![]() ![]() While bifactor models have merit, if data sampling violates assumptions of Stochastic Measurement Theory (SMT) the bifactor model requires modification prior to application. However, improved fit was observed for a bifactor-ESEM specification, in both test (χĪB - Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. Bifactor models, re-specified to account for SMT, provided good fit. Data from a cross-sectional survey of United Kingdom (UK) social workers were examined (N = 1257), with validation analyses conducted in an independent sample (N = 162). The present study compared five alternative MBI-HSS factor models using both Confirmatory Factor Analysis (CFA) and Exploratory Structural Equation Modeling (ESEM). N2 - Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. All rights reserved.Ĭopyright 2021 Elsevier B.V., All rights reserved. ![]() T1 - Measuring burnout in social work: factorial validity of the Maslach Burnout Inventory-Human Services Survey The results confirm the MBI-HSS possesses a bifactor structure in UK social workers when SMT is considered, and that bifactor-ESEM may provide a better framework to examine MBI-HSS. The results confirm the MBI-HSS possesses a bifactor structure in UK social workers when SMT is considered, and that bifactor-ESEM may provide a better framework to examine MBI-HSS.Ībstract = "Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. ![]() Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. ![]()
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