國立高雄大學圖資館 |

Test data engineeringlatent rank analysis, biclustering, and Bayesian network /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Test data engineeringby Kojiro Shojima.
其他題名:	latent rank analysis, biclustering, and Bayesian network /
作者:	Shojima, Kojiro.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xxii, 579 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-981-16-9986-3
ISBN:	9789811699863$q(electronic bk.)

Test data engineeringlatent rank analysis, biclustering, and Bayesian network /
Shojima, Kojiro.

Test data engineeringlatent rank analysis, biclustering, and Bayesian network /[electronic resource] :by Kojiro Shojima. - Singapore :Springer Nature Singapore :2022. - xxii, 579 p. :ill. (chiefly color), digital ;24 cm. - Behaviormetrics: quantitative approaches to human behavior,v. 132524-4035 ;. - Behaviormetrics: quantitative approaches to human behavior ;v.5..

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.

ISBN: 9789811699863$q(electronic bk.)

Standard No.: 10.1007/978-981-16-9986-3doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / S46 2022

Dewey Class. No.: 006.312

Test data engineeringlatent rank analysis, biclustering, and Bayesian network /
LDR:02890nmm a2200325 a 4500 001 629255
003 DE-He213
005 20220813110340.0
007 cr nn 008maaau
008 230224s2022 si s 0 eng d
020 $a 9789811699863$q(electronic bk.)
020 $a 9789811699856$q(paper)
024 7 $a 10.1007/978-981-16-9986-3 $2 doi
035 $a 978-981-16-9986-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b S46 2022
072 7 $a JHBC $2 bicssc
072 7 $a SOC027000 $2 bisacsh
072 7 $a JHBC $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b S559 2022
100 1 $a Shojima, Kojiro. $3 933934
245 1 0 $a Test data engineering $h [electronic resource] : $b latent rank analysis, biclustering, and Bayesian network / $c by Kojiro Shojima.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xxii, 579 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Behaviormetrics: quantitative approaches to human behavior, $x 2524-4035 ; $v v. 13
505 0 $a Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model.
520 $a 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.
650 0 $a Data mining. $3 184440
650 0 $a Information visualization. $3 248886
650 0 $a Educational tests and measurements $x Data processing. $3 242940
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a Cluster analysis. $3 182711
650 1 4 $a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. $3 913391
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Public Policy. $3 224661
650 2 4 $a Psychometrics. $3 182715
650 2 4 $a Machine Learning. $3 833608
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Behaviormetrics: quantitative approaches to human behavior ; $v v.5. $3 858994
856 4 0 $u https://doi.org/10.1007/978-981-16-9986-3
950 $a Mathematics and Statistics (SpringerNature-11649)