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Mindfulness

, Volume 8, Issue 4, pp 1036–1046 | Cite as

Measuring Mindfulness: Applying Generalizability Theory to Distinguish between State and Trait

  • Oleg N. MedvedevEmail author
  • Christian U. Krägeloh
  • Ajit Narayanan
  • Richard J. Siegert
ORIGINAL PAPER

Abstract

Mindfulness can be conceptualized as either a state or a trait, but currently, there is no reliable psychometric method to distinguish clearly between the two in psychological measures. Notwithstanding the clinical effectiveness of mindfulness, any specific element of mindfulness treatment can only be evaluated by comparing state and trait changes using techniques that allow such changes to be measured. Generalizability Theory (GT) is a suitable method to differentiate between state and trait variance components, and its application is illustrated here with an empirical example using the Toronto Mindfulness Scale (TMS). Person × occasion interaction is a marker of individual state changes and should explain the largest amount of variance in a valid state measure. To assess state variability, data were collected on three separate occasions: (i) after a holiday, (ii) immediately after a mindfulness exercise, and (iii) before a stressful event (i.e., exam). Generalizability analysis was applied to examine sources of true and error variances. The TMS captured a larger amount of variance attributed to a state and only a small amount associated with trait mindfulness, which is consistent with the purpose of the measure. This study has demonstrated that Generalizability Theory can be usefully applied to distinguish between state and trait components in a measure, and it is recommended as an appropriate psychometric method to validate state and trait measurement tools. These findings have far-reaching implications to improve the accuracy of the distinction between state and trait in mindfulness measurement and other areas of psychological assessment.

Keywords

State mindfulness Toronto mindfulness scale Measurement Generalizability theory Psychometrics Validation 

Notes

Acknowledgements

This study is a part of doctoral work of the first author funded by the Vice-Chancellor’s Scholarship of the Auckland University of Technology.

Compliance with Ethical Standards

The study was conducted in compliance with the guidelines of the Auckland University of Technology Ethics Committee.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Oleg N. Medvedev
    • 1
    Email author
  • Christian U. Krägeloh
    • 1
  • Ajit Narayanan
    • 1
  • Richard J. Siegert
    • 1
  1. 1.Auckland University of TechnologyAucklandNew Zealand

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