Test data engineering : latent rank analysis, biclustering, and Bayesian network / Kojiro Shojima.
Shojima, Kojiro, author.
2022
QA76.9.D343
Available Online

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Title Test data engineering : latent rank analysis, biclustering, and Bayesian network / Kojiro Shojima.
Author Shojima, Kojiro, author.
ISBN 9789811699863 (electronic bk.)
9811699860 (electronic bk.)
9811699852
9789811699856
DOI https://doi.org/10.1007/978-981-16-9986-3
Published Singapore : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (xxii, 579 pages) : illustrations (chiefly color).
Item Number 10.1007/978-981-16-9986-3 doi
Call Number QA76.9.D343
Dewey Decimal Classification 006.3/12
Summary 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. .
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Series Behaviormetrics ; v. 13.
Available in Other Form TEST DATA ENGINEERING.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Concept of Test Data Engineering
Test Data and Item Analysis
Classical Test Theory
Item Response Theory
Latent Class Analysis
Biclustering
Bayesian Network Model.

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