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Journal of Well-Being Assessment

, Volume 2, Issue 1, pp 57–73 | Cite as

Psychometric Properties of the Mental Health Continuum – Short Form in a Psychiatric Sample

  • Alexandra L. SilvermanEmail author
  • Marie Forgeard
  • Courtney Beard
  • Thröstur Björgvinsson
Original Research
  • 273 Downloads

Abstract

The Mental Health Continuum – Short Form (MHC-SF) is a well-established measure that assesses general well-being and three well-being components: emotional, social, and psychological. However, its psychometric properties have never been investigated in a psychiatric sample. We examined the psychometric properties of the MHC-SF, including factor structure, convergent validity, and sensitivity to change, in 768 patients attending a psychiatric partial hospital program. Patients completed the MHC-SF as well as self-report measures of depression, and motivation and pleasure at admission and discharge from the program. Results revealed that a Bifactor Exploratory Structural Equation Modeling (ESEM) model better fit the data than competing models (Confirmatory Factor Analysis, ESEM, and Bifactor models). This model supported the existence of a general well-being factor, but provided limited evidence for the existence of emotional, social, or psychological well-being factors. The MHC-SF negatively correlated with a measure of depression and positively correlated with a measure of motivation and pleasure, suggesting good convergent validity. General well-being increased significantly from pre- to post-treatment. Results support the use of the MHC-SF to reliably measure general well-being in a psychiatric sample.

