发表机构
London School of Economics and Political Science; Educational Testing Service; Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University（伦敦政治经济学院; 美国教育考试服务中心; 北京师范大学基础教育质量协同创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对协作决策中难以提取个体谈判特质的问题，提出两阶段潜变量测量模型，结合成员奖励与潜在特质生成团队选择并建模个体响应，模拟与实证均验证了其预测准确性和有效性。
AI 中文摘要
考虑一种协作决策情境，其中具有不同奖励结构的团队成员需要做出联合决策。重复的协作决策可以反映团队成员在谈判能力上的个体差异。然而，提取此类信息具有挑战性，因为每个观察到的响应都源于涉及多个参与者及其奖励结构的协作过程。在本文中，我们提出了一种用于结构化协作选择任务的两阶段概率测量模型。在第一阶段，已知的成员特定奖励和潜在参与者谈判特质共同决定团队选项效用，由此生成潜在的团队选择。在第二阶段，个体响应以潜在团队选择为条件进行建模，可能的偏差取决于参与者自身的奖励。参与者层面的协变量也可以通过结构模型纳入。一项模拟研究表明，在不同样本量和项目规模下，参与者特质和模型参数均能得到令人满意的恢复。对模拟协作谈判任务数据的应用表明，选择预测准确性远高于随机选择基线，且估计特质与外部标准变量之间存在有意义的关联。所提出的框架提供了一种利用协作选择数据测量参与者层面谈判特质的心理测量学方法。
英文摘要
Consider a collaborative decision-making setting in which team members with different reward structures need to make a joint decision. Repeated collaborative decisions can reflect individual differences in team members' negotiation ability. However, extracting such information is challenging because each observed response arises from a collaborative process involving multiple participants and their reward structures. In this paper, we propose a two-stage probabilistic measurement model for structured collaborative choice tasks. In the first stage, known member-specific rewards and latent participant negotiation traits jointly determine team-option utilities, from which a latent team choice is generated. In the second stage, individual responses are modeled conditional on the latent team choice, with possible deviations that depend on participants' own rewards. Participant-level covariates can also be incorporated through a structural model. A simulation study shows satisfactory recovery of the participant traits and model parameters across different sample sizes and item sizes. An application to data from simulated collaborative negotiation tasks shows choice prediction accuracy well above the random-choice baseline and meaningful associations between the estimated traits and external criterion variables. The proposed framework provides a psychometric approach to measuring participant-level negotiation traits with collaborative choice data.
CommentsSubmitted to Psychometrika
