Running head: REST, SELF-FOCUS AND NEGATIVE MOOD 1 Accepted for publication in “Clinical Psychological Science” Note: This is an uncorrected version of an author’s manuscript accepted for publication. Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication. During production and pre-press, errors may be discovered that could affect the content. Rest-related dynamics of risk and protective factors for depression: A behavioral study Igor Marchetti, Ernst H.W. Koster, & Rudi De Raedt Department of Experimental Clinical and Health Psychology Ghent University, Belgium Brief Empirical Article Word count: 4997 ________________________ Corresponding Author: Igor Marchetti, Ghent University, Department of Experimental- Clinical and Health Psychology, E-mail: [email protected]
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Running head: REST, SELF-FOCUS AND NEGATIVE MOOD
1
Accepted for publication in “Clinical Psychological Science”
Note: This is an uncorrected version of an author’s manuscript accepted for publication.
Copyediting, typesetting, and review of the resulting proofs will be undertaken on this
manuscript before final publication. During production and pre-press, errors may be
discovered that could affect the content.
Rest-related dynamics of risk and protective factors for depression:
A behavioral study
Igor Marchetti, Ernst H.W. Koster, & Rudi De Raedt
Department of Experimental Clinical and Health Psychology
Ghent University, Belgium
Brief Empirical Article
Word count: 4997
________________________
Corresponding Author: Igor Marchetti, Ghent University, Department of Experimental-
Note. ***p < .001. **p < .01. *p < .05. The values between parentheses are Cronbach’s alphas. Δ MRSI t1-t0 = differential score between MRSI indexes after (t1) and before (t0) rest-
related paradigm. Higher scores represent an increase of ruminative self-focus at time 2, controlling for time 1. Δ PANAS negative t1-t0 = differential score between PANAS negative
indexes after (t1) and before (t0) rest-related paradigm. Higher scores represent an increase of negative mood at time 2, controlling for time 1.
2
Table 2. Conditional indirect effect of internal focus on the increase of negative mood across low (-1 SD), medium (M) and high (+1) levels of cognitive reactivity and brooding via increased ruminative self-focus (upper part); conditional indirect effect across low (-1 SD), medium (M) and high (+1) levels of mindfulness (lower part)(n = 80) Cognitive reactivity
Brooding Conditional indirect effect
Boot SE Boot LL CI 95%
Boot UL CI 95%
Low
Low .16 .15 -.02 .65
Medium .21 .16 -.02 .66
High .26 .28 -.11 1.11
Medium
Low .39 .29 .01 1.15
Medium .44 .24 .01 .96
High .49 .29 .03 1.20
High
Low .62 .45 .03 1.91
Medium .66 .39 .01 1.58
High .71 .39 .01 1.55
Mindfulness Conditional indirect effect
Boot SE Boot LL CI 95%
Boot UL CI 95%
Low .74 .36 .13 1.55
Medium .49 .23 .09 1.02
High .25 .23 -.06 .92
Supplemental Online Material
Data-analytic strategy
In our study, we aimed at testing a specific statistical model where only the indirect
effect (axb) is expected to be significant, neither the total (c) nor the direct effect (c’) is
expected to be significant. Indirect effect model is a term which has been proposed to
differentiate from full and partial meditational models where significant either total or direct
effects are expected (Preacher & Hayes, 2008). In keeping with the guidelines proposed by
Mathieu and Taylor (2006), we first tested the statistical significance of the indirect effect,
operationalized as the product of path a and b. We then tested the null total effect of the focal
predictor on the outcome variable without taking into account the contribution of the
intervening variable (Figure 1A). We also tested the null effect of the direct path (c’), namely
the contribution of the focal predictor on the outcome after controlling for the intervening
variable. When these three conditions were satisfied, we could test whether the relation
between the focal predictor and the outcome is due to an indirect effect through the
contribution of the intervening variable (Hayes, 2009; Hayes, Preacher, & Myers, 2011;
Mathieu & Taylor, 2006). Nevertheless, the test of the indirect effect model was performed on
data that were only partially structured to be temporally consistent with the proposed
underlying process, with the intervening variable (increased ruminative self-focus) and the
outcome variable (increased negative mood) being measured at the same time. In order to
control for the alternative indirect path (reversed model), the data were subjected to an
analysis in which the increased negative mood served as intervening variable and the
enhanced ruminative self-focus as outcome. If the results of this reversed model were also
significant, caution would be warranted (Kenny, 2012).
To test the statistical significance of the indirect effect (path axb), according to
Preacher and Hayes’ recommendations (2008) we adopted the nonparametric bootstrapping
approach. Compared with the causal steps approach (Baron & Kenny, 1986) or the Sobel test
(Sobel, 1982), bootstrapping is considered the most powerful approach and free from
unrealistic assumptions, such as the multivariate normality in data distribution (Bollen &
Stine, 1990; Hayes, 2009). Specifically, bootstrapping circumvents this problem by relying on
confidence intervals (CIs) for the determination of the indirect effect instead of the traditional
p-value approach that uses standard errors (MacKinnon, Lockwood, & Williams, 2002).
According to Preacher and Hayes (2008), to test the significance of the indirect effect (path
axb) we estimated 10000 bootstrap bias-corrected 95% CIs, and if they did not contain zero
they were considered significant. Crucially, to determine the significance of the indirect effect
(axb path) we evaluated only the bootstrap CIs without considering the significance of path a
and b, as recommended by Hayes (2009, 2012). Regressions weights for both path a and b
were computed only to clarify the direction of the influence of the focal predictor over the
intervening variable (path a) and the intervening variable over the outcome variable (path b).
