0.15
Ideal hyperplane count: 78 (66.7%)
∗Subsample 1. Characteristics of the polychoric matrix: determinant <0.001; KMO = 0.85; Bartlett’s statistic = 3,585.80 (df = 741; p <0.001): Mardia’s statistic = 33.99
(p <0.001). r, reversed item. λ, factorial weight. S-L, second-order Schmid–Leiman approach. δ, uniqueness; IFS, index of factorial simplicity; G, general factor of the
exploratory bifactor model. †Not applied to the general factors.
In general, all factors of the new short 3D-WS showed
high and significant raw associations with all the psychological
health-related outcomes in both subsamples (Table 8)−the
cognitive and affective components presented small but
significant associations with PANAS-negative. The reflective
component had the highest raw associations with all the
psychological outcomes in both subsamples. The explanatory
power of the new short 3D-WS in relation to all psychological
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TABLE 5 | Fit indices of the exploratory and confirmatory factor analyses and invariance.
Group/model χ2df χ2/df CFI RMSEA (90% CI) 1CFI 1RMSEA
EFA (subsample 1)
Long 3-correlated-factors 964.34 627 1.54 0.974 0.042 (0.039−0.045)
Long bifactor 775.91 591 1.32 0.982 0.032 (0.029−0.035)
Short 1-advised factor 265.41 54 4.92 0.896 0.112 (0.109−0.115)
Short 3-forced factors 38.90 33 1.18 0.996 0.024 (0.021−0.037)
Short 3-forced bifactor 15.38 24 0.64 0.999 0.010 (0.007−0.013)
New-short 3-correlated factors 47.46 33 1.44 0.993 0.038 (0.035−0.041)
New-short bifactor 28.08 24 1.17 0.998 0.023 (0.020−0.026)
CFA (subsample 2)
One-factor (reference) 415.35 54 7.69 0.618 0.147 (0.134−0.160)
Three-correlated-factors 101.07 51 1.98 0.947 0.056 (0.040−0.072)
One second-order factor 101.07 51 1.98 0.947 0.056 (0.040−0.072)
G-Affective bifactor 94.72 46 2.06 0.949 0.058 (0.042−0.075)
G-Cognitive bifactor 114.91 46 2.50 0.927 0.069 (0.054−0.085)
G-Reflective bifactor 85.27 46 1.85 0.959 0.052 (0.035−0.070)
INVARIANCE (total sample)
Three-correlated factors 149.35 51 2.93 0.944 0.056 (0.45−0.066)
Subsample (n1vs. n2)
Config. 299.2 102 1.95 0.945 0.039 (0.031−0.047) –a–a
Metric 204.1 111 1.84 0.947 0.037 (0.029−0.045) +0.002 −0.002
Scalar 212.6 123 1.73 0.949 0.034 (0.026−0.042) +0.002 −0.003
Strict 234.7 135 1.74 0.944 0.034 (0.027−0.042) −0.005 0.000
Covar. 251.0 141 1.78 0.938 0.035 (0.028−0.042) −0.006 +0.001
Age (<55 vs. ≥55 years)
Config. 187.3 102 1.84 0.950 0.037 (0.029−0.045) –a–a
Metric 191.9 111 1.73 0.950 0.035 (0.026−0.043) 0.000 −0.002
Scalar 225.9 123 1.84 0.939 0.037 (0.029−0.045) −0.011 +0.002
Strict 252.5 135 1.88 0.930 0.038 (0.031−0.045) −0.009 +0.001
Covar. 268.1 141 1.90 0.925 0.039 (0.031−0.046) −0.005 +0.001
Sex (male vs. female)
Config. 203.4 102 1.99 0.942 0.040 (0.032−0.048) –a–a
Metric 207.1 111 1.87 0.945 0.037 (0.029−0.045) +0.003 −0.003
Scalar 247.6 123 2.01 0.932 0.041 (0.033−0.048) −0.013 +0.004
Strict 260.5 135 1.93 0.929 0.039 (0.032−0.046) −0.003 −0.002
Covar. 265.5 141 1.89 0.929 0.038 (0.031−0.045) 0.000 −0.001
Studies (second. vs. univ.)
