Abstract
Keyes’s theory-driven model of mental health uses a diagnosis which leads to three levels of positive feelings and positive functioning: languishing, moderately mentally healthy and flourishing (Keyes 2002). Although these three-level categories may be justified for a unidimensional factor structure, or a factorial structure using summated scales, the recent works supporting a multidimensional structure of the Mental Health Continuum Short Form (MHC-SF) suggest the adoption of shape-based rather than level-based categories of mental health (Morin et al. 2017). This research aims at testing the empirical validity of Keyes’s taxonomy and its relationship to psychosocial functioning. We first adopted a variable approach by selecting the optimal factor structure for the MHC-SF: the bifactor exploratory structural equation modeling (Bi-ESEM). Following with a person-centered approach, we used a latent profile analysis on the factor scores of the Bi-ESEM. Psychosocial risks indicators were used as outcomes for testing the criterion validity of the models on a sample of 1065 French workers. Results show that the mental health subgroups are more intricate than the three-levels categories originally theorized by Keyes (2002, 2005). While the general Bi-ESEM factor warrants three levels akin to those of Keyes, accounting for the specific factors reveals two profiles of languishing: the hedonic languishers characterized by a low level of emotional wellbeing, and the eudaimonic languishers characterized by a low level of psychological wellbeing. Both variable and person-centered approach confirm Keyes’s initial statements (Keyes 2007) that those who are languishing exhibit the highest levels of psychosocial risks while those flourishing display the lowest. A rule-based diagnosis derived from a decision tree method is suggested as an alternative to a level-based taxonomy.
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Notes
Diagnostic and Statistical Manual of Mental Disorders
http://www.who.int/mediacentre/factsheets/fs220/en/ (retrieved October 05, 2018)
The full scale is available and permission for use is also granted by Keyes (2009) at: https://www.aacu.org/sites/default/files/MHC-SFEnglish.pdf [Online, retrieved October 5, 2018]
References
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition: DSM-IV-TR®. https://doi.org/10.1176/appi.books.9780890423349.
Aronsson, G., & Gustafsson, K. (2005). Sickness presenteeism: Prevalence, attendance-pressure factors, and an outline of a model for research. Journal of Occupational and Environmental Medicine, 47(9), 958–966. https://doi.org/10.1097/01.jom.0000177219.75677.17.
Aronsson, G., Gustafsson, K., & Dallner, M. (2000). Sick but yet at work. An empirical study of sickness presenteeism. Journal of Epidemiology and Community Health, 54(7), 502–509. https://doi.org/10.1136/jech.54.7.502.
Asfaw, A. G., Chang, C. C., & Ray, T. K. (2014). Workplace mistreatment and sickness absenteeism from work: Results from the 2010 National Health Interview survey. American Journal of Industrial Medicine, 57(2), 202–213. https://doi.org/10.1002/ajim.22273.
Asparouhov, T., & Muthén, B. (2009). Exploratory Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal,16(3), 397–438. https://doi.org/10.1080/10705510903008204.
Asparouhov, T., Muthén, B., & Morin, A. J. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. SAGE Publications. https://doi.org/10.1177/0149206315591075.
Avey, J. B., Luthans, F., & Jensen, S. M. (2009). Psychological capital: A positive resource for combating employee stress and turnover. Human Resource Management, 48(5), 677–693. https://doi.org/10.1002/hrm.20294.
Baeriswyl, S., Krause, A., Elfering, A., & Berset, M. (2017). How workload and coworker support relate to emotional exhaustion: The mediating role of sickness presenteeism. International Journal of Stress Management, 24(S1), 52. https://doi.org/10.1037/str0000018.
Bornstein, M. H., Davidson, L., Keyes, C. L., & Moore, K. A. (2012). Well-being: Positive development across the life course. Psychology Press. https://www.crcpress.com/
Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and decision trees. Wadsworth, Belmont, 378. https://doi.org/10.1201/9781315139470.
Brown, H. E., Gilson, N. D., Burton, N. W., & Brown, W. J. (2012). Does physical activity impact on Presenteeism and other indicators of workplace well-being? Sports Medicine, 41(3), 249–262. https://doi.org/10.2165/11539180-000000000-00000.
