Journal of Well-Being Assessment

, Volume 2, Issue 1, pp 21–40 | Cite as

Evidence for a Bifactor Structure of the Scales of Psychological Well-Being Using Exploratory Structural Equation Modeling

  • Jose A. EspinozaEmail author
  • John P. Meyer
  • Brittney K. Anderson
  • Chelsea Vaters
  • Christina Politis
Original Research


This research investigates the much-debated factor structure of the 54-item version of Ryff’s (1989) Scales of Psychological Well-being (SPWB). Using two samples (n1 = 573; n2 = 449) of undergraduate university students, we apply confirmatory factor analysis (CFA) along with recently developed exploratory structural equation modeling (ESEM) techniques to evaluate several unidimensional and multidimensional models identified in previous research, as well as a new bifactor model. In a bifactor model, items load directly on both a global and a specific factor; when tested using ESEM, cross-loadings on other specific factors are also permitted and are targeted to be as close to zero as possible. After comparing various ESEM and traditional CFA models, the results indicate that a bifactor model estimated using ESEM provided the best fit to the data. Most items were found to reflect the global factor, but some items failed to reflect the intended specific factor. Thus, the 54-item version of the SPWB appears to be a good measure of overall psychological well-being, but may need refinement as a measure of the intended specific factors, at least among young adults. The benefits of applying ESEM to investigate the factor structure of the SPWB in other populations are discussed.


Psychological well-being Measurement Measure evaluation Scale evaluation Exploratory structural equation modeling ESEM 



This research was supported by a research grant from the Social Sciences and Humanities Research Council (435-2014-0956) awarded to the second author.

Compliance with Ethical Standards

Conflict of Interest

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

Supplementary material

41543_2018_8_MOESM1_ESM.pdf (406 kb)
ESM 1 (PDF 405 kb)


