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＜電子ブック＞
Test Data Engineering : Latent Rank Analysis, Biclustering, and Bayesian Network / by Kojiro Shojima
(Behaviormetrics: Quantitative Approaches to Human Behavior. ISSN:25244035 ; 13)

版	1st ed. 2022.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	XXII, 579 p. 242 illus., 216 illus. in color : online resource
著者標目	*Shojima, Kojiro author SpringerLink (Online service)
件　名	LCSH:Social sciences -- Statistical methods  全ての件名で検索 LCSH:Statistics  LCSH:Political planning LCSH:Psychometrics LCSH:Machine learning FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistical Theory and Methods FREE:Public Policy FREE:Psychometrics FREE:Machine Learning
一般注記	Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model 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. HTTP:URL=https://doi.org/10.1007/978-981-16-9986-3

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