Keywords

Well-being Mental health Psychopathology Bifactor ESEM 

Notes

Compliance with ethical standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16, 397–438.  https://doi.org/10.1080/10705510903008204.CrossRefGoogle Scholar
  2. Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive Therapy of Depression. New York: Guilford Press.Google Scholar
  3. Beard, C. & Björgvinsson, T. (2014). Beyond generalized anxiety disorder: Psychometric properties of the GAD-7 in a heterogeneous sample. Journal of Anxiety Disorders, 28, 547–552.  https://doi.org/10.1016/j.janxdis.2014.06.002.
  4. Beard, C., Hsu, K. J., Rifkin, L. S., Busch, A. B., & Björgvinsson, T. (2016). Validation of the PHQ-9 in a psychiatric sample. Journal of Affective Disorders, 193, 267-273.  https://doi.org/10.1016/j.jad.2015.12.075.
  5. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.  https://doi.org/10.1037/0033-2909.88.3.588.CrossRefGoogle Scholar
  6. Björgvinsson, T., Kertz, S., Bigda-Peyton, J., Rosmarin, D. H., Aderka, I., & Neuhaus, E. (2014). Effectiveness of cognitive behavior therapy for severe mood disorders in an acute psychiatric naturalistic setting: A benchmarking study. Cognitive Behaviour Therapy, 43, 209-220.  https://doi.org/10.1080/16506073.2014.901988.
  7. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 136–162). Newbury Park: Sage Publications.Google Scholar
  8. 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, 219–251.  https://doi.org/10.1111/j.1467-6494.2011.00739.x.CrossRefGoogle Scholar
  9. Cho, E. (2016). Making reliability reliable: a systematic approach to reliability coefficients. Organizational Research Methods, 19, 651–682.CrossRefGoogle Scholar
  10. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York: Routledge Academic.Google Scholar
  11. De Bruin, G. P., & Du Plessis, G. A. (2015). Bifactor analysis of the Mental Health Continuum-Short Form (MHC-SF). Psychological Reports, 116, 438–446.  https://doi.org/10.2466/03.02.PR0.116k20w6.CrossRefGoogle Scholar
  12. De Carvalho, J. S., Pereira, N. S., Pinto, A. M., & Marôco, J. (2016). Psychometric properties of the Mental Health Continuum-Short Form: a study of Portuguese speaking children/youths. Journal of Child and Family Studies, 25, 2141–2154.  https://doi.org/10.1007/s10826-016-0396-7.CrossRefGoogle Scholar
  13. Deci, E. L., & Ryan, R. M. (2008). Hedonia, eudaimonia, and well-being: an introduction. Journal of Happiness Studies, 9, 1–11.  https://doi.org/10.1007/s10902-006-9018-1.CrossRefGoogle Scholar
  14. Duckworth, A. L., Steen, T. A., & Seligman, M. E. (2005). Positive psychology in clinical practice. Annual Review of Clinical Psychology, 1, 629–651.  https://doi.org/10.1146/annurev.clinpsy.1.102803.144154.CrossRefGoogle Scholar
  15. Dziak, J. J., Coffman, D. L., Lanza, S. T., & Li, R. (2017). Sensitivity and specificity of information criteria. Peer Journal Preprints, 5, e1103v3.  https://doi.org/10.7287/peerj.preprints.1103v3.Google Scholar
  16. Gallagher, M. W., Lopez, S. J., & Preacher, K. J. (2009). The hierarchical structure of well-being. Journal of Personality, 77, 1025–1050.  https://doi.org/10.1111/j.1467-6494.2009.00573.x.CrossRefGoogle Scholar
  17. Gignac, G. E., & Watkins, M. W. (2013). Bifactor modeling and the estimation of model-based reliability in the WAIS-IV. Multivariate Behavioral Research, 48, 639–662.  https://doi.org/10.1080/00273171.2013.804398.CrossRefGoogle Scholar
  18. 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, 198–206.  https://doi.org/10.1186/s12955-015-0394-2.CrossRefGoogle Scholar
  19. Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42, 377–381.  https://doi.org/10.1016/j.jbi.2008.08.010.CrossRefGoogle Scholar
  20. 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
  21. Hone, L. C., Jarden, A., Schofield, G., & Duncan, S. (2014). Measuring flourishing: the impact of operational definitions on the prevalence of high levels of wellbeing. International Journal of Wellbeing, 4, 62–90.  https://doi.org/10.5502/ijw.v4i1.4.CrossRefGoogle Scholar
  22. Howard, J. L., Gagné, M., Morin, A. J. S., & Forest, J. (2016). Using bifactor exploratory structural equation modeling to test for a continuum structure of motivation. Journal of Management, 1–27. doi:  https://doi.org/10.1177/0149206316645653.
  23. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.  https://doi.org/10.1080/10705519909540118.CrossRefGoogle Scholar
  24. 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, 701–713.  https://doi.org/10.1002/jclp.22287.CrossRefGoogle Scholar
  25. 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, 2023–2036.  https://doi.org/10.1007/s10902-015-9683-z.CrossRefGoogle Scholar
  26. 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. doi:  https://doi.org/10.1080/09638237.2016.1222058.
  27. Joshanloo, M., & Lamers, S. M. A. (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
  28. Joshanloo, M., Wissing, M. P., Khumalo, I. P., & Lamers, S. M. A. (2013). Measurement invariance of the Mental Health Continuum-Short Form (MHC-SF) across three cultural groups. Personality and Individual Differences, 55, 755–759.  https://doi.org/10.1016/j.paid.2013.06.002.CrossRefGoogle Scholar
  29. 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. doi:  https://doi.org/10.1007/s10902-016-9767-4.
  30. 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
  31. Karaś, D., Cieciuch, J., & Keyes, C. L. M. (2014). The Polish adaptation of the Mental Health Continuum-Short Form (MHC-SF). Personality and Individual Differences, 69, 104–109.  https://doi.org/10.1016/j.paid.2014.05.011.CrossRefGoogle Scholar
  32. Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 10, 333–351.  https://doi.org/10.1207/S15328007SEM1003_1.CrossRefGoogle Scholar
  33. Keyes, C. L. (2002). The mental health continuum: from languishing to flourishing in life. Journal of Health and Social Behavior, 43, 207–222.CrossRefGoogle Scholar
  34. Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology, 73, 539–548.  https://doi.org/10.1037/0022-006X.73.3.539.CrossRefGoogle Scholar
  35. Keyes, C. L. (2007). Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. American Psychologist, 62, 95–108.  https://doi.org/10.1037/0003-066X.62.2.95.CrossRefGoogle Scholar
