ArticlePDF Available

The Association Between Mindfulness, Psychological Flexibility, and Rumination in Predicting Mental Health and Well-Being Among University Students Using Machine Learning and Structural Equation Modeling

Authors:

Figures

Content may be subject to copyright.
Vol.:(0123456789)
Mindfulness (2024) 15:359–371
https://doi.org/10.1007/s12671-023-02299-x
ORIGINAL PAPER
Adaptation andValidation oftheMindful Student Questionnaire
inChinese
QiuWang1 · YiqiWu2· RuohanFeng1· XinHao3· JoshuaC.Felver4· YingZhang5· RachelRazza6
Accepted: 25 December 2023 / Published online: 6 February 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
Abstract
Objectives The current study examined the reliability and validity of the Mindful Student Questionnaire (MSQ) among a
sample of Chinese vocational school students.
Method Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) models were fitted using MSQ and Stu-
dent Subjective Wellbeing Questionnaire (SSWQ) data collected from 2910 adolescent students. Factor structure analysis,
reliability, convergent validity, and predictive validity were examined to investigate the psychometric properties of MSQ.
Results The EFA indicated that a 3-factor solution (i.e., mindful attention, mindful acceptance, and approach and persis-
tence) was most suitable for the MSQ in the study sample. This 3-factor CFA model indicated that inter-factor correlations
were different from those originally reported by the scale developer in a US adolescent student sample. The reliability coef-
ficients (Cronbach’s alpha and composite reliability) were acceptable. Discriminative, convergent, and predictive validity
were demonstrated through the MSQ’s relation with the SSWQ.
Conclusions The 15-item, 3-factor MSQ demonstrates strong reliability and validity, offering a new multidimensional model
of MSQ to assess mindfulness among Chinese adolescents in a school setting.
Preregistration This study is not preregistered.
Keywords Mindfulness· Psychological flexibility· Adolescent· Validation· Reliability· Vocational school
Exploring and harnessing the power of mindfulness in youth
groups is a rapidly growing research trend (Dunning etal.,
2019; Schutt & Felver, 2020). Additionally, the application
of mindfulness has been steadily increasing in clinical and
educational settings to help students from kindergarten to
12th grade (Lucas-Thompson etal., 2019). School-based
Mindfulness-Based Programs (MBPs) include a wide range
of activities (e.g., meditation, yoga, body scanning, and
mindful eating) as well as various topical areas of focus,
including mental health promotion, well-being, stress
* Qiu Wang
wangqiu@syr.edu
1 School ofEducation, Syracuse University, 350 Huntington
Hall, Syracuse, NY13210, USA
2 The Education University ofHong Kong, HongKong, China
3 Department ofPsychology, Syracuse University, Syracuse,
NY, USA
4 Bronfenbrenner Center forTranslational Research, College
ofHuman Ecology, Cornell University, Ithaca, NY, USA
5 Department ofPsychology, Clarkson University, Potsdam,
NY, USA
6 Falk College ofSport & Human Dynamics, Syracuse
University, Syracuse, NY, USA
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s12671- 023- 02299-x.
reduction, and cognitive ability (Gouda etal., 2016; Zenner
etal., 2014). Developing effective curricula or treatment
packages tailored
to students’ specific nee
ds is critical to the
success of school-based MBPs (Diamond, 2010; Renshaw,
2020). Furthermore, a rigorous multidimensional mindful-
ness instrument is essential for adolescents to assess their
progress in various facets of MBPs (e.g., mindful attention
and mindful acceptance), and would be highly useful to
develop and evaluate future interventions. Although multi-
dimensional self-report mindfulness assessment scales for
adults are well-developed, there is a current lack of such
assessment scales that are theoretically coherent and con-
textually appropriate for use with culturally diverse student
populations (Goodman etal., 2017; McKeering & Hwang,
2019; Schutt & Felver, 2020).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
360 Mindfulness (2024) 15:359–371
Despite the fact that some multidimensional mindfulness
scales have been well-developed and are helpful to advance
the understanding of adults’ mindfulness, there is a relative
dearth of measurement validation research among diverse
adolescent populations (Goodman etal., 2017; Guerra
etal., 2019; Pallozzi etal., 2017). According to a systematic
review of the use of mindfulness measures with adolescents,
multidimensional measures such as the Kentucky Inventory
of Mindfulness Skills (Baer etal., 2004) and the Five Facet
Mindfulness Scale (FFMQ; Baer etal., 2006; Deng etal.,
2011) appeared to be more limited in their adaptability
for adolescents, underscoring calls for future research and
development efforts (Pallozzi etal., 2017). Additionally,
individuals’ introspective self-perception may lead to biased
ratings due to cultural influences (e.g., social desirability) on
how individual self-report mindfulness (Brown etal., 2007;
Grossman, 2015). These neglected socio-cultural influences
may significantly impact the measurement validity mindful-
ness scale in diverse populations, and further studies are thus
needed to examine whether translated mindfulness scales are
appropriate for cross-cultural use among non-native Eng-
lish speakers (Cheung etal., 2020). Thus, there is a need to
validate self-report mindfulness scales across an internation-
ally diverse population of adolescents in order to advance
the field of mindfulness research among culturally diverse
adolescent populations (Bergomi etal., 2013).
Mindfulness is currently attracting much scholarly attention
and a variety of advanced studies in China have been developed
among school population. We summarized the results from arti-
cles with adolescents in China published in the past 5years
(2018–2023; see TableS1 in the Supplementary Information)
and found a lack of studies on the development and validation
of mindfulness scales with youth in China during this time.
Although the Child and Adolescent Mindfulness Measure
(CAMM; Greco etal., 2011) and Mindful Attention Awareness
Scale (MAAS; Brown & Ryan, 2003) were the most frequent
scales in studies among Chinese students and contribute to the
broad understanding of adolescent mindfulness in China, there
are notable limitations. Mindfulness is typically considered a
multi-faceted construct that includes measurement of various
specific subordinate components (e.g., FFMQ; Baer etal.,
2006); however, the MAAS and CAMM are only measured
with a unidimensional (i.e., 1-factor) latent structure (Ma &
Fang, 2019; Wang etal., 2021; Yang etal., 2019). Furthermore,
given that heterogeneity of mindfulness practices (Felver etal.,
2023) may correspondingly result in various effects to differ-
ent components of mindfulness (Roeser etal., 2023), unidi-
mensional measurements of mindfulness may be inadequate to
capture the consequences and inner mechanisms of mindfulness
practices (Goodman etal., 2017; Hölzel etal., 2011). Also,
these scales present challenges for researchers to assess subor-
dinate facets of mindfulness among adolescents in non-Western
cultural contexts and to explore the multi-faceted mindfulness
structure than multidimensional scales (Ma & Fang, 2019;
Wang & Kong, 2020). To advance the study of mindfulness in
culturally diverse adolescent populations, the current research
aimed to translate and validate a multidimensional adolescent
self-report mindfulness questionnaire.
The scale selected for translation and adaptation, the
Mindful Student Questionnaire (MSQ; Renshaw, 2017), is a
15-item mindfulness scale designed for adolescents that con-
sists of three dimensions: mindful attention scale (MATS;
the ability to regulate an individual’s attention to focus on
the present moment), mindful acceptance scale (MACS;
the behavior of awareness and adaptability of the present
moment), and approach and persistence scale (APS; the
ability to persist in the long-term goal while accepting diffi-
cult life situations). Approach and persistence are aspects of
psychological flexibility (Hayes etal., 2006), which refers to
students’ abilities to regulate and persist with behaviors for
potentially valuable outcomes. The development and valida-
tion of the MSQ in the USA identified a 3-factor construct
that addressed an unmet need to measure the students’ mind-
fulness using a more comprehensive multidimensional scale
structure, which in turn affords the opportunity to explore
inter-factor relationships among these aspects of mindful-
ness (Renshaw, 2017). Renshaw (2017) asserted that the
MSQ’s tripartite structure of mindfulness encapsulated
an interconnected and synergistic system, thereby offer-
ing a more comprehensive representation of the underlying
mechanisms inherent in mindfulness-based practices. This
three-dimensional model, thus, responds to the scholarly
demand for a nuanced measurement and understanding of
the intricate interrelationships among mindfulness subcom-
ponents, thereby elucidating the complexity of the mind-
fulness process (Christopher etal., 2009; Grossman, 2015;
Renshaw, 2017). Additionally, the MSQ was designed for
the adolescent population by including school-specific items
(e.g., “When I am feeling bad at school, I still…”) to help
interpret mindfulness within the school context.
Renshaw (2017) preliminarily validated the MSQ with a mid-
dle school sample in the USA and recommended a larger and
more diverse youth sample for further validation. The present
study was intended to examine whether the translated version of
the MSQ demonstrates acceptable reliability and validity among
adolescent school students in China. The aim of the current study
was to assess the psychometric properties of the translated MSQ.
First, the factor structure of the translated MSQ was explored
and validated. Second, the scale reliability and internal consist-
ency were examined (Cronbach’s alpha and McDonald’s omega;
McDonald, 1999). Third, the convergent validity and discriminant
validity of MSQ were calculated with criterion indicators of the
Student Subjective Well-being Scale (SSWQ; Renshaw etal.,
2015). Last, hierarchical regression was employed to evaluate the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
361Mindfulness (2024) 15:359–371
predictive validity and incremental validity with subscale scores
of the SSWQ.
Method
Participants
Under the rule of thumb defined by Comrey and Lee (1992),
a sample size of 1000 + is an optimal condition to run fac-
tor analysis models. A minimum sample size (n = 1125)
was determined by the power analysis (MacCallum etal.,
1996; Preacher & Coffman, 2006) with the following
parameter settings: type I error (alpha) of 0.05, a preferred
power value of 0.80, the RMSEA (root mean square error
of approximation) value of 0.05, and degrees of freedom
(df = 63) of the proposed model. A total of 2910 adoles-
cents aged 15–18years (M = 17.53, SD = 0.95) participated
in this study. Participants included 1891 males (64.98%)
and 1019 females (35.02%). Participant demographics are
shown in Table1. All participants were native Mandarin
Chinese speakers and were enrolled in a vocational school
at the time of the study.
