A comparison of univariate and meta-analytic structural equation modeling approaches to reliability generalization applied to the Maslach Burnout Inventory
Metadatos
Afficher la notice complèteAuteur
Aguayo Estremera, Raimundo; Cañadas De La Fuente, Gustavo Raúl; Ariza-Castilla, Tania; Ortega-Campos, Elena; Gómez Urquiza, Jose Luis; Romero Béjar, José Luis; De la Fuente-Solana, Emilia InmaculadaEditorial
Frontiers
Materia
MBI MASEM Menta-analysis Reliability generalization Burnout
Date
2024-05-03Referencia bibliográfica
Aguayo-Estremera R, Cañadas-De la Fuente GR, Ariza-Castilla T, Ortega-Campos E, Gómez-Urquiza JL, Romero-Béjar JL and De la Fuente-Solana EI (2024) A comparison of univariate and meta-analytic structural equation modeling approaches to reliability generalization applied to the Maslach Burnout Inventory. Front. Psychol. 15:1383619. doi: 10.3389/fpsyg.2024.1383619
Patrocinador
Consejería de Universidad, Investigación e Innovación C-SEJ-043-UGR23; ERDF Andalusia Program 2021–2027Résumé
Introduction: Reliability is a property of tests scores that varies from sample
to sample. One way of generalizing reliability of a test is to perform a metaanalysis
on some reliability estimator. In 2011, a reliability generalization metaanalysis
on the Maslach Burnout Inventory (MBI) was conducted, concluding
that average alpha values for the MBI dimensions ranged from 0.71 to 0.88.
In the present study, we aimed to update the average reliability values of the
MBI by conducting a literature search from 2010 until now and comparing to
statistical procedures of meta-analysis: the Univariate approach, that were used
in the previous study, and a novel meta-analytic approach based on structural
equation modeling.
Method: An estimation of average reliability was done based on 69 independent
primary reliability coefficients for the Univariate approach. The average reliability
was based on 9 independent studies in the case of the Meta-analytic Structural
Equation Modeling (MASEM) approach. Given that MASEM has the additional
capability of testing the internal structure of a test, we also fitted several models.
Results: The data was well-suited to the bifactor model, revealing the dominance
of the general factor over the domain-specific ones. Acceptable overall alpha
and omega coefficients were achieved for the two of the MBI dimensions, having
depersonalization reliability estimates below recommendations.
Discussion: In general, the MBI can be viewed as a highly interconnected threefactor
scale, being its appropriate for research purposes.