Config. 185.7 102 1.82 0.952 0.036 (0.028−0.045) –a–a
Metric 193.9 111 1.75 0.952 0.035 (0.026−0.043) 0.000 −0.001
Scalar 216.4 123 1.76 0.946 0.035 (0.027−0.043) −0.006 0.000
Strict 249.5 135 1.85 0.934 0.037 (0.030−0.044) −0.012 +0.002
Covar. 267.3 141 1.90 0.927 0.038 (0.031−0.045) −0.007 +0.001
χ2, chi square; df, degrees of freedom; CFI, comparative fit index; RMSEA, root mean square error of approximation; 90% CI, 90% confidence interval; 1CFI, variation
in CFI from the previous least restrictive model; 1RMSEA, variation in RMSEA from the previous least restrictive model; Config., configurational invariance; metric, metric
invariance; scalar, scalar invariance; strict, strict invariance; Covar., invariance of covariances nested to strict invariance; aleast restrictive model as a reference; ‘Second.
vs. Univ.’, secondary or lower vs. university level of studies.
variables was high and significant, and it was similar across
subsamples, ranging from R2= 0.21 (PANAS-negative, subsample
1) to R2= 0.50 (boredom, subsample 2). The fit of the regression
models was adequate (all of them p<0.001). Judging by the
standard errors, the models showed similar predictions across
subsamples. Consistently, the reflective factor contributed to
explaining all the psychological variables considered, with
Beta absolute values ≥0.42 (p<0.001). The affective factor
contributed to explain purpose in life −subsample 1: Beta = 0.19
(p<0.001); subsample 2: Beta = 0.10 (p= 0.048)−and positive
affectivity −Beta = 0.18 (p= 0.001) in both subsamples. The
cognitive factor contributed to explaining boredom in both
subsamples −subsample 1: Beta = 0.11 (p= 0.026); subsample
2: Beta = 0.10 (p= 0.002). Except for PANAS-negative, residual
distributions did not present problems of asymmetry. DW values
were ≈2.00 in all the cases. Finally, tolerance and VIF values
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TABLE 6 | Exploratory factor analyses of the Spanish short and new short 3D-WS∗.
Short three-correlated-factors Short exploratory bifactor
Factor item λ1λ2λ3S-L δIFS†λ1λ2λ3GδIFS†
Cognitive 22 0.21 0.05 0.38 0.43 0.71 0.66 0.04 −0.06 −0.17 0.60 0.60 0.77
25 0.49 −0.12 0.02 0.22 0.81 0.91 0.29 −0.26 0.09 0.27 0.83 0.43
31 0.61 −0.02 −0.08 0.31 0.68 0.97 0.75 −0.30 0.21 0.21 0.46 0.71
34 0.09 −0.10 0.61 0.37 0.64 0.93 0.02 0.04 −0.41 0.49 0.60 0.98
Affective 12 −0.19 0.55 0.06 0.40 0.75 0.83 −0.08 0.34 −0.03 0.37 0.77 0.91
21 −0.03 0.62 −0.02 0.51 0.65 0.99 −0.03 0.29 0.11 0.53 0.61 0.81
33 0.53 0.18 −0.07 0.45 0.62 0.83 0.33 −0.17 0.20 0.48 0.65 0.42
36 −0.12 0.17 0.56 0.44 0.61 0.82 0.05 0.36 −0.44 0.39 0.61 0.52
Reflective 23 0.14 0.00 0.59 0.48 0.56 0.92 0.14 0.12 −0.38 0.52 0.56 0.72
26 −0.22 −0.01 0.91 0.45 0.30 0.92 0.03 0.48 −0.77 0.37 0.25 0.63
29 0.06 −0.02 0.78 0.54 0.36 0.99 0.22 0.28 −0.58 0.48 0.35 0.59
32 0.07 0.50 0.02 0.49 0.70 0.97 0.34 0.42 0.05 0.30 0.53 0.52
Loadings in the (−0.15/+0.15) hyperplane: 19 (52.8%)
Kaiser-Cerny hyperplane count: 20 (55.6%)
Kaiser-Cerny hyperplane values: 0.19; 0.13; 0.25
Ideal hyperplane count: 24 (66.7%)
Loadings in the (−0.15/+0.15) hyperplane: 14 (38.9%)
Kaiser-Cerny hyperplane count: 18 (50.0%)
Kaiser-Cerny hyperplane values: 0.15; 0.24; 0.24
Ideal hyperplane count: 24 (66.7%)