Procedure
Students and their guardians were informed at the begin-
ning of the voluntary nature of participation and the data
anonymity. Before collecting the data, the potential par-
ticipants and their guardians were asked to sign the stu-
dent assent and parental informed consent form, respec-
tively. Then, school administrative staff were invited to
distribute the questionnaire link to online WeChat group
via the widely used survey platform Tencent Question-
naire (Tencent, 2022 https:// wj. qq. com/ mine. html), and
students finished the questionnaires under the guidance
of their guardians at home.
Among 2910 participants, we randomly selected 50%
of males (n = 1891) and females (n = 1019) using the SPSS
random selection algorithm, respectively, which resulted in
two equal-sized samples for the exploratory factor analysis
(EFA; n = 1455) and the confirmatory factor analysis (CFA;
n = 1455). Either EFA or CFA sample size was greater than
the required minimum sample size (n = 1125) determined
by the power analysis (MacCallum etal., 1996; Preacher &
Coffman, 2006).
Measures
Measures are composed of demographic questions and two
questionnaires. The demographic questionnaire contained
survey items regarding the participants’ age, grade, gender,
birthplace, parents’ education level, understanding of mind-
fulness, and previous mindfulness practice experience.
Translation andCross‑cultural Adaptation
The translation process was composed of four steps (see
Supplementary FigureS1) based on Valmi’s model (Sousa
& Rojjanasrirat, 2011; Zmnako & Chalabi, 2019). First,
two independent translators who were native Chinese
speakers and graduate students in psychology translated
the original MSQ from the source language (English) into
the target language (Chinese, see TableS6 in the Supple-
mentary Information). The 1st, 2nd, 3rd, and 4th authors
discussed and compared the two forward-translated ver-
sions, and they resolved the ambiguities and discrepan-
cies. For example, researchers discussed and decided on
the final version of “when my thoughts come and go” when
two translated versions of this item appeared to be differ-
ent. The revised simplified Chinese version was ready for
the blind back-translation process. Two bilingual transla-
tors who were native English speakers back translated the
revised Chinese version into English, and they were com-
pletely blind to the original version of the MSQ. Finally,
Table 1 Socio-demographic and family characteristics of participants
(n = 2910)
Frequency %
Gender
Male 1891 64.98%
Female 1019 35.02%
Residence
Rural 2659 91.37%
Township 153 5.26%
City 98 3.37%
Grade
Grade 10 1520 52.23%
Grade 11 349 11.99%
Grade 12 1041 35.77%
Parental educational level
Father
Junior high school and below 2410 82.82%
Senior high school 460 15.81%
College/university 40 1.37%
Mother
Junior high school and below 2518 86.53%
Senior high school 354 12.16%
College/university 38 1.31%
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
362 Mindfulness (2024) 15:359–371
the 1st, 2nd, 3rd, and 4th authors joined in resolving the
inconsistencies between the two back-translated versions
and the original scale. Any ambiguities regarding cultural
background in words or sentences between the two blind
back-translations and the original English version of the
MSQ (Renshaw, 2017) were discussed to reach a full con-
sensus and complete agreement.
Mindful Student Questionnaire
The three-dimensional questionnaire (Renshaw, 2017) meas-
ures students’ mindful attention (MATS), mindful acceptance
(MACS), and approach and persistence (APS) using 15 items,
five items that tap each dimension. All MSQ items are positively
phrased, and example items include “When I am at school, I
notice when my feelings change from good to bad” (MATS),
“When I am feeling bad at school, I still have a good attitude”
(MACS), and “When I am doing something hard at school, I try to
work and work to get it right” (APS). Students responded to each
item on a 5-point Likert scale. Neither the whole-scale Cronbach’s
alpha- nor McDonald’s omega-values were available. The three
subscale Cronbach’s alpha coefficients with the US middle school
adolescents ranged from 0.77 to 0.90 (Renshaw, 2017).
Student Subjective Wellbeing Questionnaire (SSWQ)
The SSWQ is a self-report measure rated on a 4-point Likert
scale and is commonly used in assessing adolescents’ positive
psychological functioning at school (Renshaw etal., 2015). The
SSWQ consists of 16 items comprising four subscales: Joy of
Learning (e.g., “I get excited about learning new things in class”),
School Connectedness (e.g., “I feel like I belong at my school”),
Educational Purpose (e.g., “I feel like the things I do at school
are important”), and Academic Efficacy (e.g., “I am a success-
ful student”). Cronbach’s alpha coefficients ranged between 0.76
and 0.86 in the original SSWQ (Renshaw, 2017; Renshaw etal.,
2015). The Chinese version of SSWQ had satisfying composite
reliability measures (McDonald’s omega; McDonald, 1999):
0.82 for Joy of Learning, 0.82 for School Connectedness, 0.82
for Educational Purpose, 0.80 for Academic Efficacy, and 0.94
for the total (Zhang etal., 2018). The Chinese version of SSWQ
evidenced adequate reliability in our study sample; Cronbachs
alphas were as follows: 0.84 for Joy of Learning, 0.83 for School
Connectedness, 0.85 for Educational Purpose, 0.85 for Academic
Efficacy, and 0.95 for the whole scale.
Data Analyses
EFA was used to determine the factor structure of the MSQ.
In EFA, we used the first split-half sample (n = 1455) and
examined 3-factor structure solutions: 1-factor, 2-factor, and
3-factor models (Preacher etal., 2013). Besides the goodness
of model fit indices, three rules of thumb were used to
determine the number of factors: the Kaiser–Meyer–Olkin
measure of sampling adequacy (KMO > 0.80) with Bartlett’s
sphericity test, the item communalities (between 0.25 and
0.40 acceptable, closer to one the better) (Beavers etal.,
2013), and the factor loadings (> 0.32; Kahn, 2006). When
the sample size is larger than 200, Tabachnick and Fidell
(2013) argued that multivariate factor analysis is robust
using data with acceptable values of skewness and kurto-
sis. For example, kurtosis between − 2 and + 2 is considered
acceptable. Hair etal. (2018) and Byrne (2016) suggested
an acceptable skewness range from 2 to + 2, along with a
more liberal kurtosis range from 7 to + 7.
Following EFA, we further ran the CFA to examine the
3-factor model’s goodness of fit using the second split-half
sample (n = 1455). The analysis was conducted using Mplus
Version 8.8 (Muthén & Muthén, 19982017), and the maxi-
mum likelihood extraction method (ML) was employed. The
factor structure of EFA was obtained through the oblique
rotation (e.g., Geomin) to allow correlated factors. We used
multiple indices to evaluate both EFA and CFA models’
goodness of fit (Hu & Bentler, 1999). In addition to chi-
square (χ2), degree of freedom (df), Akaike’s Information
Criteria (AIC), and Bayesian Information Criteria (BIC),
other model fit indexes(West, etal., 2012), e.g., χ2/df ratio
(< 5), Comparative Fit Index (CFI > 0.95), Tucker Lewis
index (TLI > 0.95), Root Mean Square Error of Approxima-
tion (RMSEA < 0.06), and Standardized Root Mean Square
Residual (SRMR < 0.08).
Three types of reliability measures, Cronbach’s alpha, rho_a,
and rho_c, were calculated to evaluate the subscale internal
consistency. Cronbach’s alpha is the conservative lower bound
of the true internal consistency reliability (Trizano-Hermosilla
& Alvarado, 2016). The composite reliability rho_c (Jöreskog,
1971) and the reliability coefficient rho_a (Dijkstra & Henseler,
2015) are generally greater than Cronbach’s alpha. Five thou-
sand bias-corrected and accelerated (BCa) bootstrap samples
generated the 95% bias-corrected confidence interval (CI) for
each reliability coefficient’s significance testing (Hair etal.,
2022). The bias-corrected and accelerated (BCa) bootstrap was
developed by Efron (1987) and derived estimates of standard
errors and confidence intervals for complex estimators includ-
ing correlation coefficients. BCa was the most recommended
method due to its power in adjusting for both bias and skewness
in the bootstrap distribution (Hair etal., 2022). If the value of 0
is not in the 95% BCa confidence interval, it indicates that the
correlation coefficient is statistically insignificant from 0. The
reliability and internal consistency of the MSQ were assessed
by reporting its Cronbach’s alpha (> 0.70) and the McDon-
ald’s composite reliability (MCR, > 0.60; McDonald, 1999).
Based on the item loading results, AVE (average variance
extracted, > 0.50) was analyzed in reporting the discriminant
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
363Mindfulness (2024) 15:359–371
validity of the MSQ. Convergent validity was estimated by test-
ing the correlation between the MSQ and the SSWQ. The sub-
scale internal consistency reliability, convergent validity, and
discriminant validity were systematically evaluated with 5000
bootstrap samples using SmartPLS Version 4 (Ringle etal.,
2014). Finally, we examined the predictive validity through
hierarchical regression, in which MSQ scores were used to
predict scores of SSWQ with ΔR2 (change in coefficient of
determination) as the predictive criterion measure. The analysis
was performed in IBM SPSS Statistics (Version 27).
Results
Item-wise descriptive statistics are displayed in Supplemen-
tary TableS2, along with communalities that were derived
using the maximum likelihood method. The first five items
(Item 1 to Item 5) had means from 3.10 to 3.44, the second
five (Item 6 to Item 10) from 4.03 to 4.35, and the third five
(Item 11 to Item 15) from 3.75 to 4.19. The 15 communality
values were from 0.38 to 0.73, which surpassed the com-
mon minimum value of 0.30, indicating an acceptable per-
centage of variance explained by the factor model (Beavers
etal., 2013). The skewness and kurtosis values were in the
acceptable range from 2 to 2, which supported the use of
maximum likelihood estimation to retrieve factors in EFA.
