Social Indicators Research

, Volume 83, Issue 2, pp 331–350

Techniques For Developing Health Quality of Life Scales for Point of Service Use

Original Paper


Clinical and health policy research frequently involves health status measurement using generic or disease specific instruments. These instruments are generally developed to arrive at several scales, each measuring a distinct domain of health quality of life (HQOL). Clinical settings are starting to explore how to integrate patient perspectives of HQOL outcomes into patient care. However, the length of many HQOL instruments poses a challenge in terms of patient burden, as well as clinic flow time. The most popular paradigm for scale construction utilizes classical test theory methodology and can lead to excessive and redundant items in an effort to bolster reliability measurements such as Cronbach’s alpha above levels of accepted reliability.

This paper presents techniques for utilizing item response theory to arrive at single item scales that are diagnostically informative and short enough to have clinical utility. A danger of such dramatic scale reduction is that validity might be compromised. This danger is addressed in terms of criterion related validity and sensitivity to clinical changes over a 36 months period. The reduction methods are illustrated using selected scales from the Arthritis Impact Measurement Scales 2 (AIMS2) with data obtained from the study Pharmaceutical Care Outcomes: The Patient Role (PCOPR).


Arthritis Impact Measurement Scales 2 (AIMS2) health quality of life item response theory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Young-Sun Lee
    • 1
  • Jeffrey Douglas
    • 2
  • Betty Chewning
    • 3
  1. 1.Department of Human DevelopmentTeachers College Columbia UniversityNew YorkUSA
  2. 2.University of IllinoisUrbana-ChampaignUSA
  3. 3.University of WisconsinMadisonUSA

Personalised recommendations