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Journal of Well-Being Assessment

, Volume 3, Issue 2–3, pp 97–121 | Cite as

Study of the Mental Health Continuum Short Form (MHC-SF) amongst French Workers: a Combined Variable- and Person-Centered Approach

  • Franck JaotomboEmail author
Original Research
  • 22 Downloads

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.

Keywords

Mental health continuum Flourishing Bifactor ESEM Latent class analysis Factor mixture analysis Psychosocial risks Classification and regression trees 

Notes

Compliance with Ethical Standards

Conflict of Interest

The author declares that he has no conflict of interest.

Supplementary material

41543_2019_22_MOESM1_ESM.docx (34 kb)
ESM 1 (DOCX 34 kb)

References

  1. American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition: DSM-IV-TR®.  https://doi.org/10.1176/appi.books.9780890423349.
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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/
  10. Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and decision trees. Wadsworth, Belmont, 378.  https://doi.org/10.1201/9781315139470.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.Google Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.  https://doi.org/10.1037/h0040957.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. Herrman, H., Saxena, S., & Moodie, R. (2004). Promoting mental Health: Concepts, Emerging Evidence, Practice. PsycEXTRA Dataset.  https://doi.org/10.1037/e538802013-009.
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. Kahneman, D., Diener, E., & Schwarz, N. (2003). Well-being: Foundations of hedonic psychology. Russell Sage Foundation. https://www.russellsage.org/publications/well-being-1.
  39. 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.CrossRefGoogle Scholar
  40. Keyes, C. L. M. (1998). Social well-being. Social Psychology Quarterly, 121–140.  https://doi.org/10.2307/2787065.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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.
  48. 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.
  49. 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.CrossRefGoogle Scholar
  50. 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
  51. 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
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.
  55. 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.CrossRefGoogle Scholar
  56. 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.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons.  https://doi.org/10.1002/0471721182.CrossRefGoogle Scholar
  61. McLachlan, G., & Peel, D. (2005). Mixtures of factor analyzers. Finite Mixture Models, 238–256.  https://doi.org/10.1002/0471721182.ch8.
  62. 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.CrossRefGoogle Scholar
  63. 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.CrossRefGoogle Scholar
  64. 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).Google Scholar
  65. 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.CrossRefGoogle Scholar
  66. Muthén, B. O., & Muthén, L. K. (2012). Mplus User’s Guide (Muthén & Muthén, Vol. 7th Edition). Los Angeles , CA: 1998–2012.Google Scholar
  67. 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.CrossRefGoogle Scholar
  68. 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.CrossRefGoogle Scholar
  69. 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.
  70. 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.CrossRefGoogle Scholar
  71. 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.CrossRefGoogle Scholar
  72. 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.CrossRefGoogle Scholar
  73. 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.CrossRefGoogle Scholar
  74. 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.CrossRefGoogle Scholar
  75. 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.CrossRefGoogle Scholar
  76. 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.CrossRefGoogle Scholar
  77. 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.CrossRefGoogle Scholar
  78. 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.CrossRefGoogle Scholar
  79. 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.CrossRefGoogle Scholar
  80. 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.CrossRefGoogle Scholar
  81. SPSS Inc. (2012). IBM SPSS statistics for windows (version 21). NY.Google Scholar
  82. 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.CrossRefGoogle Scholar
  83. Terry, T., & Atkinson, B. (2018). Rpart: Recursive partitioning and regression trees. R package version, 4, 1–13 https://CRAN.R-project.org/package=rpart.Google Scholar
  84. 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.CrossRefGoogle Scholar
  85. 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.
  86. 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.
  87. 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.CrossRefGoogle Scholar
  88. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307.  https://doi.org/10.2307/1912557.CrossRefGoogle Scholar
  89. Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons.  https://doi.org/10.1002/9781118356258.CrossRefGoogle Scholar
  90. 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.CrossRefGoogle Scholar
  91. 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.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.AMALTEYAMarseilleFrance

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