10804nam 2200529 450 99646640970331620230619211013.03-030-69150-0(CKB)4100000011971190(MiAaPQ)EBC6648870(Au-PeEL)EBL6648870(OCoLC)1258660214(PPN)259391530(EXLCZ)99410000001197119020220319d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMultivariate humanities /Pieter M. KroonenbergCham, Switzerland :Springer,[2021]©20211 online resource (441 pages)Quantitative Methods in the Humanities and Social Sciences3-030-69149-7 Intro -- Preface -- Global table of contents -- Contents -- Part I The Actors -- 1 Introduction: Multivariate studies in the Humanities -- 1.1 Preliminaries -- 1.1.1 Audience -- 1.1.2 Before you start -- 1.1.3 Multivariate analysis -- 1.1.4 Case studies: Quantification and statistical analysis -- 1.2 The humanities-What are they? -- 1.3 Qualitative and quantitative research in the humanities -- 1.4 Multivariate data analysis -- 1.5 Data: Formats and types -- 1.5.1 Data formats -- 1.5.2 Data characteristics: Measurement levels -- 1.5.3 Characteristics of data types -- 1.5.4 From one data format to another -- 1.6 General structure of the case study chapters -- 1.7 Author references -- 1.8 Wikipedia -- 1.9 Web addresses -- 2 Data inspection: The data are in. Now what? -- 2.1 Background -- 2.1.1 A researcher's nightmare -- 2.1.2 Getting the data right -- 2.2 Data inspection: Overview -- 2.2.1 The normal distribution -- 2.2.2 Distributions: Individual numeric variables -- 2.2.3 Inspecting several univariate distributions -- 2.2.4 Bivariate inspection -- 2.3 Missing data -- 2.3.1 Unintentionally missing -- 2.3.2 Systematically missing -- 2.3.3 Handling missing data -- 2.4 Outliers -- 2.4.1 Characteristics of outliers -- 2.4.2 Types of outliers -- 2.4.3 Detection of outliers -- 2.4.4 Handling outliers -- 2.5 Testing assumptions of statistical techniques -- 2.5.1 Null hypothesis testing -- 2.5.2 Model testing -- 2.6 Content summary -- 3 Statistical framework -- 3.1 Overview -- 3.2 Data formats -- 3.2.1 Matrices: The basic data format -- 3.2.2 Contingency tables -- 3.2.3 Correlations, covariances, similarities -- 3.2.4 Three-way arrays: Several matrices -- 3.2.5 Meaning of numbers in a matrix -- 3.3 Chapter example -- 3.4 Designs, statistical models, and techniques -- 3.4.1 Data design -- 3.4.2 Model -- 3.5 From questions to statistical techniques.3.5.1 Dependence designs versus internal structure designs -- 3.5.2 Analysing variables, objects, or both -- 3.6 Dependence designs: General linear model-glm -- 3.6.1 The t test -- 3.6.2 Analysis of variance-anova -- 3.6.3 Multiple regression analysis-mra -- 3.6.4 Discriminant analysis -- 3.6.5 Logistic regression -- 3.6.6 Advanced analysis of variance models -- 3.6.7 Nonlinear multivariate analysis -- 3.7 Internal structure designs: General description -- 3.8 Internal structure designs: Variables -- 3.8.1 Principal component analysis-pca -- 3.8.2 Categorical principal component analysis-CatPCA -- 3.8.3 Factor analysis-fa -- 3.8.4 Structural equation modelling-sem -- 3.8.5 Loglinear models -- 3.9 Internal structure designs: Objects, individuals, cases, etc. -- 3.9.1 Similarities and dissimilarities -- 3.9.2 Multidimensional scaling-mds -- 3.9.3 Cluster analysis -- 3.10 Internal structure designs: Objects and variables -- 3.10.1 Correspondence analysis: Analysis of tables -- 3.10.2 Multiple correspondence analysis -- 3.10.3 Principal component analysis for binary variables -- 3.11 Internal structure designs: Three-way models -- 3.11.1 Three-mode principal component analysis-tmpca -- 3.12 Hypothesis testing versus descriptive analysis -- 3.13 Model selection -- 3.14 Model evaluation -- 3.15 Designing tables and graphs -- 3.15.1 How to improve a table -- 3.15.2 Example of table rearrangement: a binary dataset -- 3.15.3 Examples of table rearrangement: contingency tables -- 3.15.4 How to improve graphs -- 3.16 Software -- 3.17 Overview of statistics in the case studies -- 4 Statistical framework extended -- 4.1 Contents and Keywords -- 4.2 Introduction -- 4.3 Analysis of variance designs -- 4.4 Binning -- 4.5 Biplots -- 4.6 Centroids -- 4.7 Contingency tables -- 4.8 Convex hulls -- 4.9 Deviance plots -- 4.10 Discriminant analysis.4.11 Distances -- 4.12 Inner products and projection -- 4.13 Joint biplots -- 4.14 Means plot with error bars, line graph, interaction plot -- 4.15 Missing rows and columns -- 4.16 Multiple regression -- 4.17 Multivariate, multiple, multigroup, multiset, and multiway -- 4.18 Quantification, optimal scaling, and measurement levels -- 4.19 Robustness -- 4.20 Scaling coordinates -- 4.21 Singular value decomposition -- 4.22 Structural equation modelling-sem -- 4.23 Supplementary points and variables -- 4.24 Three-mode principal component analysis (tmpca) -- 4.25 X2 test (χ2 test) -- Part II The Scenes -- 5 Similarity data: Bible translations -- 5.1 Background -- 5.2 Research questions: Similarity of translations -- 5.3 Data: English and German Bible translations -- 5.4 Analysis methods -- 5.4.1 Characteristics of multidimensional scaling and