Modeling the Cross-Cultural Adaptation Process of Immigrants Using Categorical Data Clustering

— This paper introduces a quantitative method for social data analysis, which is based on the use of categorical data clustering. More specifically, we employ categorical data clustering to analyze the cross-cultural adaptation process of immigrants in a foreign cultural environment. To assess the extend to which individuals adapt themselves in a strange cultural environment we performed an experiment, where a set of cross-cultural categorical data was generated by using a questionnaire over a number of immigrants who live in Greece. The key idea is to cluster the available categorical data and to treat these clusters as patterns, each of which corresponds to a certain level of adaptation capability. Then, we detect and analyze changes of these patterns through time. These changes directly indicate how the cross-cultural adaptation process proceeds. In order to cluster the available data set we use the well-known ROCK algorithm.

• Type: Conference
• Year: 2005
• Where: CIMCA
• Authors: George E. Tsekouras
• Keywords: Categorical Data, Categorical Data Clustering, CIMCA 2005, Cross-cultural Adaptation Process