Cluster analysis in illness perception research: A Monte Carlo study to identify the most appropriate method
Cluster analysis in illness perception research: A Monte Carlo study to identify the most appropriate method
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DOI：
10.1080/14768320600774496
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发表时间：
2007-02-01
影响因子：
3.3
通讯作者：
Horne, Robert
中科院分区：
文献类型：
--
作者：
Clatworthy, Jane;Hankins, Matthew;Horne, Robert
聚类分析在疾病感知研究中具有一定的理论价值和实用价值。然而，目前还不清楚，在众多可用的方法中，哪种方法最适合用于疾病感知数据。进行了蒙特卡罗研究，生成了420个具有预定簇结构的人工数据集，以类似于修订后的疾病感知问卷(IPQ-R)数据。样本大小和簇大小的均等性被操纵。将平均关联、完全关联、Ward‘s方法和K-Means(使用Ward’s方法得出的聚类数和聚类质心)应用于人工数据集，并记录每种方法在每个数据集中正确分类的病例的百分比。4×3×2方差分析表明，K均值聚类分析是疾病知觉研究中最合适的方法。这些结果似乎可以推广到其他类似类型的健康心理学数据中的聚类分析。
Cluster analysis may have theoretical and practical value in illness perception research. It is not clear, however, which of the many methods available is the most appropriate for use with illness perception data. A Monte Carlo study was conducted, whereby 420 artificial datasets with a predetermined cluster structure were generated to resemble Revised Illness Perception Questionnaire (IPQ-R) data. Sample size and equality in cluster size were manipulated. Average Linkage, Complete Linkage, Ward's method and K-means ( using the number of clusters and cluster centroids derived from Ward's method) were applied to the artificial datasets and the percentage of cases correctly classified in each dataset by each method was recorded. A 4 x 3 x 2 ANOVA revealed that K-means cluster analysis was the most appropriate method for use in illness perception research. It is plausible that these results are generalisable to cluster analysis in other similar types of health psychology data.