cluster analysis -- 5.4.2 Multidimensional scaling -- 5.4.3 Cluster analysis -- 5.5 Bible translations: Statistical analysis -- 5.5.1 Multidimensional scaling -- 5.5.2 Cluster analysis -- 5.6 Other approaches to analysing similarities -- 5.7 Content summary -- 6 Stylometry: Authorship of the Pauline Epistles -- 6.1 Background -- 6.2 Research questions: Authorship -- 6.3 Data: Word frequencies in Pauline Epistles -- 6.4 Analysis methods -- 6.4.1 Choice of analysis method -- 6.4.2 Using correspondence analysis -- 6.5 The Pauline Epistles: Statistical analysis -- 6.5.1 Inspecting Epistle profiles -- 6.5.2 Inertia and dimensional fit -- 6.5.3 Plotting the results -- 6.5.4 Plotting the Epistles profiles -- 6.5.5 Epistles and Word categories: Biplot -- 6.5.6 Methodological summary -- 6.6 Other approaches to authorship studies -- 6.7 Content summary -- 7 Economic history: Agricultural development on Java -- 7.1 Background -- 7.2 Research questions: Historical agricultural data.7.3 Data: Agriculture development on Java -- 7.4 Analysis methods -- 7.4.1 Choice of analysis method -- 7.4.2 catpca: Characteristics of the method -- 7.5 Agricultural development on Java: Statistical analysis -- 7.5.1 Categorical principal component analysis in a miniature example -- 7.5.2 Main analysis -- 7.5.3 Agricultural history of Java: Further methodological remarks -- 7.6 Other approaches to historical data: -- 7.7 Content summary -- 8 Seriation: Graves in the Münsingen-Rain burial site -- 8.1 Background -- 8.2 Research questions: A time line for graves -- 8.3 Data: Grave contents -- 8.4 Analysis methods -- 8.5 Münsingen-Rain graves: Statistical analysis -- 8.5.1 Fashion as an ordering principle -- 8.5.2 Seriation -- 8.5.3 Validation of seriation -- 8.5.4 Other techniques -- 8.6 Other approaches to seriation -- 8.7 Content summary -- 9 Complex response data: Evaluating Marian art -- 9.1 Background -- 9.2 Research questions: Appreciation of Marian art -- 9.3 Data: Appreciation of Marian art across styles and contents -- 9.4 Analysis method -- 9.5 Marian art: Statistical analysis -- 9.5.1 Basic data inspection -- 9.5.2 A miniature example -- 9.5.3 Evaluating differences in means -- 9.5.4 Examining consistency of relations between the response variables -- 9.5.5 Principal component analyses: All painting categories -- 9.5.6 Principal component analysis: Per painting category -- 9.5.7 Scale analysis: Cronbach's alpha -- 9.5.8 Structure of the questionnaire -- 9.6 Other approaches to complex response data -- 9.7 Content summary -- 10 Rating scales: Craquelure and pictorial stylometry -- 10.1 Background -- 10.2 Research questions: Linking craquelure, paintings, and judges -- 10.3 Data: Craquelure of European paintings -- 10.4 Analysis methods -- 10.5 Craquelure: Statistical analysis -- 10.5.1 Art-historical categories: Scale means.10.5.2 Scales, judges, and paintings: Three-mode component analysis -- 10.5.3 Separation of art-historical categories -- 10.6 Other approaches to pictorial stylometry -- 10.7 Content summary -- 11 Pictorial similarity: Rock art images across the world -- 11.1 Background -- 11.2 Research questions: Evaluating Rock Art -- 11.2.1 The Kimberley versus Algerian images -- 11.2.2 The Zimbabwean, Indian, and Algerian images -- 11.2.3 The Kimberley, Arnhem Land, and Pilbara images -- 11.2.4 General considerations -- 11.3 Data: Characteristics of Barry's rock art images -- 11.4 Analysis methods -- 11.4.1 Comparison of proportions -- 11.4.2 Principal component analyses for binary variables -- 11.5 Rock art: Statistical analysis -- 11.5.1 Comparing rock art from Algeria and from the Kimberley -- 11.5.2 Comparing rock art from Zimbabwe, India, and Algeria -- 11.5.3 Comparing rock art images from within Australia -- 11.5.4 Further analytical considerations -- 11.6 Other approaches to analysing rock art images -- 11.7 Content summary -- 12 Questionnaires: Public views on deaccessioning -- 12.1 Background -- 12.2 Research questions: Public views on deaccessioning -- 12.3 Data: Public views about deaccessioning -- 12.3.1 Questionnaire respondents -- 12.3.2 Questionnaire structure -- 12.3.3 Type of data design -- 12.4 Analysis methods -- 12.5 Public views on deaccessioning: Statistical analysis -- 12.5.1 Item distributions -- 12.5.2 Item means -- 12.5.3 Item correlations -- 12.5.4 Measurement models: Preliminaries -- 12.5.5 Measurement models: Confirmatory factor analysis -- 12.5.6 Measurement models: Deaccessioning data -- 12.5.7 Item loadings -- 12.5.8 Interpretation -- 12.6 Other approaches in deaccessioning studies -- 12.7 Content summary -- 13 Stylometry: The Royal Book of Oz - Baum or Thompson? -- 13.1 Background.13.2 Research questions: Competitive authorship.Quantitative methods in the humanities and social sciences.Multivariate analysisAnàlisi multivariablethubLlibres electrònicsthubMultivariate analysis.Anàlisi multivariable519.535Kroonenberg Pieter M.103106MiAaPQMiAaPQMiAaPQBOOK996466409703316Multivariate Humanities1891966UNISA