Related Experiment Video
Updated: May 12, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
Published on: August 29, 2025
Observed agreement problems between sub-scales and summary components of the SF-36 version 2 - an alternative scoring
Graeme Tucker1, Robert Adams, David Wilson
1SA Department of Health, Adelaide, South Australia, Australia. Graeme.Tucker@health.sa.gov.au
Purpose:
A 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 scores.
Methods:
The 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.
Results:
Problems of agreement were observed with the recommended orthogonal scoring methods which were corrected using confirmatory factor analysis.
Conclusions:
Confirmatory factor analysis is the preferred method to analyse SF-36 data, allowing for the correlation between physical and mental health.
Related Concept Videos
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Friedman Two-way Analysis of Variance by Ranks
One-Way ANOVA: Unequal Sample Sizes
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Self-Discrepancy and Its Effects
SBAR II: Application of SBAR
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
