Quality of Life Research

, Volume 23, Issue 8, pp 2195–2203 | Cite as

Results from several population studies show that recommended scoring methods of the SF-36 and the SF-12 may lead to incorrect conclusions and subsequent health decisions

  • Graeme TuckerEmail author
  • Robert Adams
  • David Wilson



To compare the measurement properties of the physical component summary (PCS) and mental component summary (MCS) scores of the SF-36 and SF-12 based on the traditional orthogonal scoring algorithms with the performance of the PCS and MCS scored based on structural equation model coefficients from a correlated model.


This study used three large-scale representative population studies to compare the measurement properties of the PCS and MCS scores of the SF-36 and SF-12 with the performance of the PCS and MCS scores based on structural equation models producing coefficients from a correlated model. We assessed the relationships of these scores with selected important mental health measures and chronic conditions from three representative Australian population studies that address clinical conditions of high prevalence and health service importance.


Structural equation model scoring methods produced summary scores with higher correlations than the recommended orthogonal methods across a range of disease and health conditions. The problem experienced in using the orthogonal methods is that negative scoring coefficients are applied to negative z-scores for sub-scales, inflating the resulting summary scores. Effect sizes over a half of a standard deviation were common.


If health policy or investment decisions are made based on the results of studies employing the recommended orthogonal scoring methods then the expected outcome of such decisions or investments may not be achieved.


Self-rated health Health-related quality of life SF-36/SF-12 Correlated v orthogonal scoring 



We wish to thank the anonymous reviewer for their helpful suggestion that improved the strength of the arguments presented in this paper.

Ethical standard

This paper is based on a secondary analysis of various South Australian survey files. As such, this analysis did not require formal ethics approval; however, all of the original data collections were conducted under ethics approval with the informed consent of the participants.

Supplementary material

11136_2014_669_MOESM1_ESM.doc (152 kb)
Supplementary material 1 (DOC 151 kb)