Brunetto, Y., Teo, S. T. T., Shacklock, K., & Farr-Wharton, R. (2012). Emotional intelligence, job satisfaction, well-being and engagement: Explaining organisational commitment and turnover intentions in policing. Human Resource Management Journal, 22(4), 428–441. https://doi.org/10.1111/j.1748-8583.2012.00198.x.
Cameron, K. S., & Spreitzer, G. M. (2011). The Oxford handbook of positive organizational scholarship. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199734610.001.0001.
Chen, F. F., West, S., & Sousa, K. (2006). A comparison of Bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41(2), 189–225. https://doi.org/10.1207/s15327906mbr4102_5.
Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J.-P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80(1), 219–251. https://doi.org/10.1111/j.1467-6494.2011.00739.x.
Cousins, R., Cousins, R., MacKay, C. J., Clarke, S. D., Kelly, C., Kelly, P. J., & McCaig, R. H. (2004). ‘Management standards’ work-related stress in the UK: Practical development. Work and Stress, 18(2), 113–136. https://doi.org/10.1080/02678370410001734322.
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957.
De Bruin, G. P., & Du Plessis, G. A. (2015). Bifactor analysis of the mental health continuum-short form (MHC-SF). Psychological Reports, 116(2), 438–446. https://doi.org/10.2466/03.02.pr0.116k20w6.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276. https://doi.org/10.1037/0033-2909.125.2.276.
Donaldson, S. I., & Ko, I. (2010). Positive organizational psychology, behavior, and scholarship: A review of the emerging literature and evidence base. The Journal of Positive Psychology, 5(3), 177–191. https://doi.org/10.1080/17439761003790930.
Elangovan, A. R. (2001). Causal ordering of stress, satisfaction and commitment, and intention to quit: A structural equations analysis. Leadership and Organization Development Journal, 22(4), 159–165. https://doi.org/10.1108/01437730110395051.
Fornell, C., & Larcker, D. F. (1981a). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312.
Fornell, C., & Larcker, D. F. (1981b). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382–388. https://doi.org/10.2307/3150980.
Gallagher, M. W., Lopez, S. J., & Preacher, K. J. (2009). The hierarchical structure of well-being. Journal of Personality, 77(4), 1025–1050. https://doi.org/10.1111/j.1467-6494.2009.00573.x.
Guo, C., Tomson, G., Guo, J., Li, X., Keller, C., & Söderqvist, F. (2015). Psychometric evaluation of the mental health continuum-short form (MHC-SF) in Chinese adolescents - a methodological study. Health and Quality of Life Outcomes, 13(1), 198. https://doi.org/10.1186/s12955-015-0394-2.
Hansen, C. D., & Andersen, J. H. (2008). Going ill to work–what personal circumstances, attitudes and work-related factors are associated with sickness presenteeism? Social Science & Medicine, 67(6), 956–964. https://doi.org/10.1016/j.socscimed.2008.05.022.
Herrman, H., Saxena, S., & Moodie, R. (2004). Promoting mental Health: Concepts, Emerging Evidence, Practice. PsycEXTRA Dataset. https://doi.org/10.1037/e538802013-009.
Hides, L., Quinn, C., Stoyanov, S., Cockshaw, W., Mitchell, T., & Kavanagh, D. J. (2016). Is the mental wellbeing of young Australians best represented by a single, multidimensional or bifactor model? Psychiatry Research, 241, 1–7. https://doi.org/10.1016/j.psychres.2016.04.077.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York: Springer-Verlag https://www.springer.com/us/book/9781461471370.
Johns, G. (2008). Absenteeism and Presenteeism: Not at work or not working well. In J. Barling & C. L. Cary (Éd.), The SAGE handbook of organizational behavior: Volume I - micro approaches (p. 160–177). London: SAGE Publications Ltd. https://doi.org/10.4135/9781849200448.
Joshanloo, M. (2016a). A new look at the factor structure of the MHC-SF in Iran and the United States using exploratory structural equation modeling. Journal of Clinical Psychology, 72(7), 701–713. https://doi.org/10.1002/jclp.22287.