The Kaiser–Meyer–Olkin measure was 0.913, with a sta-
tistically significant Bartlett’s sphericity test (χ2 = 10042.72,
df = 105, p < 0.001), which indicated the sampling adequacy
to conduct EFA. Three EFA models (e.g., 1-factor, 2-fac-
tor, and 3-factor models) were conducted to examine the
factor structure of MSQ. The 3-factor model achieved the
best goodness-of-fit results: χ2 = 181.35, df = 63 (p < 0.001);
χ2/df = 2.88, RMSEA = 0.04, with a 90% CI [0.03, 0.04];
CFI = 0.99, TLI = 0.98, SRMR = 0.01, AIC = 52693.01,
and BIC = 53073.37, compared to the 1-factor solution
(χ2 = 3071.21, df = 90; χ2/df = 34.13, RMSEA = 0.15, with
a 90% CI [0.15, 0.16]; CFI = 0.70, TLI = 0.65, SRMR = 0.13,
AIC = 55528.87, BIC = 55766.59) and 2-factor solution
(χ2 = 1220.40, df = 76; χ2/df = 16.06, RMSEA = 0.10, with a
90% CI [0.10, 0.11]; CFI = 0.89, TLI = 0.84, SRMR = 0.05,
AIC = 53706.06, BIC = 54017.74). The 3-factor model, sup-
ported by the scree plot with the parallel analysis (Horn,
1965; see Supplementary FigureS2), was converged and
consistent with the original structure in Renshaw (2017). The
15 items as designed in Renshaw (2017) were significantly
Table 2 The 15-item MSQ’s unrotated factor matrix of 3-factor EFA with eigenvalues and goodness-of-fit indices (n = 1455)
MATS, mindful attention scale; MACS, mindful acceptance scale; APS, approach and persistence scale. aInter-factor correlations of the EFA model
* p < 0.05
Item statement Eigenvalues
F1 (MATS) F2 (MACS) F3 (APS)
When I am at school, I notice when my feelings change from good to bad 0.65* − 0.04 0.03
... how other people feel and act 0.70* 0.18 − 0.02
... the many things that happen around me 0.60* 0.15 − 0.00
... when my thoughts come and go 0.68* − 0.02 0.06
... how other people react to what I do 0.65* 0.01 0.00
6. When I am feeling bad at school, I still have a good attitude 0.03 0.71* 0.01
... am kind to myself − 0.02 0.79* − 0.08*
... think nice thoughts 0.00 0.77* 0.02
... stay calm − 0.02 0.61* 0.14*
... am friendly to others 0.09* 0.54* 0.24*
11. When I am doing something hard at school, I try to work and work to get it right 0.03 0.01 0.80*
... do the best I can 0.04 0.02 0.83*
... focus on doing a good job − 0.02 0.01 0.86*
... keep going until I finish − 0.02 0.05 0.79*
... do everything I can to do well − 0.02 0.02 0.79*
F11.00
F2a0.05 1.00
F3a0.09 a0.68* 1.00
χ263 = 1181.35 RMSEA = 0.04 CFI = 0.99 SRMR = 0.01
χ2/df = 2.88 90% C.I. = [0.03, 0.04] TLI = 0.98 AIC = 53,706.06
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
364 Mindfulness (2024) 15:359–371
loaded with strong loadings (> 0.50) on MATS, MACS, and
APS, respectively (Table2). The largest cross-loading of
Item 10 on APS (0.24) was significant (p < 0.05), but smaller
than the minimum cutoff value of 0.32. It further indicated
that Item 10 (“When I am feeling bad at school, I still I am
friendly to others”) should be loaded as it was originally
designed on factor MACS.
The inter-item correlations of 15 items ranged from 0.00
to 0.74 (see Supplementary TableS3), further indicating
satisfactory convergent and discriminant validity. The range
of within-factor item correlations was from 0.37 to 0.48 in
factor MATS, from 0.48 to 0.58 in MACS, and from 0.62 to
0.72 in APS. The range of between-factor item correlations
was from 0.00 to 0.19 for MATS, from 0.00 to 0.52 for
MACS, and from 0.01 to 0.52 for APS. The cross-factor
inter-item correlation between Item 10 and Item 12 was 0.52,
which is smaller than the lowest correlation coefficient in
the inter-item correlation in factor APS. This also suggested
that Item 10 should be loaded on factor MACS rather than
on APS. In order to account for this inter-item correlation,
we used “a correlated uniqueness approach” (Brown, 2015,
p. 40) by allowing Item 10’s residual to be correlated with
Item 12’s in the CFA (see FigureS3).
Following EFA, we fit the 3-factor CFA model using the
second sample (n = 1455). The 3-factor CFA model achieved
the goodness-of-fit results: χ2 = 372.91, df = 86 (p < 0.001);
χ2/df = 4.34, RMSEA = 0.05, with a 90% CI [0.04, 0.05];
CFI = 0.97, TLI = 0.97, SRMR = 0.04, AIC = 5195.17,
BIC = 52216.02). Table3 summarizes item-wise descrip-
tive statistics and factor loading values on each dimension of
MSQ. The range of items loading from 0.61 to 0.68 in factor
MATS, from 0.71 to 0.80 in MACS, and from 0.78 to 0.88
in APS. All these items fit well in each dimension, and all
15 factor loadings were statistically significant (p < 0.001;
Table3). Item 10’s residual was statically correlated with
Item 12’s (r = 0.14, p = 0.031) in the CFA (FigureS3).
Cronbach’s alpha and MCR of the whole scale were 0.85
and 0.92, respectively. The subscale internal consistency
reliability, convergent, and discriminant validity were sys-
tematically evaluated with 5000 bootstrap samples using
SmartPLS Version 4 (Ringle etal., 2014). The predictive/
incremental validity was later examined using hierarchical
regression in SPSS.
Three types of reliability measures, Cronbach’s alpha,
rho_a, and rho_c, were calculated to evaluate the subscale
internal consistency. Table3 summarizes the reliability
Table 3 The 15-item MSQ’s 3-factor CFA results (n = 1455)
a MATS’ 95% CIs: Cronbach’s alpha [0.76, 0.80]; rho_c [0.79, 0.86]; rho_a [0.86, 0.97]; AVE [0.45, 0.54]. bMACS’ 95% CIs: Cronbach’s alpha
[0.85, 0.88]; rho_c [0.89, 0.91]; rho_a [0.85, 0.88]; AVE [0.62, 0.68]. cAPS’ 95% CIs: Cronbach’s alpha [0.91, 0.92]; rho_c [0.93, 0.94]; rho_a
[0.91, 0.93]; AVE [0.73, 0.77]
*** p < 0.001
Items Standardized loading (SE)M (SD)
MATS (α = 0.78; rho_c = 0.83; rho_a = 0.87; AVE = 0.51)a3.27 (0.80)
mats1 0.61 (0.02)*** 3.44 (1.05)
mats2 0.68 (0.01)*** 3.41 (1.11)
mats3 0.68 (0.02)*** 3.41 (1.08)
mats4 0.66 (0.02)*** 3.17 (1.09)
mats5 0.67 (0.02)*** 3.10 (1.12)
MACS (α = 0.86; rho_c = 0.90; rho_a = 0.87; AVE = 0.65)b4.18 (0.79)
macs1 0.71 (0.02)*** 4.03 (1.06)
macs2 0.73 (0.01)*** 4.29 (1.02)
macs3 0.80 (0.01)*** 4.14 (1.02)
macs4 0.75 (0.01)*** 4.09 (0.99)
macs5 0.76 (0.01)*** 4.35 (0.91)
APS (α = 0.92; rho_c = 0.94; rho_a = 0.92; AVE = 0.75)c4.04 (0.81)
aps1 0.82 (0.01)*** 4.03 (0.95)
aps2 0.84 (0.01)*** 4.19 (0.91)
aps3 0.88 (0.01)*** 4.13 (0.94)
aps4 0.78 (0.01)*** 3.75 (1.03)
aps5 0.82 (0.01)*** 4.11 (0.96)
χ286 = 372.91 RMSEA = 0.05 CFI = 0.97 SRMR = 0.04
χ2/df = 4.34 90% C.I. = [0.04, 0.05] TLI = 0.97 AIC = 51,957.17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
365Mindfulness (2024) 15:359–371
coefficients and the 95% CIs of Cronbach’s alpha, rho_a,
and rho_c. The three subscale Cronbach’s alpha coefficients
were 0.783 with a 95% CI [0.76, 0.80] in MATS, 0.86 with a
95% CI [0.85, 0.88] in MACS, and 0.92 with a 95% CI [0.91,
0.92] in APS. All coefficients and their 95% CIs surpassed
the cutoff 0.70, indicating significantly satisfactory reliabili-
ties at 0.05 level. In addition, we computed the McDonald’s
composite reliability (MCR; McDonald, 1999) of each
dimension: 0.79 in MATS, 0.86 in MACS, and 0.91 in APS.
All three MCR values were larger than the cutoff 0.60 indi-
cating a desirable level of composite reliability (McDonald,
1999). All three rho_c coefficients and their 95% CIs sur-
passed the cutoff 0.70 to display significantly satisfactory
subscale reliabilities: rho_c = 0.83 with a 95% CI [0.79,
0.86] in MATS; rho_c = 0.90 with a 95% CI [0.89, 0.91]
in MACS; and rho_c = 0.94 with a 95% CI [0.93, 0.94] in
APS. Similarly, the three subscales had significantly accept-
able rho_a coefficients and their 95% CIs did not contain the
cutoff value of 0.70. MATS’ reliability rho_a was 0.87 with
the 95% CI [0.86, 0.97]. The reliability rho_a was 0.87 with
the 95% CI [0.85, 0.88] in subscale MACS. The subscale
APS’rho_a was 0.92 with the 95% CI [0.91, 0.93].
