Identifying response shift statistically at the individual level
The purpose of this study was to explore whether a longitudinal comparison between reported and predicted health could be used as a method of identifying subjects who potentially experienced response shift.
A response-shift model was developed using data from a longitudinal study of stroke in which measures of stroke impact were made at study entry and at 1, 3, 6, and 12 months post stroke. Residuals from a random effects model were centered and used to create trajectories. This model was tested against a data set from a study in which the then-test had been administered. Twenty simulated data sets were also generated to examine how much of response shift could be attributed to random error.
Group-based trajectory analysis identified seven trajectory groups. The majority (67%) of the 387 persons showed no response shift over time, whereas 15% lowered and 13% raised their health over time, disproportionally to that predicted.
Results of the validation studies were supportive that this methodology identifies response shift, but further research is required to compare results with other methodologies and other predictive models.
KeywordsResponse shift Measurement of change Quality of life Cerebrovascular accident Longitudinal
- 1.Schwartz, C. E., & Sprangers, M. A. G. (2000).Adaptation to changing health response shift in quality-of-life research(1st ed). Washington, DC: American Psychological Association.Google Scholar
- 10.Ahmed, S., Mayo, N. E., Wood-Dauphinee, S., Hanley, J. A., & Cohen, S. R. (2005). The structural equation modeling technique did not show a response shift, contrary to the results of the then test and the individualized approaches. Journal of Clinical Epidemiology, 58(11), 1125–1133.PubMedCrossRefGoogle Scholar
- 21.World Health Organization. (2001). International classification of functioning, disability and health (2nd revision ed.). Geneva.Google Scholar
- 25.Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1994). SF-36 physical & mental scales: A user’s manual. Boston, Massachusetts: The Health Institute, New England Medical Center.Google Scholar
- 26.Ware, J. E., Jr. (2000). SF-36 health survey update. Spine, 25(24), 3130–3139.Google Scholar
- 27.Ware, J.E., Jr., & Sherbourne, C. D. (1992). The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–481.Google Scholar
- 35.Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis modeling change and event occurrence. New York: Oxford University Press.Google Scholar