A solution to the pervasive problem of response bias in self-reports.

Self-reports are used ubiquitously to probe people's thoughts, feelings, and behaviors and inform medical decisions, enterprise operations, and government policy and legislation. Despite their pervasive use, self-report measures such as Likert scales have a profound problem: Standard analytic approaches do not control for the confounding effects of idiosyncratic response biases. Here, we present a model-based solution to this problem. Our model disentangles response bias from latent constructs of interest to obtain less biased scores of the latent states of respondents. Inspired by Thurstonian approaches in the psychophysics literature, the model requires nothing further than standard Likert scale design assumptions. The model uses a data-driven approach to control for response biases, without the need to prespecify bias types or response strategies. We demonstrate the model's ability to uncover more precise estimates of latent state associations, outperforming bias-affected standard scoring techniques, and garner insights into previously undetected codependencies between certain latent states and particular forms of response bias. The model is thus a tool which outperforms standard scoring methods and generates insights into, and controls for, the potentially confounding effects of response bias on self-report Likert scale data.