The average variance extracted (AVE) was the metric
used for evaluating a subscale’s convergent validity. The
minimum acceptable AVE is 0.50, indicating the factor (e.g.,
MATS) should explain at least 50% of the variance in the
five MATS items (Hair etal., 2022). As reported in Table4,
the AVE values were larger than or equal to the cutoff (0.50),
indicating the convergent validity in the subscale MATS
(AVE = 0.50, 95% CI = [0.45, 0.54]), in MACS (AVE = 0.65,
95% CI = [0.62, 0.68]), and in APS (AVE = 0.75, 95%
CI = [0.73, 0.77]). Convergent validity requires that the
square root of a factor’s AVE must be greater than the inter-
factor correlation (Fornell & Larcker, 1981). The last col-
umn of Table4 lists the square root of each factor’s AVE.
The inter-factor correlations were 0.10 (between MATS and
MACS), and 0.09 (MATS with APS), respectively, both of
which were smaller than 0.71, the square root of MATS’
AVE. The inter-factor correlation between MACS and APS
was as large as 0.72 (> 0.50; Cohen, 1988), but it was less
than either the square root of MACS’s or APS’s AVE. Thus,
this analyses ofAVE indicatessatisfactory convergent valid-
ity in the 3 subscales.
The HTMT (heterotrait-monotrait) should be less than
1, with the value < 0.85 as a favorable index of discrimi-
nant validity (Henseler etal., 2015). The HTMT between
MACS and APS was 0.78, with the 95% CI = [0.74, 0.82]
without containing 0.85, which indicated a significantly
favorable discriminant validity at 0.05 level. Similarly, dis-
criminant validity was established between MATS and APS
(HTMT = 0.18, 95% CI = [0.11, 0.25]) and between MATS
and MACS (HTMT = 0.20, 95% CI = [0.14, 0.26]).
To establish predictive and incremental validity, the 4
subscales of SSWQ were correlated with and regressed
on the 3 subscales of MSQ using SPSS. SSWQ’s 4 sub-
scales were Joy of learning (abbreviated as Joy), School
connectedness (abbreviated as Con), Educational purpose
(abbreviated as Pur), and Academic efficacy (abbreviated
Table 4 The MSQ’s subscale
correlations and HTMT indices
for validity analysis
MSQ, mindful student questionnaire. aThe four subscales of SSWQ (student subjective wellness question-
naire) were also used for predictive and incremental validity analysis (Renshaw, 2017). The correlation
effect size cutoffs (Cohen, 1988) are as follows: > 0.10 (small), > 0.30 (medium), and > 0.50 (large). bThe
minimum AVE values of 0.5 indicated convergent validity. The square root of AVE should be larger than
the inter-factor correlation to demonstrate discriminant validity. cThe heterotrait-monotrait (HTMT) values
should be lower than 0.85 to indicate discriminant validity, and/or the 95% CI should not contain 0.85
* p < 0.05; **p < 0.01; ***p < 0.00
Subscale Inter-factor correlation (HTMT index)cSqrt. (AVE)b
MATS [95% CI] MACS [95% CI] APS [95% CI]
MATS (0.20)c(0.18)c0.71 (0.50)
MACS 0.10** (0.78)c0.81 (0.65)
APS 0.09** 0.72*** 0.86 (0.75)
Joy of learninga − 0.03
[− 0.09, 0.03]
0.32**
[0.26, 0.38]
0.38**
[0.32, 0.43]
-
School connectednessa − 0.06*
[− 0.13, − 0.00]
0.32**
[0.26, 0.38]
0.33**
[0.28, 0.39]
-
Educational purposea0.003
[− 0.06, 0.06]
0.34**
[0.28, 0.39]
0.36**
[0.31, 0.42]
-
Academic efficacya − 0.07**
[− 0.14, − 0.01]
0.29***
[0.23, 0.35]
0.36**
[0.30, 0.41]
-
SSWQ − 0.04
[− 0.11, 0.02]
0.34**
[0.28, 0.40]
0.39**
[0.33, 0.44]
-
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
366 Mindfulness (2024) 15:359–371
to AE). Table4 presents the inter-subscale correlation
matrix of 3 factors of MSQ (MATS, MACS, and APS)
and four factors of SSWQ (Joy, Con, Pur, and AE). Both
MACS and APS had significant medium correlations with
well-being factors in the range from 0.29 to 0.38. Further,
we noticed that vocational school students’ APS had rela-
tively stronger correlations with well-being subscales than
MATS and MACS. MATS had statistically significant but
“trivial” (< 0.10; Cohen, 1988) negative correlations with
students’ school connectedness and academic self-efficacy.
As shown in TableS4, when we established the MSQ’s pre-
dictive and incremental validity on Joy and Pur, the irrel-
evant factor MATS was excluded from the analysis to avoid
a poor-model-fit problem of zero or negative adjusted R2
(e.g., Karch, 2020). MSQ’s MACS and APS together sig-
nificantly explained 17% (eta-square = 0.17, p < 0.001) of
the total variation of the subscale Joy, and 17% of the sub-
scale Pur (eta-square = 0.17, p < 0.001). The 3 MSQ sub-
scales together significantly predictively explained 17% of
variation in Con (eta-square = 0.17, p < 0.001), 18% of AE
(eta-square = 0.18, p < 0.001), and 21% of the whole SSWQ
scale (eta = 0.21, p < 0.001). These larger effect sizes (eta-
square > 0.14; Cohen, 1988) indicated that the MSQ sub-
scales simultaneously worked together to generate large
predictive and incremental effects in explaining the SSWQ
total and subscales.
In the final step, the hierarchical regression results from
5000 bootstrap samples further established the MSQ’s pre-
dicative and incremental validity. As demonstrated in Sup-
plementary TableS4, MSQ’s subscales MATS, MACS, and
APS were included in the first, second, and third steps in
each sequential regression. Specifically, MSQ’s 2 subscales,
MACS and APS, were statistically positively associated with
the total SSWQ and with the 4 SSWQ subscales. In con-
trast, MATS was negatively associated with the total SSWQ
and 2 SSWQ subscales (i.e., Cons and AE). These results
are summarized in TableS4. The statistically significant
R-square changes (ΔR2) of the hierarchical linear regres-
sions indicated that the subscales of the MSQ had estab-
lished predictive and incremental validity. Nevertheless, we
also found that the association between MSQ’s MATS with
Pur was positive but statistically insignificant with a trivial
effect size.
The semi-/partial correlations (sr/pr; Cohen etal., 2003)
further indicated predictive and incremental validity
through the hierarchical linear regressions (see Supplemen-
tary TableS5). Specifically, a greater absolute value of sr
indicates a larger incremental validity. That is, the squared
semi-partial correlation indicates how much R-square will
increase if each MSQ’s subscale is added to the regres-
sion. A greater absolute value of pr indicates larger predic-
tive validity. The squared partial correlation represents the
unique contribution of an MSQ’s subscale in the regression
using MSQ to predict SSWQ. The subscale APS, compared
with MATS and MACS, had the largest sr and pr values
in predictive and incremental validity analyses using the
SSWQ (pr = 0.23, sr = 0.22) and its subscales: Joy (pr = 0.23,
sr = 0.23), Con (pr = 0.17, sr = 0.16), Pur (pr = 0.19, sr = 0.18),
and AE (pr = 0.24, sr = 0.22). When the 3 MSQ subscales
were simultaneously entered in the regression, MATS
performed equally well or better than MACS to establish
predictive and incremental effects on SSWQ and its sub-
scales. For example, in predicting the SSWQ total score,
MATS (pr = − 0.12, sr = − 0.11) worked comparably well
as MACS (pr = 0.12, sr = 0.11). When predicting academic
efficacy, MATS (pr = − 0.14, sr = − 0.13) outperformed
MACS (pr = 0.08, sr = 0.07). Because APS and MACS had
significantly large correlation (r = 0.78 > 0.5; Cohen, 1988),
MACS’ predictive and incremental effects were inevitably
attenuated. In that situation, MATS played a supplementary
role to add incremental power to negatively predict the total
and sub-score of SSWQ in vocational students.
Discussion
Renshaw (2017) developed and preliminarily tested the psy-
chometric properties of MSQ, a multidimensional mindful-
ness scale containing school-specific items for testing the
youth’s mindfulness at school. The primary goal of the cur-
rent study was to examine the factor structure, reliability, and
validity of the Chinese language version of the MSQ. Our
study showed that the Chinese language version of MSQ is
a reliable and valid instrument for assessing the mindfulness
of adolescents in a school setting. Both the EFA and CFA of
the current study support the 3-factor measurement structure
among students. Consistent with the result of the previous
study with adolescents in the USA by Renshaw (2017), the
internal consistency and validity of the scale of the MSQ and
its subscales (MATS, MACS, APS) are satisfactory.
The MSQ’s 3 subscales in adolescent students had posi-
tively significant inter-factor correlations. However, the
magnitude of the correlations was different from those
in Renshaw (2017). The correlation of MATS-APS for
Chinese students was attenuated compared to values in
Renshaw (2017); however, the MACS-APS correlation
was stronger than that reported in Renshaw (2017). The
MATS-MACS correlation in Chinese students was not as
strong as the value in the US student sample reported by
Renshaw (2017). The attenuated relations of MATS with
APS in Chinese students may suggest that attention, as a
critical component in mindfulness, plays a debatable role
in connecting with psychological flexibility (Lindsay &
Creswell, 2017; Martínez-Rubio etal., 2021; Simione etal.,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
367Mindfulness (2024) 15:359–371
2021; Veehof etal., 2011). In a study on participants with
psychosis, for example, “observing attention” is negatively
correlated with psychological flexibility when people over-
emphasize their internal and external experiences (White
etal., 2013). Thus, we posit that cultural variations may
play significant roles in these observed differences (Arthur
etal., 2018; Haas & Akamatsu, 2019; Raphiphatthana etal.,
2019). Furthermore, additional research could potentially
elucidate cross-cultural differences to advance the field’s
understanding of MSQ’s between-factor relationships.
