conjoint-designlisted

# Conjoint Design Expert ## Instructions > Worked example (attribute table → power calculation → PAP tier assignment): see `references/example.md`. ### 1. Attribute Architecture - **Orthogonality:** Ensure every attribute is independent of every other attribute to allow for the estimation of causal effects for each component. - **Randomization of Order:** Order attributes randomly at the *respondent level* (not the task level) to prevent "primacy" or "recency" effects while avoiding the cognitive overload of finding information in different orders across tasks (Stantcheva 2023). A specific logical flow may override this if theoretically required. - **D-Optimal Designs:** Consider D-optimal or constrained randomization schemes rather than pure randomization. D-optimal designs choose the sets of administered conditions that maximize statistical power and may be preferable when the number of possible attribute combinations is large relative to the sample size (Auspurg & Hinz 2015; Stantcheva 2023). - **Attribute Density:** Monitor for respondent fatigue. Stefanelli and Lukac (2020) cite evidence that conjoint results remain stable with up to 10 attributes; Bansak et al. (2018) find that response quality does not degrade with up to 30 *tasks* on MTurk, and Bansak et al. (2021, "Beyond the Breaking Point") extend this to the *attribute* dimension, reporting stability at many attributes. These are the canonical sources for the task- and attribute-count claims respectively. Still