Forced-choice conjoint designs have become a staple method in the experimentalist's toolkit. However, the forced-choice outcome is neither always consistent with the types of choices individuals make in real political contexts, nor is it statistically efficient. In this paper, we formalize how ranked outcomes can be integrated into the conjoint framework. We provide a proof that rank-expanded estimators are equivalent to conventional AMCE, a theoretical account of how additional profiles increase the efficiency of conjoint designs, and design-based tests for the transitivity and independence of irrelevant alternatives assumptions that underpin the expansion. Across two pre-registered survey experiments--the first comparing forced-choice and ranked-choice designs across candidate and policy domains, and the second varying the number of ranked profiles--we find that ranked-choice conjoints yield substantively similar but more precise AMCE estimates, shrinking standard errors by 12-13% with one additional profile and up to 55% with six profiles per vignette. Based on efficiency--validity trade-offs, we recommend K = 4 profiles for most applications. We provide an accompanying open-source R package, cjrank, that implements rank expansion, AMCE estimation, efficiency diagnostics, and the assumption tests described in this paper.

翻译：强制选择联合设计已成为实验者工具箱中的标准方法。然而，强制选择结果既不符合个体在真实政治情境中做出的选择类型，也缺乏统计效率。本文形式化了如何将排序结果纳入联合分析框架。我们证明了：排序扩展估计量与传统AMCE等价；提出理论解释说明额外特征如何提升联合设计的效率；并开发基于设计的方法检验支撑扩展的传递性及无关方案独立性假设。两项预注册调查实验——第一项在候选人与政策领域比较强制选择与排序选择设计，第二项变更排序特征数量——显示：排序选择联合实验产生实质相似但更精确的AMCE估计量，每增加一个特征可使标准误差缩减12-13%，而每个场景包含六个特征时误差缩减可达55%。基于效率-有效性权衡，我们建议多数应用场景使用K=4个特征。我们提供了配套开源R包cjrank，实现排序扩展、AMCE估计、效率诊断及本文所述假设检验。