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Learning from Spiritual Models and Meditation: A Randomized Evaluation of a College Course

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Abstract

Effects of two meditation and mindfulness-based spiritual interventions were examined in college undergraduates (N=44). Compared to a control group, both interventions decreased negative religious coping (d=−0.80, p<.01) and images of God as mainly controlling (d=−.73, p<.01). One intervention provided more training in tools for learning from community and tradition-based spiritual exemplars. It produced gains in famous or traditional spiritual exemplars’ perceived influence (d=+.81, p<.05) and availability (d=+.66, p<.10), in self-efficacy for learning from spiritual exemplars (d=+.92, p<.05), and in nonmaterialistic aspirations (d=+0.65, p<.05).

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Acknowledgements

We gratefully acknowledge support for this work from Metanexus Institute (grant: “Learning from Spiritual Examples: Measures & Intervention”), John Templeton Foundation, Academic Council of Learned Societies, Contemplative Mind in Society, Fetzer Institute, Santa Clara University Internal Grants for Research, and the Spirituality and Health Institute, Santa Clara University. We also thank Sara Tsuboi and Anthony Vigliotta for their invaluable assistance.

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Authors and Affiliations

  1. School of Public Health, 140 Warren Hall MC#7360, University of California, Berkeley, CA, 94720-7360, USA

    Doug Oman

  2. Public Health Institute, 555 12th Street, Oakland, CA, 94607, USA

    Doug Oman

  3. Department of Counseling Psychology, Santa Clara University, Santa Clara, CA, USA

    Shauna L. Shapiro

  4. School of Education and Departments of Psychology and Psychiatry, Stanford University, Stanford, CA, USA

    Carl E. Thoresen

  5. Spirituality and Health Institute, Santa Clara University, Santa Clara, CA, USA

    Doug Oman, Shauna L. Shapiro, Carl E. Thoresen, Tim Flinders & Thomas G. Plante

  6. Graduate Theological Union, Pacific School of Religion, Berkeley, CA, USA

    Joseph D. Driskill

  7. Department of Psychology, Santa Clara University, Santa Clara, CA, USA

    Thomas G. Plante

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  1. Doug Oman
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Correspondence to Doug Oman.

Appendix

Appendix

Effects of interventions on the 13 outcome variables were analyzed in 13 separate hierarchical linear regression models (Raudenbush & Bryk, 2002). Hierarchical linear models (HLMs) are increasingly a tool of choice for analyzing longitudinal data, and are sometimes known, especially among physical scientists, as linear mixed models (Singer, 1998). Compared to more conventional methods such as ANOVA, HLM allows improved handling of unbalanced designs and missing data, and more flexible analyses of data gathered at multiple timepoints. In HLM terminology (Raudenbush & Bryk, 2002), our regressions used the following model:

$$ Y_{k(i),t} = c_0 + {{\beta}^{\rm (PM)}I^{\rm (PM)}}_{k,t} + {\beta^{\rm (MBSR)}I^{\rm (MBSR)}}_{k,t} + R_{k(i)} + G_k + T_t + e_{k(i),t} $$

In this formula, Y k(i),t represents the outcome for the ith individual within the kth treatment condition (k=1, 2 or 3) at exam t (t=1, 2 or 3). The treatment effect for PM (in this “time-constant” treatment effect model) is represented by β (PM), which is the coefficient of I (PM) k,t , a “Level 1” predictor that is 1 for the PM group at Exams 2 and 3, and 0 otherwise. Thus I (PM) k,t represents whether an individual at time t has received the PM intervention, but the magnitude of benefit (β (PM)) does not vary between timepoints. Similarly, β (MBSR) represents the treatment effect for MBSR, and I (MBSR) k,t is the corresponding indicator. The other terms in the model represent adjustments and an error term. Adjustment for preexisting individual differences in outcome level is included as a “Level 2” random effect, represented by R k(i). Adjustment for group assignment (e.g., baseline group differences, despite their lack of statistical significance) is included as a Level 2 fixed effect, represented by G k . Adjustment for temporal trends that affect all participants equally is included as a Level 1 fixed effect, represented by T t . Residual error, the discrepancy between the observed and expected outcome of individual k(i) at exam t, is represented by the Level 2 random effect ek(i),t, assumed to be independent and normally distributed with mean of zero and a variance of σ2. The global intercept is represented by c 0.

To explore whether the treatment effects might change or decay over time, initial regression models permitted each treatment effect to vary between Exams 2 and 3 (“time-varying” treatment effect model). These time-varying models replaced β (PM) I (PM) k,t in the above formula with β(PM) 2 I (PM,2) k +β (PM) 3 I (PM,3) k where β (PM) t is treatment effect at Exam t, and each I (PM,t) k (for t=2 or 3) is a Level 2 predictor variable equal to 1 at Exam t for PM group participants, and zero otherwise. Similarly, β(MBSR) I (MBSR) k,t was replaced with β (MBSR) 2 I (MBSR,2) k + β (MBSR) 3 I (MBSR,3) k .

We also conducted combined analyses that were based on the assumption of equal effects for the two interventions. These models replaced terms specific to PM (β (PM) I (PM) k,t ) and MBSR (β (MBSR) I (MBSR) k,t ) with generic intervention terms (β (Tx) I (Tx) k,t ).

All regression analyses were implemented using SAS Proc Mixed (Singer, 1998).

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Oman, D., Shapiro, S.L., Thoresen, C.E. et al. Learning from Spiritual Models and Meditation: A Randomized Evaluation of a College Course. Pastoral Psychol 55, 473–493 (2007). https://doi.org/10.1007/s11089-006-0062-x

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