Confirmatory Factor Analysis of the Maslach Burnout Inventory
A Bayesian Structural Equation Modeling Approach
Abstract
Abstract. In the Maslach Burnout Inventory (MBI), burnout is conceptualized as a combination of emotional exhaustion, depersonalization, and personal accomplishment. However, the factorial structure for the MBI remains controversial. We examined the factorial structure of the MBI, relying on Bayesian structural equation modeling (BSEM). BSEM allows the investigator to take into account sources of influence such as approximately-zero factor cross-loadings and between-item residual covariances. Data from a convenience sample of 5,575 French teachers were mobilized. One-, two-, three-, and bi-factor models were tested using BSEM. Maximum likelihood (ML) estimation was also implemented to examine the robustness of the BSEM analysis. The BSEM analysis showed that a two-factor model, consisting of a burnout factor combining emotional exhaustion and depersonalization and a separate personal accomplishment factor, best fitted the data. ML estimation showed a substantial enhancement of model fit when information derived from the BSEM analysis was incorporated. The MBI appears to be underlain by a unified factor involving the emotional exhaustion and depersonalization items and a separate factor involving the personal accomplishment items. These findings are compatible with a research practice that involves combining the emotional exhaustion and depersonalization dimensions of burnout and considering personal accomplishment individually.
References
2014). Relationship between burnout and depressive symptoms: A study using the person-centred approach. Burnout Research, 1, 29–37. doi: 10.1016/j.burn.2014.03.003
(2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al.. Journal of Management, 6, 1561–1577. doi: 10.1177/0149206315591075
(2007). Reexamining the factor structure of the 20-item Toronto alexithymia scale: Commentary on Gignac, Palmer, and Stough. Journal of Personality Assessment, 89, 258–264.
(2013). Comparative symptomatology of burnout and depression. Journal of Health Psychology, 18, 782–787. doi: 10.1177/1359105313481079
(2014). Is burnout a depressive disorder? A re-examination with special focus on atypical depression. International Journal of Stress Management, 21, 307–324. doi: 10.1037/a0037906
(2015a). Burnout depression overlap: A review. Clinical Psychology Review, 36, 28–41. doi: 10.1016/j.cpr.2015.01.004
(2015b). Burnout: Absence of binding diagnostic criteria hampers prevalence estimates. International Journal of Nursing Studies, 52, 789–790. doi: 10.1016/j.ijnurstu.2014.12.008
(2003). How to conduct research on burnout: Advantages and disadvantages of a unidimensional approach in burnout research. Occupational and Environmental Medicine, 60(suppl. 1), i16–i20.
(2014). Maslach Burnout Inventory – General Survey: Factorial validity and invariance among Romanian healthcare professionals. Burnout Research, 1, 103–111. doi: 10.1016/j.burn.2014.09.001
(2015). Confirmatory factor analysis for applied research. New York, NY: The Guilford Press.
(2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225.
(2010). Burnout and work engagement: A thorough investigation of the independency of both constructs. Journal of Occupational Health Psychology, 15, 209–222. doi: 10.1037/a0019408
(1994). Validation de la traduction de l’Inventaire d’épuisement professionnel de Maslach et Jackson
([Validation of a French translation of the Maslach Burnout Inventory (MBI)] . Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 26, 210–227. doi: 10.1037/0008-400x.26.2.2102015). Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach. Journal of Occupational Health, 57, 353–358.
(1974). Staff burnout. Journal of Social Issues, 30, 159–165. doi: 10.1111/j.1540-4560.1974.tb00706.x
(2006). Burnout and work engagement: Independent factors or opposite poles? Journal of Vocational Behavior, 68, 165–174.
(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.
(2000). A test of the Maslach Burnout Inventory in three samples of healthcare professionals. Work & Stress, 14, 35–50. doi: 10.1080/026783700417212
(2006). The factorial validity of the Maslach Burnout Inventory – General survey in representative samples of eight different occupational groups. Journal of Career Assessment, 14, 370–384. doi: 10.1177/1069072706286497
(1996). Consistency of the burnout construct across occupations. Anxiety, Stress and Coping: An International Journal, 9, 229–243. doi: 10.1080/10615809608249404
(1976). Burned-out. Human Behavior, 5, 16–22.
(1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2, 99–113. doi: 10.1002/job.4030020205
(1986). Maslach Burnout Inventory (2nd ed.). Palo Alto, CA: Consulting Psychologists Press.
(1996). The Maslach Burnout Inventory (3rd ed.). Palo Alto, CA: Consulting Psychologists Press.
(2008). Early predictors of job burnout and engagement. The Journal of Applied Psychology, 93, 498–512. doi: 10.1037/0021-9010.93.3.498
(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. doi: 10.1002/smi.2481
(2010). Bayesian analysis in Mplus: A brief introduction. Retrieved from http://www.statmodel.com/download/IntroBayesVersion 3.pdf
(2012a). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335. doi: 10.1037/a0026802
(2012b). New Features in Mplus v7 Lecture 3. Retrieved from http://mplus.fss.uu.nl/2012/09/12/the-workshop-new-features-of-mplus-v7/
(2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
(2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696.
(2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16, 19–31.
(1998). The burnout companion to study and practice: A critical analysis. London, UK: Taylor & Francis.
(2009). Burnout: 35 years of research and practice. Career Development International, 14, 204–220.
(2005). The conceptualization and measurement of burnout: Common ground and worlds apart. Work & Stress, 19, 256–262. doi: 10.1080/02678370500385913
(2000). The factorial validity of the Maslach Burnout Inventory‐General Survey (MBI‐GS) across occupational groups and nations. Journal of Occupational and Organizational Psychology, 73, 53–66. doi: 10.1348/096317900166877
(2005). Reflections on the study of burnout. Work & Stress, 19, 263–270. doi: 10.1080/02678370500376649
(1999). Construct validity of the Maslach Burnout Inventory-General Survey: A two-sample examination of its factor structure and correlates. Work & Stress, 13, 223–237. doi: 10.1080/026783799296039
(2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9, 486–492. doi: 10.1080/17405629.2012.686740
(2008). The factor structure of the Beck Depression Inventory–II: An evaluation. Assessment, 15, 177–187.
(1992). An exhaustive examination of the replicable factor structure of the Maslach Burnout Inventory. Educational and Psychological Measurement, 52, 309–323.
(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.
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