Brusco Michael J, Steinley Douglas
Marketing Department, College of Business, Florida State University, Tallahassee, FL 32306-1110, USA.
Psychol Methods. 2006 Sep;11(3):271-86. doi: 10.1037/1082-989X.11.3.271.
The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data.
对混淆数据的研究在心理学中是一种既定的做法。尽管有许多用于混淆数据的分析方法，但最常见的方法包括提取一个或多个刺激子集、将完整的刺激集划分为不同的组以及对刺激集进行排序。虽然标准的商业软件包有时可以促进这类分析，但不能保证它们能产生最优解。作者提出了一个用于预处理混淆矩阵的MATLAB *.m文件，其中包括相似性选择模型的拟合。还有另外两个MATLAB程序可用于根据混淆数据对刺激进行最优聚类。作者还开发了用于根据混淆矩阵中的信息对刺激进行最优排序和提取刺激子集的程序。总之，这些程序为应用研究人员在分析混淆数据时提供了几种实用的选择。虽然这些程序是在混淆数据的背景下描述的，但它们也适用于其他类型的接近度数据。
