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Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima



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Autore: Shojima Kojiro Visualizza persona
Titolo: Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima Visualizza cluster
Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Edizione: 1st ed. 2022.
Descrizione fisica: 1 online resource (xxii, 579 pages) : illustrations
Disciplina: 519.542
Soggetto topico: Social sciences - Statistical methods
Statistics
Political planning
Psychometrics
Machine learning
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Statistical Theory and Methods
Public Policy
Machine Learning
Estadística bayesiana
Anàlisi de conglomerats
Mineria de dades
Tests i proves en educació
Processament de dades
Visualització de la informació
Soggetto genere / forma: Llibres electrònics
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model.
Sommario/riassunto: 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. .
Titolo autorizzato: Test Data Engineering  Visualizza cluster
ISBN: 9789811699863
9789811699856
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 996485661803316
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Serie: Behaviormetrics: Quantitative Approaches to Human Behavior, . 2524-4035 ; ; 13