Performance comparison of Likert and binary formats of SF-36 version 1.6 across ECRHS II adults populations.

OBJECTIVES To evaluate a binary response structure of SF-36 items assessing scaling assumptions, reliability, and validity of questionnaire. METHODS An optimal scaling accounting for the nonmetric properties of the data was used to reduce SF-36 Likert item responses to give a binary coding. The binary recoding was compared with the original format regarding item analysis, underlying latent components and know-groups clinical validity using ordered correlation/regression methods. Data from the European Community Respiratory Health Survey Follow-up (ECRHS II) of 8854 subjects from 25 centers were analyzed to cross-validate the binary coding proposal. RESULTS Overall, the testing comparison produces results indicating that the binary recoding of the SF-36 scales meets at least similar standards without jeopardizing the underling structure of the original format. Internal binary consistency shows comparable values with the Likert ones and these are always higher than the minimum suggested. The Principal Component structure was well replicated and know-groups validity gives similar research findings for symptomatic, long-term illness and depression differences. CONCLUSIONS Although there is lost of information due to the reduction of response's chance, our results indicate that the SF-36 binary recoding gives the possibility to suggest a new version of smarter and easier methodology of administration, compilation, score calculation, and data processing. Consequently, it may be an alternative to the existing shorter versions, suitable in administering in clinical setting and clinical trials, in subjects with serious diseases, and by telephone.

[1]  M. Hill,et al.  Nonlinear Multivariate Analysis. , 1990 .

[2]  A. Lloyd Assessment of the SF-36 version 2 in the United Kingdom. , 1999, Journal of epidemiology and community health.

[3]  J. Ware,et al.  What information do consumers want and how will they use it? , 1995, Medical care.

[4]  H. Goldstein Multilevel Statistical Models , 2006 .

[5]  E Svensson,et al.  Construction of a single global scale for multi‐item assessments of the same variable , 2001, Statistics in medicine.

[6]  R. Clarke,et al.  Theory and Applications of Correspondence Analysis , 1985 .

[7]  P. Fayers,et al.  Quality of Life: Assessment, Analysis, and Interpretation , 2000 .

[8]  A. Donner A Review of Inference Procedures for the Intraclass Correlation Coefficient in the One-Way Random Effects Model , 1986 .

[9]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[10]  W. Akkermans Polytomous item scores and Guttman dependence , 1999 .

[11]  J. Ware,et al.  Conceptualization and Measurement of Health for Adults in the Health Insurance Study , 1979 .

[12]  Julia Knox ECRHS II steering committee. The European community respiratory health survey II , 2003 .

[13]  P. Kind,et al.  Comparison of the MOS short form-12 (SF12) health status questionnaire with the SF36 in patients with rheumatoid arthritis. , 1998, British journal of rheumatology.

[14]  J. Ware,et al.  International quality of life assessment (IQOLA) project , 1992, Quality of Life Research.

[15]  A. Venot,et al.  Methodological and statistical problems in the construction of composite measurement scales: a survey of six medical and epidemiological journals. , 1995, Statistics in medicine.

[16]  J C Grip,et al.  Ordinal scales and foundations of misinference. , 1989, Archives of physical medicine and rehabilitation.

[17]  Fritz Drasgow,et al.  Polychoric and Polyserial Correlations , 2006 .

[18]  C. McHorney,et al.  The MOS 36‐Item Short‐Form Health Survey (SF‐36): II. Psychometric and Clinical Tests of Validity in Measuring Physical and Mental Health Constructs , 1993, Medical care.

[19]  W H Rogers,et al.  Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. , 1995, Medical care.

[20]  I. Jolliffe,et al.  Nonlinear Multivariate Analysis , 1992 .

[21]  P. Robert,et al.  A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient , 1976 .

[22]  D. Jarvis,et al.  The European Community Respiratory Health Survey II , 1994, European Respiratory Journal.

[23]  M. Splaine,et al.  Use of the Reliable Change Index to evaluate clinical significance in SF-36 outcomes , 2002, Quality of Life Research.

[24]  S. Embretson,et al.  Item response theory for psychologists , 2000 .

[25]  Paul Horst,et al.  The prediction of personal adjustment. , 1942 .

[26]  L. Guttman,et al.  The Quantification of a class of attributes : A theory and method of scale construction , 1941 .

[27]  M Sullivan,et al.  The factor structure of the SF-36 Health Survey in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. , 1998, Journal of clinical epidemiology.

[28]  C. Sherbourne,et al.  The MOS 36-Item Short-Form Health Survey (SF-36) , 1992 .

[29]  R W Sanson-Fisher,et al.  An examination of self- and telephone-administered modes of administration for the Australian SF-36. , 1998, Journal of clinical epidemiology.

[30]  Risto Lethonen Multilevel Statistical Models (3rd ed.) , 2005 .

[31]  T. Perneger,et al.  A simple imputation algorithm reduced missing data in SF-12 health surveys. , 2005, Journal of clinical epidemiology.