Joshanloo, M. (2016b). Revisiting the empirical distinction between hedonic and eudaimonic aspects of well-being using exploratory structural equation modeling. Journal of Happiness Studies, 17(5), 2023–2036. https://doi.org/10.1007/s10902-015-9683-z.
Joshanloo, M., & Jovanović, V. (2016). The factor structure of the mental health continuum-short form (MHC-SF) in Serbia: An evaluation using exploratory structural equation modeling. Journal of Mental Health, 1–6. https://doi.org/10.1080/09638237.2016.1222058.
Joshanloo, M., & Lamers, S. M. (2016). Reinvestigation of the factor structure of the MHC-SF in the Netherlands: Contributions of exploratory structural equation modeling. Personality and Individual Differences, 97, 8–12. https://doi.org/10.1016/j.paid.2016.02.089.
Joshanloo, M., & Niknam, S. (2017). The tripartite model of mental well-being in Iran: Factorial and discriminant validity. Current Psychology, 1–6. https://doi.org/10.1007/s12144-017-9595-7.
Joshanloo, M., Jose, P. E., & Kielpikowski, M. (2016). The value of exploratory structural equation modeling in identifying factor overlap in the mental health continuum-short form (MHC-SF): A study with a New Zealand sample. Journal of Happiness Studies, 1–14. https://doi.org/10.1007/s10902-016-9767-4.
Jovanović, V. (2015). Structural validity of the mental health continuum-short form: The bifactor model of emotional, social and psychological well-being. Personality and Individual Differences, 75, 154–159. https://doi.org/10.1016/j.paid.2014.11.026.
Kahneman, D., Diener, E., & Schwarz, N. (2003). Well-being: Foundations of hedonic psychology. Russell Sage Foundation. https://www.russellsage.org/publications/well-being-1.
Kendler, K. S., Myers, J. M., Maes, H. H., & Keyes, C. L. (2011). The relationship between the genetic and environmental influences on common internalizing psychiatric disorders and mental well-being. Behavior Genetics, 41(5), 641–650. https://doi.org/10.1007/s10519-011-9466-1.
Keyes, C. L. M. (1998). Social well-being. Social Psychology Quarterly, 121–140. https://doi.org/10.2307/2787065.
Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207–222. https://doi.org/10.2307/3090197.
Keyes, C. L. M. (2005). Mental illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology, 73(3), 539–548. https://doi.org/10.1037/0022-006x.73.3.539.
Keyes, C. L. M. (2007). Promoting and protecting mental health as flourishing: A complementary strategy for improving national mental health. American Psychologist, 62(2), 95–108. https://doi.org/10.1037/0003-066X.62.2.95.
Keyes, C. L., & Simoes, E. J. (2012). To flourish or not: Mental health and all-cause mortality. American Journal of Public Health, 102(11), 2164–2172. https://doi.org/10.2105/ajph.2012.300918.
Keyes, C. L., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 1007. https://doi.org/10.1037/0022-3514.82.6.1007.
Keyes, C. L., Wissing, M., Potgieter, J. P., Temane, M., Kruger, A., & van Rooy, S. (2008). Evaluation of the mental health continuum–short form (MHC–SF) in setswana-speaking south Africans. Clinical Psychology & Psychotherapy, 15(3), 181–192. https://doi.org/10.1002/cpp.572.
Keyes, C. L., Michalec, B., Scheid, T. L., & Brown, T. N. (2010). Viewing mental health from the complete state paradigm. In A Handbook for the Study of Mental Health (p. 125-134). Consulté à l’adresse https://doi.org/10.1017/CBO9780511984945.010.
Keyes, C. L., Martin, C. C., Slade, M., & Martin, C. C. (2017). The complete state model. In Wellbeing, Recovery and Mental Health (p. 75-85). Cambridge University press. https://doi.org/10.1017/9781316339275.009.
Kim, H., & Stoner, M. (2008). Burnout and turnover intention among social workers: Effects of role stress, job autonomy and social support. Administration in Social Work, 32(3), 5–25. https://doi.org/10.1080/03643100801922357.
