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|本期目录/Table of Contents|

 基于模型聚类与基于距离聚类在表现标准设定中的比较研究(PDF)

《心理学探新》[ISSN:1003-5184/CN:36-1228/B]

期数:
 2025年06期
页码:
 564-574
栏目:
 
出版日期:
 2025-12-30

文章信息/Info

Title:
 A Comparative Study of Model-Based and Distance-Based Clustering in Performance Standard Setting
文章编号:
1003-5184(2025)06-0564-11
作者:
 梁正妍1; 李 浩2; 张 潮2; 张敏强2
 (1.广东技术师范大学教育科学学院,广州 510665; 2.华南师范大学心理学院,广州 510631)
Author(s):
 Liang Zhengyan1; Li Hao2; Zhang Chao2; Zhang Minqiang2
 (1.School of Education Science,Guangdong Polytechnic Normal University,Guangzhou 510665; 2.School of Psycholog,South China Normal University,Guangzhou 510631)
关键词:
 表现标准设定;  混合题型测验;  KAMILA聚类;  Modha-Spangler聚类;  基于模型的聚类
Keywords:
 performance standard setting;  mixed-format tests;  KAMILA clustering;  Modha-Spangler clustering;  Model-based clustering
分类号:
 B841.2
DOI:
 -
文献标识码:
 A
摘要:
 传统表现标准设定方法(如Angoff法)依赖专家主观判断,在结构复杂的混合题型测验中易受质疑。本研究比较三种聚类方法——基于模型聚类、Modha-Spangler聚类与KAMILA聚类在混合题型测验中的标准设定效果。模拟研究表明,类别概率与类间距对分类准确率有显著影响,Modha-Spangler和KAMILA方法对类间距变化敏感,而基于模型的方法在小类间距且分布满足假定时更具适应性。实证研究显示,KAMILA聚类在组内同质性和临界分数可解释性方面表现最优。建议在混合题型测验中优先采用KAMILA聚类,以提升标准设定的客观性与稳健性。
Abstract:
 Traditional performance standard-setting methods(e.g.,the Angoff method)rely heavily on subjective expert judgments,which often raises concerns when applied to tests with complex structures and mixed item types.This study compares the effectiveness of three clustering approaches—Model-based clustering,Modha-Spangler clusteringand KAMILA clustering—in performance standard setting for mixed-format tests.Simulation results indicate that both categorical probability and inter-class distance significantly affect classification accuracy.While Modha-Spangler and KAMILA methods are sensitive to changes in inter-class distance,the Model-based approach shows greater adaptability when the class distance is small and distributional assumptions are met.Empirical analysis further demonstrates that KAMILA clustering outperforms the others in terms of within-class homogeneity and the interpretability of cut-off scores.It is recommended that KAMILA clustering be prioritized in mixed-format testing contexts to enhance the objectivity and robustness of performance standard setting.

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备注/Memo

备注/Memo:
 基金项目:2025年度国家社会科学基金教育学青年项目(CSA250341)。
通信作者:张敏强,E-mail:2640726401@qq.com。
更新日期/Last Update:  2025-12-30