The Unified Flexibility and Mindfulness model (UFM;
Bergomi etal., 2013; Rogge & Daks, 2021) provided a much
broader conceptualized framework of mindfulness that uni-
fies the psychological flexibility and dimensions of mindful-
ness into a multistage framework. The UFM also proposed
mindful acceptance and attention would facilitate the dimen-
sions of “committed action” and “value” of psychological
flexibility. Corresponding with the hypothesized structure
in UFM, the present study found that psychological flex-
ibility positively correlates with students’ well-being (Daks
& Rogge, 2020). In our analyses, the APS subscale stood out
as a prominent predictor for both the overall SSWQ and its
individual subscales including Joy of Learning, School Con-
nectedness, Educational Purpose, and Academic Efficacy.
This highlights that the Approach and Persistence subscale
of MSQ may play a critical role in establishing predictive
and incremental validity with the whole scale and subscales
of SSWQ. A larger APS score indicates students’ greater
abilities to regulate and persist with behaviors that may
result in potentially valuable outcomes while being adapt-
able to difficult life situations. The improved psychological
flexibility was found to be beneficial in improving self-regu-
lation in learning and coping with the stress among students
(Asikainen etal., 2019; Hudyma, 2019).
The MSQ, specifically, the mindful attention scale
(MATS), performed differently in the vocational students,
which may be reflective of the academic and cultural dif-
ferences in the population. MATS was not predictive of
SSWQ’s subscales, “Joy of learning,” or “educational pur-
pose.” This finding is congruent with previous findings (Baer
etal., 2006; Carpenter etal., 2019). Specifically, studies on
FFMQ discovered that the “observing” factor (attention) is
significantly related to psychological well-being in the popu-
lation of meditators, but not in the populations of students
or highly educated individuals (Baer etal., 2006). When
the 3 MSQ subscales were simultaneously in the regres-
sion, MACS performed equally well or more powerfully
than MATS to establish predictive and incremental effects
on SSWQ and its subscales. This finding can be interpreted
with Monitor and Acceptance Theory (Lindsay & Creswell,
2017), which posits that attention monitoring and acceptance
are the fundamental mechanisms underlying the effects of
mindfulness and mindfulness training. For example, accept-
ance skills alter how one responds to present-moment expe-
riences and work in conjunction with attention skills in
reducing affective reactivity and stress-related health con-
sequences (Lindsay & Creswell, 2017). The current study
supports the interactive Gestalt effect of acceptance and
attention on well-being. More importantly, MATS cannot
be evaluated out of the tri-factor structure of MSQ because
attention skills are insufficient for cognitive functioning out-
comes (Lindsay & Creswell, 2017). The MATS subscale
holds significant importance as attention, a core feature of
mindfulness, plays a critical role in fostering awareness. This
underscores its essential nature in both theoretical under-
standing and practical application of mindfulness within the
vocational student population.
MBPs in schools are becoming increasingly popular across
the world (Zenner etal., 2014). However, there has been
limited focus on the use of MBPs among Chinese youth at
school, resulting in a lack of empirical evidence to support
future intervention development (Jing etal., 2021). In terms
of the necessity for cultural adaptation of existing evidence-
based interventions, there is a clear need to test measurement
validity cross-culturally to facilitate the extrapolation of the
effectiveness of intervention in different countries (Bergomi
etal., 2013). Additionally, given that adolescents may interpret
the items on the mindfulness scale differently from other popu-
lations, the current study offers reliable evidence to support
the MSQ is not only cultural adjustable but also adaptable for
adolescents because of its school-based items (Pallozzi etal.,
2017). Therefore, the results of this research suggest that the
MSQ could be useful for future research regarding the rela-
tionship between mindfulness and clinical aspects, in terms
of external behaviors (e.g., higher suicide rate in secondary
school students), and for collecting data to the effectiveness of
mindfulness intervention within the Chinese school context.
However, future studies are warranted to examine the practical
utility of the MSQ as outcomes measure on the school-based
intervention cross-culturally and in various school settings
(e.g., vocational school).
Limitations andFuture Research
There are several limitations that exist in the present study.
First, adolescents who attend vocational schools often are
perceived as academically less competent by the main Chi-
nese cultural values, which may result in unequal educa-
tional resources and social bias (Woronov, 2015), limited
future academic success, lower level of well-being (Schoon
& Silbereisen, 2009), increased life pressure, and less resil-
ience in adversity (Chen etal., 2021; Seery & Quinton,
2016). Vocational school students in many countries (e.g.,
Israel, Denmark, and Dutch) faced the similar challenges
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
368 Mindfulness (2024) 15:359–371
as students in China, such as high rate of dropout, lack of
social support, higher frequency of suicidal behavior, and
mental health problems (Andersen etal., 2015; Benatov
etal., 2017; de Kroon etal., 2016). Thus, the future study
may apply MSQ to students at general high schools to be
able to compare with vocational school students and test the
performance of MBPs among vocational students in other
countries because the neglect of vocational high school stu-
dents can be a global problem. Second, the current study
followed Renshaw (2017) and did not include other mind-
fulness measures used for youth as criteria in the validity
analysis. Third, the sole criterion measure, the SSWQ, is
a self-report measure, which might be subject to different
biases and limitations (e.g., common-method variance;
Podsakoff etal., 2003; Poewakoff etal., 2012). Lastly, as
Grossman and Van Dam (2011) pointed out, there is a lack
of a “gold standard” to construct the mindfulness scale and
most of the unidimensional scales are hard to present the
mindfulness process. The novel model of MSQ proposed
in this study may contribute to capturing the complexity of
the mindfulness process, but further validation is required
to articulate its generalizability to youth in diverse context.
Acknowledgements We gratefully acknowledge Sichun Yang and
Ming Zhu, teachers at a vocational school in Yunnan, China, for their
assistance in recruiting participants. It is impossible to complete this
project without their contribution.
Author Contribution QW: designedand executed the studycol-
lected and analyzed the data, wrote the manuscript, contributed to
the final version of the manuscript, and supervised the project. YW:
designedand executed thestudy collected and analyzed the data, and
wrote the manuscript. RF: designedand executed the study , collected
and analyzed the data, searchedand reviewed the literature, and wrote
the manuscript. XH: designedand executed thestudy, searchedand
reviewed the literature, and wrote the manuscript. JF: contributed to the
final version of the manuscript. YZ: contributed to the final version of
the manuscript. RR: contributed to the final version of the manuscript.
All authors approved the final version of the manuscript for submission.
Data Availability The authors were not allowed to share or upload the
dataset following the ethics protocol and informed consent procedure
of this study that ensured the participants that all data we collected
were confidential and would not be shared. We will be glad to answer
any questions about the data collected in this study and to share unpub-
lished information on this dataset and code for data analysis.
Declarations
Ethics Approval All procedures were approved by the Institu-
tional Review Board (IRB) at Syracuse University (Reference No.
22–173)and were in accordance with the ethical standards of the IRB
and with the Helsinki Declaration of 1964 and its later amendments.
Informed consent was obtained from all adolescents and their guardians
included in the study.
Conflict of Interest The authors declare no competing interests.
Use of Artificial Intelligence AI was not used.
References
Andersen, S., Tolstrup, J. S., Rod, M. H., Ersbøll, A. K., Sørensen, B.
B., Holmberg, T., Johansen, C., Stock, C., Laursen, B., Zinck-
ernagel, L., Øllgaard, A. L., & Ingholt, L. (2015). Shaping the
Social: Design of a settings-based intervention study to improve
well-being and reduce smoking and dropout in Danish vocational
schools. BMC Public Health, 15, 568. https:// doi. org/ 10. 1186/
s12889- 015- 1936-6
Arthur, D., Dizon, D., Jooste, K., Li, Z., Salvador, M., & Yao, X.
(2018). Mindfulness in nursing students: Five-facet Mindfulness
Questionnaire in samples of Nursing Students in China, the Philip-
pines, and South Africa. International Journal of Mental Health
Nursing, 27(3), 975–986. https:// doi. org/ 10. 1111/ inm. 12405
Asikainen, H., Kaipainen, K., & Katajavuori, N. (2019). Understanding
and promoting students’ well-being and performance in univer-
sity studies. Journal of University Teaching & Learning Practice,
16(5), 4–19. https:// doi. org/ 10. 53761/1. 16.5.2
Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of
mindfulness by self-report: The Kentucky of Mindfulness Skills.
Assessment, 11(3), 191–206. https:// doi. org/ 10. 1177/ 10731 91104
268029
Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L.
(2006). Using self-report assessment methods to explore facets of
mindfulness. Assessment, 13(1), 27–45. https:// doi. org/ 10. 1177/
10731 91105 283504
Beavers, A. S., Lounsbury, J. W., Richards, J. K., Huck, S. W., Skolits,
G. J., & Esquivel, S. L. (2013). Practical considerations for using
exploratory factor analysis in educational research. Practical
Assessment, Research & Evaluation, 18(1), 6. https:// doi. org/ 10.
7275/ qv2q- rk76
Benatov, J., Nakash, O., Chen-Gal, S., & BrunsteinKlomek, A. (2017).
The association between gender, ethnicity, and suicidality among
vocational students in Israel. Suicide & Life-Threatening Behav-
ior, 47(6), 647–659. https:// doi. org/ 10. 1111/ sltb. 12332
Bergomi, C., Tschacher, W., & Kupper, Z. (2013). The assessment
of mindfulness with self-report measures: Existing scales and
open issues. Mindfulness, 4(3), 191–202. https:// doi. org/ 10. 1007/
s12671- 012- 0110-9
Brown, T. A. (2015). Confirmatory factor analysis for applied research
(2nd ed.). The Guilford Press.
Brown, K. W., & Ryan, R. M. (2003). The benefits of being present:
Mindfulness and its role in psychological well-being. Journal of
Personality and Social Psychology, 84(4), 822–848. https:// doi.
org/ 10. 1037/ 0022- 3514. 84.4. 822
Brown, K. W., Ryan, R. M., & Creswell, J. D. (2007). Mindfulness:
Theoretical foundations and evidence for its salutary effects. Psy-
chological Inquiry, 18(4), 211–237. https:// doi. org/ 10. 1080/ 10478
40070 15982 98
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic
concepts, applications, and programming (3rd ed.). Routledge.
