Compare the Result of Clustering Using Latent Class and Two-Step Cluster Analysis

• Main Authors: Lee Pei-Yu, 李佩隃
• Other Authors: 陳柏熹
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/60160158361393750393

碩士 === 國立臺灣師範大學 === 教育心理與輔導學系 === 99 === The purpose of this research is to compare the results of clustering using latent class analysis and two-step cluster analysis. The research is composed of two sub-researches, simulation and empirical study. In Study 1, the type of observed variables, the number of observed variables and sample size were be manipulated. And compare the features of clustering, the accuracy of clustering with two clustering methods. In Study 2, we apply two clustering methods on creativity performances and compare the results of clustering. Four hundreds university students were asked to complete the “computerized creativity test”. Their creating process were automatically recorded and scored by computer using seven criterions. The creating product was scored using nine criterions by two raters. The results of Study 1 indicate that latent class analysis performs better than two-step cluster, when the observed variables contain categorical variables. Besides, the number of observed variables and sample size has significant effect. The ANOVA result shows that the more observed variables and sample size, the less misclassification rate of both methods. The results of Study 2 show that the results of two clustering methods are very similar either in creating process or in creating product. In creating product, there are three types of creativity performances in the subjects which were named as “Creative designer”, “Practical designer”, and “Imaginable designer”. In creating process, there are three groups in the subjects too. They were named as “Excellence ability to creative operate”, “Excellence ability to practical operate”, and “Poor ability to use material”. According to the results of this study, two recommendations were proposed: 1. It’s better to use latent class analysis, when the observed variables contain categorical variables. The results of latent class analysis have more correct decisions and less misclassification. In addition, to reduce the misclassification rate, researcher can add more observed variables and sample size. 2. Several pedagogical implications can be drawn from Study 2. Teacher can design different programs for different type of students, such as strengthening the link between innovative and practical of creating product, or provided more training for the poor performance of creating process.