Measuring Burnout in Social Work

Abstract. Several studies challenge the three-dimensional structure of Maslach Burnout Inventory – Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. While have merit, if data sampling violates assumptions Stochastic Measurement Theory (SMT) model requires modification prior to application. The present study compared five MBI-HSS factor using both Confirmatory Factor Analysis (CFA) and Exploratory Structural Equation Modeling (ESEM). Data from a cross-sectional survey United Kingdom (UK) social workers were examined ( N = 1257), with validation analyses conducted in an independent sample 162). Bifactor models, re-specified account for SMT, provided good fit. However, improved fit was observed bifactor-ESEM specification, test (χ 2 1,112.93, df 149, p < .001, CFI .969, RMSEA .072, 90% CI [.068, .076]) 227.89, .978, .057, [.042, .072]) samples. results confirm possesses UK when SMT is considered, that may provide better framework examine MBI-HSS.