Purpose: Methods for response shift (RS) detection at the individual level could be of great interest when analyzing changes in patient-reported outcome data. Guttman errors (GEs), which measure discrepancies in respondents' answers compared to the average sample responses, might be useful for detecting RS at the individual level between two time points, as RS may induce an increase in the number of discrepancies over time. This study aims to establish the link between recalibration RS and the change in the number of GEs over time (denoted index [Formula: see text]) via simulations and explores the discriminating ability of this index.
Methods: We simulated the responses of individuals affected or not affected by recalibration RS (defined as changes in the patients' standard of measurement) to determine whether simulated individuals with recalibration had a greater change in the number of GEs over time than individuals without recalibration. The effects of factors related to the sample, the questionnaire structure and recalibration were investigated. As an illustrative example, the change in the number of GEs was computed in patients suffering from eating disorders.
Results: Within simulations, simulated individuals affected by recalibration had, on average, a greater change in the number of GEs over time than did individuals without RS. Some of the parameters related to the questionnaire structure and recalibration magnitude appeared to have substantial effects on the values of [Formula: see text]. Discriminating abilities appeared, however, globally low.
Conclusion: Some evidence of the link between recalibration and the change in GEs was found in this study. GEs could be a valuable nonparametric tool for RS detection at a more individual level, but further investigation is needed.
Keywords: Guttman errors; Individual level; Recalibration; Response shift.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.