To keep card sorting with a lot of cards concise, a common strategy for gauging mental models involves presenting participants with fewer randomly selected cards instead of the full set. This is a decades-old practice, but its effects lacked systematic examination. To assess how randomized subsets affect data, we conducted an experiment with 160 participants. We compared results between full and randomized 60\% card sets, then analyzed sample size requirements and the impacts of individual personality and cognitive factors. Our results demonstrate that randomized subsets can yield comparable similarity matrices to standard card sorting, but thematic patterns in categories can differ. Increased data variability also warrants larger sample sizes (25-35 for 60% card subset). Results indicate that personality traits and cognitive reflection interact with card sorting. Our research suggests evidence-based practices for conducting card sorting while exposing the influence of study design and individual differences on measurement of mental models.

翻译：为使包含大量卡片的卡片分类过程保持简洁，一种评估心智模型的常用策略是向参与者呈现随机抽取的较少卡片而非完整集合。这项沿用数十年的实践方法，其实际效果却缺乏系统性检验。为评估随机子集对数据的影响，我们开展了包含160名参与者的实验。通过对比完整卡片组与随机抽取60%卡片组的结果，进而分析样本量需求以及个体人格与认知因素的影响。实验结果表明：随机子集能够产生与标准卡片分类可比的相似性矩阵，但类别中的主题模式可能存在差异。数据变异性的增加也要求更大的样本量（60%卡片子集需25-35个样本）。研究还发现人格特质与认知反思能力会与卡片分类产生交互作用。本研究为开展卡片分类提供了循证实践指导，同时揭示了研究设计和个体差异对心智模型测量的影响。