Jyoti Meditation Article 2016
Jyoti Meditation Article 2016
Jyoti Meditation Article 2016
net/publication/303717259
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3 authors, including:
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The authors examined whether Jyoti meditation (JM), a spiritually based medita-
tion (Singh, 2012), influenced student counselors’ (N = 60) level of stress and
emotional intelligence (EI). Results from a randomized controlled trial and
growth curve analysis provided a multilevel model in which JM reduced stress
and EI moderated the effect.
Keywords: meditation, stress, counselor education, emotional intelligence, model
Slope Intercept
EI 1 EI 2 EI 3
Figure 1
Hypothesized Model for Research Question 2
Note. EI = emotional intelligence.
Method
Participants and Procedures
The appropriate institutional review board approved the study. We recruited
mental health, marriage and family, and school counseling students from
the Introduction to Counseling course at a large counselor education pro-
gram in the southeastern United States that was accredited by Council for
Accreditation of Counseling and Related Educational Programs. This course
was among the first taken by students in their master’s-level counseling pro-
gram. Course requirements included participation in laboratory groups. We
informed students that both groups would be included in a research study,
but only one laboratory group would feature a meditation technique. Stu-
dents could withdraw from the study at any time without prejudice. There
was an alternative group that met the course requirement. Students not
selected for the meditation group knew they would have the opportunity
to join it later in the semester. All students in the class elected to take part
in the research study.
Sixty students participated in the study. We randomly assigned them to
either the meditation or the comparison group using research randomizer
tools (www.randomizer.org). The participants were blind to the expected
outcomes and hypotheses of the research study.
Eighty-eight percent (n = 53) of the participants were female, and 12% (n
= 7) were male. Participants’ mean age was 24 years (SD = 4.35), with ages
ranging from 20 to 47 years. The modal age of participants was 22 years.
Participants identified as White (n = 38, 63%), Latino (n = 7, 12%), Black
(n = 9, 15%), and Asian (n = 5, 8%), and one participant declined to report
race. The course was open to nondegree-seeking students (n = 3, 5%), school
Results
Statistical Assumptions and Data Cleaning
Before the analyses, data were screened for missing data and outliers. We
found neither. Therefore, there was no need to address or eliminate ex-
treme cases. The Shapiro–Wilk test of normality indicated that the data were
normal (p > .10). Visual inspection of plots of the variables confirmed the
data’s normality and linearity, meeting statistical assumptions (Tabachnick
& Fidell, 2013).
Using PROC MIXED (SAS Institute, 2013), we created a two-level model
in which Level 1 consisted of the participant’s individual growth and Level
2 expressed the variation parameters between participants. In other words,
the three observation points were nested into participants for the analysis.
Time was coded as 0, 1, and 2, where 0 represented the pretest scores, 1 the
midtest scores, and 2 the posttest scores. In addition, time was specified as
both a random and a fixed effect to capture an estimate of the intraindividual
variance (Singer, 1998). We developed three models: (a) an unconditional
linear growth model, (b) a conditional model with time and the interaction
between time and group, and (c) a conditional model with EI, stress, time,
and the interaction between time and group.
14
13
12
11
1 2 3
Time
Figure 2
Plot of Mean Stress Scores Over Time
We created a third model to explore the effects of EI on stress, time, and
the Time × Group interaction. In this analysis, we used the full group of
participants (N = 60). All of the tests for significance of the fixed effects
were significant. More specifically, the estimate for the intercept increased
to 47.96 from 15.95 in the unconditional linear growth model (p < .01). The
effects of time were also significant (β = –2.77, p < .01), as was EI (β = –0.19,
p < .01). Furthermore, when EI was added to the model (i.e., compared with
the fixed effects in the second conditional model with the full sample), the
interaction between time and group was significant (β = 1.26, p < .05.) Thus,
in the full sample of meditators the Time × Group interaction is significant
when controlling for EI.
As for the random effects, the residuals remained significant (p < .01),
but the estimate decreased to 12.33 from the 13.04 found in the uncondi-
tional linear growth model, indicating that the inclusion of EI did explain
some of the variation within participants. Likewise, the variance component
for the intercepts decreased, revealing that EI helped explain some of the
variation in the intercepts. The inclusion of EI accounts for 27% ([20.07 –
14.71] / 20.07) of the variation in intercepts, a large effect size (J. Cohen,
1988). Preacher, Wichman, MacCallum, and Briggs (2008) recommended
interpreting this type of cross-level interaction as a moderation effect. To
confirm the moderation effect of EI, we included a three-way interaction in
this model: time, group, and EI. As expected, the results for the interaction
were significant (β = −0.06, p < .001), indicating that EI does moderate the
effects of meditation on stress.
Discussion
The findings show that JM had a statistically significant effect on student
counselors’ level of stress. Moreover, EI moderated the influence of the
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