Exercise-related biases in stated choice experiments, such as position effects and screen fatigue, can reduce model accuracy by introducing skewed responses. We evaluate two mitigation strategies: adding contextual information to utility models and adjusting pooling based on respondent behavior. Meta-analytic findings will assess their effectiveness across studies. Attendees will gain practical tools for identifying quasi-straightliners, cleaning noisy data, and applying recommendations to model, simulate, and improve the overall quality of choice experiment results.