New short three-correlated-factors New short exploratory bifactor
Reflective 23 0.68 0.00 0.13 0.55 0.43 0.95 −0.09 0.42 −0.07 0.70 0.33 0.90
26 0.81 −0.03 −0.08 0.44 0.43 0.98 −0.09 0.74 0.17 0.32 0.31 0.91
29 0.78 −0.06 0.01 0.47 0.43 0.99 −0.04 0.64 0.19 0.40 0.42 0.88
37 0.76 0.09 −0.04 0.53 0.39 0.98 −0.20 0.48 0.03 0.65 0.34 0.78
Cognitive 5 −0.06 0.77 −0.07 0.45 0.49 0.98 −0.13 −0.14 0.58 0.42 0.49 0.85
7 0.15 0.60 0.04 0.55 0.50 0.91 −0.02 0.07 0.53 0.47 0.49 0.97
13 −0.04 0.68 −0.07 0.40 0.60 0.98 −0.09 −0.09 0.54 0.34 0.60 0.92
15 −0.09 0.82 0.03 0.53 0.37 0.98 −0.07 −0.18 0.59 0.52 0.37 0.86
Affective 17r −0.04 −0.12 0.66 0.41 0.65 0.95 0.62 0.07 0.12 0.25 0.50 0.93
21r 0.04 0.09 0.44 0.43 0.74 0.93 0.32 0.06 0.18 0.34 0.73 0.64
24 0.09 0.01 0.59 0.53 0.58 0.97 0.35 0.02 0.03 0.56 0.58 0.98
33 −0.07 −0.06 0.67 0.44 0.63 0.97 0.40 −0.10 −0.04 0.47 0.62 0.90
Loadings in the (−0.15/+0.15) hyperplane: 24 (66.7%)
Kaiser-Cerny hyperplane count: 24 (66.7%)
Kaiser-Cerny hyperplane values: 0.22; 0.18; 0.17
Ideal hyperplane count: 24 (66.7%)
Loadings in the (−0.15/+0.15) hyperplane: 19 (52.8%)
Kaiser-Cerny hyperplane count: 23 (63.9%)
Kaiser-Cerny hyperplane values: 0.17; 0.20; 0.20
Ideal hyperplane count: 24 (66.7%)
∗Subsample 1. Characteristics of the short 3D-WS polychoric matrix: Determinant = 0.102; KMO = 0.82; Bartlett’s statistic = 698.80 (df = 66, p <0.001); Mardia’s
statistic = 15.57 (p <0.001). Characteristics of the new short 3D-WS polychoric matrix: Determinant = 0.057; KMO = 0.80; Bartlett’s statistic = 876.00 (df = 66,
p<0.001); Mardia’s statistic = 15.96 (p <0.001). r, reversed item; λ, factorial weight; S-L, second-order Schmid–Leiman approach; δ, uniqueness terms; IFS, index of
factorial simplicity; G, general factor of the exploratory bifactor model. †Not applied to the general factors.
did not point to collinearity problems [reflective (subsample
1: T ≥0.78, VIF ≤1.28; subsample 2: T ≥0.82, VIF ≤1.21),
affective (subsample 1: T ≥0.79, VIF ≤1.26; subsample 2:
T≥0.87, VIF ≤1.16), cognitive (subsample 1: T ≥0.83,
VIF ≤1.20; subsample 2: T ≥0.88, VIF ≤1.19)].
DISCUSSION
Spanish 3D-WS
The primary purpose of the present study was to validate a
Spanish version of the 3D-WS. To the best of our knowledge,
there are no robust validations of any questionnaires used to
assess the difficult to gauge wisdom construct in the Spanish
language. In our study, the factorial structure of the original
long 3D-WS (Ardelt, 2003) was not replicated through EFA,
and although the three-dimensionality fitted to the data, few
items loaded adequately onto their corresponding theoretical
factor. Most of the 3D-WS items were not of original design,
but were taken from other existing questionnaires from a
variety of associated content domains, e.g., Need for cognition
(Cacioppo and Richard, 1983), attitudes about reality (Unger
et al., 1986), dogmatism (Rokeach, 1960), ambiguity Tolerance
(MacDonald, 1970), ideas of reference (Sears, 1937), perspective-
taking (Davis, 1980), personal problem-solving (Heppner and
Petersen, 1982), resentment (Bachman et al., 1967), empathy
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TABLE 7 | Confirmatory factor analysis of the new short three-correlated-factors 3D-WS†.
