k -Centres Functional Clustering
提出k中心函数聚类方法，用于识别随时间变化的复杂非线性模式的人群，并应用于心理契约违背后的承诺变化数据，发现两种有意义的反应模式。
In the present paper, we introduce k -centres functional clustering ( k -centres FC), a person-centered method that clusters people with similar patterns of complex, highly nonlinear change over time. We review fundamentals of the methodology and argue how it addresses some of the limitations of the traditional approaches to modeling repeated measures data. The usefulness of k -centres FC is demonstrated by applying the method to weekly measured commitment data from 109 participants who reported psychological contract breach events. The k -centres FC analysis shows two substantively meaningful clusters, the first cluster showing reaction patterns with general growth in commitment after breach and the second cluster showing general decline in commitment after breach. Further, the reaction patterns in the second cluster appear to be the result of a combination of two interesting reaction logics: immediate and delayed reactions. We conclude by outlining how future organizational research can incorporate this methodology.