Are People Mindful in Different Ways? Disentangling the Quantity and Quality of Mindfulness in Latent Profiles and Exploring their Links to Mental Health and Life Effectiveness

European Journal of Personality

Volumn 31, Issue 4, 2017, Pages 347-365

  Sahdra, Baljinder K ^a   Ciarrochi, Joseph ^a   Parker, Philip D ^a   Basarkod, Geetanjali ^a   Bradshaw, Emma L ^a   Baer, Ruth ^b  

^a AUSTRALIAN CATHOLIC UNIVERSITY   (Australia)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

latent profile analysis; mindfulness; mixture models; nonattachment; person centred analysis

Indexed keywords

AMERICAN; EXPLORATORY RESEARCH; FEMALE; HUMAN; LIFE SATISFACTION; MAJOR CLINICAL STUDY; MALE; MENTAL HEALTH; MINDFULNESS; STATISTICAL MODEL;

EID: 85020865898     PISSN: 08902070     EISSN: 10990984     Source Type: Journal    
DOI: 10.1002/per.2108     Document Type: Article

References (74)

1. 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, 27–45. https://doi.org/10.1177/1073191105283504.
2. Baer, R. A., Smith, G. T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., & …Williams, J. M. G. (2008). Construct validity of the five facet mindfulness questionnaire in meditating and nonmeditating samples. Assessment, 15, 329–342.
3. Baptiste, A. (2016). ridExtra: Miscellaneous functions for “grid” graphics. (Version 2.2.1). Retrieved from https://CRAN.R-project.org/package=gridExtra
4. Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338–363.
5. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.
6. Bravo, A. J., Boothe, L. G., & Pearson, M. R. (2016). Getting personal with mindfulness: A latent profile analysis of mindfulness and psychological outcomes. Mindfulness, 7, 420–432. https://doi.org/10.1007/s12671-015-0459-7.
7. 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, 822–848.
8. Ciarrochi, J., Atkins, P. W., Hayes, L. L., Sahdra, B. K., & Parker, P. (2016). Contextual positive psychology: Policy recommendations for implementing positive psychology into schools. Frontiers in Psychology, 7.
9. Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Submitted for publication. Retrieved from http://www.statmodel.com/download/relatinglca.pd
10. Cudek, R., & Henly, S. (2003). A realistic perspective on pattern representation in growth data: Comment on Bauer and Curran (2003). Psychological Methods, 8, 378–383.
11. Desrosiers, A., Vine, V., Curtiss, J., & Klemanski, D. H. (2014). Observing nonreactively: A conditional process model linking mindfulness facets, cognitive emotion regulation strategies, and depression and anxiety symptoms. Journal of Affective Disorders, 165, 31–37.
12. Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75. https://doi.org/10.1207/s15327752jpa4901_13.
13. Dobkin, P. L., Irving, J. A., & Amar, S. (2012). For whom may participation in a mindfulness-based stress reduction program be contraindicated? Mindfulness, 3, 44–50.
14. Elphinstone, B., Sahdra, B. K., & Ciarrochi, J. (2015). Living well by letting go: Reliability and validity of a brief measure of nonattachment. Unpublished manuscript.
15. Enders, C. K. (2010). Applied missing data analysis. New York: Guilford Press.
16. Farias, M., & Wikholm, C. (2015). The Buddha pill: Can meditation change you. London: Watkins.
17. Fox, J., & Weisberg, S. (2010). An R companio+n to applied regression (2nd ed.). Oaks, CA: Sage.
18. Fresco, D. M., Moore, M. T., van Dulmen, M. H., Segal, Z. V., Ma, S. H., Teasdale, J. D., & Williams, J. M. G. (2007). Initial psychometric properties of the experiences questionnaire: Validation of a self-report measure of decentering. Behavior Therapy, 38, 234–246.
19. Gelman, A., & Su, Y.-S. (2015). Arm: Data analysis using regression and multilevel/hierarchical models (version 1.8-6). Retrieved from https://CRAN.R-project.org/package=arm
20. Goldberg, D. (1978). Manual of the general health questionnaire. Windsor: National Foundation of Education Research.
21. Goldberg, D., McDowell, I., & Newell, C. (1996). Measuring health: A guide to rating scales and questionnaires. New York: Oxford University Press.
22. Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned missing data designs in psychological research. Psychological Methods, 11, 323–343. https://doi.org/10.1037/1082-989X.11.4.323.
23. Grossmann, I., Sahdra, B. K., & Ciarrochi, J. (2016). A heart and a mind: Self-distancing facilitates the association between heart rate variability, and wise reasoning. Frontiers in Behavioral Neuroscience, 10.
24. Hallquist, M., & Wiley, J. (2013). MplusAutomation: Automating Mplus model estimation and interpretation (R package version 0.6-3).
25. Harris, W. D. III, & Moore, D. J. (1990). Affect intensity as an individual difference variable in consumer response to advertising appeals. Advances in Consumer Research, 17, 792–797.
26. Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36–53.
27. Honaker, J., King, G., & Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45, 1–47.
28. Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
29. Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2014). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44, 486–507.
30. Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology, 73, 539.
31. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
32. Lesnoff, M., & Lancelot, R. (2012). Aod: Analysis of overdispersed data (version 1.3). Retrieved from http://cran.r-project.org/package=aod
33. Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
34. Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture model. Biometrika, 88, 767–788.
35. Lubke, G., & Muthen, B. (2007). Performance of factor mixture models as a function of model size, criterion measure effects, and class-specific parameters. Structural Equation Modeling, 14, 26–47.
36. Lumley, T. (2014). mitools: Tools for multiple imputation of missing data (version 2.3). Retrieved from http://CRAN.R-project.org/package=mitools
37. Lutz, A., Slagter, H. A., Dunne, J. D., & Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends in Cognitive Sciences, 12, 163–169. https://doi.org/10.1016/j.tics.2008.01.005.
38. MacLean, K. A., Ferrer, E., Aichele, S. R., Bridwell, D. A., Zanesco, A. P., Jacobs, T. L., … Shaver, P. R. (2010). Intensive meditation training improves perceptual discrimination and sustained attention. Psychological Science, 21, 829–839.
39. Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16, 191–225.
40. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.
41. Morin, A. J., Arens, A. K., & Marsh, H. W. (2016a). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling: A Multidisciplinary Journal, 23, 116–139.
42. Morin, A. J., Boudrias, J.-S., Marsh, H. W., Madore, I., & Desrumaux, P. (2016b). Further reflections on disentangling shape and level effects in person-centered analyses: An illustration exploring the dimensionality of psychological health. Structural Equation Modeling: A Multidisciplinary Journal, 23, 438–454.
43. Morin, A. J., & Marsh, H. W. (2015). Disentangling shape from level effects in person-centered analyses: An illustration based on university teachers' multidimensional profiles of effectiveness. Structural Equation Modeling: A Multidisciplinary Journal, 22, 39–59.
44. Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8, 369–377.
45. Muthén, B., & Muthén, L. (2015). Mplus: A general latent variable modeling program (version 7.4). Los Angeles, CA: Muthén & Muthén.
46. Neill, J. T., Marsh, H. W., & Richards, G. E. (2003). The life effectiveness questionnaire: Development and psychometrics. Retrieved from http://www.wilderdom.com/abstracts/NeillMarchRichards2003LEQDevelopmentPsychometrics.htm
47. Nylund, K., Asparouhov, T., & Muthen, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.
48. Oehlert, G. W. (1992). A note on the delta method. The American Statistician, 46, 27–29.
49. Pearson, M. R., Lawless, A. K., Brown, D. B., & Bravo, A. J. (2015). Mindfulness and emotional outcomes: Identifying subgroups of college students using latent profile analysis. Personality and Individual Differences, 76, 33–38. https://doi.org/10.1016/j.paid.2014.11.009.
