Exploratory Item Classification Via Spectral Graph Clustering
Abstract
Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 01, 2017
- Source ID
- 10.1177/0146621617692977
Entities
People
- Gongjun Xu
- Jingchen Liu
- Xiaoou Li
- Yunxiao Chen
- Zhiliang Ying
Organizations
- Army Research Office
- Columbia University
- Division of Information and Intelligent Systems
- Division of Social and Economic Sciences
- Emory University
- Institute of Education Sciences
- National Institutes of Health
- National Security Agency
- University of Michigan
- University of Minnesota