Carpenter, J. K., Conroy, K., Gomez, A. F., Curren, L. C., & Hof-
mann, S. G. (2019). The relationship between trait mindfulness
and affective symptoms: A meta-analysis of the Five Facet Mind-
fulness Questionnaire (FFMQ). Clinical Psychology Review, 74,
101785. https:// doi. org/ 10. 1016/j. cpr. 2019. 101785
Chen, Y., Xie, X., & Huang, C. C. (2021). Resilience of vocational
students with disadvantaged characteristics in China: The role of
mindfulness. Children and Youth Services Review, 122, 105917.
https:// doi. org/ 10. 1016/j. child youth. 2020. 105917
Cheung, D. S. K., Kor, P. P. K., Jones, C., Davies, N., Moyle, W.,
Chien, W. T., Yip, A. L. K., Chambers, S., Yu, C. T. K., & Lai,
C. K. Y. (2020). The use of modified mindfulness-based stress
reduction and mindfulness-based cognitive therapy program for
family caregivers of people living with dementia: A feasibility
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
369Mindfulness (2024) 15:359–371
study. Asian Nursing Research, 14(4), 221–230. https:// doi. org/
10. 1016/j. anr. 2020. 08. 009
Christopher, M. S., Christopher, V., & Charoensuk, S. (2009). Assess-
ing “western” mindfulness among Thai Theravada Buddhist
monks. Mental Health, Religion & Culture, 12(3), 303–314.
https:// doi. org/ 10. 1080/ 13674 67080 26514 87
Cohen, J. (1988). Statistical power analysis for the behavioral sciences
(2nd ed.). Lawrence Erlbaum Associates. https:// doi. org/ 10. 4324/
97802 03771 587
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied mul-
tiple regression/correlation analysis for the behavioral sciences
(3rd ed.). Lawrence Erlbaum Associates Publishers.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis
(2nd ed.). Erlbaum.
Daks, J. S., & Rogge, R. D. (2020). Examining the correlates of psy-
chological flexibility in romantic relationship and family dynam-
ics: A meta-analysis. Journal of Contextual Behavioral Science,
18, 214–238. https:// doi. org/ 10. 1016/j. jcbs. 2020. 09. 010
de Kroon, M., Bulthuis, J., Mulder, W., Schaafsma, F. G., & Anema,
J. (2016). Reducing sick leave of Dutch vocational school stu-
dents: Development of a sick leave protocol using the intervention
mapping process. International Journal of Public Health, 61(9),
1039–1047. https:// doi. org/ 10. 1007/ s00038- 016- 0840-x
Deng, Y. Q., Liu, X. H., Rodriguez, M. A., & Xia, C. Y. (2011). The
five-facet mindfulness questionnaire: Psychometric properties of
the Chinese version. Mindfulness, 2(2), 123–128. https:// doi. org/
10. 1007/ s12671- 011- 0050-9
Diamond, A. (2010). The evidence base for improving school outcomes
by addressing the whole child and by addressing skills and atti-
tudes, not just content. Early Education and Development, 21(5),
780–793. https:// doi. org/ 10. 1080/ 10409 289. 2010. 514522
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares
path modeling. MIS Quarterly, 39(2), 297–316. https:// doi. org/ 10.
25300/ MISQ/ 2015/ 39.2. 02
Dunning, D. L., Griffiths, K., Kuyken, W., Crane, C., Foulkes, L.,
Parker, J., & Dalgleish, T. (2019). Research Review: The effects of
mindfulness-based interventions on cognition and mental health
in children and adolescents – a meta-analysis of randomized con-
trolled trials. Journal of Child Psychology and Psychiatry, 60(3),
244–258. https:// doi. org/ 10. 1111/ jcpp. 12980
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the
American Statistical Association, 82(397), 171–185. https:// doi.
org/ 10. 1080/ 01621 459. 1987. 10478 410
Felver, J. C., Cary, E. L., Helminen, E. C., Schutt, M. K. A., Gould, L.
F., Greenberg, M. T., Roeser, R. W., Baelen, R. N., & Schussler,
D. L. (2023). Identifying core program components of mindful-
ness-based programming for youth: Delphi approach consensus
outcomes. Mindfulness, 14(2), 279–292. https:// doi. org/ 10. 1007/
s12671- 022- 02015-1
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation
models with unobservable variables and measurement error. Jour-
nal of Marketing Research, 18(1), 39–50. https:// doi. org/ 10. 2307/
31513 12
Goodman, M. S., Madni, L. A., & Semple, R. J. (2017). Measuring
mindfulness in youth: Review of current assessments, challenges,
and future directions. Mindfulness, 8(6), 1409–1420. https:// doi.
org/ 10. 1007/ s12671- 017- 0719-9
Gouda S., Luong, M. T., Schmidt, S., & Bauer, J. (2016). Students
and teachers benefit from mindfulness-based stress reduction in a
school-embedded pilot study. Frontiers in Psychology, 7:590–590.
https:// doi. org/ 10. 3389/ fpsyg. 2016. 00590
Greco, L. A., Baer, R. A., & Smith, G. T. (2011). Assessing mindful-
ness in children and adolescents: Development and validation of the
Child and Adolescent Mindfulness Measure (CAMM). Psychologi-
cal Assessment, 23(3), 606–614. https:// doi. org/ 10. 1037/ a0022 819
Grossman, P. (2015). Mindfulness: Awareness informed by an embod-
ied ethic. Mindfulness, 6(1), 17–22. https:// doi. org/ 10. 1007/
s12671- 014- 0372-5
Grossman, P., & Van Dam, N. T. (2011). Mindfulness, by any other
name and tribulations of sati in western psychology and science.
Contemporary Buddhism, 12(1), 219–239. https:// doi. org/ 10.
1080/ 14639 947. 2011. 564841
Guerra, J., García-Gómez, M., Turanzas, J., Cordón, J. R., Suárez-
Jurado, C., & Mestre, J. M. (2019).A brief spanish version of
the child and adolescent mindfulness measure (camm). a dispo-
sitional mindfulness measure. International Journal of Environ-
mental Research and Public Health, 16(8), 1355. https:// doi. org/
10. 3390/ ijerp h1608 1355
Haas, B. W., & Akamatsu, Y. (2019). Psychometric investigation of the
five facets of mindfulness and well-being measures in the King-
dom of Bhutan and the USA. Mindfulness, 10(7), 1339–1351.
https:// doi. org/ 10. 1007/ s12671- 018- 1089-7
Hair, J., Black, W., Babin, B., & Anderson, R. (2018). Multivariate
data analysis (8th ed.). Cengage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A
primer on partial least squares structural equation modeling
(PLS-SEM) (3rd ed.). Sage.
Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J.
(2006). Acceptance and Commitment Therapy: Model, processes
and outcomes. Behaviour Research and Therapy, 44(1), 1–25.
https:// doi. org/ 10. 1016/j. brat. 2005. 06. 006
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion
for assessing discriminant validity in variance-based structural
equation modeling. Journal of the Academy of Marketing Sci-
ence, 43(1), 115–135. https:// doi. org/ 10. 1007/ s11747- 014- 0403-8
Hölzel, B. K., Lazar, S. W., Gard, T., Schuman-Olivier, Z., Vago, D.
R., & Ott, U. (2011). How does mindfulness meditation work?
proposing mechanisms of action from a conceptual and neural per-
spective. Perspectives on Psychological Science, 6(6), 537–559.
https:// doi. org/ 10. 1177/ 17456 91611 419671
Horn, J. L. (1965). A rationale and test for the number of factors in
factor analysis. Psychometrika, 30, 179–185. https:// doi. org/ 10.
1007/ BF022 89447
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covari-
ance structure analysis: Conventional criteria versus new alterna-
tives. Structural Equation Modeling, 6(1), 1–55. https:// doi. org/
10. 1080/ 10705 51990 95401 18
Hudyma, A. (2019). The role of psychological flexibility in graduate
student stress and well-being [Electronic Theses and Disserta-
tions, University of Denver]. ProQuest Dissertations and Theses
Global.
Jing, S., Zhang, A., Chen, Y., Shen, C., Currin-McCulloch, J., &
Zhu, C. (2021). Mindfulness-based interventions for breast
cancer patients in China across outcome domains: Systematic
review and meta-analysis of the Chinese literature. Supportive
Care in Cancer, 29(10), 5611–5621. https:// doi. org/ 10. 1007/
s00520- 021- 06166-0
Jöreskog, K. G. (1971). Simultaneous factor analysis in several popu-
lations. Psychometrika, 36(4), 409–426. https:// doi. org/ 10. 1007/
BF022 91366
Kahn, J. H. (2006). Factor analysis in counseling psychology research,
training, and practice: Principles, advances, and applications. The
Counseling Psychologist, 34(5), 684–718. https:// doi. org/ 10. 1177/
00110 00006 286347
Karch, J. (2020). Improving on adjusted R-Squared. Collabra Psychol-
ogy, 6(1). https:// doi. org/ 10. 1525/ colla bra. 343
Lindsay, E. K., & Creswell, J. D. (2017). Mechanisms of mindful-
ness training: Monitor and Acceptance Theory (MAT). Clini-
cal Psychology Review, 51, 48–59. https:// doi. org/ 10. 1016/j. cpr.
2016. 10. 011
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
370 Mindfulness (2024) 15:359–371
Lucas-Thompson, R. G., Miller, R. L., Seiter, N. S., & Prince, M.
A. (2019). Dispositional mindfulness predicts cortisol, cardio-
vascular, and psychological stress responses in adolescence.
Psychoneuroendocrinology, 110, 104405–104405. https:// doi.
org/ 10. 1016/j. psyne uen. 2019. 104405
Ma, Y., & Fang, S. (2019).Adolescents’ Mindfulness and Psycho-
logical Distress: The Mediating Role of Emotion Regulation.
