Multivariate Humanities (Quantitative Methods in the Humanities and Social Sciences)

✍ Scribed by Pieter M. Kroonenberg

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 443
• Category: Library

✦ Synopsis

This case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of datasets 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. Datasets in SPSS are included online.

✦ Table of Contents

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
13.3 Data: Occurrence of function words
13.3.1 Preprocessing
13.3.2 Dataset
13.4 Analysis methods
13.4.1 Significance testing
13.4.2 Distributions and graphics
13.4.3 Principal component analysis and graphics
13.4.4 Cluster analysis
13.5 Wizard of Oz: Statistical analyses
13.5.1 Principal component analysis
13.5.2 Cluster analysis
13.6 Other approaches in authorship studies
13.7 Content summary
14 Linguistics: Accentual prose rhythm in mediæval Latin
14.1 Background
14.2 Research questions: Accentual prose rhythm in mediæval Latin
14.3 Data: Janson's data tables
14.4 Analysis methods
14.4.1 Contingency tables
14.4.2 Ordinal principal component analysis
14.5 Accentual prose rhythm: Statistical analysis
14.5.1 Internal structure of individual authors' cadences
14.5.2 Similarities in accentual prose rhythm
14.6 Content summary
15 Linguistics: Chronology of Plato's works
15.1 Background
15.2 Research questions: Plato's chronology
15.3 Data: Kaluscha's clausulae data
15.4 Analysis methods
15.5 Plato's chronology: Statistical analysis
15.5.1 Text similarities
15.5.2 Clausulae and texts
15.6 Other approaches to Plato's chronology
15.7 Content summary
16 Binary judgments: Reading preferences
16.1 Background
16.2 Research questions: Binary variables
16.3 Data: Reading preferences
16.4 Analysis methods
16.4.1 Loglinear modelling
16.4.2 Multiple correspondence analysis
16.4.3 Supplementary variables
16.5 Reading preferences: Statistical analysis
16.5.1 Co-occurrence of quality and popular reading
16.5.2 Complexity of the relations
16.5.3 Multiple correspondence analysis
16.5.4 Supplementary background variables
16.6 Other approaches to binary judgments
16.7 Content summary
17 Music appreciation: The Chopin Preludes
17.1 Background
17.2 Research questions: Appreciation and musical knowledge
17.3 Data: Semantic differential scales
17.3.1 Musical database: Semantic differential scales
17.3.2 Design and data collection
17.3.3 Data format: Students, Preludes, and Scales
17.4 Analysis methods
17.4.1 Two-way multivariate analysis of variance
17.4.2 Tucker's three-mode model
17.4.3 Joint biplots
17.5 The Chopin preludes: Statistical analysis
17.5.1 Individual differences
17.5.2 Three-mode principal component analysis—tmpca
17.5.3 Scale components
17.5.4 Prelude components
17.5.5 Joint biplot—Consensus
17.5.6 Preludes as characterised by the scales
17.5.7 Circle of fifths: Judgments and keys
17.5.8 Joint biplot—Individual differences
17.6 Other approaches to evaluating music appreciation
17.7 Content summary
18 Musical stylometry: Characterisation of music
18.1 Background
18.2 Research questions: Differences in musical style
18.3 Data: Melodic intervals and pitch
18.3.1 Musical database
18.3.2 Musical styles: Features
18.3.3 Design
18.4 Analysis methods
18.4.1 Binary logistic regression
18.4.2 Multinomial logistic regression
18.4.3 Discriminant analysis
18.5 Characterisation of music: Statistical analysis
18.5.1 Data description
18.5.2 Data inspection
18.5.3 Preliminaries for the analyses
18.5.4 Predicting Bach versus Haydn + Beethoven
18.5.5 Genre: Heterogeneity of the Bach pieces
18.5.6 Genre: Discriminating between Bach pieces
18.6 Other approaches to analysing musical styles:
18.7 Content summary
Part III The Finale
19 Final Musings
Correction to: Multivariate Humanities
Correction to: P. M. Kroonenberg, Multivariate Humanities, Quantitative Methods in the Humanities and Social Sciences, https://doi.org/10.1007/978-3-030-69150-9
Appendix A Discipline-orientated statistics books
Appendix Statistical Glossary
Appendix References
Index