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Salud mental

versão impressa ISSN 0185-3325

Resumo

DANIEL-GONZALEZ, Leopoldo; MORAL DE LA RUBIA, José; VALLE DE LA O, Adrián  e  GARCIA-CADENA, Cirilo H.. Structure analysis of subjective well-being. Salud Ment [online]. 2020, vol.43, n.3, pp.119-127.  Epub 28-Ago-2020. ISSN 0185-3325.  https://doi.org/10.17711/sm.0185-3325.2020.017.

Introduction

There is controversy regarding the structure of subjective well-being (SWB) and the possibility of calculating a SWB total score.

Objective

To test and compare five models proposed for the description of SWB.

Method

The study was implemented with a cross-sectional, ex-post-facto design using an incidental sampling method. The Positive and Negative Experience Scale and the Satisfaction with Life Scale were applied to a sample composed of 600 students of health sciences from two universities of Nuevo Leon, Mexico. Data were analyzed through a structural equation modeling, using Maximum Likelihood and Corrected-Bias Percentile methods.

Results

The bifactor model comprising three specific factors, vis-à-vis the model composed of three correlated factors, had the best data fit (Δχ2/Δdf = 8.166 > 5, ΔNFI = .018, ΔNNFI = .015, and ΔCFI = .016 > .01), and all its fit indices were close; however, the specific factor related to positive affect had a poor contribution. Nevertheless, the model composed of three correlated factors had the greatest parsimony (PR = .853, PNFI = .804, PNNFI = .813, PCFI = .819, and PGFI = .706) and its three factors showed convergent validity, discriminant validity, and internal consistency reliability.

Discussion and conclusion

The two models with the best properties justify the use of a composite score of SWB based on the scores of positive affect, negative affect, and satisfaction with life, as well as scores for these three specific domains of content. From a psychometric perspective, the model composed of three correlated factors yielded the best result.

Palavras-chave : Subjective well-being; personal satisfaction; affect; psychometrics; latent class analysis; students.

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