Assessing measurement invariance of MSQOL-54 across Italian and English versions
The Multiple Sclerosis Quality of Life-54 (MSQOL-54) is a specific multiple sclerosis (MS) health-related quality of life inventory consisting of 52 items organized into 12 subscales plus two single items. No study was found in literature assessing its measurement invariance across language versions. We investigated whether MSQOL-54 items provide unbiased measurements of underlying constructs across Italian and English versions.
Three constrained levels of measurement invariance were evaluated: configural invariance where equivalent numbers of factors/factor patterns were required; metric invariance where equivalent factor loadings were required; and scalar invariance where equivalent item intercepts between groups were required. Comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) fit indices and their changes between nested models were used to assess tenability of invariance constraints.
Overall, the dataset included 3669 MS patients: 1605 (44%) Italian, mean age 41 years, 62% women, 69% with mild level of disability; 2064 (56%) English-speaking (840 [41%] from North America, 797 [39%] from Australasia, 427 [20%] from UK and Ireland), mean age 46 years, 83% women, 54% with mild level of disability. The configural invariance model showed acceptable fit (RMSEA = 0.052, CFI = 0.904, SRMR = 0.046); imposing loadings and intercepts equality constraints produced negligible worsening of fit (ΔRMSEA < 0.001, ΔCFI = − 0.002, ΔSRMR = 0.002 for metric invariance; ΔRMSEA = 0.003, ΔCFI = − 0.013, ΔSRMR = 0.003 for scalar invariance).
These findings support measurement invariance of the MSQOL-54 across the two language versions, suggesting that the questionnaire has the same meaning and the same measurement paramaters in the Italian and English versions.
KeywordsMultiple sclerosis Measurement invariance Multi-group confirmatory factor analysis Health-related quality of life MSQOL-54
We thank all the PwMS who participated.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
Patients gave written informed consent to being included in the original projects. Additional consent was not required for this secondary analysis, for which patients’ privacy and anonymity was guaranteed.
- 1.Compston, A., McDonald, I., Noseworthy, J., Lassmann, H., Miller, D., Smith, K., et al. (2006). McAlpine’s multiple sclerosis (4th ed.). Edinburgh: Churchill Livingstone Elsevier.Google Scholar
- 12.Idiman, E., Uzunel, F., Ozakbas, S., Yozbatiran, N., Oguz, M., Callioglu, B., et al. (2006). Cross-cultural adaptation and validation of multiple sclerosis quality of life questionnaire (MSQOL-54) in a Turkish multiple sclerosis sample. Journal Neurological Sciences,240, 77–80.CrossRefGoogle Scholar
- 27.Hadgkiss, E. J., Jelinek, G. A., Weiland, T. T., Pereira, N. G., Marck, C. H., & van derMeer, D. M. (2013). Methodology of an international study of people with multiple sclerosis recruited through web 2.0 platforms: demographics, lifestyle, and disease characteristics. Neurology Research International,2013, 580–596.Google Scholar
- 28.Jelinek, G. A., De Livera, A. M., Marck, C. H., Brown, C. R., Neate, S. L., Keryn, L., et al. (2016). Lifestyle, medication and socio-demographic determinants of mental and physical health-related quality of life in people with multiple sclerosis. BMC Neurology,16, 235. https://doi.org/10.1186/s12883-016-0763-4.CrossRefPubMedPubMedCentralGoogle Scholar
- 39.Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 health survey manual and interpretation guide. Boston, MA: The Health Institute.Google Scholar
- 43.Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling (pp. 76–99)., Concepts, issues, and applications London: Sage.Google Scholar
- 46.van Bebber, J., Flens, G., Wigman, J. T. W., de Beurs, E., Sytema, S., Wunderink, L., et al. (2018). Application of the patient-reported outcomes measurement information system (PROMIS) item parameters for anxiety and depression in the Netherlands. International Journal of Methods in Psychiatric Research,27(4), e1744. https://doi.org/10.1002/mpr.1744.CrossRefPubMedGoogle Scholar
- 49.Muthén, L. K., Muthén, B. O. (1998-2011). Mplus user’s guide. 6th edn. Los Angeles, CA: Muthén & Muthén.Google Scholar