Addressing Reporting Heterogeneity in Visual Analogue scales: A Double-Index Model Approach using Anchoring Vignette

Abstract

In this study, we propose methods to account for reporting heterogeneity in self reported data coming from Visual Analogue Scales (VAS) using corresponding VAS-based anchoring vignettes. Though widely used as a measurement tool in many disciplines, VAS may suffer from individual-specific reporting heterogeneity. Such reporting heterogeneity and potential solutions to solve this problem in the context of VAS measures have not yet been addressed in the literature. Using VAS-based anchoring vignettes and standard vignettes assumptions, we show how double-index models can be used to address individual-specific reporting heterogeneity in VAS. We then apply our methods to real data assessing reporting heterogeneity in VAS-measured Quality of Life (QoL) among students in Switzerland. We show that the findings of previous studies showing positive associations between being a female and QoL might be entirely driven by reporting heterogeneity.