Validity Evidence for the Internal Structure of the Maslach Burnout Inventory-Student Survey: A Comparison between Classical CFA Model and the ESEM and the Bifactor Models
Autor:
Aguayo-Estremera, Raimundo
; Cañadas, Gustavo R.
; Ortega-Campos, Elena
; Ariza, Tania
; De La Fuente-Solana, Emilia I.
Fecha:
2023Palabra clave:
Revista / editorial:
MathematicsCitación:
Aguayo-Estremera, R.; Cañadas, G.R.; Ortega-Campos, E.; Ariza, T.; De la Fuente-Solana, E.I. Validity Evidence for the Internal Structure of the Maslach Burnout Inventory-Student Survey: A Comparison between Classical CFA Model and the ESEM and the Bifactor Models. Mathematics 2023, 11, 1515. https://doi.org/10.3390/math11061515Tipo de Ítem:
Articulo Revista IndexadaDirección web:
https://www.mdpi.com/2227-7390/11/6/1515Resumen:
Academic burnout is a psychological problem characterized by three dimensions: emotional exhaustion, depersonalization, and personal accomplishment. This paper studies the internal structure of the MBI-SS, the most widely used instrument to assess burnout in students. The bifactor model and the ESEM approach have been proposed as alternatives, capable of overcoming the classical techniques of CFA to address this issue. Our study considers the internal structure of the MBI-SS by testing the models most frequently referenced in the literature, along with the bifactor model and the ESEM. After determining which model best fits the data, we calculate the most appropriate reliability index. In addition, we examined the validity evidence using other variables, namely the concurrent relationships with depression, anxiety, neuroticism, and conscientiousness, and the discriminant relationships with the dimensions of engagement, extraversion, and agreeableness. The results obtained indicate that the internal structure of the MBI-SS is well reflected by the three-factor congeneric oblique model, reaching good values of reliability and convergent and discriminant validity. Therefore, when the scale is used in applied contexts, we recommend considering the total scores obtained for each of the dimensions. Finally, we recommend using the omega coefficient and not the alpha coefficient as an estimator of reliability.
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