Adjusting for subgroup differences in extreme response tendency in ratings of health care: impact on disparity estimates

Health Serv Res. 2009 Apr;44(2 Pt 1):542-61. doi: 10.1111/j.1475-6773.2008.00922.x. Epub 2008 Nov 24.


Objective: Adjust for subgroup differences in extreme response tendency (ERT) in ratings of health care, which otherwise obscure disparities in patient experience.

Data source: 117,102 respondents to the 2004 Consumer Assessment of Healthcare Providers and Systems (CAHPS) Medicare Fee-for-Service survey.

Study design: Multinomial logistic regression is used to model respondents' use of extremes of the 0-10 CAHPS rating scales as a function of education. A new two-stage model adjusts for both standard case-mix effects and ERT. Ratings of subgroups are compared after these adjustments.

Principal findings: Medicare beneficiaries with greater educational attainment are less likely to use both extremes of the 0-10 rating scale than those with less attainment. Adjustments from the two-stage model may differ substantially from standard adjustments and resolve or attenuate several counterintuitive findings in subgroup comparisons.

Conclusions: Addressing ERT may be important when estimating disparities or comparing providers if patient populations differ markedly in educational attainment. Failures to do so may result in misdirected resources for reducing disparities and inaccurate assessment of some providers. Depending upon the application, ERT may be addressed by the two-stage approach developed here or through specified categorical or stratified reporting.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Bias
  • Diagnosis-Related Groups
  • Educational Status
  • Health Care Surveys*
  • Healthcare Disparities*
  • Humans
  • Medicare
  • Middle Aged
  • Observer Variation*
  • Patient Satisfaction*
  • Quality of Health Care*
  • Surveys and Questionnaires
  • United States
  • Vulnerable Populations