Abstract
PurposeA number of previous studies have shown inconsistencies between sub-scale scores and component summary scores using traditional scoring methods of the SF-36 version 1. This study addresses the issue in Version 2 and asks if the previous problems of disagreement between the eight SF-36 Version 1 sub-scale scores and the Physical and Mental Component Summary persist in version 2. A second study objective is to review the recommended scoring methods for the creation of factor scoring weights and the effect on producing summary scale scoresMethodsThe 2004 South Australian Health Omnibus Survey dataset was used for the production of coefficients. There were 3,014 observations with full data for the SF-36. Data were analysed in LISREL V8.71. Confirmatory factor analysis models were fit to the data producing diagonally weighted least squares estimates. Scoring coefficients were validated on an independent dataset, the 2008 South Australian Health Omnibus Survey.ResultsProblems of agreement were observed with the recommended orthogonal scoring methods which were corrected using confirmatory factor analysis.ConclusionsConfirmatory factor analysis is the preferred method to analyse SF-36 data, allowing for the correlation between physical and mental health.
Highlights
The SF-36 and the shorter form SF-12 health status questionnaires have been used extensively in international studies to obtain summary measures of health status
Tucker et al [5], acknowledged this connection in the SF-36 version 1 by demonstrating that the use of the recommended orthogonal scoring methods, which do not allow for the correlation, created important discrepancies between the physical health summary scores (PCS) and mental component summary scores (MCS) and their underlying sub-scale scores, and that this could be corrected by use of confirmatory factor analysis (CFA)
The Akaike Information Criteria (AIC) value from the CFA model fit in LISREL V8.7 [25] is based on the Satorra-Bentler Chi-squared value, and the AIC from the Exploratory factor analysis (EFA) model fit in Stata SE V12 [24] is based on the model chi-square which is -2*log likelihood
Summary
The SF-36 and the shorter form SF-12 health status questionnaires have been used extensively in international studies to obtain summary measures of health status. International concern has been raised questioning the validity of the recommended orthogonal scoring methods of Version 1 of the SF-36 to produce Physical and Mental Component Summary scores (PCS & MCS) [2,3,4,5,6,7,8,9]. These scoring methods remain in widespread use, they are the default scoring approach around the world. Given the extensive use of Version 2 [12] it is important to again compare recommended orthogonal scoring methods with CFA, assess if the problems found in Version 1 persist and resolve which methods may best analyse Version 2 to produce summary scores consistent with the sub-scales
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