Using cluster analysis to validate the angoff standard setting method in mixed-format assessments

Fen Lan Tseng, Jia Min Chiou, Yao Ting Sung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Test validity is a property of the interpretation assigned to test scores. To provide an objective validating evidence for a standard-referenced assessment is especially important. In this study we utilize a statistical technique, cluster analysis, to explore the validity of one of the expert judgement technique-Yes/No Angoff standard setting method. We first segregated each examinee ability cluster using the hierarchical clustering (HC). Assume that each ability cluster is a Gaussian distribution and that the distribution of each test subject data can be modeled by mixture of Gaussians (MoG), where the mean, variance and the proportion of each cluster were initialized by the HC results. Finally, the ability clustering was implemented by the expectation maximization (EM) method. The results from the traditional standard-setting procedure and cluster analysis were compared. The study concludes that cluster analysis appears useful for helping to set standards on educational tests. In addition, it suggested that cluster analysis could be applied as a support tool to provide validating information in the process of standard setting for high-stakes achievement tests.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2540-2546
Number of pages7
ISBN (Electronic)9781538621653
DOIs
Publication statusPublished - 2018 Jun 21
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 2017 Jul 292017 Jul 31

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Other

Other13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
CountryChina
CityGuilin, Guangxi
Period17/7/2917/7/31

Keywords

  • cluster analysis
  • hierarchical clustering
  • mixture of Gaussian model
  • standard setting

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Logic
  • Modelling and Simulation
  • Statistics and Probability

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