Furthermore, we put forward the indirect effect to vary across different levels of the
moderator(s), either cognitive reactivity and brooding (Figure 1B) or mindfulness (Figure
1C). For both models, we estimated different conditional indirect effects of the focal predictor
over the outcome variable at low (one SD below the mean), moderate (sample mean), and
high (one SD above the mean) values of the moderator(s). Following Cohen, Cohen, West,
and Aiken (2003), both the focal predictor and the moderator(s) were mean-centered prior
calculating the interaction term(s).
Additional Analyses
We tested the reversed models, where increased negative mood acts as intervening variable
and increased ruminative self-focus acts as outcome variable, by estimating the indirect effect.
Specifically, we ruled out the statistical significance of the reversed models for both cognitive
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Psychological-Research - Conceptual, Strategic, and Statistical Considerations.
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estimates of variability. Sociological Methodology, 20, 115-140.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple
Regression/Correlation Analysis for the Behavioral Sciences (3rd Edition). Mahwah,
NJ.: Erlbaum.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical Mediation Analysis in the New
Millennium. Communication Monographs, 76(4), 408-420.
Hayes, A. F. (2012). An Analytical Primer and Computational Tool for observed Variable
Mediation, Moderation, and Conditional Process Modeling. Manuscript submitted for
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Kenny, D. (2012). Mediation: Specification error. Retrieved 3rd April 2012, from
http://davidakenny.net/cm/mediate.htm#SE
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Table S1. Summary regressions for the total and direct effect of internal focus over increased negative mood conditioned to cognitive reactivity and brooding via increased ruminative self-focus (n = 80) Δ MRSI t1-t0 Predictor B SE B β R2
BDI-II -.01 .07 -.01
Reflection -.37 .13 -.29**
Brooding .35 .16 .25*
LEIDS-R .03 .04 .09
MAAS 1.59 .86 .20
External focus 1.01 .62 .17
Internal focus 2.76 .54 .53***
Internal focus x LEIDS-R (a) .08 .04 .28*
Internal focus x Brooding (a) .08 .21 .05 .401***
Δ PANAS negative t1-t0
Predictor B SE B β R2
BDI-II -.05 .06 -.11
Reflection .00 .10 .00
MAAS .22 .60 .04
External focus .44 .48 .11
Internal focus (c’) .13 .48 .04
Δ MRSI t1-t0 (b) .16 .08 .24 .086
Δ PANAS negative t1-t0
Predictor B SE B β R2
BDI-II -.03 .06 -.06
Reflection .04 .11 .05
Brooding -.05 .13 -.06
LEIDS-R -.03 .03 -.17
MAAS -.12 .68 -.02
External focus .48 .49 .13
Internal focus (c) .52 .44 .15 .073
Note. ***p < .001; **p < .01; *p < .05. Δ MRSI t1-t0 = differential score between MRSI indexes after (t1) and before (t0) rest-related paradigm. Higher scores represent an increase of ruminative self-focus at time 2, controlling for time 1. Δ PANAS negative t1-t0 = differential score between PANAS negative indexes after (t1) and before (t0) rest-related paradigm. Higher scores represent an increase of negative mood at time 2, controlling for time 1.
Table S2. Conditional indirect effect of internal focus on the increase of ruminative self-focus across low (-1 SD), medium (M) and high (+1)levels of cognitive reactivity and brooding via increased negative mood (reversed model) (upper part); conditional indirect effect across low (-1 SD), medium (M) and high (+1) levels of mindfulness (reversed model) (lower part)(n = 80) Cognitive reactivity
Brooding Conditional indirect effect
Boot SE Boot LL CI 95%
Boot UL CI 95%
Low
Low -.03 .19 -.51 .31
Medium .11 .23 -.21 .78
High .26 .37 -.24 1.37
Medium
Low .01 .21 -.34 .53
Medium .16 .18 -.03 .72
High .30 .30 -.06 1.18
High
Low 05 .31 -.43 .92
Medium .20 .24 -.05 .97
High .34 .31 -.02 1.20
Mindfulness Conditional indirect effect
Boot SE Boot LL CI 95%
Boot UL CI 95%
High .27 .26 -.05 .98
Medium .16 .19 -.08 .74
Low .06 .26 -.42 .68
Table S3. Summary regressions for the total and direct effect of internal focus over increased negative mood conditioned to mindfulness via increased ruminative self-focus (n = 80) Δ MRSI t1-t0 Predictor B SE B β R2
BDI-II -.00 .08 -.01
Reflection -.28 .14 -.22*
Brooding .26 .17 .19
LEIDS-R .01 .04 .05
MAAS 1.30 .89 .16
External focus .88 .63 .15
Internal focus 2.52 .57 .48***
Internal focus x MAAS (a) -2.00 1.15 -.18 .335***
Δ PANAS negative t1-t0
Predictor B SE B β R2
BDI-II -.02 .06 -.05
Reflection .09 .10 .11
Brooding -.12 .13 -.13
LEIDS-R -.03 .03 -.16
External focus .32 .48 .08
Internal focus (c’) .02 .48 .00
Δ MRSI t1-t0 (b) .20 .09 .30* .135
Δ PANAS negative t1-t0
Predictor B SE B β R2
BDI-II -.03 .06 -.06
Reflection .04 .11 .05
Brooding -.05 .13 -.06
LEIDS-R -.03 .03 -.17
MAAS -.12 .68 -.02
External focus .48 .49 .13
Internal focus (c) .52 .44 .15 .073
Note. ***p < .001; **p < .01; *p < .05. Δ MRSI t1-t0 = differential score between MRSI indexes after (t1) and before (t0) rest-related paradigm. Higher scores represent an increase of ruminative self-focus at time 2, controlling for time 1. Δ PANAS negative t1-t0 = differential score between PANAS negative indexes after (t1) and before (t0) rest-related paradigm. Higher scores represent an increase of negative mood at time 2, controlling for time 1.