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Simply Clustering. Making New Sense In The Five Facets Mindfulness Questionnaire

Author

Listed:
  • Elena Druica

    (University of Bucharest, Romania)

  • Rodica Ianole – Calin

    (University of Bucharest, Romania)

Abstract

A common approach in examining data collected based on different scales is to look at their structure by means of factor analysis. This article provides a way to look not only into the overall mindfulness score as an individual characteristic, but also at how the mindfulness dimensions cluster together to provide potentially consistent individual profiles. The novelty of our contribution is two fold: we reached our goal with the help of cluster analysis, and not by the means of previous methodological approaches. Also, we applied the most popular tools used to measure mindfulness, the face facets mindfulness questionnaire, on a sample of Romanian participants which makes this research the first study on mindfulness conducted on a Romanian sample. We found that, despite the existence of some stable groups that share similar characteristics, the degree of homogeneity across individuals is pretty high. In addition, the levels of mindfulness corresponding to our participants seems to be unrelated with background variables like age, gender, and working place.

Suggested Citation

  • Elena Druica & Rodica Ianole – Calin, 2018. "Simply Clustering. Making New Sense In The Five Facets Mindfulness Questionnaire," Romanian Statistical Review, Romanian Statistical Review, vol. 66(1), pages 61-81, March.
    • Handle: RePEc:rsr:journl:v:66:y:2018:i:1:p:61-81
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    More about this item

    Keywords

    Mixed methods; Health care; Survey research; Regression analysis;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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