Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/90761
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: 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
Author: Tucker, G.
Adams, R.
Wilson, D.
Citation: Quality of Life Research, 2014; 23(8):2195-2203
Publisher: Springer
Issue Date: 2014
ISSN: 0962-9343
1573-2649
Statement of
Responsibility: 
Graeme Tucker, Robert Adams, David Wilson
Abstract: PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Keywords: Humans; Health Status Indicators; Reproducibility of Results; Psychometrics; Algorithms; Quality of Life; Adult; Aged; Middle Aged; Australia; Female; Male; Self-Assessment; Surveys and Questionnaires
Rights: © Springer International Publishing Switzerland 2014
RMID: 0030025059
DOI: 10.1007/s11136-014-0669-9
Appears in Collections:Medicine publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.