Exploring the Influence of Response Styles on Continuous Scale Assessments: Insights From a Novel Modeling Approach.

The use of discrete categorical formats to assess psychological traits has a long-standing tradition that is deeply embedded in item response theory models. The increasing prevalence and endorsement of computer- or web-based testing has led to greater focus on continuous response formats, which offer numerous advantages in both respondent experience and methodological considerations. Response styles, which are frequently observed in self-reported data, reflect a propensity to answer questionnaire items in a consistent manner, regardless of the item content. These response styles have been identified as causes of skewed scale scores and biased trait inferences. In this study, we investigate the impact of response styles on individuals' responses within a continuous scale context, with a specific emphasis on extreme response style (ERS) and acquiescence response style (ARS). Building upon the established continuous response model (CRM), we propose extensions known as the CRM-ERS and CRM-ARS. These extensions are employed to quantitatively capture individual variations in these distinct response styles. The effectiveness of the proposed models was evaluated through a series of simulation studies. Bayesian methods were employed to effectively calibrate the model parameters. The results demonstrate that both models achieve satisfactory parameter recovery. Neglecting the effects of response styles led to biased estimation, underscoring the importance of accounting for these effects. Moreover, the estimation accuracy improved with increasing test length and sample size. An empirical analysis is presented to elucidate the practical applications and implications of the proposed models.