Factor Item Mn SD Skew Kurt Item-rest (f) Item-rest (t)λ δ
Reflective 23 3.94 1,10 −0.91 0.10 0.68 0.62 0.85∗0.28
26 3.65 1.13 −0.51 −0.50 0.67 0.63 0.77∗0.41
29 3.53 1.16 −0.52 −0.50 0.60 0.62 0.71∗0.50
37 3.91 1.13 −0.93 0.12 0.59 0.64 0.75∗0.44
Cognitive 5 3.82 1.00 −0.68 −0.10 0.55 0.53 0.70∗0.51
7 4.40 0.86 −1.82 3.69 0.59 0.45 0.76∗0.42
13 3.52 1.13 −0.60 −0.34 0.55 0.46 0.68∗0.54
15 4.20 0.99 −1.47 2.04 0.66 0.55 0.87∗0.24
Affective 17r 4.28 0.68 −0.46 −0.50 0.39 0.38 0.58∗0.66
21r 3.79 1.03 −0.68 −0.10 0.31 0.32 0.51∗0.73
24 4.03 1.05 −1.20 1.07 0.37 0.37 0.63∗0.60
33 4.06 0.98 −1.18 1.27 0.39 0.38 0.64∗0.59
†Subsample 2. Characteristics of the polychoric matrix: Determinant = 0.038; KMO = 0.80; Bartlett’s statistic = 997.20 (df = 66, p <0.001); Mardia’s coefficient = 18.25
(p <0.001). Mn, mean; SD, standard deviation; skew, skewness; kurt, kurtosis; item-rest, discrimination coefficient for the factor (f) and total (t). λ, factorial weight; δ,
uniqueness; r, reversed item; ∗p<0.001.
(Mehrabian and Epstein, 1972), acceptance of others (Fey, 1955),
compassion (Beutel and Marini, 1995), empathic concern
(Davis, 1980), helping disposition (Severy, 1975), overt but safe
aggression (Webster et al., 1955), Liking People (Filsinger, 1981),
acceptance of self and others (Shaw and Wright, 1967). The
fact that the 3D-WS items cover a large range of personality
characteristics attempting to capture a broad essence of wisdom
(Ardelt, 2003), might be why many of them did not load strongly
onto their expected factor, displaying a complex cross-loading
structure. Despite this, factor determinacy, marginal reliability
and construct replicability were acceptable, showing a moderately
high pattern of inter-factor correlations which, nonetheless,
explained a low percentage of variance. The Schmid–Leiman
solution showed moderate factorial weights for the second-order
factors, and an exploratory bifactor structure improved the fit to
the data, but the explained common variance suggested a weak
common factor, and the problem of cross-loadings remained.
The original short 3D-WS factorial structure (Thomas et al.,
2017) presented greater difficulties because it was necessary to
force the theoretical three-dimensional structure through EFA.
The factor determinacy and marginal reliability of this solution
were appropriate, explaining a considerable percent of the total
variance, with moderately high inter-factorial correlations and
adequate fit indices. However, construct replicability did not
reach acceptable values, and only half of the items loaded
where they corresponded. The loadings in the Schmid–Leiman
approach were moderate, and the bifactor solution improved
the fit and the percentage of explained variance −but simplicity,
determinacy, marginal reliability and construct replicability
were not adequate, with the appearance of negative loadings
that hindered interpretability. Nevertheless, explained common
variance was insufficient, and thus, the original short 3D-WS
could not be considered as minimally acceptable.