  36. 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, 181–192.  https://doi.org/10.1002/cpp.572.CrossRefGoogle Scholar
  37. Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling (2nd ed.). New York: Guilford Press.Google Scholar
  38. Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: a new depression diagnostic and severity measure. Psychiatric Annals, 32, 509–515.  https://doi.org/10.3928/0048-5713-20020901-06.CrossRefGoogle Scholar
  39. Lahey, B. B., Applegate, B., Hakes, J. K., Zald, D. H., Hariri, A. R., & Rathouz, P. J. (2012). Is there a general factor of prevalent psychopathology during adulthood? Journal of Abnormal Psychology, 121, 971–977.  https://doi.org/10.1037/a0028355.CrossRefGoogle Scholar
  40. Lamers, S. M. A., Westerhof, G. J., Bohlmeijer, E. T., ten Klooster, P. M., & Keyes, C. L. M. (2011). Evaluating the psychometric properties of the Mental Health Continuum-Short Form (MHC-SF). Journal of Clinical Psychology, 67, 99–110.  https://doi.org/10.1002/jclp.20741.CrossRefGoogle Scholar
  41. Lamers, S. M. A., Glas, C. A. W., Westerhof, G. J., & Bohlmeijer, E. T. (2012). Longitudinal evaluation of the Mental Health Continuum-Short Form (MHC-SF). European Journal of Psychological Assessment, 28, 290–296.  https://doi.org/10.1027/1015-5759/a000109.CrossRefGoogle Scholar
  42. Linehan, M. (1993). Cognitive-behavioral Treatment of Borderline Personality Disorder. New York, NY: Guilford Press.Google Scholar
  43. Litalien, D., Morin, A. J., Gagné, M., Vallerand, R. J., Losier, G. F., & Ryan, R. M. (2017). Evidence of a continuum structure of academic self-determination: a two-study test using a bifactor-ESEM representation of academic motivation. Contemporary Educational Psychology, 51, 67–82.  https://doi.org/10.1016/j.cedpsych.2017.06.010.CrossRefGoogle Scholar
  44. Llerena, K., Park, S. G., McCarthy, J. M., Couture, S. M., Bennett, M. E., & Blanchard, J. J. (2013). The Motivation and Pleasure Scale-Self-Report (MAP-SR): reliability and validity of a self-report measure of negative symptoms. Comprehensive Psychiatry, 54, 568–574.  https://doi.org/10.1016/j.comppsych.2012.12.001.CrossRefGoogle Scholar
  45. 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, Advanced online publication. doi:  https://doi.org/10.1177/1073191117748394.
  46. Marsh, H. W., Morin, A. J. S., 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
  47. 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, 1–21.  https://doi.org/10.1111/j.2044-8317.1970.tb00432.x.CrossRefGoogle Scholar
  48. McDonald, R. P. (1999). Test Theory: a Unified Treatment. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  49. 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, 116–139.  https://doi.org/10.1080/10705511.2014.961800.CrossRefGoogle Scholar
  50. Muthén, L. K., & Muthén, B. O. (2017). Mplus statistical modeling software: release 8.0. Los Angeles: Muthén & Muthén.Google Scholar
  51. 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, 291–312.  https://doi.org/10.1007/s11205-014-0629-3.CrossRefGoogle Scholar
  52. Rapp, C. A. (1998). The strengths model: case management with people suffering from severe and persistent mental illness. New York: Oxford University Press.Google Scholar
  53. Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013a). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95, 129–140.CrossRefGoogle Scholar
  54. Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013b). Multidimensionality and structural coefficient bias in structural equation modeling: a bifactor perspective. Educational and Psychological Measurement, 73, 5–26.  https://doi.org/10.1177/0013164412449831.CrossRefGoogle Scholar
  55. Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: calculating and interpreting statistical indices. Psychological Methods, 21, 137–150.CrossRefGoogle Scholar
  56. 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, 1069–1081.  https://doi.org/10.1037/0022-3514.57.6.1069.CrossRefGoogle Scholar
  57. 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, 13–39.  https://doi.org/10.1007/s10902-006-9019-0.CrossRefGoogle Scholar
  58. Sánchez-Oliva, D., Morin, A. J., 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
  59. 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, 142–158. doi:  https://doi.org/10.1177/2156869317707793.
  60. Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Special issue: positive psychology. American Psychologist, 55, 5–14.  https://doi.org/10.1007/978-94-017-9088-8_18.CrossRefGoogle Scholar
  61. Stenling, A., Ivarsson, A., Hassmén, P., & Lindwall, M. (2015). Using bifactor exploratory structural equation modeling to examine global and specific factors in measures of sports coaches’ interpersonal styles. Frontiers in Psychology, 6, 1–12.  https://doi.org/10.3389/fpsyg.2015.01303.CrossRefGoogle Scholar
  62. Wang, P. S., Lane, M., Olfson, M., Pincus, H. A., Wells, K. B., & Kessler, R. C. (2005). Twelve- month use of mental health services in the United States: results from the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 629–640.  https://doi.org/10.1001/archpsyc.62.6.629.CrossRefGoogle Scholar
  63. Watkins, M. W. (2017). The reliability of multidimensional neuropsychological measures: from alpha to omega. The Clinical Neuropsychologist, 31, 1113–1126.CrossRefGoogle Scholar
  64. Westerhof, G. J., & Keyes, C. L. M. (2010). Mental illness and mental health: the two continua model across the lifespan. Journal of Adult Development, 17, 110–119.  https://doi.org/10.1007/s10804-009-9082-y.CrossRefGoogle Scholar
  65. Wood, A. M., & Tarrier, N. (2010). Positive clinical psychology: a new vision and strategy for integrated research and practice. Clinical Psychology Review, 30, 819–829.  https://doi.org/10.1016/j.cpr.2010.06.003.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alexandra L. Silverman
    • 1
    Email author
  • Marie Forgeard
    • 1
    • 2
  • Courtney Beard
    • 1
    • 2
  • Thröstur Björgvinsson
    • 1
    • 2
  1. 1.Behavioral Health Partial ProgramMcLean HospitalBelmontUSA
  2. 2.Harvard Medical SchoolBostonUSA

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