  1. 1.
    Hawthorne, G., Osborne, R. H., Taylor, A., & Sansoni, J. (2007). The SF-36 Version 2: Critical analysis of population weighting, scoring algorithms and population norms. Quality of Life Research, 16, 661–673.PubMedCrossRefGoogle Scholar
  2. 2.
    Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). The SF-36 health survey manual and interpretation guide. Boston, MA: The Health Institute, New England Medical Centre.Google Scholar
  3. 3.
    Sorensen, L., Stokes, J. A., Purdie, D. M., et al. (2004). Medication reviews in the community: Results of a randomized, controlled effectiveness trial. British Journal of Clinical Pharmacology, 58, 648–664.PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Commonwealth Department of Health and Aged Care. (1999). The Australian coordinated care trials: Background and trial descriptions. Canberra: Department of Health and Aged Care.Google Scholar
  5. 5.
    McCallum, J. (1995). The new SF-36 health status measure: Australian validity tests. Canberra: National Centre for Epidemiology and Population Health. Paper presented to the Health Outcomes and Quality of Life Measurement Conference.Google Scholar
  6. 6.
    McCallum, J. (1995). The SF-36 in an Australian sample: Validating a new, generic health status measure. Australian Journal of Public Health, 19, 160–166.PubMedCrossRefGoogle Scholar
  7. 7.
    Sanson-Fisher, R. W., & Perkins, J. J. (1998). Adaptation and validation of the SF-36 health survey for use in Australia. Journal of Clinical Epidemiology, 51(11), 961–967.PubMedCrossRefGoogle Scholar
  8. 8.
    Ware, J. E., Kosinski, M., & Keller, S. D. (1994). SF-36 physical and mental health summary scales: A users manual. Boston, MA: The Health Institute, New England Medical Centre.Google Scholar
  9. 9.
    Hays, R. D., Sherbourne, C. D., & Mazel, R. M. (1993). The RAND 36-item health survey 1.0. Health Economics, 2, 217–227.PubMedCrossRefGoogle Scholar
  10. 10.
    Hays, R. D., Prince-Embury, S., & Chen, H. (1998). RAND-36 health status inventory. San Antonio, TX: The Psychological Corporation.Google Scholar
  11. 11.
    Hays, R. D., & Morales, L. S. (2001). The RAND-36 measure of health-related quality of life. Annals of Medicine, 33, 350–357.PubMedCrossRefGoogle Scholar
  12. 12.
    Simon, G. E., Revicki, D. A., Grothaus, L., & Vonkorf, M. (1998). SF-36 summary scores: Are physical and mental health truly distinct? Medical Care, 36, 567–572.PubMedCrossRefGoogle Scholar
  13. 13.
    Wilson, D., Parsons, J., & Tucker, G. (2000). The SF-36 summary scales: Problems and solutions. Sozial-und Präventivmedizin, 45, 239–246.PubMedCrossRefGoogle Scholar
  14. 14.
    Taft, C., Karlson, J., & Sullivan, M. (2001). Do SF-36 summary component scores accurately summarise subscale scores? Quality of Life Research, 10, 395–404.PubMedCrossRefGoogle Scholar
  15. 15.
    Farivar, S. S., Cunningham, W. E., & Hays, R. D. (2007). Correlated physical and mental health summary scores for the SF-36 and SF-12 health survey, V. 1. Health and Quality of Life Outcomes, 5, 54.PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Hann, M., & Reeves, D. (2008). The SF-36 scales are not accurately summarised by independent physical and mental component scores. Quality of Life Research, 17, 413–423.PubMedCrossRefGoogle Scholar
  17. 17.
    Anagnostopoulos, F., Niakas, D., & Tountas, Y. (2009). Comparison between exploratory factor-analytic and SEM-based approaches to constructing SF-36 summary scores. Quality of Life Research, 18, 53–63.PubMedCrossRefGoogle Scholar
  18. 18.
    Fleishman, J. A., Selim, A. J., & Kazis, L. E. (2010). Deriving SF-12v2 physical and mental health summary scores: A comparison of different scoring algorithms. Quality of Life Research, 19(2), 231–241.PubMedCrossRefGoogle Scholar
  19. 19.
    Tucker, G., Adams, R., & Wilson, D. (2010). New Australian population scoring coefficients for the old version of the SF-36 and SF-12 health status questionnaires. Quality of Life Research, 19(7), 1069–1076.PubMedCrossRefGoogle Scholar
  20. 20.
    Tucker, G. R., Adams, R. J., & Wilson, D. H. (2013). Observed agreement problems between Sub-scales and summary components of the SF-36 version 2-an alternative scoring method can correct the problem. Plos One 8(4):e61191. doi:  10.1371/journal.pone.0061191.
  21. 21.
    Chapman, D. P., & Perry, G. S. (2008). Depression as a major component of public health for older adults. Preventing Chronic Disease 5(1). Accessed 21 May 2013.
  22. 22.
    Chapman, D. P., Perry, G. S., Strine, & T. W. (2005). The vital link between chronic disease and depressive disorders. Preventing Chronic Disease. Accessed 21 May 2013.
  23. 23.
    Cheok, F., Schrader, G., Banham, D., Marker, J., & Hordacre, A. L. (2003). Identification, course, and treatment of depression after admission for a cardiac condition: Rationale and patient characteristics for the Identifying Depression As a Comorbid Condition (IDACC) project. American Heart Journal, 146(6), 978–984.PubMedCrossRefGoogle Scholar
  24. 24.
    Schrader, G., Cheok, F., Hordacre, A. L., & Guiver, N. (2004). Predictors of depression three months after cardiac hospitalization. Psychosomatic Medicine, 66(4), 514–520.PubMedCrossRefGoogle Scholar
  25. 25.
    Wilson, D. H., Appleton, S. L., Taylor, A. W., Tucker, G., Ruffin, R. E., Wittert, G., et al. (2010). Depression and obesity in adults with asthma: Multiple comorbidities and management issues. Medical Journal of Australia, 192(7), 381–383.PubMedGoogle Scholar
  26. 26.
