Multivariate humanities / Pieter M. Kroonenberg.

Format:

Book

Author/Creator:

Kroonenberg, Pieter M., author.

Series:

Quantitative methods in the humanities and social sciences.

Quantitative Methods in the Humanities and Social Sciences

Language:

English

Subjects (All):

Multivariate analysis.

Physical Description:

1 online resource (441 pages)

Place of Publication:

Cham, Switzerland : Springer, [2021]

Summary:

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.

Contents:

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.

Notes:

Description based on print version record.

ISBN:

3-030-69150-0

OCLC:

1258660214