  1. Abbott, R. A., Ploubidis, G. B., Huppert, F. A., Kuh, D., Wadsworth, M. E., & Croudace, T. J. (2006). Psychometric evaluation and predictive validity of Ryff's psychological well-being items in a UK birth cohort sample of women. Health and Quality of Life Outcomes, 4(1), 76–92.CrossRefGoogle Scholar
  2. Allport, G. W. (1961). Pattern and growth in personality. New York, NY: Holt, Rinehart, and Winston.Google Scholar
  3. Aristotle (1985). Nicomachean ethics (T. Irwin, Trans.). Indianapolis, IN: Hackett.Google Scholar
  4. Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397–438.CrossRefGoogle Scholar
  5. Bandalos, D.L., & Boehm-Kaufman, M.R. (2009). Four common misconceptions in exploratory factor analysis. In. C.E. Lance & R.J. Vandenberg (Eds.), Statistical and methodological myths and urban legends (pp. 61–87). New York: Routledge.Google Scholar
  6. 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, CA: Sage.Google Scholar
  7. Burns, R. A., & Machin, M. A. (2009). Investigating the structural validity of Ryff’s psychological well-being scales across two samples. Social Indicators Research, 93(2), 359–375.CrossRefGoogle Scholar
  8. Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504.CrossRefGoogle Scholar
  9. Clarke, P. J., Marshall, V. W., Ryff, C. D., & Rosenthal, C. J. (2000). Well-being in Canadian seniors: Findings from the Canadian study of health and aging. Canadian Journal on Aging/La Revue Canadienne du Vieillissement, 19(2), 139–159.CrossRefGoogle Scholar
  10. Clarke, P. J., Marshall, V. W., Ryff, C. D., & Wheaton, B. (2001). Measuring psychological well-being in the Canadian study of health and aging. International Psychogeriatrics, 13(1), 79–90.CrossRefGoogle Scholar
  11. Cronbach, L. J. (1970). Essentials of psychological testing. Macmillan: New York: Macmillan.Google Scholar
  12. Finney, S. J., & DiStefano, C. (2013). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (2nd ed., pp. 439–492). Greenwich, CO: IAP.Google Scholar
  13. Frankl, V. E., & Lasch, I. (1992). Man’s search for meaning: An introduction to logotherapy. Boston, MA: Beacon Press. (Originally work published 1959).Google Scholar
  14. Gallagher, M. W., Lopez, S. J., & Preacher, K. J. (2009). The hierarchical structure of well- being. Journal of Personality, 77(4), 1025–1050.CrossRefGoogle Scholar
  15. Gignac, G. E. (2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68.CrossRefGoogle Scholar
  16. Guay, F., Morin, A. J., Litalien, D., Valois, P., & Vallerand, R. J. (2015). Application of exploratory structural equation modeling to evaluate the academic motivation scale. The Journal of Experimental Education, 83(1), 51–82.CrossRefGoogle Scholar
  17. Howard, J. L., Gagné, M., Morin, A. J., & Forest, J. (2016). Using bifactor exploratory structural equation modeling to test for a continuum structure of motivation. Journal of Management, 1–27.Google Scholar
  18. Hu, L. T., & 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), 1–55.CrossRefGoogle Scholar
  19. Kafka, G. J., & Kozma, A. (2002). The construct validity of Ryff's scales of psychological well- being (SPWB) and their relationship to measures of subjective well-being. Social Indicators Research, 57(2), 171–190.CrossRefGoogle Scholar
  20. Kam, C. C. S., & Meyer, J. P. (2015). How careless responding and acquiescence response bias can influence construct dimensionality: The case of job satisfaction. Organizational Research Methods, 18(3), 512–541.CrossRefGoogle Scholar
  21. Litalien, D., Morin, A. J. S., 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.Google Scholar
  22. Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares & J. McArdle (Eds.), Contemporary psychometrics. A Festschrift for Roderick P. McDonald (pp. 275–340). Mahwah NJ: Erlbaum.Google Scholar
  23. 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.CrossRefGoogle Scholar
  24. Maslow, A. H. (1968). Toward a psychology of being. New York: D. Van Nostrand Company.Google Scholar
  25. Morin, A. J. S., Arens, A., & Marsh, H. (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.CrossRefGoogle Scholar
  26. Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335.CrossRefGoogle Scholar
  27. Muthén, L. K., & Muthén, B. O. (2014). Mplus user’s guide. Los Angeles, CA: Muthén &. Muthén.Google Scholar
  28. Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696.CrossRefGoogle Scholar
  29. Rogers, C. R. (1962). The interpersonal relationship: The core of guidance. Harvard Educational Review, 32, 416–429.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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), 069–1081.CrossRefGoogle Scholar
  32. Ryff, C. D. (2014). Psychological well-being revisited: Advances in the science and practice of eudaimonia. Psychotherapy and Psychosomatics, 83(1), 10–28.CrossRefGoogle Scholar
  33. Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69(4), 719–727.CrossRefGoogle Scholar
  34. Ryff, C. D., & Singer, B. H. (2006). Best news yet on the six-factor model of well-being. Social. Science Research, 35(4), 1103–1119.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Schwartz, S. J., Waterman, A. S., Vazsonyi, A. T., Zamboanga, B. L., Whitbourne, S. K., Weisskirch, R. S., et al. (2011). The association of well-being with health risk behaviors in college-attending young adults. Applied Developmental Science, 15(1), 20–36.CrossRefGoogle Scholar
  37. Springer, K. W., & Hauser, R. M. (2006). An assessment of the construct validity of Ryff’s scales of psychological well-being: Method, mode, and measurement effects. Social Science Research, 35(4), 1080–1102.CrossRefGoogle Scholar
  38. Springer, K. W., Hauser, R. M., & Freese, J. (2006). Bad news indeed for Ryff’s six-factor model of well-being. Social Science Research, 35(4), 1120–1131.CrossRefGoogle Scholar
  39. Thurstone, L. L. (1945). Multiple factor analysis. Chicago: University of Chicago Press.Google Scholar
  40. Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003–1017.CrossRefGoogle Scholar
  41. van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85, 842–860.CrossRefGoogle Scholar
  42. van Dierendonck, D. (2004). The construct validity of Ryff's scales of psychological well-being and its extension with spiritual well-being. Personality and Individual Differences, 36(3), 629–643.CrossRefGoogle Scholar
  43. Waterman, A. S. (2011). Eudaimonic identity theory: Identity as self-discovery. In Handbook of Identity Theory and Research (pp. 357–379). New York, NY: Springer.Google Scholar
  44. Wiggins, J. S. (1973). Personality and prediction: Principles of personality assessment. Menlo Park, CA: Addison-Wesley.Google Scholar
  45. Zyphur, M. J., & Oswald, F. L. (2015). Bayesian estimation and inference: A user’s guide. Journal of Management, 41, 390–420.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jose A. Espinoza
    • 1
    Email author
  • John P. Meyer
    • 1
    • 2
  • Brittney K. Anderson
    • 1
  • Chelsea Vaters
    • 1
  • Christina Politis
    • 1
  1. 1.Department of PsychologyThe University of Western OntarioLondonCanada
  2. 2.Curtin Business SchoolCurtin UniversityBentleyAustralia

Personalised recommendations