    Sullivan, M. D., O’Connor, P., Feeney, P., Hire, D., Simmons, D. L., Raisch, D. W., et al. (2012). Depression predicts all-cause mortality: Epidemiological evaluation from the ACCORD HRQL substudy. Diabetes Care, 35(8), 1708–1715. doi: 10.2337/dc11-1791.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Lin, E. H., Von Korff, M., Ciechanowski, P., Peterson, D., Ludman, E. J., Rutter, C. M., et al. (2012). Treatment adjustment and medication adherence for complex patients with diabetes, heart disease, and depression: A randomized controlled trial. The Annals of Family Medicine, 10(1), 6–14. doi: 10.1370/afm.1343.CrossRefGoogle Scholar
  28. 28.
    Katon, W. J. (2011). Epidemiology and treatment of depression in patients with chronic medical illness. Dialogues in Clinical Neuroscienc, 13(1), 7–23.Google Scholar
  29. 29.
    Davydow, D. S., Katon, W. J., & Zatzick, D. F. (2009). Psychiatric morbidity and functional impairments in survivors of burns, traumatic injuries, and ICU stays for other critical illnesses: A review of the literature. International Review of Psychiatry, 21(6), 531–538. doi: 10.3109/09540260903343877.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Llaneza, P., García-Portilla, M. P., Llaneza-Suárez, D., Armott, B., & Pérez-López, F. R. (2012). Depressive disorders and the menopause transition. Maturitas, 71(2), 120–130. doi: 10.1016/j.maturitas.2011.11.017.PubMedCrossRefGoogle Scholar
  31. 31.
    Australian Bureau of Statistics (1995). National Health Survey. SF-36 Population Norms Australia. Canberra: Australian Bureau of Statistics, Catalogue Number 4399.0.Google Scholar
  32. 32.
    Ware, J., Kosinski, M., & Keller, S. (1995). SF-12: How to score the SF-12 physical and mental health summary scales (2nd ed.). Boston: The Health Institute, New England Medical Center.Google Scholar
  33. 33.
    Ware J. E. Jr., Kosinski M., Turner-Bowker, D., Sundaram M., Gandeck, B., & Maruish M. E. (2002). User’s manual for the SF-12 V2 health survey, Second Edition. Quality Metric, 24 Albion Rd, Building 400, Lincoln, RI 02865, USA.Google Scholar
  34. 34.
  35. 35. Accessed 25 July 2013.
  36. 36.
    Hawthorne, G. & Richardson, J. (1997). The assessment of quality of life (AQoL) instrument construction, initial validation and utility scaling. Centre for Health Program Evaluation, Melbourne (15 pages) (ISBN 1 875677 85 2).Google Scholar
  37. 37.
    Hawthorne, G., Korn, S., & Richardson, J. (2013). Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing. Australian and New Zealand Journal of Public Health, 37(1), 7–16.PubMedCrossRefGoogle Scholar
  38. 38.
    Selim, A., Ren, X., Fincke, G., Rogers, W., Lee, A., & Kazis, L. (1997). A symptom-based measure of the severity of chronic lung disease: results from the Veterans Health Study. Chest, 111, 1607–1614.PubMedCrossRefGoogle Scholar
  39. 39.
    Ruffin, R. E., Wilson, D. H., Chittleborough, C. R., Southcott, A. M., Smith, B., & Christopher, D. J. (2000). Multiple respiratory symptoms predict quality of life in chronic lung disease: A population-based study of Australian adults. Quality of Life Research, 9, 1031–1039.PubMedCrossRefGoogle Scholar
  40. 40.
    Goldberg, D. P., & Hillier, V. F. (1979). A scaled version of the General Health Questionnaire. Psychological Medicine, 9, 139–145.PubMedCrossRefGoogle Scholar
  41. 41.
    Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.CrossRefGoogle Scholar
  42. 42.
    Kessler, R. C., Andrews, G., Colpe, L. J., Hripi, E., Mrocsek, D. K., Normand, S.-L. T., et al. (2002). Short screening scales to monitor population prevalences and trends in nonspecific psychological distress. Psychological Medicine, 32(6), 959–976.PubMedCrossRefGoogle Scholar
  43. 43.
    Wilson, D., Wakefield, M., & Taylor, A. (1992). The South Australian health omnibus survey. Health Promotion Journal of Australia, 2, 47–49.Google Scholar
  44. 44.
    Grant, J. F., Taylor, A. W., Ruffin, R. E., Wilson, D. H., Phillips, P. J., Adams, R. J. T., et al. (2009). Cohort profile: The North West Adelaide Health Study (NWAHS). International Journal of Epidemiology, 38, 1479–1486. doi: 10.1093/ije/dyn262.PubMedCrossRefGoogle Scholar
  45. 45.
    WANTS Health West. (2001). Collaborative health and wellbeing survey design and methodology. Perth, WA: Western Australian Government.Google Scholar
  46. 46.
    Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Hillsdale, NJ: Eribaum.Google Scholar
  47. 47.
    Kolappe, K., Henderson, D. C., & Kishare, S. P. (2013). No physical health without mental health: Lessons unlearned? Bulletin of the World Health Organization, 91, 3.CrossRefGoogle Scholar
  48. 48.
    Naylor, C., Amy Galea, Parsonage, M., McDaid, D., Knapp, M., Fossey, M. (2012). Long term conditions and mental health. Accessed 29 Aug 2013.
  49. 49.
    Academy of Medical Royal Colleges. (2009). No health without mental health: The alert summary report. London: Millbank Medical Ltd.Google Scholar
  50. 50.
    World Federation of Mental Health (2010). Mental Health and Chronic Illness. The Need for Continued and Integrated care.
  51. 51.
    Adams, R. J., Wilson, D. H., Taylor, A. W., Daly, A., Tursan d’Espaignet, E., Dal Grande, E., et al. (2004). Psychological factors and asthma quality of life: A population based study. Thorax, 59, 930–935.PubMedCentralPubMedCrossRefGoogle Scholar
  52. 52.
    Jain, A., & Lolak, S. (2009). Psychiatric aspects of chronic lung disease. Current Psychiatry Reports, 11, 219–225.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of MedicineUniversity of AdelaideAdelaideAustralia

Personalised recommendations