Measuring Burnout in Social Work
Factorial Validity of the Maslach Burnout Inventory – Human Services Survey
Abstract
Abstract. Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory – Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. While bifactor models have merit, if data sampling violates assumptions of Stochastic Measurement Theory (SMT) the bifactor model requires modification prior to application. The present study compared five alternative MBI-HSS factor models using both Confirmatory Factor Analysis (CFA) and Exploratory Structural Equation Modeling (ESEM). 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). Bifactor models, re-specified to account for SMT, provided good fit. However, improved fit was observed for a bifactor-ESEM specification, in both test (χ2 = 1,112.93, df = 149, p < .001, CFI = .969, RMSEA = .072, 90% CI [.068, .076]) and validation (χ2 = 227.89, df = 149, p < .001, CFI = .978, RMSEA = .057, 90% CI [.042, .072]) samples. 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.
References
2015). Measuring resilience with the RS-14: A tale of two samples. Journal of Personality Assessment, 97, 291–300. https://doi.org/10.1080/00223891.2014.951445
(2011). Factorial validity and consistency of the Maslach Burnout Inventory among staff working with persons with intellectual disability and dementia. Journal of Intellectual Disability Research, 55, 529–536. https://doi.org/10.1111/j.1365-2788.2011.01413.x
(2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36, 462–494. https://doi.org/10.1177/0049124108314720
(1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ: Erlbaum.
(2001). Re-thinking burnout. Journal of Organizational Behavior, 22, 833–847. https://doi.org/10.1002/job.115
(2017). Bifactor Indices Calculator: A Microsoft Excel-based tool to calculate various indices relevant to bifactor CFA models [Software]. https://doi.org/10.13023/edp.tool.01
(2017). Anomalous results in G-factor models: Explanations and alternatives. Psychological Methods, 22, 541. https://doi.org/10.1037/met0000083
(2010). General social care council annual report and accounts 2009–10. London, UK: The Stationery Office Limited.
. (2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68. https://doi.org/10.1016/j.intell.2016.01.006
(2016). Using bifactor exploratory structural equation modeling to test for a continuum structure of motivation. Journal of Management, 44, 2638–2664. https://doi.org/10.1177/0149206316645653
(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118
(2006). Factor structure of the Maslach Burnout Inventory among Finnish nursing staff. Nursing & Health Sciences, 8, 201–207.
(2009). Factor structure and longitudinal invariance of the Maslach Burnout Inventory. Research on Social Work Practice, 19, 325–339. https://doi.org/10.1177/1049731508318550
(1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology, 81, 123.
(2008). A two-process model of burnout and work engagement: Distinct implications of demands and values. Giornale Italianol di Medicina del Lavoro ed Ergonomia, 30(1, Suppl A), 54–58.
(1996). Maslach Burnout Inventory Manual. Mountain View, CA: CPP and Davies-Black.
(2019). Explaining self-reported Resilience in child-protection social work: The role of organisational factors, demographic information and job characteristics. The British Journal of Social Work, 49, 198–216. https://doi.org/10.1093/bjsw/bcy015
(2014). The bifactor model of the Maslach Burnout Inventory – Human Services Survey (MBI-HSS) – an alternative measurement model of burnout. Stress and Health, 30, 82–88. https://doi.org/10.1002/smi.2481
(2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling, 23, 116–139. https://doi.org/10.1080/10705511.2014.961800
(2018). Mplus statistical modeling software: Release 8.1. Los Angeles, CA: Muthén & Muthén.
(2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696. https://doi.org/10.1080/00273171.2012.715555
(2008). Confirmatory factor analysis of the Maslach Burnout Inventory: A replication with Canadian child welfare workers. Journal of Public Child Welfare, 1, 77–94.
(2014). Relationships among perceived psychological growth, resilience and burnout in physicians. Personality and Individual Differences, 59, 120–123.
(1987). How big is big enough? Sample size and goodness of fit in structural equation models with latent variables. Child Development, 58, 134–146.
(2009). The 14-Item Resilience Scale (RS-14). Wordem, MT: Resilience Center.
(1992). An exhaustive examination of the replicable factor structure of the Maslach Burnout Inventory. Educational and Psychological Measurement, 52, 309–323. https://doi.org/10.1177/0013164492052002006
(2012). Concurrent validity of single-item measures of emotional exhaustion and depersonalization in burnout assessment. Journal of General Internal Medicine, 27(11), 1445–1452. https://doi.org/10.1007/s11606-012-2015-7
(2008). Factor structure of scores from the Maslach Burnout Inventory a review and meta-analysis of 45 exploratory and confirmatory factor-analytic studies. Educational and Psychological Measurement, 68, 797–823. https://doi.org/10.1177/0013164408315268
(1995). Confirmatory factor analysis of the Maslach Burnout Inventory. Social Work Research, 19, 184–192. https://doi.org/10.1093/swr/19.3.184
(1999). On the relationship between the higher-order factor model and the hierarchical factor model. Psychometrika, 64, 113–128. https://doi.org/10.1007/BF02294531
(