Overview
- Demonstrates how test data can be engineered to transform it into information useful for examinees and teachers
- Shows how information underlying the test data can be visualized after structuring and extracting it
- Expands readers’ field of vision in analyzing test data by demonstrating various useful statistical models
Part of the book series: Behaviormetrics: Quantitative Approaches to Human Behavior (BQAHB, volume 13)
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About this book
This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the society. The author does not differentiate test data analysis from data engineering and information visualization. This monograph introduces the following methods of engineering or processing test data, including the latest machine learning techniques: classical test theory (CTT), item response theory (IRT), latent class analysis (LCA), latent rank analysis (LRA), biclustering (co-clustering), and Bayesian network model (BNM). CTT and IRT are methods for analyzing test data and evaluating students’ abilities on a continuous scale. LCA and LRA assess examinees by classifying them into nominal and ordinal clusters, respectively, where the adequate number of clusters is estimated from the data. Biclustering classifies examinees into groups (latent clusters) while classifying items into fields (factors). Particularly, the infinite relational model discussed in this book is a biclustering method feasible under the condition that neither the number of groups nor the number of fields is known beforehand. Additionally, the local dependence LRA, local dependence biclustering, and bicluster network model are methods that search and visualize inter-item (or inter-field) network structure using the mechanism of BNM. As this book offers a new perspective on test data analysis methods, it is certain to widen readers’ perspective on test data analysis.
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Keywords
Table of contents (11 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Kojiro Shojima is Associate Professor at The National Center for University Entrance Examinations. He is a psychometrician living in Tokyo with his (lovely) wife and two (angelic) daughters.
Bibliographic Information
Book Title: Test Data Engineering
Book Subtitle: Latent Rank Analysis, Biclustering, and Bayesian Network
Authors: Kojiro Shojima
Series Title: Behaviormetrics: Quantitative Approaches to Human Behavior
DOI: https://doi.org/10.1007/978-981-16-9986-3
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-16-9985-6Published: 14 August 2022
Softcover ISBN: 978-981-16-9988-7Published: 15 August 2023
eBook ISBN: 978-981-16-9986-3Published: 13 August 2022
Series ISSN: 2524-4027
Series E-ISSN: 2524-4035
Edition Number: 1
Number of Pages: XXII, 579
Number of Illustrations: 26 b/w illustrations, 216 illustrations in colour
Topics: Statistics for Social Sciences, Humanities, Law, Statistical Theory and Methods, Public Policy, Psychometrics, Machine Learning