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Multivariate humanities

Kroonenberg, Pieter M.

Multivariate humanities

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate humanitiesby Pieter M. Kroonenberg.
作者:	Kroonenberg, Pieter M.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xxi, 435 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis.
電子資源:	https://doi.org/10.1007/978-3-030-69150-9
ISBN:	9783030691509$q(electronic bk.)

Multivariate humanities
Kroonenberg, Pieter M.

Multivariate humanities[electronic resource] /by Pieter M. Kroonenberg. - Cham :Springer International Publishing :2021. - xxi, 435 p. :ill., digital ;24 cm. - Quantitative methods in the humanities and social sciences,2199-0956. - Quantitative methods in the humanities and social sciences..

1. Introduction: Multivariate studies in the Humanities -- 2. Data inspection: The data are in. Now what?- 3. Statistical framework -- 4. Statistical framework extended -- 5. Similarity data: Bible translations (coauthor: Zachary Bleemer) -- 6. Stylometry: Authorship of the Pauline Epistles -- 7. Economic history: Agriculture development on Java -- 8. Seriation: Graves in the Munsingen-Rain burial site -- 9. Complex response data: Evaluating Marian art (coauthor: Donald Polzella) -- 10. Rating scales: Craquelure and pictorial stylometry (coauthor: Spike Bucklow) -- 11. Pictorial similarity: Rock art images across the world -- 12 Questionnaires: Public views on deaccessioning (coauthor: Marilena Vecco) -- 13. Sylometry: The Royal Book of Oz: Baum or Thompson?- 14. Linguistics: Accentual prose rhythm in medieval Latin -- 15. Linguistics: Chronology of Plato's works -- 16. Binary judgments: Reading preferences -- 17. Music appreciation: The Chopin Preludes (coauthor: Takashi Murakami) -- 18. Musical stylometry: Characterisation of music (coauthor: Dorien Herremans) -- 19. Final musings.

This case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of data sets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide applicability. Fields featured include, but are not limited to, historical agriculture, arts (music and painting), theology, and stylometrics (authorship issues) Most analyses are based on existing data, earlier analysed in published peer-reviewed papers. Four preliminary methodological and statistical chapters provide general technical background to the case studies. The multivariate statistical methods presented and illustrated include data inspection, several varieties of principal component analysis, correspondence analysis, multidimensional scaling, cluster analysis, regression analysis, discriminant analysis, and three-mode analysis. The bulk of the text is taken up by 14 case studies that lean heavily on graphical representations of statistical information such as biplots, using descriptive statistical techniques to support substantive conclusions. Each study features a description of the substantive background to the data, followed by discussion of appropriate multivariate techniques, and detailed results interpreted through graphical illustrations. Each study is concluded with a conceptual summary.

ISBN: 9783030691509$q(electronic bk.)

Standard No.: 10.1007/978-3-030-69150-9doiSubjects--Topical Terms:

181905
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Multivariate humanities
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520 $a This case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of data sets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide applicability. Fields featured include, but are not limited to, historical agriculture, arts (music and painting), theology, and stylometrics (authorship issues) Most analyses are based on existing data, earlier analysed in published peer-reviewed papers. Four preliminary methodological and statistical chapters provide general technical background to the case studies. The multivariate statistical methods presented and illustrated include data inspection, several varieties of principal component analysis, correspondence analysis, multidimensional scaling, cluster analysis, regression analysis, discriminant analysis, and three-mode analysis. The bulk of the text is taken up by 14 case studies that lean heavily on graphical representations of statistical information such as biplots, using descriptive statistical techniques to support substantive conclusions. Each study features a description of the substantive background to the data, followed by discussion of appropriate multivariate techniques, and detailed results interpreted through graphical illustrations. Each study is concluded with a conceptual summary.
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