In this context, it was necessary to explore a new 3D-WS
proposal using those items with the best factorial behavior, which
could provide a reasonable solution in terms of adjustment,
simplicity and possibilities of interpretation. This was not the
first time that the 3D-WS had to be adapted; for example,
the Korean 3D-WS showed a distinct factor structure and
item content as a result of adding culturally specific factors of
wisdom such as modesty and unobtrusiveness (Kim and Knight,
2014). Thus, 12 items were selected, four in each dimension,
representing wisdom in a more concise way, but in accordance
with the original proposal (Ardelt, 2000, 2003). The reflective
factor was operationalized as the ability to overcome self-
centredness and subjectivity (e.g., “Sometimes I get so charged
up emotionally that I am unable to consider many ways of
dealing with my problems,” −reversed). The cognitive was
defined as the ability to understand the ambiguity of human
nature and the limits of knowledge (e.g., “People are either
good or bad,” −reversed). The affective included the presence
of sympathetic and compassionate behaviors toward others (e.g.,
“I don’t like to get involved in listening to another person’s
troubles,” −reversed). This new proposal overcame the previous
limitations, showing a three-factor structure with moderately
high inter-factor correlations and adequate fit. All the items
loaded where they corresponded, and an important percentage
of the variance was explained. Moreover, simplicity was very
good, as was determinacy, suggesting the factor score estimates
unambiguously reflected the latent levels they attempted to
estimate (Beauducel, 2011). Marginal reliability was acceptable,
as was construct replicability, which suggested all the factors were
well defined (Hancock and Mueller, 2000;Brown and Croudace,
2015;Rodriguez et al., 2016). The loadings in the Schmid–
Leiman approximation and the exploratory bifactor analysis were
moderately high, and the latter improved the model fit and the
percent of variance explained, maintaining factorial simplicity
and item distribution. However, factor determinacy, marginal
reliability and construct replicability were not sufficient in all
the components, with a general factor presenting an explained
common variance of around 50%.
The new short three-correlated-factor model was also tested
on a second subsample using CFA, obtaining adequate loadings
and fit indices. Likewise, factor determinacy and construct
replicability were appropriate, as were the average variance
extracted and composite reliability, and although these two were a
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TABLE 8 | Explanatory power of the new short 3D-WS on psychological health-related outcomes.
DV/IVs R2Se F df par b Se Beta t pb
Subsample 1
PIL 0.42 13.78 70.78 3/288 <0.001
Reflective 0.63∗1.04 0.10 0.55 10.94 <0.001
Cognitive 0.25∗0.01 0.09 0.01 0.11 0.916
Affective 0.41∗0.35 0.09 0.19 3.77 <0.001
MSBS 0.40 28.51 66.42 3/299 <0.001
Reflective −0.62∗−2.14 0.19 −0.56 −11.04 <0.001
Cognitive −0.32∗−0.41 0.18 −0.11 −2.22 0.026
Affective −0.32∗−0.24 0.19 −0.06 −1.26 0.208
PANAS-p 0.33 6.88 48.00 3/290 <0.001
Reflective 0.55∗0.39 0.05 0.46 8.45 <0.001
Cognitive 0.28∗0.05 0.05 0.05 1.01 0.313
Affective 0.38∗0.15 0.05 0.18 3.25 0.001
PANAS-n 0.21 5.67 27.89 3/296 <0.001
Reflective −0.45∗−0.30 0.04 −0.46 −7.99 <0.001
Cognitive −0.23∗−0.07 0.04 −0.10 −1.79 0.075
Affective −0.12†0.07 0.04 0.10 1.82 0.069
DERS 0.47 14.35 86.47 3/287 <0.001
Reflective −0.68∗−1.27 0.10 −0.63 −12.95 <0.001
Cognitive −0.30∗−0.11 0.09 −0.05 −1.13 0.261
Affective −0.36∗−0.17 0.10 −0.09 −1.77 0.078
Subsample 2
PIL 0.36 13.84 55.61 3/290 <0.001
Reflective 0.59∗0.91 0.09 0.54 10.59 <0.001
Cognitive 0.26∗0.12 0.09 0.06 1.33 0.916
Affective 0.29∗0.17 0.09 0.10 1.97 <0.001
MSBS 0.50 26.14 100.13 3/301 <0.001
Reflective −0.70∗−2.31 0.16 −0.66 −14.72 <0.001
Cognitive −0.35∗−0.49 0.16 −0.14 −3.12 0.002
Affective −0.21∗0.14 0.16 0.04 0.88 0.381
PANAS-p 0.26 7.12 35.19 3/292 <0.001
Reflective 0.49∗0.33 0.04 0.42 7.58 <0.001
Cognitive 0.20∗0.02 0.04 0.02 0.40 0.401
Affective 0.32∗0.14 0.04 0.18 3.29 0.001
PANAS-n 0.31 5.52 46.25 3/296 <0.001
Reflective −0.56∗−0.38 0.03 −0.59 −11.16 <0.001
Cognitive −0.16‡0.01 0.03 −0.01 0.08 0.933
Affective −0.13†0.05 0.03 0.08 1.46 0.147
DERS 0.47 13.96 83.34 3/280 <0.001