Kuhn, M., Contributions from Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., the R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., & Tyler Hunt. (2018). Caret: Classification and regression training. R package version 6.0–80. https://CRAN.R-project.org/package=caret
Leka, S., Cox, T., & Zwetsloot, G. (2008). The European framework for psychosocial risk management. PRIMA-EF. I-WHO Publications, Nottingham. http://www.prima-ef.org/prima-ef-book.html
Leka, S., Jain, A., Zwetsloot, G., & Cox, T. (2010). Policy-level interventions and work-related psychosocial risk management in the European Union. Work and Stress, 24(3), 298–307. https://doi.org/10.1080/02678373.2010.519918.
Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778. https://doi.org/10.1093/biomet/88.3.767.
Longo, Y., Jovanović, V., Sampaio de Carvalho, J., & Karaś, D. (2017). The general factor of well-being: Multinational evidence using Bifactor ESEM on the mental health continuum-short form. Assessment, 1073191117748394. https://doi.org/10.1177/1073191117748394.
MacKay, C. J., MacKay, C. J., Cousins, R., Kelly, P. J., Lee, S., & McCaig, R. H. (2004). ‘Management standards’ and work-related stress in the UK: Policy background and science. Work and Stress, 18(2), 91–112. https://doi.org/10.1080/02678370410001727474.
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 439–476. https://doi.org/10.1080/10705510903008220.
Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700.
McDonald, R. P. (1970). The theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23(1), 1–21. https://doi.org/10.1111/j.2044-8317.1970.tb00432.x.
McLachlan, G. J. (1987). On bootstrapping the likelihood ratio test Stastistic for the number of components in a Normal mixture. Applied Statistics, 36(3), 318. https://doi.org/10.2307/2347790.
McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons. https://doi.org/10.1002/0471721182.
McLachlan, G., & Peel, D. (2005). Mixtures of factor analyzers. Finite Mixture Models, 238–256. https://doi.org/10.1002/0471721182.ch8.
Morin, A. J., Arens, A. K., & Marsh, H. W. (2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 116–139. https://doi.org/10.1080/10705511.2014.961800.
Morin, A. J. S., Boudrias, J.-S., Marsh, H. W., McInerney, D. M., Dagenais-Desmarais, V., Madore, I., & Litalien, D. (2017). Complementary variable- and person-centered approaches to the dimensionality of psychometric constructs: Application to psychological wellbeing at work. Journal of Business and Psychology, 32(4), 395–419. https://doi.org/10.1007/s10869-016-9448-7.
Morin, A. J. S., Myers, N. D., & Lee, S. (in Press). Modern factor analytic techniques: Bifactor models, exploratory structural equation modeling (ESEM) and bifactor-ESEM. In G. Tenenbaum & R. C. Eklund (Éd.), Handbook of Sport Psychology 4 th Edition (Wiley).
Muthén, B. (2004). Latent variable analysis. The Sage handbook of quantitative methodology for the social sciences, 345, 368. https://doi.org/10.4135/9781412986311.n19.
Muthén, B. O., & Muthén, L. K. (2012). Mplus User’s Guide (Muthén & Muthén, Vol. 7th Edition). Los Angeles , CA: 1998–2012.
O’Leary-Kelly, S. (1998). The empirical assessment of construct validity. Journal of Operations Management, 16(4), 387–405. https://doi.org/10.1016/s0272-6963(98)00020-5.
Petrillo, G., Capone, V., Caso, D., & Keyes, C. L. (2015). The mental health continuum–short form (MHC–SF) as a measure of well-being in the Italian context. Social Indicators Research, 121(1), 291–312. https://doi.org/10.1007/s11205-014-0629-3.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. URL https://www.R-project.org/. Accessed 5 Oct 2018.
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. https://doi.org/10.1080/00273171.2012.715555.
Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150. https://doi.org/10.1037/met0000045.
Rogoza, R., Truong Thi, K. H., Różycka-Tran, J., Piotrowski, J., & Żemojtel-Piotrowska, M. (2018). Psychometric properties of the MHC-SF: An integration of the existing measurement approaches. Journal of Clinical Psychology. https://doi.org/10.1002/jclp.22626.
