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Validating a multidimensional measure of wellbeing in Greece: Translation, factor structure, and measurement invariance of the PERMA Profiler

Abstract

The aim of the present study was to create the Greek version of the PERMA Profiler questionnaire, which measures the five pillars of wellbeing: positive emotions, engagement, positive relationships, meaning in life, and accomplishment based on Seligman’s (2011) theory, and to examine its factor structure, measurement invariance, reliability, and convergent and discriminant validity using data from a lifespan sample of 2539 participants. We have tested two models of wellbeing through confirmatory factor analysis, but the first-order five-factor structure of the wellbeing was finally supported. The results also demonstrated acceptable internal consistency and test-retest reliability for the overall wellbeing items and for almost all wellbeing components. The Greek version of PERMA Profiler demonstrated good convergent validity with several wellbeing indices and discriminant validity with psychological symptoms and experiencing of negative emotions. Limitations, recommendations for future studies and the significance of using a multidimensional measure of wellbeing are discussed.

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Acknowledgements

This research was supported by a doctoral scholarship to the first author by Alexander S. Onassis Public Benefit Foundation.

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This study was funded by Alexander S. Onassis Public Benefit Foundation (10.200€).

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Correspondence to Christos Pezirkianidis.

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Christos Pezirkianidis has received doctoral scholarship from Alexander S. Onassis Public Benefit Foundation. The other three authors declare that they have no conflict of interest.

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Pezirkianidis, C., Stalikas, A., Lakioti, A. et al. Validating a multidimensional measure of wellbeing in Greece: Translation, factor structure, and measurement invariance of the PERMA Profiler. Curr Psychol 40, 3030–3047 (2021). https://doi.org/10.1007/s12144-019-00236-7

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Keywords

  • Wellbeing
  • PERMA
  • Translation
  • Validation
  • Invariance
  • Factor analysis