Читать книгу Methods in Psychological Research - Annabel Ness Evans - Страница 99

Cluster analysis includes a range of algorithms and methods used to group similar objects into categories, or clusters. The members of each cluster are thus more similar to each other than they are to members of other clusters. Unlike factor analysis, where the goal is to group similar variables together, in cluster analysis, the idea is to group similar members. Organizing data into meaningful structures or taxonomies is a task many researchers face. Cluster analysis is a method that can discover structure in data, but it does not in and of itself have any explanatory function. In other words, the analysis can find structure but does not explain it.

Imagine a hospital where patients are assigned to wards based on similar symptoms or perhaps similar treatments. Each ward could be considered a cluster. A cluster analysis might discover the similarities among the patients in each ward, and the researcher then has the job of determining why the cluster or ward is similar (i.e., symptoms, treatment, age, etc.).

Cluster analysis is often used when researchers have no a priori hypotheses and are in the beginning phase of their research. As such, statistical significance testing often has no role in such analyses.