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Quality of Life Research

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01-05-2007 | Original Paper

The SF36 Version 2: critical analyses of population weights, scoring algorithms and population norms

Auteurs: Graeme Hawthorne, Richard H. Osborne, Anne Taylor, Jan Sansoni

Gepubliceerd in: Quality of Life Research | Uitgave 4/2007

Abstract

Background

The SF36 Version 2 (SF36V2) is a revision of the SF36 Version 1, and is a widely used health status measure. It is important that guidelines for interpreting scores are available.

Method

A population sample of Australians (n = 3015) weighted to achieve representativeness was administered the SF36V2. Comparisons between published US weights and sample derived weights were made, and Australian population norms computed and presented.

Major findings

Significant differences were observed on 7/8 scales and on the mental health summary scale. Possible causes of these findings may include different sampling and data collection procedures, demographic characteristics, differences in data collection time (1998 vs. 2004), differences in health status or differences in cultural perception of the meaning of health. Australian population norms by age cohort, gender and health status are reported by T-score as recommended by the instrument developers. Additionally, the proportions of cases within T-score deciles are presented and show there are important data distribution issues.

Principal conclusions

The procedures reported here may be used by other researchers where local effects are suspected. The population norms presented may be of interest. There are statistical artefacts associated with T-scores that have implications for how SF36V2 data are analysed and interpreted.

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Titel: The SF36 Version 2: critical analyses of population weights, scoring algorithms and population norms
Auteurs: Graeme Hawthorne
Richard H. Osborne
Anne Taylor
Jan Sansoni
Publicatiedatum: 01-05-2007
Uitgeverij: Springer Netherlands
Gepubliceerd in: Quality of Life Research / Uitgave 4/2007
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-006-9154-4

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Abstract

Background

Method

Major findings

Principal conclusions

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