Reflective −0.68∗−1.21 0.09 −0.65 −13.64 <0.001
Cognitive −0.30∗−0.20 0.09 −0.10 −2.23 0.027
Affective −0.24∗−0.01 0.09 −0.01 −0.02 0.985
DV, dependent variable; IVs, independent variables; R2, adjusted determination coefficient. F, Snedecor’s F. df, degrees of freedom. Se, standard error. pa, p-value
associated with the ANOVA test for the model adjustment. r, Pearson’s raw correlation. b, regression coefficient. Beta, standardized regression coefficient. t, Student’s
t-value related to the Wald test on regression coefficients. pb, p-value associated with the Wald test on regression coefficients. ∗p<0.001, ‡p<0.01, †p<0.05.
bit fair in the affective component −something that well deserves
to be the subject of future research−, it was possible to establish
convergent and discriminant validity following the Fornell and
Larcker (1981) criteria. Interestingly, inter-factor correlations
presented the same pattern as that obtained in the original design
of the long 3D-WS (Ardelt, 2003), with the reflective component
showing moderately high relationships with the other two (i.e.,
cognitive, affective), which in comparison were less associated
between them.
General Factor
The existence of a possible general wisdom factor was firstly
explored by means of the Schmid–Leiman approach and
exploratory bifactor analysis using EFA, as explained above.
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FIGURE 2 | G-reflective bifactor structure for the new proposed short 3D-WS. G-reflective, G-reflective general factor; cognitive, cognitive factor; affective, affective
factor. ∗p<0.001.
After that, it was explored by a second-order solution using
CFA and subsample 2, which demonstrated the same fit to the
data as that of the three-correlated-factor solution. So far, the
idea of a G-Reflective general factor that would explain the
possible influences of the reflective traits on all the wisdom
aspects was supported by: (a) the three-factor structure of
data suggested by the dimensionality test; (b) the differential
strength of inter-factor correlations in the first-order approach
−with higher values when the reflective component was implied;
and (c) a loading structure from the second-order general
factor with values four times greater toward the reflective
component, compared with the others. Thus, the G-Reflective
structure was tested in these conditions by means of CFA
and the bifactor model using subsample 2, achieving better fit
than previous models and obtaining significant loadings from
the G-Reflective onto all the items −with higher values for
the specific reflective items, highlighting the general reflective
tendency. The other alternative bifactor models showed worse
fit or did not reach identification. Therefore, the G-Reflective
bifactor model was accepted as the best approach to the
general factor for the new short 3D-WS. This G-Reflective
accounted for around 75% of the reliable variance in total
scores. The percent of reliable variance in subscales due to
the effects of the G-Reflective was roughly 33%. The factor
determinacy and construct replicability for the G-Reflective were
fair, but they were insufficient for the other two subscales.
About one-half of explained common variance was attributable
to the G-Reflective general factor. Therefore, it was equally
split into two sources, the G-Reflective, and the cognitive
and affective components. Thus, the new short 3D-WS should
not be considered primarily unidimensional in the context of
uncontaminated correlations found (Rodriguez et al., 2016),
although the G-Reflective factor was a relevant source of
explained common variability.
Invariance
Multi-group CFA demonstrated strict invariance −including the
level of covariances between latent components−for the three-
correlated-factor solution of the new short 3D-WS with respect
to subsample, and three important factors that might be related
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to wisdom, such as age, sex and study level (Ardelt, 2000, 2003;
Thomas et al., 2017). This result provides support for the
possibilities of generalizing the described assessment model for
wisdom. As expected, random subsampling did not determine
differences in the latent means of the wisdom factors. However,
those participants ≥55 years old showed higher levels in the
reflective and affective factors, with moderately low effects.