Frontiers in Psychology, 10, 1358. https:// doi. org/ 10. 3389/
fpsyg. 2019. 01358
Martínez-Rubio, D., Martínez-Brotons, C., Monreal-Bartolomé, A.,
Barceló-Soler, A., Campos, D., Pérez-Aranda, A., Colomer-
Carbonell, A., Cervera-Torres, S., Solé, S., Moreno, Y., &
Montero-Marín, J. (2021). Protective role of mindfulness, self-
compassion and psychological flexibility on the burnout sub-
types among psychology and nursing undergraduate students.
Journal of Advanced Nursing, 77(8), 3398–3411. https:// doi.
org/ 10. 1111/ jan. 14870
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power
analysis and determination of sample size for covariance structure
modeling. Psychological Methods, 1, 130–149.
Preacher, K. J., & Coffman, D. L. (2006, May). Computing power and
minimum sample size for RMSEA [Computer software]. Available
from http:// quant psy. org/.
McDonald, R. P. (1999). Test theory: A unified treatment. Erlbaum.
McKeering, P., & Hwang, Y.-S. (2019). A systematic review of mindful-
ness-based school interventions with early adolescents. Mindfulness,
10(4), 593–610. https:// doi. org/ 10. 1007/ s12671- 018- 0998-9
Muthén, L.K. and Muthén, B.O. (1998–2017). Mplus User’s Guide
(8th ed.). Muthén & Muthén.
Pallozzi, R., Wertheim, E., Paxton, S., & Ong, B. (2017). Trait
mindfulness measures for use with adolescents: A systematic
review. Mindfulness, 8(1), 110–125. https:// doi. org/ 10. 1007/
s12671- 016- 0567-z
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P.
(2003). Common method biases in behavioral research: A criti-
cal review of the literature and recommended remedies. Journal
of Applied Psychology, 88(5), 879–903. https:// doi. org/ 10. 1037/
0021- 9010. 88.5. 879
Poewakoff, P. M., Mackenzie, S. B., & Podsakoff, N. P. (2012). Sources
of method bias in social science research and recommendations on
how to control it. Annual Review of Psychology, 63(1), 539–569.
https:// doi. org/ 10. 1146/ annur ev- psych- 120710- 100452
Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the
optimal number of factors in exploratory factor analysis: A model
selection perspective. Multivariate Behavioral Research, 48(1),
28–56. https:// doi. org/ 10. 1080/ 00273 171. 2012. 710386
Raphiphatthana, B., Jose, P. E., & Chobthamkit, P. (2019). The asso-
ciation between mindfulness and grit: An East vs West Cross-
Cultural Comparison. Mindfulness, 10(1), 146–158. https:// doi.
org/ 10. 1007/ s12671- 018- 0961-9
Renshaw, T. L. (2017). Preliminary development and validation of the
mindful student questionnaire. Assessment for Effective Interven-
tion, 42(3), 168–175. https:// doi. org/ 10. 1177/ 15345 08416 678971
Renshaw, T. L. (2020). Mindfulness-based intervention in schools. In
C. Maykel & M. A. Bray (Eds.), Promoting mind–body health
in schools: Interventions for mental health professionals (pp.
145–160). American Psychological Association. https:// doi. org/
10. 1037/ 00001 57- 010
Renshaw, T. L., Long, A. C., & Cook, C. R. (2015). Assessing adoles-
cents’ positive psychological functioning at school: Development
and validation of the Student Subjective Wellbeing Questionnaire.
School Psychology Quarterly, 30(4), 534–552. https:// doi. org/ 10.
1037/ spq00 00088
Ringle, C. M., da Silva, D., & Bido, D. (2014). Structural equation
modeling with the smartpls. Revista Brasileira de Marketing,
13(2), 56–73. https:// doi. org/ 10. 5585/ remark. v13i2. 2717
Roeser, R. W., Greenberg, M. T., Frazier, T., Galla, B. M., Semenov, A.
D., & Warren, M. T. (2023). Beyond all splits: Envisioning the next
generation of science on mindfulness and compassion in schools
for students. Mindfulness, 14(2), 239–254. https:// doi. org/ 10. 1007/
s12671- 022- 02017-z
Rogge, R. D., & Daks, J. S. (2021). Embracing the intricacies of the
path toward mindfulness: Broadening our conceptualization of the
process of cultivating mindfulness in day-to-day life by develop-
ing the unified flexibility and mindfulness model. Mindfulness,
12(3), 701–721. https:// doi. org/ 10. 1007/ s12671- 020- 01537-w
Schoon, I., & Silbereisen, R. K. (2009). Transitions from school to work:
Globalization, individualization, and patterns of diversity. Cambridge
University Press. https:// doi. org/ 10. 1017/ CBO97 80511 605369
Schutt, M. K. A., & Felver, J. (2020). Mindfulness in education. In N.
Singh & S. Joy (Eds.), Mindfulness-based interventions with chil-
dren and adolescents (pp. 57–73). Routledge. https:// doi. org/ 10. 4324/
97813 15563 862
Seery, M. D., & Quinton, W. J. (2016). Understanding resilience: From
negative life events to everyday stressors. In J. M. Olson & M. P.
Zanna (Eds.), Advances in experimental social psychology (pp.
181–245). Elsevier Academic Press. https:// doi. org/ 10. 1016/ bs.
aesp. 2016. 02. 002
Simione, L., Raffone, A., & Mirolli, M. (2021). Acceptance, and not
its interaction with attention monitoring, increases psychological
well-being: Testing the monitor and acceptance theory of mind-
fulness. Mindfulness, 12(6), 1398–1411. https:// doi. org/ 10. 1007/
s12671- 021- 01607-7
Sousa, V. D., & Rojjanasrirat, W. (2011). Translation, adaptation and
validation of instruments or scales for use in cross-cultural health
care research: A clear and user-friendly guideline. Journal of
Evaluation in Clinical Practice, 17(2), 268–274. https:// doi. org/
10. 1111/j. 1365- 2753. 2010. 01434.x
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics
(6th ed.). Allyn & Bacon/Pearson Education.
Tencent. (2022). Tencent Questionnaire-Free questionnaire system.
https:// wj. qq. com.
Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to
Cronbach’s alpha reliability in realistic conditions: Congeneric
and asymmetrical measurements. Frontiers in Psychology, 7, 769.
https:// doi. org/ 10. 3389/ fpsyg. 2016. 00769
Veehof, M. M., ten Klooster, P. M., Taal, E., Westerhof, G. J., &
Bohlmeijer, E. T. (2011). Psychometric properties of the Dutch
Five Facet Mindfulness Questionnaire (FFMQ) in patient with
fibromyalgia. Clinical Rheumatology, 30(8), 1045–1054. https://
doi. org/ 10. 1007/ s10067- 011- 1690-9
Wang, K., & Kong, F. (2020). Linking trait mindfulness to life satisfac-
tion in adolescents: Mediating role of resilience and self-esteem.
Child Indicators Research, 13(1), 321–335. https:// doi. org/ 10.
1007/ s12187- 019- 09698-4
Wang, Y., Yu, M., & Zhou, H. (2021). Co-development of mindful
awareness and Chinese problem: Based on parallel-process latent
growth curve model. Journal of Affective Disorders, 295, 997–
1004. https:// doi. org/ 10. 1016/j. jad. 2021. 08. 113
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model
selection in structural equation modeling. In R. H. Hoyle (Ed.),
Handbook of structural equation modeling (pp. 209-231). The
Guilford Press.
White, R. G., Gumley, A. I., McTaggart, J., Rattrie, L., McConville,
D., Cleare, S., & Mitchell, G. (2013). Depression and anxi-
ety following psychosis: Associations with and psychological
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
371Mindfulness (2024) 15:359–371
flexibility. Behavioural and Cognitive Psychotherapy, 41(1),
34–51. https:// doi. org/ 10. 1017/ S1352 46581 20002 39
Woronov, T. (2015). Class work: Vocational schools and China’s urban
youth. Stanford University Press.
Yang, X., Zhou, Z., Liu, Q., & Fan, C. (2019). Mobile phone addic-
tion and adolescents’ anxiety and depression: Moderating role of
mindfulness. Journal of Child and Family Studies, 28(3), 822–
830. https:// doi. org/ 10. 1007/ s10826- 018- 01323-2
Zenner, C., Herrnleben-Kurz, S., & Walach, H. (2014). Mindfulness-
based interventions in schools-a systematic review and meta-
analysis. Frontiers in Psychology, 5, 603. https:// doi. org/ 10. 3389/
fpsyg. 2014. 00603
Zhang, Y., Yu, Q., & Renshaw, T. (2018). Adaptation of the student
subjective wellbeing questionnaire (SSWQ) for Chinese schools:
Validation and generalizability study. [Poster presentation]. 40th
Annual Conference of the International School Psychology Asso-
ciation, Tokyo, Japan.
Zmnako, S. S. F., & Chalabi, Y. I. (2019). Cross-cultural adaptation,
reliability, and validity of the Vertigo symptom scale–short form
in the central Kurdish dialect. Health and Quality of Life Out-
comes, 17, 125. https:// doi. org/ 10. 1186/ s12955- 019- 1168-z
Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with the
author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of
such publishing agreement and applicable law.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
The present study examined the psychometric properties of the Personalized Psychological Flexibility Index (PPFI) in a Turkish adult community sample, which consisted of 837 people (65% female) aged between 18 and 68 (Mage = 29.93, SD = 13.21). Exploratory factor analysis yielded a three-factor structure corresponding to acceptance, avoidance, and harnessing, in accordance with the original PPFI. Confirmatory factor analysis showed that the three-factor model outperformed the one-factor model and showed a good model fit. The Turkish adaptation of the PPFI and its subscales exhibited high internal consistency, convergent validity with dispositional hope, and distress endurance in expected directions. Notably, the Turkish adaptation of the PPFI also showed excellent divergent validity from indices of negative emotionality. This study has offered additional support for the construct validity of the Turkish adaptation of the PPFI by investigating the relationship between psychological flexibility, dispositional hope, and psychological distress. Psychological flexibility emerged as a mediator between dispositional hope and psychological distress, and potential explanations were discussed. The present study offers evidence of the reliability and validity of the PPFI and its potential applicability in the Turkish adult population.
