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The online version of this article (doi:10.1007/s11136-013-0393-x) contains supplementary material, which is available to authorized users.
The purpose of this paper was to examine if pain and functioning can be distinguished in the Oxford Knee Score (OKS) in a meaningful way. This was done by (1) conducting exploratory factor analysis to explore the factorial structure of the OKS and (2) conducting confirmatory factor analysis to examine whether a two-factor solution is superior to a one-factor solution.
Secondary data analysis of four independent datasets containing OKS scores on 161,973 patients was performed. Four independent datasets contained data on: (1) 156, 788 patients from the NHS HES/PROMS dataset, (2) 2,405 consecutive patients from the South West London Elective Operating Centre, (3) 2,353 patients enrolled in the Knee Arthroplasty Trial and (4) 427 consecutive patients listed for knee replacement surgery at the Nuffield Orthopaedic Centre in Oxford.
Factor extraction methods suggested that, depending on the method employed, both one- and two-factor solutions are meaningful. Overall and in each data set some cross-loading occurred and item loadings were consistent across two factors. On confirmatory factor analysis, both one- and two-factor models had acceptable fit indices. This allowed the creation of the ‘OKS pain component’ and the ‘OKS functional component’ subscales.
Factor analysis confirmed the original conceptual basis of the OKS but offered an option to perform additional analyses using pain and functional subscales. Further research should focus on providing further evidence on construct validity and responsiveness of the newly derived subscales.
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Supplementary material 1 (DOCX 21 kb)11136_2013_393_MOESM1_ESM.docx
Agency for Healthcare Research and Quality (February, 2012). Facts and figures. Statistics on hospital-based care in the United States, 2009. www.hcup-us.ahrq.gov/reports/factsandfigures/2009/exhibit3_1.jsp. Accessed 11 June 2012.
National Joint Registry for England and Wales (2011). 8th annual report. Hemel Hempstead, Hertfordshire, UK.
Dawson, J., Fitzpatrick, R., Murray, D., & Carr, A. (1998). Questionnaire on the perceptions of patients about total knee replacement. Journal of Bone and Joint Surgery. British Volume, 80(1), 63–69. CrossRef
Devlin, N. J., & Appleby, J. (2010). Getting the most out of PROMs. London: King’s Fund, Office of Health Economics.
Gooberman-Hill, R., Woolhead, G., MacKichan, F., Ayis, S., Williams, S., & Dieppe, P. (2007). Assessing chronic joint pain: Lessons from a focus group study. Arthritis Care & Research, 57(4), 666–671. CrossRef
Dawson, J., Fitzpatrick, M., Churchman, D., Verjee-Lorenz, A., & Claysonm, D. (2010). User manual for the Oxford Knee Score (OKS). Isis Innovation Limited.
Conaghan, P. G., Emerton, M., & Tennant, A. (2007). Internal construct validity of the Oxford Knee Scale: Evidence from Rasch measurement. Arthritis Care & Research, 57(8), 1363–1367. CrossRef
Baker, P., Van der Meulen, J., Lewsey, J., & Gregg, P. (2007). The role of pain and function in determining patient satisfaction after total knee replacement: Data from the National Joint Registry for England and Wales. Journal of Bone and Joint Surgery. British Volume, 89(7), 893.
Scott, C., Howie, C., MacDonald, D., & Biant, L. (2010). Predicting dissatisfaction following total knee replacement: a prospective study of 1217 patients. Journal of Bone and Joint Surgery-British Volume, 92(9), 1253.
The Health and Social Care Information Centre Annual publication data quality notes library. Available at: http://www.hesonline.nhs.uk/Ease/servlet/ContentServer?siteID=1937&categoryID=1189. Accessed 26 March 2012.
Murray, D., Fitzpatrick, R., Rogers, K., Pandit, H., Beard, D., Carr, A., et al. (2007). The use of the Oxford hip and knee scores. Journal of Bone and Joint Surgery-British Volume, 89(8), 1010.
Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443–460. CrossRef
Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The bare essentials. PMPH USA Ltd.
Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286. CrossRef
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20, 141–151.
Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99(3), 432. CrossRef
Ledesma, R. D., & Valero-Mora, P. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research & Evaluation, 12(2), 1–11.
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. CrossRef
David, L. S. (1998). Factors affecting reliability of interpretations of scree plots. Psychological Reports, 83(2), 687–694. CrossRef
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327. CrossRef
O’connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, 32(3), 396–402.
Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191. CrossRef
Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In: Problems and solutions in human assessment 2000 (pp. 41–71). USA: Springer.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272. CrossRef
Maruyama, G. (1998). Basics of structural equation modelling. California: Thousand Oaks.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Testing structural equation models, 154, 136–162.
Stewart, A. L., & Ware, J. E. (1992). Measuring functioning and well-being: The medical outcomes study approach. USA: RAND Corporation.
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.
Kline, R. B. (2010). Principles and practice of structural equation modelling. New York: Guilford Press.
Cliff, N. (1983). Some cautions concerning the application of causal modelling methods. Multivariate Behavioral Research, 18(1), 115–126. CrossRef
- Can pain and function be distinguished in the Oxford Knee Score in a meaningful way? An exploratory and confirmatory factor analysis
Richard E. Field
David W. Murray
Andrew J. Price
David J. Beard
- Springer Netherlands