50. Peugh, J., & Fan, X. (2013). Modeling unobserved heterogeneity using latent profile analysis: A Monte Carlo simulation. Structural Equation Modeling, 20, 616–639.
51. Purdie, N., Neill, J. T., & Richards, G. E. (2002). Australian identity and the effect of an outdoor education program. Australian Journal of Psychology, 54, 32–39. https://doi.org/10.1080/00049530210001706493.
52. R_Core_Team (2015). R: A language and environment for statistical computing (version 3.2.0). Vienna, Austria: R Foundation for Statistical Computing Retrieved from http://www.R-project.org/.
53. Revelle, W. (2015). psych: Procedures for personality and psychological research (version 1.5.4). Retrieved from http://CRAN.R-project.org/package=psych
54. Robitzsch, A., Grund, S., & Henke, T. (2014). Miceadds: Some additional multiple imputation functions, especially for mice. Retrieved from http://CRAN. R-project. org/package= miceadds
55. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys (Vol. 81). New York: Wiley.
56. Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57, 1069–1081.
57. Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69, 719–727.
58. Sahdra, B. K., Ciarrochi, J., & Parker, P. (2016a). Nonattachment and mindfulness: Related but distinct constructs. Psychological Assessment, 28, 819–829. https://doi.org/10.1037/pas0000264.
59. Sahdra, B. K., Ciarrochi, J., Parker, P., Marshall, S., & Heaven, P. (2015). Empathy and nonattachment independently predict peer nominations of prosocial behavior of adolescents. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00263.
60. Sahdra, B. K., Ciarrochi, J., Parker, P., & Scrucca, L. (2016b). Using genetic algorithms in a large nationally representative American sample to abbreviate the multidimensional experiential avoidance questionnaire. Frontiers in Psychology, 7.
61. Sahdra, B. K., MacLean, K. A., Ferrer, E., Shaver, P. R., Rosenberg, E. L., Jacobs, T. L., … Bridwell, D. A. (2011). Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning. Emotion, 11, 299–312.
62. Sahdra, B. K., & Shaver, P. R. (2013). Comparing attachment theory and Buddhist psychology. International Journal for the Psychology of Religion, 23, 282–293. https://doi.org/10.1080/10508619.2013.795821.
63. Sahdra, B. K., Shaver, P. R., & Brown, K. W. (2010). A scale to measure nonattachment: A Buddhist complement to western research on attachment and adaptive functioning. Journal of Personality Assessment, 92, 116–127. https://doi.org/10.1080/00223890903425960.
64. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman Hall.
65. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3–15.
66. Steinley, D., & McDonald, R. (2007). Examining factor scores distributions to determine the nature of latent spaces. Multivariate Behavioral Research, 42, 133–156.
67. Tein, J., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling, 20, 640–657.
68. Thera, N. (1994). The foundations of mindfulness: Satipatthana Sutta. Retrieved from http://www.accesstoinsight.org/lib/authors/nyanasatta/wheel019.html#further
69. Tofighi, D., & Enders, D. (2008). Identifying the correct number of classes in growth mixture models. In G. Hancock, & K. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 640–657). Charlotte, NC: Information Age.
70. Tran, U. S., Gluck, T. M., & Nader, I. W. (2013). Investigating the five facet mindfulness questionnaire (FFMQ): Construction of a short form and evidence of a two-factor higher order structure of mindfulness. Journal of Clinical Psychology, 69, 951–965. https://doi.org/10.1002/jclp.21996.
71. Venables, W. N., & Ripley, B. D. (2013). Modern applied statistics with S-PLUS. New York: Springer.
72. Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21, 1–20.
73. Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag.
74. Yang, C. (2006). Evaluating latent class analyses in qualitative phenotype identification. Computational Statistics and Data Analysis, 50, 1090–1104.