Article
Full-text available
In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships.
Article
Full-text available
Objectives This paper describes the emergence of the scientific study of mindfulness in schools; summarizes findings of experimental research on the impacts of school-based mindfulness programs (SBMPs) on student outcomes in prekindergarten, primary, and secondary school settings (ages 4–18 years); discusses scientific limitations and wider critiques of this work; and offers suggestions for future research. Methods Public data are used to describe the emergence of science on SBMPs, the foci of this research, and the academic disciplines contributing to it. A narrative summary of scientific findings regarding the impacts of SBMPs on students, and critiques of this work, is also presented. Results Research is increasing and is primarily psychological and prevention-oriented. Evidence shows SBMPs can enhance students’ self-regulation abilities, but SBMPs’ impacts on other student outcomes at different ages are equivocal. The current research has significant limitations, and these, alongside wider critiques of the work, suggest important directions for research. Conclusions In the next generation of science, we suggest (a) improving the experimental research; (b) expanding developmental research; and (c) re-envisioning assumptions, theories, and methods in research to go “beyond all splits” towards a non-dualistic and relationally, culturally, contextually, ethically, and developmentally grounded science on mindfulness and compassion for students in schools.
Article
Full-text available
Objectives The implementation of mindfulness-based programming/interventions (MBP) for youth, and corresponding research, has proliferated in recent years. Although preliminary evidence is promising, one pressing concern is that the heterogeneity of MBP for youth makes it difficult to infer the essential constituent program elements that may be driving specified outcomes (i.e., core program components (CPCs)). Methods This research employed the Delphi method to survey expert MBP scientists and instructors to identify consensus of CPCs of MBP for youth. Results The study’s advisory board identified scientists based on topical publication record and peer nomination. Delphi Round 1 surveyed scientists (n = 19) to name and define potential CPCs of MBP for youth; responses were qualitatively analyzed yielding 22 MBP categorical codes. Delphi Round 2 recruited MBP instructors (n = 21) identified by scientist participants and peer instructor nomination. In Rounds 2 and 3, the full participant sample (scientists and instructors) were asked to consider the preceding Round’s results and whether each of the 22 identified codes were an essential CPC of MBPs for youth. Final Round 3 results indicated consensus (≥ 75% endorsement) of 9 of the 22 identified codes as CPCs of MBP for youth, including self-awareness, non-judging, focused attention, orienting to present moment, acceptance, compassion, somatic awareness, non-reacting, and decentering. Two additional codes (skillful responding and loving-kindness) were indicated by the instructor subgroup only. Conclusions These findings are the first to report expert consensus of identified CPCs of MBP for youth, and results have significant implications for future youth MBP evaluation, implementation, and curriculum development.
Article
Full-text available
In a study on 346 college students, we investigated how mindfulness (broadly construed as a manifold of self-awareness, self-regulation, and self-transcendence) influences three aspects of self-view (comprehensive intellectual humility, self-consciousness, and positive self-view) and eudemonic well-being (personal growth, self-acceptance, purpose in life, and positive relationships). Path analysis uncovered that: (a) both positive self-view and self-consciousness were related to mindfulness, be it each in their own way; (b) intellectual humility was not related to mindfulness once the influence of personality was taken into account; and (c) positive self-view was a mediator for the effects of mindfulness on most aspects of eudemonic well-being, self-consciousness was not, and intellectual humility was a (positive) determinant for most aspects of eudemonic well-being.
Article
Full-text available
Background: We examined how a newly proposed facet of rumination, that is, the (in)ability to let go, might relate to other aspects of rumination and to psychological outcomes. Methods: In two independent samples (n = 423 and 329, resp.) of college students, we measured a broad set of rumination and rumination-related measures, letting go, anxiety and dysphoria; in the second sample, we also collected data on mindfulness, self-compassion and eudemonic well-being. Results: Factor analysis of rumination and rumination-related measures yielded three factors: (a) negative intrusive thought; (b) reflectiveness; and (c) the inability to let go. Repetitive intrusive thought and the ability to let go were significant (and thus partially independent) predictors for the three outcomes of anxiety, dysphoria, and wellbeing. The inability to let go and repetitive intrusive thought significantly mediated between mindfulness and all three outcomes. Conclusions: The findings suggest that letting go is a potentially interesting aspect of rumination not fully captured in the traditional concept of rumination and its standard measures.
Article
Full-text available
Introduction Adolescence is a critical period of growth. Mental health during adolescence is one of the most important determinants of mental health in adulthood. The aim of this study was to analyze the relationship between mindfulness and psychological well-being of adolescents considering the mediating role of self-compassion, emotional dysregulation and cognitive flexibility. Methods The method of this research is cross-sectional. The research population was adolescents (elementary, first and second high school) in Zanjan, Iran in 2021, whose approximate number was 14,000. Data through adolescent mindfulness questionnaires (Brown, West, Loverich, and Biegel, 2011), short form of psychological well-being questionnaire (Ryff and Keyes, 1995), short form of self-compassion scale (Raes et al., 2011), difficulty in Emotion regulation (Gratz and Roemer, 2004) and cognitive flexibility (Dennis and Vander Wal, 2010) were collected. Data analysis was performed using Pearson correlation coefficient and path analysis with SPSS-26 and lisrel-10.2 software. Results According the results, in addition to the fact that mindfulness is directly and positively related to psychological well-being (p < 0.05), it is also indirectly through self-compassion and Cognitive flexibility has a positive and significant relationship with psychological well-being and also mindfulness has an indirect, negative and significant relationship with psychological well-being through emotional dysregulation (p < 0.05). The results supported the goodness of model fit and confirmation of hypotheses. Conclusion Therefore, it is recommended that practitioners provide the basis for promoting psychological well-being through mindfulness, emotional dysregulation, self-compassion and cognitive flexibility.
Article
Full-text available
Background We investigated the relationship between mindfulness and compassion in a broader way than is typically done by (a) using a recent, comprehensive conceptualization of mindfulness as a manifold of self-awareness, self-regulation, and self-transcendence, and (b) by casting a wide net of compassion measures [i.e., the Compassionate Love for Humanity Scale (Sprecher and Fehr in J Soc Pers Relatsh 22(5):629–651, 2005); Compassion Scale (Martins et al. in J Health Care Poor Underserved 24:1235–1246, 2013); Compassion Scale (Pommier in Assessment 27:21–39, 2020); Relational Compassion Scale (Hacker in The relational compassion scale: Development and validation of a new self-rated scale for the assessment of self-other compassion, University of Glasgow, 2008); and the SOCS-O scale (Gu et al. in Clin Psychol Rev 37:1–12, 2020)]. Additionally, we examined the interplay between mindfulness, compassion, and ethical sensitivities by assessing the influence of the moral foundations (individualizing and binding) on compassion, and the influence of mindfulness, the moral foundations, and compassion on awareness of privilege. Methods We surveyed 407 undergraduate students. Factor analysis was used to examine the dimensionality of the compassion measures; path analysis to examine the relationships between all variables. Results Factor analysis revealed distinct affective (empathy, indifference), cognitive (common humanity, recognizing suffering), and motivational (willingness to act) aspects of compassion. Mindfulness, under its aspects of reflective awareness, self-compassion, and self-transcendence, was associated with compassion, with reflective awareness predicting multiple aspects of compassion over and beyond the normal mechanisms of the mindfulness manifold and the moral foundations. Individualizing was associated with all aspects of compassion; binding was only connected to recognizing suffering and a willingness to act. Awareness of privilege was positively connected to mindfulness through individualizing and the recognition of common humanity; it was also directly negatively related to the moral foundation of binding. Conclusions Mindfulness and compassion have synergistic and distinct positive effects on ethical sensitivities. Given that both compassion and ethical sensitivities have roots in mindfulness, mindfulness interventions might be one possible venue to enhance these positive aspects of individuals’ psychology.
Article
Recent reform efforts have pushed toward a better understanding of the distinction between exploratory and confirmatory research, and appropriate use of each. As some utilize more exploratory tools, it may be tempting to employ multiple linear regression models. In this paper, we advocate for the use of random forest (RF) models. RF is able to obtain better predictive performance than traditional regression, while also inherently protecting against overfitting as well as detecting nonlinear effects and interactions among predictors. Given the advantages of RF compared to other statistical procedures, it is a tool commonly used within a plethora of industries, including stock trading, banking, pharmaceuticals, and patient healthcare planning. However, we find RF is used within the field of psychology comparatively less frequently. In the current paper, we advocate for RF as an important statistical tool within the context of behavioral and psychological research. In hopes of increasing the use of RF in the field of psychology, we provide information pertaining to the limitations one might confront in using RF and how to overcome such limitations. Moreover, we discuss various methods for how to optimally utilize RF with psychological data, such as nonparametric modeling, interaction and nonlinearity detection, variable selection, prediction and classification modeling, and assessing parameters of Monte Carlo simulations. Throughout, we illustrate the use of RF with visualization strategies, aimed to make RF models more comprehensible and intuitive.
Chapter
Mindfulness as an applied topic of scientific inquiry has become increasingly popular during the past 30 years. In parallel with this general interest, clinicians and researchers have also become more interested in exploring the utility of mindfulness-based programs (MBPs) in educational settings. This chapter synthesizes the existing research literature on the use of MBP practices with children and adolescents in school settings. The first section of the chapter summarizes existing systematic reviews and meta-analyses that have been conducted to date. The second section reviews and summarizes research that has been conducted since 2014. The third and final section of the chapter provides recommendations for future research and for those seeking to implement evidence-based MBPs in school settings. The chapter presents two overall findings of some importance: (a) the evidence strongly suggests that MBPs appear to be feasible to implement in school systems, are acceptable as a practice, and do not appear to cause harm, and (b) the existing studies indicate that MBPs would seem to benefit students across multiple domains of functioning, including reducing symptoms of psychopathology, improving their general psychosocial well-being, enhancing certain aspects of cognitive functioning (e.g., attention, self-regulation), and improving academic outcomes.