The Robustness of MDS Configurations in the Case of Incomplete Data
作者研究了当输入数据不完整（而非完整）时，多维尺度分析（MDS）配置的稳健性。通过两项实证研究，他们发现稳健性随不完整数据量的增加而变化，并且随机删除数据的方法与循环设计效果相当。这些结果为早期关于该主题的蒙特卡洛文献提供了实证支持。作者还表明，受访者的个体特征（即认知整合和意象）会影响使用不完整数据获得的配置质量。
The authors examine the robustness of MDS configurations when incomplete rather than complete input data are used. Using two empirical studies, they show that robustness varies as the amount of incomplete data increases and that random methods of data deletion perform as well as cyclic designs. These findings provide empirical support for earlier Monte Carlo literature on the topic. The authors also show that individual characteristics of respondents, namely cognitive integration and imagery, influence the quality of configurations obtained with incomplete data.