Detecting Response Styles in Visual Analogue Scales
Abstract
Response styles are prevalent in self-report data and may bias parameter estimates. While there are various approaches for detecting response styles in Likert-type data, models for visual analogue scales remain scarce. We propose a novel modeling framework that extends the beta item response model by Noel and Dauvier (2007) to jointly detect extreme response style, midpoint response style, and acquiescent response style. Estimated in a Bayesian framework using Stan, the model performed well in a comprehensive simulation study. Parameter recovery was primarily influenced by the number of (reverse-scored) items and the variance of response styles, whereas sample size had a negligible effect. The model’s practical utility is illustrated using empirical data (Kuhlmann et al., 2017), for which it demonstrated superior model fit compared to the original beta item response model. Overall, the proposed framework provides researchers with a principled approach to account for response styles in visual analogue scale data.
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BibTeX
@misc{vollbracht2026detecting,
title = {{Detecting Response Styles in Visual Analogue Scales}},
author = {Dominik Vollbracht and Tanja Lischetzke and Mirka Henninger},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/xteku_v2},
url = {https://osf.io/preprints/psyarxiv/xteku_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Detecting Response Styles in Visual Analogue Scales
AU - Dominik Vollbracht
AU - Tanja Lischetzke
AU - Mirka Henninger
PY - 2026
DA - 2026/09/28
PB - PsyArXiv
DO - 10.31234/osf.io/xteku_v2
UR - https://osf.io/preprints/psyarxiv/xteku_v2/
AB - Response styles are prevalent in self-report data and may bias parameter estimates. While there are various approaches for detecting response styles in Likert-type data, models for visual analogue scales remain scarce. We propose a novel modeling framework that extends the beta item response model by Noel and Dauvier (2007) to jointly detect extreme response style, midpoint response style, and acquiescent response style. Estimated in a Bayesian framework using Stan, the model performed well in a comprehensive simulation study. Parameter recovery was primarily influenced by the number of (reverse-scored) items and the variance of response styles, whereas sample size had a negligible effect. The model’s practical utility is illustrated using empirical data (Kuhlmann et al., 2017), for which it demonstrated superior model fit compared to the original beta item response model. Overall, the proposed framework provides researchers with a principled approach to account for response styles in visual analogue scale data.
ER -