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166. https://doi.org/10.1146/annurev.psych.52.1.141.
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069. https://doi.org/10.1037/0022-3514.57.6.1069.
Ryff, C. D., & Singer, B. H. (2006). Know thyself and become what you are: A Eudaimonic approach to psychological well-being. Journal of Happiness Studies, 9(1), 13–39. https://doi.org/10.1007/s10902-006-9019-0.
Sánchez-Oliva, D., Morin, A. J. S., Teixeira, P. J., Carraça, E. V., Palmeira, A. L., & Silva, M. N. (2017). A bifactor exploratory structural equation modeling representation of the structure of the basic psychological needs at work scale. Journal of Vocational Behavior, 98, 173–187. https://doi.org/10.1016/j.jvb.2016.12.001.
Schutte, L., & Wissing, M. P. (2017). Clarifying the factor structure of the mental health continuum short form in three Languages: A Bifactor Exploratory Structural Equation Modeling Approach. Society and Mental Health. https://doi.org/10.1177/2156869317707793.
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107. https://doi.org/10.1007/s11336-008-9101-0.
Silverman, A. L., Forgeard, M., Beard, C., & Björgvinsson, T. (2018). Psychometric properties of the mental health continuum – Short form in a psychiatric sample. Journal of Well-Being Assessment, 2(1), 57–73. https://doi.org/10.1007/s41543-018-0011-3.
Siu, O. L., Cheung, F., & Lui, S. (2014). Linking positive emotions to work well-being and turnover intention among Hong Kong police officers: The role of psychological capital. Journal of Happiness Studies, 16(2), 367–380. https://doi.org/10.1007/s10902-014-9513-8.
SPSS Inc. (2012). IBM SPSS statistics for windows (version 21). NY.
Strümpfer, D. J. W., Hardy, A., de Villiers, J. S., & Rigby, S. (2009). Organisationally relevant variables and Keyes’s mental health continuum scale: An exploratory study. SA Journal of Industrial Psychology, 35(1), 165–171. https://doi.org/10.4102/sajip.v35i1.763.
Terry, T., & Atkinson, B. (2018). Rpart: Recursive partitioning and regression trees. R package version, 4, 1–13 https://CRAN.R-project.org/package=rpart.
Tett, R. P., & Meyer, J. P. (2006). Job satisfaction, organizational commitment, turnover intention, and turnover: Path analyses based on meta-analytic findings. Personnel Psychology, 46(2), 259–293. https://doi.org/10.1111/j.1744-6570.1993.tb00874.x.
Trochim, W., Donnelly, J. P., & Arora, K. (2015). Research methods: The essential Knowledge Base (2nd ed.). Boston, MA: Wadsworth Publishing http://www.socialresearchmethods.net/kb/index.php.
Therneau, T., Atkinson, B., & Ripley, B. (2018). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-13. https://CRAN.R-project.org/package=rpart. Accessed 5 Oct 2018.
Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4.
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307. https://doi.org/10.2307/1912557.
Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons. https://doi.org/10.1002/9781118356258.
Wetzel, E., Leckelt, M., Gerlach, T. M., & Back, M. D. (2016). Distinguishing subgroups of narcissists with latent class analysis. European Journal of Personality, 30(4), 374–389. https://doi.org/10.1002/per.2062.
Żemojtel-Piotrowska, M., Piotrowski, J. P., Osin, E. N., Cieciuch, J., Adams, B. G., Ardi, R., & Maltby, J. (2018). The mental health continuum-short form: The structure and application for cross-cultural studies - a 38 nation study. Journal of Clinical Psychology, 74(6), 1034–1052. https://doi.org/10.1002/jclp.22570.
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Jaotombo, F. Study of the Mental Health Continuum Short Form (MHC-SF) amongst French Workers: a Combined Variable- and Person-Centered Approach. J well-being assess 3, 97–121 (2019). https://doi.org/10.1007/s41543-019-00022-z
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DOI: https://doi.org/10.1007/s41543-019-00022-z