Data from the 36-Item Short-Form Health Survey (SF-36) do not follow a normal distribution and should not be analyzed using parametric techniques. A novel type of analysis, top-box analysis, may add to the interpretation of these data.
Review of SF-36 data from preoperative and postoperative patients.
Tertiary care hospital and clinic.
One thousand randomly selected preoperative and postoperative patients with a variety of surgical diseases completed the SF-36 (8 domains: physical functioning, role physical, role emotional, bodily pain, vitality, mental health, social functioning, and general health). The best possible score was 100; the worst possible score, 0. One item assessed “health transition.” The best score was 1; the worst score, 5. The health transition item and each domain were analyzed for mean with standard deviation, median, mode skewness, kurtosis, and normality. A “top-box” assessment was done by determining the frequency of patients scoring 100 in each domain or 1 in the health transition item. In addition, preoperative and postoperative scores were compared.
The results for all 1000 questionnaires demonstrated that none of the domains had data that followed a normal distribution. The means, medians, and modes were different. Five domains had the mode and median at the top box.
The SF-36 data did not follow a normal distribution in any of the domains. Data were always skewed to the left, with means, medians, and modes different. These data need to be statistically analyzed using nonparametric techniques. Of the 8 domains, 5 had a significant frequency of top-box scores, which also were the domains in which the mode was at 100, implying that change in top-box score may be an informative method of presenting change in SF-36 data.