Wisdom does not need to increase automatically with age (Ardelt,
1997;Staudinger, 1999;Webster, 2003), because the development
of wisdom would require time but also active experience in
overcoming subjectivity and projections (Kekes, 1983;Ardelt,
2003). However, it is accepted that wisdom seems to reach a
maximum peak at around the mid-50s, usually being higher
when this group is compared to younger people, although it
might show a decrease in the final stage of life, like other
capabilities (Wink and Helson, 1997;Ardelt, 2011;Webster
et al., 2014). On the other hand, female participants showed
lower levels in the reflective factor, but higher values in the
affective, with low and moderately low effects, respectively. In
a previous study, differences were found in wisdom according
to gender, with females presenting lower scores in the cognitive
factor (Ardelt, 2003). These differences were attributed to the
fact that men have usually been more encouraged to develop
their cognitive capacities, and to know the deeper meaning
of phenomena and events than women have, and maybe this
could be extensive to their reflective capacities. Likewise, and
particularly in the Spanish context, women have traditionally
assumed the principal role of providing informal care (García-
Calvente et al., 2004), and this gender-work identity may
facilitate experiences so that they develop higher levels of
emotional wisdom. Finally, a university study level was related
to higher levels in the cognitive factor with a moderately low
effect, compared to secondary and lower levels of education.
It has been said that individuals in search of wisdom are
more likely to pursue more advanced educational levels (Ardelt,
2003), and the same could be applied here, but the cross-
sectional nature of this study makes causality difficult to
establish.
Explanatory Power
The new short 3D-WS proposal was convergent with the
original long version of the scale (which although not entirely
recommended because of its weak factorial structure, was taken
into account as a content reference). The new short 3D-WS was
highly associated with all psychological health-related variables
in the expected directions using multivariate regression models,
highlighting the relevance of wisdom in terms of mental health
and general well-being, similar to other studies (Ardelt, 1997,
2000, 2003;Le, 2011;Bergsma and Ardelt, 2012;Jeste et al.,
2013;Roháriková et al., 2013;Zacher et al., 2013;Webster
et al., 2014;Thomas et al., 2017). The reflective component
was the most powered wisdom factor to explain well-being
across both subsamples, and it was significantly related to all
psychological health-related variables when controlling the other
wisdom components. This is congruent with the idea that
an ego-decentred mindset enables wise thinking and behavior
regarding personally meaningful issues (Grossmann, 2017). It
therefore reinforces the idea of the reflective component as
the core dimension of wisdom from which the other wisdom
components may be developed (Ardelt, 2003;Thomas et al.,
2017). Interestingly, the cognitive factor contributed to inversely
explain boredom (adverse outcome), an important outcome
that is related to distinct mental disorders (Alda et al., 2015),
whereas the affective factor contributed to explain positive
affectivity and purpose in life (favorable outcomes), which
is in line with the definition and valence of these wisdom
dimensions.
Strengths and Limitations
As strengths, we highlight the large sample size used, which
enabled us to properly develop the statistical analyses by dividing
the total group into two randomly selected subsamples in which
to independently perform the exploratory and confirmatory
analyses. In addition, the basic assumptions for the type of
data analysis utilized were accepted, although the distribution
of residuals was not normal in the case of negative affect,
which may have affected the confidence intervals, but with
minor consequences as a result of the large sample size used.
Nevertheless, the present study has several limitations. First, the
sample selection procedure used obligated us to be cautious
when generalizing our results; therefore, this work should be
considered a heuristic guide to drive future research. Second,
because this work was oriented in extension rather than in depth,
as a result of the early and preliminary stage in the development
of a complex, difficult-to-grasp construct but with promising
perspectives (Staudinger and Glück, 2011), the relationships
between wisdom and the other health-related constructs were
established using general regression models, which did not
consider measurement errors. Third, the use of a cross-sectional
design did not enable us to drive the analysis toward testing
causal hypotheses; it only allowed us to speak in terms of
relationships and explanatory power. Fourth, we did not employ