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Analysis of Psychological Well-Being from a Compositional Data Analysis Perspective: A New Approach
María Cortés-Rodríguez1,2,3, Purificación Galindo-Villardón1,4,5, Mercedes Sánchez-Barba1,2
1Department of Statistics, University of Salamanca, 37008 Salamanca, Spain.
Behavioral Sciences (Basel, Switzerland)
|November 24, 2023
Summary
This study introduces compositional data analysis to measure psychological well-being proportions. New indicators reveal the balance between well-being dimensions for a richer interpretation.
Area of Science:
- Psychology
- Data Analysis
Background:
- Psychological well-being is a key area of study.
- Carol Ryff's Scale of Psychological Well-Being (SPWB) measures six dimensions.
- Traditional interpretation relies on raw scores per dimension.
Purpose of the Study:
- To enhance psychological well-being (PWB) score interpretation.
- To incorporate Compositional Data Analysis (CDA) for PWB assessment.
- To analyze inter-dimensional balance and interactions within PWB.
Main Methods:
- Applied Compositional Data Analysis to SPWB data.
- Introduced position ratios (PR1, PR2) and a General Indicator of Subjective Psychological Well-Being (GISPW).
- Utilized data from 628 university students.
Main Results:
- The GISPW, PR1, and PR2 provide compositional insights into PWB.
- These indicators offer relevant information on the balance across PWB dimensions.
- The new approach enriches the interpretation of SPWB results.
Conclusions:
- Compositional Data Analysis offers a novel perspective on psychological well-being.
- The GISPW and position ratios enhance understanding of PWB dimensional balance.
- This method provides a more nuanced interpretation of PWB assessment.
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