Psychological well-beinganalysis and interpretation applying Compositional Data Analysis methods

  1. M. Cortés 1
  2. M. Sánchez 1
  3. P. Galindo 1
  4. E. Jarauta Bragulat 2
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 Universitat Politècnica de Catalunya
    info

    Universitat Politècnica de Catalunya

    Barcelona, España

    ROR https://ror.org/03mb6wj31

Buch:
Proceedings of the 8th International Workshop on Compositional Data Analysis (CoDaWork2019): Terrassa, 3-8 June, 2019
  1. J.J. Egozcue
  2. J. Graffelman
  3. M.I. Ortego

Verlag: Universidad Politécnica de Cataluña / Universitat Politècnica de Catalunya

ISBN: 978-84-947240-2-2

Datum der Publikation: 2019

Seiten: 19-24

Kongress: International Workshop on Compositional Data Analysis (8. 2019. Terrassa)

Art: Konferenz-Beitrag

Zusammenfassung

One of the applications of Psychology that makes use of Statisticas is that which refers to the analysis of psychological well-being tests. However, this analysis was not systematic until Carol Riff proposed in 1989 a test to describe, analyze and interpret the psychological well-being of people. The model is based on six descriptive fields or dimensions of psychological well-being: self-acceptance, positive relations, autonomy, environmental mastery, purpose in life and personal growth. To measure these theoretical dimensions, an instrument known as "Scale of Physiological Well Being (SPWB)" was developed, with 120 items forming the original scale. Nowadays, there exist several different versions of the instruent in different languages and with different numbers of items. To interpret the results of the different dimensions, the scores of each of them must be added up and compared with the maximum and minimum possible score, since there are no existing ready-reckoners. Application of the previous methodology generates certain problems when interpreting the results individually, since there is no normative or reference group with which to compare the results obtained. Applying methods of Compositional Data Analysis such as the centered log-ratio transformation (CLR), relative position ratios (RPR) of each individual can be obtained in relation to each of the indicators used. Positive values of the position ratio indicate that the subject is above the (geometric) mean of the normative group and negative values of the position ratio indicate that the subject is below the mean of the normative group. The average of the relative position ratios allows to obtain a global indicator or profile of subjective wellbeing for each individual, nor only in relation to himself but also in the context of the group in which he has been analyzed.